171055 20260505142650.0 doi 10.1007/s12021-026-09780-7 sideral 149161 ART-2026-149161 eng Gascón, Elisa Universidad de Zaragoza (orcid)0009-0008-3380-0465 Unveiling an als blood transcriptomic signature: a machine learning classifier distinct from neurodegenerative controls 2026 The absence of accessible and reliable biomarkers constitutes a critical barrier for the early diagnosis and stratification of neurodegenerative diseases. While peripheral blood offers a minimally invasive window into systemic pathophysiology, identifying molecular signatures that survive biological heterogeneity and technical noise remains an unresolved challenge. In this study, this issue was addressed through a comparative systemic transcriptomic analysis of Amyotrophic Lateral Sclerosis (ALS), Alzheimer’s disease (AD), and Parkinson’s disease (PD) in whole blood, implementing a comprehensive workflow integrating unsupervised network analysis and supervised machine-learning methods. By employing LASSO regression and cross-validation across independent external cohorts, a stable and specific transcriptomic signature for ALS was identified, comprising key crosstalk genes involved in systemic immune dysregulation and microglial function, including CTSS, PTEN, IL18, PTPRC, and CSF1R. In contrast, AD and PD exhibited weak transcriptomic signatures with poor predictive reproducibility, suggesting a distinctive systemic pathology in ALS. In addition, the study confirms the superiority of linear modeling for this genomic signature: while complex non-linear algorithms, specifically Radial Basis Function (RBF) kernel Support Vector Machine (SVM) and Random Forest, displayed high initial performance, they collapsed due to overfitting during external validation. Conversely, the linear LASSO model demonstrated superior robustness and generalizability (AUC 0.74). In conclusion, this study not only defines a unique systemic immunotranscriptomic signature for ALS, distinguishable from other neurodegenerative pathologies, but also establishes interpretability and linear simplicity as essential factors for developing reproducible blood-based biomarkers with clinical translational potential. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Calvo, Ana Cristina Universidad de Zaragoza (orcid)0000-0001-5193-7782 Zaragoza, Pilar Universidad de Zaragoza (orcid)0000-0001-5740-0185 Osta, Rosario Universidad de Zaragoza (orcid)0000-0001-5687-6704 1001 420 Universidad de Zaragoza Dpto. Anatom.,Embri.Genét.Ani. Área Genética 24, 2 (2026), 26 [20 pp.] Neuroinformatics Neuroinformatics 1539-2791 2768142 http://zaguan.unizar.es/record/171055/files/texto_completo.pdf Versión publicada 2164025 http://zaguan.unizar.es/record/171055/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:171055 articulos driver 2026-05-05-13:36:44 ARTICLE