We extracted 619 inflammation-related genes from Gene Ontology (GO) and GO annotations (https://www.ebi.ac.uk/QuickGO/) and compiled detailed annotation information for these genes (Supplementary Table S1). The annotation included gene function descriptions, cellular locations, and biological processes, which are crucial for understanding how these genes may influence the prognosis of IPF.

The publicly available GSE70866 dataset, obtained from GEO, was used for this study. This dataset includes mRNA expression data from 176 patients with IPF, along with prognostic information. In addition, we utilized the GSE175457 dataset as a validation set, comprising 188 normal samples and 234 IPF samples. Since the GSE70866 and GSE175457 dataset is publicly accessible, ethical approval from the local ethics committee was not required.

We analyzed the inflammation-related genes in the BALF of patients with IPF and healthy controls using the GSE70866 dataset with R’s “limma” package. A threshold was set with a false discovery rate of less than 0.05 and a log2 |fold change| greater than 1. The results were visualized using the “heatmap” and “ggplot2” packages. We further investigated the biological functions of differentially expressed inflammatory genes through GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses using the R packages “clusterProfiler,” “org.Hs.e.g.,.db,” “enrichplot,” and “ggplot2.” Additionally, gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment of these genes.

We used WGCNA to identify gene modules in IPF that exhibited strong correlations (Langfelder and Horvath, 2008). Inflammation-related genes were further analyzed by performing WGCNA using the R package. A soft thresholding power of five was selected to construct the gene network and compute the similarity and proximity in coexpression, which was then transformed into a topological overlap matrix (TOM). Based on the TOM, hierarchical clustering was applied to group the modules. Finally, we identified modules that exhibited significant associations with clinical traits.

To minimize the risk of overfitting, we used LASSO penalized Cox regression analysis via the “glmnet” package in R to develop the prognostic model. The risk score for each patient was calculated using the regression coefficients corresponding to the normalized expression levels of prognostic inflammation-related genes. Using the median risk score as a threshold, we divided patients with IPF into two groups: high-risk and low-risk. The Kaplan–Meier survival plots and time-varying receiver operating characteristic (ROC) curves were generated using the “survival,” “survminer,” and “timeROC” R packages to assess the forecasting power of our prognostic model. Additionally, the “heatmap” software was used to visually depict gene expression patterns in each sample of the two risk groups.

To examine the interactions among these genes, we constructed a PPI network of inflammation-related genes using the STRING database. STRING, a public online resource, provides detailed information on gene and protein interactions, helping to better understand the functions and mechanisms of inflammation-related genes during the prognostic process.

We used the CIBERSORT method to investigate the association between prognostic genes, risk scores, and immune cell infiltration. Subsequently, the “ggplot2” package, in combination with the “limma” R package, was used to examine the relationships between hub genes, immune cells and immune checkpoints.

Fifteen male Sprague–Dawley (SD) rats aged 6–8 weeks were randomly divided into the control, model, and anti-CCL2 intervention groups. Bleomycin A5 (0.2 mL of physiological saline containing 5 mg/kg) was injected into the trachea to induce the disease model, ensuring proper anesthesia to minimize stress reactions. During the modeling process, 10 μL of anti-CCL2 lentivirus solution (titer of 1 × 10⁹) was simultaneously administered through the trachea to the anti-CCL2 group. The lentivirus was used to knock down the expression of CCL2, with a successful infection confirmed by PCR. After 14 days of normal feeding, the rats were euthanized and weighed, and samples were collected. The entire lung was excised and weighed, and the left lungs were immediately frozen in liquid nitrogen or fixed for histological analysis.

Total RNA was extracted using a nuclear reaction reagent supplied by McRea-Nagel Corporation. RNA content was measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, United States). Complementary DNA (cDNA) was synthesized using the SuperScript III First-Strand Synthesis System (18080051; Invitrogen). qPCR was performed on a CFX96 qPCR system (Bio-Rad Laboratories) using a 2× SYBR Green master mix from Yeasen Biotech. The data were used to quantify the comparative mRNA expression of target genes, with GAPDH serving as the internal control. The primer sequences used were:

• Forward primer: CAG​CCA​GAT​GCA​ATC​AAT​GCC

To analyze protein expression in the lung tissue of IPF model rats, we employed standardized biochemical methods. Tissues were stored in liquid nitrogen, and proteins were extracted using a radioimmunoprecipitation assay buffer and quantified with a BCA Protein Assay Kit. Proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, transferred to polyvinylidene fluoride membranes, and incubated with MCP-1-specific antibodies. Protein bands were visualized and analyzed using ImageJ software to quantify MCP-1 expression levels.

