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.
Keywords: combinatorial biomarker; indicator panel; machine learning; molecular subtype; serum proteome.
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