Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis

Fang Jia; Bingchang Zhang; Weijie Yu; Zheng Chen; Wenbin Xu; Wenpeng Zhao; Zhanxiang Wang

doi:10.1016/j.compbiomed.2023.107776

Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis

Comput Biol Med. 2024 Jan:168:107776. doi: 10.1016/j.compbiomed.2023.107776. Epub 2023 Dec 3.

Authors

Fang Jia¹, Bingchang Zhang¹, Weijie Yu², Zheng Chen¹, Wenbin Xu¹, Wenpeng Zhao¹, Zhanxiang Wang³

Affiliations

¹ Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
² The School of Clinical Medicine, Fujian Medical University, Fuzhou, China.
³ Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. Electronic address: wangzx@xmu.edu.cn.

PMID: 38056214
DOI: 10.1016/j.compbiomed.2023.107776

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.

Keywords: Amyotrophic lateral sclerosis; Cuproptosis-related genes; Immune infiltration; Molecular cluster; Prediction model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Amyotrophic Lateral Sclerosis* / genetics
Apoptosis
Calibration
Cluster Analysis
Humans
Neurodegenerative Diseases*