Cuproptosis-related genes reveal related typing, prognosis, and immunological features of amyotrophic lateral sclerosis: A comprehensive landscape assessed through machine learning algorithms, risk model construction, and verification
4 affiliations
- 1Faculty of Medicine, Macau University of Science and Technology, Taipa, Macao
- 2Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China
- 3Department of Laboratory Medicine, Guangdong Provincial Key Laboratory of Precision Medical Diagnostics, Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Guangdong Provincial Key Laboratory of Single-cell and Extracellular Vesicles, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- 4Guangdong Provincial Clinical Research Center for Laboratory Medicine, China
Abstract
Background: Cuproptosis, a recently identified cell death form, is closely associated with amyotrophic lateral sclerosis (ALS), a debilitating neurological disease characterized by motor neuron degeneration. This study investigated the potential involvement of Cuproptosis-Related Genes (CRGs) in ALS pathogenesis.
Methods: The ALS-related peripheral blood dataset (GSE112681) from the Gene Expression Omnibus (GEO) database was used to identify CR-DEGs, followed by enrichment analysis. CIBERSORT algorithm and correlation analysis revealed the immune cell infiltration in ALS patients and the correlation between immune cells and CR-DEGs. Machine learning algorithms were used to identify key CR-DEGs, perform GSEA, and construct a risk model. Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) was used to detect the expression level of key CR-DEGs. Consensus clustering analysis was used to divide 397 ALS patients into two subclusters (C1 and C2) based on key CR-DEGs. Multivariate Cox regression analyzed clinical attributes and survival. Model accuracy was validated using receiver operating characteristic (ROC) curves, calibration curves, and two external datasets.
Results: Eight CR-DEGs including Nuclear Factor Erythroid 2-Like 2 (NFE2L2), Ferredoxin 1 (FDX1), Lipoyltransferase 1 (LIRT1), Glutaminase (GLS), and Dihydrolipoamide S-Succinyltransferase (DLST) involved in tricarboxylic acid (TCA) cycle and various mitochondrial metabolic pathways, were identified. Various types of immune cells infiltrated in the peripheral blood of ALS patients, which was correlated with CR-DEGs. Machine learning algorithms identified five key CR-DEGs enriched in multi-substance metabolism, immune- and ALS-related pathways. The nomogram constructed based on CR-DEGs highly predicted ALS incidence risk and prognosis. RT-qPCR results indicated that compared to normal individuals, the expression levels of NFE2L2, FDX1, and LIPT1 were increased in the peripheral blood of ALS patients, whereas those of GLS and DLST were decreased. Subsequently, GSEA results demonstrated significant upregulation of cuproptosis, apoptosis, multiple immune responses, and substance metabolism pathways in C1, which corresponded to a shortened survival period. Conversely, C2 exhibited upregulation of the myogenesis pathway, indicating a relatively favorable prognosis. The nomogram confirmed cuproptosis as a significant prognostic risk factor, especially for ALS patients with early- and bulbar-onset symptoms. Nomogram based on cuproptosis levels accurately predicted ALS prognosis and survival.
Conclusions: This study offers initial insights into the association between CR-DEGs and ALS and presents a risk prediction model for future investigations into ALS mechanisms and potential biomarkers.
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BibTeX
@article{gong2026cuproptosisrelated,
title = {{Cuproptosis-related genes reveal related typing, prognosis, and immunological features of amyotrophic lateral sclerosis: A comprehensive landscape assessed through machine learning algorithms, risk model construction, and verification}},
author = {Ziwei Gong and Binbin Wei},
journal = {Journal of Genetic Engineering \& Biotechnology},
year = {2026},
month = sep,
doi = {10.1016/j.jgeb.2026.100835},
url = {https://doi.org/10.1016/j.jgeb.2026.100835},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Cuproptosis-related genes reveal related typing, prognosis, and immunological features of amyotrophic lateral sclerosis: A comprehensive landscape assessed through machine learning algorithms, risk model construction, and verification
AU - Ziwei Gong
AU - Binbin Wei
PY - 2026
DA - 2026/09/01
JO - Journal of Genetic Engineering & Biotechnology
DO - 10.1016/j.jgeb.2026.100835
UR - https://doi.org/10.1016/j.jgeb.2026.100835
AB - Background: Cuproptosis, a recently identified cell death form, is closely associated with amyotrophic lateral sclerosis (ALS), a debilitating neurological disease characterized by motor neuron degeneration. This study investigated the potential involvement of Cuproptosis-Related Genes (CRGs) in ALS pathogenesis. Methods: The ALS-related peripheral blood dataset (GSE112681) from the Gene Expression Omnibus (GEO) database was used to identify CR-DEGs, followed by enrichment analysis. CIBERSORT algorithm and correlation analysis revealed the immune cell infiltration in ALS patients and the correlation between immune cells and CR-DEGs. Machine learning algorithms were used to identify key CR-DEGs, perform GSEA, and construct a risk model. Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) was used to detect the expression level of key CR-DEGs. Consensus clustering analysis was used to divide 397 ALS patients into two subclusters (C1 and C2) based on key CR-DEGs. Multivariate Cox regression analyzed clinical attributes and survival. Model accuracy was validated using receiver operating characteristic (ROC) curves, calibration curves, and two external datasets. Results: Eight CR-DEGs including Nuclear Factor Erythroid 2-Like 2 (NFE2L2), Ferredoxin 1 (FDX1), Lipoyltransferase 1 (LIRT1), Glutaminase (GLS), and Dihydrolipoamide S-Succinyltransferase (DLST) involved in tricarboxylic acid (TCA) cycle and various mitochondrial metabolic pathways, were identified. Various types of immune cells infiltrated in the peripheral blood of ALS patients, which was correlated with CR-DEGs. Machine learning algorithms identified five key CR-DEGs enriched in multi-substance metabolism, immune- and ALS-related pathways. The nomogram constructed based on CR-DEGs highly predicted ALS incidence risk and prognosis. RT-qPCR results indicated that compared to normal individuals, the expression levels of NFE2L2, FDX1, and LIPT1 were increased in the peripheral blood of ALS patients, whereas those of GLS and DLST were decreased. Subsequently, GSEA results demonstrated significant upregulation of cuproptosis, apoptosis, multiple immune responses, and substance metabolism pathways in C1, which corresponded to a shortened survival period. Conversely, C2 exhibited upregulation of the myogenesis pathway, indicating a relatively favorable prognosis. The nomogram confirmed cuproptosis as a significant prognostic risk factor, especially for ALS patients with early- and bulbar-onset symptoms. Nomogram based on cuproptosis levels accurately predicted ALS prognosis and survival. Conclusions: This study offers initial insights into the association between CR-DEGs and ALS and presents a risk prediction model for future investigations into ALS mechanisms and potential biomarkers.
SN - 1687-157X
SN - 2090-5920
ER -