Medicine, Faculty of
Pathology and Laboratory Medicine, Department of
DSpace
Cancers 17 (6): 996 (2025)
BC Cancer Research Centre
University of British Columbia. Interdisciplinary Oncology Program
Souza, Vanessa G. P.
Benard, Katya H.
Stewart, Greg L.
Enfield, Katey S. S.
Lam, Wan
Background/Objectives: Non-small cell lung cancer (NSCLC) is the most common type of cancer, with lung adenocarcinoma (LUAD) as the predominant subtype. Despite advancements in targeted therapies, many NSCLC patients still experience poor outcomes due to treatment resistance and disease progression. Genomic instability (GI), a hallmark of cancer, defined as the increased tendency of DNA mutations and alterations, is closely linked to cancer initiation, progression, and resistance to therapy. Emerging evidence suggests that long non-coding RNAs (lncRNAs)—molecules longer than 200 nucleotides that do not encode proteins but regulate gene expression—play critical roles in cancer biology and are associated with GI. However, the relationship between GI and lncRNA expression in LUAD remains poorly understood. Methods: In this study, we analyzed the transcript profiles of lncRNAs and mRNAs from LUAD samples in The Cancer Genome Atlas (TCGA) database and classified them based on their Homologous Recombination Deficiency (HRD) score. The HRD score is an unweighted sum of three independent DNA-based measures of genomic instability: loss of heterozygosity, telomeric allelic imbalance, and large-scale transitions. We then performed a differential gene expression analysis to identify lncRNAs and mRNAs that were either upregulated or downregulated in samples with high HRD scores compared to those with low HRD scores. Following this, we conducted a correlation analysis to assess the significance of the association between HRD scores and the expression of both lncRNAs and mRNAs. Results: We identified 30 differentially expressed lncRNAs and 200 mRNAs associated with genomic instability. Using an RNA interactome database from sequencing experiments, we found evidence of interactions between GI-associated lncRNAs (GI-lncRNAs) and GI-associated mRNAs (GI-mRNAs). Further investigation showed that some GI-lncRNAs play regulatory and functional roles in LUAD and other diseases. We also found that GI-lncRNAs have potential as prognostic biomarkers, particularly when integrated with HRD stratification. The expression of specific GI-lncRNAs was associated with primary therapy response and immune infiltration in LUAD. Additionally, we identified existing drugs that could modulate GI-lncRNAs, offering potential therapeutic strategies to address GI in LUAD. Conclusions: Our findings suggest that GI-associated lncRNAs could serve as valuable biomarkers for LUAD prognosis and therapeutic response. Furthermore, modulating these lncRNAs presents potential treatment avenues to address genomic instability in LUAD.
Academic Editor: Akiteru GotoReceived: 12 February 2025Revised: 11 March 2025Accepted: 13 March 2025Published: 15 March 2025Citation: Souza, V.G.P.; Benard, K.H.;Stewart, G.L.; Enfield, K.S.S.; Lam,W.L. Identification of GenomicInstability-Associated LncRNAs asPotential Therapeutic Targets in LungAdenocarcinoma. Cancers 2025, 17, 996.https://doi.org/10.3390/cancers17060996Copyright: © 2025 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).ArticleIdentification of Genomic Instability-Associated LncRNAs asPotential Therapeutic Targets in Lung AdenocarcinomaVanessa G. P. Souza 1,*,† , Katya H. Benard 1,2,†, Greg L. Stewart 1,2, Katey S. S. Enfield 1,2,3 and Wan L. Lam 1,2,31 British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada; wanlam@bccrc.ca (W.L.L.)2 Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC V6T 1Z4, Canada3 Department of Pathology and Laboratory Medicine, University of British Columbia,Vancouver, BC V6T 1Z7, Canada* Correspondence: vsouza@bccrc.ca† These authors contributed equally to this work.Simple Summary: Non-small cell lung cancer (NSCLC) is the most common type of lungcancer, with lung adenocarcinoma (LUAD) being its predominant subtype. Despite ad-vancements in treatment, LUAD outcomes remain poor due to treatment resistance, whichis in part driven by genomic instability (GI)—an increased tendency for DNA mutationsand other genetic changes that occur during cell division. Using LUAD samples fromThe Cancer Genome Atlas (TCGA) database, we classified cases by Homologous Recom-bination Deficiency (HRD) score—a measure of GI—and identified 30 long non-codingRNAs (lncRNAs) and 200 messenger RNAs (mRNAs) associated with GI. We found thatGI-associated lncRNAs (GI-lncRNAs) could serve as prognostic biomarkers, particularlywhen combined with HRD score. These lncRNAs were also linked to therapy response andimmune infiltration in high-GI samples. Additionally, we identified existing drugs thatcould interact with specific GI-lncRNAs, offering opportunities for drug repurposing inLUAD or for experimental research into their mechanistic roles in tumor progression.Abstract: Background/Objectives: Non-small cell lung cancer (NSCLC) is the most com-mon type of cancer, with lung adenocarcinoma (LUAD) as the predominant subtype.Despite advancements in targeted therapies, many NSCLC patients still experience pooroutcomes due to treatment resistance and disease progression. Genomic instability (GI), ahallmark of cancer, defined as the increased tendency of DNA mutations and alterations,is closely linked to cancer initiation, progression, and resistance to therapy. Emergingevidence suggests that long non-coding RNAs (lncRNAs)—molecules longer than 200 nu-cleotides that do not encode proteins but regulate gene expression—play critical roles incancer biology and are associated with GI. However, the relationship between GI andlncRNA expression in LUAD remains poorly understood. Methods: In this study, weanalyzed the transcript profiles of lncRNAs and mRNAs from LUAD samples in TheCancer Genome Atlas (TCGA) database and classified them based on their HomologousRecombination Deficiency (HRD) score. The HRD score is an unweighted sum of threeindependent DNA-based measures of genomic instability: loss of heterozygosity, telom-eric allelic imbalance, and large-scale transitions. We then performed a differential geneexpression analysis to identify lncRNAs and mRNAs that were either upregulated ordownregulated in samples with high HRD scores compared to those with low HRD scores.Following this, we conducted a correlation analysis to assess the significance of the associa-tion between HRD scores and the expression of both lncRNAs and mRNAs. Results: Weidentified 30 differentially expressed lncRNAs and 200 mRNAs associated with genomicinstability. Using an RNA interactome database from sequencing experiments, we foundevidence of interactions between GI-associated lncRNAs (GI-lncRNAs) and GI-associatedCancers 2025, 17, 996 https://doi.org/10.3390/cancers17060996Cancers 2025, 17, 996 2 of 34mRNAs (GI-mRNAs). Further investigation showed that some GI-lncRNAs play regulatoryand functional roles in LUAD and other diseases. We also found that GI-lncRNAs havepotential as prognostic biomarkers, particularly when integrated with HRD stratification.The expression of specific GI-lncRNAs was associated with primary therapy responseand immune infiltration in LUAD. Additionally, we identified existing drugs that couldmodulate GI-lncRNAs, offering potential therapeutic strategies to address GI in LUAD.Conclusions: Our findings suggest that GI-associated lncRNAs could serve as valuablebiomarkers for LUAD prognosis and therapeutic response. Furthermore, modulating theselncRNAs presents potential treatment avenues to address genomic instability in LUAD.Keywords: genomic instability; homologous recombination deficiency (HRD); long non-coding RNAs (lncRNAs); non-small cell lung cancer (NSCLC)1. IntroductionLung cancer is the leading cause of cancer-related deaths worldwide, representingroughly 18% of all cancer-related deaths [1]. Non-small cell lung cancer (NSCLC) is thepredominant subtype, accounting for approximately 87% of cases [2]. Due to the lack ofsymptoms, a substantial proportion of NSCLC patients are diagnosed at locally advancedor metastatic stages, with frequent spread to organs such as the liver, brain, and bones,leading to poor survival outcomes [3,4]. The five-year survival rate for NSCLC dependson the stage at diagnosis, varying from 65% when diagnosed at an early stage to 9% inadvanced stages [5].Targeted therapies have significantly improved outcomes for NSCLC patients, mak-ing broad-panel molecular testing essential for identifying actionable genetic alterations.Tyrosine kinase inhibitors (TKIs) that target genetic mutations in EGFR, ALK, ROS1, RET,BRAF, MET exon 14, and NTRK have been approved for different subtypes of NSCLC [6].However, only a modest fraction of patients have these mutations, and for most patientswithout driver mutations, chemoimmunotherapy is the preferred first-line treatment [7].Combining chemotherapy with immunotherapy typically results in an increased medianprogression-free survival (PFS) and median overall survival (OS) for both squamous andnon-squamous NSCLC in advanced stages [8,9]. Despite initial benefits, most patientsexperience disease progression within the first year of treatment. Consequently, second-linetreatment with docetaxel monotherapy remains the standard of care for most patients withadvanced NSCLC without actionable genomic alterations, despite its modest efficacy andnotable toxicity [7,10].A central challenge in NSCLC treatment is therapy resistance, with systemic progres-sion often arising due to drug resistance in tumor cells [11]. Although the molecular basisof drug resistance is complex, genomic instability (GI)—a key driver of tumor evolutionand intratumor heterogeneity—is increasingly recognized as a major contributor [12–14].Through GI, tumor cells can adopt diverse phenotypes, enhancing their survival underthe selective pressures of treatment [14,15]. This adaptability highlights GI’s role in cancerprogression and poor therapeutic outcomes, fueling a growing interest in targeting GI andaneuploidy as therapeutic strategies [16].Lung adenocarcinoma (LUAD) exhibits higher GI scores than many other tumortypes [17], with chromosomal instability linked to poorer prognosis and metastasisrates [18]. In LUAD, GI-related genes such as PPAT and PAICS, as well as immune-relatedpathways like MEC2C and CCL14-CCR1, have been proposed as potential therapeutictargets [19,20].Cancers 2025, 17, 996 3 of 34While studies have shown that GI may serve as a prognostic marker in cancers likecervical and breast cancer [21,22] and emerging evidence links GI to lung cancer tumori-genesis [23,24], the prognostic significance of GI in LUAD remains inconsistent acrossdifferent studies and subgroups. This inconsistency suggests that the prognostic role of GIin LUAD may vary based on factors such as genomic alterations, clinical characteristics,and treatment regimens [25–28].GI in malignant tumors can be broadly classified into four types: chromosomal in-stability (CIN), intrachromosomal instability, microsatellite instability, and epigenetic in-stability [29]. CIN is the most common form of GI observed in over 90% of solid tumorsand many blood cancers [30–32]. Although GI is a recognized cancer hallmark [33], itremains unclear whether it is primarily a cause or consequence of cancer progression, andits underlying genetic vulnerabilities are not fully understood [29].Recently, studies have shown that GI can be evaluated using the Homologous Re-combination Deficiency (HRD) score [25,34–36]. The HRD score is used to assess the dys-function in the homologous recombination repair (HRR) pathway at the cellular level [37].HRR is responsible for precise DNA damage repair, especially double-strand break (DSB)repair [26,37,38]. When HRR is impaired, as in some cancers, cells accumulate geneticalterations such as abnormal copy numbers and chromosomal cross-linking, leading toboth genomic instability and CIN [37]. By calculating a combined score of events suchas loss of heterozygosity (LOH), large-scale state transitions (LSTs), and telomere alleleimbalance (TAI), the HRD score provides a quantifiable assessment of genomic scarring,offering a comprehensive score for the evaluation of GI in cancer [34,35,37]. The concept ofHRD scoring was first introduced and validated in cancer genomics studies, such as thoseby Abkevich et al. (2012) [39] and Timms et al. (2014) [34], and HRD scoring was furthervalidated as a predictor of therapy response, particularly for DNA-damaging treatmentslike platinum-based chemotherapy and PARP inhibitors [40–42].Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides that lackprotein-coding potential but play essential roles in maintaining normal cellular functionsand homeostasis. They regulate gene expression at multiple levels, including epigeneticmodification, transcriptional regulation, and post-transcriptional control [43,44]. The rolesof lncRNAs in cancer biology are increasingly recognized, particularly in processes likecell proliferation, differentiation, and genomic stability [45,46]. Notably, certain lncRNAs,including NORAD, have been shown to play direct roles in chromosomal stability [47,48].However, only a few genomic instability-associated lncRNAs (GI-lncRNAs) have beenidentified, and their involvement in LUAD and their association with HRD scores have notbeen fully explored [47].Recent studies have shown that genomic instability-related lncRNA signatures (GILnc-Sig) could serve as independent predictors of survival in LUAD [49,50]. For instance, amodel incorporating five GI-associated lncRNAs, developed using data from The Can-cer Genome Atlas (TCGA), was identified as an independent predictor of OS in LUADpatients [50]. These findings underscore the promising role of GI-associated lncRNAs asbiomarkers for prognosis.Beyond their biomarker potential, lncRNAs are promising pharmacological targets [51].However, developing new drugs is a lengthy process, often requiring years to complete alldesign and approval phases. Drug repurposing has been utilized to identify new thera-peutic roles for existing drugs [52]. LncRNAs have emerged as important players in drugrepurposing efforts. For example, one study identified drugs that could modulate lncRNAsinvolved in the cross-talk between diabetes mellitus and Alzheimer’s disease, revealingnew therapeutic possibilities [53]. Another analysis identified over 27,000 lncRNA–druginteractions with 265 chemotherapy drugs, uncovering “multi-drug resistant” lncRNAsCancers 2025, 17, 996 4 of 34linked to resistance against more than 100 chemotherapy agents, suggesting significantroles in drug metabolism and disposition [54]. Additionally, LncTx—a network-basedtool—identifies potential drugs that act on survival-related lncRNAs in lung cancer [55].Given this, identifying GI-lncRNAs in LUAD could provide new insights into the molecularunderpinnings of this cancer and reveal potential therapeutic targets.Therefore, the rationale for this study is to investigate the role of GI-lncRNAs in LUAD,with the aim of identifying potential biomarkers for prognosis and novel therapeutictargets to improve clinical outcomes. We explore the potential mechanisms regulating theselncRNAs using in silico analysis. Additionally, by examining the relationship betweenHRD scores and the mutational landscape, we analyze how alterations in key driver genesmay influence genomic instability and contribute to tumorigenesis and LUAD progression.We conclude that GI-lncRNAs could serve as potential therapeutic targets and offer newopportunities for drug repurposing and precision treatment strategies for LUAD.2. Materials and MethodsA schematic overview of the methodology is provided in Figure 1.Cancers 2025, 17, x 4 of 36 revealing new therapeutic possibilities [53]. Another analysis identified over 27,000 lncRNA–drug interactions with 265 chemotherapy drugs, uncovering “multi-drug re-sistant” lncRNAs linked to resistance against more than 100 chemotherapy agents, sug-gesting significant roles in drug metabolism and disposition [54]. Additionally, LncTx—a network-based tool—identifies potential drugs that act on survival-related lncRNAs in lung cancer [55]. Given this, identifying GI-lncRNAs in LUAD could provide new insights into the molecular underpinnings of this cancer and reveal potential therapeutic targets. Therefore, the rationale for this study is to investigate the role of GI-lncRNAs in LUAD, with the aim of identifying potential biomarkers for prognosis and novel thera-peutic targets to improve clinical outcomes. We explore the potential mechanisms regu-lating these lncRNAs using in silico analysis. Additionally, by examining the relationship between HRD scores and the mutational landscape, we analyze how alterations in key driver genes may influence genomic instability and contribute to tumorigenesis and LUAD progression. We conclude that GI-lncRNAs could serve as potential therapeutic targets and offer ew opportunities for drug r purposin and precision treatment strate-gies f r LUAD. 2. Materials and Methods A schematic overview of the methodology is provided in Figure 1. Figure 1. Homologous Recombination Deficiency (HRD) scores for LUAD-TCGA samples were obtainedfrom a previous study, which calculated HRD scores based on three indicators: loss of heterozygosityCancers 2025, 17, 996 5 of 34(LOH), telomeric allelic imbalance (NtAI), and large-scale state transitions (LSTs) [37]. Using theseHRD scores, samples were classified into two groups: high HRD scores and low HRD scores.RNA sequencing data and corresponding clinicopathological features for these selected sampleswere retrieved from the TCGA database (details provided in the methodology section). LncRNAand mRNA expression profiles were extracted from TCGA gene expression quantification files(counts), and differential expression analysis was performed to identify lncRNAs and mRNAs up-or downregulated in the high-HRD group. Thresholds for differential expression were set to log2fold change ≥ |2| and FDR < 0.05. A Spearman correlation test was then conducted to identifydifferentially expressed lncRNAs and mRNAs that also correlated with HRD scores. LncRNAsmeeting both differential expression (log2 fold change ≥ |2|, FDR < 0.05) and correlation (Spearmancoefficient ≥ |0.2|, p-value < 0.05) criteria were classified as genomic instability-associated lncRNAs(GI-lncRNAs). Similarly, mRNAs meeting a Spearman coefficient ≥ |0.1| with a p-value < 0.05 wereclassified as genomic instability-associated mRNAs (GI-mRNAs). To explore the regulatory roles andfunctions of GI-lncRNAs in human diseases, we used the LncTarD 2.0 database [56], and RNA–RNAinteractions between GI-lncRNAs and GI-mRNAs were examined using the RISE database [57].The clinical significance of GI-lncRNAs as prognostic biomarkers in LUAD was assessed throughsurvival analysis, stratifying LUAD patients by HRD scores. Additionally, we investigated thepotential relevance of GI-lncRNAs in predicting therapy response in patients with high HRD scores.Differences in immune cell infiltration between patients with high and low HRD scores were analyzedusing the CIBERSORTx algorithm [58]. Finally, potential drug candidates targeting GI-lncRNAs wereidentified using the ncRNADrug database [59].2.1. Data Collection and HRD ScoresHomologous Recombination Deficiency (HRD) scores for LUAD-TCGA samples wereobtained from a previous study [37], which utilized three key indicators—loss of het-erozygosity (LOH), telomeric allelic imbalance (NtAI), and large-scale state transitions(LSTs)—to determine the HRD status of each sample. Based on these HRD scores, weclassified LUAD-TCGA samples into quartiles (Q1–Q4) and then selected the top 25% ofsamples with the highest HRD scores (Q4: high HRD scores) and the bottom 25% withthe lowest HRD scores (Q1: low HRD scores), following the methodology outlined in aprevious study [60] (Supplementary Table S1). Using the sample IDs for LUAD-TCGAsamples provided in this study, we downloaded the RNA sequencing transcriptome dataand the corresponding clinicopathological features of LUAD patients from the TCGAdatabase [61]. Comparative statistical analysis was performed using the Chi-Square testto assess the association between clinicopathological characteristics and HRD scores. Theclinicopathological characteristics of the patients in this study are presented in Table 1.Detailed clinical information for each sample can be found in Supplementary Table S2.Table 1. Clinicopathological features of lung adenocarcinoma patients.Characteristics High HRD Scores(n = 120)Low HRD Scores(n = 134)p-Value *Age ≤65 70 (58.33%) 48 (35.82%) 0.001>65 46 (38.33%) 79 (59.03%)Unknown 4 (3.33%) 7 (5.22%)Sex Female 57 (47.50%) 73 (54.48%) 0.287Male 63 (52.50%) 61 (45.52%)Race Caucasian/White85 (70.8%) 102 (76.1%) 0.281Black/African American 20 (16.7%) 12 (9.0%)Asian 2 (1.7%) 1 (0.7%)Unknown 13 (10.8%) 19 (14.2%)Cancers 2025, 17, 996 6 of 34Table 1. Cont.Characteristics High HRD Scores(n = 120)Low HRD Scores(n = 134)p-Value *Stage Stages I–II 97 (80.83%) 103 (76.87%) 0.757Stages III–IV 21 (17.50%) 26 (19.40%)Unknown 2 (1.67%) 5 (3.73%)T T1–2 106 (88.33%) 113 (84.33%) 0.519T3–4 14 (11.67%) 21 (15.67%)N N0 79 (65.83%) 87 (64.93%) 0.893N1–3 41 (34.17%) 41 (30.60%)NX 0 (0.00%) 6 (4.48%)M M0 77 (64.17%) 95 (70.90%) 0.950M1 4 (3.33%) 5 (3.73%)MX 38 (31.67%) 32 (23.88%)Unknown 1 (0.83%) 2 (1.49%)* Chi-Square test: The statistical analysis excludes the Unknown, MX, and NX sample categories. The primarytumor (T) category is used to define the size and extent of the main tumor, with values ranging from T0, indicatingno evidence of the primary tumor, to T4, representing a larger or more invasive tumor. The regional lymphnodes (N) category indicates whether cancer has spread to nearby lymph nodes, with N0 indicating no spreadand higher numbers (N1, N2, and N3), reflecting the increasing number and/or size of affected lymph nodes.The distant metastasis (M) category assesses whether cancer has spread to distant parts of the body, with M0indicating no metastasis and M1 indicating that the cancer has spread. The NX and MX stages are used when theextent of cancer in lymph nodes or metastasis cannot be measured, respectively.2.2. Analysis of Gene Mutational Status and HRD Scores in Lung AdenocarcinomaThe mutational status of key driver genes in LUAD (TP53, EGFR, KRAS, PIK3CA,and CDKN2A) was retrieved from the UCSC Xena database [62] using the GDC TCGALung Adenocarcinoma (LUAD) dataset. Somatic mutation data for each selected gene weredownloaded and imported into R software (v.4.4.1) [63]. In the R environment, we filteredthe GDC TCGA LUAD dataset to include only samples with high and low HRD scores.To explore the relationship between mutational status and HRD scores (high vs. low), aChi-Square test was performed using the chisq.test function from the stats package (v.4.4.1)available in R.2.3. Identification of Genomic Instability-Associated Long Non-Coding RNAs (GI-lncRNAs) andmRNAs (GI-mRNAs), and Differentially Expressed miRNAs (DE-miRNAs)Transcriptome data for RNA, miRNA, and clinicopathological features were obtainedfrom the TCGA database and analyzed in R software (v.4.4.1) [61,64]. Seven transcriptomicsamples with incomplete clinical data were excluded to ensure that only fully annotatedsamples were included in the analysis, maintaining the integrity of the dataset. LncRNAand mRNA expression profiles were extracted from the gene expression quantification files(counts), which contain raw count data for each gene in the dataset. Genes identified as“lncRNA” in the count file were categorized as lncRNAs (n = 16,901), and genes labeled as“protein_coding” were categorized as mRNAs (n = 19,962). MiRNA expression levels wereextracted from the corresponding miRNA quantification files (mirnas.quantification.txt).To explore sources of variation in the