Dewaxed tissue sections were rinsed with distilled after treatment with an eco-friendly solution and ethanol. Antigens retrieval was performed by microwaving the slides in a retrieval solution, followed by natural cooling. The slides were then rinsed three times with phosphate-buffered saline (PBS), treated with 3% hydrogen peroxide (H₂O₂) in the dark, and washed again in PBS. Subsequently, the sections were blocked with 3% bovine serum albumin (BSA), incubated overnight at 4°C with primary antibodies, and washed in PBS. HRP-labeled secondary antibodies were added, followed by incubation. Staining was performed using 3,3’-diaminobenzidine, stopped with tap water, counterstained with hematoxylin, dehydrated, mounted, and observed under a microscope.

A total of 100 mg of tissue was weighed and homogenized with 900 μL of buffer. Standard, sample, and blank wells were set up. Subsequently, 100 μL of standards and samples were added to the respective wells. The mixture was incubated at 37°C for 1 h. After incubation, the liquid was discarded, and 100 μL of Detection Solution A was added. The wells were incubated again. The wells were washed five times, and then 100 μL of Detection Solution B was added, followed by incubation for 30 min. The wells were washed again, and 90 μL of 3,3’,5,5’-Tetramethylbenzidine substrate was added. The color change was monitored, and the optical density values were measured.

For HE staining, fresh lung tissue was fixed in a fixative solution for more than 24 h to ensure adequate preservation. The tissues were trimmed, dehydrated, and embedded in paraffin. Wax blocks were sectioned into 4 μm slices. The slices were deparaffinized, stained with hematoxylin, rinsed, differentiated, and counterstained. Finally, the stained sections were mounted for observation.

For Masson staining, paraffin sections were dewaxed using an environmentally friendly solution and anhydrous ethanol, then rinsed with tap water. Frozen sections were returned to room temperature and fixed in a tissue fixative. The slices were stained with Masson A solution overnight, rinsed with tap water, and stained with a mixture of Masson B and C for 1 min. Differentiation was performed, followed by immersion in Masson D for 6 min and Masson E for 1 min. The sections were then stained with Masson’s F solution, rinsed with 1% acetic acid, dehydrated, cleared, and mounted using neutral balsam.

Statistical analyses were conducted using SPSS Statistics 23 (IBM, Armonk, NY, United States) and R software (version 4.3.2). For data with a normal distribution, means of continuous variables were compared using independent t-tests and are described as mean ± standard deviation. For non-normally distributed data, the Mann-Whitney U test was employed, with results presented as median [interquartile range]. Survival rate comparisons were performed using log-rank tests and Kaplan-Meier analyses. Statistical visualization was generated using GraphPad Prism 8. All tests were two-sided, with a significance level set at α = 0.05.

We analyzed gene expression profiles in the bronchoalveolar lavage fluid (BALF) of 176 patients with idiopathic pulmonary fibrosis (IPF) and 20 healthy controls using the GSE70866 dataset. Our investigation concentrated on inflammation-related genes, selected from the QuickGO database and comprising a total of 619 genes. The objective was to identify differentially expressed inflammation-related genes in IPF patients compared to healthy individuals, aiming to elucidate inflammatory processes underlying IPF pathogenesis. Through rigorous differential expression analysis, we identified 41 inflammation-related genes exhibiting significant expression differences between the two groups. To visually represent these results, a heatmap illustrating the top 20 differentially expressed genes was generated (Figure 1A), facilitating a comparative overview of gene expression patterns.

Furthermore, a volcano plot summarizing the differential expression analysis was constructed (Figure 1B). This plot revealed that 13 inflammation-related genes were downregulated in IPF patients, potentially reflecting suppression of specific inflammatory pathways. In contrast, 28 inflammation-related genes were upregulated, suggesting an intensified inflammatory response characteristic of IPF. These findings are important as they pinpoint specific inflammation-related genes that may contribute critically to IPF pathogenesis and progression. Identification of these genes lays the groundwork for the development of novel therapeutic strategies targeting inflammatory mechanisms, which may ultimately improve disease outcomes by mitigating inflammation-driven fibrosis.

