A five-gene mitochondrial–lipid metabolism signature for Alzheimer's disease diagnosis identified by machine learning and validated in independent cohorts
Bo Liu, Jingjing Wu, Jun Liu, Yingmin Mo, Guiqing Lin, Yi Zhang, Chunying Wei, Ran TaoBackground
Mitochondrial dysfunction and lipid metabolism dysregulation represent early key events in Alzheimer's disease (AD), yet integrated transcriptomic signatures combining these two biological processes for AD diagnosis remain poorly defined.
Objective
This study aimed to construct and validate a mitochondrial-lipid metabolism (MLM) gene-based diagnostic classifier for AD using machine-learning approaches and explore its underlying biological mechanisms.
Methods
Two public GEO datasets GSE5281 (training) and GSE28146 (independent validation) were included. Candidate MLM genes were retrieved from MSigDB. Differentially expressed MLM genes were screened by limma, followed by two-step feature selection with LASSO regression and random forest. Four machine-learning algorithms were compared. Model performance was evaluated by AUC, calibration, decision-curve analysis and subgroup analysis across AD severity. GSVA and hypergeometric test were applied for mechanistic exploration.
Results
A five-gene signature (
Conclusions
This validated five-gene MLM signature exhibits favorable diagnostic performance for AD and reflects disturbed mitochondrial-lipid metabolic network. It provides a promising transcriptomic biomarker for AD diagnosis.