dataset, we performed Principal Component Anal-ysis (PCA), a widely used dimensionality reduction and visualization method for high-dimensional data. This analysis was conducted using the prcomp function from the statspackage (v.4.4.1) in R. The expression matrix was used as input to assess potential batch-related variation in the data.Cancers 2025, 17, 996 7 of 34Subsequently, we employed the EdgeR package (v.3.19) [65] for differential expressionanalysis. EdgeR is a widely used tool for RNA-seq data due to its robust normalization andstatistical modeling capabilities. It employs the Trimmed Mean of M-values (TMM) methodvia the calcNormFactors() function, a robust approach for handling variations in librarysize, which is a common characteristic of RNA-seq data [66,67]. The filterByExpr functionwas used to exclude genes that were not expressed in sufficient samples to ensure statisticalrobustness as genes with low expression levels are more likely to produce unreliable results.For differential expression analysis of lncRNAs and mRNAs, we set thresholds of a log2fold change ≥ |2| and an FDR < 0.05. For miRNAs, we did not consider log2 fold changeand used only an FDR < 0.05 as the threshold.In addition to differential expression, we performed a Spearman correlation test usingthe cor.test function from the stats package (v.4.4.1) available in R to assess the associa-tion between HRD scores and the expression of lncRNAs and mRNAs. The Spearmancorrelation was selected because it is a non-parametric test that does not assume a linearrelationship, making it appropriate for capturing more complex associations between HRDscores and gene expression data. We set the threshold for correlation significance at ap-value < 0.05, with a minimum Spearman coefficient ≥ |0.2|, a threshold commonly usedto highlight meaningful but potentially weak correlations in large genomic datasets.LncRNAs were classified as genomic instability-associated long non-coding RNAs(GI-lncRNAs) if they met both of the following criteria: (a) differential expression (log2 foldchange≥ |2| and an FDR < 0.05) and (b) Spearman coefficient≥ |0.2| with p-value < 0.05.Similarly, mRNAs that met the criteria of (a) differential expression (log2 fold change ≥ |2|and an FDR < 0.05) and (b) Spearman coefficient ≥ |0.1| with p-value < 0.05 were clas-sified as genomic instability-associated mRNAs (GI-mRNAs). The genomic locations forboth GI-lncRNAs and GI-mRNAs were identified using GeneCards®: The Human GeneDatabase [68], based on the latest assembly (GRCh38/hg38).2.4. Pathway Enrichment AnalysisFor GI-associated lncRNAs, Kyoto Encyclopedia of Genes and Genomes (KEGG)pathway enrichment analysis was conducted using the LncRNAs2Pathways tool [69],with an FDR < 0.05 considered significant. For GI-mRNAs, Kyoto Encyclopedia of Genesand Genomes (KEGG) and Gene Ontology (biological process) enrichment analyses wereperformed using Enrichr [70–72]. We set p < 0.05 as the threshold for statistical significance.2.5. Interactions Between GI-lncRNA and GI-mRNAs, Along with the Regulatory Mechanismsand Functional Roles of GI-lncRNAsWe aimed to select the most reliable interactions involving GI-lncRNAs and GI-mRNAs. To achieve this, we downloaded RNA–RNA interactions from the RISEdatabase [57]. This comprehensive database of RNA interactomes from sequencing experi-ments includes 10,941 lncRNA–mRNA interactions. RISE provides an extensive collectionof RNA–RNA interactions (RRIs) identified in humans, mice, and yeast, along with de-tailed molecular annotations for each RRI. The RRIs were curated from transcriptome-wideand targeted sequencing studies, as well as other public databases and datasets [57]. Wedownloaded the complete RNA–RNA interactome for humans and imported the datasetinto R for further analysis. In the R environment, we selected only interactions involvingthe GI-lncRNAs of interest and GI-mRNAs. Subsequently, we conducted a Spearmancorrelation analysis between the GI-lncRNAs and GI-mRNAs to identify only those withinteractions in our cohort (p < 0.05). The lncRNA–mRNA interaction network in ourmodel was constructed based on these interactions using Cytoscape software (v.3.10.1) [73].Additionally, the LncTarD 2.0 database was used to explore the associated functions andregulatory mechanisms of GI-lncRNAs in human diseases, including LUAD [56].Cancers 2025, 17, 996 8 of 342.6. Patient Survival AnalysisRecurrence, death, and progression-free survival data were downloaded from theTCGA database [61] and used to assess overall survival (OS) and progression-free survival(PFS). We normalized gene expression using the z-score formula, i.e., z = (x− x−)/s, wherex represents the gene expression value, x− is the mean of gene expression values, and s isthe standard deviation. Associations between gene expression and OS were investigatedfollowing Sha’s method [74]. Briefly, patients were categorized based on gene expressionlevels into ’high’ (above the median) and ’low’ (below the median) groups. The prognosticsignificance of these classifications was assessed through survival analysis. OS and PFSwere estimated using the Kaplan–Meier method by making comparisons between groupsusing the log-rank test. The analysis utilized the survival (v.3.7-0) [75] and survminer(v.0.4.9) [76] packages in R. Statistical significance was defined as a p < 0.05.2.7. Patient Treatment ResponseData on primary therapy outcome success and follow-up treatment success for LUAD-TCGA samples were downloaded from the UCSC Xena database [62] using the GDC TCGALung Adenocarcinoma (LUAD) dataset. Gene expression was normalized using the z-score,as previously described. Patients were categorized based on gene expression levels into’high’ (above the median) and ’low’ (below the median) groups. We then performed aFisher’s Exact Test to assess the relationship between gene expression categories (“high”vs. “low”) and therapy outcome success (“complete remission/response”, “stable disease”,and “progressive disease”) for each group (high HRD scores and low HRD scores). TheFisher’s Exact Test was performed using the fisher.test function from the stats package(v.4.4.1). Statistical significance was defined as an adjusted p-value < 0.05.2.8. Immune-Related AnalysisTo assess the immune composition of the samples, we utilized the CIBERSORTxtool [58]. This tool incorporates the LM22 reference file, which is a signature matrix contain-ing 547 genes that differentiate 22 distinct human hematopoietic cell populations, including7 T cell subtypes, B cells, plasma cells, and NK cells and various myeloid subsets. Prior toanalysis in CIBERSORTx, raw RNA-seq counts were converted to transcripts per million(TPM) using the countToTpm() function from the R package GeoTcgaData (v.2.4.0) [77].Immune cell profiling was conducted with CIBERSORTx, using 1000 permutations forenhanced statistical reliability, while quantile normalization was disabled. The resultingoutput files were downloaded in tab-delimited format and subsequently imported into R(v.4.4.1), which was used to identify differences in immune composition between high andlow HRD scores. The normality test was performed using the shapiro.test function. Differ-ences were considered significant when p < 0.05 based on the Wilcoxon–Mann–Whitneytest. Then, we explored the association between the populations of immune cells and theexpression of GI-lncRNAs; for this, the Spearman correlation analysis was performed usingthe cor.test function of the R stats package (v.4.4.1) [63]. Values were considered significantwhen p < 0.05. Both raw and TPM-normalized counts were used in the correlation analysisto ensure that the correlations remained consistent across both methods. The plots weregenerated using the package ggplot2 (v.3.5.1) [78].2.9. Identification of Candidate Drugs Targeting GI-lncRNAsTo identify potential drug candidates targeting GI-lncRNAs, we utilized the ncR-NADrug database, a comprehensive resource for drug and ncRNA associations. Thisdatabase includes both experimentally validated and computationally predicted ncRNAslinked to drug resistance, as well as ncRNAs targeted by specific drugs [79].Cancers 2025, 17, 996 9 of 343. Results3.1. Characterization of HRD Scores and Their Association with Clinicopathological Features inLUAD-TCGA SamplesHRD scores for LUAD-TCGA samples were obtained as outlined in the methodology(Figure 1). Based on these HRD scores, we classified LUAD-TCGA samples into quartiles(Q1–Q4) (Supplementary Table S1). We then selected the top 25% of samples with thehighest HRD scores (Q4: high HRD scores, n = 122) and the bottom 25% with the lowestHRD scores (Q1: low HRD scores, n = 134), following the methodology outlined in aprevious study [60]. The samples TCGA-50-5066-02 and TCGA-50-5946-02 were excludedfrom our subsequent analysis due to their classification as recurrent solid tumors. Researchhas shown that LUAD recurrent tumors have a higher mutation burden than non-recurrenttumors [80]. Following this removal, we retained 120 samples with high HRD scores and134 samples with low HRD scores.The distribution of HRD scores across LUAD samples is displayed in Figure 2A. Basedon these scores, samples were categorized into two groups: high HRD (n = 120) and lowHRD (n = 134). The median HRD scores were 44 for the high-HRD group and 9 for thelow-HRD group (Figure 2B). Median values for NtAI, LSTs, and LOH were markedlydifferent between the groups, with the high-HRD group showing values of 19, 15, and 11,respectively, compared to 4, 2, and 2 in the low-HRD group (Figure 2C–E).We analyzed the associations between HRD scores and clinicopathological features,including pathological stage, vital status, sex, race, tumor size (T), lymph node involvement(N), and metastasis (M). HRD scores were not significantly associated with most clinicalcharacteristics, except for age (p = 0.001, Chi-Square test) (Table 1). Additionally, there wereno differences in OS or PFS between patients with high or low HRD scores (SupplementaryFigure S1). These results align with those of previous studies [25].Given the reported association between GI/HRD scores and genomic alterations inNSCLC [25], we investigated the relationship between the mutational status of key drivergenes in LUAD and HDR scores (Table 2). TP53 somatic mutations were significantlymore prevalent in the group with high HRD scores, with 80% of patients exhibiting TP53alterations, compared to only 13.43% in the group with low HRD scores (p = 0.0001). Wefound no significant associations with other assessed genes.Table 2. Mutational status of key driver genes in lung adenocarcinoma patients stratified byHRD scores.Genes High HRD Scores(n = 120)Low HRD Scores(n = 134)p-Value *TP53 Mutant 96 (80%) 18 (13.43%) 1 × 10−4Wildtype 22 (18.33%) 111 (82.09%)Unknown 2 (1.67%) 5 (3.73%)EGFR Mutant 10 (8.33%) 19 (14.18%) 0.184Wildtype 108 (90%) 110 (82.09%)Unknown 2 (1.67%) 5 (3.73%)KRAS Mutant 25 (20.83%) 38 (28.36%) 0.179Wildtype 93 (77.5%) 91 (67.91%)Unknown 2 (1.67%) 5 (3.73%)PIK3CA Mutant 5 (4.17%) 6 (4.48%) 1Wildtype 113 (94.17%) 123 (91.79%)Unknown 2 (1.67%) 5 (3.73%)Cancers 2025, 17, 996 10 of 34Table 2. Cont.Genes High HRD Scores(n = 120)Low HRD Scores(n = 134)p-Value *CDKN2A Mutant 9 (7.50%) 5 (3.73%) 0.318Wildtype 109 (90.83%) 124 (92.54%)Unknown 2 (1.67%) 5 (3.73%)* Chi-Square test: The statistical analysis excludes categories with unknown values.Cancers 2025, 17, x 10 of 36 alterations, compared to only 13.43% in the group with low HRD scores (p = 0.0001). We found no significant associations with other assessed genes. Figure 2. (A) Distribution of Homologous Recombination Deficiency (HRD) scores across all LUAD samples obtained from Shi et al., 2023 [37], with the median HRD score highlighted in red. (B) Dis-tribution of HRD scores for the top 25% of samples classified as high HRD scores compared to the bottom 25% classified as low HRD scores used in this study. (C) Distribution of telomeric allelic imbalance (NtAI) scores among samples categorized by high and low HRD scores. (D) Distribution of large-scale state transition (LST) scores among samples categorized by high and low HRD scores. (E) Distribution of loss of heterozygosity (LOH) scores among samples categorized by high and low HRD scores. The HRD scores and NtAI, LST, and LOH values were obtained from Shi et al. (2023) [37]. In all plots, boxes represent the standard deviation, with whiskers extending to the 95th per-centile. Table 2. Mutational status of key driver genes in lung adenocarcinoma patients stratified by HRD scores. Genes High HRD Scores (n = 120) Low HRD Scores (n = 134) p-Value * TP53 Mutant 96 (80%) 18 (13.43%) 1 × 10⁻⁴ Wildtype 22 (18.33%) 111 (82.09%) Unknown 2 (1.67%) 5 (3.73%) EGFR Mutant 10 (8.33%) 19 (14.18%) 0.184 Figure 2. (A) Distribution of Homologous Recombination Deficiency (HRD) scores across all LUADsamples obtained from Shi et al., 2023 [37], with the median HRD score highlighted in red. (B) Dis-tribution of HRD scores for the top 25% of samples classified as high HRD scores compared to thebottom 25% classified as low HRD scores used in this study. (C) Distribution of telomeric allelici balance (NtAI) score among samples categorized by high an low HRD cores. (D) Distributionof large-scale state transition (LST) scores among samples categorized by high and low HRD scores.