{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T06:01:59Z","timestamp":1792476119140,"version":"4.5.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Despite progress in serum biomarker research, reliable tools for early diagnosis and patient stratification in multiple sclerosis (MS) remain limited. This study uses proteomic profiling in untreated MS patients to identify early disease-associated biomarkers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted an unbiased proteomic screen to capture broad serum protein expression profiles in a well-characterized discovery sample: 7 relapsing remitting MS (RRMS), 7 secondary progressive MS (SPMS), 4 with primary progressive MS (PPMS) alongside 6 healthy controls (HC). Twelve candidate biomarkers were subsequently validated by ELISA in an independent sample comprising 80 untreated MS patients (38 RRMS, 21 SPMS, 21 PPMS) and 21 age- and sex-matched HC from southern Spain.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the discovery phase, 393 proteins were identified; 13 showed significant differences between MS patients and controls and 4 were dysregulated between PPMS and relapsing-onset MS (ROMS). These proteins were involved in immune responses, oxidative stress, and complement regulation. ELISA validation confirmed six differentially abundant proteins (DAPs) in MS patients compared to controls. Among these, BST1 levels were elevated in ROMS (<jats:italic>P<\/jats:italic><jats:sub>adj<\/jats:sub> = 0.0017), while FCGR3A showed significant increases in PPMS (<jats:italic>P<\/jats:italic><jats:sub>adj<\/jats:sub> = 0.034). PRDX6 levels were consistently elevated in both ROMS (<jats:italic>P<\/jats:italic><jats:sub>adj<\/jats:sub> = 0.044) and PPMS (<jats:italic>P<\/jats:italic><jats:sub>adj<\/jats:sub> = 0.001), as were APEH levels (ROMS vs. HC: <jats:italic>P<\/jats:italic><jats:sub>adj<\/jats:sub> = 0.038; PPMS vs. HC: <jats:italic>P<\/jats:italic><jats:sub>adj<\/jats:sub> = 0.009), both correlating with higher disability scores. In contrast, CFHR5 and MST1 levels were significantly reduced in ROMS (<jats:italic>P<\/jats:italic><jats:sub>adj<\/jats:sub> \u2264 0.001 for both). Besides, disease severity was significantly associated with higher MST1 and APEH levels. Functional enrichment analyses linked these proteins to <jats:italic>innate immunity, neuroinflammation<\/jats:italic>, and <jats:italic>metabolic regulation<\/jats:italic>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our study identified six proteins involved in key pathological mechanisms such as inflammation, oxidative stress, immune regulation, and blood-brain barrier (BBB) integrity. Notably, the upregulation of PRDX6\u2014linked to protein repair and neuroprotection in EAE models\u2014may reflect a compensatory response to neuroinflammatory damage. Conversely, the downregulation of MST1, a molecule involved in immune signaling, could impair neuroprotective signaling and may drive neuroinflammation. These findings highlight PRDX6 and MST1 as particularly promising biomarkers for the diagnosis and monitoring of MS, meriting further validation in larger, longitudinal cohorts.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1579045","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T04:11:53Z","timestamp":1755749513000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Discovery and early validation of serum protein signatures in untreated multiple sclerosis patients: identification of candidate biomarkers for diagnosis and 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