Abstract

Currently, the aetiology and pathogenesis of idiopathic pulmonary fibrosis (IPF) are still largely unclear. Moreover, patients with IPF exhibit a considerable difference in clinical presentation, treatment, and prognosis. Optimal biomarkers or models for IPF prognosis are lacking. Therefore, this study quantified the levels of various hallmarks using a single-sample gene set enrichment analysis algorithm. The hazard ration was calculated using Univariate Cox regression analysis based on the transcriptomic profile of bronchoalveolar lavage cells and clinical survival information. Afterwards, weighted Gene Co-expression Network Analysis was performed to construct a network between gene expression, inflammation response, and hypoxia. Subsequently, univariate Cox, random forest, and multivariate Cox regressions were applied to develop a robust inflammation and hypoxia-related gene signature for predicting clinical outcomes in patients with IPF. Furthermore, a nomogram was constructed to calculate risk assessment. The inflammation response and hypoxia were identified as latent risk factors for patients with IPF. Five genes, including HS3ST1, WFDC2, SPP1, TFPI, and CDC42EP2, were identified that formed the inflammation-hypoxia-related gene signature. Kaplan-Meier plotter showed that the patients with high-risk scores had a worse prognosis than those with low-risk scores in training and validation cohorts. The time-dependent concordance index and the receiver operating characteristic analysis revealed that the risk model could accurately predict the clinical outcome of patients with IPF. Therefore, this study contributes to elucidating the role of inflammation and hypoxia in IPF, which can aid in assessing individual prognosis and personalised treatment decisions.

Highlights

  • Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease of unknown cause, which is characterized by diffused alveolitis and alveolar structure disorder [1]

  • The z-scores of various hallmarks were calculated based on the transcriptome profiling of the training set and gene sets of Molecular Signatures Database (MSigDB) using single-sample gene set enrichment analysis (ssGSEA)

  • The results indicated that the inflammatory response, angiogenesis, epithelial-mesenchymal transition, hypoxia, immune response, and other signaling pathways impacted the overall survival of patients with IPF

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Summary

Introduction

Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease of unknown cause, which is characterized by diffused alveolitis and alveolar structure disorder [1]. IPF involves the progressive deterioration of lung function, which is characterized by reduced volume of both lungs and thickening of visceral pleura resulting in dyspnea and, eventually, respiratory failure [2–4]. The etiology of IPF is still unclear; In addition, there is a lack of specific drugs for treatment [5]. Two clinical drugs were approved by Food and Drug Administration, none of them reverse the lung injury caused by disease or reduces the mortality of IPF [7]. Researchers have achieved encouraging results in the diagnosis and treatment of IPF recently, IPF patients remains incurable with remarkably mortality [10]. It is crucial to identify reliable biomarkers of IPF for prognosis and targeted therapy

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