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International Journal of Molecular Sciences 23 (3): 1215 (2022)
Haince, Jean-François
Joubert, Philippe
Bach, Horacio
Ahmed Bux, Rashid
Tappia, Paramjit S.
Ramjiawan, Bram
The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.
Citation: Haince, J.-F.; Joubert, P.;Bach, H.; Ahmed Bux, R.; Tappia, P.S.;Ramjiawan, B. MetabolomicFingerprinting for the Detection ofEarly-Stage Lung Cancer: From theGenome to the Metabolome. Int. J.Mol. Sci. 2022, 23, 1215. https://doi.org/10.3390/ijms23031215Academic Editor: Pavel SoucekReceived: 29 November 2021Accepted: 17 January 2022Published: 21 January 2022Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2022 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/). International Journal of Molecular SciencesReviewMetabolomic Fingerprinting for the Detection of Early-StageLung Cancer: From the Genome to the MetabolomeJean-François Haince 1, Philippe Joubert 2, Horacio Bach 3 , Rashid Ahmed Bux 1, Paramjit S. Tappia 4,*and Bram Ramjiawan 4,51 BioMark Diagnostics Inc., Richmond, BC V6X 2W8, Canada; jhaince@biomarkdiagnostics.com (J.-F.H.);Rashid.biomark@gmail.com (R.A.B.)2 Institut Universitaire de Cardiologie et de Pneumologie de Québec, Department of Pathology,Laval University, Quebec, QC G1V 4G5, Canada; philippe.joubert@criucpq.ulaval.ca3 Department of Medicine, Division of Infectious Diseases, University of British Columbia,Vancouver, BC V6H 3Z6, Canada; horacio.bach@gmail.com4 Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada;bramjiawan@sbrc.ca5 Department of Pharmacology & Therapeutics, Rady Faculty of Health Sciences, University of Manitoba,Winnipeg, MB R3E 0T6, Canada* Correspondence: ptappia@sbrc.ca; Tel.: +1-204-258-1230Abstract: The five-year survival rate of lung cancer patients is very low, mainly because most newlydiagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosisis key to the successful treatment and management of lung cancer. Unfortunately, early detectionmethods of lung cancer are not ideal. In this brief review, we described early detection methods suchas chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, andlow-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomicfingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost,high-throughput blood-based test that is both feasible and affordable for early-stage lung cancerscreening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as analternative to molecular and proteomic-based markers in lung cancer screening, which will enableblood-based routine testing and be accessible to those patients at the highest risk for lung cancer.Keywords: lung cancer; diagnosis; molecular targets; proteomics; metabolomics; early detection1. IntroductionCancer is a leading cause of death worldwide, accounting for nearly 10 million deathsin 2020 [1]. Among the most common global cancer-related death in 2020, lung canceraccounted for 1.8 million deaths. In addition, there were 2.21 million new cases of lungcancer around the world [1]. According to the World Cancer Research Fund, lung cancer isthe most common cancer in men and the third most common cancer in women [2]. Althoughthe American Cancer Society recommends that those over 40 years undergo yearly cancercheck-ups, this recommendation is usually not followed. Consequently, survival tends tobe poorer, when patients show cancer symptoms at a later stage of development [3].The early diagnosis of lung cancer is key to a successful treatment regimen andimproved prognosis; however, current early detection methods for lung cancer are notadequate. Furthermore, some of these technologies used in those methods are invasive,cause discomfort and pain to the patients and may present a greater risk of complications.Personalized disease risk based on genomic information has assisted therapeutic deci-sions. However, genomics remains relatively limited in predicting disease onset, mainlybecause genomic information does not account for the dynamic environmental influences(phenotype). Thus, to better understand lung cancer, the examination of downstreamInt. J. Mol. Sci. 2022, 23, 1215. https://doi.org/10.3390/ijms23031215 https://www.mdpi.com/journal/ijmsInt. J. Mol. Sci. 2022, 23, 1215 2 of 17changes occurring at the level of the protein and metabolites might provide helpful infor-mation about the disease. Although there are a number of recent reviews on the topic ofmetabolomics in human health and disease [4–13], there is a paucity of information regard-ing the utility of metabolomics as a robust cancer diagnostic platform. Accordingly, thepurpose of this brief review was to chart a general cancer diagnostic landscape and variousapproaches in the diagnosis of lung cancer, but with a major focus on the use of liquidbiopsy. We also highlighted the role of metabolomics and how it has been underutilized asa tool in cancer diagnostics, particularly for the detection of early-stage lung cancer. We,therefore, searched PubMed for primary research articles regarding the potential of clinicalapplications of metabolomics. Specifically, the search strategy used the following searchterms for relevant articles over the last 10 years: biomarkers, metabolomics, lung cancer,early-stage lung cancer, and liquid Biopsy.2. Challenges and Importance of Better Screening Approaches for Early-Stage LungCancerThe two main types of lung cancer are small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), with NSCLC accounting for 85–90% of observedcancers [14,15]. The vast majority (85–90%) of lung cancer cases are due to long-termtobacco use, while 10–15% of cases occur in people who have never smoked [16]. Non-smokers typically develop lung cancer through exposure to radon gas, asbestos, second-hand smoke, air pollution, toxic metals, soot, sawdust, and/or coal dust [17–20]. Thetypical age at diagnosis is 70, with slightly more men diagnosed than women [21]. Thefive-year survival rate of lung cancer patients is approximately 21%, mainly because mostnewly diagnosed patients present with advanced or metastatic disease.Lung cancer is histologically categorized into adenocarcinoma, large-cell carcinoma,and squamous cell carcinoma. The stages of lung cancer are based on the staging systemestablished by the American Joint Committee of Cancer (AJCC). The staging system isacronymized as TNM, where T stands for the size of the primary tumor, N stands for thespread of the tumor to lymph nodes, and M stands for metastasis. Despite decades ofresearch and the introduction of many advanced therapeutics, survival rates for lung cancerhave remained essentially unchanged [22]. However, when lung cancer is detected at stageI (T < 5 cm, no spread to lymph nodes, no metastasis), the 10-year survival rate increasesto 88%, and if the tumor is resected within one month of detection, the survival rate is92% [23]. The five-year survival rates for NSCLC have been reported to range from 14% to49% for stage I to stage IIIa lung cancer and <5% for stages IIIb/IV (Table 1), but with theadvancement of treatment regimens, some improvements have been observed [24].Although advances in multimodality therapeutics for lung cancer have been made,the overall five-year survival rate among newly diagnosed lung cancer patients at a latestage remains in the range of 15–17% [26,27]. Although surgical resection is the treatmentof choice for early-stage NSCLC, if lung cancer is detected at an early stage, the 5-yearsurvival is reported to range from 36% to 70% [28–30]. On the other hand, multimodalitytreatment with adjuvant chemotherapy has improved survival rates by only 5% [28,30]. Itshould be noted that until the last decade, the five-year overall survival rate for patientswith metastatic NSCLC was <5%; however, with an improved understanding of the patho-physiology of lung cancer, the overall survival rate has improved to 25% to 40% [31]. Since90% of lung cancer cases are detected among smokers and former smokers, this high-riskpopulation group would benefit from a screening test with the goal of detecting lung cancerwhile it is in stage I [32] or earlier.Int. J. Mol. Sci. 2022, 23, 1215 3 of 17Table 1. TNM of malignant tumor classification.Tumor Size (cm) Lymph Nodes MetastasisT1: <3 N0: no lymph nodes M0: noT2 T2a: 3–5T2b: 5–7N1: ipsilateralbronchopulmonar/hilar lymphnodesM1: presentT3: >7N2: ipsilateralmediastinal/subcarinal