Open Access

Gene set enrichment analysis and meta‑analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma

  • Authors:
    • Wenwu He
    • Liangmin Fu
    • Qunlun Yan
    • Qiuxi Zhou
    • Kun Yuan
    • Linxin Chen
    • Yongtao Han
  • View Affiliations

  • Published online on: April 9, 2019     https://doi.org/10.3892/ol.2019.10236
  • Pages: 5608-5618
  • Copyright: © He et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

The aim of the present study was to analyze lung adenocarcinoma‑associated microarray data and identify potentially crucial genes. The gene expression profiles were downloaded from the Gene Expression Omnibus database and 6 datasets, of which 2 were discarded and 4 were retained, were preprocessed using packages in the R computing language. Subsequently, Gene Set Enrichment Analysis (GSEA) and meta‑analysis was used to screen the common pathways and differentially expressed genes at the transcriptional level. The genes detected from GSEA through The Cancer Genome Atlas databases were subsequently examined, and the crucial genes by survival data were identified. Pathways of the crucial genes were obtained using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the online website Database for Annotation, Visualization and Integrated Discovery (DAVID) tool, and the pathways of crucial genes that were upregulated or downregulated were matched using the Venn method to identify the common crucial pathways. Furthermore, on the basis of the common crucial pathways, key genes that are closely associated with the development and progression of lung adenocarcinoma were identified with the KEGG pathway of DAVID. Additional information was obtained through Gene Ontology annotation. A total of two key pathways, including cell cycle and DNA replication, as well as 12 key genes [DNA polymerase δ subunit 2, DNA replication licensing factor MCM4, MCM6, mitotic checkpoint serine/threonine‑protein kinase BUB1, BUB1β, mitotic spindle assembly checkpoint protein MAD2A, dual specificity protein kinase TTK, M‑phase inducer phosphatase 1, cell division control protein 45 homolog, cyclin‑dependent kinase inhibitor 1C, pituitary tumor‑transforming gene 1 protein and polo‑like kinase 1] were identified. These key pathways and genes may be studied in future studies involving gene transfection/knockdown, which may provide insights into the prognosis of lung adenocarcinoma. Additional studies are required to confirm their biological function.

References

1 

Torre LA, Siegel RL and Jemal A: Lung Cancer Statistics. Adv Exp Med Biol. 893:1–19. 2016. View Article : Google Scholar : PubMed/NCBI

2 

de Castro J, Tagliaferri P, de Lima VCC, Ng S, Thomas M, Arunachalam A, Cao X, Kothari S, Burke T, Myeong H, et al: Systemic therapy treatment patterns in patients with advanced non-small cell lung cancer (NSCLC): PIvOTAL study. Eur J Cancer Care (Engl). 26:e127342017. View Article : Google Scholar

3 

Selvaggi G and Scagliotti GV: Histologic subtype in NSCLC: Does it matter? Oncology (Williston Park). 23:1133–1140. 2009.PubMed/NCBI

4 

Janssen-Heijnen ML, Schipper RM, Klinkhamer PJ, Crommelin MA, Mooi WJ and Coebergh JW: Divergent changes in survival for histological types of non-small-cell lung cancer in the southeastern area of The Netherlands since 1975. Br J Cancer. 77:2053–2057. 1998. View Article : Google Scholar : PubMed/NCBI

5 

Huang H, Tang Y, He W, Huang Q, Zhong J and Yang Z: Key pathways and genes controlling the development and progression of clear cell renal cell carcinoma (ccRCC) based on gene set enrichment analysis. Int Urol Nephrol. 46:539–553. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M and Edgar R: NCBI GEO: Mining tens of millions of expression profiles - database and tools update. Nucleic Acids Res 35 (Database). D760–D765. 2007. View Article : Google Scholar

7 

Subramanian A, Kuehn H, Gould J, Tamayo P and Mesirov JP: GSEA-P: A desktop application for Gene Set Enrichment Analysis. Bioinformatics. 23:3251–3253. 2007. View Article : Google Scholar : PubMed/NCBI

8 

Greenbaum D, Jansen R and Gerstein M: Analysis of mRNA expression and protein abundance data: An approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics. 18:585–596. 2002. View Article : Google Scholar : PubMed/NCBI

9 

Manchia M, Piras IS, Huentelman MJ, Pinna F, Zai CC, Kennedy JL and Carpiniello B: Pattern of gene expression in different stages of schizophrenia: Down-regulation of NPTX2 gene revealed by a meta-analysis of microarray datasets. Eur Neuropsychopharmacol. 27:1054–1063. 2017. View Article : Google Scholar : PubMed/NCBI

