Serum Proteomics Identifies Biomarkers Associated With the Pathogenesis of Idiopathic Pulmonary Fibrosis

• Published in: Molecular & cellular proteomics 2023-04, Vol.22 (4), p.100524-100524, Article 100524
• Main Authors: Wang, Lan, Zhu, Minghui, Li, Yan, Yan, Peishuo, Li, Zhongzheng, Chen, Xiuping, Yang, Juntang, Pan, Xin, Zhao, Huabin, Wang, Shenghui, Yuan, Hongmei, Zhao, Mengxia, Sun, Xiaogang, Wan, Ruyan, Li, Fei, Wang, Xiaobo, Yu, Hongtao, Rosas, Ivan, Ding, Chen, Yu, Guoying
• Format: Article
• Language: English
• Subjects: Biomarkers; Blood Proteins; Chaperonin Containing TCP-1; combinatorial biomarker; Humans; Idiopathic Pulmonary Fibrosis - diagnosis; Idiopathic Pulmonary Fibrosis - metabolism; Idiopathic Pulmonary Fibrosis - pathology; indicator panel; machine learning; molecular subtype; Proteomics; serum proteome
• ISSN: 1535-9476; 1535-9484
• DOI: 10.1016/j.mcpro.2023.100524

The heterogeneity of idiopathic pulmonary fibrosis (IPF) limits its diagnosis and treatment. The association between the pathophysiological features and the serum protein signatures of IPF currently remains unclear. The present study analyzed the specific proteins and patterns associated with the clinical parameters of IPF based on a serum proteomic dataset by data-independent acquisition using MS. Differentiated proteins in sera distinguished patients with IPF into three subgroups in signal pathways and overall survival. Aging-associated signatures by weighted gene correlation network analysis coincidently provided clear and direct evidence that aging is a critical risk factor for IPF rather than a single biomarker. Expression of LDHA and CCT6A, which was associated with glucose metabolic reprogramming, was correlated with high serum lactic acid content in patients with IPF. Cross-model analysis and machine learning showed that a combinatorial biomarker accurately distinguished patients with IPF from healthy individuals with an area under the curve of 0.848 (95% CI = 0.684–0.941) and validated from another cohort and ELISA assay. This serum proteomic profile provides rigorous evidence that enables an understanding of the heterogeneity of IPF and protein alterations that could help in its diagnosis and treatment decisions. [Display omitted] •Three proteomic subgroups of IPF patients with distinct molecular features.•Aging-associated signatures provided direct evidence that aging is a critical risk factor.•LDHA and CCT6A expression were correlated with high serum lactic acid content in IPF.•A combinatorial biomarker accurately distinguished IPF patients from healthy subjects. Serum proteomics distinguished IPF patients into three subgroups in signal pathways and overall survival. Aging-associated signatures provided clear and direct evidence that aging is a critical risk factor for IPF rather than to a single biomarker. LDHA and CCT6A expression, which were associated with glucose metabolic reprogramming, were correlated with high serum lactic acid content in IPF patients. Cross-model analysis and machine learning showed that the combinatorial biomarker is applicable and validated from another cohort and ELISA assay.