Jia F, Zhang B, Yu W, Chen Z, Xu W, Zhao W, Wang Z. Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis.
Comput Biol Med 2024;
168:107776. [PMID:
38056214 DOI:
10.1016/j.compbiomed.2023.107776]
[Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND
Amyotrophic lateral sclerosis (ALS) is a progressive and lethal neurodegenerative disease. Several studies have suggested the involvement of cuproptosis in its pathogenesis. In this research, we intend to explore the cuproptosis-related molecular clusters in ALS and develop a novel cuproptosis-related genes prediction model.
METHODS
The peripheral blood gene expression data was downloaded from the Gene Expression Omnibus (GEO) online database. Based on the GSE112681 dataset, we investigated the critical cuproptosis-related genes (CuRGs) and pathological clustering of ALS. The immune microenvironment features of the peripheral blood in ALS patients were also examined using the CIBERSORT algorithm. Cluster-specific hub genes were determined by the WGCNA. The most accurate prediction model was selected by comparing the performance of four machine learning techniques. ROC curves and two independent datasets were applied to validate the prediction accuracy. The available compounds targeting these hub genes were filtered to investigate their efficacy in treating ALS.
RESULTS
We successfully determined four critical cuproptosis-related genes and two pathological clusters with various immune profiles and biological characteristics in ALS. Functional analysis showed that genes in Cluster1 were primarily enriched in pathways closely associated with immunity. The Support Vector Machine (SVM) model exhibited the best discrimination properties with a large area under the curve (AUC = 0.862). Five hub prediction genes (BAP1, SMG1, BCLAF1, DHX15, EIF4G2) were selected to establish a nomogram model, suggesting significant risk prediction potential for ALS. The accuracy of this model in predicting ALS incidence was also demonstrated through calibration curves, nomograms, and decision curve analysis. Finally, three drugs targeting BAP1 were determined through drug-gene interactions.
CONCLUSION
This study elucidated the complex associations between cuproptosis and ALS and constructed a satisfactory predictive model to explore the pathological characteristics of different clusters in ALS patients.
Collapse