Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC. CIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan-Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy. Red module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs. We developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

Liu J ，Geng R ，Yang S ，Shao F ，Zhong Z ，Yang M ，Ni S ，Cai L ，Bai J ... -  《Frontiers in Immunology》

Cuproptosis-Associated lncRNA Gene Signature Establishes New Prognostic Profile and Predicts Immunotherapy Response in Endometrial Carcinoma.

Jiang XY ，Hu JJ ，Wang R ，Zhang WY ，Jin QQ ，Yang YT ，Mei J ，Hong L ，Yao H ，Tao F ，Li JJ ，Liu Y ，Zhang L ，Chen SX ，Chen G ，Song Y ，Zhou SG ... -  《-》

Prognostic signature construction and immunotherapy response analysis for Uterine Corpus Endometrial Carcinoma based on cuproptosis-related lncRNAs.

As a general female malignant tumor, Uterine Corpus Endometrial Carcinoma (UCEC) has high mortality and relapses. Cuproptosis was found to play an essential role in tumor by more and more researches. However, it is still unclear of the prognostic value and function of cuproptosis related Long non-coding RNA (lncRNA) in UCEC. Sequencing data with the corresponding clinical data and cuproptosis-related genes (CRGs) data were obtained from the Cancer Gene Atlas (TCGA) database and cuproptosis related studies. Pearson test was applied to select cuproptosis-related lncRNAs (CRLs). Prognosis associated CRLs was identified by univariate Cox analysis and the predictors were determined by least absolute shrinkage and selection operator (Lasso)-Cox and multivariate Cox analyses to construct the cuproptosis-related lncRNA prognostic signature (CRLPS). The performance of the CRLPs was evaluated by consistency index (C-index) and Kaplan-Meier analysis. A nomogram model was constructed for survival prediction and the accuracy of the model was evaluated by calibration curve. Finally, immune related analyses were applied to predict immune responses and identify drugs with potential efficacy for the overall survival (OS). A total of 734 CRLs were found and 29 of them were identified as prognosis related lncRNAs. 12 CRLs were finally determined to build the CRLPS which revealed good ability on prognosis predicting. Subsequently, risk score of the CRLPS and grade were assessed as independent prognosis factors for UCEC, based on which the prognostic model provided the highest prediction accuracy of 99.7%. The calibration curve suggested that the prediction results consisted well with the observation. Enrichment analysis showed the CRLPS was mainly associated with tumor development and immune response. Patients in low tumor mutation burden (TMB) group had poorer OS. Significant difference was found in tumor immune dysfunction and exclusion (TIDE) score between different risk score groups. Finally, based on the CRLPs, drug sensitivity analysis identified nine anticancer drugs with potential efficacy on prognosis. Cuproptosis-related lncRNA prognostic signature was constructed for UCEC for the first time. Its high reliability and accuracy on predicting prognosis and immunotherapy response provided new perspective to explore the tumor mechanism and improve clinical prognosis. Nine discovered sensitive drugs provided important clues for personalized treatment of UCEC.

Zhang X ，Ye Z ，Xiao G ，He T ... -  《-》

Construction of the novel immune risk scoring system related to CD8(+) T cells in uterine corpus endometrial carcinoma.

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. 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. 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. 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.

Zhang G ，Yin Z ，Fang J ，Wu A ，Chen G ，Cao K ... -  《Cancer Cell International》

An miRNA signature associated with tumor mutation burden in endometrial cancer.

Tumor mutation burden (TMB) is an essential biomarker to predict immunotherapy response. TMB measurement was mainly evaluated by whole-exome sequencing (WES), which was costly and difficult to be widely applied. In the present study, we aimed to establish and validate a miRNA signature to predict TMB level in endometrial cancer using The Cancer Genome Atlas (TCGA) database. MiRNA expression and somatic mutation profiles of uterine corpus endometrial carcinoma (UCEC) were downloaded from TCGA database. Total 518 patients with UCEC were randomly classified into training set (n=311) and validation set (n=207). Thirty-five differentially expressed miRNAs between high-TMB and low-TMB group were identified in training set. Least absolute shrinkage and selection operator (LASSO) method was performed to select out 26 miRNAs to establish the optimal signature. The accuracy of the miRNA signature for predicting TMB level was 0.833 for training set, 0.749 for validation set and 0.799 for total set. Moreover, the miRNA signature had significant correlation with immune checkpoints related genes (PD-1, PD-L1, CTLA-4) and mismatch repair related genes (BRCA1, BRCA2, MLH1, MSH6) expression. In conclusion, this miRNA signature could predict TMB level in endometrial cancer and might have some merits in providing guidance for immunotherapy in endometrial cancer.

Zhou H ，Chen L ，Qin M ，Lei Y ，Li T ，Li H ，Cheng X ... -  《-》