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基于甘油磷脂代谢探讨克罗恩病的关键基因:利用孟德尔随机化、多组学整合、机器学习和 SHAP 方法的综合分析。

Explore key genes of Crohn's disease based on glycerophospholipid metabolism: A comprehensive analysis Utilizing Mendelian Randomization, Multi-Omics integration, Machine Learning, and SHAP methodology.

机构信息

Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, PR China.

Department of Medical Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, PR China.

出版信息

Int Immunopharmacol. 2024 Nov 15;141:112905. doi: 10.1016/j.intimp.2024.112905. Epub 2024 Aug 21.

Abstract

BACKGROUND AND AIMS

Crohn's disease (CD) is a chronic, complex inflammatory condition with increasing incidence and prevalence worldwide. However, the causes of CD remain incompletely understood. We identified CD-related metabolites, inflammatory factors, and key genes by Mendelian randomization (MR), multi-omics integration, machine learning (ML), and SHAP.

METHODS

We first performed a mediation MR analysis on 1400 serum metabolites, 91 inflammatory factors, and CD. We found that certain phospholipids are causally related to CD. In the scRNA-seq data, monocytes were categorized into high and low metabolism groups based on their glycerophospholipid metabolism scores. The differentially expressed genes of these two groups of cells were extracted, and transcription factor prediction, cell communication analysis, and GSEA analysis were performed. After further screening of differentially expressed genes (FDR<0.05, log2FC>1), least absolute shrinkage and selection operator (LASSO) regression was performed to obtain hub genes. Models for hub genes were built using the Catboost, XGboost, and NGboost methods. Further, we used the SHAP method to interpret the models and obtain the gene with the highest contribution to each model. Finally, qRT-PCR was used to verify the expression of these genes in the peripheral blood mononuclear cells (PBMC) of CD patients and healthy subjects.

RESULT

MR results showed 1-palmitoyl-2-stearoyl-gpc (16:0/18:0) levels, 1-stearoyl-2-arachidonoyl-GPI (18:0/20:4) levels, 1-arachidonoyl-gpc (20:4n6) levels, 1-palmitoyl-2-arachidonoyl-gpc (16:0/20:4n6) levels, and 1-arachidonoyl-GPE (20:4n6) levels were significantly associated with CD risk reduction (FDR<0.05), with CXCL9 acting as a mediation between these phospholipids and CD. The analysis identified 19 hub genes, with Catboost, XGboost, and NGboost achieving AUC of 0.91, 0.88, and 0.85, respectively. The SHAP methodology obtained the three genes with the highest model contribution: G0S2, S100A8, and PLAUR. The qRT-PCR results showed that the expression levels of S100A8 (p = 0.0003), G0S2 (p < 0.0001), and PLAUR (p = 0.0141) in the PBMC of CD patients were higher than healthy subjects.

CONCLUSION

MR findings suggest that certain phospholipids may lower CD risk. G0S2, S100A8, and PLAUR may be potential pathogenic genes in CD. These phospholipids and genes could serve as novel diagnostic and therapeutic targets for CD.

摘要

背景和目的

克罗恩病(CD)是一种慢性、复杂的炎症性疾病,其发病率和患病率在全球范围内呈上升趋势。然而,CD 的病因仍不完全清楚。我们通过孟德尔随机化(MR)、多组学整合、机器学习(ML)和 SHAP 鉴定了与 CD 相关的代谢物、炎症因子和关键基因。

方法

我们首先对 1400 种血清代谢物、91 种炎症因子和 CD 进行了中介 MR 分析。我们发现某些磷脂与 CD 有因果关系。在 scRNA-seq 数据中,根据甘油磷脂代谢评分将单核细胞分为高代谢组和低代谢组。提取这两组细胞的差异表达基因,并进行转录因子预测、细胞通讯分析和 GSEA 分析。进一步筛选差异表达基因(FDR<0.05,log2FC>1),采用最小绝对收缩和选择算子(LASSO)回归得到关键基因。采用 Catboost、XGboost 和 NGboost 方法构建关键基因模型。进一步采用 SHAP 方法对模型进行解释,得到每个模型贡献最高的基因。最后,采用 qRT-PCR 检测 CD 患者和健康对照者外周血单个核细胞(PBMC)中这些基因的表达。

结果

MR 结果表明,1-棕榈酰基-2-硬脂酰基-GPC(16:0/18:0)水平、1-硬脂酰基-2-花生四烯酰基-GPI(18:0/20:4)水平、1-花生四烯酰基-GPC(20:4n6)水平、1-棕榈酰基-2-花生四烯酰基-GPC(16:0/20:4n6)水平和 1-花生四烯酰基-GPE(20:4n6)水平与 CD 风险降低显著相关(FDR<0.05),CXCL9 作为这些磷脂与 CD 之间的中介。分析鉴定了 19 个关键基因,Catboost、XGboost 和 NGboost 的 AUC 分别为 0.91、0.88 和 0.85。SHAP 方法得到了模型贡献最高的三个基因:G0S2、S100A8 和 PLAUR。qRT-PCR 结果显示,CD 患者 PBMC 中 S100A8(p=0.0003)、G0S2(p<0.0001)和 PLAUR(p=0.0141)的表达水平高于健康对照者。

结论

MR 研究结果表明,某些磷脂可能降低 CD 的发病风险。G0S2、S100A8 和 PLAUR 可能是 CD 的潜在致病基因。这些磷脂和基因可能成为 CD 的新型诊断和治疗靶点。

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