lymphnodesT4 InvasionMediastinal organsVertebral bodiesN3: contralateral/hilar/mediastinalsupraclavicular lymph nodesStage I Stage IIIa: T1, N0, M0 IIa: T2b, N0, M0Ib: T2a, N0, M0 IIa: T1, N1, M0IIa: T2a, N1, M0IIb: T2b, N1, M0IIb: T3, N0, M0(Adapted from Nooreldeen and Bach [25]). For primary tumor, T0 = No tumor has been found or the originaltumor cannot be detected; T1 to T4= Indication of increasing tumor size and extent, i.e., T1, a small tumor, T3 alarger tumor. For lymph nodes, N0 = no tumors in the lymph nodes; N1 to N3 stand for location and number ofaffected regional lymph nodes. Tumors found in lymph nodes that are not in the drainage area of the affectedorgan are regarded as distant metastases; for metastases, M0 = no distant metastases observed, M1 = distantmetastases found. Stage I refers to presence of a small cancer and only in one area. This is also called early-stagecancer. Stage II means the cancer is larger and has grown to nearby tissues or lymph nodes. Addition of thelowercase letter a or b (i.e., T2a, T2b) in the classification has been used to subdivide the tumour, lymph nodes ormetastasis categories to make them more specific.These data strongly support the contention that early diagnosis is fundamental to thesuccessful treatment of lung cancer. Unfortunately, current early detection methods of lungcancer are not ideal. These methods include chest X-rays, bronchoscopy, sputum analysisfollowed by cytological analysis, and low-dose computed tomography (LDCT). Althoughradiation exposure during chest X-rays may be problematic, a major reason for not usingX-rays may be attributed to the poor performance of the test concerning sensitivity andspecificity. Accordingly, as of 2016, the Canadian Task Force on Preventative Health Care(CTFPHC) has recommended against using chest X-rays for lung cancer screening [33].Some of the issues with bronchoscopies are that they are invasive, not readily available andassociated with potential complications. In addition, false-negative bronchoscopic resultsare commonly experienced, and the diagnostic accuracy of this technique is sub-optimal,with sensitivities ranging between 34% and 88%, particularly for the diagnosis of peripheralmalignant lesions [34]. Indeed, these sensitivities depend on the size and the localization ofthe primary tumor and the number of parallel tests performed per bronchoscopy [35–37].The diagnosis of lung cancer using standard cytological sputum analysis can betedious, prone to unsatisfactory sample collection and often exhibits poor sensitivity [38].To achieve a diagnosis, indeterminate sputum results may necessitate repeated sputumcollection or escalation to more costly and invasive testing methods. LDCT, while moresensitive than chest X-rays, is expensive, exposes patients to a higher radiation dose thanchest X-rays, is not routinely accessible to many patients and has a 96% rate of false positives(with a 4 mm non-calcified nodule size cut-off) [39]. In an assessment conducted by theNational Lung Screening Trial in the USA, the over-diagnosis rate of lung cancer by LDCTwas estimated to be 18.5% [40]. LDCT screening can thus lead to invasive interventions(needle biopsy and/or surgery) [41] for many patients with benign lesions, leading topatient morbidity and high health care costs. Additionally, cumulative radiation exposurefrom repeated scans may increase the risk of developing cancers [42]. Indeed, a widespreadInt. J. Mol. Sci. 2022, 23, 1215 4 of 17issue with LDCT lung cancer screening is the potential harm attributed to exposure toionizing radiation as well as in cases during the work-up of suspicious lesions discoveredat LDCT [43]. However, in the Continuous Observation of Smoking Subject (COSMOS)lung cancer study, it was found that one radiation-induced major cancer would be expectedfor every 108 (259/2.4) lung cancers detected through screening. It was concluded thatradiation exposure and cancer risk associated with lung cancer LDCT screening are notinsignificant but acceptable due to a highly significant reduction in mortality achieved withscreening [44]. Nevertheless, a 20% reduction in lung cancer mortality has been reportedusing intensive LDCT screening of heavy smokers [45].It should be mentioned that the Nederland’s Leuvens Longkanker Screenings Onder-zoek (NELSON) trial was conducted based on a volumetry-based screening strategy [46].Of the lung cancers diagnosed, around 71% were diagnosed at stage I and approximately8% at stages IIIb–IV, and 51.2% were adenocarcinomas, thus demonstrating that, unlikeother comparable trials, the NELSON trial screen-detected lung cancers are more oftendiagnosed at stage I and less frequently at stages IIIb–IV. However, it was suggested thatthe screening strategy of the NELSON trial results in a favorable diagnosis [46] with areduction in mortality from lung cancer [47]. Still, the complete consensus for a systematiclung cancer screening is yet to be attained [47].3. Molecular Approaches and Directions in Lung Cancer DetectionGiven the limitations detailed above, it is clear that better, less expensive, less invasive,and broader screening approaches to early lung cancer detection are urgently needed. To ad-dress this need, many researchers have looked to the development of non-invasive or mildlyinvasive molecular tests using breath [48], urine [49], sputum [50], breath condensate [51],or blood-based assays [52]. The molecular markers being used include proteins [53,54],tumor-associated autoantibodies [55], circulating DNA [56], circulating tumor cells [57],microRNA [58], methylated DNA [59], and metabolites [60]. Many of these reports tend tofocus on diagnosing late-stage lung cancer as opposed to early-stage lung cancer. Amongthose focused on early-stage lung cancer, some of the better molecular tests reported thearea under the receiver operating characteristic (AUROCs) curves of 0.84 [61,62] to 0.90 [63].To date, very few molecular tests have been validated on larger populations. Only onemolecular blood test for early-stage lung cancer has reached the market; unfortunately, thistest only shows a 37% sensitivity and a 90% specificity [64,65]. However, it should be notedthat a randomized ongoing clinical trial led by Oncimmune and Lung Cancer Scotland(ECLS) was designed to confirm these findings [66]. Dama et al. [67] have recently reviewedsome of the current technologies used in the early detection of lung cancer. Overall, liquidbiopsy has shown some clinical applicability as a tool for the early detection of lung cancer,and its use as a screening/diagnostic/prognostic test for cancer detection remains to befully validated for implementation in clinical practice [68].4. The Interrelationship between Genes, Proteins, and MetabolitesThere are a number of factors that can influence the metabolome of an individual,including age, sex, diet, geographical location, environment, ethnicity, time of day, andeven the individual’s endogenous genetic make-up [69,70]. Metabolites and genes areintimately connected [71]. In this regard, it has been suggested that a single DNA basechange in a given gene can lead to 10,000-fold endogenous metabolite shift levels [72]. Thissubstantial increase in metabolite concentration is attributed to the fact that metabolites arethe downstream products and interactions of multiple intracellular elements, includinggenes, transcriptional activators, RNA transcripts, protein transporters, and enzymes [73].This amplification of the metabolomic signal, depicted in Figure 1 (adapted from [72]),involves the transmission of the message from DNA to proteins to metabolites. In otherwords, it can also be described in terms of the number of genes (20,000) versus the numberof (expected) metabolites (~1 million) and the diversity of chemicals used to assemblegenes (4 nucleotides) or proteins (20 amino acids) versus the variety of chemicals seen inInt. J. Mol. Sci. 2022, 23, 1215 5 of 17the metabolome (3000 chemical classes) [72]. The organs of the body produce or utilizespecific metabolites that can serve as metabolomic fingerprints in health and differentpathophysiological conditions [72]. Thus, metabolomic fingerprinting provides a uniqueopportunity for the use of metabolites as a panel of biomarkers of different diseases,including cancer, which can exhibit highly specific metabolic signatures that have bothdiagnostic and prognostic value.Figure 