Decoding Non-Neuronal Mechanisms and Therapeutic Targets in Huntington's Disease Through Integrative Transcriptomics and Machine Learning.

Huntington's disease (HD) is a rare, inherited neurodegenerative disorder caused by the expanded CAG repeats in the huntingtin gene. The HD domain still lacks detailed knowledge of validated drug targets, limiting the effectiveness of classical methods. To address this gap, we have applied an integrated computational approach, combining machine learning (ML) with transcriptomic analysis, to identify novel therapeutic targets. Differential expression analysis was performed on eight publicly available datasets, comprising 209 healthy control and 193 Huntington's disease patient samples, followed by ML-based screening of differentially expressed genes (DEGs). Feature selection using mRMR and RFE, in combination with four classifiers (Linear SVC, Stochastic Gradient Descent, Logistic regression, and Ridge regression), yielded 138 DEG candidates. Subsequent literature curation, drug target analysis, and gene regulatory network (GRN) construction highlighted several key genes, including TXNIP, TNIP3, HTR1D, ADRB1, and FOXP1, which may play pivotal roles in disease progression. Furthermore, our findings highlight the contribution of non-neuronal mechanisms, such as endothelial dysfunction, vascular neurodegeneration, thermoregulation, metabolic imbalance, and impaired phagocytosis, providing a broader perspective into HD pathophysiology. This comprehensive strategy advances our HD knowledge regarding therapeutic targets, molecular pathways, transcription factors (TFs), and complex gene interactions beyond classical HD processes. In summary, the study successfully identifies a promising set of novel drug targets, indicating potential implications in HD therapy.