Identification of key lipid metabolism-related genes in Alzheimer's disease

被引:3
|
作者
Zeng, Youjie [1 ]
Cao, Si [1 ]
Li, Nannan [2 ]
Tang, Juan [2 ]
Lin, Guoxin [1 ]
机构
[1] Cent South Univ, Xiangya Hosp 3, Dept Anesthesiol, Changsha 410013, Hunan, Peoples R China
[2] Cent South Univ, Xiangya Hosp 3, Dept Nephrol, Changsha 410013, Hunan, Peoples R China
关键词
Alzheimer's disease; Bioinformatics; Biomarkers; Lipid metabolism; Differentially expressed genes; Differential expression analysis; Hub genes; Immune cell infiltration; Key genes; CEREBROSPINAL-FLUID; DEHYDROGENASE; BRAIN; ACTIVATION; DATABASE; BLOOD; RISK;
D O I
10.1186/s12944-023-01918-9
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Background Alzheimer's disease (AD) represents profound degenerative conditions of the brain that cause significant deterioration in memory and cognitive function. Despite extensive research on the significant contribution of lipid metabolism to AD progression, the precise mechanisms remain incompletely understood. Hence, this study aimed to identify key differentially expressed lipid metabolism-related genes (DELMRGs) in AD progression. Methods Comprehensive analyses were performed to determine key DELMRGs in AD compared to controls in GSE122063 dataset from Gene Expression Omnibus. Additionally, the ssGSEA algorithm was utilized for estimating immune cell levels. Subsequently, correlations between key DELMRGs and each immune cell were calculated specifically in AD samples. The key DELMRGs expression levels were validated via two external datasets. Furthermore, gene set enrichment analysis (GSEA) was utilized for deriving associated pathways of key DELMRGs. Additionally, miRNA-TF regulatory networks of the key DELMRGs were constructed using the miRDB, NetworkAnalyst 3.0, and Cytoscape software. Finally, based on key DELMRGs, AD samples were further segmented into two subclusters via consensus clustering, and immune cell patterns and pathway differences between the two subclusters were examined. Results Seventy up-regulated and 100 down-regulated DELMRGs were identified. Subsequently, three key DELMRGs (DLD, PLPP2, and PLAAT4) were determined utilizing three algorithms [(i) LASSO, (ii) SVM-RFE, and (iii) random forest]. Specifically, PLPP2 and PLAAT4 were up-regulated, while DLD exhibited downregulation in AD cerebral cortex tissue. This was validated in two separate external datasets (GSE132903 and GSE33000). The AD group exhibited significantly altered immune cell composition compared to controls. In addition, GSEA identified various pathways commonly associated with three key DELMRGs. Moreover, the regulatory network of miRNA-TF for key DELMRGs was established. Finally, significant differences in immune cell levels and several pathways were identified between the two subclusters. Conclusion This study identified DLD, PLPP2, and PLAAT4 as key DELMRGs in AD progression, providing novel insights for AD prevention/treatment.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Comprehensive Analysis of the Expression and Prognosis for Lipid Metabolism-Related Genes in Hepatocellular Carcinoma
    Fan, Wen-Jie
    Ding, Hao
    Chen, Xiang-Xun
    Yang, Lin
    SOUTH ASIAN JOURNAL OF CANCER, 2023, 12 (02) : 126 - 134
  • [42] Identification of Key Regulatory Genes and Pathways in Prefrontal Cortex of Alzheimer’s Disease
    Fuzhang Yang
    Xin Diao
    Fushuai Wang
    Quanwei Wang
    Jiamin Sun
    Yan Zhou
    Jiang Xie
    Interdisciplinary Sciences: Computational Life Sciences, 2020, 12 : 90 - 98
  • [43] cDNA microarray and bioinformatic analysis for the identification of key genes in Alzheimer's disease
    Gu, Chao
    Shen, Ting
    INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE, 2014, 33 (02) : 457 - 461
  • [44] Identification and Interaction of Key Genes in Alzheimer's Disease via Bioinformatics Analysis
    Zheng, Yanting
    Huang, Suli
    Zhou, Huijing
    Zhou, W.
    Jin, Guixiang
    Liu, Q.
    Wu, Yiming
    INDIAN JOURNAL OF PHARMACEUTICAL SCIENCES, 2022, 84 : 159 - 167
  • [45] Alzheimer’s Disease and Aging Association: Identification and Validation of Related Genes
    T. Liu
    K. Hou
    J. Li
    T. Han
    S. Liu
    Jianshe Wei
    The Journal of Prevention of Alzheimer's Disease, 2024, 11 : 196 - 213
  • [46] Identification of Key Regulatory Genes and Pathways in Prefrontal Cortex of Alzheimer's Disease
    Yang, Fuzhang
    Diao, Xin
    Wang, Fushuai
    Wang, Quanwei
    Sun, Jiamin
    Zhou, Yan
    Xie, Jiang
    INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES, 2020, 12 (01) : 90 - 98
  • [47] Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease
    Gu, Xuefeng
    Lai, Donglin
    Liu, Shuang
    Chen, Kaijie
    Zhang, Peng
    Chen, Bing
    Huang, Gang
    Cheng, Xiaoqin
    Lu, Changlian
    FRONTIERS IN AGING NEUROSCIENCE, 2022, 14
  • [48] Lipid metabolism in Alzheimer's and Parkinson's disease
    Xu, Qin
    Huang, Yadong
    FUTURE LIPIDOLOGY, 2006, 1 (04): : 441 - 453
  • [49] Identification of iron metabolism-related genes in coronary heart disease and construction of a diagnostic model
    Zhu, Lin
    Zhang, Jianxin
    Fan, Wenhui
    Su, Chen
    Jin, Zhi
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2024, 11
  • [50] Lipid Profiling of Alzheimer's Disease Brain Highlights Enrichment in Glycerol(phospho)lipid, and Sphingolipid Metabolism
    Akyol, Sumeyya
    Ugur, Zafer
    Yilmaz, Ali
    Ustun, Ilyas
    Gorti, Santosh Kapil Kumar
    Oh, Kyungjoon
    McGuinness, Bernadette
    Passmore, Peter
    Kehoe, Patrick G.
    Maddens, Michael E.
    Green, Brian D.
    Graham, Stewart F.
    CELLS, 2021, 10 (10)