Anti-SOX1 Antibodies in Paraneoplastic Neurological Syndrome

副肿瘤性神经综合征中的抗SOX1抗体

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Abstract

Anti-Sry-like high mobility group box (SOX) 1 antibodies (abs) are partly characterized onconeural autoantibodies (autoabs) due to their correlation with neoplastic diseases. Anti-SOX1 abs are associated with various clinical manifestations, including Lambert-Eaton myasthenic syndrome (LEMS) and paraneoplastic cerebellar degeneration (PCD). However, the clinical characteristics of patients with anti-SOX1 abs have not been described in detail. This review systematically explores the reported patients with anti-SOX1 abs and analyzes these cases for demographic characteristics, clinical features, coexisting neuronal autoabs, neuroimaging findings, treatment, and clinical outcomes. In addition, considering that PCD is the most common paraneoplastic neurological syndrome and that the association between PCD and anti-SOX1 abs remains unclear, we focus on the presence of autoabs in relation to PCD and associated tumors. PCD-associated autoabs include various intracellular autoabs (e.g., anti-Hu, anti-Yo, anti-Ri, and anti-SOX1) and cell-surface autoabs (anti-P/Q-type voltage-gated calcium channel). Commonly involved tumors in PCD are small-cell lung cancer (SCLC), gynecological, and breast tumors. LEMS is the most common clinical symptom in patients with anti-SOX1 abs, followed by PCD, and multiple neuronal autoabs coexist in 47.1% of these patients. SCLC is still the predominant tumor in patients with anti-SOX1 abs, while non-SCLC is uncommon. No consistent imaging feature is found in patients with anti-SOX1 abs, and there is no consensus on either the therapy choice or therapeutic efficacy. In conclusion, the presence of anti-SOX1 abs alone is a potential predictor of an uncommon paraneoplastic neurological disorder, usually occurring in the setting of LEMS, PCD, and SCLC. The detection of anti-SOX1 abs contributes to an early diagnosis of underlying tumors, given the diversity of clinical symptoms and the absence of characteristic neuroimaging features.

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