Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis

Yejinpeng Wang; Liang Chen; Lingao Ju; Kaiyu Qian; Xuefeng Liu; Xinghuan Wang; Yu Xiao

doi:10.3389/fonc.2019.01030

Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis

Front Oncol. 2019 Oct 11:9:1030. doi: 10.3389/fonc.2019.01030. eCollection 2019.

Authors

Yejinpeng Wang¹, Liang Chen¹, Lingao Ju^{2

3

4}, Kaiyu Qian^{2

3

4}, Xuefeng Liu⁵, Xinghuan Wang^{1

6}, Yu Xiao^{1

2

3

4

7}

Affiliations

¹ Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.
² Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.
³ Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China.
⁴ Human Genetics Resource Preservation Center of Wuhan University, Wuhan, China.
⁵ Department of Pathology, Lombardi Comprehensive Cancer Center, Georgetown University Medical School, Washington, DC, United States.
⁶ Laboratory of Urology, Medical Research Institute, Wuhan University, Wuhan, China.
⁷ Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.

Abstract

Our study's goal was to screen novel biomarkers that could accurately predict the progression and prognosis of bladder cancer (BC). Firstly, we used the Gene Expression Omnibus (GEO) dataset GSE37815 to screen differentially expressed genes (DEGs). Secondly, we used the DEGs to construct a co-expression network by weighted gene co-expression network analysis (WGCNA) in GSE71576. We then screened the brown module, which was significantly correlated with the histologic grade (r = 0.85, p = 1e-12) of BC. We conducted functional annotation on all genes of the brown module and found that the genes of the brown module were mainly significantly enriched in "cell cycle" correlation pathways. Next, we screened out two real hub genes (ANLN, HMMR) by combining WGCNA, protein-protein interaction (PPI) network and survival analysis. Finally, we combined the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the predict value of the real hub genes for BC progression and prognosis. A gene-set enrichment analysis (GSEA) revealed that the real hub genes were mainly enriched in "bladder cancer" and "cell cycle" pathways. A survival analysis showed that they were of great significance in predicting the prognosis of BC. In summary, our study screened and confirmed that two biomarkers could accurately predict the progression and prognosis of BC, which is of great significance for both stratification therapy and the mechanism study of BC.

Keywords: The Cancer Genome Atlas (TCGA) dataset; bladder cancer (BC); gene-set enrichment analysis (GSEA); protein-protein interaction (PPI); weighted co-expression network analysis (WGCNA).