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通过生物信息学分析寻找子宫肌瘤中的关键基因、关键信号通路和免疫细胞浸润。

Search for key genes, key signaling pathways, and immune cell infiltration in uterine fibroids by bioinformatics analysis.

机构信息

Shandong University of Traditional Chinese Medicine, Shandong, China.

The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Shandong, China.

出版信息

Medicine (Baltimore). 2023 May 19;102(20):e33815. doi: 10.1097/MD.0000000000033815.

Abstract

Uterine fibroids grow in the myometrium and are benign tumors. The etiology and molecular mechanism are not fully understood. Here, we hope to study the potential pathogenesis of uterine fibroids by bioinformatics. Our aim is to search for the key genes, signaling pathways and immune infiltration about the development of uterine fibroids. The GSE593 expression profile was downloaded from the Gene Expression Omnibus database, which contains 10 samples, including 5 uterine fibroids samples and 5 normal controls. Bioinformatics methods were used to find differentially expressed genes (DEGs) in tissues and further analyze the DEGs. R (version 4.2.1) software was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analysis of DEGs in uterine leiomyoma tissues and normal control. STRING database was used to generate protein-protein interaction (PPI) networks of key genes. Then, CIBERSORT was used to assess the infiltration of immune cells in uterine fibroids. A total of 834 DEGs were identified, of which 465 were up-regulated and 369 were down-regulated. GO andKEGG pathway analysis showed that the DEGs were mainly concentrated in extracellular matrix and cytokine related signaling pathways. We identified 30 key genes in DEGs from the PPI network. There were some differences in infiltration immunity between the 2 tissues. This study indicated that screening key genes, signaling pathways and immune infiltration by comprehensive bioinformatics analysis is helpful to understand the molecular mechanism of uterine fibroids and provide new insights into understanding the molecular mechanism.

摘要

子宫肌瘤生长在子宫肌层,是良性肿瘤。其病因和分子机制尚未完全阐明。在这里,我们希望通过生物信息学研究子宫肌瘤的潜在发病机制。我们的目的是寻找与子宫肌瘤发展相关的关键基因、信号通路和免疫浸润。从基因表达综合数据库(GEO)中下载了 GSE593 表达谱,其中包含 10 个样本,包括 5 个子宫肌瘤样本和 5 个正常对照。使用生物信息学方法在组织中寻找差异表达基因(DEGs),并进一步分析 DEGs。R(版本 4.2.1)软件用于对子宫肌瘤组织和正常对照中的 DEGs 进行京都基因与基因组百科全书(KEGG)和基因本体论(GO)通路富集分析。STRING 数据库用于生成关键基因的蛋白质-蛋白质相互作用(PPI)网络。然后,使用 CIBERSORT 评估子宫肌瘤中的免疫细胞浸润。共鉴定出 834 个 DEGs,其中 465 个上调,369 个下调。GO 和 KEGG 通路分析表明,DEGs 主要集中在外泌体和细胞因子相关信号通路。我们从 PPI 网络中确定了 30 个 DEGs 中的关键基因。这 2 种组织的浸润免疫存在一些差异。本研究表明,通过综合生物信息学分析筛选关键基因、信号通路和免疫浸润有助于了解子宫肌瘤的分子机制,并为深入了解分子机制提供新的思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6935/10194444/cc8196fcdb05/medi-102-e33815-g001.jpg

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