Open AccessAn immune relevant signature for predicting prognoses and immunotherapeutic responses in patients with muscle‐invasive bladder cancer (MIBC)
Author(s) -
Jiang Wen,
Zhu Dandan,
Wang Chenghe,
Zhu Yu
Publication year - 2020
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.2942
Subject(s) - immune system , oncology , proportional hazards model , bladder cancer , immune checkpoint , immunotherapy , receiver operating characteristic , biomarker , cohort , medicine , biology , immunology , cancer , genetics
Immune checkpoint inhibitors (ICIs) are novel treatments that significantly improve the survival time of MIBC patients, but immunotherapeutic responses are different among MIBC patients. Therefore, it is urgent to find predictive biomarkers that can accurately identify MIBC patients who are sensitive to ICIs. In this study, we computed the relative abundances of 24 immune cells based on the expression profiles of MIBC patients using single‐sample gene set enrichment analysis (ssGSEA). Unsupervised clustering analysis of the 24 immune cells was performed to classify MIBC patients into different immune‐infiltrating groups. Genome (gene mutation and copy number variation), transcriptome (mRNA, lncRNA, and miRNA), and functional enrichment were found to be heterogeneous among different immune‐infiltrating groups. We identified 282 differentially expressed genes (DEGs) associated with immune infiltration by comparing the expression profiles of patients with different immune infiltration profiles, and 20 core prognostic DEGs were identified by univariate Cox regression analysis. An immune‐relevant gene signature (TIM signature) consisting of nine key prognostic DEGs (CCDC80, CD3D, CIITA, FN1, GBP4, GNLY, SPINK1, UBD, and VIM) was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Receiver operating characteristic (ROC) curves and subgroup analysis confirmed that the TIM signature was an ideal biomarker for predicting the prognosis of MIBC patients. Its value in predicting immunotherapeutic responses was also validated in The Cancer Genome Atlas (TCGA) cohort (AUC = 0.69, 95% CI = 0.63‐0.74) and the IMvigor210 cohort (AUC = 0.64, 95% = 0.55‐0.74). The TIM signature demonstrates a powerful ability to distinguish MIBC patients with different prognoses and immunotherapeutic responses, but more prospective studies are needed to assess its reliability in the future.