000171055 001__ 171055
000171055 005__ 20260505142650.0
000171055 0247_ $$2doi$$a10.1007/s12021-026-09780-7
000171055 0248_ $$2sideral$$a149161
000171055 037__ $$aART-2026-149161
000171055 041__ $$aeng
000171055 100__ $$0(orcid)0009-0008-3380-0465$$aGascón, Elisa$$uUniversidad de Zaragoza
000171055 245__ $$aUnveiling an als blood transcriptomic signature: a machine learning classifier distinct from neurodegenerative controls
000171055 260__ $$c2026
000171055 5060_ $$aAccess copy available to the general public$$fUnrestricted
000171055 5203_ $$aThe 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.
000171055 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000171055 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000171055 700__ $$0(orcid)0000-0001-5193-7782$$aCalvo, Ana Cristina$$uUniversidad de Zaragoza
000171055 700__ $$0(orcid)0000-0001-5740-0185$$aZaragoza, Pilar$$uUniversidad de Zaragoza
000171055 700__ $$0(orcid)0000-0001-5687-6704$$aOsta, Rosario$$uUniversidad de Zaragoza
000171055 7102_ $$11001$$2420$$aUniversidad de Zaragoza$$bDpto. Anatom.,Embri.Genét.Ani.$$cÁrea Genética
000171055 773__ $$g24, 2 (2026), 26 [20 pp.]$$pNeuroinformatics$$tNeuroinformatics$$x1539-2791
000171055 8564_ $$s2768142$$uhttps://zaguan.unizar.es/record/171055/files/texto_completo.pdf$$yVersión publicada
000171055 8564_ $$s2164025$$uhttps://zaguan.unizar.es/record/171055/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000171055 909CO $$ooai:zaguan.unizar.es:171055$$particulos$$pdriver
000171055 951__ $$a2026-05-05-13:36:44
000171055 980__ $$aARTICLE