Identifying candidate diagnostic markers for early stage of non-small cell lung cancer


Autoři: Zhen Wu aff001;  Xu Zhang aff001;  Zhihui He aff002;  Liyun Hou aff001
Působiště autorů: School of Mathematics and Statistics, Southwest University, Chongqing 400715, China aff001;  Department of Pediatric Respiration, Chongqing Ninth People’s Hospital, Chongqing 400700, China aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225080

Souhrn

We performed a series of bioinformatics analysis on a set of important gene expression data with 76 samples in early stage of non-small cell lung cancer, including 40 adenocarcinoma samples, 16 squamous cell carcinoma samples and 20 normal samples. In order to identify the specific markers for diagnosis, we compared the two subtypes with the normal samples respectively to determine the gene expression characteristics. Through the multi-dimensional scaling classification, we found that the samples were clustered well according to the disease cases. Based on the classification results and using empirical Bayes moderation and treat method, 486 important genes associated with the disease were identified. We constructed gene functions and gene pathways to verify our result and explain the pathogenicity factor and process. We generated a protein-protein interaction network based on the mutual interaction between the selected genes and found that the top thirteen hub genes were highly associated with lung cancer or some other cancers including five newly found genes through our method. The results of this study indicated that contrast on the gene expression between different subtypes and normal samples provides important information for the detection of non-small cell lung cancer and helps exploration of the disease pathogenesis.

Klíčová slova:

Cell binding – Cell cycle and cell division – Gene expression – Genetic networks – Lung and intrathoracic tumors – Non-small cell lung cancer – Protein kinases – Small cell lung cancer


Zdroje

1. Siegel RL, Miller KD and Jemal A. Cancer statistics, 2018. Ca A Cancer Journal for Clinicians. 2018;60(5):277–300.

2. Raponi M. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung. Cancer Research.2006;66(15):7466–7472. doi: 10.1158/0008-5472.CAN-06-1191 16885343

3. Spira A and Ettinger DS. Multidisciplinary management of lung cancer. New England Journal of Medicine. 2004;350:2008–2010. doi: 10.1056/NEJM200405063501921

4. Dalmay T and Edwards DR. MicroRNAs and the hallmarks of cancer. Oncogene. 2006;25(46):6170–6175. doi: 10.1038/sj.onc.1209911 17028596

5. Liang B, Li C and Zhao J. Identification of key pathways and genes in colorectal cancer using bioinformatics analysis. Medical Oncology. 2016;33(10):111. doi: 10.1007/s12032-016-0829-6 27581154

6. Kulasingam V and Diamandis EP. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nature Clinical Practice Oncology. 2008;5:588–599. doi: 10.1038/ncponc1187 18695711

7. Robinson MD, Mccarthy DJ and Smyth GK. EdgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–140. doi: 10.1093/bioinformatics/btp616 19910308

8. Ritchie ME, Phipson B, Wu D et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015;43(7):e47. doi: 10.1093/nar/gkv007 25605792

9. Huang WD, Sherman BT and Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. doi: 10.1038/nprot.2008.211

10. Martucci D, Masseroli M and Pinciroli F. Gene ontology application to genomic functional annotation, statistical analysis and knowledge mining. Studies in health technology and informatics. 2004;102:108–131. 15853267

11. Ogata H, Goto S, Sato K et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research. 2000;28(1):27–30. doi: 10.1093/nar/28.1.27

12. Szklarczyk D, Morris JH, Cook H et al. The STRING database in 2017: quality-controlled protein-protein association networks made broadly accessible. Nucleic Acids Research.2017;45(D1):D362–D368. doi: 10.1093/nar/gkw937 27924014

13. Saito R, Smoot ME, Ono K et al. A travel guide to Cytoscape plugins. Nature Methods. 2012;9(11):1069–1076. doi: 10.1038/nmeth.2212 23132118

14. Robinson MD and Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology. 2010;11(3):1–9.

15. Huang H, Huang QD, Tang TY et al. Differentially expressed gene screening, biological function enrichment, and correlation with prognosis in non-small cell lung cancer. Medicine Science Monitor. 2019;25:4333–4341. doi: 10.12659/MSM.916962

16. Li Y, Gu J, Xu FK et al. Transcriptomic and functional network features of lung squamous cell carcinoma through integrative analysis of GEO and TCGA data. Scientific Reports. 2018;8(1):15834. doi: 10.1038/s41598-018-34160-w 30367091

17. Tang Q, Zhang HM, Kong M et al. Hub genes and key pathways of non-small lung cancer identified using bioinformatics. Oncology Letters. 2018;16(2):2344–2354. doi: 10.3892/ol.2018.8882 30008938

18. Li SC, Xuan YP, Gao B et al. Identification of an eight-gene prognostic signature for lung adenocarcinoma. Cancer Management and Research. 2018;10:3383–3392. doi: 10.2147/CMAR.S173941 30237740

19. Law CW, Alhamdoosh M, Su S et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000research. 2016;5:1408. doi: 10.12688/f1000research.9005.1

20. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3(3):Article ID 3. doi: 10.2202/1544-6115.1027 16646809

21. Mccarthy DJ and Smyth GK. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics. 2009;25(6):765–771. doi: 10.1093/bioinformatics/btp053 19176553

22. Smogorzewska A, Matsuoka S, Vinciguerra P et al. Identification of the FANCI protein, a monoubiquitinated FANCD2 paralog required for DNA repair. Cell. 2007;129(2):289–301. doi: 10.1016/j.cell.2007.03.009 17412408

23. Taniguchi T, D’Andrea AD. Molecular pathogenesis of Fanconi anemia:recent progress. Blood. 2006;107(11):4223–4233. doi: 10.1182/blood-2005-10-4240 16493006

