Mining TCGA database for tumor mutation burden and their clinical significance in bladder cancer

Abstract

Background: Bladder cancer is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Tumor mutational burden (TMB) is an emerging biomarker in cancer characterized by microsatellite instability. TMB has been described as a powerful predictor of tumor behavior and response to immunotherapy.

Methods: A total of 443 bladder cancer samples obtained from The Cancer Genome Atlas (TCGA) were analyzed for mutation types, TMB values, and prognostic value of TMB. Differentially expressed genes (DEGs) were identified from the TMB groupings. Functional analysis was performed to assess the prognostic value of the first 30 core genes. CIBERSORT algorithm was used to determine the correlation between the immune cells and TMB subtypes.

Results: Single nucleotide polymorphism (SNP) and C>T were reported as the most common missense mutations and we also identified a high rate of mutations in TP53, TTN, KMT2D. Bladder cancer patients with high TMB showed a better prognosis. Enrichment analysis of the DEGs revealed that they were involved in the regulation of the P13K-Akt signaling pathway, cytokine-cytokine receptor interaction, and Ras signaling pathway. The high expression of hub genes ADRA2A, CXCL12, S1PR1, ADAMTS9, F13A1, and SPON1 was correlated with poor overall survival. Besides, significant differences in the composition of the immune cells of T cells CD8, T cells CD4 memory activated, NK cells resting and Mast cells resting were observed.

Conclusions: The present study provides a comprehensive and systematic analysis of the prediction of TMB in bladder cancer and its clinical significance. Also, the study provides additional prognostic information and opportunities for immunotherapy in bladder cancer.

Keywords: Bladder Cancer; Immune cell infiltration; TCGA; Tumor mutation burden.

Figure 1. TCGA bladder cancer mutation cohort

(A) Overview of TGCA bladder cancer cohort mutations. (B) Waterfall of the top 30 mutated genes in the TCGA bladder cancer cohort.

Figure 2. TMB correlation analysis

(A) Kaplan–Meier curves of overall survival of the high- and low-TMB groups. (B) Wilcox test for patients stratified by gender. (C) Wilcox test for patients stratified by grade.

Figure 3. Hierarchical clustering heatmap of DEGs between high- and low-TMB groups

The higher and lower expressed genes were shown in red and green, respectively, and genes with the same expression level in black.

Figure 4. Functional enrichment analysis of DEGs

(A) Functional analysis of the top ten enriched biological processes (BPs), cell composition (CC), and molecular function (MF) of GO analysis. (B) KEGG enrichment diseases analysis.

Figure 5. PPI network analysis

(A) PPI network. The color and size of the map node was determined by the degree value, which was a gradual process. Green and small circles represent low values, and orange and large circles represent high values. (B) Histogram of core genes.

Figure 6. The overall survival of bladder cancer patients with high or low expression

of ADRA2D (A) CXCL12 (B) S1PR1 (C) ADAMTS9 (D) F13A1 (E) and SPON1(F).

Figure 7. The average proportion of each type of tumor-infiltrating immune cells in in the low- and high-TMB groups

Figure 8. Differential analysis of tumor-infiltrating immune cells (TIICs) between high- and low-TMB groups