Temiz Kubra, Gul Aytac, Gov Esra
Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Building M1, Office: 202 Saricam, 01250, Adana, Türkiye.
Department of Biotechnology, Faculty of Science, Bartin University, Bartin, Türkiye.
Neuromolecular Med. 2025 Apr 3;27(1):24. doi: 10.1007/s12017-025-08847-z.
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，会导致运动神经元退化、肌肉无力和呼吸衰竭。尽管研究不断，但针对ALS的有效治疗方法仍然有限。本研究旨在应用网络生物学和机器学习（ML）技术，识别用于ALS的新型重新利用药物候选物。在本研究中，我们使用ALS患者（包括运动神经元和肌肉组织）及健康对照的4个转录组数据进行了荟萃分析。通过该分析，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为代理，我们识别出两个高度聚集的差异共表达聚类基因的不同聚类：肌肉组织的“肌肉组织聚类”和运动神经元的“运动神经元聚类”。然后，我们使用ML算法（KNN、SVM和随机森林）评估这两个模块节点区分患病和健康状态的性能。此外，我们进行了药物重新利用分析和文本挖掘分析，将聚类节点作为药物靶点来识别用于ALS的新型药物候选物。使用线性回归、支持向量回归（SVR）、随机森林、梯度提升和神经网络算法预测了药物候选物对聚类基因表达 的潜在影响。结果，我们识别出5种用于治疗ALS的新型药物候选物：尼洛替尼、曲伐沙星、阿普拉毒素A、卡铂和克林沙星。这些发现突出了药物重新利用在ALS治疗中的潜力，并表明通过实验研究进行进一步验证可能会带来新的治疗途径。
