Objectives: Bladder urothelial carcinoma (BLCA) is one of the most common malignancies in urinary system. With the development of next-generation sequencing technology, we intended to investigate prognostic immune cells and related signature to predict the prognosis of BLCA and potential therapeutic targets.
Methods: We obtained the transcriptome profiles of 573 BLCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The fractions of immune cells in each sample was calculated by "CIBERSORT" algorithm. Tumor Infiltrating Immune Cells Scores (TIICS) was accordingly derived and Receiver Operating Characteristic (ROC) curve was conducted to evaluate the predictive efficiency. Moreover, differential analysis was performed between two TIICS groups and hub TIICS-related immune signature was identified. The correlation of key immune genes and immune-infiltrating immune cells was evaluated based on the TIMER database. An Immune Signature Prognostic Index (ISPI) based on these signatures was constructed with superior predictive accuracy. Last, the TIICS model or related immune signature were all validated in an independent cohort from the GSE13507.
Results: The least absolute shrinkage and selection operator (LASSO) algorithm was utilized to screen the 6 hub tumor-infiltrating immune cells in TCGA cohort, where higher infiltrating levels of M0 Macrophages, M2 Macrophages and Neutrophils were hazard factors, while CD8+ T cells and memory activated CD4+ T cells were protective factors.
Conclusion: Taken together, our study identified several prognostic immune cells and related immune signature in BLCA, shedding insight on the individualized immunotherapy or potential drug targets.
Keywords: Tumor-infiltrating immune cells; bladder urothelial carcinoma; immune signature; prognosis.
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