Integrated WGCNA retrieval of T-cell exhaustion genes related to radiosensitivity in locally advanced cervical cancer and prediction of immunotherapy efficacy.

For more than two decades, concurrent chemoradiotherapy (CCRT) has been considered the standard treatment for locally advanced cervical cancer (LACC) and has achieved remarkable clinical results. Nevertheless, 30-40% of treated patients would appear recurrence within 5 years. Recently, Immunotherapy-based programs have been attempted for effective treatment of LACC, while T cell exhaustion greatly limits the application of immunotherapy in cancer radiotherapy (RT). This study aimed to comprehensively characterize the specific prognostic factors of radiosensitivity-related T cell exhaustion (rrTex) in LACC, and identify gene signatures that contribute to predict the efficacy of immunotherapy. LACC samples in several datasets were categorized into two groups (benefit vs. no benefit) according to their responses after RT. A total of 2878 differential genes were selected as candidate genes. Based on TCGA training dataset, WGCNA, LASSO regression analysis, 11 rrTex genes were selected to establish the rrTex gene signature. The Kaplan-Meier curves showed that the prognosis of the low-risk group was better than that of the high-risk group. The immune infiltration score of the high-risk group was significantly different from that of the low-risk group. Subsequently, the PRJEB23709 immunotherapy cohort was used to explore the efficacy of immunotherapy. The rrTex gene signature may facilitate to predict prognosis and assess the efficacy of immunotherapy, and has the potential to facilitate the personalized and precise treatment of LACC in the future.