(E) Distribution of loss of heterozygosity (LOH) scores among samples categorized by high and lowHRD scores. The HRD scores and NtAI, LST, and LOH values were obtained from Shi et al. (2023) [37].In all plots, boxes represent the standard deviation, with whiskers extending to the 95th percentile.3.2. Identification of Genomic Instability-Associated Long Non-Coding RNAs (GI-lncRNAs) inLung AdenocarcinomaTo identify GI-lncRNAs in LUAD, we first compared lncRNA expression betweensamples with high HRD scores and those with low HRD scores. This analysis revealed atotal of 43 d fferenti lly expr ssed lncRNAs (DE- ncRNAs) (log2 FC≥ |2| and FDR < 0.05).Among the DE-lncRNAs, 27 were upregulated and 16 were downregulated. The list ofCancers 2025, 17, 996 11 of 34DE-lncRNAs is shown in Supplementary Table S3. Following the identification of DE-lncRNAs, we performed a correlation analysis to evaluate the significance and strengthof the associations between HRD scores and DE-lncRNA expression levels. DE-lncRNAswith a Spearman correlation coefficient (|r| ≥ 0.2) and p-value < 0.05 were classifiedas genome instability-associated lncRNAs (GI-lncRNAs; Supplementary Table S4). Thisanalysis identified 30 GI-lncRNAs, as illustrated in Figure 3A.Cancers 2025, 17, x 12 of 36 Figure 3. (A) Volcano plot showing upregulated and downregulated genes in samples with high HRD scores compared to those with low HRD scores. Red points represent upregulated long non-coding RNAs (lncRNAs), and blue points indicate downregulated lncRNAs, defined by a log2 fold change ≥ |2| and an FDR < 0.05. GI-associated lncRNAs—those that are both differentially ex-pressed and significantly correlated with HRD scores (Spearman correlation)—are labeled. Gray points represent genes that are not significantly differentially expressed. (B) Box plots showing the expression levels (log2-transformed) of the top three upregulated GI-lncRNAs. (C) Box plots show-ing the expression levels (log2-transformed) of the top three downregulated GI-lncRNAs. Figure 3. (A) Volcano plot showing upregulated and downregulated genes in samples with high HRDscores compared to those with low HRD scores. Red points represent upregulated long non-codingRNAs (lncRNAs), and blue points indicate downregulated lncRNAs, defined by a log2 fold change≥ |2| and an FDR < 0.05. GI-associated lncRNAs—those that are both differentially expressed andsignificantly correlated with HRD scores (Spearman correlation)—are labeled. Gray points representgenes that are not significantly differentially expressed. (B) Box plots showing the expression levels(log2-transformed) of the top three upregulated GI-lncRNAs. (C) Box plots showing the expressionlevels (log2-transformed) of the top three downregulated GI-lncRNAs.Among the GI-lncRNAs, DLX6-AS1 as the most upregulated (log2FC = 2.58)(Figure 3B), while LINC02688 was the most downregulated (log2FC = −2.81) (Figure 3C).Using the LncRNAs2Pathways tool, we found that these GI-lncRNAs were significantlyCancers 2025, 17, 996 12 of 34enriched in several KEGG pathways, including oxidative phosphorylation, chemokinesignaling, and homologous recombination (FDR < 0.05) (Supplementary Table S5). Addi-tionally, we explored the chromosomal locations of the GI-lncRNAs and found that theyare widely distributed across the genome (Figure 4 and Supplementary Table S6).Cancers 2025, 17, x 13 of 36 Figure 4. The circos plot illustrates the chromosomal distribution of the genomic instability-associ-ated long non-coding RNAs (GI-lncRNAs). In this plot, each circle from the periphery to the center represents the following specifications: chromosomal location and the positions of the known genes in the genome. The upregulated GI-lncRNAs are colored in red, while the downregulated ones are colored in blue (with a log2 fold change ≥ |2| and an FDR < 0.05). This figure was created using shinyCircos-V2.0 [81]. 3.3. Identification of Genomic Instability-Associated mRNAs (GI-mRNAs) in LUAD To identify GI-mRNAs in LUAD, we first compared mRNA expression between sam-ples with high HRD scores and those with low HRD scores to identify differentially ex-pressed mRNAs (DE-mRNAs). This analysis revealed 257 DE-mRNAs, with 123 upregu-lated genes and 134 downregulated genes (log2 FC ≥ |2| and FDR < 0.05). The complete list of DE-mRNAs is provided in Supplementary Table S7. After identifying the DE-mRNAs, we conducted a correlation analysis to assess the significance and nature of the association between HRD scores and DE-mRNA expression. We defined DE-mRNAs with a p-value < 0.05 and a Spearman coefficient ≥ |0.1| as GI-mRNAs. We identified a total of 200 GI-mRNAs (85 upregulated and 115 downregulated; Supplementary Table S8). The top 10 upregulated and downregulated GI-mRNAs are highlighted in Figure 5A. The biological process and enrichment annotation analysis were performed on the GI-mRNAs with the same significant difference direction. The results indicated that the upregulated GI-mRNAs are primarily associated with pathways related to ’transcrip-tional misregulation in cancer’, while the downregulated GI-mRNAs are predominantly linked to metabolic processes (Figure 5B). Biological process analysis revealed that key processes include those previously implicated in the function of lncRNAs, such as the ’Regulation of Immune Response’, ’CRD-mediated mRNA Stabilization’, and ’mRNA Figure 4. The ci cos plot illustrate the chromosomal distributio f the genomic instability-associatedlong non-coding RNAs (GI-lncRNAs). In this plot, each circle from the periphery to the centerrepresents the following specifications: chromosomal location and the positions of the known genesin the genome. The upregulated GI-lncRNAs are colored in red, while the downregulated ones arecolored in blue (with a log2 fold change ≥ |2| and an FDR < 0.05). This figure was created usingshinyCircos-V2.0 [81].3.3. Identification of Genomic Instability-Associated mRNAs (GI-mRNAs) in LUADTo identify GI-mRNAs in LUAD, we first compared mRNA expression betweensamples with high HR scores and those with low HRD scores to identify differentiallyexpressed mRNAs (DE-mRNAs). This analysis revealed 257 DE-mRNAs, with 123 up-regulated genes and 134 downregulated genes (log2 FC ≥ |2| and FDR < 0.05). Thecomplete list of DE-mRNAs is provided in Supplementary Table S7. After identifying theDE-mRNAs, we conducted a correlation analysis to assess the significance and nature ofthe association between HRD scores and DE-mRNA expression. We defined DE-mRNAswith a p-value < 0.05 and a Spearman coefficient ≥ |0.1| as GI-mRNAs. We identified atotal of 200 GI-mRNAs (85 upregulated and 115 downregulated; Supplementary Table S8).The top 10 upregulated and downregulated GI-mRNAs are highlighted in Figure 5A.Cancers 2025, 17, 996 13 of 34Cancers 2025, 17, x 14 of 36 Stabilization’ (Figure 5C). The complete list of KEGG pathways and biological processes are shown in the Supplementary Tables S9 and S10. Figure 5. (A) Volcano plot showing upregulated and downregulated genes. Red points represent upregulated mRNAs, and blue points indicate downregulated mRNAs in samples with high HRD scores compared to samples with low HRD scores, defined by a log2 fold change ≥ |2| and an FDR < 0.05. The top 10 upregulated and downregulated GI-associated mRNAs—those that are both dif-ferentially expressed and significantly correlated with HRD scores (Spearman correlation)—are Fi . (A) Volcano plot showing upr gulated a d downregulated gene . Red points representupr lated mRNAs, and blue points in icate downregulated mRNAs in samples with high HRDscores compared to samples with low HRD scores, defined by a log2 fold change ≥ |2| and anFDR < 0.05. The top 10 upregulated and downregulated GI-associated mRNAs—those that are bothdifferentially expressed and significantly correlated with HRD scores (Spearman correlation)—arelabeled. Gray points represent genes that are not differentially expressed. (B) Bar graph displayingthe top 10 enriched pathways based on the p-value from the Kyoto Encyclopedia of Genes andGenomes (KEGG) pathway enrichment analysis for GI-mRNAs. (C) Bar graph displaying thetop 10 enriched biological processes, based on the p-value, from Gene Ontology (GO) enrichmentanalysis for GI-mRNAs.The biological process and enrichment annotation analysis were performed on theGI-mRNAs with the same significant difference direction. The results indicated that theupregulated GI-mRNAs are primarily associated with pathways related to ’transcriptionalCancers 2025, 17, 996 14 of 34misregulation in cancer’, while the downregulated GI-mRNAs are predominantly linkedto metabolic processes (Figure 5B). Biological process analysis revealed that key processesinclude those previously implicated in the function of lncRNAs, such as the ’Regulationof Immune Response’, ’CRD-mediated mRNA Stabilization’, and ’mRNA Stabilization’(Figure 5C). The complete list of KEGG pathways and biological processes are shown inthe Supplementary Tables S9 and S10.3.4. Identifying Interactions Between GI-lncRNAs and GI-mRNAsLncRNAs can bind directly to mRNAs or form complexes with RNA-binding proteins,stabilizing mRNAs to promote their translation or marking them for degradation. Recentstudies on lncRNA–mRNA interactions have demonstrated their regulatory roles in variousbiological processes [82–84], highlighting the utility of comprehensive lncRNA–mRNAinteraction predictions in elucidating lncRNA functions [85].To investigate the potential interactions between GI-lncRNAs and GI-mRNAs inLUAD, we utilized the RISE database, an RNA interactome database from sequencing exper-iments [57]. From this analysis, we identified 132 interactions involving GI-lncRNAs (Sup-plementary Table S11). Notably, 12 out of 30 GI-lncRNAs were found to interact with otherprotein-coding and non-coding RNA molecules. These GI-lncRNAs include DLX6-AS1,C8orf34-AS1, LINC01224, LINC00664, LHFPL3-AS2, MIR2052HG, CALML3-AS1, SRGAP3-AS2, TRPM2-AS, SFTA1P, ATP13A4-AS1, and MGC27382. The GI-lncRNAs C8orf34-AS1 andDLX6-AS1 exhibited the highest number of interactions among all identified GI-lncRNAs(Figure 6A,B). Specifically, C8orf34-AS1 demonstrated a total of 70 interactions, including54 with mRNAs (protein-coding genes) and 5 with other lncRNAs (Figure 6A).From the initial 132 interactions involving GI-lncRNAs, we filtered the dataset toinclude only interactions involving GI-mRNAs, identifying three significant interactionswith DLX6, FOXI3, and CDH20. Specifically, DLX6 was found to interact with DLX6-AS1,while FOXI3 and CDH20 were found to interact with C8orf34-AS1. In our cohort, we founda significant positive correlation between DLX6-AS1 