A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

Abstract

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 tumors. 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 GSVA and WGCNA in bulk RNA-seq data, 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 tumor 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.

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

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Identification of molecular characteristics of FUT8 and alteration of core fucosylation in kidney renal clear cell cancer

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Multi‐omics analysis reveals that ferroptosis‐related gene CISD2 is a prognostic biomarker of head and neck squamous cell carcinoma

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“…15 Research has shown a significant upregulation of CISD3 expression in diverse cancers such as liver cancer, lung cancer, breast cancer, and colorectal cancer. [18][19][20] The upregulation of CISD3 may be linked to tumor cell growth, proliferation, and metastasis. 18 Although our understanding of the functionality and mechanisms of the NEET family in tumors remains limited, these research findings underscore the critical regulatory role of NEET family proteins in tumor initiation and progression.…”