Identification of key candidate genes for pancreatic cancer by bioinformatics analysis

Lv,Kui; Yang,Jianying; Sun,Junfeng; Guan,Jianguo

doi:10.3892/etm.2019.7619

Identification of key candidate genes for pancreatic cancer by bioinformatics analysis

Authors:
- Kui Lv
- Jianying Yang
- Junfeng Sun
- Jianguo Guan
View Affiliations

Affiliations: Department of Emergency Medicine, Anhui No. 2 Provincial People's Hospital, Hefei, Anhui 230041, P.R. China
Published online on: May 28, 2019 https://doi.org/10.3892/etm.2019.7619
Pages: 451-458
Copyright: © Lv 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: )

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

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Although pancreatic cancer has the highest mortality rate among all neoplasms worldwide, its exact mechanism remains poorly understood. In the present study, three Gene Expression Omnibus (GEO) datasets were integrated to elucidate the potential genes and pathways that contribute to the development of pancreatic cancer. Initially, a total of 226 differentially expressed genes (DEGs) were identified in the three GEO datasets, containing 179 upregulated and 47 downregulated DEGs. Furthermore, function and pathway enrichment analyses were performed to explore the function and pathway of these genes, and the results indicated that the DEGs participated in extracellular matrix (ECM) processes. In addition, a protein‑protein interaction network was constructed and 163 genes of the 229 DEGs were filtered into the network, resulting in a network complex of 163 nodes and 438 edges. Finally, 24 hub genes were identified in the network, and the top 2 most significant modules were selected for function and pathway analysis. The hub genes were involved in several processes, including activation of matrix, degradation of ECM and ECM organization. Taken collectively, the data demonstrated potential key genes and pathways in pancreatic cancer, which may provide novel insights to the mechanism of pancreatic cancer. In addition, these hub genes and pathways may be considered as targets for the treatment of pancreatic cancer.

