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溃疡性结肠炎中PAN细胞焦亡与自噬相关分子特征及免疫格局:综合分析与实验验证

PANoptosis and Autophagy-Related Molecular Signature and Immune Landscape in Ulcerative Colitis: Integrated Analysis and Experimental Validation.

作者信息

Lu Jiali, Li Fei, Ye Mei

机构信息

Department of Gastroenterology, Zhongnan Hospital, Wuhan University, Wuhan, Hubei, 430071, People's Republic of China.

Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Zhongnan Hospital, Wuhan University, Wuhan, Hubei, 430071, People's Republic of China.

出版信息

J Inflamm Res. 2024 May 20;17:3225-3245. doi: 10.2147/JIR.S455862. eCollection 2024.

Abstract

BACKGROUND

Ulcerative colitis (UC) is an autoimmune inflammatory disorder of the gastrointestinal tract. Programmed cell death (PCD), including PANoptosis and autophagy, plays roles in inflammation and immunity. This study aimed to investigate the molecular signature and immune landscape of the PANoptosis- and autophagy-related differentially expressed genes (DEGs) in UC.

METHODS

Analyzing UC dataset GSE206285 yielded DEGs. Differentially expressed PANoptosis- and autophagy-related genes were identified using DEGs and relevant gene collections. Functional and pathway enrichment analyses were conducted. A protein-protein interaction (PPI) network was established to identify hub genes. TRRUST database predicted transcription factors (TFs), pivotal miRNAs, and drugs interacting with hub genes. Immune infiltration analysis, UC-associated single-cell sequencing data analysis, and construction of a competing endogenous RNA (ceRNA) network for hub genes were conducted. Machine learning identified key candidate genes, evaluated for diagnostic value via receiver operating characteristic (ROC) curves. A UC mice model verified expression of key candidate genes.

RESULTS

Identifying ten PANoptosis-related hub DEGs and four autophagy-related hub DEGs associated them with cell chemotaxis, wound healing and positive MAPK cascade regulation. Immune infiltration analysis revealed increased immunocyte infiltration in UC patients, with hub genes closely linked to various immune cell infiltrations. Machine learning identified five key candidate genes, TIMP1, TIMP2, TIMP3, IL6, and CCL2, with strong diagnostic performance. At the single-cell level, these genes exhibited high expression in inflammatory fibroblasts (IAFs). They showed significant expression differences in the colon mucosa of both UC patients and UC mice model.

CONCLUSION

This study identified and validated novel molecular signatures associated with PANoptosis and autophagy in UC, potentially influencing immune dysregulation and wound healing, thus opening avenues for future research and therapeutic interventions.

摘要

背景

溃疡性结肠炎(UC)是一种胃肠道自身免疫性炎症性疾病。程序性细胞死亡(PCD),包括全程序死亡和自噬,在炎症和免疫中发挥作用。本研究旨在探讨UC中与全程序死亡和自噬相关的差异表达基因(DEGs)的分子特征和免疫格局。

方法

分析UC数据集GSE206285得到DEGs。使用DEGs和相关基因集鉴定差异表达的全程序死亡和自噬相关基因。进行功能和通路富集分析。建立蛋白质-蛋白质相互作用(PPI)网络以识别枢纽基因。TRRUST数据库预测与枢纽基因相互作用的转录因子(TFs)、关键微小RNA(miRNAs)和药物。进行免疫浸润分析、UC相关单细胞测序数据分析以及构建枢纽基因的竞争性内源性RNA(ceRNA)网络。机器学习识别关键候选基因,并通过受试者工作特征(ROC)曲线评估其诊断价值。UC小鼠模型验证关键候选基因的表达。

结果

鉴定出10个与全程序死亡相关的枢纽DEGs和4个与自噬相关的枢纽DEGs,它们与细胞趋化性、伤口愈合和正向丝裂原活化蛋白激酶(MAPK)级联调节相关。免疫浸润分析显示UC患者免疫细胞浸润增加,枢纽基因与各种免疫细胞浸润密切相关。机器学习识别出5个关键候选基因,即金属蛋白酶组织抑制因子1(TIMP1)、金属蛋白酶组织抑制因子2(TIMP2)、金属蛋白酶组织抑制因子3(TIMP3)、白细胞介素6(IL6)和趋化因子配体2(CCL2),具有较强的诊断性能。在单细胞水平,这些基因在炎症成纤维细胞(IAFs)中高表达。它们在UC患者和UC小鼠模型的结肠黏膜中均表现出显著的表达差异。

结论

本研究鉴定并验证了UC中与全程序死亡和自噬相关的新型分子特征,可能影响免疫失调和伤口愈合,从而为未来的研究和治疗干预开辟了途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/716e/11122227/ffe6a48d4c64/JIR-17-3225-g0001.jpg

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