miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer

Open access

Abstract

Lung cancer (LC), which includes small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), is common and has a high fatality rate. This study aimed to reveal the prognostic mechanisms of LC. GSE30219 was extracted from the Gene Expression Omnibus (GEO) database, and included 293 LC samples and 14 normal lung samples. Differentially expressed genes (DEGs) were identified using the Limma package, and subjected to pathway enrichment analysis using DAVID. MicroRNAs (miRNAs) targeting the DEGs were predicted using Webgestalt. Cytoscape software was used to build a protein-protein interaction (PPI) network and to identify significant network modules. Survival analysis was conducted using Survminer and Survival packages, and validation was performed using The Cancer Genome Atlas (TCGA) dataset. The good and poor prognosis groups contained 518 DEGs. miR-190, miR-493, and miR-218 for the upregulated genes and miR-302, miR-200, and miR-26 for the downregulated genes were predicted. Three network modules (module 1, 2, and 3) were identified from the PPI network. CDK1, MCM10, and NDC80 were the core nodes of module 1, 2, and 3, respectively. In module 1, CDK1 interacted with both CCNB1 and CCNB2. Additionally, CDK1, CCNB1, CCNB2, MCM10, and NDC80 expression levels correlated with clinical survival and were identified as DEGs in both GSE30219 and the TCGA dataset. miR-190, miR-493, miR-218, miR-200, and miR-302 might act in LC by targeting the DEGs. CDK1, CCNB1, CCNB2, MCM10, and NDC80 might also influence the prognosis of LC.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Travis WD Travis LB Devesa SS. Lung cancer. Cancer. 2015;75(S1):191-202. DOI: 10.1002/1097-0142(19950101)75:1+<191::AID-CNCR2820751307>3.0.CO;2-Y

  • 2. Collins LG Haines C Perkel R Enck RE. Lung cancer: diagnosis and management. Am Fam Physician. 2007;75(1):56-63.

  • 3. Mcguire S. World Cancer Report 2014. Geneva Switzerland: World Health Organization International Agency for Research on Cancer WHO Press 2015. Adv Nutr. 2016;7(2):418. DOI: 10.3945/an.116.012211

  • 4. Tan X Fang Z Wan J Jie H Chen Z Li B et al. Pin1 expression contributes to lung cancer prognosis and carcinogenesis. Cancer Biol Ther. 2010;9(2):111-9. DOI: 10.4161/cbt.9.2.10341

  • 5. Yoon HE Kim SA Choi HS Ahn MY Yoon JH Ahn SG. Inhibition of Plk1 and Pin1 by 5′-nitro-indirubinoxime suppresses human lung cancer cells. Cancer Lett. 2012;316(1):97-104. DOI: 10.1016/j.canlet.2011.10.029

  • 6. Dong QZ Wang Y Dong XJ Li ZX Tang ZP Cui QZ et al. CIP2A is Overexpressed in Non-Small Cell Lung Cancer and Correlates with Poor Prognosis. Ann Surg Oncol. 2011;18(3):857. DOI: 10.1245/s10434-010-1313-8

  • 7. Xu P Xu XL Huang Q Zhang ZH Zhang YB. CIP2A with survivin protein expressions in human non-small. Med Oncol. 2012;29(3):1643-7. DOI: 10.1007/s12032-011-0053-3

  • 8. Ni S Xu L Huang J Feng J Zhu H Wang G et al. Increased ZO-1 expression predicts valuable prognosis in non-small cell lung cancer. Int J Clin Exp Pathol. 2013;6(12):2887-95.

  • 9. Gao W Yu Y Cao H Shen H Li X Pan S et al. Deregulated expression of miR-21 miR-143 and miR-181a in non small cell lung cancer is related to clinicopathologic characteristics or patient prognosis. Biomed Pharmacother. 2010;64(6):399. DOI: 10.1016/j.biopha.2010.01.018

  • 10. Rousseaux S Debernardi A Jacquiau B Vitte AL Vesin A Nagymignotte H et al. Ectopic Activation of Germline and Placental Genes Identifies Aggressive Metastasis-Prone Lung Cancers. Sci Transl Med. 2013;5(186):186ra66. DOI: 10.1126/scitranslmed.3005723

  • 11. Irizarry RA Wu Z Jaffee HA. Comparison of Affymetrix GeneChip expression measures. Bioinformatics. 2006;22(7):789. DOI: 10.1093/bioinformatics/btk046

  • 12. Ritchie ME Phipson B Wu D Hu Y Law CW Shi W et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7). DOI: 10.1093/nar/gkv007

  • 13. Huang DW Sherman BT Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44. DOI: 10.1038/nprot.2008.211

  • 14. Kanehisa M Sato Y Kawashima M Furumichi M Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2015;44(D1):D457-D62. DOI: 10.1093/nar/gkv1070

  • 15. He L Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004;5(7):522-31. DOI: 10.1038/nrg1379

  • 16. Wang J Duncan D Shi Z Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41(W1):77-83. DOI: 10.1093/nar/gkt439

  • 17. Franceschini A Szklarczyk D Frankild S Kuhn M Simonovic M Roth A et al. STRING v9. 1: protein-protein interaction networks with increased coverage and integration. Nucleic Acids Res. 2013;41(D1):D808-D15. DOI: 10.1093/nar/gks1094

  • 18. Saito R Smoot ME Ono K Ruscheinski J Wang P-L Lotia S et al. A travel guide to Cytoscape plugins. Nat Methods. 2012;9(11):1069-76. DOI: 10.1038/nmeth.2212

  • 19. Morris JH Apeltsin L Newman AM Baumbach J Wittkop T Su G et al. clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC bioinformatics. 2011;12:436 DOI: 10.1186/1471-2105-12-436. DOI: 10.1186/1471-2105-12-436

  • 20. Consortium TGO. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015;43(Database issue):1049-56. DOI: 10.1093/nar/gku1179

  • 21. Maere S Heymans K Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005 Aug 15;21(16):3448-9 DOI: 10.1093/bioinformatics/bti551. DOI: 10.1093/bioinformatics/bti551

  • 22. Kassambara A. survminer: Drawing Survival Curves using ‘ggplot2’. R package version 0.2.2. ed. https://CRAN.R-project.org/package=survminer. 2016.

