Occupational silica exposure drives systemic immune dysregulation and tumor microenvironment susceptibility: evidence from a real-world study

Background: Occupational exposure to carcinogenic dusts such as silica is a well-established risk factor for cancer. However, the molecular mechanisms linking early exposure to tumor-promoting microenvironmental changes remain poorly defined. Emerging evidence suggests that chronic immune dysregulation and remodeling of the tumor microenvironment (TME) may serve as critical intermediates. Methods: We analyzed occupational health data from 5,482 industrial workers in Anhui Province, China. Explainable machine learning models were constructed using exposure profiles and hematological immune parameters to predict carcinoembryonic antigen (CEA) positivity, with feature contributions interpreted via SHAP values. Experimental validation involved silica-stimulated THP-1 monocytes and colorectal cancer (CRC) cell lines to assess inflammatory activation and paracrine regulation of CEA. Silica- and CRC-associated genes were integrated from public databases to construct protein-protein interaction networks, identify hub genes, and evaluate prognostic significance using TCGA and GSE39582 datasets. Single-cell RNA sequencing (scRNA-seq) analysis was used to resolve cell type-specific expression patterns. Results: , silica activated NF-κB-dependent IL-6 secretion in THP-1 cells, and conditioned media dose-dependently upregulated CEA expression in CRC cells-an effect attenuated by NF-κB inhibition or IL-6 neutralization. Multi-omics analysis identified 42 overlapping genes linking silica exposure to CRC, with enrichment in cytokine signaling, adhesion, and matrix remodeling pathways. A hub gene-based risk score was significantly associated with overall survival. scRNA-seq analysis revealed elevated expression of inflammation- and adhesion-related genes in tumor-associated macrophages. Conclusions: Occupational silica exposure induces macrophage-driven inflammatory signaling that promotes early CEA elevation and TME remodeling. Integrating machine learning with experimental and multi-omics validation provides a translational framework for identifying exposure-responsive biomarkers and immune-related cancer risk in occupational settings.