Large‐Scale Proteome Profiling Identifies Biomarkers Associated with Suspected Neurosyphilis Diagnosis

Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as “the great imitator,” NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients. This study presents an innovative approach to diagnosing Neurosyphilis (NS), a complex central nervous system (CNS) infection. By analyzing 223 cerebrospinal fluid samples using proteomic techniques and machine learning models, researchers identifies three key biomarkers (SEMA7A, SERPINA3, and ITIH4) for NS. These findings, validated through multicenter research with additional 115 samples, offer new diagnostic tools and deepen understanding of NS pathogenesis.