## 4 Discussion  
In this study, we have utilized three HD microarray datasets studied postmortem brain tissue to disclose a potential biomarker with prognostic value that can detect disease progression during the prolonged presymptomatic stage and monitor the treatment efficacy. Although efforts from previous studies on biomarker identification mostly rely on analyzing blood transcriptome profiling, the most reliable way to understand the molecular basis of disease processes specific to a particular tissue is by analyzing RNA expression in postmortem tissue, because of its sensitivity to local pathology, superiority to cell type resolution and lack of systemic confounding [39; 40; 41]. To find reliable biomarkers for HD, we used a two-step workflow in this study: first, a thorough meta-analysis of HD microarray datasets; and second, supervised ML. In many disease contexts, meta-analysis of microarray data has increased statistical power and reproducibility, providing a more meaningful understanding of the underlying biological processes [42]. Our findings extend this paradigm to the discovery of HD biomarkers. In addition, this approach addresses sample heterogeneity by employing data aggregation, stringent quality control, and statistical modeling that explicitly considers inter-study variations [43; 44; 45]. We used two criteria for sorting the candidate DEGs (a relaxed and stringent), where a relaxed criterion (adjusted \(p\)-value \(<\) 0.05, and absolute log2 fold change \(\geq\) 0.85), which identifies 41 genes subjected to ML modeling. A lowered threshold (relaxed criterion) ensures the model picks up weak but biologically significant signals, keeping a broader range of potentially informative features. These genes may not meet stringent statistical cut-offs but could contribute meaningfully to classification performance. Perhaps, GO enrichment analysis suggests that the dysregulation of these 41 genes is responsible for alterations in some essential signaling pathways, including synapse function and dendritic arborization, which are known to exacerbate HD pathophysiology [46; 47; 48; 49; 50] and contribute to motor and cognitive symptoms. Furthermore, striatal neurons, which are particularly affected in HD, show alterations in action potentials and membrane potentials, including changes in the patterns of action potential firing and a shift towards the depolarisation of  
Figure 7: Meta-analysis of area under the receiver operating characteristic curve (AUC) estimates for the final selected genes across human and mouse cohorts. (**a**) Summary table showing each gene’s meta AUC, standard error (SE), and 95% confidence interval (CI). (**b**) The corresponding forest plot illustrates each gene’s same meta AUC with 95% CI. resting membrane potentials, which are also associated with motor and cognitive symptoms [51; 52; 53]. To refine these candidates, we leveraged the strength of 19 ML classifiers, as not all models are suitable for all data types. For example, training a deep neural network with high-dimensional data of a few hundred patient samples will likely result in a highly overfitted model. Hence, it is necessary to carefully choose the right types of models a priori and not purely rely on brute force computational power [54]. When estimating a model's generalization performance from observational data, the variability in biomedical datasets is often high, due to both technical and biological sources of variation. To address this challenge, we evaluated each model using cross-validation to avoid hyperparameter-tuning bias and obtain nearly unbiased performance estimates [55]. AUC selected the top three models from these ML models, providing consistent model evaluation. AUC evaluates the model using all thresholds, effectively addressing the effect of changing prevalence (proportion of positive cases). Models' feature rankings were interpreted using SHAP, a game-theoretically grounded method that provides consistent, locally accurate attributions for any model class [31].