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<p>Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has multiple factors that make its molecular pathogenesis difficult to understand and its diagnosis and treatment during the early stages difficult to determine. Discovering novel biomarkers in ALS for diagnostic and therapeutic potential has become important. Consequently, bioinformatics and machine learning algorithms are useful for identifying differentially expressed genes (DEGs) and potential biomarkers, as well as understanding the molecular mechanisms and intricacies of diseases such as ALS. To achieve the aim of the present study, six datasets obtained from the Gene Expression Omnibus (GEO) were utilized and analyzed using an integrative bioinformatics and machine learning approach. Log transformation was done during data preprocessing, RMA normalization was performed, and the batch effect was corrected. Differential expression analysis identified 206 DEGs that were significantly associated with different&#xa0;biological processes, including muscle function, energy metabolism, and mitochondrial membrane activity. Functional enrichment analysis highlighted pathways, including those related to prion disease, Parkinson’s disease, and ATP synthesis via chemiosmotic coupling. We employed a multi-step machine learning framework incorporating random forest, LASSO regression, and SVM-RFE to identify robust biomarkers. This approach identified three key genes, <i>CHRNA1</i>, <i>DLG5</i>, and <i>PLA2G4C</i>, which could be explored as promising biomarkers for ALS after further validation. The internal validation, including&#xa0;principal component analysis (PCA) and ROC-AUC analysis, demonstrated strong diagnostic potential of these hub genes, achieving an AUC of 0.96. This work highlights the utility of bioinformatics and machine learning in identifying key genes as biomarkers for diagnostic and therapeutic potential in ALS.</p>

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Discovering Novel Biomarkers and Potential Therapeutic Targets of Amyotrophic Lateral Sclerosis Through Integrated Machine Learning and Gene Expression Profiling

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Farah Anjum,
  • Abdulaziz Alsharif,
  • Maha Bakhuraysah,
  • Alaa Shafie,
  • Md.Imtaiyaz Hassan,
  • Taj Mohammad

摘要

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has multiple factors that make its molecular pathogenesis difficult to understand and its diagnosis and treatment during the early stages difficult to determine. Discovering novel biomarkers in ALS for diagnostic and therapeutic potential has become important. Consequently, bioinformatics and machine learning algorithms are useful for identifying differentially expressed genes (DEGs) and potential biomarkers, as well as understanding the molecular mechanisms and intricacies of diseases such as ALS. To achieve the aim of the present study, six datasets obtained from the Gene Expression Omnibus (GEO) were utilized and analyzed using an integrative bioinformatics and machine learning approach. Log transformation was done during data preprocessing, RMA normalization was performed, and the batch effect was corrected. Differential expression analysis identified 206 DEGs that were significantly associated with different biological processes, including muscle function, energy metabolism, and mitochondrial membrane activity. Functional enrichment analysis highlighted pathways, including those related to prion disease, Parkinson’s disease, and ATP synthesis via chemiosmotic coupling. We employed a multi-step machine learning framework incorporating random forest, LASSO regression, and SVM-RFE to identify robust biomarkers. This approach identified three key genes, CHRNA1, DLG5, and PLA2G4C, which could be explored as promising biomarkers for ALS after further validation. The internal validation, including principal component analysis (PCA) and ROC-AUC analysis, demonstrated strong diagnostic potential of these hub genes, achieving an AUC of 0.96. This work highlights the utility of bioinformatics and machine learning in identifying key genes as biomarkers for diagnostic and therapeutic potential in ALS.