Table Info

Table 2.

Genes discriminating between SCC and ADC

Gene	Function	Upregulation (SCC/ADC)	Reference
DSC3	Ca²⁺-dependent glycoprotein involved in cell adherence	SCC	[22]
VSNL1	Neuronal Ca²⁺ sensor protein; tumour suppressor gene	SCC	[33, 34]
IRF6	Transcription factor	SCC	[35]
DST	Cell adhesion	SCC	[36]
CLCA2	Cell adhesion; tumour suppressor	SCC	[37]
PIGX	Tumour suppressor	SCC	-
DSG3	Cell adhesion	SCC	[38–40]
LPCAT1	Cancer progression and metastasis	ADC	[41, 42]
SLC6A10	Neurotransmitter transporter; pseudogene of SLC6A8*	ADC	[43]
CGN	Tight junction	ADC	[21]

-: blank cell; Ca²⁺: calcium ion; *: pseudogene of its parent gene SLC6A8

Declarations

Author contributions

JG: Conceptualization, Methodology, Resources, Writing—review & editing, Supervision, Project administration, Funding acquisition. MC: Conceptualization, Visualization. MC and BQ: Methodology, Validation, Formal analysis, Investigation, Data curation, Writing—original draft, Writing—review & editing. JZ, SY and AB: Software. SY: Validation. LP and JG: Writing—review & editing. LP: Project administration.

Conflicts of interest

JG is a major shareholder of NetraMark Corp, where NetraMark is a technology company providing clinical trial support to pharmaceutical companies. LP has previously acted as a scientific consultant for AbbVie USA; Acadia USA; BCG Switzerland; Boehringer Ingelheim International GmbH; Compass Pathways; EDRA-Publishing, Italy; Ferrer Spain; Gedeon-Richter, Hungary; Inpeco SA, Switzerland; Johnson & Johnson USA; NeuroCog Trials USA; Novartis-Gene Therapies, Switzerland; Otsuka USA; Pfizer Global USA; PharmaMar Spain; Relmada Therapeutics USA; Takeda, USA; VeraSci, USA; Vifor Switzerland.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Data was obtained from publicly available datasets GSE10245 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10245 and GSE18842 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18842.

Funding

Part of this research was funded by NetraMark Corp in the form of salary for Dr. Joseph Geraci, and computational resources. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

References

Ridge CA, McErlean AM, Ginsberg MS. Epidemiology of lung cancer. Semin Intervent Radiol. 2013;30:93–8. [PubMed]

Thomas A, Liu SV, Subramaniam DS, Giaccone G. Refining the treatment of NSCLC according to histological and molecular subtypes. Nat Rev Clin Oncol. 2015;12:511–26. [DOI] [PubMed]

Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014;505:495–501. [DOI] [PubMed] [PMC]

Pikor LA, Ramnarine VR, Lam S, Lam WL. Genetic alterations defining NSCLC subtypes and their therapeutic implications. Lung Cancer. 2013;82:179–89. [DOI] [PubMed]

Manegold C. Treatment algorithm in 2014 for advanced non-small cell lung cancer: therapy selection by tumour histology and molecular biology. Adv Med Sci. 2014;59:308–13. [DOI] [PubMed]

Carnio S, Novello S, Bironzo P, Scagliotti GV. Moving from histological subtyping to molecular characterization: new treatment opportunities in advanced non-small-cell lung cancer. Expert Rev Anticancer Ther. 2014;14:1495–513. [DOI] [PubMed]

Yu L, Tao G, Zhu L, Wang G, Li Z, Ye J, et al. Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis. BMC Cancer. 2019;19:464. [DOI] [PubMed] [PMC]

Tau N, Stundzia A, Yasufuku K, Hussey D, Metser U. Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. AJR Am J Roentgenol. 2020;215:192–7. [DOI] [PubMed]

Kriegsmann M, Haag C, Weis CA, Steinbuss G, Warth A, Zgorzelski C, et al. Deep learning for the classification of small-cell and non-small-cell lung cancer. Cancers (Basel). 2020;12:1604. [DOI] [PubMed] [PMC]

10.

Mu W, Jiang L, Zhang J, Shi Y, Gray JE, Tunali I, et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun. 2020;11:5228. [DOI] [PubMed] [PMC]

11.

