Ejlalidiz Mahsa, Mehri-Ghahfarrokhi Ameneh, Saberiyan Mohammadreza
Medical Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Clinical Research Developmental Unit, Hajar Hospital, Shahrekord University of Medical Sciences, Shahrekord, Iran.
Biochem Biophys Rep. 2024 Nov 1;40:101860. doi: 10.1016/j.bbrep.2024.101860. eCollection 2024 Dec.
Uterine corpus endometrial carcinoma (UCEC), derived from the endometrium, is the most common type of endometrial malignasis. This gynecological malignancy is very common all over the world, especially in developed countries and shows a potentially rising trend correlated with the increase in obese women.
Differentially Expressed Genes (DEGs) analysis was conducted on GSE7305 and GSE25628 datasets from the Gene Expression Omnibus (GEO). DEGs were identified using GEO2R (adjusted p-value <0.05, |logFC| > 1). Pathway analysis employed KEGG and Gene Ontology databases, while protein-protein interactions were analyzed using Cytoscape and Gephi. GEPIA was used for target gene validation.
We have identified 304 common DEGs and 78 hub genes using GEO and PPI analysis, respectively. The GO and KEGG pathways analysis revealed enrichment of DEGs in extracellular matrix structural constituent, extracellular space, cell adhesion, and ECM-receptor interaction. GEPIA analysis identified three genes, ENG, GNG4, and ECT2, whose expression significantly differed between normal and tumor samples.
This analysis study identified the hub genes and associated pathways involved in the pathogenesis of UCEC. The identified hub genes exhibit remarkable potential as diagnostic biomarkers, providing a significant opportunity for early diagnosis and more effective therapeutic approaches for UCEC.
子宫内膜癌（UCEC）起源于子宫内膜，是最常见的子宫内膜恶性肿瘤类型。这种妇科恶性肿瘤在全球范围内都很常见，尤其是在发达国家，并且随着肥胖女性数量的增加呈现出潜在的上升趋势。
对来自基因表达综合数据库（GEO）的GSE7305和GSE25628数据集进行差异表达基因（DEG）分析。使用GEO2R识别差异表达基因（调整后的p值<0.05，|logFC|>1）。通路分析采用KEGG和基因本体数据库，而蛋白质-蛋白质相互作用则使用Cytoscape和Gephi进行分析。使用GEPIA进行靶基因验证。
我们分别使用GEO和PPI分析鉴定出304个常见差异表达基因和78个核心基因。GO和KEGG通路分析显示差异表达基因在细胞外基质结构成分、细胞外空间、细胞粘附和ECM-受体相互作用中富集。GEPIA分析确定了ENG、GNG4和ECT2这三个基因，其在正常样本和肿瘤样本中的表达存在显著差异。
本分析研究确定了参与子宫内膜癌发病机制的核心基因和相关通路。所确定的核心基因作为诊断生物标志物具有显著潜力，为子宫内膜癌的早期诊断和更有效的治疗方法提供了重要契机。
