Significance of TP53 mutation in bladder cancer disease progression and drug selection

Abstract

Background: The tumor protein p53 (TP53) mutant is one of the most frequent mutant genes in bladder cancer. In this study, we assessed the importance of the TP53 mutation in bladder cancer progression and drug selection, and identified potential pathways and core genes associated with the underlying mechanisms.

Methods: Gene expression data used in this study were downloaded from The Cancer Genome Atlas and cBioportal databases. Drug sensitivity data were obtained from the Genomics of Drug Sensitivity in Cancer. We did functional enrichment analysis by gene set enrichment analysis (GSEA) and the Database for Annotation, Visualization and Integrated Discovery (DAVID).

Results: We found the TP53 mutation in 50% of bladder cancer patients. Patients with the TP53 mutation were associated with a lower TP53 mRNA expression level, more advanced tumor stage and higher histologic grade. Three drugs, mitomycin-C, doxorubicin and gemcitabine, were especially more sensitive to bladder cancer with the TP53 mutation. As for the mechanisms, we identified 863 differentially expressed genes (DEGs). Functional enrichment analysis suggested that DEGs were primarily enriched in multiple metabolic progressions, chemical carcinogenesis and cancer related pathways. The protein-protein interaction network identified the top 10 hub genes. Our results have suggested the significance of TP53 mutation in disease progression and drug selection in bladder cancer, and identified multiple genes and pathways related in such program, offering novel basis for bladder cancer individualized treatment.

Keywords: Bioinformatics analysis; Bladder cancer; Drug selection; RNA sequencing; TCGA; TP53 mutation.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2019 Wu et al.

Figures

Figure 1. Mutation frequency and types of TP53 in bladder cancer from the cancer Genome Atlas (TCGA) database.

(A) Mutation frequency of TP53 in MIBC. (B) TP53 protein mutation diagram showed the mutation types of TP53 in MIBC. P53_TAD: P53 transactivation motif, P53_tetramer: P53 tetramerisation motif. (C) Mutation frequency of TP53 in NMIBC. (D) TP53 protein mutation diagram showed the mutation types of TP53 in NMIBC. P53_TAD: P53 transactivation motif, P53_tetramer: P53 tetramerisation motif.

Figure 2. TP53 mutation and bladder cancer prognosis.

(A) Correlation between TP53 mutation and mRNA expression. (B & C) Kaplan–Meier survival and disease recurrence curves for bladder cancer patients stratified by TP53 mutation.

Figure 3. TP53 mutation influences drug selection of bladder cancer.

(A) Volcano plotting showed that bladder cancer with TP53 mutation was significantly sensitive to mitomycin-c, doxorubicin and gemcitabine. (B–G) Reproduction of GDSC database showed that bladder cancer cells with TP53 mutation, but not cancer of other types, was significantly inhibited by Mitomycin-C, Doxorubicin and Gemcitabine. NS: not significant, * P < 0.05, ** P < 0.01, *** P < 0.001.

Figure 4. GSEA results of TP53 mutation in bladder cancer patients.

Including DNA repair (A), G2M check point (B), glycolysis (C), PI3K-AKT-mTOR signaling pathway (D), MYC target (E), spermatogenesis (F), mTORC1 signaling pathway (G), heme metabolism (H), UV response (I), cholesterol homeostasis (J), protein secretion (K), peroxisome (L), E2F targets (M), androgen response (N), reactive oxygen species pathway (O), and mitotic spindle (P).

Figure 5. DAVID enrichment results of differentially expressed genes.

(A) Volcano plot for differentially expressed genes. (B) GO enrichment terms of differentially expressed genes. (C) KEGG pathway analysis of differentially expressed genes.