Rabbani M, Kanevsky J, Kafi K, Chandelier F, Giles FJ. Role of artificial intelligence in the care of patients with nonsmall cell lung cancer. Eur J Clin Invest. 2018;48:e12901. [DOI] [PubMed]

12.

Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499:214–8. [DOI] [PubMed] [PMC]

13.

Podolsky MD, Barchuk AA, Kuznetcov VI, Gusarova NF, Gaidukov VS, Tarakanov SA. Evaluation of machine learning algorithm utilization for lung cancer classification based on gene expression levels. Asian Pac J Cancer Prev. 2016;17:835–8. [DOI] [PubMed]

14.

Li J, Li D, Wei X, Su Y. In silico comparative genomic analysis of two non-small cell lung cancer subtypes and their potentials for cancer classification. Cancer Genomics Proteomics. 2014;11:303–10. [PubMed]

15.

Yuan F, Lu L, Zou Q. Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms. Biochim Biophys Acta Mol Basis Dis. 2020;1866:165822. [DOI] [PubMed]

16.

Hu F, Zhou Y, Wang Q, Yang Z, Shi Y, Chi Q. Gene expression classification of lung adenocarcinoma into molecular subtypes. IEEE/ACM Trans Comput Biol Bioinform. 2020;17:1187–97. [DOI] [PubMed]

17.

Shen Y, Xiang Y, Huang X, Zhang Y, Yue Z. Pharmacogenomic cluster analysis of lung cancer cell lines provides insights into preclinical model selection in NSCLC. Interdiscip Sci. 2022;14:712–21. [DOI] [PubMed]

18.

Mostavi M, Chiu YC, Chen Y, Huang Y. CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training. BMC Bioinformatics. 2021;22:244. [DOI] [PubMed] [PMC]

19.

Robinson GA, Peng J, Dönnes P, Coelewij L, Naja M, Radziszewska A, et al. Disease-associated and patient-specific immune cell signatures in juvenile-onset systemic lupus erythematosus: patient stratification using a machine-learning approach. Lancet Rheumatol. 2020;2:e485–96. [DOI] [PubMed] [PMC]

20.

Qorri B, Tsay M, Agrawal A, Au R, Geraci J. Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity. Explor Med. 2020;1:377–95. [DOI]

21.

Kuner R, Muley T, Meister M, Ruschhaupt M, Buness A, Xu EC, et al. Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes. Lung Cancer. 2009;63:32–8. [DOI] [PubMed]

22.

Sanchez-Palencia A, Gomez-Morales M, Gomez-Capilla JA, Pedraza V, Boyero L, Rosell R, et al. Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer. Int J Cancer. 2011;129:355–64. [DOI] [PubMed]

23.

Wu Z, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F. A model-based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc. 2004;99:909–17.

24.

Tsay M, Geraci J, Agrawal A. Next-gen AI for disease definition, patient stratification, and placebo effect. OSF Preprints [Preprint]. 2020 [cited 2023 Jan 21]. Available from: https://osf.io/pc7ak/

25.

Choi J, Bodenstein DF, Geraci J, Andreazza AC. Evaluation of postmortem microarray data in bipolar disorder using traditional data comparison and artificial intelligence reveals novel gene targets. J Psychiatr Res. 2021;142:328–36. [DOI] [PubMed]

26.

Lai C, Reinders MJ, van’t Veer LJ, Wessels LF. A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets. BMC Bioinformatics. 2006;7:235. [DOI] [PubMed] [PMC]

27.

Chen X, Ishwaran H. Random forests for genomic data analysis. Genomics. 2012;99:323–9. [DOI] [PubMed] [PMC]

28.

van der Maaten L, Hinton G. Visualizing data using t-SNE. JMLR. 2008;9:2579–605.

29.

McInnes L, Healy J. Accelerated hierarchical density based clustering. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 2017 IEEE International Conference on Data Mining Workshops (ICDMW); 2017 Nov 18–21; New Orleans, LA, USA. IEEE; 2017. pp. 33–42. [DOI]

30.

