Identification of biomarkers associated with histological grade and prognosis of gastric cancer by co‑expression network analysis

Chen,Wenjing; Zhang,Weiteng; Wu,Ruisen; Cai,Yiqi; Xue,Xiangyang; Cheng,Jun

doi:10.3892/ol.2019.10869

Identification of biomarkers associated with histological grade and prognosis of gastric cancer by co‑expression network analysis

Authors:
- Wenjing Chen
- Weiteng Zhang
- Ruisen Wu
- Yiqi Cai
- Xiangyang Xue
- Jun Cheng
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Affiliations: Department of General Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China, Department of Microbiology and Immunology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325035, P.R. China
Published online on: September 13, 2019 https://doi.org/10.3892/ol.2019.10869
Pages: 5499-5507
Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The biological characteristics and clinical outcomes of gastric cancer (GC) are largely dependent on the histopathological type and degree of differentiation. The identification of the molecular mechanisms underlying the histological grade of GC may provide information about tumorigenesis and tumor progression, and may subsequently be used to develop novel therapeutic agents. The present study obtained the RNA sequencing data and clinical characteristics of patients with GC from The Cancer Genome Atlas. A total of 1,400 differentially expressed genes (DEGs) were screened between two histological grades. Weighted gene co‑expression network analysis (WGCNA) was subsequently used to identify nine co‑expressed gene modules, and the black module was found to be the most significant for prognosis prediction of tumor. Additionally, the black module was associated with overall survival time, death event, N stage and tumor‑node‑metastasis (TNM) stage. Functional enrichment analysis revealed that the biological processes of the genes in the black module included ‘Wnt signaling pathway’ and ‘structural molecule activity’. Additionally, 10 network hub genes that were significantly associated with the progression of GC were identified from the black module, and the significance of each hub gene was determined across different TNM stages. Kaplan‑Meier survival curves revealed that keratin 40 and glycine decarboxylase were significantly associated with patient prognosis (P<0.05), suggesting that these genes may serve as potential progression and prognosis biomarkers in GC. The present study identified molecular markers that correlated with histological grade in GC. Therefore, the results obtained in the present study may have important clinical implications on treatment selection, risk stratification and prognosis prediction in patients with GC.

