解析染色质调控因子调控的肿瘤微环境的综合特征，预测子宫体子宫内膜癌的预后并指导治疗。

Deciphering comprehensive features of tumor microenvironment controlled by chromatin regulators to predict prognosis and guide therapies in uterine corpus endometrial carcinoma.

机构信息

Department of Gynecology, Xiangya Hospital, Central South University, Changsha, China.

National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China.

出版信息

Front Immunol. 2023 Mar 3;14:1139126. doi: 10.3389/fimmu.2023.1139126. eCollection 2023.

BACKGROUND

Dysregulation of chromatin regulators (CRs) can perturb the tumor immune microenvironment, but the underlying mechanism remains unclear. We focused on uterine corpus endometrial carcinoma (UCEC) and used gene expression data from TCGA-UCEC to investigate this mechanism.

METHODS

We used weighted gene co-expression network analysis (WGCNA) and consensus clustering algorithm to classify UCEC patients into Cluster_L and Cluster_H. TME-associated CRs were identified using WGCNA and differential gene expression analysis. A CR risk score (CRRS) was constructed using univariate Cox and LASSO-Cox regression analyses. A nomogram was developed based on CRRS and clinicopathologic factors to predict patients' prognosis.

RESULTS

Lower CRRS was associated with lower grade, more benign molecular subtypes, and improved survival. Patients with low CRRS showed abundant immune infiltration, a higher mutation burden, fewer CNVs, and better response to immunotherapy. Moreover, low CRRS patients were more sensitive to 24 chemotherapeutic agents.

CONCLUSION

A comprehensive assessment of CRRS could identify immune activation and improve the efficacy of UCEC treatments.

背景

染色质调控因子（CRs）的失调会扰乱肿瘤免疫微环境，但潜在的机制仍不清楚。我们专注于子宫体子宫内膜癌（UCEC），并使用 TCGA-UCEC 的基因表达数据来研究这一机制。

方法

我们使用加权基因共表达网络分析（WGCNA）和共识聚类算法将 UCEC 患者分为 Cluster_L 和 Cluster_H。使用 WGCNA 和差异基因表达分析鉴定与 TME 相关的 CRs。使用单因素 Cox 和 LASSO-Cox 回归分析构建 CR 风险评分（CRRS）。根据 CRRS 和临床病理因素开发列线图来预测患者的预后。