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INTRODUCTION

Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD.

METHODS

Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.

RESULTS

113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.

DISCUSSION

Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

INTRODUCTION

METHODS

RESULTS

DISCUSSION

引言

在过去几十年中，众多研究一致表明免疫失调是子宫内膜异位症（EM）和炎症性肠病（IBD）的共同特征。然而，其潜在的病理机制仍不清楚。在本研究中，运用生物信息学技术筛选大规模基因表达数据，以在分子水平上寻找可能的相关性，从而确定EM和IBD之间的共同致病途径。

方法

基于EM转录组数据集GSE7305和GSE23339，以及IBD转录组数据集GSE87466和GSE126124，在R环境中使用limma软件包进行差异基因分析。识别共表达的差异表达基因，并使用STRING数据库11.5版本构建差异表达基因的蛋白质-蛋白质相互作用（PPI）网络。Cytoscape中的MCODE工具便于筛选出蛋白质相互作用子网。通过CytoHubba插件的两种拓扑分析算法（MCC和Degree）确定PPI网络中的关键基因。使用Upset对这些关键基因进行可视化。使用受试者工作特征（ROC）曲线和曲线下面积（AUC）评估这些关键基因的基因表达水平的诊断价值。CIBERSORT算法确定22种免疫细胞亚型的浸润状态，探索EM和IBD患者在对照组和疾病组中的差异。最后，将EM和IBD共有的不同基因表达趋势输入CMap，以识别具有潜在治疗作用的小分子化合物。

结果

已鉴定出113个在EM和IBD中共表达的差异表达基因（DEG），包括28个下调基因和86个上调基因。在功能富集分析中，EM和IBD的共表达差异基因集中在免疫反应激活、循环免疫球蛋白介导的体液免疫反应和体液免疫反应。通过蛋白质-蛋白质相互作用网络和MCODE鉴定出5个枢纽基因（SERPING1、VCAM1、CLU、C3、CD55）。5个枢纽基因的受试者工作特征（ROC）曲线的曲线下面积（AUC）值较高，表明其对疾病发生的预测能力。这些枢纽基因可用作EM和IBD发展的潜在生物标志物。此外，CMap数据库共鉴定出9种靶向EM和IBD治疗基因的小分子化合物（TTNPB、CAY-10577、PD-0325901等）。