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1	Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI
2	Folli S, Morgagni P, Roviello F, De Manzoni G, Marrelli D, Saragoni L, Di Leo A, Gaudio M, Nanni O, Carli A, et al: Risk factors for lymph node metastases and their prognostic significance in early gastric cancer (EGC) for the Italian research group for gastric cancer (IRGGC). Jpn J Clin Oncoly. 31:495–499. 2001. View Article : Google Scholar
3	Popiela T, Kulig J, Kolodziejczyk P and Sierzega M and Sierzega M; Polish Gastric Cancer Study Group, : Long-term results of surgery for early gastric cancer. Brit J Surg. 89:1035–1042. 2002. View Article : Google Scholar : PubMed/NCBI
4	Adachi Y, Yasuda K, Inomata M, Sato K, Shiraishi N and Kitano S: Pathology and prognosis of gastric carcinoma: Well versus poorly differentiated type. Cancer. 89:1418–1424. 2000. View Article : Google Scholar : PubMed/NCBI
5	Noda S, Soejima K and Inokuchi K: Clinicopathological analysis of the intestinal type and diffuse type of gastric carcinoma. Jpn J Surg. 10:277–283. 1980. View Article : Google Scholar : PubMed/NCBI
6	Ribeiro MM, Sarmento JA, Sobrinho Simões MA and Bastos J: Prognostic significance of Lauren and Ming classifications and other pathologic parameters in gastric carcinoma. Cancer. 47:780–784. 1981. View Article : Google Scholar : PubMed/NCBI
7	Kim BS, Oh ST, Yook JH and Kim BS: Signet ring cell type and other histologic types: Differing clinical course and prognosis in T1 gastric cancer. Surgery. 155:1030–1035. 2014. View Article : Google Scholar : PubMed/NCBI
8	Qiu MZ, Cai MY, Zhang DS, Wang ZQ, Wang DS, Li YH and Xu RH: Clinicopathological characteristics and prognostic analysis of Lauren classification in gastric adenocarcinoma in China. J Transl Med. 11:582013. View Article : Google Scholar : PubMed/NCBI
9	Flucke U, Mönig SP, Baldus SE, Zirbes TK, Bollschweiler E, Thiele J, Dienes HP and Hölscher AH: Differences between biopsy- or specimen-related Laurén and world health organization classification in gastric cancer. World J Surg. 26:137–140. 2002. View Article : Google Scholar : PubMed/NCBI
10	Langfelder P and Horvath S: WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI
11	Udyavar AR, Hoeksema MD, Clark JE, Zou Y, Tang Z, Li Z, Li M, Chen H, Statnikov A, Shyr Y, et al: Co-expression network analysis identifies spleen tyrosine kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer. BMC Syst Biol. 7 (Suppl 5):S12013. View Article : Google Scholar : PubMed/NCBI
12	Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, et al: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA. 103:17402–17407. 2006. View Article : Google Scholar : PubMed/NCBI
13	Shi Z, Derow CK and Zhang B: Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression. BMC Syst Biol. 4:742010. View Article : Google Scholar : PubMed/NCBI
14	Liu X, Hu AX, Zhao JL and Chen FL: Identification of key gene modules in human osteosarcoma by co-expression analysis weighted gene co-expression network analysis (WGCNA). J Cell Biochem. 118:3953–3959. 2017. View Article : Google Scholar : PubMed/NCBI
15	Perumal D, Pillai S, Nguyen J, Schaal C, Coppola D and Chellappan SP: Nicotinic acetylcholine receptors induce c-Kit ligand/stem cell factor and promote stemness in an ARRB1/ β-arrestin-1 dependent manner in NSCLC. Oncotarget. 5:10486–10502. 2014. View Article : Google Scholar : PubMed/NCBI
16	Shi K, Bing ZT, Cao GQ, Guo L, Cao YN, Jiang HO and Zhang MX: Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis. Int J Ophthalmol. 8:269–274. 2015.PubMed/NCBI
17	Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR and Winchester DP: The eighth edition AJCC cancer staging manual: Continuing to build a bridge from a population-based to a more ‘personalized’ approach to cancer staging. CA Cancer J Clin. 67:93–99. 2017. View Article : Google Scholar : PubMed/NCBI
18	Team RC, . R: A language and environment for statistical computingR Foundation for Statistical Computing; Vienna: 2012, http://www.R-project.org/
19	RStudio Team, . RStudio: Integrated Development for R. RStudio, Inc.; Boston, MA: 2015, http://www.rstudio.com/
20	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
21	Wisniewski N, Cadeiras M, Bondar G, Cheng RK, Shahzad K, Onat D, Latif F, Korin Y, Reed E, Fakhro R and Deng MC: Weighted gene coexpression network analysis (WGCNA) modeling of multiorgan dysfunction syndrome after mechanical circulatory support therapy. J Heart Lung Transpl. 32 (Suppl):S2232013. View Article : Google Scholar
22	Goel MK, Khanna P and Kishore J: Understanding survival analysis: Kaplan-Meier estimate. Int J Ayurveda Res. 1:274–278. 2010. View Article : Google Scholar : PubMed/NCBI
23	Lánczky A, Nagy Á, Bottai G, Munkácsy G, Szabó A, Santarpia L and Győrffy B: miRpower: A web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 160:439–446. 2016. View Article : Google Scholar : PubMed/NCBI
24	Fiocca R, Luinetti O, Villani L, Mastracci L, Quilici P, Grillo F and Ranzani GN: Molecular mechanisms involved in the pathogenesis of gastric carcinoma: Interactions between genetic alterations, cellular phenotype and cancer histotype. Hepatogastroenterology. 48:1523–1530. 2001.PubMed/NCBI
25	Liu W and Rodgers GP: Olfactomedin 4 expression and functions in innate immunity, inflammation, and cancer. Cancer Metastasis Rev. 35:201–212. 2016. View Article : Google Scholar : PubMed/NCBI
