Multi-omics analysis of tumor mutation burden combined with immune infiltrates in bladder urothelial carcinoma.
Chuanjie ZhangLuping ShenFeng QiJin-Cheng WangJun LuoPublished in: Journal of cellular physiology (2019)
To explore the prognosis of tumor mutation burden (TMB) and underlying relationships with tumor-infiltrating immune cells in bladder cancer (BLCA). Transcriptome profiles and somatic mutation data from The Cancer Genome Atlas database by the GDC tool. A total of 437 samples were included, consisted of 412 BLCA patients and matched 25 normal samples. Specific mutation information was summarized and illustrated in waterfall plot. Higher TMB levels revealed improved overall survival (OS) and lower tumor recurrence. We found 68 differentially expressed genes in two TMB groups and identified eight independent hub TMB-related signature. Pathway analysis suggested that differential TMB-related signature correlated with multiple cancer-related crosstalk, including cell cycle, DNA replication, cellular senescence, and p53 signaling pathway. Besides, the tumor mutation burden related signature (TMBRS) model based on eight signature possessed well predictive value with area under curve (AUC) = 0.753, and patients with higher TMBRS scores showed worse OS outcomes (p < .001). Moreover, we exhibited the inferred immune cell fractions in box plot and differential abundance of immune cells were shown in the heatmap. The Wilcoxon rank-sum test suggested that CD8+ T cell (p = .001) and memory activated CD4+ T cell (p = .004) showed higher infiltrating levels in high-TMB group, while the density of resting mast cells showed lower infiltrating level in high-TMB group (p = .016). Finally, it is significant to note that CD8+ T cell and memory activated CD4+ T cell subsets not only revealed higher infiltrating abundance in high-TMB group but correlated with prolonged OS and lower risk of tumor recurrence, respectively.
Keyphrases
- cell cycle
- single cell
- signaling pathway
- genome wide
- gene expression
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- ejection fraction
- risk factors
- squamous cell carcinoma
- peritoneal dialysis
- dna damage
- type diabetes
- big data
- working memory
- insulin resistance
- pi k akt
- antibiotic resistance genes
- prognostic factors
- rna seq
- heart rate variability
- machine learning
- peripheral blood
- stress induced
- dna methylation
- adverse drug
- induced apoptosis
- bioinformatics analysis
- artificial intelligence
- health information