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Cancer Cell International
Published in: Cancer Cell International 1/2019

Open Access 01-12-2019 | Endometrial Cancer | Primary research

Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis

Authors: JinHui Liu, ShuLin Zhou, SiYue Li, Yi Jiang, YiCong Wan, XiaoLing Ma, WenJun Cheng

Published in: Cancer Cell International | Issue 1/2019

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Abstract

Background

Endometrial cancer (EC) is one of the female malignant tumors. Endometrial cancer predominately affects post-menopausal women. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression.

Methods

Download the gene expression profile from Gene Expression Omnibus (GEO). Calculate raw expression data according to pre-processing procedures. We performed the “limma” R language package to screen DEGs between Endometrial cancer tissue samples and normal uterus tissue samples. Enrichment of the functions and pathways was analyzed by using clusterprofiler. We utilized Search Tool for the Retrieval of Interacting Genes Database (STRING) to assess protein–protein interaction (PPI) information, and then we used plug-in Molecular Complex Detection (MCODE) to screen hub modules of PPI network in Cytoscape. We also performed functional analysis on the genes in the hub module by using clusterprofiler. Next, we utilized the “WGCNA” package in R to establish co-expression network for the DEGs. The Venn diagram was performed to overlap the gene in key module and hub PPI cluster. We validated the key genes in TCGA, GEPIA, UALCAN and Immunohistochemistry staining obtained from The Human Protein Atlas database. And then we did ROC curve analysis by SPSS. Gene set enrichment analysis (GSEA) and mutation analysis were also performed for hub genes.

Results

Functional and pathway enrichment analysis demonstrated that the upregulated differentially expressed genes (DEGs) were significantly enriched in CXCR chemokine receptor binding, chemokine activity, chemokine receptor binding, G-protein coupled receptor binding, RAGE receptor binding, cytokine activity, microtubule binding, receptor regulator activity and microtubule motor activity, and the down-regulated genes were highly enriched in collagen binding. After using STRING software to construct PPI network, 30 prominent proteins were identified and the first two significant modules were selected. In co-expression network, 5 EC-related modules were identified. Among them, the turquoise module has the highest correlation with the EC. We further analyzed the genes in the PPI and turquoise module, and selected eleven key genes related to EC after validation of TCGA database, GEPIA, UALCAN and immunohistochemistry. Six of them had mutation significance.

