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Abstract：
The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.
摘要：
免疫球蛋白A肾病（IgAN）与炎症性肠病（IBD）之间复杂相关的潜在机制尚不清楚。本研究旨在确定最佳的串扰基因，潜在的途径，以及IBD和IgAN之间相互免疫浸润的微环境，以阐明IBD和IgAN患者之间的联系。IgAN和IBD数据集从基因表达综合(GEO)获得。三种算法，CIBERSORTx,ssGSEA,和xcell,用于评估两种疾病之间浸润微环境的相似性。在IBD数据集中实施加权基因共表达网络分析(WGCNA)以鉴定主要的免疫浸润模块,和Boruta算法，RFE算法，和LASSO回归用于过滤串扰基因。接下来,应用多个机器学习模型来确认最佳串扰基因。最后,相关发现通过IBD小鼠的组织学和免疫组织化学分析得到验证.免疫浸润剖析显示IBD和IgAN样品在多数免疫细胞中没有显著差别。这三种算法确定了10个诊断基因，MAPK3,NFKB1,FDX1,EPHX2,SYNPO,KDF1,METTL7A,Rida,HSDL2和RIPK2;FDX1和NFKB1在IBD小鼠的肾脏中增强。京都基因百科全书和基因组分析显示了两种疾病之间的15条相互通路，脂质代谢在串扰中起着至关重要的作用。我们的发现为IgAN和IBD的共同免疫机制提供了见解。这些共同的途径，诊断串扰基因，细胞介导的异常免疫可能为进一步的实验研究提供信息。