24. Duan W, Gao L, Aguila B et al. Fanconi Anemia Repair Pathway Dysfunction, a Potential Therapeutic Target in Lung Cancer. Front Oncol. 2014;4:368. doi: 10.3389/fonc.2014.00368 25566506

25. Cole SW, Sood AK. Molecular pathways: beta-adrenergic signaling in cancer. Clinical Cancer Research An Official Journal of the American Association for Cancer Research. 2012;18(5):1201. doi: 10.1158/1078-0432.CCR-11-0641 22186256

26. Fimia GM, Sassone-Corsi P. Cyclic AMP signalling. Journal of Cell Science. 2001;114:1971–1972. 11493633

27. Park JY, Juhnn YS. cAMP signaling increases histone deacetylase 8 expression via the Epac2-Rap1A-Akt pathway in H1299 lung cancer cells. Experimental and Molecular Medicine. 2017;49(2):e297. doi: 10.1038/emm.2016.152 28232663

28. Deguchi A, Das KK, Xing SW et al. Down-regulation of the cGMP/PKG pathway in primary human colon cancers and cancer cell lines. Cancer Research. 2005;65(9):2330.

29. Liu Z, Khalil RA. Evolving Mechanisms of Vascular Smooth Muscle Contraction Highlight Key Targets in Vascular Disease. Cancer Research. 2018;(153):91–122.

30. Shigeishi H, Oue N, Kuniyasu H et al. Expression of Bub1 gene correlates with tumor proliferating activity in human gastric carcinomas. Pathobiology. 2001;69(1):24–29. doi: 10.1159/000048754 11641614

31. Soria J, Jang SJ, Khuri FR et al. Advances in brief overexpression of Cyclin B1 in early-stage non-small cell lung cancer and its clinical implication 1. Cancer Research. 2000;60(15):4000–4004. 10945597

32. Guo Y, Zhang X, Yang M et al. Functional evaluation of missense variations in the human MAD1L1 and MAD2L1 genes and their impact on susceptibility to lung cancer. Journal of Medical Genetics. 2010;47(9):616–622. doi: 10.1136/jmg.2009.074252 20516147

33. Huang H, Liu J, Meng Q et al. Multidrug resistance protein and topoisomerase 2 alpha expression in non-small cell lung cancer are related with brain metastasis postoperatively. International Journal of Clinical and Experimental Pathology. 2015;8(9):11537–11542. 26617887

34. Schneider MA, Christopoulos P, Muley T et al. AURKA, DLGAP5, TPX2, KIF11 and CKAP5: Five specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patients. International Journal of Oncology. 2017;50(2):365–372. doi: 10.3892/ijo.2017.3834 28101582

35. Kato T, Daigo Y, Aragaki M et al. Overexpression of CDC20 predicts poor prognosis in primary non-small cell lung cancer patients. Journal of Surgical Oncology. 2012;106(4):423–430. doi: 10.1002/jso.23109 22488197

36. Chen H, Lee J, Kljavin NM et al. Abstract 2259: Requirement for BUB1B in tumor progression of lung adenocarcinoma. Cancer Research. 2015;75(S15):2259.

37. Shih MC, Chen JY, Wu YC et al. TOPK/PBK promotes cell migration via modulation of the PI3K/PTEN/AKT pathway and is associated with poor prognosis in lung cancer. Oncogene. 2012;31(19):2389–2400. doi: 10.1038/onc.2011.419 21996732

38. Kuo WY, Wu CY, Hwu L et al. Enhancement of tumor initiation and expression of KCNMA1, MORF4L2 and ASPM genes in the adenocarcinoma of lung xenograft after vorinostat treatment. Oncotarget. 2015;6(11):8663–8675. doi: 10.18632/oncotarget.3536 25796627

39. Liu W, Liang B, Liu H et al. Overexpression of non-SMC condensin I complex subunit G serves as a promising prognostic marker and therapeutic target for hepatocellular carcinoma. International Journal of Molecular Medicine. 2017;40(3):731–738. doi: 10.3892/ijmm.2017.3079 28737823

40. Varis A, Salmela AL, Kallio MJ. CENPF (mitosin) is more than a mitotic marker. Chromosoma (Berlin). 2006;115(4):288–295. doi: 10.1007/s00412-005-0046-0

41. Liao, H et al. CENPF is a protein of the nuclear matrix that assembles onto kinetochores at late G2 and is rapidly degraded after mitosis. The Journal of Cell Biology. 1995;130(3):507–518. doi: 10.1083/jcb.130.3.507 7542657

42. Teresa LM, Tatiana D, Melissa R et al. Gene Expression Signature of Cigarette Smoking and Its Role in Lung Adenocarcinoma Development and Survival. PLoS ONE. 2008;3(2):e1651. doi: 10.1371/journal.pone.0001651

43. Smith SL, Bowers NL, Betticher DC et al. Overexpression of aurora B kinase (AURKB) in primary non-small cell lung carcinoma is frequent, generally driven from one allele, and correlates with the level of genetic instability. British journal of cancer. 2005;93(6):719–729. doi: 10.1038/sj.bjc.6602779 16222316

44. Williams GH and Stoeber K. The cell cycle and cancer. Proceedings of the National Academy of Sciences of the United States of America. 2012;226(2):352–364.

45. Wang W, Spitz MR, Yang H et al. Genetic variants in cell cycle control pathway confer susceptibility to lung cancer. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research. 2007;13(19):5974–5981. doi: 10.1158/1078-0432.CCR-07-0113

46. Caputi M, Russo G, Esposito V et al. Role of cell-cycle regulators in lung cancer. Journal of Cellular Physiology. 2005;205(3):319–327. doi: 10.1002/jcp.20424 15965963


Článek vyšel v časopise

PLOS One


2019 Číslo 11