26	Piao Y, Li Y, Xu Q, Liu JW, Xing CZ, Xie XD and Yuan Y: Association of MTOR and AKT gene polymorphisms with susceptibility and survival of gastric cancer. PLoS One. 10:e01364472015. View Article : Google Scholar : PubMed/NCBI
27	Streit M, Schmidt R, Hilgenfeld RU, Thiel E and Kreuser ED: Adhesion receptors in malignant transformation and dissemination of gastrointestinal tumors. Recent Results Cancer Res. 142:19–50. 1996. View Article : Google Scholar : PubMed/NCBI
28	Carlomagno N, Incollingo P, Tammaro V, Peluso G, Rupealta N, Chiacchio G, Sandoval Sotelo ML, Minieri G, Pisani A, Riccio E, et al: Diagnostic, predictive, prognostic, and therapeutic molecular biomarkers in third millennium: A breakthrough in gastric cancer. Biomed Res Int. 2017:78698022017. View Article : Google Scholar : PubMed/NCBI
29	Choi YJ and Kim N: Gastric cancer and family history. Korean J Intern Med. 31:1042–1053. 2016. View Article : Google Scholar : PubMed/NCBI
30	Panarese I, De Vita F, Ronchi A, Romano M, Alfano R, Di Martino N, Zito Marino F, Ferraraccio F and Franco R: Predictive biomarkers along gastric cancer pathogenetic pathways. Expert Rev Anticancer Ther. 17:417–425. 2017. View Article : Google Scholar : PubMed/NCBI
31	Shin VY and Chu KM: MiRNA as potential biomarkers and therapeutic targets for gastric cancer. World J Gastroenterol. 20:10432–10439. 2014. View Article : Google Scholar : PubMed/NCBI
32	Tahara T and Arisawa T: DNA methylation as a molecular biomarker in gastric cancer. Epigenomics. 7:475–486. 2015. View Article : Google Scholar : PubMed/NCBI
33	Chou WC, Cheng AL, Brotto M and Chuang CY: Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer. BMC Genomics. 15:3002014. View Article : Google Scholar : PubMed/NCBI
34	Luo Y, Coskun V, Liang A, Yu J, Cheng L, Ge W, Shi Z, Zhang K, Li C, Cui Y, et al: Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells. Cell. 161:1175–1186. 2015. View Article : Google Scholar : PubMed/NCBI
35	Kunowska N, Rotival M, Yu L, Choudhary J and Dillon N: Identification of protein complexes that bind to histone H3 combinatorial modifications using super-SILAC and weighted correlation network analysis. Nucleic Acids Res. 43:1418–1832. 2015. View Article : Google Scholar : PubMed/NCBI
36	Huang H, Chen Z and Ni X: Tissue transglutaminase-1 promotes stemness and chemoresistance in gastric cancer cells by regulating Wnt/β-catenin signaling. Exp Biol Med (Maywood). 242:194–202. 2017. View Article : Google Scholar : PubMed/NCBI
37	Katoh M: Canonical and non-canonical WNT signaling in cancer stem cells and their niches: Cellular heterogeneity, omics reprogramming, targeted therapy and tumor plasticity (Review). Int J Oncol. 51:1357–1369. 2017. View Article : Google Scholar : PubMed/NCBI
38	Song Y, Li ZX, Liu X, Wang R, Li LW and Zhang Q: The Wnt/β-catenin and PI3K/Akt signaling pathways promote EMT in gastric cancer by epigenetic regulation via H3 lysine 27 acetylation. Tumour Biol. 39:10104283177126172017. View Article : Google Scholar : PubMed/NCBI
39	Wang H, Duan XL, Qi XL, Meng L, Xu YS, Wu T and Dai PG: Concurrent hypermethylation of SFRP2 and DKK2 activates the Wnt/β-catenin pathway and is associated with poor prognosis in patients with gastric cancer. Mol Cells. 40:45–53. 2017. View Article : Google Scholar : PubMed/NCBI
40	Woo DK, Kim HS, Lee HS, Kang YH, Yang HK and Kim WH: Altered expression and mutation of beta-catenin gene in gastric carcinomas and cell lines. Int J Cancer. 95:108–113. 2001. View Article : Google Scholar : PubMed/NCBI
41	Kim D, Fiske BP, Birsoy K, Freinkman E, Kami K, Possemato RL, Chudnovsky Y, Pacold ME, Chen WW, Cantor JR, et al: SHMT2 drives glioma cell survival in ischaemia but imposes a dependence on glycine clearance. Nature. 520:363–367. 2015. View Article : Google Scholar : PubMed/NCBI
42	Min HL, Kim J, Kim WH, Jang BG and Kim MA: Epigenetic silencing of the putative tumor suppressor gene GLDC (Glycine Dehydrogenase) in gastric carcinoma. Anticancer Res. 36:179–187. 2016.PubMed/NCBI
43	Zhuang H, Li Q, Zhang X, Ma X, Wang Z, Liu Y, Yi X, Chen R, Han F, Zhang N and Li Y: Downregulation of glycine decarboxylase enhanced cofilin-mediated migration in hepatocellular carcinoma cells. Free Radic Biol Med. 120:1–12. 2018. View Article : Google Scholar : PubMed/NCBI
44	Berezowska S, Galvan JA, Langer R, Bubendorf L, Savic S, Gugger M, Schmid RA and Marti TM: Glycine decarboxylase and HIF-1α expression are negative prognostic factors in primary resected early-stage non-small cell lung cancer. Virchows Arch. 470:323–330. 2017. View Article : Google Scholar : PubMed/NCBI
45	Zhang WC, Shyh-Chang N, Yang H, Rai A, Umashankar S, Ma S, Soh BS, Sun LL, Tai BC, Nga ME, et al: Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis. Cell. 148:259–272. 2012. View Article : Google Scholar : PubMed/NCBI
46	Yu Z, Wildermoth JE, Wallace OA, Gordon SW, Maqbool NJ, Maclean PH, Nixon AJ and Pearson AJ: Annotation of sheep keratin intermediate filament genes and their patterns of expression. Exp Dermatol. 20:582–588. 2011. View Article : Google Scholar : PubMed/NCBI
47	Pei Z, Wang B, Chen G, Nagao M, Nakafuku M and Campbell K: Homeobox genes Gsx1 and Gsx2 differentially regulate telencephalic progenitor maturation. Proc Natl Acad Sci USA. 108:1675–1680. 2011. View Article : Google Scholar : PubMed/NCBI
48	Haria D, Trinh BQ, Ko SY, Barengo N, Liu J and Naora H: The homeoprotein DLX4 stimulates NF-κB activation and CD44-mediated tumor-mesothelial cell interactions in ovarian cancer. Am J Pathol. 185:2298–2308. 2015. View Article : Google Scholar : PubMed/NCBI