Xiong Jian, Chen Junyuan, Guo Zhongming, Zhang Chaoyue, Yuan Li, Gao Kefei
Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
China Medical University, Shenyang, China.
Front Oncol. 2023 Jul 18;13:1224071. doi: 10.3389/fonc.2023.1224071. eCollection 2023.
To explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model.
The RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods.
We identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters consistent clustering analysis. Patients in cluster B had higher tumor purity, higher T stage, and worse prognoses compared to those in cluster A. The latter generally showed higher immune infiltration. A prognostic model was constructed using 11 genes (GZMA, ASNS, GLS, PRKAA2, VLDLR, PRDX6, PSAT1, CDKN2A, SIRT3, TNFRSF1A, LRPPRC), and exhibited good diagnostic performance. Patients with high-risk scores were older, and had higher stage and grade tumors, along with worse prognoses. The frequency of mutations in PCD-related genes was correlated with the risk score. LRPPRC, an adverse prognostic gene in EC, was strongly correlated with proliferation-related genes and multiple PCD-related genes. LRPPRC expression was higher in patients with higher clinical staging and in the deceased patients. In addition, a positive correlation was observed between LRPPRC and infiltration of multiple immune cell types.
We identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.
探讨子宫内膜癌（EC）患者程序性细胞死亡（PCD）相关基因的潜在机制，并建立预后模型。
从TCGA下载RNA测序数据（RNAseq）、单核苷酸变异（SNV）数据及相应临床数据。筛选出与预后相关的PCD基因并进行一致性聚类分析。通过加权基因共表达网络分析（WGCNA）、免疫浸润分析等对两个聚类进行比较。采用最小绝对收缩和选择算子（LASSO）算法构建PCD相关预后模型。通过多种生物信息学方法评估PCD相关基因特征的生物学意义。
我们鉴定出43个与EC患者预后显著相关的PCD相关基因，并通过一致性聚类分析将它们分为两个聚类。与A聚类患者相比，B聚类患者具有更高的肿瘤纯度、更高的T分期和更差的预后。后者通常表现出更高的免疫浸润。使用11个基因（GZMA、ASNS、GLS、PRKAA2、VLDLR、PRDX6、PSAT1、CDKN2A、SIRT3、TNFRSF1A、LRPPRC）构建了一个预后模型，该模型具有良好的诊断性能。高危评分患者年龄较大，肿瘤分期和分级较高，预后较差。PCD相关基因的突变频率与风险评分相关。LRPPRC是EC中的一个不良预后基因，与增殖相关基因和多个PCD相关基因密切相关。LRPPRC在临床分期较高的患者和死亡患者中表达较高。此外，观察到LRPPRC与多种免疫细胞类型的浸润呈正相关。
我们鉴定出一个PCD相关基因特征，可预测EC患者的预后，并为治疗干预提供潜在靶点。