GO and KEGG pathway enrichment analyses were performed to investigate the functional roles of the differentially expressed inflammation-related genes. GO enrichment analysis revealed significant overrepresentation of Biological Process (BP) terms associated with leukocyte migration. In the Cellular Component (CC) category, enriched terms included secretory granules, cytoplasmic vesicles, and vesicle lumens. Molecular Function (MF) terms showed enrichment in cytokine receptor binding, cytokine activity, and G protein-coupled receptor binding (Figure 2A). KEGG pathway analysis further highlighted significant enrichment in the cytokine–cytokine receptor interaction pathway (Figure 2B). Together, these results indicate that the differentially expressed genes participate in key biological functions and signaling pathways, providing insights into the potential mechanisms driving their involvement in IPF pathogenesis.

We conducted WGCNA and identified the brown module as exhibiting the highest correlation with IPF (Figures 3A,B). From the genes in the brown module, we identified several hub genes associated with IPF, including SPP1, CCR3, FFAR3, CCL2, and STAB1. Using LASSO regression analysis, we further refined the selection and identified 18 genes suitable for constructing the prognostic model, such as CXCR6, BDKRB1, TPST1, PPBP, CLU, CCL2, BMP6, LIPA, CXCL5, CAMP, ELF3, IL1RL1, AOC3, PLD4, S1PR3, CMKLR1, PROK2, STAB1 (Figures 3C,D). Based on the calculated risk scores, the IPF samples were divided into high-risk and low-risk groups. The prognostic model’s predictive ability was then evaluated through survival rate and ROC curve analyses. Kaplan-Meier analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group (Figure 3E). Additionally, time-dependent ROC analysis revealed that the areas under the curve (AUC) for survival rates at 1, 3, and 5 years were 0.836, 0.864, and 0.974, respectively (Figure 3F).

Through the intersection of WGCNA and LASSO, we identified two genes, CCL2 and STAB1 (Figure 4A). Based on this prognostic inflammation-related gene set, we used the STRING online platform to generate a PPI network (Figure 4B). The results revealed that the CCL2 gene had significantly more interactions than the other genes, suggesting its potentially more crucial role. Differential analysis and ROC curve result for CCL2 and STAB1 showed elevated expression levels in IPF, which correlated with patient prognosis (Figures 4C–F). Finally, we used the GSE175457 dataset as a validation set to generate boxplots of CCL2 and STAB1 expression levels (Supplementary Figure S1). The results showed that CCL2 expression was significantly higher in IPF samples compared to normal samples (p = 0.0038), whereas STAB1 expression showed no significant difference (p = 0.44). Based on these findings, we focused our investigation on CCL2.

We investigated immune function and immune cell infiltration in IPF patients by comparing the high- and low-risk groups. Immune function was significantly enhanced in the high-risk group, particularly in antigen-presenting cell co-stimulation, CCR, parainflammation, and T-cell co-inhibition (Figure 5A). Immune cell infiltration analysis showed elevated levels of macrophages M0, activated dendritic cells, and neutrophils in the high-risk group, while those of T cells (CD4 memory resting) and resting mast cells were reduced (Figure 5B). These findings suggest that high-risk patients exhibit greater immune activity and immune cell infiltration.

KEGG pathway enrichment analysis was conducted using GSEA to compare high-CCL2 and low-CCL2 expression groups. The “CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION” pathway demonstrated the highest enrichment in patients with high-CCL2 IPF (Figure 6). Notably, pathways strongly associated with CCL2 expression overlapped with those enriched for differentially expressed inflammatory genes, suggesting a critical role for CCL2 and inflammatory pathways in IPF pathogenesis.

Although immune checkpoint blockade therapies have shown remarkable success, a substantial proportion of patients exhibit marked resistance (Mei et al., 2021; Nie et al., 2022). To explore the association between CCL2 expression and immunotherapy in patients with IPF, we analyzed its relationship with various immune checkpoints. Our findings revealed that CCL2 was significantly and positively correlated with immune checkpoints, including CD276 and CD40 (Figures 7A,B). These results suggest that patients in the high-CCL2 group may benefit more from immune therapy, particularly with immune checkpoint inhibitors.