Conclusions

In summary, these 11 genes may become new therapy targets for EC treatment.
Appendix
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Literature
1.
Fang F, Munck J, Tang J, Taverna P, Wang Y, Miller DF, Pilrose J, Choy G, Azab M, Pawelczak KS, et al. The novel, small-molecule DNA methylation inhibitor SGI-110 as an ovarian cancer chemosensitizer. Clin Cancer Res. 2014;20(24):6504–16.CrossRef
2.
Xia L, Su X, Shen J, Meng Q, Yan J, Zhang C, Chen Y, Wang H, Xu M. ANLN functions as a key candidate gene in cervical cancer as determined by integrated bioinformatic analysis. Cancer Manage Res. 2018;10:663–70.CrossRef
3.
Yuan L, Zeng G, Chen L, Wang G, Wang X, Cao X, Lu M, Liu X, Qian G, Xiao Y, et al. Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis. Int J Biol Sci. 2018;14(3):266–79.CrossRef
4.
Kuzu OF, Noory MA, Robertson GP. The role of cholesterol in cancer. Can Res. 2016;76(8):2063–70.CrossRef
5.
Yin L, Cai Z, Zhu B, Xu C. Identification of key pathways and genes in the dynamic progression of HCC based on WGCNA. Genes. 2018;9(2):92.CrossRef
6.
Zhang J, Lan Q, Lin J. Identification of key gene modules for human osteosarcoma by co-expression analysis. World J Surg Oncol. 2018;16(1):89.CrossRef
7.
Yao S, Liu T. Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database. Oncol Lett. 2018;15(6):8319–24.PubMedPubMedCentral
8.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.CrossRef
9.
Wang F, Chang Y, Li J, Wang H, Zhou R, Qi J, Liu J, Zhao Q. Strong correlation between ASPM gene expression and HCV cirrhosis progression identified by co-expression analysis. Digest Liver Dis. 2017;49(1):70–6.CrossRef
10.
Clarke C, Madden SF, Doolan P, Aherne ST, Joyce H, O'Driscoll L, Gallagher WM, Hennessy BT, Moriarty M, Crown J, et al. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis. 2013;34(10):2300–8.CrossRef
11.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447–D452.CrossRef
12.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.CrossRef
13.
Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003;4:2.CrossRef
14.
Mason MJ, Fan G, Plath K, Zhou Q, Horvath S. Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. BMC Genomics. 2009;10:327.CrossRef
15.
Horvath S, Dong J. Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol. 2008;4(8):e1000117.CrossRef
16.
Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 2011;12:35.CrossRef
17.
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–W102.CrossRef
18.
Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, Varambally S. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19(8):649–58.CrossRef
19.
Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjostedt E, Asplund A et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):12604CrossRef
20.
Subramanian A, Kuehn H, Gould J, Tamayo P, Mesirov JP. GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics. 2007;23(23):3251–3.CrossRef
21.
Zhou W, Wang Z, Shen N, Pi W, Jiang W, Huang J, Hu Y, Li X, Sun L. Knockdown of ANLN by lentivirus inhibits cell growth and migration in human breast cancer. Mol Cell Biochem. 2015;398(1–2):11–9.CrossRef
22.
Wang G, Shen W, Cui L, Chen W, Hu X, Fu J. Overexpression of Anillin (ANLN) is correlated with colorectal cancer progression and poor prognosis. Cancer Biomark. 2016;16(3):459–65.CrossRef
23.
Zeng S, Yu X, Ma C, Song R, Zhang Z, Zi X, Chen X, Wang Y, Yu Y, Zhao J, et al. Transcriptome sequencing identifies ANLN as a promising prognostic biomarker in bladder urothelial carcinoma. Sci Rep. 2017;7(1):3151.CrossRef
24.
Chen X, Thiaville MM, Chen L, Stoeck A, Xuan J, Gao M, Shih Ie M, Wang TL. Defining NOTCH3 target genes in ovarian cancer. Can Res. 2012;72(9):2294–303.CrossRef
25.
Liu R, Guo CX, Zhou HH. Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen. Cancer Biol Ther. 2015;16(2):317–24.CrossRef