and DLX6 (ρ = 0.93, p < 0.05), as wellas between C8orf34-AS1 and CDH20 (ρ = 0.32) (Figure 7A,B).Cancers 2025, 17, x 16 of 36 Figure 6. Interaction network depicting the associations involving the genomic instability-associ-ated long non-coding RNAs (GI-lncRNAs) (A) C8orf34-AS1 and (B) DLX6-AS1. The network was constructed using Cytoscape. rRNA: ribosomal RNA. lncRNA: long non-coding RNA. snRNA: small nuclear RNA. Figure 6. Cont.Cancers 2025, 17, 996 15 of 34Cancers 2025, 17, x 16 of 36 Figure 6. Interaction network depicting the associations involving the genomic instability-associ-ated long non-coding RNAs (GI-lncRNAs) (A) C8orf34-AS1 and (B) DLX6-AS1. The network was constructed using Cytoscape. rRNA: ribosomal RNA. lncRNA: long non-coding RNA. snRNA: small nuclear RNA. Figure 6. Interaction network depicting the associations involving the genomic instability-associatedlong non-coding RNAs (GI-lncRNAs) (A) C8orf34-AS1 and (B) DLX6-AS1. The network was con-structed using Cytoscape. rRNA: ribosomal RNA. lncRNA: long non-coding RNA. snRNA: smallnuclear RNA.The genomic localization of GI-lncRNAs and their associated GI-mRNAs suggests thatthese lncRNAs may have both cis- and trans-regulatory roles. For example, the lncRNADLX6-AS1 overlaps its corresponding protein-coding gene, DLX6, on chromosome 7q21.3,while C8orf34-AS1 and CDH20 localize to chromosomes 8 and 18, respectively.Cancers 2025, 17, x 17 of 36 Figure 7. Genomic positions of GI-lncRNAs and GI-mRNAs with interactions identified in the RISE database. (A) The genomic positions of GI-lncRNAs and their corresponding GI-mRNAs are dis-played. The figure was created using shinyCircos-V2.0 [86]. Grey arrows indicate interactions be-tween GI-lncRNAs and GI-mRNAs. The zoomed-in box highlights chromosome 7, showing the overlap between the gene DLX6 and its antisense, DLX6-AS1. (B) Spearman’s rank correlation com-paring the expression levels of GI-lncRNAs and GI-mRNAs. 3.5. Regulatory Mechanisms and Functional Roles of GI-lncRNAs in LUAD and Other Diseases The interaction analysis in Section 3.4 offers insights into how GI-lncRNAs, such as DLX6-AS1 and C8orf34-AS1, could regulate GI-mRNAs through cis- and trans-regulatory mechanisms, revealing a framework for their potential roles in LUAD biology. These spe-cific interactions, such as the ones between DLX6-AS1 and DLX6 and between C8orf34-AS1 and CDH20, highlight a direct molecular interplay that may influence tumor behavior in the context of genomic instability. Building upon these findings, Section 3.5 expands the focus to explore the broader regulatory mechanisms and functional roles of GI-lncRNAs in LUAD and other diseases by leveraging the LncTarD 2.0 database [56]. This comprehensive resource provides key lncRNA–target interactions, associated functions, Figure 7. Cont.Cancers 2025, 17, 996 16 of 34Cancers 2025, 17, x 17 of 36 Figure 7. Genomic positions of GI-lncRNAs and GI-mRNAs with interactions identified in the RISE database. (A) The genomic positions of GI-lncRNAs and their corresponding GI-mRNAs are dis-played. The figure was created using shinyCircos-V2.0 [86]. Grey arrows indicate interactions be-tween GI-lncRNAs and GI-mRNAs. The zoomed-in box highlights chromosome 7, showing the overlap between the gene DLX6 and its antisense, DLX6-AS1. (B) Spearman’s rank correlation com-paring the expression levels of GI-lncRNAs and GI-mRNAs. 3.5. Regulatory Mechanisms and Functional Roles of GI-lncRNAs in LUAD and Other Diseases The interaction analysis in Section 3.4 offers insights into how GI-lncRNAs, such as DLX6-AS1 and C8orf34-AS1, could regulate GI-mRNAs through cis- and trans-regulatory mechanisms, revealing a framework for their potential roles in LUAD biology. These spe-cific interactions, such as the ones between DLX6-AS1 and DLX6 and between C8orf34-AS1 and CDH20, highlight a direct molecular interplay that may influence tumor behavior in the context of genomic instability. Building upon these findings, Section 3.5 expands the focus to explore the broader regulatory mechanisms and functional roles of GI-lncRNAs in LUAD and other diseases by leveraging the LncTarD 2.0 database [56]. This comprehensive resource provides key lncRNA–target interactions, associated functions, 7. Genomic positi ns of GI-lncRNAs and GI-mRNAs wi h interactions identified in theRISE database. (A) The gen mic positions of GI-lncRNAs and their corresponding GI-mRNAs aredisplayed. The figure was created us g shinyCircos-V2.0 [86]. Grey arrows indi ate int ractionsbetween GI-lncRNAs and GI-mRNAs. The zoomed-in box highlights chromosome 7, showing thel betw en the gene DLX6 and it antisense, DLX6-AS1. (B) Spearman’s ra k correlationcomparing the expression levels of GI-lncRNAs and GI-mRNAs.3.5. Regulatory Mechanisms and Functional Roles of GI-lncRNAs in LUAD and Other Diseasese interaction analysis in Section 3.4 offers insights into how GI-lncRNAs, such as1 and C8orf34-AS1, could regulate GI-mRNAs through cis- d trans-regulatoryisms, revealing a framework fo their potential roles in LUAD biology. Thesespecific interactions, such as the ones between DLX6-AS1 and DLX6 and between C8orf34-AS1 and CDH20, highlight a direct molecular interplay that may influence tumor behaviorin the context of genomic instability. Building upon these findings, Section 3.5 expands thefocus to explore the broader regulatory mechanisms and functional roles of GI-lncRNAs inLUAD and other diseases by leveraging the LncTarD 2.0 database [56]. This comprehensiveresource provides key lncRNA–target interactions, associated functions, and regulatorymechanisms in human diseases, encompassing over 70,000 interactions supported bymanually curated experimental evidence. By integrating these two approaches, we developa synergistic strategy to explore the dual aspects of GI-lncRNAs: their direct molecularinteractions as well as their broader regulatory roles and implications across variousdisease contexts.Among the 30 identified GI-lncRNAs, six upregulated lncRNAs (DLX6-AS1, ELFN1-AS1, MIR2052HG, TRPM2-AS, LINC01224, and CALML3-AS1) were found in the database,with functions predominantly linked to ceRNA activity, miRNA sponging, and transcrip-tional regulation across various cancer types, including NSCLC and LUAD.For example, DLX6-AS1, the most highly expressed GI-lncRNA (logFC = 2.58), wasimplicated in multiple ceRNA interactions in cancer, acting as a miRNA sponge for severaltargets, such as miR-16-5p, miR-505-3p, and miR-497-5p. Notably, miR-505 (logFC = 0.37)and miR-497 (logFC = −0.54) were also significantly dysregulated in GI-LUAD samples(p < 0.05; Supplementary Table S12). However, whether these dysregulations are directlymediated by DLX6-AS1 or occur through indirect mechanisms in the context of GI in LUADrequires further investigation.Tumor suppressor miRNAs such as miR-497-5p and miR-140-3p were also identified astargets of GI-lncRNA-mediated sponging. For instance, TRPM2-AS sponges miR-140-3p, aknown tumor suppressor in NSCLC [87]. Similarly, DLX6-AS1 accelerates cell proliferationby regulating the miR-497-5p/SNCG pathway in prostate cancer [88].Cancers 2025, 17, 996 17 of 34In addition to miRNA sponging, GI-lncRNAs are also involved in transcriptionalregulation. TRPM2-AS interacts with the transcription factor ELK1 in gastric cancer, whileLINC01224 interacts with YY1 in colorectal cancer, both of which influence cell cycleprogression and stress responses.GI-lncRNAs have also been shown to interact with oncogenic targets. DLX6-AS1was found to regulate PIK3CA and MTOR in colorectal cancer. These genes are criticalcomponents of pathways governing cell survival, proliferation, and chromatin remodeling,such as the PI3K/AKT/mTOR signaling cascade. This illustrates the potential contributionof GI-lncRNAs to tumor progression in LUAD with genomic instability.Table 3 summarizes the regulatory mechanisms and functional roles of GI-lncRNAsin human diseases, as identified in the LncTarD 2.0 database. The table highlights pre-dicted functions such as ceRNA activity, miRNA sponging, and transcriptional regulation,providing a functional framework for these GI-lncRNAs in LUAD.Table 3. Functions involving GI-lncRNAs retrieved from the LncTarD 2.0 database.lncRNA Function Target miRNA DiseaseDLX6-AS1 ceRNA RUNX2 miR-505-3p Breast cancerARPP19 miR-16-5p Cervical cancerCDK4 miR-124-3p Ewing’s sarcomaPDK1 miR-4290 Gastric cancerPOU2F1 miR-204-5p Gastric cancerMMP2 miR-203a Hepatocellular carcinomaWEE1 miR-424-5p Liver cancerE2F1 miR-197-5p Malignant gliomaYAP1 miR-497-5p NeuroblastomaPRR11 miR-144 NSCLCDLK1 miR-129-5p OsteosarcomaGADD45A miR-376c Pre-eclampsiaERP44 miR-149-5p Pre-eclampsiaPTEN miR-26a Renal cell carcinomaprotein interaction NOTCH1 Ovarian cancerUPF1 Thyroid cancerNA PIK3CA Colorectal cancerAKT1 Colorectal cancerMTOR Colorectal cancerDLX6 Lung adenocarcinomasponge miR-199a Cervical cancermiR-497-5p Neuroblastoma/PDACmiR-223 Urinary bladder cancertranscript reg CADM1 Hepatocellular carcinomaCancers 2025, 17, 996 18 of 34Table 3. Cont.lncRNA Function Target miRNA DiseaseLINC01224 ceRNA MYO6 miR-485-5p Colorectal cancerAKT3 miR-485-5p Endometrial cancersponge MIR2467 Colorectal cancer/NSCLCtranscript reg YY1 Colorectal cancerELFN1-AS1 ceRNA MTA1 miR-1250 Colorectal cancerTRIM44 miR-4644 Colorectal cancerGFPT1 miR-183-3p Esophageal cancerNA Erk Colon adenocarcinomaIRS1 Colon adenocarcinomavimentin Colon adenocarcinomaTRPM2-AS ceRNA PYCR1 miR-140-3p Breast cancerHMGA1 miR-195 Gastric cancerWEE1 miR-497 Retinoblastomaprotein interaction TAF15 Colorectal cancerNA SHC1 NSCLCsponge miR-140-3p Breast cancertranscript reg ELK1 Gastric cancerCALML3-AS1 ceRNA ZBTB2 miR-4316 Urinary bladder cancerMIR2052HG histone mod andtranscript regLMTK3 Breast cancerLegend: ceRNA (competing endogenous RNA): a regulatory mechanism where lncRNAs act as molecular sponges,sequestering miRNAs to influence gene expression. NA (not available): interactions or functions that have notbeen annotated. NSCLC: non-small cell lung cancer. PDAC: pancreatic cancer. transcript reg: transcriptionalregulation. histone mod: histone modification.Interestingly, supporting our previous findings, there is evidence of an interactionbetween DLX6-AS1 and DLX6 in LUAD. However, the exact mechanism of this interactionremains unknown, and further studies are necessary to elucidate this.3.6. GI-lncRNAs as Potential Prognostic Biomarkers in LUADThe mechanistic interactions identified in Section 3.4, particularly those involvingGI-lncRNAs such as DLX6-AS1 and C8orf34-AS1, emphasize their potential regulatory rolesin LUAD through both cis- and trans-regulatory mechanisms. These findings, coupledwith evidence from the LncTarD 2.0 database in Section 3.5, establish a framework forunderstanding how GI-lncRNAs can contribute to LUAD-related processes, includingmiRNA sponging, ceRNA activity, and transcriptional regulation. The interplay betweenGI-lncRNAs and pivotal oncogenic pathways like PI3K/AKT/mTOR further highlightstheir potential as mediators of tumor progression.Building on these mechanistic insights, in this section, we explore the clinical signifi-cance of GI-lncRNAs as prognostic biomarkers in LUAD.Our survival analysis revealed that specific GI-lncRNAs have significant prognosticvalue in LUAD patients stratified by HRD scores. For example, in the group with highHRD scores, low expression of GI-lncRNAs such as AC046195.1, LHFPL3-AS2, LINC02772,and LINC01224 was associated with worse OS, suggesting their potential as biomarkers forpoor outcomes in highly unstable genomic contexts (Table 4 and Figure 8).Cancers 2025, 17, 996 19 of 34Table 4. Key results of the survival analysis for GI-lncRNAs in LUAD patients stratified by HRDscores. The table includes the log-rank test-derived p-values, hazard ratios (HRs), and log-foldchange (LogFC) values for GI-lncRNAs that demonstrated significant prognostic value in the high-HRD group.Gene HR (Hazard Ratio) p-Value LogFCHigh HRD Scores AC046195.1 2.255680678 0.014686109 −2.24LHFPL3-AS2 