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Landscape of the intratumroal microenvironment in bladder cancer: Implications for prognosis and immunotherapy.

ABSTRACT:

Introduction

This study aims to present the landscape of the intratumoral microenvironment and by which establish a classification system that can be used to predict the prognosis of bladder cancer patients and their response to anti-PD-L1 immunotherapy.

Methods

The expression profiles of 1554 bladder cancer cases were downloaded from seven public datasets. Single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression analysis, and meta-analysis were employed to establish the bladder cancer immune prognostic index (BCIPI). Extensive analyses were executed to investigate the association between BCIPI and overall survival, tumor-infiltrated immunocytes, immunotherapeutic response, mutation load, etc.

Results

The results obtained from seven independent cohorts and meta-analyses suggested that the BCIPI is an effective classification system for estimating bladder cancer patients' overall survival. Patients in the BCIPI-High subgroup revealed different immunophenotypic outcomes from those in the BCIPI-Low subgroup regarding tumor-infiltrated immunocytes and mutated genes. Subsequent analysis suggested that patients in the BCIPI-High subgroup were more sensitive to anti-PD-L1 immunotherapy than those in the BCIPI-Low subgroup.

Conclusions

The newly established BCIPI is a valuable tool for predicting overall survival outcomes and immunotherapeutic responses in patients with bladder cancer.

SUBMITTER: Bian Z

PROVIDER: S-EPMC9730156 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Landscape of the intratumroal microenvironment in bladder cancer: <i>Implications for prognosis and immunotherapy</i>.

Bian Zichen Z Chen Jia J Liu Chang C Ge Qintao Q Zhang Meng M Meng Jialin J Liang Chaozhao C

Computational and structural biotechnology journal 20221201

<h4>Introduction</h4>This study aims to present the landscape of the intratumoral microenvironment and by which establish a classification system that can be used to predict the prognosis of bladder cancer patients and their response to anti-PD-L1 immunotherapy.<h4>Methods</h4>The expression profiles of 1554 bladder cancer cases were downloaded from seven public datasets. Single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression analysis, and meta-analysis were employed to e ...[more]

PMID: 36514337

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