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通过生物信息学分析和实验验证鉴定卵巢癌预后相关的枢纽基因。

Identification of prognosis-related hub genes of ovarian cancer through bioinformatics analyses and experimental verification.

机构信息

Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.

出版信息

Medicine (Baltimore). 2022 Sep 9;101(36):e30374. doi: 10.1097/MD.0000000000030374.

Abstract

Ovarian cancer (OC) is a lethal and highly prevalent disease in women worldwide. The disease is often diagnosed in late stages, which leads to its rapid progression and low survival rate. This study aims to identify new prognostic genes for OC. Based on 2 datasets from the National Center for Biotechnology Information Gene Expression Omnibus public database, we constructed 2 Weighted Gene Co-expression Network Analysis networks. Then, we selected and intersected 2 key modules to screen key genes. Enrichment analyses were performed, and a protein-protein interaction network was constructed. The cytoHubba plugin of Cytoscape and survival analysis were used to screen hub genes related to prognosis. The expression of hub genes was analyzed by GEPIA and verified by quantitative Real-Time PCR. Gene alteration frequency analysis, gene set variation analysis, immune infiltration analysis, drug sensitivity analysis, tumor mutation burden, and neoantigen analyses were conducted to determine the prognostic value and molecular mechanisms of the hub genes. In total, 214 key genes were selected from 2 Weighted Gene Co-expression Network Analysis networks, and 3 hub genes, namely ALDH1A2, CLDN4, and GPR37, were identified as prognostic candidates through cytoHubba and survival analysis. Three hub genes were significantly associated with overall survival of OC patients. GEPIA and quantitative Real-Time PCR indicated that ALDH1A2 expression was significantly downregulated, while expression of CLDN4 and GPR37 was upregulated in OC samples compared with normal samples. CIBERSORT showed that 3 hub genes were closely associated with the infiltrating immune cells. GDSC showed that hub genes expression influenced IC50 values of chemotherapeutic drugs. OC patients with high expression of ALDH1A2 and CLDN4 had lower TMB and low ALDH1A2 expression could produce a larger number of neoantigens. In conclusion, the 3 hub genes (ALDH1A2, CLDN4 and GPR37) identified through bioinformatics analyses in the present study may serve as OC prognosis biomarkers. The study findings offer valuable insights into OC progression and mechanisms.

摘要

卵巢癌(OC)是全球女性致命且高发的疾病。该疾病通常在晚期诊断,导致其快速进展和低生存率。本研究旨在鉴定 OC 的新预后基因。基于国家生物技术信息中心基因表达综合数据库的 2 个数据集,我们构建了 2 个加权基因共表达网络分析网络。然后,我们选择并交叉 2 个关键模块以筛选关键基因。进行了富集分析,并构建了蛋白质-蛋白质相互作用网络。使用 Cytoscape 的 cytoHubba 插件和生存分析筛选与预后相关的枢纽基因。通过 GEPIA 分析和定量实时 PCR 验证分析枢纽基因的表达。进行基因改变频率分析、基因集变异分析、免疫浸润分析、药物敏感性分析、肿瘤突变负荷和新抗原分析,以确定枢纽基因的预后价值和分子机制。总共从 2 个加权基因共表达网络分析网络中选择了 214 个关键基因,通过 cytoHubba 和生存分析鉴定了 3 个枢纽基因,即 ALDH1A2、CLDN4 和 GPR37,作为预后候选基因。3 个枢纽基因与 OC 患者的总生存显著相关。GEPIA 和定量实时 PCR 表明,与正常样本相比,OC 样本中 ALDH1A2 的表达显著下调,而 CLDN4 和 GPR37 的表达上调。CIBERSORT 表明 3 个枢纽基因与浸润免疫细胞密切相关。GDSC 表明枢纽基因表达影响化疗药物的 IC50 值。ALDH1A2 和 CLDN4 高表达的 OC 患者 TMB 较低,而 ALDH1A2 低表达可产生更多的新抗原。综上所述,本研究通过生物信息学分析鉴定的 3 个枢纽基因(ALDH1A2、CLDN4 和 GPR37)可能作为 OC 预后标志物。研究结果为 OC 进展和机制提供了有价值的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d585/10980417/d22294d5fae3/medi-101-e30374-g001.jpg

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