1. The interrelationship between genes, proteins, and metabolites. This scheme illustrates theamplification of the metabolomic signal, which involves the initial message transfer from DNA tofunctional proteins and then organ-specific metabolites. Adapted from Wishart, DS [72].5. Metabolic FingerprintsIt has been known for a long time that cancer can be regarded as a metabolic dis-ease [74]. Since metabolism reflects the biochemical state of a healthy or unhealthy cell,then the unique metabolic characteristics (fingerprints) may assist in the determination ofthe stage and location of a carcinoma in a non-invasive manner and allow for the distinctionbetween metabolic processes in healthy cells as well as cancer cells [74]. Such metabolicdisorder helps to support high proliferative rates despite poor vascularization that lim-its oxygen and nutrient supply [75,76]. Accordingly, the field of cancer metabolomics isprimarily based on the fact that metabolic activities are altered in cancer cells relative tonormal cells that represent a characteristic metabolic profile that sustains malignancy andis thus considered a hallmark of cancer [77,78]. In cancer cells, biochemical pathways aredisturbed due to the disruption of gene regulation and genetic polymorphisms, unusualchromosome repetitions, and mutations [78,79]. These disruptions may occur within thepathway itself or from downstream effects caused by dysfunctional proteins in adjacentpathways. Dysfunctional proteins in pathways are often related to cancers and can causean imbalance in metabolite concentrations that are different from healthy cells [72,74]. Itis thus conceivable that each other type of cancer may be caused by different mutations(differing dysfunctional proteins) with unique metabolomic profiles. However, the exactnature and mechanisms responsible for metabolic reprogramming in cancer cells remain tobe fully explored [79].From the aforementioned, lung cancer diagnosis generally occurs at the late stagesof the disease, when curative treatments are not efficacious or possible anymore. Thus,new methodologies were explored to identify early signs of the disease. One of them,the presence of specific metabolites in different body fluids, might offer an answer for anearly diagnosis. Metabolomics has been used to detect and quantify metabolites related tocancer. Fluids such as serum, plasma, urine, and sputum have been evaluated as potentialsources of specific metabolites. A summary of the studies that evaluated the metabolomicsof lung cancer versus healthy controls using body fluids is described in Table 2. In thesestudies, metabolites with sufficient power to discriminate the lung cancer groups from thehealthy controls were indicated after statistical analyses. Although this finding constitutes aInt. J. Mol. Sci. 2022, 23, 1215 6 of 17significant advance in discovering universal biomarkers, no matching was observed whencomparing the metabolites between the studies.Table 2. Dysregulated metabolites among various types of lung cancer.Sample Type of Cancer Relevant Metabolites ReferenceAd LCC N Sq S LC M H P ComTissue14 20 * A DS Early stage (selected metabolites thatdifferentiate the groups): creatine,creatinine, GP-N-heptacenyl EA,GP-N-heptadecanoyl EA, GP-NAE,GP-N-hexodecenyl EA,GP-N-octadecanoyl EA,GP-N-octadecenyl EA, DHTH, LysoPEP-16:0, and xanthine.[80]33 54 12 20 * A DS Advanced stage (selected metabolitesthat differentiate the groups):arachidonic acid, LysoPC 16:0 plasm,oleic acid, PC 18:1 plasm/0:0, PC 32:0plasm, adrenic acid, DPA, LysoPC 16:2plasm, PC 34:2(OH), PE 0–18:2/0:0,and P-18:1/0:0.Plasma21 18 20* A DSEarly stage (selected metabolites thatdifferentiate the groups): PC 15:0/22:6and PC 18:1/22:6.[80]11 7 15Advanced stage (selected metabolitesthat differentiate the groups):deoxycholic acid, glycocholic acid, PEP-18:1/20:5, PE P-16:0/22:5, PCO-16:0/18:2, PE P-19:1, PC 40:1, PC38:0, linoleic acid, and arachidonicacid.6 6 7 5 2 29 B DCValine, LysoPC (18:2),decadienyl-L-carnitine (C10:2)phosphatidylcholine, acyl-alkyl C36:0(PC aa C36:0), phosphatidylcholinediacyl C32:2 (PC aa C32:2,phosphatidylcholine diacyl C30:2 (PCaa C30:2), spermine, putrescine, anddiacetylspermine.[81]31 28 C DCAlanine, glutamine, glycine, threonine,5-hydroxytryptophan,5-methoxytryptophan, L-arginine,proline, N(6)-methyllysine,deoxycholic acid glycine conjugate, PC(34:4), PE (34:2), PE (36:1), PE (36:2),PE (36:4), PE (38:4), PE (38:6), PE (40:4),PE (40:5), PS (38:4), ceramides (42:0),palmitic acid, linoleic acid, oleic acid,amylose, maltitol, testosterone sulfate,androsterone sulfate, pregnenolonesulfate, 3-hydroxybutyric acid,pipecolic acid, uric acid, bilirubin,ubiquinone, ubiquinol,3,4,5-trimethoxycinnamic acid, andN-palmitoleoyl ethanolamide.[82]Int. J. Mol. Sci. 2022, 23, 1215 7 of 17Table 2. Cont.Sample Type of Cancer Relevant Metabolites ReferenceAd LCC N Sq S LC M H P Com12 9 4 25 D DCLDL/VLDL, β-hydroxybutyrate,unsaturated lipids, acetoacetate,α-glucose, β-glucose, lactate,glutamate, glutamine, tyrosine,histidine, choline, phosphocholine,glycerophosphocholine, betaine, andTMAO.[83]EAspartate, asparagine, glutamate,glutamine, cysteine, methionine,isoleucine, leucine, and tryptophan.Biofluid plasma110 46 60 B DCβ-hydroxybutyric acid, LysoPC (20:3),PC ae (C40:6), citric acid, fumaric acid,and carnitine.[84]BALF8 6 7 3 30 ˆ F DCLactic acid, acetic acid, glycerol,L-glycine, L-aspartate, L-proline,L-glutamine, fructose, phosphoricacid, isocitric acid, inositol, galactose,palmitic acid, stearic acid, inosine, andoleic acid.[85]Serum9 9 6 8 29 F DCL-valine, L-glycine, tartaric acid,L-serine, L-threonine, uridine, malonicacid, L-proline, L-cysteine,L-glutamine, L-phenylalanine,fructose, phosphoric acid, isocitricacid, L-asparagine, inositol,L-ornithine, deoxy-glucose, glucose,palmitic acid, uric acid, stearic acid,L-cystine, myristic acid, margaric acid,and arachidonic acid.[85]15 3 12 30 G DC 8-OH guanine, phenylglyoxylic acid,L-pipecolic acid, L-carnitine, caffeicacid 3-sulfate, dimethyl fumarate,L-adrenaline, adrenosterone,3-OH-3-methyl-glutaric acid,acetylcarnitine, L-valine, uric acid,oxalosuccinic acid, cortisol, Prasteronesulfate, uridine, sphinganine,sphingosine, phosphorylcholine,PC(15:0),glycerophospho-N-arachidonoylethanolamine, palmitoyl -L-carnitine,PC(16:0), oleamide,glycerophospho-N-oleylethanolamine, PC(17:0),1,25-hydroxyvitamin D3, arachidylcarnitine, PC(18:0), glycocholic acid,γ-linolenic acid, and linoleic acid.[86]Int. J. Mol. Sci. 2022, 23, 1215 8 of 17Table 2. Cont.Sample Type of Cancer Relevant Metabolites ReferenceAd LCC N Sq S LC M H P Com15 3 12 30 H DC Lactic acid, alanine,α-hydroxyisobutyric acid,α-hydroxybutyric acid, L-valine, urea,serine, L-leucine, phosphate,L-isoleucine, glycine, L-threonine,aminomalonic acid, pyroglutamic acid,2,3,4-trihydroxybutyric acid,L-phenylalanine, tetradecanoic acid,glucose, palmitic acid, myo-inositol,9,12-octadecadienoic acid, oleic acid,and cholesterol.43 43 H DCMaltose, maltotriose, cystine,3-phosphoglycerate, citrulline,pyrophosphate, tryptophan,adenosine-5-phosphate, Bin_226841,Bin_367991, Bin_715929, Bin_299216,and cellobiotol.[87]65 65 I DCCysteine, serine, glycine, leucine,aspartic acid, cholesterol,2-hydroxyglutaric acid, and 1-monooleoylglycerol.[88]4 3 5 5 30 J DC2015 dataset: valine, leucine, ornithine,methionine, histidine, phenylalanine,arginine, citrulline, tyrosine, aspartate+ asparagine, C3-carnitine,C4-carnitine, C5-carnitine,C8-carnitine, C14-carnitine, andC16-carnitine.2017 dataset: glycine,valine, leucine, methionine, histidine,citrulline, and arginine.[89]35 70 K DC Bisphenol A, retinol, L-proline. [90]25 18 50 L DCHypoxanthine, inosine, L-tryptophan,indoleacrylic acid, acyl-carnitine C10:1,and LysoPC (18:2).[91]31 29 H DCIndole-3-lactate, erythritol,adenosine-5-phosphate, paracetamol,threitol, oxalic acid, fructose, inosine,naproxen, lyxose, caprylic acid,dodecanol, and cystine.[92]50 41 H DCCholesterol, oleic acid, myo-inositol,2-hydroxybutyric acid,4-hydroxybutyric acid, and pelargonicacid.[93]Sputum23 33 C DCIsobutyl decanoate, putrescine, diethylglutarate, cysteamine, hexanal, cysteicacid, and hydropyruvic acid.