McInnes L, Healy J, Saul N, Großberger L. UMAP: uniform manifold approximation and projection. J Open Source Softw. 2018;3:861. [DOI]

31.

Liu K, Chen Z, Wu J, Tan Y, Wang L, Yan Y, et al. Big medical data decision-making intelligent system exploiting fuzzy inference logic for prostate cancer in developing countries. IEEE Access. 2019;7:2348–63. [DOI]

32.

Zhou J, Khushi M, Moni MA, Uddin S, Poon SK. Lung cancer prediction using curriculum learning based deep neural networks. In: 2021 IEEE International Conference on Digital Health (ICDH). 2021 IEEE International Conference on Digital Health (ICDH); 2021 Sep 5–10;Chicago, IL, USA. IEEE; 2021. pp.11–8. [DOI]

33.

Fu J, Fong K, Bellacosa A, Ross E, Apostolou S, Bassi DE, et al. VILIP-1 downregulation in non-small cell lung carcinomas: mechanisms and prediction of survival. PLoS One. 2008;3:e1698. [DOI] [PubMed] [PMC]

34.

Gonzalez Guerrico AM, Jaffer ZM, Page RE, Braunewell KH, Chernoff J, Klein-Szanto AJ. Visinin-like protein-1 is a potent inhibitor of cell adhesion and migration in squamous carcinoma cells. Oncogene. 2005;24:2307–16. [DOI] [PubMed]

35.

Liu Y, Shao G, Yang Z, Lin X, Liu X, Qian B, et al. Interferon regulatory factor 6 correlates with the progression of non-small cell lung cancer and can be regulated by miR-320. J Pharm Pharmacol. 2021;73:682–91. [DOI] [PubMed]

36.

Chae YK, Choi WM, Bae WH, Anker J, Davis AA, Agte S, et al. Overexpression of adhesion molecules and barrier molecules is associated with differential infiltration of immune cells in non-small cell lung cancer. Sci Rep. 2018;8:1023. [DOI] [PubMed] [PMC]

37.

Shinmura K, Igarashi H, Kato H, Kawanishi Y, Inoue Y, Nakamura S, et al. CLCA2 as a novel immunohistochemical marker for differential diagnosis of squamous cell carcinoma from adenocarcinoma of the lung. Dis Markers. 2014;2014:619273. [DOI] [PubMed] [PMC]

38.

Savci-Heijink CD, Kosari F, Aubry MC, Caron BL, Sun Z, Yang P, et al. The role of desmoglein-3 in the diagnosis of squamous cell carcinoma of the lung. Am J Pathol. 2009;174:1629–37. [DOI] [PubMed] [PMC]

39.

Fukuoka J, Dracheva T, Shih JH, Hewitt SM, Fujii T, Kishor A, et al. Desmoglein 3 as a prognostic factor in lung cancer. Hum Pathol. 2007;38:276–83. [DOI] [PubMed]

40.

Dong Y, Li S, Sun X, Wang Y, Lu T, Wo Y, et al. Desmoglein 3 and keratin 14 for distinguishing between lung adenocarcinoma and lung squamous cell carcinoma. Onco Targets Ther. 2020;13:11111–24. [DOI] [PubMed] [PMC]

41.

Liu F, Wu Y, Liu J, Ni RJ, Yang AG, Bian K, et al. A miR-205-LPCAT1 axis contributes to proliferation and progression in multiple cancers. Biochem Biophys Res Commun. 2020;527:474–80. [DOI] [PubMed]

42.

Wei C, Dong X, Lu H, Tong F, Chen L, Zhang R, et al. LPCAT1 promotes brain metastasis of lung adenocarcinoma by up-regulating PI3K/AKT/MYC pathway. J Exp Clin Cancer Res. 2019;38:95. [DOI] [PubMed] [PMC]

43.

Yuan K, Gao ZJ, Yuan WD, Yuan JQ, Wang Y. High expression of SLC6A10P contributes to poor prognosis in lung adenocarcinoma. Int J Clin Exp Pathol. 2018;11:720–6. [PubMed] [PMC]

44.

Lucchetta M, da Piedade I, Mounir M, Vabistsevits M, Terkelsen T, Papaleo E. Distinct signatures of lung cancer types: aberrant mucin O-glycosylation and compromised immune response. BMC Cancer. 2019;19:824. [DOI] [PubMed] [PMC]

45.

