Background Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy. Methods CIBERSORT combined with weighted gene co-expression network analysis (WGCNA), non-negative matrix factorization (NMF), and random forest algorithms to screen the module associated with CD8+ T cells, and key genes related to prognosis were selected out by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to develop the novel immune risk score (NIRS). Kaplan–Meier (K-M) analysis was used to compare the difference of survival between high- and low- NIRS groups. We  also explored the correlations between NIRS, immune infiltration and immunotherapy, and three external validation sets were used to verify the predictive performance of NIRS. Furthermore, clinical subgroup analysis, mutation analysis, differential expression of immune checkpoints, and drug sensitivity analysis were performed to generate individualized treatments for patients with different risk scores. Finally, gene set variation analysis (GSVA) was conducted to explore the biological functions of NIRS, and qRT-PCR was applied to verify the differential expressions of three trait genes at cellular and tissue levels. Results Among the modules clustered by WGCNA, the magenta module was most positively associated with CD8+ T cells. Three genes (CTSW, CD3D and CD48) were selected to construct NIRS after multiple screening procedures. NIRS was confirmed as an independent prognostic factor of UCEC, and patients with high NIRS had significantly worse prognosis compared to those with low NIRS. The high NIRS group showed lower levels of infiltrated immune cells, gene mutations, and expression of multiple immune checkpoints, indicating reduced sensitivity to immunotherapy. Three module genes were identified as protective factors positively correlated with the level of CD8+ T cells. Conclusions In this study, we constructed NIRS as a novel predictive signature of UCEC. NIRS not only differentiates patients with distinct prognoses and immune responsiveness, but also guides their therapeutic regimens.

Hepatocellular carcinoma (HCC) is a highly prevalent and deadly cancer, with limited treatment options for advanced-stage patients. This study aimed to explore the potential of disulfidptosis, a novel form of cell death, as a prognostic and therapeutic marker in HCC.We classified HCC patients into two disulfidptosis subtypes (C1 and C2) based on the transcriptional profiles of 31 disulfrgs using a non-negative matrix factorization (NMF) algorithm. The low disulfidptosis subtype (C2) demonstrated better overall survival (OS) and progression-free survival (PFS) prognosis, along with lower levels of immunosuppressive cell infiltration and activation of the glycine/serine/threonine metabolic pathway. Five key signature genes (SLC7A11, SLC2A1, ADAM9, ITGAV, and PFKP) were identified to distinguish between the subgroups, and the constructed model exhibited high accuracy. The study also investigated the association of disulfidptosis with microsatellite instability, tumor immune microenvironment, and genomic mutational burden. Additionally, the low disulfidptosis group showed better responses to immunotherapy and potential antagonism with sorafenib treatment. The key genes SLC7A11 and SLC2A1 were identified as crucial for molecular typing and had excellent predictive power for patient survival. RT-qPCR was used to determine the mRNA levels of the two key genes mentioned above. Classification is a highly effective tool for predicting the prognosis and therapeutic response of patients, providing a valuable reference for accurate individualized treatment. The present study indicates that novel biomarkers related to disulfidptosis may serve as useful clinical diagnostic indicators for liver cancer, enabling the prediction of prognosis and identification of potential treatment targets.

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought huge survival benefits for some specific patients, the traditional evaluation indicators cannot accurately identify all beneficiaries. To construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy, key genes related to CD8+T cells and prognosis were selected out to develop the novel immune risk score (NIRS). Subsequently, correlations between NIRS and other prognostic features such as clinical characteristics, microsatellite status, immune infiltration and tumor mutation burden were investigated. Five module genes (GPR18, CD48, LCK, LTA, and SLAMF1) were selected to construct NIRS after multiple screening procedures. NIRS is considered as an independent prognostic factor of UCEC. The increase in NIRS is accompanied by decreases in infiltrated immune cells and immune checkpoint expression; thus, indicating a lower sensibility to immune checkpoint inhibitors. Five module genes were considered protective factors and positively linked to the level of CD8+ T cells by single gene multi-omics analyses. In this research, we constructed NIRS as a novel prognostic signature of UCEC. NIRS can not only distinguish patients with different prognoses and immune responsiveness, but also guide their therapeutic regimens.