5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression.
{"title":"5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression.","authors":"Kubra Temiz, Aytac Gul, Esra Gov","doi":"10.1007/s12017-025-08847-z","DOIUrl":null,"url":null,"abstract":"<p><p>Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to apply network biology and machine learning (ML) techniques to identify novel repurposed drug candidates for ALS. In this study, we conducted a meta-analysis using 4 transcriptome data in ALS patients (including motor neuron and muscle tissue) and healthy controls. Through this analysis, we uncovered common shared differentially expressed genes (DEGs) separately for motor neurons and muscle tissue. Using common DEGs as proxies, we identified two distinct clusters of highly clustered differential co-expressed cluster genes: the 'Muscle Tissue Cluster' for muscle tissue and the 'Motor Neuron Cluster' for motor neurons. We then evaluated the performance of the nodes of these two modules to distinguish between diseased and healthy states with ML algorithms: KNN, SVM, and Random Forest. Furthermore, we performed drug repurposing analysis and text-mining analyses, employing the nodes of clusters as drug targets to identify novel drug candidates for ALS. The potential impact of the drug candidates on the expression of cluster genes was predicted using linear regression, SVR, Random Forest, Gradient Boosting, and neural network algorithms. As a result, we identified five novel drug candidates for the treatment of ALS: Nilotinib, Trovafloxacin, Apratoxin A, Carboplatin, and Clinafloxacin. These findings highlight the potential of drug repurposing in ALS treatment and suggest that further validation through experimental studies could lead to new therapeutic avenues.</p>","PeriodicalId":19304,"journal":{"name":"NeuroMolecular Medicine","volume":"27 1","pages":"24"},"PeriodicalIF":3.3000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11968496/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NeuroMolecular Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12017-025-08847-z","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to apply network biology and machine learning (ML) techniques to identify novel repurposed drug candidates for ALS. In this study, we conducted a meta-analysis using 4 transcriptome data in ALS patients (including motor neuron and muscle tissue) and healthy controls. Through this analysis, we uncovered common shared differentially expressed genes (DEGs) separately for motor neurons and muscle tissue. Using common DEGs as proxies, we identified two distinct clusters of highly clustered differential co-expressed cluster genes: the 'Muscle Tissue Cluster' for muscle tissue and the 'Motor Neuron Cluster' for motor neurons. We then evaluated the performance of the nodes of these two modules to distinguish between diseased and healthy states with ML algorithms: KNN, SVM, and Random Forest. Furthermore, we performed drug repurposing analysis and text-mining analyses, employing the nodes of clusters as drug targets to identify novel drug candidates for ALS. The potential impact of the drug candidates on the expression of cluster genes was predicted using linear regression, SVR, Random Forest, Gradient Boosting, and neural network algorithms. As a result, we identified five novel drug candidates for the treatment of ALS: Nilotinib, Trovafloxacin, Apratoxin A, Carboplatin, and Clinafloxacin. These findings highlight the potential of drug repurposing in ALS treatment and suggest that further validation through experimental studies could lead to new therapeutic avenues.
肌萎缩侧索硬化症(ALS)是一种进行性神经退行性疾病,会导致运动神经元变性、肌肉无力和呼吸衰竭。尽管研究在不断进行,但有效治疗 ALS 的方法仍然有限。本研究旨在应用网络生物学和机器学习(ML)技术来确定治疗 ALS 的新型再用途候选药物。在这项研究中,我们利用 ALS 患者(包括运动神经元和肌肉组织)和健康对照组的 4 个转录组数据进行了荟萃分析。通过分析,我们分别发现了运动神经元和肌肉组织中共同的差异表达基因(DEGs)。以共同的 DEGs 为代表,我们发现了两个不同的高聚类差异共表达基因簇:肌肉组织的 "肌肉组织簇 "和运动神经元的 "运动神经元簇"。然后,我们评估了这两个模块的节点的性能,并使用多模型算法来区分疾病和健康状态:KNN、SVM 和随机森林。此外,我们还进行了药物再利用分析和文本挖掘分析,将聚类节点作为药物靶点,以确定治疗 ALS 的新型候选药物。我们使用线性回归、SVR、随机森林、梯度提升和神经网络算法预测了候选药物对聚类基因表达的潜在影响。结果,我们确定了五种治疗 ALS 的候选新药:这五种候选新药是:尼洛替尼、特罗伐他星、Apratoxin A、卡铂和克林沙星。这些发现凸显了药物再利用在 ALS 治疗中的潜力,并表明通过实验研究进一步验证可能会带来新的治疗途径。
期刊介绍:
NeuroMolecular Medicine publishes cutting-edge original research articles and critical reviews on the molecular and biochemical basis of neurological disorders. Studies range from genetic analyses of human populations to animal and cell culture models of neurological disorders. Emerging findings concerning the identification of genetic aberrancies and their pathogenic mechanisms at the molecular and cellular levels will be included. Also covered are experimental analyses of molecular cascades involved in the development and adult plasticity of the nervous system, in neurological dysfunction, and in neuronal degeneration and repair. NeuroMolecular Medicine encompasses basic research in the fields of molecular genetics, signal transduction, plasticity, and cell death. The information published in NEMM will provide a window into the future of molecular medicine for the nervous system.