10 

Lai Y, Zhang F, Nayak TK, Modarres R, Lee NH and McCaffrey TA: Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets. BMC Genomics. 15 (Suppl 1):S62014. View Article : Google Scholar : PubMed/NCBI

11 

Knopp-Sihota JA, Newburn-Cook CV, Homik J, Cummings GG and Voaklander D: Calcitonin for treating acute and chronic pain of recent and remote osteoporotic vertebral compression fractures: A systematic review and meta-analysis. Osteoporos Int. 23:17–38. 2012. View Article : Google Scholar : PubMed/NCBI

12 

Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 5:R802004. View Article : Google Scholar : PubMed/NCBI

13 

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H and Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27:29–34. 1999. View Article : Google Scholar : PubMed/NCBI

14 

Pinelli NR, Cha R, Brown MB and Jaber LA: Addition of thiazolidinedione or exenatide to oral agents in type 2 diabetes: A meta-analysis. Ann Pharmacother. 42:1541–1551. 2008. View Article : Google Scholar : PubMed/NCBI

15 

Tian H: Detection of differentially expressed genes involved in osteoarthritis pathology. J Orthop Surg Res. 13:492018. View Article : Google Scholar : PubMed/NCBI

16 

Shinmura K, Kato H, Kawanishi Y, Igarashi H, Inoue Y, Yoshimura K, Nakamura S, Fujita H, Funai K, Tanahashi M, et al: WDR62 overexpression is associated with a poor prognosis in patients with lung adenocarcinoma. Mol Carcinog. 56:1984–1991. 2017. View Article : Google Scholar : PubMed/NCBI

17 

Fahrmann JF, Grapov DD, Wanichthanarak K, DeFelice BC, Salemi MR, Rom WN, Gandara DR, Phinney BS, Fiehn O, Pass H, et al: Integrated Metabolomics and Proteomics Highlight Altered Nicotinamide- and Polyamine Pathways in Lung Adenocarcinoma. Carcinogenesis bgw205. 2017. View Article : Google Scholar

18 

Gu MM, Gao D, Yao PA, Yu L, Yang XD, Xing CG, Zhou J, Shang ZF and Li M: p53-inducible gene 3 promotes cell migration and invasion by activating the FAK/Src pathway in lung adenocarcinoma. Cancer Sci. 109:3783–3793. 2018. View Article : Google Scholar : PubMed/NCBI

19 

Wang Z, Wei Y, Zhang R, Su L, Gogarten SM, Liu G, Brennan P, Field JK, McKay JD, Lissowska J, et al: Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma. EBioMedicine. 32:93–101. 2018. View Article : Google Scholar : PubMed/NCBI

20 

Collins FS and Mansoura MK; The Human Genome Project, : The Human Genome Project. Revealing the shared inheritance of all humankind. Cancer. 91 (Suppl):221–225. 2001. View Article : Google Scholar : PubMed/NCBI

21 

Tang Y, He W, Wei Y, Qu Z, Zeng J and Qin C: Screening key genes and pathways in glioma based on gene set enrichment analysis and meta-analysis. J Mol Neurosci. 50:324–332. 2013. View Article : Google Scholar : PubMed/NCBI

22 

Unger G: Antibody formation after cat gut application. Zentralbl Chir. 95:290–292. 1970.(In German). PubMed/NCBI

23 

Deng H, Liu C, Zhang G, Wang X and Liu Y: Lung adenocarcinoma with concurrent ALK and ROS1 rearrangement: A case report and review of the literatures. Pathol Res Pract. 214:2103–2105. 2018. View Article : Google Scholar : PubMed/NCBI

24 

Tane S, Sakai Y, Hokka D, Okuma H, Ogawa H, Tanaka Y, Uchino K, Nishio W, Yoshimura M and Maniwa Y: Significant role of Psf3 expression in non-small-cell lung cancer. Cancer Sci. 106:1625–1634. 2015. View Article : Google Scholar : PubMed/NCBI

25 

Jiang W, Wang H, Cui Y, Lei Y, Wang Y, Xu D, Jiang N, Chen Y, Sun Y, Zhang Y, et al: Polymer nanofiber-based microchips for EGFR mutation analysis of circulating tumor cells in lung adenocarcinoma. Int J Nanomedicine. 13:1633–1642. 2018. View Article : Google Scholar : PubMed/NCBI

