Hu Jianlan, Wu Youxing, Zhang Danxia, Wang Xiaoyang, Sheng Yaohui, Liao Hui, Ou Yangpeng, Chen Zhen, Shu Baolian, Gui Ruohu
Department of Gastroenterology, The Central Hospital of Hengyang City, Hengyang, Hunan Province, PR China.
Department of Oncology, Huizhou Third People's Hospital, Guangzhou Medical University, Huizhou, Guangdong Province, PR China.
Genes Immun. 2024 Dec;25(6):492-513. doi: 10.1038/s41435-024-00304-4. Epub 2024 Nov 5.
The present study utilized large-scale genome-wide association studies (GWAS) summary data (731 immune cell subtypes and three primary sclerosing cholangitis (PSC) GWAS datasets), meta-analysis, and two PSC transcriptome data to elucidate the pivotal role of Tregs proportion imbalance in the occurrence of PSC. Then, we employed weighted gene co-expression network analysis (WGCNA), differential analysis, and 107 combinations of 12 machine-learning algorithms to construct and validate an artificial intelligence-derived diagnostic model (Tregs classifier) according to the average area under curve (AUC) (0.959) in two cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) verified that compared to control, Akap10, Basp1, Dennd3, Plxnc1, and Tmco3 were significantly up-regulated in the PSC mice model yet the expression level of Klf13, and Scap was significantly lower. Furthermore, immune cell infiltration and functional enrichment analysis revealed significant associations of the hub Tregs-related gene with M2 macrophage, neutrophils, megakaryocyte-erythroid progenitor (MEP), natural killer T cell (NKT), and enrichment scores of the autophagic cell death, complement and coagulation cascades, metabolic disturbance, Fc gamma R-mediated phagocytosis, mitochondrial dysfunction, potentially mediating PSC onset. XGBoost algorithm and SHapley Additive exPlanations (SHAP) identified AKAP10 and KLF13 as optimal genes, which may be an important target for PSC.
本研究利用大规模全基因组关联研究(GWAS)汇总数据(731个免疫细胞亚型和三个原发性硬化性胆管炎(PSC)GWAS数据集)、荟萃分析和两个PSC转录组数据,以阐明调节性T细胞(Tregs)比例失衡在PSC发生中的关键作用。然后,我们采用加权基因共表达网络分析(WGCNA)、差异分析以及12种机器学习算法的107种组合,根据两个队列中的曲线下平均面积(AUC)(0.959)构建并验证了一个人工智能衍生的诊断模型(Tregs分类器)。定量实时聚合酶链反应(qRT-PCR)证实,与对照组相比,Akap10、Basp1、Dennd3、Plxnc1和Tmco3在PSC小鼠模型中显著上调,而Klf13和Scap的表达水平显著降低。此外,免疫细胞浸润和功能富集分析显示,关键的Tregs相关基因与M2巨噬细胞、中性粒细胞、巨核细胞-红系祖细胞(MEP)、自然杀伤T细胞(NKT)以及自噬性细胞死亡、补体和凝血级联反应、代谢紊乱、FcγR介导的吞噬作用、线粒体功能障碍的富集分数显著相关,可能介导PSC的发病。XGBoost算法和SHapley加性解释(SHAP)确定AKAP10和KLF13为最佳基因,它们可能是PSC的重要靶点。