2.226216673 0.015577954 −2.52LINC02772 2.202470692 0.021184545 −2.09LINC01224 2.150362909 0.0234937 2.30p < 0.05 based on the log-rank test.Cancers 2025, 17, x 20 of 36 and LINC01224 was associated with worse OS, suggesting their potential as biomarkers for poor outcomes in highly unstable genomic contexts (Table 4 and Figure 8). We also explored if the GI-mRNAs (DLX6 and CDH20) predicted to interact with GI-lncRNAs could be associated with OS, and curiously, we found that although DLX6-AS1 and C8orf34-AS1 were not associated with OS in samples with high HRD scores, high ex-pression of GI-mRNA DLX6 (logFC 3.33) was associated with worse OS in these patients (Supplementary Figure S2). Table 4. Key results of the survival analysis for GI-lncRNAs in LUAD patients stratified by HRD scores. The table includes the log-rank test-derived p-values, hazard ratios (HRs), and log-fold change (LogFC) values for GI-lncRNAs that demonstrated significant prognostic value in the high-HRD group. Gene HR (Hazard Ratio) p-Value LogFC High HRD Scores AC046195.1 2.255680678 0.014686109 −2.24 LHFPL3-AS2 2.226216673 0.015577954 −2.52 LINC02772 2.202470692 0.021184545 −2.09 LINC01224 .150362909 0.0234937 2.30 p < 0.05 based on the log-rank test. Figure 8. Kaplan–Meier survival curves for LUAD patients with high HRD scores, stratified by high and low expression levels of the target genes. Blue curves represent patients with low gene expres-sion, while red curves denote those with high expression. The log-rank test was used to calculate the p-value, with p < 0.05 considered statistically significant. Dashed lines mark the 5-year survival time. The gene expression is normalized by z-scores, as described in the methodology section. Figure 8. Kaplan–Meier survival curves for LUAD patients with high HRD scores, stratified by highand low expression levels of the target genes. Blue curves represent patients with low gene expression,whi e red curves d note those with high expression. The log-rank test was us d to calculate thep-value, with p < 0.05 considered statistically significant. Dashed lines mark the 5-year survival time.The gene expression is normalized by z-scores, as described in the methodology section.We also explored if the GI-mRNA (DLX6 and CDH20) predicted to i teract withGI-lncRNAs could be associated with OS, and curiously, we found that although DLX6-AS1and C8orf34-AS1 were not associated with OS in samples with high HRD scores, highexpression of GI-mRNA DLX6 (logFC 3.33) was associated with worse OS in these patients(Supplementary Figure S2).Cancers 2025, 17, 996 20 of 343.7. Association of GI-lncRNAs with Primary Therapy OutcomesGiven the association between GI-lncRNAs and survival in patients with high HRDscores, we further investigated their potential relevance in predicting therapy responsein this subgroup. Our analysis revealed significant differences in the expression levelsof AC046195.1 (log2FC = −2.24) and AC009244.1 (log2FC = −2.06), both of which werenotably downregulated in patients with high HRD scores. Notably, lower expression levelsof these lncRNAs were associated with progressive or stable disease, whereas patients withhigher expression levels demonstrated a more favorable therapeutic response.Interestingly, when we extended our analysis to patients with low HRD scores, nosignificant association was observed between the expression of these lncRNAs and primarytherapy outcomes. This suggests that these GI-lncRNAs may function as specific predictivebiomarkers for therapy response in patients with high HRD scores.Figure 9 illustrates the expression levels of these GI-lncRNAs in patients stratified byHRD scores, while comprehensive data for all GI-lncRNAs are provided in SupplementaryTables S13 and S14.Cancers 2025, 17, x 21 of 36 3.7. Association of GI-lncRNAs with Primary Therapy Outcomes Given the association between GI-lncRNAs and survival in patients with high HRD scores, we further investigated their potential relevance in predicting therapy response in this subgroup. Our analysis revealed significant differences in the expression levels of AC046195.1 (log2FC = −2.24) and AC009244.1 (log2FC = −2.06), both of which were notably downregulated in patients with high HRD scores. Notably, lower expression levels of these lncRNAs were associated with progressive or stable disease, whereas patients wit higher expression levels demonstrate a more favorable therapeutic response. Interestingly, when we extended our analysis to patients with low HRD scores, no significant association was observed between the expression of these lncRNAs and pri-mary therapy outcomes. This suggests that these GI-lncRNAs may function as specific predictive biomarkers for therapy response in p ients with high HRD s ores. Figure 9 illustrates the expression levels of these GI-lncRNAs in patients stratified by HRD scores, while comprehensive data for all GI-lncRNAs are provided in Supplemen-tary Tables S13 and S14. Figure 9. Fisher’s Exact Test results for the relationship between gene expression and therapy out-come success in samples with high and low HRD scores. (A) Expression of AC046195.1 and ther-apy success in high HRD score patients. (B) Expression of AC046195.1 and therapy success in low HRD score patients. (C) Expression of AC009244.1 and therapy success in high HRD score pa-tients. (D) Expression of AC009244.1 and therapy success in low HRD score patients. 3.8. Analysis of Immune Cell Infiltration in Tumors with High and Low HRD Scores Given that the tumor immune microenvironment can influence therapeutic response [89–91], we analyzed immune cell infiltration differences between patients with high and low HRD scores. Using the CIBERSORTx algorithm, we quantified immune cell infiltra-tion in tumor tissues and identified significant differences (p < 0.05) between the two groups. In the low-HRD group, CD4 memory resting T cells comprised the largest Figure 9. Fisher’s Exact Test results for the relationship between gene expression and therapyoutcome success in samples with high and low HRD scores. (A) Expression of AC046195.1 andtherapy success in high HRD score patients. (B) Expression of AC046195.1 and therapy success in lowHRD score patients. (C) Expression of AC009244.1 and therapy success in high HRD score patients.(D) Expression of AC009244.1 and therapy success in low HRD score patients.Cancers 2025, 17, 996 21 of 343.8. Analysis of Immune Cell Infiltration in Tumors with High and Low HRD ScoresGiven that the tumor immune microenvironment can influence therapeutic re-sponse [89–91], we analyzed immune cell infiltration differences between patients withhigh and low HRD scores. Using the CIBERSORTx algorithm, we quantified immune cellinfiltration in tumor tissues and identified significant differences (p < 0.05) between thetwo groups. In the low-HRD group, CD4 memory resting T cells comprised the largestproportion of immune cells (16.96%), whereas in the high-HRD group, M0 macrophageswere the most abundant (16.04%) (Supplementary Table S15).We observed significant differences in the infiltration levels of various immune cells,including plasma cells, T cells (CD4 memory resting, CD4 memory activated, and follicularhelper), monocytes, macrophages (M0 and M1), dendritic cells (resting and activated), mastcells (resting), and neutrophils, between the groups (p < 0.05, Wilcoxon–Mann–Whitneytest) (Supplementary Figures S3 and S4 and Supplementary Table S16).To further investigate these findings, we examined the correlation between the in-filtration of these immune cell types, which differed between the groups with high andlow HRD scores, and the expression levels of GI-lncRNAs (Supplementary Table S17). Weidentified significant correlations between the expression of 21 out of 30 GI-lncRNAs andimmune cell infiltration (Figure 10).Cancers 2025, 17, x 22 of 36 proportion of immune cells (16.96%), whereas in the high-HRD group, M0 macrophages were the most abundant (16.04%) (Supplementary Table S15). We observed significant differences in the infiltration levels of various immune cells, including plasma cells, T cells (CD4 memory resting, CD4 memory activated, and follicu-lar helper), monocytes, macrophages (M0 and M1), dendritic cells (resting and activated), mast cells (resting), and neutrophils, between the groups (p < 0.05, Wilcoxon–Mann–Whit-ney test) (Supplementary Figures S3 and S4 and Supple entary Table S16). To further investigate these findings, we examined the c rrelation betw en t infil-tration of these immune cell types, which differed between the groups with high and low HRD scores, and the expression levels of GI-lncRNAs (Supplementary Table S17). We identified significant correlations between the expression of 21 out of 30 GI-lncRNAs and immune cell infiltrati n (Figure 10). C8orf34-AS1 was significantly associated with the greatest number of immune cell types, including B cells (memory), plasma cells, T cells (CD4 memory resting), monocytes, macrophages (M0), and mast cells (resting). Additionally, several GI-lncRNAs were found to be associated with the infiltration of T cells (CD4 memory resting), including LHFPL3-AS2, LINC02555, C8orf34-AS1, AC046195.1, SRGAP3-AS2, MGC27382, ATP13A4-AS1, LINC01612, and CALML3-AS1. Interestingly, the GI-lncRNA AC046195.1, which is associated with primary therapy outcomes (Figure 9A), was correlated with the infiltration of plasma cells, T cells (CD4 memory resting), macrophages (M1), and mast cells (resting). Figure 10. Heat map showing the correlation between GI-lncRNA expression and immune cell in-filtration in the group with high HRD scores, using Spearman’s rank correlation. Red indicates a positive correlation, while blue indicates a negative correlation, with pale blue representing non-significant relationships (p ≥ 0.05). Figure 10. Heat map showing the correlation between GI-lncRNA expression and immune cellinfiltration in the group with high HRD scores, using Spearman’s rank correlation. Red indicatesa positive correlation, while blue indicates a negative correlation, with pale blue representing non-significant relationship (p ≥ 0.05).Cancers 2025, 17, 996 22 of 34C8orf34-AS1 was significantly associated with the greatest number of immune celltypes, including B cells (memory), plasma cells, T cells (CD4 memory resting), mono-cytes, macrophages (M0), and mast cells (resting). Additionally, several GI-lncRNAs werefound to be associated with the infiltration of T cells (CD4 memory resting), includingLHFPL3-AS2, LINC02555, C8orf34-AS1, AC046195.1, SRGAP3-AS2, MGC27382, ATP13A4-AS1, LINC01612, and CALML3-AS1.Interestingly, the GI-lncRNA AC046195.1, which is associated with primary therapyoutcomes (Figure 9A), was correlated with the infiltration of plasma cells, T cells (CD4memory resting), macrophages (M1), and mast cells (resting).3.9. Exploring Drugs Targeting GI-lncRNAsOur analysis identified 30 GI-lncRNAs with distinct expression patterns in LUAD,suggesting potential functional roles in tumor biology that warrant further investigationfor both mechanistic insights and therapeutic development.To investigate possible drug interactions with GI-lncRNAs, we utilized the ncR-NADrug database, a comprehensive repository of experimentally validated and computa-tionally predicted interactions between drugs and ncRNAs [79]. This approach enabled usto identify existing drugs that could interact with specific GI-lncRNAs, offering opportu-nities for drug repurposing in LUAD or for experimental research into their mechanisticroles in tumor progression.Our findings show that most of the identified GI-lncRNAs are associated with oneor more drugs (Supplementary Table S18). Table 5 details curated associations betweenselect GI-lncRNAs and their corresponding drugs, including the cancer type in which theseinteractions were originally observed.Table 5. Drugs interacting with GI-lncRNAs using the ncRNADrug database.GI-lncRNA Drug DrugBank Accession Number Cancer Type PMID FDAKCNMB2-AS1 Panobinostat DB06603 