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He L, Zhou Q, Xiu C, Shao Y, Shen D, Meng H, Le W, Chen S. Circulating proteomic biomarkers for diagnosing sporadic amyotrophic lateral sclerosis: a cross-sectional study. Neural Regen Res 2024; 19:1842-1848. [PMID: 38103252 PMCID: PMC10960292 DOI: 10.4103/1673-5374.389357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/02/2023] [Accepted: 08/29/2023] [Indexed: 12/18/2023] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202408000-00039/figure1/v/2023-12-16T180322Z/r/image-tiff Biomarkers are required for the early detection, prognosis prediction, and monitoring of amyotrophic lateral sclerosis, a progressive disease. Proteomics is an unbiased and quantitative method that can be used to detect neurochemical signatures to aid in the identification of candidate biomarkers. In this study, we used a label-free quantitative proteomics approach to screen for substantially differentially regulated proteins in ten patients with sporadic amyotrophic lateral sclerosis compared with five healthy controls. Substantial upregulation of serum proteins related to multiple functional clusters was observed in patients with sporadic amyotrophic lateral sclerosis. Potential biomarkers were selected based on functionality and expression specificity. To validate the proteomics profiles, blood samples from an additional cohort comprising 100 patients with sporadic amyotrophic lateral sclerosis and 100 healthy controls were subjected to enzyme-linked immunosorbent assay. Eight substantially upregulated serum proteins in patients with sporadic amyotrophic lateral sclerosis were selected, of which the cathelicidin-related antimicrobial peptide demonstrated the best discriminative ability between patients with sporadic amyotrophic lateral sclerosis and healthy controls (area under the curve [AUC] = 0.713, P < 0.0001). To further enhance diagnostic accuracy, a multi-protein combined discriminant algorithm was developed incorporating five proteins (hemoglobin beta, cathelicidin-related antimicrobial peptide, talin-1, zyxin, and translationally-controlled tumor protein). The algorithm achieved an AUC of 0.811 and a P-value of < 0.0001, resulting in 79% sensitivity and 71% specificity for the diagnosis of sporadic amyotrophic lateral sclerosis. Subsequently, the ability of candidate biomarkers to discriminate between early-stage amyotrophic lateral sclerosis patients and controls, as well as patients with different disease severities, was examined. A two-protein panel comprising talin-1 and translationally-controlled tumor protein effectively distinguished early-stage amyotrophic lateral sclerosis patients from controls (AUC = 0.766, P < 0.0001). Moreover, the expression of three proteins (FK506 binding protein 1A, cathelicidin-related antimicrobial peptide, and hemoglobin beta-1) was found to increase with disease progression. The proteomic signatures developed in this study may help facilitate early diagnosis and monitor the progression of sporadic amyotrophic lateral sclerosis when used in combination with current clinical-based parameters.
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Affiliation(s)
- Lu He
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinming Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chaoyang Xiu
- Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
| | - Yaping Shao
- Center for Translational Research on Neurological Diseases, the First Affiliated Hospital, Dalian Medical University, Dalian, Liaoning Province, China
| | - Dingding Shen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu Province, China
| | - Huanyu Meng
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weidong Le
- Institute of Neurology, Sichuan Academy of Medical Sciences-Sichuan Provincial Hospital, Chengdu, Sichuan Province, China
| | - Sheng Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu Province, China
- Department of Neurology, Xinrui Hospital, Wuxi, Jiangsu Province, China
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