Chen Xiaoqi, Han Qianqian, Song Jing, Pu Yongqiang
Department of Gynecology, Affiliated Hospital of Qinghai University, Xining, China.
Department of Colorectal and Anal Surgery, Shanxi Provincial People's Hospital, Taiyuan, China.
Medicine (Baltimore). 2024 Nov 22;103(47):e40608. doi: 10.1097/MD.0000000000040608.
Ovarian cancer (OC) is a malignant gynecological cancer with an extremely poor prognosis. Stress granules (SGs) are non-membrane organelles that respond to stressors; however, the correlation between SG-related genes and the prognosis of OC remains unclear. This systematic analysis aimed to determine the expression levels of SG-related genes between high- and low-risk groups of patients with OC and to explore the prognostic value of these genes. RNA-sequencing data and clinical information from GSE18520 and GSE14407 in the Gene Expression Omnibus (GEO) and ovarian plasmacytoma adenocarcinoma in The Cancer Genome Atlas (TCGA) were downloaded. SG-related genes were obtained from GeneCards, the Molecular Signatures Database, and the literature. First, 13 SG-related genes were identified in the prognostic model using least absolute shrinkage and selection operator (LASSO) Cox regression. The prognostic value of each SG-related gene for survival and its relationship with clinical characteristics were evaluated. Next, we performed a functional enrichment analysis of SG-related genes. The protein-protein interactions (PPI) of SG-related genes were visualized using Cytoscape with STRING. According to the median risk score from the LASSO Cox regression, a 13-gene signature was created. All patients with OC in TCGA cohort and GEO datasets were classified into high- and low-risk groups. Five SG-related genes were differentially expressed between the high- and low-risk OC groups in the GEO datasets. The 13 SG-related genes were related to several important oncogenic pathways (TNF-α signaling, PI3K-AKT-mTOR signaling, and WNT-β-catenin signaling) and several cellular components (cytoplasmic stress granule, cytoplasmic ribonucleoprotein granule, and ribonucleoprotein granule). The PPI network identified 11 hub genes with the strongest interactions with ELAVL1. These findings indicate that SG-related genes (DNAJA1, ELAVL1, FBL, GRB7, MOV10, PABPC3, PCBP2, PFN1, RFC4, SYNCRIP, USP10, ZFP36, and ZFP36L1) can be used to predict OC prognosis.
卵巢癌（OC）是一种预后极差的妇科恶性肿瘤。应激颗粒（SGs）是对应激源作出反应的无膜细胞器；然而，SG相关基因与OC预后之间的相关性仍不清楚。这项系统分析旨在确定OC患者高风险组和低风险组之间SG相关基因的表达水平，并探索这些基因的预后价值。从基因表达综合数据库（GEO）中的GSE18520和GSE14407以及癌症基因组图谱（TCGA）中的卵巢浆细胞瘤腺癌下载了RNA测序数据和临床信息。SG相关基因从基因卡片、分子特征数据库和文献中获取。首先，使用最小绝对收缩和选择算子（LASSO）Cox回归在预后模型中鉴定出13个SG相关基因。评估了每个SG相关基因对生存的预后价值及其与临床特征的关系。接下来，我们对SG相关基因进行了功能富集分析。使用Cytoscape和STRING可视化SG相关基因的蛋白质-蛋白质相互作用（PPI）。根据LASSO Cox回归的中位风险评分，创建了一个13基因特征。将TCGA队列和GEO数据集中的所有OC患者分为高风险组和低风险组。在GEO数据集中，高风险和低风险OC组之间有5个SG相关基因差异表达。这13个SG相关基因与几个重要的致癌途径（TNF-α信号传导、PI3K-AKT-mTOR信号传导和WNT-β-连环蛋白信号传导）以及几个细胞成分（细胞质应激颗粒、细胞质核糖核蛋白颗粒和核糖核蛋白颗粒）相关。PPI网络确定了11个与ELAVL1相互作用最强的枢纽基因。这些发现表明，SG相关基因（DNAJA1、ELAVL1、FBL、GRB7、MOV10、PABPC3、PCBP2、PFN1、RFC4、SYNCRIP、USP10、ZFP36和ZFP36L1）可用于预测OC预后。