Nephroblastoma 26176219 approvedMIR9-1HG Panobinostat DB06603 Nephroblastoma 26176219 approvedMIR9-1HG Tetraarsenic Oxide NA Breast Cancer 33932728 NAELFN1-AS1 Dexamethasone DB01234 Hepatocellular Carcinoma 29409992 approvedDLX6-AS1 Panobinostat DB06603 Nephroblastoma 26176219 approvedLHFPL3-AS2 Oxaliplatin DB00526 Gastric Cancer 29156779 approvedLHFPL3-AS2 Panobinostat DB06603 Nephroblastoma 26176219 approvedLINC01612 Oxaliplatin DB00526 Gastric Cancer 29156779 approvedSFTA1P Panobinostat DB06603 Nephroblastoma 26176219 approvedLINC02688 Panobinostat DB06603 Nephroblastoma 26176219 approvedNA: not available.The lncRNA DLX6-AS1—which we identified as being associated with high HRDscores, with its predicted target protein-coding gene DLX6 being linked to poorerprognosis—was found to interact with Panobinostat, an FDA-approved drug. This interac-tion could be leveraged in functional studies to investigate whether modulating DLX6-AS1affects genomic instability in LUAD, potentially revealing new therapeutic strategies.Other notable GI-lncRNA-drug associations include Panobinostat with KCNMB2-AS1 and MIR9-1HG (previously studied in nephroblastoma) and Dexamethasone withELFN1-AS1 (investigated in hepatocellular carcinoma). Many of these drugs are alreadyapproved for use in other cancers (for example, Oxaliplatin), providing a foundation forpreclinical studies in LUAD, where their interactions with GI-lncRNAs could help uncovernew mechanisms or therapeutic pathways.Cancers 2025, 17, 996 23 of 344. DiscussionLung cancers often exhibit asymptomatic progression, leading to diagnoses at ad-vanced stages. Despite advancements in treatments, the five-year survival rate for lungcancer patients remains low [92], highlighting a critical need for new therapeutic strategiesand diagnostic tools that can detect the disease earlier, where treatment is more effective. GIhas been reported in various malignant cancers, including lung cancer [20,21,23,93], withincreasing evidence suggesting that lncRNAs play crucial roles in regulating GI throughdiverse mechanisms [47]. Signatures of these lncRNAs hold promise as prognostic biomark-ers in multiple cancer types. Previous studies have demonstrated that understandinggenomic instability-related lncRNAs could help predict therapy responses and identifyhigh-risk patients [94], and these lncRNAs may serve as novel therapeutic targets to addressGI in cancers [47,50]. Recently, a signature comprising five genomic instability-associatedlncRNAs was identified, demonstrating potential for predicting prognosis and serving asbiomarkers in lung adenocarcinoma [50].Numerous lncRNAs regulate DNA damage repair and genomic instability path-ways [94–96]. In this study, we proposed identifying GI-associated lncRNAs in LUADand their potential regulatory mechanisms and functional roles. We also explored therelationship between GI-lncRNAs and clinical outcomes, therapy responses, and immuneinfiltration. Additionally, we investigated potential drugs targeting GI- lncRNAs, aim-ing to identify existing drugs that could interact with them, offering opportunities fordrug repositioning in LUAD or for experimental research into their mechanistic roles intumor progression.To classify TCGA samples, we used HRD scores, a well-established measure ofGI [50,97]. HRD scores have been shown to be predictive of therapeutic outcomes inNSCLC patients undergoing neoadjuvant immunotherapy [98]. In ovarian cancer, individ-uals with HRD tumors experience a significant improvement in progression-free survival(PFS) with olaparib treatment [99], and the FDA has approved HRD as a companiondiagnostic for the use of niraparib and olaparib in ovarian cancer therapy [100].In this study, we explored the association between HRD scores and various clinico-pathological characteristics, such as pathological stage, age, vital status, sex, race, andTNM classification. Although we did not observe a direct correlation between HRD scoresand most of these features (Table 1), consistent with previous reports [25], we identified anotable association between HRD scores and age, suggesting that younger LUAD patientsmay exhibit higher levels of GI. This observation aligns with studies that have shown anegative association between age and GI in lung cancer, potentially due to tobacco exposure,which may lead to an earlier onset of mutation-dense lung cancers [101,102].We also investigated the association between HRD scores and patient survival out-comes, specifically, OS and PFS. Patients with higher HRD scores exhibited lower OSand PFS, but the differences were not significant between the groups with high and lowHRD scores. This lack of significance may seem counterintuitive given the establishedassociation between high HRD scores and lower PFS in eight cancer types (glioblastoma,esophageal cancer, thyroid carcinoma, kidney renal clear cell carcinoma, uterine corpusendometrial carcinoma, prostate adenocarcinoma, kidney renal papillary cell carcinoma,and adrenocortical carcinoma) [26]. However, it is important to note that the role of HRDin LUAD remains complex. A comprehensive pan-cancer analysis revealed that HRDscores varied significantly between patients with the same type of cancer, including lungcancers [37]. While HRD has been associated with favorable prognosis in some cancers,such as ovarian cancer and triple-negative breast cancer, its relationship with LUAD isnot yet fully understood. A higher HRD score was also associated with worse PFS inmultiple cancer types but not in lung squamous cell carcinoma (LUSC) and TCGA-LUADCancers 2025, 17, 996 24 of 34cohorts [26]. Similar results were found by Feng et al., who did not observe a relationshipbetween poorer OS and HRD score in the TCGA-LUAD cohort; however, in a differentLUAD cohort, OS was significantly decreased in patients with high HRD scores [25].Several factors, including the heterogeneity of the tumor microenvironment, the pres-ence of co-mutations, and the intricate nature of genomic instability itself, could contributeto this lack of significant difference in survival outcomes [25]. Given the conflicting evi-dence, it would be accurate to say that the association between HRD scores and survivalin LUAD is not consistently observed across all studies and may depend on various fac-tors. Further studies with larger and more diverse cohorts and standardized methods forassessing genomic instability may help clarify this relationship. For now, it is important tonote that GI impacts multiple aspects of tumor biology beyond survival, including tumorevolution and heterogeneity, immune evasion, and treatment resistance [103–107].Although our findings suggest that HRD alone may not be a robust predictor of PFSor OS in LUAD, they highlight the potential of HRD as part of a composite biomarker.This emphasizes the need for an integrative approach that combines HRD with additionalmolecular features, such as specific lncRNAs, to improve patient stratification. Notably,combining HRD scores with TP53 mutation status has been shown to enhance prognosticaccuracy in LUAD [25].In our study, LUAD patients with high HRD scores frequently harbored TP53 muta-tions, while no significant differences were observed in the mutational status of other keydriver genes. These findings are consistent with previous reports [25]. TP53 mutations arestrongly associated with genomic instability, including CIN, oncogene amplification, andtumor suppressor gene deletions [108]. TP53 mutation-driven genomic instability is knownto contribute to chemoresistance and recurrence, underscoring its broader implications intumor progression [109].Next, by comparing patients with high and low HRD scores, we identified 30 lncRNAsand 200 mRNAs associated with high genomic instability. Although numerous studies havefocused on the individual roles of lncRNAs in a genomic instability context [50,110,111], agrowing body of research has begun to investigate the combined influence of GI-associatedlncRNAs and mRNAs [112–114]. In this study, we extended this approach by examiningboth GI-lncRNAs and GI-mRNAs, aiming to uncover potential interactions and regulatorymechanisms. This dual investigation enabled a more comprehensive understanding of howthese molecular players collectively contribute to the GI phenotype in LUAD, potentiallyunveiling novel targets for therapeutic intervention and biomarkers for patient stratification.We identified three lncRNAs—FAM83A-AS1, MIR193BHG, and LINC01116—that havebeen previously reported in the literature as being associated with genomic instability inLUAD [42]. Although these lncRNAs did not meet our criterion of log2 fold change ≥ |2|,they all had an FDR < 0.05, indicating their potential relevance.DLX6-AS1 was the most upregulated GI-lncRNA (log2FC = 2.58). Recently, researchgroups have explored the expression of DLX6-AS1 in human tissues and its clinical valuein various cancers. All studies consistently concluded that DLX6-AS1 is upregulated inpatients with cancer (reviewed by Xue et al., 2020 [115]). Its oncogenic role has been exten-sively demonstrated in various cancer models, where it promotes an aggressive phenotypein cancer cell lines. Studies have shown that DLX6-AS1 contributes to tumor progressionby enhancing cell proliferation, migration, invasion, and epithelial–mesenchymal transition(EMT) while simultaneously suppressing apoptosis in cancer cells (reviewed by Ghafouri-Fard et al., 2022 [116]). In NSCLC, DLX6-AS1 knockdown has been reported to suppresstumorigenesis and inhibit cancer progression [117]. Similarly, in pancreatic cancer (PC),the overexpression of DLX6-AS1 was shown to drive tumor growth, as evidenced by in-creased tumor volume and weight, as well as a higher number of liver and lung metastaticCancers 2025, 17, 996 25 of 34foci [118]. Conversely, the knockdown of DLX6-AS1 led to significant reductions in theseoncogenic features, highlighting its potential as a therapeutic target. The overexpression ofDLX6-AS1 was also detected in lung cancer tissues and patients’ serum and exosomes andwas positively correlated with a poor prognosis [119].We found that GI-associated lncRNAs are distributed across the genome. This obser-vation aligns with previous studies demonstrating the widespread nature of GI-relatedlncRNAs across chromosomal loci [47,50,120]. For instance, research on colon cancer identi-fied a range of GI-associated lncRNAs and mRNAs spread throughout the genome [120].Functional studies of lncRNA–mRNA co-expression networks highlight their varied influ-ence in essential processes like immune responses, cell proliferation, and DNA damagerepair [120].One significant mechanism by which lncRNAs regulate gene expression is throughpost-transcriptional regulation [121–123], where they stabilize or destabilize messengerRNAs (mRNAs) via complementary base pairing, affecting mRNA degradation or trans-lation. One class of lncRNAs involved in post-transcriptional regulation are known ascompeting endogenous RNAs (ceRNAs) or sponges [121,124]. LncRNAs can act as ceRNAsby two different mechanisms. On one hand, they are able to sequester miRNAs, avoidingtheir binding to target mRNAs, and on the other hand, they can directly interact withtarget mRNA transcripts to block miRNA binding sites [121–123]. Most lncRNAs that blockmiRNA activity to enhance mRNA stability are transcribed from the opposite DNA strandto their paired (sometimes complementary) sense protein-coding genes and are known asnatural antisense transcripts (NATs) [125]. However, there are some examples of lncRNAsthat can exert trans-regulatory actions, meaning they regulate genes located on differentchromosomes or distant regions of the same chromosome, for example, LincRNA-p21,which recognizes mRNA targets by base pairing and regulates their translation [124,126].Recent studies have highlighted the importance of lncRNA–mRNA interactions in regulat-ing gene expression and cellular functions, underscoring the utility of computational andexperimental approaches