Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis - Wang, Yejinpeng; Chen, Liang; Ju, Lingao; Qian, Kaiyu; Liu, Xuefeng; Wang, Xinghuan; Xiao, Yu (Frontiers in Oncology, 2019)
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 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 used the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the real hub genes of the value of BC progression and prognosis. Gene-set enrichment analysis (GSEA) analysis revealed the real hub genes were mainly enriched in “bladder cancer” and “cell cycle” pathways. 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 mechanism study of BC.