Wang T, Zhang L, Tian P, Tian S. Identification of differentially-expressed genes between early-stage adenocarcinoma and squamous cell carcinoma lung cancer using meta-analysis methods. Oncol Lett. 2017;13:3314–22. [DOI] [PubMed] [PMC]

46.

Warth A, Muley T, Herpel E, Meister M, Herth FJ, Schirmacher P, et al. Large-scale comparative analyses of immunomarkers for diagnostic subtyping of non-small-cell lung cancer biopsies. Histopathology. 2012;61:1017–25. [DOI] [PubMed]

47.

Tsuta K, Tanabe Y, Yoshida A, Takahashi F, Maeshima AM, Asamura H, et al. Utility of 10 immunohistochemical markers including novel markers (desmocollin-3, glypican 3, S100A2, S100A7, and Sox-2) for differential diagnosis of squamous cell carcinoma from adenocarcinoma of the lung. J Thorac Oncol. 2011;6:1190–9. [DOI] [PubMed]

48.

Angulo B, Suarez-Gauthier A, Lopez-Rios F, Medina PP, Conde E, Tang M, et al. Expression signatures in lung cancer reveal a profile for EGFR-mutant tumours and identify selective PIK3CA overexpression by gene amplification. J Pathol. 2008;214:347–56. [DOI] [PubMed]

49.

Cui T, Chen Y, Yang L, Knösel T, Huber O, Pacyna-Gengelbach M, et al. The p53 target gene desmocollin 3 acts as a novel tumor suppressor through inhibiting EGFR/ERK pathway in human lung cancer. Carcinogenesis. 2012;33:2326–33. [DOI] [PubMed]

50.

Wainer Z, Wright GM, Gough K, Daniels MG, Russell PA, Choong P, et al. Sex-dependent staging in non-small-cell lung cancer; analysis of the effect of sex differences in the eighth edition of the tumor, node, metastases staging system. Clin Lung Cancer. 2018;19:e933–44. [DOI] [PubMed]

51.

Radkiewicz C, Dickman PW, Johansson ALV, Wagenius G, Edgren G, Lambe M. Sex and survival in non-small cell lung cancer: a nationwide cohort study. PLoS One. 2019;14:e0219206. [DOI] [PubMed] [PMC]

52.

Ivanova MM, Mazhawidza W, Dougherty SM, Klinge CM. Sex differences in estrogen receptor subcellular location and activity in lung adenocarcinoma cells. Am J Respir Cell Mol Biol. 2010;42:320–30. [DOI] [PubMed] [PMC]

53.

Rubin JB, Lagas JS, Broestl L, Sponagel J, Rockwell N, Rhee G, et al. Sex differences in cancer mechanisms. Biol Sex Differ. 2020;11:17. [DOI] [PubMed] [PMC]

54.

Ruch R. Gap junctions and connexins in cancer formation, progression, and therapy. Cancers (Basel). 2020;12:3307. [DOI] [PubMed] [PMC]

55.

Soini Y. Tight junctions in lung cancer and lung metastasis: a review. Int J Clin Exp Pathol. 2012;5:126–36. [PubMed] [PMC]

56.

Bhat AA, Uppada S, Achkar IW, Hashem S, Yadav SK, Shanmugakonar M, et al. Tight junction proteins and signaling pathways in cancer and inflammation: a functional crosstalk. Front Physiol. 2019;9:1942. [DOI] [PubMed] [PMC]

57.

Feng Y, Guo X, Tang H. SLC6A8 is involved in the progression of non-small cell lung cancer through the Notch signaling pathway. Ann Transl Med. 2021;9:264. Erratum in: Ann Transl Med. 2022;10:845. [DOI] [PubMed] [PMC]

58.

Nakakido M, Tamura K, Chung S, Ueda K, Fujii R, Kiyotani K, et al. Phosphatidylinositol glycan anchor biosynthesis, class X containing complex promotes cancer cell proliferation through suppression of EHD2 and ZIC1, putative tumor suppressors. Int J Oncol. 2016;49:868–76. [DOI] [PubMed] [PMC]