to predict and elucidate these interactions [82–85].In this study, we explored potential interactions between GI-lncRNAs and GI-mRNAsin LUAD using the RISE database, which integrates RNA interactome data derived fromsequencing experiments. Our analysis revealed notable interactions between C8orf34-AS1and CDH20 and between DLX6-AS1 and DLX6. DLX6-AS1 is an antisense transcriptof DLX6, a type of lncRNA that constitutes approximately 40% of all lncRNAs [127–129].Antisense lncRNAs typically regulate their overlapping sense protein-coding genes throughvarious mechanisms, often acting in cis due to their close genomic proximity. Thesemechanisms can influence the expression of neighboring protein-coding genes in eithera concordant or discordant manner [130,131]. In our analysis, both DLX6-AS1 and DLX6were found to be upregulated in high-HRD samples, suggesting a potential concordantregulatory relationship. Previous reports demonstrate that the expression level of DLX6-AS1 in LUAD tissues is significantly higher compared to paired adjacent normal lungtissues. Furthermore, the downregulation of DLX6-AS1 has been shown to decrease bothDLX6 mRNA and protein levels, providing further evidence of the regulatory interplaybetween these transcripts and highlighting their potential role in LUAD pathogenesis [132].Additionally, using the LncTarD 2.0 database, a source that offers key lncRNA–targetinteractions, their associated functions, and regulatory mechanisms in human diseases,we found evidence of an interaction between DLX6-AS1 and DLX6 in LUAD. However,the exact molecular mechanisms underlying this interaction remain unclear, and furtherstudies are needed to fully elucidate the biological significance and potential therapeuticimplications of this regulatory relationship.Cancers 2025, 17, 996 26 of 34We also investigated the potential association between GI-lncRNAs and GI-mRNAspredicted to interact with them, based on the RISE database and their association with OS.Although DLX6-AS1 and C8orf34-AS1 were not associated with OS in samples with highHRD scores, high expression of the GI-mRNA DLX6 (logFC = 3.33) was linked to worse OSin these patients (Supplementary Figure S2). This observation underscores the importanceof further investigating the role of GI-mRNAs and their interactions with lncRNAs in LUADprognosis and progression. This area opens avenues for directly targeting lncRNAs toaffect the expression of their protein-coding partners. Recent advances in lipid nanoparticle(LNP) delivery systems, similar to those used in COVID-19 vaccines, enhance the feasibilityof RNA-based therapies aimed at lncRNAs [133]. Furthermore, techniques like gapmerscan be utilized to upregulate the expression of sense partner genes linked to antisenselncRNAs [134,135]. These gene therapies could potentially be used to overcome GI inLUAD by targeting lncRNAs. Further experimental validation of these interactions couldprovide deeper insights into their functional significance and therapeutic potential.We identified significant prognostic implications for specific GI-lncRNAs in LUADpatients when stratified by HRD scores. Notably, in the group with high HRD scores,low expression of GI-lncRNAs such as AC046195.1, LHFPL3-AS2, and LINC02772 wasassociated with worse OS. This suggests that these lncRNAs could serve as biomarkersfor poor outcomes in tumors with high levels of genomic instability; however, furthervalidation is required in independent data. Interestingly, these associations were notobserved in patients with low HRD scores, highlighting the distinct biological dynamicsin LUAD subgroups defined by their HRD levels. We also investigated the relationshipbetween GI-lncRNA expression and primary therapy outcomes. In patients with highHRD scores, a lower expression of AC046195.1 and AC009244.1 was linked to progressiveor stable disease, whereas no significant differences were observed in patients with lowHRD scores. These findings highlight the context-dependent prognostic significance ofGI-lncRNAs, particularly in relation to the genomic instability captured by HRD scores.GI involves a complex interplay between immune evasion and immune activation.While increased genomic heterogeneity among tumor cells can facilitate immune escape,high levels of genomic alterations may enhance immunogenicity by generating neoanti-gens recognized by the immune system, potentially leading to greater immune infiltra-tion [104,136]. To investigate the relationship between GI-lncRNA expression and immuneinfiltration in LUAD, we analyzed immune cell populations across groups with high andlow HRD scores. Significant variations in the infiltration of plasma cells, CD4+ T cells,monocytes, macrophages, dendritic cells, and neutrophils were observed. Notably, tumorswith high HRD scores exhibited elevated levels of M0 and M1 macrophages comparedto those with low HRD scores. This finding aligns with previous studies indicating thatmacrophages can contribute to DNA damage and GI in adjacent cells [137–139]. Thesemacrophages release diffusible factors that induce stress signaling, DNA damage, andCIN in neighboring cells [140]. The observed associations between GI-lncRNA expressionand immune cell infiltration point to a potential involvement of GI-lncRNAs in shapingthe interaction between the tumor and the immune system in LUAD. However, furtherresearch is needed to unravel the mechanisms driving these relationships.Finally, we explored drugs targeting GI-lncRNAs, which could offer therapeutic pos-sibilities or serve as tools for studying GI-lncRNA mechanisms. The lncRNA UCA1, forexample, has been associated with drug resistance in several cancers, including bladderand breast cancer. The overexpression of UCA1 is linked to resistance to chemotherapeuticagents like cisplatin and gemcitabine [141,142], and targeting UCA1 through knockdownstrategies has been shown to restore drug sensitivity in resistant cancer cells [143]. Thepotential of targeting ncRNAs, particularly lncRNAs and miRNAs, to overcome cancer GICancers 2025, 17, 996 27 of 34and therapy resistance has been widely explored [144]. Two therapeutic strategies havebeen proposed to restore radio- and chemotherapy response via the CIN pathway: acceler-ating CIN to generate less-fit karyotypes or inhibiting CIN to target a stable and geneticallyfrozen cancer cell population [144]. It has been proposed that by overexpressing or inhibit-ing CIN-associated lncRNAs, CIN-induced resistance to therapy can be manipulated [144].Our findings revealed several drugs targeting GI-lncRNAs, including those upregulatedin samples with high HRD scores. One promising finding is Panobinostat, which targetsDLX6-AS1, a highly promising GI-lncRNA that warrants further investigation as a potentialcandidate for future studies.While we acknowledge that the therapeutic implications of targeting GI-lncRNAsare promising, we emphasize that our study is exploratory, aiming to identify potentialbiomarkers and investigate their functional roles. The feasibility and clinical application ofthese findings, particularly the targeting of DLX6-AS1 with drugs like Panobinostat, requirefurther experimental validation and clinical trials to confirm their therapeutic potentialin LUAD.5. ConclusionsIn this study, we explored the role of GI-lncRNAs and GI-mRNAs in LUAD, focusingon their interactions and potential as prognostic biomarkers, particularly when combinedwith HRD scores. We also investigated their association with immune infiltration inhigh-GI samples and identified FDA-approved drugs targeting these GI-lncRNAs, whichcould be explored further in future therapeutic and functional studies. However, furtherresearch is necessary to delve deeper into the molecular mechanisms underlying GI-lncRNAinteractions and their contribution to tumor progression in LUAD. Future studies shouldfocus on validating these findings in larger cohorts, exploring the therapeutic potentialof targeting GI-lncRNAs, and conducting functional assays to better understand theirroles in LUAD. Additionally, experimental studies involving drug repositioning and RNA-based therapies could provide insights into overcoming therapy resistance in LUAD. Theidentification of novel lncRNA-targeted therapies offers exciting prospects for clinicalapplications in LUAD treatment.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers17060996/s1, Figure S1: Kaplan–Meier survival curvesfor (A) overall survival and (B) progression-free survival comparing the groups with high and lowHRD scores; Figure S2: Kaplan–Meier survival curves for LUAD patients with high HRD scores,stratified by the target genes’ high and low expression levels; Figure S3: Bar graph illustrating thecomposition of infiltrating immune cells in groups with high and low HRD scores, summarized bythe mean values for each group; Figure S4: Box plot showing immune cell infiltration levels betweenhigh and low HRD scores. Table S1: Homologous Recombination Deficiency (HRD) scores for LUAD-TCGA samples; Table S2: The clinicopathological characteristics of the patients included in thisstudy; Table S3: The list of differentially expressed long non-coding RNAs (DE-lncRNAs); Table S4:Correlation between DE-lncRNA expression and HRD scores; Table S5: KEGG pathways enrichedby genomic instability-associated lncRNAs (GI-lncRNAs); Table S6: Chromosomal locations ofgenomic instability-associated long non-coding RNAs (GI-lncRNAs); Table S7: The list of differentiallyexpressed mRNAs (DE-mRNAs); Table S8: Correlation between DE-mRNA expression and HRDscores; Table S9: Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysisfor GI-mRNAs; Table S10: Gene Ontology (GO) enrichment analysis for GI-mRNAs; Table S11: RNA–RNA interactions involving GI-lncRNAs; Table S12: The list of differentially expressed miRNAs(DE-miRNAs); Table S13: Fisher’s Exact Test results for the relationship between gene expression andtherapy outcome success in samples with high HRD scores; Table S14: Fisher’s Exact Test results forthe relationship between gene expression and therapy outcome success in samples with low HRDCancers 2025, 17, 996 28 of 34scores; Table S15: Infiltrating immune cell composition in groups with high and low HRD scores,summarized by the mean values calculated for each group; Table S16: Wilcoxon–Mann–Whitneytest results comparing immune cell infiltration levels between high and low HRD scores; Table S17:Correlation between the immune cell infiltration levels and expression levels of GI-lncRNAs (highHRD scores); and Table S18: Existing drugs that may interact with GI-lncRNAs, according to thencRNADrug database.Author Contributions: V.G.P.S. and K.H.B. designed, researched, and analyzed the data for the projectand wrote the article. G.L.S. interpreted the data. K.S.S.E. and W.L.L. are the principal investigators.All authors have read and agreed to the published version of the manuscript.Funding: This work was supported by funds from the Canadian Institutes of Health Research (CIHR,FRN-143345 and 183775), the Terry Fox Foundation, the Lotte and John Hecht Memorial Foundation,and the BC Cancer Foundation. V.G.P.S. was supported by the BC Cancer Rising Stars Fellowship.Institutional Review Board Statement: Not applicable.Informed Consent Statement: Not applicable.Data Availability Statement: Transcriptome profiling for RNA and miRNA and clinicopathologicalfeatures were retrieved from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/, accessed on 20 July 2024). Patient clinical information, including survival data and recurrence,was obtained from the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/, accessedon 12 August 2024). 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genomic instability
homologous recombination deficiency (HRD)
long non-coding RNAs (lncRNAs)
non-small cell lung cancer (NSCLC)
Identification of Genomic Instability-Associated LncRNAs as Potential Therapeutic Targets in Lung Adenocarcinoma
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