Ma Ruyue, Zheng Yu, Wang Jianing, Xu Hong, Zhang Ruirui, Xie Zhijia, Zhang Lei, Zhao Ruiheng
Department of Obstetrics and Gynecology, Suzhou Ninth People's Hospital, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
Front Oncol. 2024 Nov 22;14:1387860. doi: 10.3389/fonc.2024.1387860. eCollection 2024.
Endometriosis (EMS) is acknowledged as a risk factor for the development of endometrial cancer (EC), although the precise molecular mechanisms that underpin this association have yet to be fully elucidated. The primary objective of this investigation is to harness bioinformatics methodologies to identify pivotal genes and pathways that may be implicated in both EMS and EC, potentially offering novel therapeutic biomarkers for the management of endometriosis.
We acquired four datasets pertaining to EMS and one dataset concerning EC from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in EMS and EC cohorts, in comparison to controls, were ascertained utilizing the limma package. Subsequently, we conducted a series of bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis, to delineate pathways associated with the identified DEGs.
Our bioinformatics analyses disclosed 141 shared DEGs between EMS and EC groups relative to the control cohort. GO analysis demonstrated that these genes are predominantly involved in the regulation of growth and development, as well as signal transduction pathways. KEGG analysis underscored the significance of these genes in relation to the JAK-STAT signaling pathway and leukocyte transendothelial migration. Furthermore, PPI analysis pinpointed ten central genes (APOE, FGF9, TIMP1, BGN, C1QB, MX1, SIGLEC1, BST2, ICAM1, MME) exhibiting high interconnectivity. Notably, the expression levels of APOE, BGN, C1QB, and BST2 were found to correlate with cancer genomic atlas data, and were implicated in tumor immune infiltration. Strikingly, only APOE and BGN demonstrated a significant correlation with patient prognosis.
This comprehensive bioinformatics analysis has successfully identified key genes that may serve as potential biomarkers for EC. These findings significantly enhance our comprehension of the molecular underpinnings of EC pathogenesis and prognosis, and hold promise for the identification of novel drug targets.
子宫内膜异位症（EMS）被认为是子宫内膜癌（EC）发生发展的一个危险因素，尽管支撑这种关联的精确分子机制尚未完全阐明。本研究的主要目的是利用生物信息学方法来识别可能与EMS和EC均相关的关键基因和通路，这可能为子宫内膜异位症的治疗提供新的生物标志物。
我们从基因表达综合数据库（GEO）中获取了四个与EMS相关的数据集和一个与EC相关的数据集。利用limma软件包确定了EMS和EC队列中与对照组相比的差异表达基因（DEG）。随后，我们进行了一系列生物信息学分析，包括基因本体论（GO）、京都基因与基因组百科全书（KEGG）通路分析以及蛋白质-蛋白质相互作用（PPI）分析，以描绘与所鉴定的DEG相关的通路。
我们的生物信息学分析揭示了EMS和EC组相对于对照组有141个共享的DEG。GO分析表明，这些基因主要参与生长发育的调控以及信号转导通路。KEGG分析强调了这些基因在JAK-STAT信号通路和白细胞跨内皮迁移方面的重要性。此外，PPI分析确定了十个具有高度互连性的核心基因（载脂蛋白E（APOE）、成纤维细胞生长因子9（FGF9）、金属蛋白酶组织抑制因子1（TIMP1）、基底膜聚糖（BGN）、补体C1q亚基B（C1QB）、Mx蛋白1（MX1）、唾液酸结合免疫球蛋白样凝集素1（SIGLEC1）、骨髓基质细胞抗原2（BST2）、细胞间黏附分子1（ICAM1）、膜联蛋白M（MME））。值得注意的是，发现APOE、BGN、C1QB和BST2的表达水平与癌症基因组图谱数据相关，并与肿瘤免疫浸润有关。令人惊讶的是，只有APOE和BGN与患者预后存在显著相关性。
这项全面的生物信息学分析成功识别出了可能作为EC潜在生物标志物的关键基因。这些发现显著增强了我们对EC发病机制和预后分子基础的理解，并有望识别出新的药物靶点。