  • 23. Therneau TM April. A Package for Survival Analysis in S. version 2.38 ed. http://CRAN.R-project.org/package=survival. 2015.

  • 24. Robinson MD Mccarthy DJ Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139. DOI: 10.1093/bioinformatics/btp616

  • 25. Oliveros JC. VENNY. An interactive tool for comparing lists with Venn Diagrams. http://bioinfogp.cnb.csic.es/tools/venny/index.html. 2007.

  • 26. Jia WZ Tao Y Qi A Hua Y Zhu Z Xiao L et al. MicroRNA-190 regulatesFOXP2genes in human gastric cancer. Onco Targets Ther. 2016;9(Issue 1):3643-51.

  • 27. Yu Y Luo W Yang ZJ Chi JR Li YR Ding Y et al. miR-190 suppresses breast cancer metastasis by regulation of TGF-β-induced epithelial-mesenchymal transition. Mol Cancer. 2018;17(1):70. DOI: 10.1186/s12943-018-0818-9

  • 28. Liang Z Kong R He Z Lin LY Qin SS Chen CY et al. High expression of miR-493-5p positively correlates with clinical prognosis of non small cell lung cancer by targeting oncogene ITGB1. Oncotarget. 2017;8(29):47389-99. DOI: 10.18632/oncotarget.17650

  • 29. Peng Z Pan L Niu Z Li W Dang X Lin W et al. Identification of microRNAs as potential biomarkers for lung adenocarcinoma using integrating genomics analysis. Oncotarget. 2017;8(38):64143. DOI: 10.18632/oncotarget.19358

  • 30. Si L Tian H Yue W Li L Li S Gao C et al. Potential use of microRNA-200c as a prognostic marker in non-small cell lung cancer. Oncol Lett. 2017;14(4):4325. DOI: 10.3892/ol.2017.6667

  • 31. Li J Yu J Zhang H Wang B Guo H Bai J et al. Exosomes-Derived MiR-302b Suppresses Lung Cancer Cell Proliferation and Migration via TGF beta RII Inhibition. Cell Physiol Biochem. 2016;38(5):1715. DOI: 10.1159/000443111

  • 32. Shi YX Zhu T Zou T Zhuo W Chen YX Huang MS et al. Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma. Oncotarget. 2016;7(51):85235. DOI: 10.18632/oncotarget.13252

  • 33. Huang SH Xiao-Li MA Qiu C Huang JA Kong WH Xie JW et al. The overexpression of cyclin B1 and CDK1 in lung carcinoma and its clinical significance. Journal of Shandong University. 2004;39(5):122-4.

  • 34. Jacquot C Rousseau B Carbonnelle D Chinou I Malleter M Tomasoni C et al. Cucurbitacin-D-induced CDK1 mRNA up-regulation causes proliferation arrest of a non-small cell lung carcinoma cell line (NSCLC-N6). Anticancer Res. 2014;34(9):4797-806.

  • 35. Cooper WA Kohonencorish MR Mccaughan B Kennedy C Sutherland RL Lee CS. Expression and prognostic significance of cyclin B1 and cyclin A in non-small cell lung cancer. Histopathology. 2009;55(1):28-36. DOI: 10.1111/j.1365-2559.2009.03331.x

  • 36. Takashima S Saito H Takahashi N Imai K Kudo S Atari M et al. Strong expression of cyclin B2 mRNA correlates with a poor prognosis in patients with non-small cell lung cancer. Tumour Biol. 2014;35(5):4257-65. DOI: 10.1007/s13277-013-1556-7

  • 37. Liu YZ Wang BS Jiang YY Cao J Hao JJ Zhang Y et al. MCMs expression in lung cancer: implication of prognostic significance. J Cancer. 2017;8(18):3641-7. DOI: 10.7150/jca.20777

  • 38. Liu YZ Jiang YY Hao JJ Lu SS Zhang TT Shang L et al. Prognostic significance of MCM7 expression in the bronchial brushings of patients with non-small cell lung cancer (NSCLC). Lung Cancer. 2012;77(1):176. DOI: 10.1016/j.lungcan.2012.03.001

  • 39. Kikuchia J Kinoshitab I Shimizub Y Kikuchia E Takedab K Abu H. Minichromosome maintenance (MCM) protein 4 as a marker for proliferation and its clinical and clinicopathological significance in non-small cell lung cancer. Lung Cancer. 2011;72(2):229-37. DOI: 10.1016/j.lungcan.2010.08.020

  • 40. Chao W. Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes. BMC bioinformatics. 2012;13(1):182. DOI: 10.1186/1471-2105-13-182

  • 41. Hayama S Daigo Y Kato T Ishikawa N Yamabuki T Miyamoto M et al. Activation of CDCA1-KNTC2 Members of Centromere Protein Complex Involved in Pulmonary Carcinogenesis. 2006;66(21):10339-48.

Search
Journal information
Impact Factor

IMPACT FACTOR 2018: 0.800
5-year IMPACT FACTOR: 0.655

CiteScore 2017: 0.31

SCImago Journal Rank (SJR) 2018: 0.194
Source Normalized Impact per Paper (SNIP) 2018: 0.306

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 220 220 9
PDF Downloads 170 170 5