[94]Int. J. Mol. Sci. 2022, 23, 1215 9 of 17Table 2. Cont.Sample Type of Cancer Relevant Metabolites ReferenceAd LCC N Sq S LC M H P ComUrine9 9 6 8 29 F DCL-alanine, acetic acid, malonic acid,urea, L-glycine, succinic acid, glycericacid, L-serine, L-threonine, butanoicacid, threonic acid, creatinine,glutaconic acid, L-aspartate, ribonicacid, adipic acid, arabitol, aconiticacid, phosphoric acid, isocitric acid,hippuric acid, purine, inositol,gluconic acid, sorbitol, glucaric acid,galactaric acid, palmitic acid, uric acid,and stearic acid.[85]51 10 14 78 M DC Creatine riboside andN-acetylneuraminic acid. [95]13 22 3 42 N DCGlycyl-glycine, 7-ethyl-5,6-dihydro-1,4-dimethylazulene, 4-pyridoxic acid,crithmumdiol, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone,1-methylhistidine, indoxyl sulfate,tryptophan, deacetyldiltiazem,maltulose, porson,8-hydroxynevirapine, austinol,dehydroepiandrosterone sulfate,dehydroepiandrosterone3-glucuronide,5-alpha-dihydrotestosteroneglucuronide, dulciol C, imidazoelacticacid, glutamine, lyciumoside IV,neuromedin C 1–8, withaphysacarpin,noradrenochrome o-semiquinone,S-prenyl-L-cysteine,tetrahydroaldosterone-3-glucuronide,ax-4′-hydroxy-3′-methoxymasin,dulxanthone, androsterone sulfate,cortolone-3-glucuronide, tyrosine,xanthosine. tyrosyl tyrosine, 3beta-dihydroxymarasmene, indoxyl,and cis-zeatin-O-glucoside.[96]Ad, adenocarcinoma; LCC, large cell carcinoma; N, non-small-cell lung carcinoma; Sq, squamous cell carcinoma;LC, undefined lung cancer type; S, small-cell carcinoma; M, metastasis; H, healthy controls; P, platform used; Com,comparison; DS, comparison between different disease subgroups; DC, comparison between the disease and thecontrol; BALF, bronchoalveolar lavage fluid; *, COPD patients; ˆ, non-cancerous lung disease; A, LC-QTOF-MS;B, ABI4000 Qtrap tandem MS; C, Agilent 6890 GC-MS + Agilent 6540 quadrupole-time of flight (Q-TOF); D, 1HNMR; E, Agilent 1260 Rapid Resolution LC; F, Trace GC Ultra GC-MS; G, Agilent 6530 accurate-Mass Q-TOF/MS;H, Agilent 7890A GC-MS; I, QP2010 GC−MS; J, Agilent 6430 triple quadrupole-MS; K, Agilent Q-TOF 6550; L,SCIEX X500R QTOF-MS; M, Waters quadrupole time-of-flight (QTOF)-MS; N, Waters SNAPT G2-Si Q-TOF/HRMS.The metabolites described in the Relevant Metabolites column include both discovery and validation datasets.Furthermore, although changes in similar biochemical pathways were observed, nospecific metabolite(s) have been found, as represented in all of the studies. It shouldbe mentioned that while the use of various fluids such as serum, plasma, urine, andsputum for metabolic studies has been described, another important fluid proposed morerecently for lung cancer studies is bronchoalveolar lavage fluid (BALF), due to its vicinityto cancerous tissue. In this regard, technological advances in separating and detectingexfoliated tumor cells from BALF for lung cancer diagnosis have recently been reported [97].In addition, cell-free DNA (cfDNA) from BALF has also been used to identify lung cancer.Int. J. Mol. Sci. 2022, 23, 1215 10 of 17Indeed, cfDNA profiling can distinguish small malignant tumors (≤2cm in diameter) frombenign pulmonary nodules with a reported 83% sensitivity and an 87.5% specificity, thuspotentially being of diagnostic value [98]. In addition, exhaled breath condensate (EBC)and breath biomarkers for lung cancer have also been proposed as a testing sample for lungcancer diagnosis [99]. However, the cost and reproducibility are concerning. Furthermore,interference effects and staging of patients for early detection still need to be explored toestablish the clinical applicability of metabolomics of EBC and breath biomarkers in lungcancer diagnostics.It should be noted that the data presented in Table 2 reflect the dysregulated metabo-lites in different biofluids, even though the metabolomics of lung cancer tissue were alsoreported in some of these studies. It is the intent of this review to focus on biofluids only asthe trend is now on liquid biopsy as a valuable diagnostic tool. However, finding universalbiomarkers for disease diagnosis is difficult because of the complexity of the analyzedfluids, especially serum and plasma. In addition, the lack of a standardization protocolgenerates variations between the studies. Some of the concerns in the performance of thestudies are as follows.5.1. Variations of MetabolitesMetabolite variation in individuals has been analyzed, and the need for thousandsof patients has been suggested to obtain robust epidemiological studies [100]. Metabolitelevels could fluctuate daily during the year and significantly if diets are changed. Generally,studies do not assess the effect of ethnic groups of the cohort, and the variability in thestudies can be caused by different nutrients. In addition, biochemical pathways mayundergo changes based on ethnicity [101,102].5.2. Collection, Storage, and ProcessingThe stability of metabolites during collection until the analysis is critical to obtainconsistent results. For example, plasma was shown to be more stable than serum, and otherfactors should be considered, such as clotting time, ambient temperature, and freezing-thawing cycles [103]. Moreover, studies have shown that glycolysis intermediates, aminoacids (e.g., histidine), acetate, and diacylglycerol levels might be compromised by thisprocessing [104]. In the case of urine samples, different profiles were observed, when thesame sample was stored at 4 ◦C and −20 ◦C. The results showed a significant reduction in14 metabolites (e.g., N-acetyl glycine, adenosine, creatine, pyridoxal, and succinic acid) insamples stored at 4 ◦C compared to a more stable level at −20 ◦C [105].5.3. Variations of Manufacturing KitsThe comparison of the results obtained by the platforms Biocrates and Metabolonshowed some discrepancies that should be considered a metabolite variation. For example,the metabolite lysoPC a C20:4 showed a low correlation with the same cohort results [106].This variation probably originated from the way both platforms analyzed this specificmetabolite. For example, Metabolon quantified lysoPC 20:4 fatty acid chain at position sn1,whereas Biocrates did not discriminate between positions sn1 and sn2 and it quantifiedonly the total levels of both fatty acids.5.4. Instrumentation and Data ProcessingTwo types of mass spectrometry (MS) instruments are used in metabolomics. Theyinclude gas- and liquid-chromatography (GC and LC, respectively). Upon measuring themasses of the metabolites, a library is necessary to determine the identity of each one. Thisanalysis represents a potential impact on data analysis and processing, as different librariesare available. Each one has different algorithms that might bring slight differences in astudy. For example, the libraries accurately identify known metabolites but may be lessefficient for unknown molecules.Int. J. Mol. Sci. 2022, 23, 1215 11 of 17Other factors in the performance of the instruments are related to the analysis ofhundreds of samples that might bring differences based on the instrument performance.For example, analytical changes result from column degradation, GC/LC conditions,mass spectrometer contamination, or metabolite degradation/decay due to the waitingtime [107,108].5.5. Effect of Underlying Diseases or Non-Related MetabolitesIt is important to determine the impact of medications on underlying diseases/conditions when analyzing metabolomics results. For example, therapies related to un-derlying diseases in lung cancer comorbidities are metabolized mainly in the liver. Thismetabolism could generate novel metabolites by introducing errors in the metabolomicanalysis. In addition, the microbiome might also metabolize drugs that can be reabsorbedin the bloodstream [109], introducing confounding factors. Other examples include thepresence of paracetamol as a metabolite with high significance, which is probably theresidual of a drug consumed by the patients [87].Moreover, the presence of bisphenol A in the list of metabolites is problematic as thisis a synthetic molecule, and its presence can be related either to an error in the identificationof the molecule or, less probably, to high-consumption bottle beverages as bisphenol A isused in the manufacturing of polycarbonate bottles. In addition, as bisphenol A is poorlysoluble in water, the presence in blood can result from high levels in drinking water [92].Finally, it should be pointed out that a number of different factors need to be consideredwhen undertaking metabolomic studies, including diet [110] and comorbidities [111–119].In this regard, obesity [111,112], chronic obstructive pulmonary disease [113–115], kidneydisease [116], diabetes [117,118], and cardiovascular disease [94] have all been reported tohave a major impact on the metabolome that can confound metabolomic studies in cancer,and thus, their influence cannot be underestimated.Although the application of metabolomics is growing for cancer diagnosis, morestudies are necessary to be implemented for a final use for biomarker measurement. Asmentioned above, there is an inconsistency in the chosen metabolites that discriminatelung cancer from healthy individuals. In addition, the heterogeneity of samples relatedto the types of lung cancer, together with the different stages in the patients, is addingmore variability. Overall, the validation of new biomarkers in lung cancer diagnosis needsstudies that include thousands of patients stratified according to their cancer types andstages of the disease and effects of other clinical parameters, including comorbidities anddrug interactions.We have focused on the polyamine pathway due to its connection to many types ofcancer. Multiple proteins constitute this pathway, and when one of them does not functionproperly, the levels of metabolite at the point of dysfunction will be altered until they reacha new equilibrium that allows cancer to grow. Using this concept, we have defined andvalidated the preliminary fingerprint for lung cancer and are in the process of determiningthe fingerprint for breast cancer. Using quantitative metabolite tests instead of genetic tests,protein tests, or X-ray imaging allows such a test to be quickly and inexpensively integratedinto the existing clinical testing infrastructure. It also makes the test far more reproducibleand much more accurate [96]. As of yet, no other high-performing chemical/metabolitetest for lung cancer screening has been developed anywhere in the world.6. Potential Impact on PatientsThe Canadian Task Force on Preventative Health Care recommends screening byLDCT for adults aged 55–74 years old who smoke or used to smoke in the last 15 yearsand smoked 30 pack years. However, there are significant challenges with implementingand adopting such a program, including radiation exposure, poor patient uptake due tolengthy follow-up processes, and resources required to implement and successfully executea provincial LDCT screening program. Furthermore, LDCT has a very high false-positiverate contributing to further health care resource depletion through unnecessary follow-upInt. J. Mol. Sci. 2022, 23, 1215 12 of 17procedures and biopsies, increased health care costs, increased patient risk, and patientanxiety. A more straightforward, inexpensive, more accurate early-detection lung cancertest would benefit cancer patients as follows: (1) Earlier, more accurate diagnosis will leadto better patient health for high-risk individuals. Smokers would immediately benefit fromlow-cost population-based screening for lung cancer. (2) Early detection will save livesand reduce costs related to treatment at advanced stages, targeting the detection of stageI lung cancer (over 53% of patients are asymptomatic), would significantly improve thecure rates as compared to detection at late-stage disease. (3) The dynamics of patient triageand quality of care will change. A metabolomics blood-based test will enable physiciansto make evidence-based prevention and treatment decisions. A blood-based test makessample collection very simple, and it is more likely to encourage patient participation inscreening programs. (4) Patient anxiety will be reduced. Obtaining the result of a lungcancer test within hours will decrease the anxiety associated with waiting. If the test ispositive, patients will be placed in a correct health care stream for immediate treatment.This test would be expected to fit into the physician’s workflow and current programsquickly.There are bottlenecks associated with the identification and application of metabolomics.Outstanding concerns preventing the widespread clinical use of metabolomics as a diagnos-tic/predictive tool are the scalability of data interpretation, the standardization of samplehandling practice, and e-infrastructure [120]. However, as these issues become resolved, itis conceivable that the routine utilization of metabolomics at the patient and populationlevels will constitute an integral part of future healthcare provision [120] and the improvedmanagement of patients with cancer [121].7. ConclusionsCancer is a metabolic disease caused by mutations in key metabolic pathways andkey metabolic regulators. Metabolomics has led to identifying several oncometabolites andmany high-performing cancer metabolite biomarkers. Thus, the use of metabolomic assaysand multi-metabolite markers allows for the rapid, accurate diagnosis and monitoringof not only lung cancer but has the potential applicability in multiple types of cancer.The utility of metabolomics as a fast, accurate, cheap liquid biopsy technique in cancer isunder-exploited and thus represents an enormous opportunity in cancer diagnostics andprognostics. We hope the nature of this review contributes to existing knowledge in the fieldof metabolomics in cancer and how it can be utilized as a robust diagnostic tool for the earlydetection of lung cancer. Therefore, developing a low-cost, high-throughput metabolomictest will make early-stage lung cancer screening feasible and affordable globally, wheresurvival rates are low, particularly for at-risk populations. In addition, such a paradigmshift in lung cancer screening will make blood-based testing routines accessible to thosepatients at the highest risk for lung cancer and serve as a viable alternative to molecularand proteomic-based markers for the early detection of lung cancer.Author Contributions: Conceptualization, R.A.B., J.-F.H., P.S.T.; writing-original draft preparation,J.-F.H., P.S.T. and R.A.B., responsibility for the final draft and the scientific background of themanuscript, R.A.B., H.B., P.S.T., P.J. and B.R. All authors have read and agreed to the publishedversion of the manuscript.Funding: This research received no external funding.Institutional Review Board Statement: Not applicable.Informed Consent Statement: Note applicable.Data Availability Statement: Not appliable.Acknowledgments: Infrastructural support was provided by the Albrechtsen Research Centre,St. Boniface Hospital.Int. J. Mol. Sci. 2022, 23, 1215 13 of 17Conflicts of Interest: R.A.B. is the President and Chief Executive Officer of BioMark Diagnostics Inc.and J.-F. H. is Chief Scientific Officer and General Manager of BioMark Diagnostics Inc. P.S.T. is ami-nor shareholder of BioMark Diagnostics Inc. The authors have no other relevant affilia-tions orfinancial involvement with any organization or entity with a financial interest or financial conflictwith the subject matter or materials discussed in the manuscript apart from those disclosed.References1. Ferlay, J.; Ervik, M.; Lam, F.; Colombet, M.; Mery, L.; Piñeros, M.; Znaor, A.; Soerjomataram, I.; Bray, F. Global Cancer Observatory:Cancer Today. Lyon: International Agency for Research on Cancer; 2020. Available online: https://gco.iarc.fr/today (accessed on16 November 2021).2. World Cancer Research Fund. Available online: https://www.wcrf.org/dietandcancer/lung-cancer-statistics/ (accessed on 16November 2021).3. Chadder, J.; Dewar, R.; Shack, L.; Nishri, D.; Niu, J.; Lockwood, G. A first look at relative survival by stage for colorectal and lungcancers in Canada. Curr. Oncol. 2016, 23, 119–124. [CrossRef] [PubMed]4. Jin, Q.; Ma, R.C.W. Metabolomics in diabetes and diabetic complications: Insights from epidemiological studies. Cells 2021, 10,2832. [CrossRef]5. Brennan, L.; Hu, F.B.; Sun, Q. Metabolomics meets nutritional epidemiology: Harnessing the potential in metabolomics data.Metabolites 2021, 11, 709. [CrossRef] [PubMed]6. Onuh, J.O.; Qiu, H. Metabolic profiling and metabolites fingerprints in human hypertension: Discovery and potential. Metabolites2021, 11, 687. [CrossRef]7. McGranaghan, P.; Kirwan, J.A.; Garcia-Rivera, M.A.; Pieske, B.; Edelmann, F.; Blaschke, F.; Appunni, S.; Saxena, A.; Rubens, M.;Veledar, E.; et al. Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from humanstudies. Metabolites 2021, 11, 621. [CrossRef] [PubMed]8. LeVatte, M.; Keshteli, A.H.; Zarei, P.; Wishart, D.S. Applications of metabolomics to precision nutrition. Lifestyle Genom. 