26.
Liao W, Liu W, Yuan Q, Liu X, Ou Y, He S, Yuan S, Qin L, Chen Q, Nong K, et al. Silencing of DLGAP5 by siRNA significantly inhibits the proliferation and invasion of hepatocellular carcinoma cells. PLoS ONE. 2013;8(12):e80789.CrossRef
27.
Stangeland B, Mughal AA, Grieg Z, Sandberg CJ, Joel M, Nygard S, Meling T, Murrell W, Vik Mo EO, Langmoen IA. Combined expressional analysis, bioinformatics and targeted proteomics identify new potential therapeutic targets in glioblastoma stem cells. Oncotarget. 2015;6(28):26192–215.CrossRef
28.
Schneider MA, Christopoulos P, Muley T, Warth A, Klingmueller U, Thomas M, Herth FJ, Dienemann H, Mueller NS, Theis F, et al. AURKA, DLGAP5, TPX2, KIF11 and CKAP5: Five specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patients. Int J Oncol. 2017;50(2):365–72.CrossRef
29.
Xu XS, Miao RC, Wan Y, Zhang LQ, Qu K, Liu C. FoxM1 as a novel therapeutic target for cancer drug therapy. Asian Pac J Cancer Prev. 2015;16(1):23–9.CrossRef
30.
Cui J, Shi M, Xie D, Wei D, Jia Z, Zheng S, Gao Y, Huang S, Xie K. FOXM1 promotes the warburg effect and pancreatic cancer progression via transactivation of LDHA expression. Clin Cancer Res. 2014;20(10):2595–606.CrossRef
31.
Zhao H, Zhang H, Du Y, Gu X. Prognostic significance of BRCA1, ERCC1, RRM1, and RRM2 in patients with advanced non-small cell lung cancer receiving chemotherapy. Tumour Biol. 2014;35(12):12679–88.CrossRef
32.
Putluri N, Maity S, Kommagani R, Creighton CJ, Putluri V, Chen F, Nanda S, Bhowmik SK, Terunuma A, Dorsey T, et al. Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance. Neoplasia. 2014;16(5):390–402.CrossRef
33.
Grolmusz VK, Karaszi K, Micsik T, Toth EA, Meszaros K, Karvaly G, Barna G, Szabo PM, Baghy K, Matko J, et al. Cell cycle dependent RRM2 may serve as proliferation marker and pharmaceutical target in adrenocortical cancer. Am J Cancer Res. 2016;6(9):2041–53.PubMedPubMedCentral
34.
Wang N, Zhan T, Ke T, Huang X, Ke D, Wang Q, Li H. Increased expression of RRM2 by human papillomavirus E7 oncoprotein promotes angiogenesis in cervical cancer. Br J Cancer. 2014;110(4):1034–44.CrossRef
35.
Zhong Z, Cao Y, Yang S, Zhang S. Overexpression of RRM2 in gastric cancer cell promotes their invasiveness via AKT/NF-kappaB signaling pathway. Pharmazie. 2016;71(5):280–4.PubMed
36.
Yin L, Jiang LP, Shen QS, Xiong QX, Zhuo X, Zhang LL, Yu HJ, Guo X, Luo Y, Dong J, et al. NCAPH plays important roles in human colon cancer. Cell Death Dis. 2017;8(3):e2680.CrossRef
37.
Sellick G, Fielding S, Qureshi M, Catovsky D, Houlston R. Germline mutations in RAD51, RAD51AP1, RAD51B, RAD51C, RAD51D, RAD52 and RAD54L do not contribute to familial chronic lymphocytic leukemia. Leuk Lymphoma. 2008;49(1):130–3.CrossRef
38.
Pelttari LM, Kiiski J, Nurminen R, Kallioniemi A, Schleutker J, Gylfe A, Aaltonen LA, Leminen A, Heikkila P, Blomqvist C, et al. A Finnish founder mutation in RAD51D: analysis in breast, ovarian, prostate, and colorectal cancer. J Med Genet. 2012;49(7):429–32.CrossRef
39.
Lin SY, Pan HW, Liu SH, Jeng YM, Hu FC, Peng SY, Lai PL, Hsu HC. ASPM is a novel marker for vascular invasion, early recurrence, and poor prognosis of hepatocellular carcinoma. Clin Cancer Res. 2008;14(15):4814–20.CrossRef
40.
Xie JJ, Zhuo YJ, Zheng Y, Mo RJ, Liu ZZ, Li BW, Cai ZD, Zhu XJ, Liang YX, He HC, et al. High expression of ASPM correlates with tumor progression and predicts poor outcome in patients with prostate cancer. Int Urol Nephrol. 2017;49(5):817–23.CrossRef
41.
Wang WY, Hsu CC, Wang TY, Li CR, Hou YC, Chu JM, Lee CT, Liu MS, Su JJ, Jian KY, et al. A gene expression signature of epithelial tubulogenesis and a role for ASPM in pancreatic tumor progression. Gastroenterology. 2013;145(5):1110–20.CrossRef
42.
