利用机器学习算法鉴定阿尔茨海默病和代谢综合征的诊断基因。

Identification of diagnostic genes for both Alzheimer's disease and Metabolic syndrome by the machine learning algorithm.

机构信息

Department of Neurosurgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.

General Surgery, The First Affiliated Hospital of Dali University, Dali, China.

出版信息

Front Immunol. 2022 Nov 2;13:1037318. doi: 10.3389/fimmu.2022.1037318. eCollection 2022.

DOI:10.3389/fimmu.2022.1037318

PMID:36405716

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9667080/

Abstract

BACKGROUND

Alzheimer's disease is the most common neurodegenerative disease worldwide. Metabolic syndrome is the most common metabolic and endocrine disease in the elderly. Some studies have suggested a possible association between MetS and AD, but few studied genes that have a co-diagnostic role in both diseases.

METHODS

The microarray data of AD (GSE63060 and GSE63061 were merged after the batch effect was removed) and MetS (GSE98895) in the GEO database were downloaded. The WGCNA was used to identify the co-expression modules related to AD and MetS. RF and LASSO were used to identify the candidate genes. Machine learning XGBoost improves the diagnostic effect of hub gene in AD and MetS. The CIBERSORT algorithm was performed to assess immune cell infiltration MetS and AD samples and to investigate the relationship between biomarkers and infiltrating immune cells. The peripheral blood mononuclear cells (PBMCs) single-cell RNA (scRNA) sequencing data from patients with AD and normal individuals were visualized with the Seurat standard flow dimension reduction clustering the metabolic pathway activity changes each cell with ssGSEA.

RESULTS

The brown module was identified as the significant module with AD and MetS. GO analysis of shared genes showed that intracellular transport and establishment of localization in cell and organelle organization were enriched in the pathophysiology of AD and MetS. By using RF and Lasso learning methods, we finally obtained eight diagnostic genes, namely , , , , , , and . Their AUC were all greater than 0.7. Higher immune cell infiltrations expressions were found in the two diseases and were positively linked to the characteristic genes. The scRNA-seq datasets finally obtained seven cell clusters. Seven major cell types including CD8 T cell, monocytes, T cells, NK cell, B cells, dendritic cells and macrophages were clustered according to immune cell markers. The ssGSEA revealed that immune-related gene () was significantly regulated in the glycolysis-metabolic pathway.

CONCLUSION

We identified genes with common diagnostic effects on both MetS and AD, and found genes involved in multiple metabolic pathways associated with various immune cells.

摘要

背景

阿尔茨海默病是全球最常见的神经退行性疾病。代谢综合征是老年人最常见的代谢和内分泌疾病。一些研究表明代谢综合征与 AD 之间可能存在关联，但很少有研究涉及在这两种疾病中具有共同诊断作用的基因。

方法

从 GEO 数据库中下载 AD（GSE63060 和 GSE63061 在去除批次效应后合并）和 MetS（GSE98895）的微阵列数据。使用 WGCNA 识别与 AD 和 MetS 相关的共表达模块。使用 RF 和 LASSO 识别候选基因。机器学习 XGBoost 提高了 AD 和 MetS 中枢纽基因的诊断效果。使用 CIBERSORT 算法评估 MetS 和 AD 样本中的免疫细胞浸润，并研究生物标志物与浸润免疫细胞之间的关系。使用 Seurat 标准流程对来自 AD 患者和正常个体的外周血单核细胞（PBMC）单细胞 RNA（scRNA）测序数据进行降维可视化，通过 ssGSEA 对每个细胞的代谢途径活性变化进行聚类。

结果

棕色模块被鉴定为与 AD 和 MetS 相关的显著模块。共享基因的 GO 分析表明，细胞内运输和细胞及细胞器定位的建立在 AD 和 MetS 的病理生理学中得到了富集。通过使用 RF 和 Lasso 学习方法，我们最终获得了 8 个诊断基因，即、、、、、、。它们的 AUC 均大于 0.7。在这两种疾病中发现了更高的免疫细胞浸润表达，并与特征基因呈正相关。最终获得的 scRNA-seq 数据集包含 7 个细胞簇。根据免疫细胞标志物，将 7 种主要细胞类型（包括 CD8 T 细胞、单核细胞、T 细胞、NK 细胞、B 细胞、树突状细胞和巨噬细胞）聚类。ssGSEA 显示免疫相关基因（）在糖酵解代谢途径中受到显著调控。