26 

Xu L, Lan H, Su Y, Li J and Wan J: Clinicopathological significance and potential drug target of RUNX3 in non-small cell lung cancer: A meta-analysis. Drug Des Devel Ther. 9:2855–2865. 2015. View Article : Google Scholar : PubMed/NCBI

27 

He W, Qi B, Zhou Q, Lu C, Huang Q, Xian L and Chen M: Key genes and pathways in thyroid cancer based on gene set enrichment analysis. Oncol Rep. 30:1391–1397. 2013. View Article : Google Scholar : PubMed/NCBI

28 

Ishimi Y and Irie D: G364R mutation of MCM4 detected in human skin cancer cells affects DNA helicase activity of MCM4/6/7 complex. J Biochem. 157:561–569. 2015. View Article : Google Scholar : PubMed/NCBI

29 

Elgaaen BV, Haug KB, Wang J, Olstad OK, Fortunati D, Onsrud M, Staff AC, Sauer T and Gautvik KM: POLD2 and KSP37 (FGFBP2) correlate strongly with histology, stage and outcome in ovarian carcinomas. PLoS One. 5:e138372010. View Article : Google Scholar : PubMed/NCBI

30 

Ding Y, Hubert CG, Herman J, Corrin P, Toledo CM, Skutt-Kakaria K, Vazquez J, Basom R, Zhang B, Risler JK, et al: Cancer-Specific requirement for BUB1B/BUBR1 in human brain tumor isolates and genetically transformed cells. Cancer Discov. 3:198–211. 2013. View Article : Google Scholar : PubMed/NCBI

31 

Szambowska A, Tessmer I, Kursula P, Usskilat C, Prus P, Pospiech H and Grosse F: DNA binding properties of human Cdc45 suggest a function as molecular wedge for DNA unwinding. Nucleic Acids Res. 42:2308–2319. 2014. View Article : Google Scholar : PubMed/NCBI

32 

Liu C, Zhang YH, Huang T and Cai Y: Identification of transcription factors that may reprogram lung adenocarcinoma. Artif Intell Med. 83:52–57. 2017. View Article : Google Scholar : PubMed/NCBI

33 

Zhang W, Gong W, Ai H, Tang J and Shen C: Gene expression analysis of lung adenocarcinoma and matched adjacent non-tumor lung tissue. Tumori. 100:338–345. 2014.PubMed/NCBI

34 

Sanchez-Palencia A, Gomez-Morales M, Gomez-Capilla JA, Pedraza V, Boyero L, Rosell R and Fárez-Vidal ME: Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer. Int J Cancer. 129:355–364. 2011. View Article : Google Scholar : PubMed/NCBI

35 

Lu TP, Lai LC, Tsai MH, Chen PC, Hsu CP, Lee JM, Hsiao CK and Chuang EY: Integrated analyses of copy number variations and gene expression in lung adenocarcinoma. PLoS One. 6:e248292011. View Article : Google Scholar : PubMed/NCBI

36 

Landi MT, Dracheva T, Rotunno M, Figueroa JD, Liu H, Dasgupta A, Mann FE, Fukuoka J, Hames M, Bergen AW, et al: Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival. PLoS One. 3:e16512008. View Article : Google Scholar : PubMed/NCBI

37 

Su LJ, Chang CW, Wu YC, Chen KC, Lin CJ, Liang SC, Lin CH, Whang-Peng J, Hsu SL, Chen CH, et al: Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme. BMC Genomics. 8:1402007. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

June 2019
Volume 17 Issue 6

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
He, W., Fu, L., Yan, Q., Zhou, Q., Yuan, K., Chen, L., & Han, Y. (2019). Gene set enrichment analysis and meta‑analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma. Oncology Letters, 17, 5608-5618. https://doi.org/10.3892/ol.2019.10236
MLA
He, W., Fu, L., Yan, Q., Zhou, Q., Yuan, K., Chen, L., Han, Y."Gene set enrichment analysis and meta‑analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma". Oncology Letters 17.6 (2019): 5608-5618.
Chicago
He, W., Fu, L., Yan, Q., Zhou, Q., Yuan, K., Chen, L., Han, Y."Gene set enrichment analysis and meta‑analysis identified 12 key genes regulating and controlling the prognosis of lung adenocarcinoma". Oncology Letters 17, no. 6 (2019): 5608-5618. https://doi.org/10.3892/ol.2019.10236