IntroductionIdiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease (ILD) of unknown etiology, which is characterized by interstitial, distal airway, and alveolar remodeling1,2. The prevalence of IPF ranges from 20 to 80 patients per 100,000 individuals, and the prognosis is poor, with a 5-year survival rate similar to various cancers2,3. Currently, the treatment options for IPF are limited, only nintedanib and pirfenidone have been utilized to slow the progression of the disease4.Obesity is a major global public health concern, primarily resulting from an imbalance between calorie intake and expenditure5. It is characterized by a chronic inflammatory state, with elevated levels of systemic pro-inflammatory mediators6,7. Obesity has been implicated in the development of pulmonary fibrosis through several potential mechanisms, including chronic low-grade inflammation, oxidative stress, and metabolic dysregulation8. Recent studies have identified a causal association between obesity and IPF9,10, emphasizing the need to consider obesity as a contributing factor in the management of IPF. However, studies addressing the underlying molecular mechanisms of obesity-related pulmonary fibrosis remain limited.Some studies have attempted to investigate the association between obesity and pulmonary fibrosis. Obesity induced by a high-fat diet (HFD) can lead to elevated levels of important mediators associated with the development of IPF, such as tumor necrosis factor (TNF)-α and transforming growth factor (TGF)-β7,11. These inflammatory mediators stimulate the proliferation and differentiation of fibroblasts into myofibroblasts, which then secrete large amounts of extracellular matrix (ECM), leading to excessive ECM deposition and the formation of pulmonary fibrosis12. Furthermore, a study has shown that excessive intake of saturated fatty acids and meat may increase the risk of developing IPF13. Another study suggests that obesity induced by neonatal overfeeding may be a potential risk factor for pulmonary fibrosis14. These results indicate an association between obesity and IPF. However, the potential pathogenic mechanisms by which obesity leads to IPF are complex and not fully understood. Investigating the possible pathogenesis of IPF is crucial for improving management and treatment strategies for IPF. Therefore, more molecular mechanism studies are needed to further elucidate the enigma between obesity and IPF.With the rapid advancement in life sciences and computer technology, bioinformatics analysis offers a promising approach to deciphering complex disease patterns in large amounts of biological data and exploring the molecular mechanisms of disease pathophysiology. Several studies have used bioinformatics analysis to explore the disease mechanisms of IPF and potential avenues for therapeutic intervention, providing new clues for subsequent research15,16,17. Machine learning, a scientific discipline at the intersection of statistics and computer science, focuses on how computers learn from data18. The application of machine learning in bioinformatics is evolving, allowing researchers to identify the best interpretable features in the data effectively19. This integration enhances understanding of the underlying information and patterns, facilitating deeper insights into the relationships within the data.In this study, we performed histological analysis in the animal models. Then we identified key genes associated with obesity-related IPF by analyzing obesity and IPF datasets from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the Limma package, and key module genes were selected through weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) networks were constructed, followed by machine learning and receiver operating characteristic (ROC) curve analysis to pinpoint hub genes linked to obesity-related IPF. These hub genes were validated via qRT-PCR, and their relationship with immune cell infiltration was explored to better understand the molecular immunological mechanisms underlying obesity-related IPF. By identifying shared gene signatures and pathways, this study aims to deepen our understanding of the pathogenesis of obesity-related IPF and highlight potential therapeutic targets to improve patient outcomes.MethodsData collectionWe downloaded gene expression datasets for obesity (GSE151839) and IPF (GSE28042, GSE24206, and GSE53845) from the GEO database (http://www.ncbi.nlm.nih.gov/geo). Supplementary Table 1 provides more details about the aforementioned datasets. The research design of this study is clearly displayed in Fig. 1.Fig. 1The workflow chart of this study. (A) Flow chart of research design. (B) Schematic of mouse experiment. (C) Flow chart for identifying shared genes.Full size imageAnimal models and experimental designAll experimental procedures were in compliance with the guidelines published by the National Institutes of Health (Guide for the Care and Use of Laboratory Animals, 8th edition) and they fulfilled the ARRIVE guidelines. All experimental procedures were performed with the approval of the Animal Care and Utilization Committee of Xiamen Medical College.C57BL/6 male mice, obtained from Shanghai Slac Laboratory Animal Co., LTD., were used in this study. All mice were housed under standard laboratory conditions with ad libitum access to water and chow diets, and maintained on a 12-hour light/12-hour dark cycle. These mice were randomly divided into three groups: the control (Ctrl) group, the bleomycin (BLM) group, and the obesity + bleomycin (obe + BLM) group. Mice in the Ctrl group and BLM group were fed a normal diet (ND), while those in the obe + BLM group were fed a high-fat diet (HFD), the composition of which is provided in Supplementary Table 2. After 14 weeks, pulmonary fibrosis was induced in the BLM group and the obe + BLM group by intratracheal instillation of BLM (2 U/kg). Twenty-one days post-instillation, all mice were euthanized using cervical dislocation, and lung tissues were harvested for subsequent analysis.Histological analysisMouse lung tissues were obtained and immediately fixed in a 4% paraformaldehyde solution. After fixation, the tissues were dehydrated and embedded in paraffin, and sections of 5 μm thick slices. Subsequently, Masson’s trichrome staining was utilized for collagen detection. Additionally, the degree of fibrosis was assessed using the Collagen volume fraction (CVF, calculated as the collagen-positive area divided by the total tissue area) and the Ashcroft score20.Identification of DEGs and modular genesDifferential expression analysis was performed using the Limma package to identify DEGs between obesity and control samples in GSE151839, as well as between IPF and control samples in GSE2804221. The analysis was conducted with thresholds of |log2 Fold change (FC)| > 1 and adjusted P value