Background: Idiopathic pulmonary fibrosis (IPF) is caused by aberrant repair because of alveolar epithelial injury and can only be effectively treated with several compounds. Several metabolism-related biomolecular processes were found to be involved in IPF. We aimed to identify IPF subtypes based on metabolism-related pathways and explore potential drugs for each subtype.Methods: Gene profiles and clinical information were obtained from the Gene Expression Omnibus (GEO) database (GSE70867 and GSE93606). The enrichment scores for 41 metabolism-related pathways, immune cells, and immune pathways were calculated using the Gene Set Variation Analysis (GSVA) package. The ConsensusClusterPlus package was used to cluster samples. Novel modules and hub genes were identified using weighted correlation network analysis (WGCNA). Receiver operating characteristic (ROC) and calibration curves were plotted, and decision curve analysis (DCA) were performed to evaluate the model in the training and validation cohorts. A connectivity map was used as a drug probe.Results: Two subtypes with significant differences in prognosis were identified based on the metabolism-related pathways. Subtype C1 had a poor prognosis, low metabolic levels, and a unique immune signature. CDS2, LCLAT1, GPD1L, AGPAT1, ALDH3A1, LAP3, ADH5, AHCYL2, and MDH1 were used to distinguish between the two subtypes. Finally, subtype-specific drugs, which can potentially treat IPF, were identified.Conclusion: The aberrant activation of metabolism-related pathways contributes to differential prognoses in patients with IPF. Collectively, our findings provide novel mechanistic insights into subtyping IPF based on the metabolism-related pathway and potential treatments, which would help clinicians provide subtype-specific individualized therapeutic management to patients.

Background Invasive pulmonary aspergillosis (IPA) can occur in both immunocompromised and non‐immunocompromised hosts, and early diagnosis of IPA is difficult. Metagenomic next‐generation sequencing (mNGS) is a novel non‐migratory pathogen detection method; however, utilising this method for IPA diagnosis is challenging due to the current lack of a unified clinical interpretation standard following Aspergillus detection using mNGS. Objectives To investigate the accuracy of IPA diagnosis by positive bronchoalveolar lavage fluid (BALF) mNGS results in immunocompromised and immunocompetent patients. Methods We retrospectively included patients with confirmed pulmonary infections having a BALF mNGS result of Aspergillus reads ≥1. We compared the accuracy of using mNGS for IPA diagnosis in patients with different immune statuses based on the revised EORTC/MSG criteria. Results Overall, 62 mNGS Aspergillus‐positive patients were divided into two groups: with (41) and without IPA (21). In univariate logistic regression analysis, immunocompromised function, fever, halo sign on CT image, and multiple masses or nodules were associated with mNGS Aspergillus‐positive IPA diagnosis. In multivariate logistic regression analysis, immunocompromised function (OR = 6.68, 95% CI: 1.73–25.87, p = .006) and a halo sign (OR = 7.993, 95% CI: 2.07–30.40, p = .003) were independent risk factors. The concordance rate of IPA diagnosis was significantly higher in immunocompromised patients [82.1% (23/28)] than in non‐immunocompromised patients [52.9% (18/34); p = .016]. Conclusions For immunocompromised patients, a combination of mNGS testing and lung CT imaging can be used for IPA diagnosis. However, caution is required in IPA diagnosis based on positive mNGS results in non‐immunocompromised patients.

Background: Idiopathic pulmonary fibrosis (IPF), a chronic, progressive lung disease characterized by interstitial remodeling and tissue destruction, affects people worldwide and places a great burden on society. Cellular senescence is thought to be involved in the mechanisms and development of IPF. The aim of this study was to predictively investigate subtypes of IPF according to cellular senescence-related genes and their correlation with the outcome of patients with IPF, providing possible treatment and management options for disease control.Methods: Gene expression profiles and follow-up data were obtained from the GEO database. Senescence-related genes were obtained from the CSGene database and analyzed their correlation with the outcome of IPF. A consensus cluster was constructed to classify the samples based on correlated genes. The GSVA and WGCNA packages in R were used to calculate the immune-related enriched fractions and construct gene expression modules, respectively. Metascape and the clusterProfiler package in R were used to enrich gene functions. The ConnectivityMap was used to probe suitable drugs for potential treatment.Results: A total of 99 cellular senescence-related genes were associated with IPF prognosis. Patients with IPF were divided into two subtypes with significant prognostic differences. Subtype S2 was characterized by enhanced fibrotic progression and infection, leading to acute exacerbation of IPF and poor prognosis. Finally, five cellular senescence-related genes, TYMS, HJURP, UBE2C, BIRC5, and KIF2C, were identified as potential biomarkers in poor prognostic patients with IPF.Conclusion: The study findings indicate that cellular senescence-related genes can be used to distinguish the prognosis of patients with IPF. Among them, five genes can be used as candidate biomarkers to predict patients with a poor prognostic subtype for which anti-fibrosis and anti-infection treatments could be suitable.