TOWARD PRECISION MEDICINE IN AMYOTROPHIC LATERAL SCLEROSIS: BLOOD-BASED TRANSCRIPTOMIC BIOMARKER DISCOVERY

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by pronounced molecular heterogeneity and prolonged diagnostic delay. Although RNA dysregulation and mitochondrial dysfunction are central features of ALS pathology, robust and accessible biomarkers reflecting these processes remain limited. Whole-blood transcriptomics offers a minimally invasive approach to interrogate systemic molecular alterations associated with neurodegeneration and supports disease stratification within precision neuroscience frameworks. We analyzed whole-blood RNA sequencing data from ALS patients and matched healthy controls (GSE277709) using covariate-adjusted negative binomial modeling accounting for age and sex. Variance-stabilized expression profiles were used for principal component analysis, clustering, and visualization, followed by functional enrichment and protein–protein interaction network analyses. Covariate-adjusted results revealed persistent transcriptional separation between ALS and control samples, indicating disease-specific molecular signatures beyond demographic effects. ALS samples exhibited marked upregulation of mitochondrial oxidative phosphorylation and RNA-processing genes, including components of mitochondrial complex I and ATP synthase, alongside coordinated downregulation of extracellular matrix (ECM) and collagen-associated genes. Network topology analysis identified two dominant molecular modules: a densely interconnected mitochondrial module and a collagen-rich ECM module, with COL1A1, COL1A2, and LUM emerging as central downregulated hubs. Functional enrichment highlighted mitochondrial respiratory chain activity, spliceosomal processes, and ECM structural organization as key dysregulated pathways. Together, these findings reveal convergent mitochondrial and ECM-related transcriptional signatures in ALS whole blood, supporting a systems-level model of neurodegeneration involving metabolic stress, RNA dysregulation, and altered matrix homeostasis, and demonstrate the value of covariate-aware, network-based transcriptomic approaches for ALS stratification and biomarker discovery.