2021, 8,1–9. [CrossRef] [PubMed]9. Schultheiss, U.T.; Kosch, R.; Kotsis, F.; Altenbuchinger, M.; Zacharias, H.U. Chronic kidney disease cohort studies: A guide tometabolome analyses. Metabolites 2021, 11, 460. [CrossRef]10. Walker, A.; Schmitt-Kopplin, P. The role of fecal sulfur metabolome in inflammatory bowel diseases. Int. J. Med. Microbiol. 2021,311, 151513. [CrossRef]11. Liang, L.; Sun, F.; Wang, H.; Hu, Z. Metabolomics, metabolic flux analysis and cancer pharmacology. Pharmacol. Ther. 2021, 224,107827. [CrossRef]12. Alldritt, I.; Greenhaff, P.L.; Wilkinson, D.J. Metabolomics as an important tool for determining the mechanisms of human skeletalmuscle deconditioning. Int. J. Mol. Sci. 2021, 22, 13575. [CrossRef] [PubMed]13. Daisley, B.A.; Koenig, D.; Engelbrecht, K.; Doney, L.; Hards, K.; Al, K.F.; Reid, G.; Burton, J.P. Emerging connections between gutmicrobiome bioenergetics and chronic metabolic diseases. Cell Rep. 2021, 37, 110087. [CrossRef]14. Zhou, J.; Huang, Z.A.; Kumar, U.; Chen, D.D.Y. Review of recent developments in determining volatile organic compounds inexhaled breath as biomarkers for lung cancer diagnosis. Anal. Chim. Acta. 2017, 996, 1–9. [CrossRef]15. Sher, T.; Dy, G.K.; Adjei, A.A. Small cell lung cancer. Mayo. Clin. Proc. 2008, 83, 355–367. [CrossRef] [PubMed]16. Samet, J.M.; Avila-Tang, E.; Boffetta, P.; Hannan, L.M.; Olivo-Marston, S.; Thun, M.J.; Rudin, C.M. Lung cancer in never smokers:Clinical epidemiology and environmental risk factors. Clin. Cancer Res. 2009, 15, 5626–5645. [CrossRef]17. Hubaux, R.; Becker-Santos, D.D.; Enfield, K.S.; Lam, S.; Lam, W.L.; Martinez, V.D. Arsenic, asbestos and radon: Emerging playersin lung tumorigenesis. Environ. Health 2012, 11, 89. [CrossRef] [PubMed]18. Vineis, P.; Forastiere, F.; Hoek, G.; Lipsett, M. Outdoor air pollution and lung cancer: Recent epidemiologic evidence. Int. J. Cancer.2004, 111, 647–652. [CrossRef] [PubMed]19. Vallières, E.; Pintos, J.; Parent, M.E.; Siemiatycki, J. Occupational exposure to wood dust and risk of lung cancer in twopopulation-based case-control studies in Montreal, Canada. Environ. Health 2015, 14, 1. [CrossRef] [PubMed]20. Öberg, M.; Jaakkola, M.S.; Woodward, A.; Peruga, A.; Prüss-Ustün, A. Worldwide burden of disease from exposure to second-handsmoke: A retrospective analysis of data from 192 countries. Lancet 2011, 377, 139–146. [CrossRef]21. Sagerup, C.M.; Småstuen, M.; Johannesen, T.B.; Helland, Å.; Brustugun, O.T. Sex-specific trends in lung cancer incidence andsurvival: A population study of 40,118 cases. Thorax 2011, 66, 301–307. [CrossRef] [PubMed]22. CancerControl AB, Surveillance & Reporting: 2012 Report on Cancer Statistics in Alberta. 2015. Available online: https://www.albertahealthservices.ca/cancer/Page2407.aspx (accessed on 16 November 2021).23. International Early Lung Cancer Action Program Investigators; Henschke, C.I.; Yankelevitz, D.F.; Libby, D.M.; Pasmantier, M.W.;Smith, J.P.; Miettinen, O.S. Survival of patients with stage I lung cancer detected on CT screening. N. Engl. J. Med. 2006, 355,1763–1771.24. Ko, E.C.; Raben, D.; Formenti, S.C. The integration of radiotherapy with immunotherapy for the treatment of non-small cell lungcancer. Clin. Cancer Res. 2018, 24, 5792–5806. [CrossRef]25. Nooreldeen, R.; Bach, H. Current and future development in lung cancer diagnosis. Int. J. Mol. Sci. 2021, 22, 8661. [CrossRef]26. Wald, O. CXCR4 Based therapeutics for non-small cell lung cancer (NSCLC). J. Clin. Med. 2018, 7, 303. [CrossRef]Int. J. Mol. Sci. 2022, 23, 1215 14 of 1727. Tang, Y.; Qiao, G.; Xu, E.; Xuan, Y.; Liao, M.; Yin, G. Biomarkers for early diagnosis, prognosis, prediction, and recurrencemonitoring of non-small cell lung cancer. Onco. Targets Ther. 2017, 10, 4527–4534. [CrossRef]28. Gatteschi, L.; Iannopollo, M.; Gonfiotti, A. Neoadjuvant immunotherapy in resectable non-small cell lung cancer. A narrativereview. Life 2021, 11, 1036. [CrossRef] [PubMed]29. Knight, S.B.; Crosbie, P.A.; Balata, H.; Chudziak, J.; Hussell, T.; Dive, C. Progress and prospects of early detection in lung cancer.Open Biol. 2017, 7, 170070. [CrossRef] [PubMed]30. Gobbini, E.; Levra, M.G. Is there a room for immune checkpoint inhibitors in early stage non-small cell lung cancer? J. Thorac. Dis.2018, 10, S1427–S1437. [CrossRef]31. Arbour, K.C.; Riely, G.J. Systemic therapy for locally advanced and metastatic non-small cell lung cancer: A review. JAMA. 2019,322, 764–774. [CrossRef]32. Dominioni, L.; Strauss, G.M.; Imperatori, A.; Rovera, F.; Dionigi, G. Screening for lung cancer. Chest Surg Clin. N. Am. 2000, 10,729–736. [PubMed]33. Canadian-Task-Force-on-Preventive-Health-Care. Recommendations on screening for lung cancer. CMAJ 2016, 188, 425–432.[CrossRef]34. Labbé, C.; Beaudoin, S.; Martel, S.; Delage, A.; Joubert, P.; Drapeau, C.; Provencher, S. Diagnostic yield of non-guided flexiblebronchoscopy for peripheral pulmonary neoplasia. Thorac. Cancer 2015, 6, 517–523. [CrossRef]35. Rivera, M.P.; Mehta, A.C.; Wahidi, M.M. Establishing the diagnosis of lung cancer: Diagnosis and management of lung cancer, 3rded: American College of Chest Physicians evidence-based clinical practice guidelines. Chest 2013, 143, e142S–e165S. [CrossRef][PubMed]36. Shirakawa, T.; Imamura, F.; Hamamoto, J.; Honda, I.; Fukushima, K.; Sugimoto, M.; Shirkakusa, T. Usefulness of endobronchialultrasonography for transbronchial lung biopsies of peripheral lung lesions. Respiration 2004, 71, 260–268. [CrossRef] [PubMed]37. Schreiber, G.; McCrory, D.C. Performance characteristics of different modalities for diagnosis of suspected lung cancer: Summaryof published evidence. Chest 2003, 123, 115s–128s. [CrossRef] [PubMed]38. Rivera, M.P.; Detterbeck, F.; Mehta, A.C. Diagnosis of lung cancer: The guidelines. Chest 2003, 123, 129s–136s. [CrossRef]39. Aberle, D.R.; Abtin, F.; Brown, K. Computed tomography screening for lung cancer: Has it finally arrived? Implications of thenational lung screening trial. J. Clin. Oncol. 2013, 31, 1002–1008. [CrossRef]40. Patz, E.F., Jr.; Pinsky, P.; Gatsonis, C.; Sicks, J.D.; Kramer, B.S.; Tammemägi, M.C.; Chiles, C.; Black, W.C.; Aberle, D.R.; NLSTOverdiagnosis Manuscript Writing Team. Overdiagnosis in low-dose computed tomography screening for lung cancer. JAMAIntern. Med. 2014, 174, 269–274. [CrossRef] [PubMed]41. Sharma, D.; Newman, T.G.; Aronow, W.S. Lung cancer screening: History, current perspectives, and future directions. Arch. Med.Sci. 2015, 11, 1033–1043. [PubMed]42. Griffey, R.T.; Sodickson, A. Cumulative radiation exposure and cancer risk estimates in emergency department patients undergo-ing repeat or multiple CT. AJR Am. J. Roentgenol. 2009, 192, 887–892. [CrossRef] [PubMed]43. Mascalchi, M.; Sali, L. Lung cancer screening with low dose CT and radiation harm-from prediction models to cancer incidencedata. Ann. Transl. Med. 2017, 5, 360. [CrossRef]44. Rampinelli, C.; De Marco, P.; Origgi, D.; Maisonneuve, P.; Casiraghi, M.; Veronesi, G.; Spaggiari, L.; Bellomi, M. Exposure tolow dose computed tomography for lung cancer screening and risk of cancer: Secondary analysis of trial data and risk-benefitanalysis. BMJ 2017, 356, j347. [CrossRef]45. Berg, C.D. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 2011, 365, 395–409.46. Horeweg, N.; van der Aalst, C.M.; Thunnissen, E.; Nackaerts, K.; Weenink, C.; Groen, H.J.; Lammers, J.W.; Aerts, J.G.; Scholten,E.T.; van Rosmalen, J.; et al. Characteristics of lung cancers detected by computer tomography screening in the randomizedNELSON trial. Am. J. Respir. Crit. Care Med. 2013, 187, 848–854. [CrossRef]47. Duquenne, J.B.; Paulus, A.; Sibille, A.; Corhay, J.L.; Louis, R.; Duysinx, B. Pre-cancerous lung lesions and lung cancer screening.Rev. Med. Liege. 2021, 76, 440–445.48. Nardi-Agmon, I.; Peled, N. Exhaled breath analysis for the early detection of lung cancer: Recent developments and futureprospects. Lung Cancer 2017, 8, 31–38. [CrossRef] [PubMed]49. Zhang, C.; Leng, W.; Sun, C.; Lu, T.; Chen, Z.; Men, X.; Wang, Y.; Wang, G.; Zhen, B.; Qin, J. Urine Proteome profiling predictslung cancer from control cases and other tumors. EBioMedicine 2018, 30, 120–128. [CrossRef]50. Hubers, A.J.; Prinsen, C.F.; Sozzi, G.; Witte, B.I.; Thunnissen, E. Molecular sputum analysis for the diagnosis of lung cancer. Br. J.Cancer 2013, 109, 530–537. [CrossRef]51. Mehta, A.; Cordero, J.; Dobersch, S.; Romero-Olmedo, A.J.; Savai, R.; Bodner, J.; Chao, C.M.; Fink, L.; Guzmán-Díaz, E.; Singh, I.;et al. Non-invasive lung cancer diagnosis by detection of GATA6 and NKX2-1 isoforms in exhaled breath condensate. EMBOMol.Med. 2016, 8, 1380–1389. [CrossRef]52. Mamdani, H.; Ahmed, S.; Armstrong, S.; Mok, T.; Jalal, S.I. Blood-based tumor biomarkers in lung cancer for detection andtreatment. Transl. Lung Cancer Res. 2017, 6, 648–660. [CrossRef] [PubMed]53. Paci, M.; Rapicetta, C.; Maramotti, S. New biomarkers for lung cancer. Expert Opin Med. Diagn. 