Sporadic Amyotrophic lateral sclerosis (sALS) is a complex, invariably fatal, disease with a poorly understood cause, despite many studies. Diagnostic biomarkers that precede active symptoms would be an immense help to clinicians, for patient management, following the progress of clinical studies, and uncovering early events in the development and progression of the disease. Combining bioinformatics of microarrays and molecular biology assays we analyzed and extended the results from experiments performed on peripheral blood lymphocyte (PBL) fractions from an sALS and a normal-matched coronary artery disease (CAD) study. We developed a novel computational pipeline (LO-BaFL) to improve the power and discrimination of identifying differentially expressed (DE) genes on long-oligonucleotide arrays. From sALS samples we performed quantitative polymerase chain reaction (qPCR) validation assays that linked three novel genes, ACTG1, B2M, and ILKAP, to sALS. Selected regions of the DE transcripts were sequenced, which revealed a new, albeit non ALS-linked mutation. Genes revealed as DE by LO-BaFL were examined through pathway and network interaction analysis. Heightened profiles are seen in the immune response signature, apoptosis and responses to chemical stimulus; these correspond well to phenotypes associated with sALS and are good candidates for a simplified blood-based biomarker signature.