Identification of diagnostic gene signatures and molecular mechanisms for non-alcoholic fatty liver disease and Alzheimer's disease through machine learning algorithms

作者全名：Jiang, Liqing; Wang, Qian; Jiang, Yingsong; Peng, Dadi; Zong, Kezhen; Li, Shan; Xie, Wenyuan; Zhang, Cheng; Li, Kaili; Wu, Zhongjun; Huang, Zuotian

作者地址：[Jiang, Liqing; Jiang, Yingsong; Peng, Dadi; Zong, Kezhen; Wu, Zhongjun; Huang, Zuotian] Chongqing Med Univ, Affiliated Hosp 1, Dept Hepatobiliary Surg, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Wang, Qian] Chengdu Seventh Peoples Hosp, Dept Gen Practice, Chengdu, Peoples R China; [Li, Shan; Xie, Wenyuan; Zhang, Cheng; Li, Kaili; Wu, Zhongjun; Huang, Zuotian] Chongqing Univ, Canc Hosp, Dept Hepatobiliary Pancreat Tumor Ctr, Chongqing, Peoples R China

通信作者：Wu, ZJ; Huang, ZT (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Hepatobiliary Surg, 1 Youyi Rd, Chongqing 400016, Peoples R China.

来源：CLINICA CHIMICA ACTA

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001223040200001

JCR分区：Q2

影响因子：3.2

年份：2024

卷号：557

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Non-alcoholic fatty liver disease; Alzheimer's disease; Machine learning; Immunity; Inflammation

摘要：Background: Non-alcoholic fatty liver disease (NAFLD) and Alzheimer 's disease (AD) pose significant global health challenges. Recent studies have suggested a link between these diseases; however, the underlying mechanisms remain unclear. This study aimed to decode the shared molecular landscapes of NAFLD and AD using bioinformatic approaches. Methods: We analyzed three datasets for NAFLD and AD from the Gene Expression Omnibus (GEO). This study involved identifying differentially expressed genes (DEGs), using weighted gene co -expression network analysis (WGCNA), and using machine learning for biomarker discovery. The diagnostic biomarkers were validated using expression analysis, receiver operating characteristic (ROC) curves, and nomogram models. Furthermore, Gene Set Enrichment Analysis (GSEA) and CIBERSORT were used to investigate molecular pathways and immune cell distributions related to GADD45G and NUPR1. Results: This study identified 14 genes that are common to NAFLD and AD. Machine learning identified six biomarkers for NAFLD, four for AD, and two crucial shared biomarkers: GADD45G and NUPR1. Validation confirmed their expression patterns and robust predictive abilities. GSEA revealed the intricate roles of these biomarkers in disease -associated pathways. Immune cell profiling highlighted the importance of macrophages under these conditions. Conclusion: This study highlights GADD45G and NUPR1 as key biomarkers for NAFLD and AD, and provides novel insights into their molecular connections. These findings revealed potential therapeutic targets, particularly in macrophage -mediated pathways, thus enriching our understanding of these complex diseases.

基金机构：Chongqing Research Performance Incentive and Guidance Project [cstc2022jxjl120032]; Chongqing tech-nology innovation and application development key project [cstc2021jscx-gksbX0060]; Doctoral program of the first affiliated hospital of Chongqing Medical University [CYYY-BSYJSCXXM-202305]

基金资助正文：This study was supported by Chongqing Research Performance Incentive and Guidance Project (cstc2022jxjl120032) , Chongqing technology innovation and application development key project (cstc2021jscx-gksbX0060) , Doctoral program of the first affiliated hospital of Chongqing Medical University (CYYY-BSYJSCXXM-202305) .