In Molecular & cellular proteomics : MCP
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 (DIA) using mass spectrometry. Differentiated proteins in sera distinguished in IPF patients into three subgroups in signal pathways and overall survival. Aging-associated signatures by WGCNA coincidently provided clear and direct evidence that aging is a critical risk factor for IPF rather than a single biomarker. LDHA and CCT6A expression, which were associated with glucose metabolic reprogramming, were correlated with high serum lactic acid content in the patients with IPF. Cross-model analysis and machine learning showed that a combinatorial biomarker accurately distinguished IPF patients from healthy subjects with an AUC 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 understanding of the heterogeneity of IPF and protein alterations that could help in its diagnosis and treatment decisions.
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
2023-Mar-02
Molecular subtype, Serum proteome, combinatorial biomarker, indicator panel, machine learning
