A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients - Yuan, Kemiao; Zhao, Songyun; Ye, Bicheng; Wang, Qi; Liu, Yuan; Zhang, Pengpeng; Xie, Jiaheng; Chi, Hao; Chen, Yu; Cheng, Chao; Liu, Jinhui (Frontiers in Pharmacology, 2023)
The phenomenon of T cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumours. In the context of ovarian cancer immunotherapy, the development and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through genomic variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA) in bulk RNA-seq, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumour mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.