Role of inflammation-related genes as prognostic biomarkers and mechanistic implications in idiopathic pulmonary fibrosis

Bing Bai^1,2,3Wenfei Zhao⁴Fazhan Li¹Yang Mi¹Zheng Pengyuan^1*

• ¹Henan Key Laboratory for Helicobacter Pylori and Microbiota and GI Cancer, Marshall Medical Research Center, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
• ²Zhengzhou Fuhua Street Community Health Service Center, Zhengzhou, China
• ³Fuhua Street Branch of the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
• ⁴Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive lung disorder characterized by excessive fibrosis and structural remodeling of lung tissue. The role of inflammation in developing and progressing IPF is increasingly recognized as critical. However, the precise mechanisms and pathways of inflammation in IPF remain unclear. This study aimed to identify inflammation-related genes in IPF and develop a prognostic risk model using machine learning approaches. The IPF dataset GSE70866 from the Gene Expression Omnibus database was analyzed to identify inflammationrelated genes. Unsupervised clustering algorithms were used to classify IPF samples, followed by weighted gene co-expression network analysis (WGCNA) to identify highly correlated genes. Least absolute shrinkage and selection operator (LASSO) regression was then applied, and the intersection of results pinpointed critical hub genes, primarily CCL2 and STAB1. A rat model of pulmonary fibrosis was established, and lentivirus transfection was used to knock down CCL2 expression. The transfection effect and hub gene expression were validated using Quantitative polymerase chain reaction, Western blot, immunohistochemistry, enzyme-linked immunosorbent assay, hematoxylin-eosin staining, and Masson's trichrome staining. Levels of α-SMA and COL1A1 were also assessed. WGCNA and LASSO regression analyses identified CCL2 and STAB1 as significant contributors to IPF, closely associated with patient prognosis and immune cell infiltration. Protein-protein interaction network analysis established CCL2 as a novel biomarker for IPF. In a rat model of IPF, CCL2 expression was significantly elevated compared to that in the controls. Knockdown of CCL2 expression alleviated pulmonary fibrosis and reduced the expression of COL1A1 protein and α-SMA protein. CCL2 promotes the expression of COL1A1 protein and α-SMA proteins, suggesting that the mechanism of inflammation-induced pulmonary fibrosis may involve the regulation of COL1A1 and α-SMA by CCL2. These findings establish CCL2 as a promising biomarker and potential therapeutic target for IPF.