To validate the role of CCL2, immune cell infiltration was analyzed in IPF patients stratified by CCL2 expression levels. High CCL2 expression was associated with increased infiltration of M0 macrophages and activated dendritic cells, whereas low CCL2 expression corresponded to elevated levels of resting memory CD4⁺ T cells and M2 macrophages (Figure 5C). Correlation analysis further demonstrated a positive association between CCL2 and neutrophils as well as activated dendritic cells, alongside a negative correlation with resting memory CD4⁺ T cells (Figure 5D).

Subsequent investigation focused on the relationship between CCL2 and macrophage polarization markers in IPF patients. CCL2 exhibited a significant positive correlation with the M2 macrophage marker CD206 (R = 0.18, p = 0.02), whereas no significant correlation was observed with the M1 macrophage marker NOS2 (R = 0.016, p = 0.83; Figures 8B,C). These findings highlight the pivotal role of CCL2 in modulating macrophage phenotypes, with its strong association with M2 macrophages implicating it in immune regulation and disease progression in IPF.

To validate CCL2 expression, quantitative PCR (qPCR) and Western blot analyses were performed on lung tissue from IPF-induced rats, while qPCR was also conducted on infected rats to assess the efficiency of lentiviral transfection. As shown in Figure 9, compared to normal rat lung tissue, CCL2 expression at both the mRNA and protein levels was significantly elevated in IPF lung tissue. These results are consistent with our database analysis, supporting the notion that increased CCL2 expression may contribute to IPF pathogenesis. Furthermore, following chronic lentiviral transfection, CCL2 expression in the anti-CCL2 treatment group was significantly reduced relative to the IPF group, indicating that lentiviral transfection effectively suppresses CCL2 expression.

To further validate the model’s effectiveness, hematoxylin and eosin (HE) and Masson’s trichrome staining were performed. HE staining provided a detailed visualization of tissue architecture, allowing assessment of inflammatory cell infiltration and structural alterations in lung tissue. In the model group, HE staining (Figure 10) revealed prominent infiltration of inflammatory cells, primarily lymphocytes, monocytes, and macrophages, indicating a pronounced chronic inflammatory response. Additionally, notable pathological features such as thickened alveolar walls and airway remodeling were observed, consistent with the hallmark characteristics of idiopathic pulmonary fibrosis (IPF). Masson’s trichrome staining was employed to evaluate collagen deposition. In the model group, a marked increase in blue-stained collagen fibers was evident, demonstrating a positive correlation between collagen accumulation and fibrosis severity. These observations align with previously reported collagen deposition patterns in IPF, underscoring the critical role of aberrant collagen accumulation in disease pathology (Figure 10). Importantly, collagen deposition was significantly reduced in the anti-CCL2 treatment group compared to the model group, suggesting that lentiviral-mediated knockdown of CCL2 partially attenuates fibrosis progression.

We subsequently evaluated the successful construction of the IPF model using ELISA. In the IPF rat model, detecting COL1A1 (collagen I) holds significant biological importance. COL1A1, the most abundant collagen protein, constitutes the matrix of lung tissue and plays an important role in tissue repair and remodeling. In IPF, lung tissue undergoes abnormal fibrosis, leading to excessive deposition of COL1A1. This deposition serves as a key marker of fibrosis, reflecting the structural changes and severity of fibrosis in lung tissue. Our results demonstrated a marked increase in COL1A1 expression in the model group, indicating extensive fibrosis. Conversely, the anti-CCL2 group exhibited a significant decrease in COL1A1 expression levels (Figure 11A), suggesting that CCL2 knockdown mitigated fibrosis. In immunohistochemical analysis, we assessed the expression levels of α-SMA and COL1A1. α-SMA, a classical marker of myofibroblasts, is typically upregulated during fibrosis. The results revealed a pronounced increase in α-SMA and COL1A1 in the model group, indicating enhanced myofibroblast transformation and collagen deposition during IPF progression (Figure 11B). Treatment with anti-CCL2 significantly reduced the expression of α-SMA and COL1A1, highlighting that CCL2 likely promotes the expression of these proteins. These findings underscore the pivotal role of CCL2 in driving the fibrosis process.