Specifically, this investigation aims to address the following knowledge gaps: (1) validate CXCL14 as a pivotal immune-related biomarker through SHAP-driven feature importance analysis; (2) decipher the mechanistic associations between CXCL14 expression and immune cell infiltration (e.g., Treg and macrophage polarization); and (3) develop an interpretable machine learning framework for predicting IPF progression based on CXCL14 and associated molecular signatures. This evidence concerns the gene CXCL14 and idiopathic pulmonary fibrosis.