View Figures

View References

1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2018. CA Cancer J Clin. 68:7–30. 2018. View Article : Google Scholar : PubMed/NCBI
2	Gillen S, Schuster T, Meyer Zum Buschenfelde C, Friess H and Kleeff J: Preoperative/neoadjuvant therapy in pancreatic cancer: A systematic review and meta-analysis of response and resection percentages. PLoS Med. 7:e10002672010. View Article : Google Scholar : PubMed/NCBI
3	Kamisawa T, Wood LD, Itoi T and Takaori K: Pancreatic cancer. Lancet. 388:73–85. 2016. View Article : Google Scholar : PubMed/NCBI
4	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:D760–D765. 2007. View Article : Google Scholar : PubMed/NCBI
5	Li J, Tan W, Peng L, Zhang J, Huang X, Cui Q, Zheng J, Tan W, Wu C and Lin D: Integrative analysis of gene expression profiles reveals specific signaling pathways associated with pancreatic duct adenocarcinoma. Cancer Commun (Lond). 38:132018. View Article : Google Scholar : PubMed/NCBI
6	Wang H, Zhan M, Yang R, Shi Y, Liu Q and Wang J: Elevated expression of NFE2L3 predicts the poor prognosis of pancreatic cancer patients. Cell Cycle. 17:2164–2174. 2018. View Article : Google Scholar : PubMed/NCBI
7	Kanehisa M, Sato Y, Furumichi M, Morishima K and Tanabe M: New approach for understanding genome variations in KEGG. Nucleic Acids Res. 47:D590–D595. 2019. View Article : Google Scholar : PubMed/NCBI
8	Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
9	Huang da W, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
10	Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D and Thomas PD: PANTHER version 11: Expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45:D183–D189. 2017. View Article : Google Scholar : PubMed/NCBI
11	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
12	Ilic M and Ilic I: Epidemiology of pancreatic cancer. World J Gastroenterol. 22:9694–9705. 2016. View Article : Google Scholar : PubMed/NCBI
13	Khadka R, Tian W, Hao X and Koirala R: Risk factor, early diagnosis and overall survival on outcome of association between pancreatic cancer and diabetes mellitus: Changes and advances, a review. Int J Surg. 52:342–346. 2018. View Article : Google Scholar : PubMed/NCBI
14	Kleeff J, Korc M, Apte M, La Vecchia C, Johnson CD, Biankin AV, Neale RE, Tempero M, Tuveson DA, Hruban RH and Neoptolemos JP: Pancreatic cancer. Nat Rev Dis Primers. 2:160222016. View Article : Google Scholar : PubMed/NCBI
15	Cancer Research UK, . Pancreatic cancer statistics 2015. https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/pancreatic-cancerSeptember 20–2018
16	Mammoto T and Ingber DE: Mechanical control of tissue and organ development. Development. 137:1407–1420. 2010. View Article : Google Scholar : PubMed/NCBI
17	Geiger B, Spatz JP and Bershadsky AD: Environmental sensing through focal adhesions. Nat Rev Mol Cell Biol. 10:21–33. 2009. View Article : Google Scholar : PubMed/NCBI
18	Theocharis AD, Skandalis SS, Gialeli C and Karamanos NK: Extracellular matrix structure. Adv Drug Deliv Rev. 97:4–27. 2016. View Article : Google Scholar : PubMed/NCBI
19	Abedin M and King N: Diverse evolutionary paths to cell adhesion. Trends Cell Biol. 20:734–742. 2010. View Article : Google Scholar : PubMed/NCBI
20	Engler AJ, Sen S, Sweeney HL and Discher DE: Matrix elasticity directs stem cell lineage specification. Cell. 126:677–689. 2006. View Article : Google Scholar : PubMed/NCBI
21	Gumbiner BM: Cell adhesion: The molecular basis of tissue architecture and morphogenesis. Cell. 84:345–357. 1996. View Article : Google Scholar : PubMed/NCBI
22	Hynes RO: The extracellular matrix: Not just pretty fibrils. Science. 326:1216–1219. 2009. View Article : Google Scholar : PubMed/NCBI
23	Lu P, Weaver VM and Werb Z: The extracellular matrix: A dynamic niche in cancer progression. J Cell Biol. 196:395–406. 2012. View Article : Google Scholar : PubMed/NCBI
24	Pickup MW, Mouw JK and Weaver VM: The extracellular matrix modulates the hallmarks of cancer. EMBO Rep. 15:1243–1253. 2014. View Article : Google Scholar : PubMed/NCBI
25	Radisky ES and Radisky DC: Matrix metalloproteinase-induced epithelial-mesenchymal transition in breast cancer. J Mammary Gland Biol Neoplasia. 15:201–212. 2010. View Article : Google Scholar : PubMed/NCBI
26	Avraamides CJ, Garmy-Susini B and Varner JA: Integrins in angiogenesis and lymphangiogenesis. Nat Rev Cancer. 8:604–617. 2008. View Article : Google Scholar : PubMed/NCBI
27	Sorokin L: The impact of the extracellular matrix on inflammation. Nat Rev Immunol. 10:712–723. 2010. View Article : Google Scholar : PubMed/NCBI
28	Egeblad M and Werb Z: New functions for the matrix metalloproteinases in cancer progression. Nat Rev Cancer. 2:161–174. 2002. View Article : Google Scholar : PubMed/NCBI
29	Garcea G, Neal CP, Pattenden CJ, Steward WP and Berry DP: Molecular prognostic markers in pancreatic cancer: A systematic review. Eur J Cancer. 41:2213–2236. 2005. View Article : Google Scholar : PubMed/NCBI
30	Jiang Y, Goldberg ID and Shi YE: Complex roles of tissue inhibitors of metalloproteinases in cancer. Oncogene. 21:2245–2252. 2002. View Article : Google Scholar : PubMed/NCBI
31	Bonnans C, Chou J and Werb Z: Remodelling the extracellular matrix in development and disease. Nat Rev Mol Cell Biol. 15:786–801. 2014. View Article : Google Scholar : PubMed/NCBI
32	Nerenberg PS, Salsas-Escat R and Stultz CM: Collagen - A necessary accomplice in the metastatic process. Cancer Genomics Proteomics. 4:319–327. 2007.PubMed/NCBI
33	Verrecchia F, Chu ML and Mauviel A: Identification of novel TGF-beta/Smad gene targets in dermal fibroblasts using a combined cDNA microarray/promoter transactivation approach. J Biol Chem. 276:17058–17062. 2001. View Article : Google Scholar : PubMed/NCBI