Bruning-Richardson A, Bond J, Alsiary R, Richardson J, Cairns DA, McCormack L, Hutson R, Burns P, Wilkinson N, Hall GD, et al. ASPM and microcephalin expression in epithelial ovarian cancer correlates with tumour grade and survival. Br J Cancer. 2011;104(10):1602–10.CrossRef
43.
Alsiary R, Bruning-Richardson A, Bond J, Morrison EE, Wilkinson N, Bell SM. Deregulation of microcephalin and ASPM expression are correlated with epithelial ovarian cancer progression. PLoS ONE. 2014;9(5):e97059.CrossRef
44.
Jiao DC, Lu ZD, Qiao JH, Yan M, Cui SD, Liu ZZ. Expression of CDCA8 correlates closely with FOXM1 in breast cancer: public microarray data analysis and immunohistochemical study. Neoplasma. 2015;62(3):464–9.CrossRef
45.
Bi Y, Chen S, Jiang J, Yao J, Wang G, Zhou Q, Li S. CDCA8 expression and its clinical relevance in patients with bladder cancer. Medicine. 2018;97(34):e11899.CrossRef
46.
Cao R, Wang G, Qian K, Chen L, Qian G, Xie C, Dan HC, Jiang W, Wu M, Wu CL, et al. Silencing of HJURP induces dysregulation of cell cycle and ROS metabolism in bladder cancer cells via PPARgamma-SIRT1 feedback loop. J Cancer. 2017;8(12):2282–95.CrossRef
47.
Hu Z, Huang G, Sadanandam A, Gu S, Lenburg ME, Pai M, Bayani N, Blakely EA, Gray JW, Mao JH. The expression level of HJURP has an independent prognostic impact and predicts the sensitivity to radiotherapy in breast cancer. Breast Cancer Res. 2010;12(2):R18.CrossRef
48.
Li L, Li X, Meng Q, Khan AQ, Chen X. Increased expression of Holliday Junction-Recognizing Protein (HJURP) as an independent prognostic biomarker in advanced-stage serous ovarian carcinoma. Med Sci Monit. 2018;24:3050–5.CrossRef
49.
Chen T, Huang H, Zhou Y, Geng L, Shen T, Yin S, Zhou L, Zheng S. HJURP promotes hepatocellular carcinoma proliferation by destabilizing p21 via the MAPK/ERK1/2 and AKT/GSK3beta signaling pathways. J Exp Clin Cancer Res. 2018;37(1):193.CrossRef
50.
Hu P, Chen X, Sun J, Bie P, Zhang LD. siRNA-mediated knockdown against NUF2 suppresses pancreatic cancer proliferation in vitro and in vivo. Biosci Rep. 2015;35(1):e00170.CrossRef
51.
Sugimasa H, Taniue K, Kurimoto A, Takeda Y, Kawasaki Y, Akiyama T. Heterogeneous nuclear ribonucleoprotein K upregulates the kinetochore complex component NUF2 and promotes the tumorigenicity of colon cancer cells. Biochem Biophys Res Commun. 2015;459(1):29–35.CrossRef
52.
Fu HL, Shao L. Silencing of NUF2 inhibits proliferation of human osteosarcoma Saos-2 cells. Eur Rev Med Pharmacol Sci. 2016;20(6):1071–9.PubMed
53.
Huang SK, Qian JX, Yuan BQ, Lin YY, Ye ZX, Huang SS. SiRNA-mediated knockdown against NUF2 suppresses tumor growth and induces cell apoptosis in human glioma cells. Cell Mol Biol. 2014;60(4):30–36.PubMed
54.
Liu Q, Dai SJ, Li H, Dong L, Peng YP. Silencing of NUF2 inhibits tumor growth and induces apoptosis in human hepatocellular carcinomas. Asian Pac J Cancer Prev. 2014;15(20):8623–9.CrossRef
55.
Akent'eva NP, Shushanov SS, Kotel'nikov AI. Effects of RHAMM/HMMR-Selective Peptides on Survival of Breast Cancer Cells. Bull Exp Biol Med. 2015;159(5):658–61.CrossRef
56.
Tilghman J, Wu H, Sang Y, Shi X, Guerrero-Cazares H, Quinones-Hinojosa A, Eberhart CG, Laterra J, Ying M. HMMR maintains the stemness and tumorigenicity of glioblastoma stem-like cells. Can Res. 2014;74(11):3168–79.CrossRef
57.
Day RS, McDade KK, Chandran UR, Lisovich A, Conrads TP, Hood BL, Kolli VS, Kirchner D, Litzi T, Maxwell GL. Identifier mapping performance for integrating transcriptomics and proteomics experimental results. BMC Bioinform. 2011;12:213.CrossRef
Metadata
Title
Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis
Authors
JinHui Liu
ShuLin Zhou
SiYue Li
Yi Jiang
YiCong Wan
XiaoLing Ma
WenJun Cheng
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2019
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-019-0859-1

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