Identification of Biomarkers for Controlling Cancer Stem Cell Characteristics in Bladder Cancer by Network Analysis of Transcriptome Data Stemness Indices.

Shen Pan,Xiaonan Chen,Yunhong Zhan,Bin Wu,Bitian Liu

doi:10.3389/fonc.2019.00613

Abstract

Background: Stem cells characterized by self-renewal and therapeutic resistance play crucial roles in bladder cancer (BLCA). However, the genes modulating the maintenance and proliferation of BLCA stem cells are still unclear. In this study, we aimed to characterize the expression of stem cell-related genes in BLCA.Methods: The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Corrected mRNAsi were further analyzed with regard to muscle-invasive bladder cancer molecular subtypes, survival analysis, pathological staging characteristics, and outcomes after primary treatment. Next, weighted gene co-expression network analysis was used to find modules of interest and key genes. Functional enrichment analysis was performed to functionally annotate the modules and key genes. The expression levels of key genes in all cancers were validated using Oncomine and Gene Expression Omnibus (GEO) database containing molecular subtypes in BLCA. Protein interaction networks were used to identify upstream genes, and the relationships between genes were analyzed at the protein and transcription levels.Findings: mRNAsi was significantly upregulated in cancer tissues. Corrected mRNAsi in BLCA increased as tumor stage increased, with T3 having the highest stem cell characteristics. Lower corrected mRNAsi scores had better overall survival and treatment outcome. The modules of interest and key genes were determined based on topological overlap measurement clustering results and the inclusion criteria. For 13 key genes (AURKA, BUB1B, CDCA5, CDCA8, KIF11, KIF18B, KIF2C, KIFC1, KPNA2, NCAPG, NEK2, NUSAP1, and RACGAP1), enriched gene ontology terms related to cell proliferation (e.g., mitotic nuclear division, spindle, and microtubule binding) were determined. Their expression did not differ according to the pathological stages of BLCA, and these genes were clearly overexpressed in many types of cancers. In GEO database, the expression levels of 13 key genes were higher in basal subtype with the highest stem cell characteristics than in luminal and its subtypes. AURKB and PLK1 may be regulated upstream of other key genes, and the key genes were found to be strongly correlated with each other and with upstream genes.Interpretation: The 13 key genes identified in this study were found to play important roles in the maintenance of BLCA stem cells. Controlling the upstream genes AURKB and PLK1 may have applications in the treatment of BLCA. These genes may act as therapeutic targets for inhibiting the stemness characteristics of BLCA.

Highlights

Bladder cancer (BLCA) is one of the most common cancers worldwide and results in ∼150,000 deaths each year
MRNAsi in Molecular Subtypes and Clinical Characteristics in BLCA mRNA expression-based stemness index (mRNAsi) is an index that describes the degree of similarity between tumor cells and stem cells and can be considered a quantitative representation of cancer stem cells (CSCs)
We identified key genes associated with CSC characteristics using Weighted gene co-expression network analysis (WGCNA) based on mRNAsi scores, as calculated by Tathiane et al The corrected mRNAsi scores increased as the tumor pathological stage increased, and T3 stage tumors have the highest stem cell characteristics

Summary

Introduction

Bladder cancer (BLCA) is one of the most common cancers worldwide and results in ∼150,000 deaths each year. Populations of undifferentiated cells with stem cell-like properties in BLCA have been identified as the main factors affecting recurrence and progression [2] Such cancer stemness features have been extensively studied using artificial intelligence and deep learning methods. Higher mRNAsi scores are associated with active biological processes in cancer stem cells (CSCs) and greater tumor dedifferentiation, as reflected by histopathological grades. These stemness indices have been applied to datasets from The Cancer Genome Atlas (TCGA) in order to calculate the mRNAsi and mDNAsi scores of the samples [3]. We aimed to characterize the expression of stem cell-related genes in BLCA

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