2010, 4, 201–224. [CrossRef][PubMed]54. Zamay, T.N.; Zamay, G.S.; Kolovskaya, O.S.; Zukov, R.A.; Petrova, M.M.; Gargaun, A.; Berezovski, M.V.; Kichkailo, A.S. Currentand prospective protein biomarkers of lung cancer. Cancers 2017, 9, 155. [CrossRef]Int. J. Mol. Sci. 2022, 23, 1215 15 of 1755. Zhong, L.; Coe, S.P.; Stromberg, A.J.; Khattar, N.H.; Jett, J.R.; Hirschowitz, E.A. Profiling tumor-associated antibodies for earlydetection of non-small cell lung cancer. J. Thorac. Oncol. 2006, 1, 513–519. [CrossRef] [PubMed]56. Heitzer, E.; Ulz, P.; Geigl, J.B. Circulating tumor DNA as a liquid biopsy for cancer. Clin. Chem. 2015, 61, 112–123. [CrossRef][PubMed]57. Hussain, S.H.; Huertas, C.S.; Mitchell, A.; Deman, A.L.; Laurenceau, E. Biosensors for circulating tumor cells (CTCs)-biomarkerdetection in lung and prostate cancer: Trends and prospects. Biosens. Bioelectron. 2021, 197, 113770. [CrossRef]58. Rabinowits, G.; Gerçel-Taylor, C.; Day, J.M.; Taylor, D.D.; Kloecker, G.H. Exosomal microRNA: A diagnostic marker for lungcancer. Clin. Lung Cancer 2009, 10, 42–46. [CrossRef]59. Ostrow, K.L.; Hoque, M.O.; Loyo, M.; Brait, M.; Greenberg, A.; Siegfried, J.M.; Grandis, J.R.; Davis, A.G.; Bigbee, W.L.; Rom, W.;et al. Molecular analysis of plasma DNA for the early detection of lung cancer by quantitative methylation-specific PCR. Clin.Cancer Res. 2010, 16, 3463–3472. [CrossRef] [PubMed]60. Duarte, I.F.; Rocha, C.M.; Gil, A.M. Metabolic profiling of biofluids: Potential in lung cancer screening and diagnosis. Expert Rev.Mol. Diagn. 2013, 13, 737–748. [CrossRef]61. Blanco-Prieto, S.; De Chiara, L.; Rodríguez-Girondo, M.; Vázquez-Iglesias, L.; Rodríguez-Berrocal, F.J.; Fernández-Villar, A.;Botana-Rial, M.I.; de la Cadena, M.P. Highly sensitive marker panel for guidance in lung cancer rapid diagnostic units. Sci. Rep.2017, 7, 41151. [CrossRef] [PubMed]62. Boeri, M.; Verri, C.; Conte, D.; Roz, L.; Modena, P.; Facchinetti, F.; Calabrò, E.; Croce, C.M.; Pastorino, U.; Sozzi, G. MicroRNAsignatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer. Proc. Natl.Acad. Sci. USA 2011, 108, 3713–3718. [CrossRef]63. Ostroff, R.M.; Bigbee, W.L.; Franklin, W.; Gold, L.; Mehan, M.; Miller, Y.E.; Pass, H.I.; Rom, W.N.; Siegfried, J.M.; Stewart, A.; et al.Unlocking biomarker discovery: Large scale application of aptamer proteomic technology for early detection of lung cancer. PLoSONE 2010, 5, e15003. [CrossRef] [PubMed]64. Boyle, P.; Chapman, C.J.; Holdenrieder, S.; Murray, A.; Robertson, C.; Wood, W.C.; Maddison, P.; Healey, G.; Fairley, G.H.; Barnes,A.C.; et al. Clinical validation of an autoantibody test for lung cancer. Ann. Oncol. 2011, 22, 383–389. [CrossRef]65. Jett, J.R.; Peek, L.J.; Fredericks, L.; Jewell, W.; Pingleton, W.W.; Robertson, J.F. Audit of the autoantibody test, EarlyCDT(R)-lung,in 1600 patients: An evaluation of its performance in routine clinical practice. Lung Cancer 2014, 83, 51–55. [CrossRef] [PubMed]66. Canadian Cancer Statistics Advisory Committee. Canadian Cancer Statistics: A 2020 Special Report on Lung Cancer; CanadianCancer Society: Toronto, ON, Canada, 2020.67. Dama, E.; Colangelo, T.; Fina, E.; Cremonesi, M.; Kallikourdis, M.; Veronesi, G.; Bianchi, F. Biomarkers and lung cancer earlydetection: State of the art. Cancers 2021, 13, 3919. [CrossRef] [PubMed]68. Freitas, C.; Sousa, C.; Machado, F.; Serino, M.; Santos, V.; Cruz-Martins, N.; Teixeira, A.; Cunha, A.; Pereira, T.; Oliveira, H.P.; et al.The role of liquid biopsy in early diagnosis of lung cancer. Front. Oncol. 2021, 11, 634316. [CrossRef]69. Kim, K.; Mall, C.; Taylor, S.L.; Hitchcock, S.; Zhang, C.; Wettersten, H.I.; Jones, A.D.; Chapman, A.; Weiss, R.H. Mealtime,temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment. PLoS ONE2014, 9, e86223. [CrossRef]70. Suhre, K.; Raffler, J.; Kastenmüller, G. Biochemical insights from population studies with genetics and metabolomics. Arch.Biochem. Biophys. 2016, 589, 168–176. [CrossRef]71. Wishart, D.S. Systems biology resources arising from the human metabolome project. In Genetics Meets Metabolomics: FromExperiment to Systems Biology; Suhre, K., Ed.; Springer: New York, NY, USA, 2012; pp. 157–175.72. Wishart, D.S. Metabolomics for investigating physiological and pathophysiological processes. Physiol. Rev. 2019, 99, 1819–1875.[CrossRef]73. Fiehn, O. Metabolomics–the link between genotypes and phenotypes. Plant. Mol. Biol. 2002, 48, 155–171. [CrossRef]74. Wishart, D.S.; Mandal, R.; Stanislaus, A.; Ramirez-Gaona, M. Cancer metabolomics and the human metabolome database.Metabolites 2016, 6, 10. [CrossRef]75. Vanhove, K.; Graulus, G.J.; Mesotten, L.; Thomeer, M.; Derveaux, E.; Noben, J.P.; Guedens, W.; Adriaensens, P. The metaboliclandscape of lung cancer: New insights in a disturbed glucose metabolism. Front. Oncol. 2019, 9, 1215. [CrossRef] [PubMed]76. Anastasiou, D. Tumour microenvironment factors shaping the cancer metabolism landscape. Br. J. Cancer 2017, 116, 277–286.[CrossRef] [PubMed]77. Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [CrossRef]78. Pavlova, N.N.; Thompson, C.B. The emerging hallmarks of cancer metabolism. Cell Metab. 2016, 23, 27–47. [CrossRef] [PubMed]79. DeBerardinis, R.J.; Chandel, N.S. Fundamentals of cancer metabolism. Sci. Adv. 2016, 2, e1600200. [CrossRef]80. Kowalczyk, T.; Kisluk, J.; Pietrowska, K.; Godzien, J.; Kozlowski, M.; Reszec´, J.; Sierko, E.; Naumnik, W.; Mróz, R.; Moniuszko, M.;et al. The Ability of metabolomics to discriminate non-small-cell lung cancer subtypes depends on the stage of the disease andthe type of material studied. Cancers 2021, 13, 3314. [CrossRef] [PubMed]81. Singhal, S.; Rolfo, C.; Maksymiuk, A.W.; Tappia, P.S.; Sitar, D.S.; Russo, A.; Akhtar, P.S.; Khatun, N.; Rahnuma, P.; Rashiduzzaman,A.; et al. Liquid biopsy in lung cancer screening: The contribution of metabolomics. Results of a pilot study. Cancers 2019, 11,1069. [CrossRef] [PubMed]82. Wen, T.; Gao, L.; Wen, Z.; Wu, C.; Tan, C.S.; Toh, W.Z.; Ong, C.N. Exploratory investigation of plasma metabolomics in humanlung adenocarcinoma. Mol. Biosyst. 2013, 9, 2370–2378. [CrossRef]Int. J. Mol. Sci. 2022, 23, 1215 16 of 1783. Zhang, X.; Zhu, X.; Wang, C.; Zhang, H.; Cai, Z. Non-targeted and targeted metabolomics approaches to diagnosing lung cancerand predicting patient prognosis. Oncotarget 2016, 7, 63437–63448. [CrossRef] [PubMed]84. Zhang, L.; Zheng, J.; Ahmed, R.; Huang, G.; Reid, J.; Mandal, R.; Maksymiuk, A.; Sitar, D.S.; Tappia, P.S.; Ramjiawan, B.; et al. Ahigh-performing plasma metabolite panel for early-stage lung cancer detection. Cancers 2020, 12, 622. [CrossRef]85. Callejón-Leblic, B.; García-Barrera, T.; Pereira-Vega, A.; Gómez-Ariza, J.L. Metabolomic study of serum, urine and bronchoalveolarlavage fluid based on gas chromatography mass spectrometry to delve into the pathology of lung cancer. J. Pharm. Biomed. Anal.2019, 163, 122–129. [CrossRef] [PubMed]86. Chen, Y.; Ma, Z.; Li, A.; Li, H.; Wang, B.; Zhong, J.; Min, L.; Dai, L. Metabolomic profiling of human serum in lung cancer patientsusing liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry and gas chromatography/mass spectrometry.J. Cancer Res. Clin. Oncol. 2015, 141, 705–718. [CrossRef] [PubMed]87. Fahrmann, J.F.; Kim, K.; DeFelice, B.C.; Taylor, S.L.; Gandara, D.R.; Yoneda, K.Y.; Cooke, D.T.; Fiehn, O.; Kelly, K.; Miyamoto, S.Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer. Cancer Epidemiol. Biomark. Prev.2015, 24, 1716–1723. [CrossRef] [PubMed]88. Mu, Y.; Zhou, Y.; Wang, Y.; Li, W.; Zhou, L.; Lu, X.; Gao, P.; Gao, M.; Zhao, Y.; Wang, Q.; et al. Serum metabolomics study of nonsmoking female patients with non-small cell lung cancer using gas chromatography-mass spectrometry. J. Proteome Res. 2019, 18,2175–2184. [CrossRef] [PubMed]89. Ni, J.; Xu, L.; Li, W.; Zheng, C.; Wu, L. Targeted metabolomics for serum amino acids and acylcarnitines in patients with lungcancer. Exp. Ther. Med. 2019, 18, 188–198. [CrossRef]90. Pamungkas, A.D.; Park, C.; Lee, S.; Jee, S.H.; Park, Y.H. High resolution metabolomics to discriminate compounds in serum ofmale lung cancer patients in South Korea. Respir. Res. 2016, 17, 100. [CrossRef] [PubMed]91. Ruiying, C.; Zeyun, L.; Yongliang, Y.; Zijia, Z.; Ji, Z.; Xin, T.; Xiaojian, Z. A comprehensive analysis of metabolomics andtranscriptomics in non-small cell lung cancer. PLoS ONE 2020, 15, e0232272. [CrossRef] [PubMed]92. Sun, Q.; Zhao, W.; Wang, L.; Guo, F.; Song, D.; Zhang, Q.; Zhang, D.; Fan, Y.; Wang, J. Integration of metabolomic andtranscriptomic profiles to identify biomarkers in serum of lung cancer. J. Cell. Biochem. 2019. Epub Ahead of Print. [CrossRef]93. Zheng, Y.; He, Z.; Kong, Y.; Huang, X.; Zhu, W.; Liu, Z.; Gong, L. Combined metabolomics with transcriptomics reveals importantserum biomarkers correlated with lung cancer proliferation through a calcium signaling pathway. J. Proteome Res. 2021, 20,3444–3454. [CrossRef] [PubMed]94. Cameron, S.J.; Lewis, K.E.; Beckmann, M.; Allison, G.G.; Ghosal, R.; Lewis, P.D.; Mur, L.A. The metabolomic detection of lungcancer biomarkers in sputum. Lung Cancer 2016, 94, 88–95. [CrossRef] [PubMed]95. Mathé, E.A.; Patterson, A.D.; Haznadar, M.; Manna, S.K.; Krausz, K.W.; Bowman, E.D.; Shields, P.G.; Idle, J.R.; Smith, P.B.; Anami,K.; et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res.2014, 74, 3259–3270. [CrossRef]96. Zhao, C.; Kong, X.; Han, S.; Li, X.; Wu, T.; Zhou, J.; Guo, Y.; Bu, Z.; Liu, C.; Zhang, C.; et al. Analysis of differential metabolites inlung cancer patients based on metabolomics and bioinformatics. Future Oncol. 2020, 16, 1269–1287. [CrossRef] [PubMed]97. Li, T.; Liu, Y.; Zhang, W.; Lin, L.; Zhang, J.; Xiong, Y.; Nie, L.; Liu, X.; Li, H.; Wang, W. A rapid liquid biopsy of lung cancer byseparation and detection of exfoliated tumor cells from bronchoalveolar lavage fluid with a dual-layer “PERFECT” filter system.Theranostics 2020, 10, 6517–6529. [CrossRef] [PubMed]98. Jiang, J.; Mu, C.; Zhao, J.; Zeng, D.; Wang, C.; Ye, J.; Xu, J.; Su, J.; Han-Zhang, H.; Li, B.; et al. Cell-free DNA from bronchoalveolarlavage fluid (BALF) for the identification of lung cancer: A new medium of liquid biopsy. Cancer Res. 2020, 80, 2311.99. Pérez-Sánchez, C.; Barbarroja, N.; Pantaleão, L.C.; López-Sánchez, L.M.; Ozanne, S.E.; Jurado-Gámez, B.; Aranda, E.; Lopez-Pedrera, C.; Rodríguez-Ariza, A. Clinical Utility of microRNAs in Exhaled Breath Condensate as Biomarkers for Lung Cancer. J.Pers. Med. 2021, 11, 111. [CrossRef]100. Sampson, J.N.; Boca, S.M.; Shu, X.O.; Stolzenberg-Solomon, R.Z.; Matthews, C.E.; Hsing, A.W.; Tan, Y.T.; Ji, B.T.; Chow, W.H.; Cai,Q.; et al. Metabolomics in epidemiology: Sources of variability in metabolite measurements and implications. Cancer Epidemiol.Biomark. Prev. 2013, 22, 631–640. [CrossRef]101. Sanchez, O.A.; Duprez, D.A.; Bahrami, H.; Daniels, L.B.; Folsom, A.R.; Lima, J.A.; Maisel, A.; Peralta, C.A.; Jacobs, D.R., Jr.The associations between metabolic variables and NT-proBNP are blunted at pathological ranges: The Multi-Ethnic Study ofAtherosclerosis. Metabolism 2014, 63, 475–483. [CrossRef] [PubMed]102. Sato, S.; Parr, E.B.; Devlin, B.L.; Hawley, J.A.; Sassone-Corsi, P. Human metabolomics reveal daily variations under nutritionalchallenges specific to serum and skeletal muscle. Mol. Metab. 2018, 16, 1–11. [CrossRef] [PubMed]103. Hirayama, A.; Sugimoto, M.; Suzuki, A.; Hatakeyama, Y.; Enomoto, A.; Harada, S.; Soga, T.; Tomita, M.; Takebayashi, T. Effects ofprocessing and storage conditions on charged metabolomic profiles in blood. Electrophoresis 2015, 36, 2148–2155. [CrossRef]104. Ferreira, D.L.S.; Maple, H.J.; Goodwin, M.; Brand, J.S.; Yip, V.; Min, J.L.; Groom, A.; Lawlor, D.A.; Ring, S. The effect ofpre-analytical conditions on blood metabolomics in epidemiological studies. Metabolites 2019, 9, 64. [CrossRef]105. Laparre, J.; Kaabia, Z.; Mooney, M.; Buckley, T.; Sherry, M.; Le Bizec, B.; Dervilly-Pinel, G. Impact of storage conditions on theurinary metabolomics fingerprint. Anal. Chim. Acta 2017, 951, 99–107. [CrossRef] [PubMed]106. Yet, I.; Menni, C.; Shin, S.Y.; Mangino, M.; Soranzo, N.; Adamski, J.; Suhre, K.; Spector, T.D.; Kastenmüller, G.; Bell, J.T. Geneticinfluences on metabolite levels: A comparison across metabolomic platforms. PLoS ONE 2016, 11, e0153672. [CrossRef]Int. J. Mol. Sci. 2022, 23, 1215 17 of 17107. Zelena, E.; Dunn, W.B.; Broadhurst, D.; Francis-McIntyre, S.; Carroll, K.M.; Begley, P.; O’Hagan, S.; Knowles, J.D.; Halsall, A.;Wilson, I.D.; et al. Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of humanserum. Anal. Chem. 2009, 81, 1357–1364. [CrossRef]108. Gika, H.G.; Macpherson, E.; Theodoridis, G.A.; Wilson, I.D. Evaluation of the repeatability of ultra-performance liquidchromatography-TOF-MS for global metabolic profiling of human urine samples. J. Chromatogr. B Anal. Technol. Biomed.Life Sci. 2008, 871, 299–305. [CrossRef]109. Noh, K.; Kang, Y.R.; Nepal, M.R.; Shakya, R.; Kang, M.J.; Kang, W.; Lee, S.; Jeong, H.G.; Jeong, T.C. Impact of gut microbiota ondrug metabolism: An update for safe and effective use of drugs. Arch. Pharm. Res. 2017, 40, 1345–1355. [CrossRef]110. Emwas, A.M.; Al-Rifai, N.; Szczepski, K.; Alsuhaymi, S.; Rayyan, S.; Almahasheer, H.; Jaremko, M.; Brennan, L.; Lachowicz, J.I.You are what you eat: Application of metabolomics approaches to advance nutrition research. Foods 2021, 10, 1249. [CrossRef][PubMed]111. Cheng, D.; Zhao, X.; Yang, S.; Cui, H.; Wang, G. Metabolomic signature between metabolically healthy overweight/obese andmetabolically unhealthy overweight/obese: A systematic review. Diabetes Metab. Syndr. Obes. 2021, 14, 991–1010. [CrossRef][PubMed]112. Handakas, E.; Lau, C.H.; Alfano, R.; Chatzi, V.L.; Plusquin, M.; Vineis, P.; Robinson, O. A systematic review of metabolomicstudies of childhood obesity: State of the evidence for metabolic determinants and consequences. Obes. Rev. 2021, e13384.[CrossRef] [PubMed]113. Fuschillo, S.; Paris, D.; Tramice, A.; Ambrosino, P.; Palomba, L.; Maniscalco, M.; Motta, A. Metabolomic profiling of exhaledbreath condensate and plasma/serum in chronic obstructive pulmonary disease. Curr. Med. Chem. 2021. Epub Ahead of Print.[CrossRef]114. Esther, C.R., Jr.; O’Neal, W.K.; Anderson, W.H.; Kesimer, M.; Ceppe, A.; Doerschuk, C.M.; Alexis, N.E.; Hastie, A.T.; Barr, R.G.;Bowler, R.P.; et al. SPIROMICS. Identification of sputum biomarkers predictive of pulmonary exacerbations in chronic obstructivepulmonary disease. Chest 2021, in press.115. Choudhury, P.; Bhattacharya, A.; Dasgupta, S.; Ghosh, N.; Senpupta, S.; Joshi, M.; Bhattacharyya, P.; Chaudhury, K. Identificationof novel metabolic signatures potentially involved in the pathogenesis of COPD associated pulmonary hypertension. Metabolomics2021, 17, 94. [CrossRef]116. Lin, B.M.; Zhang, Y.; Yu, B.; Boerwinkle, E.; Thygarajan, B.; Yunes, M.; Daviglus, M.L.; Qi, Q.; Kaplan, R.; Lash, J.; et al.Metabolome-wide association study of estimated glomerular filtration rates in Hispanics. Kidney Int. 2021, 101, 144–151.[CrossRef]117. Hameed, A.; Mojsak, P.; Buczynska, A.; Suleria, H.A.R.; Kretowski, A.; Ciborowski, M. Altered metabolome of lipids and aminoacids species: A source of early signature biomarkers of T2DM. J. Clin. Med. 2020, 9, 2257. [CrossRef] [PubMed]118. Chen, Z.Z.; Gerszten, R.E. Metabolomics and proteomics in type 2 diabetes. Circ. Res. 2020, 126, 1613–1627. [CrossRef] [PubMed]119. Cheng, S.; Shah, S.H.; Corwin, E.J.; Fiehn, O.; Fitzgerald, R.L.; Gerszten, R.E.; Illig, T.; Rhee, E.P.; Srinivas, P.R.; Wang, T.J.; et al.Potential impact and study considerations of metabolomics in cardiovascular health and disease: A scientific statement from theAmerican Heart Association. Circ. Cardiovasc. Genet. 2017, 10, e000032. [CrossRef] [PubMed]120. Ashrafian, H.; Sounderajah, V.; Glen, R.; Ebbels, T.; Blaise, B.J.; Kalra, D.; Kultima, K.; Spjuth, O.; Tenori, L.; Salek, R.M.; et al.Metabolomics: The Stethoscope for the Twenty-First Century. Med Princ. Pract. 2021, 30, 301–310. [CrossRef]121. Schmidt, D.R.; Patel, R.; Kirsch, D.G.; Lewis, C.A.; Heiden, M.G.V.; Locasale, J.W. Metabolomics in cancer research and emergingapplications in clinical oncology. CA Cancer J. Clin. 2021, 71, 333–358. [CrossRef]
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Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome
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