Air Pollution Drives Liver Cancer Through Immune System Disruption
New study identifies 7-gene signature linking air pollutants to hepatocellular carcinoma prognosis via immune dysfunction.
Summary
Researchers analyzed data from 607 liver cancer patients to understand how air pollution affects cancer outcomes through immune system changes. Using machine learning on genetic data, they identified 19 air pollutant-related immune genes and created a 7-gene signature that predicts patient survival. The study found that air pollutants like PM2.5 and nitrogen dioxide may promote liver cancer by disrupting immune responses, particularly affecting macrophages. One gene, CDC25C, emerged as a key target linking air pollution to multiple cancer types.
Detailed Summary
Air pollution's role in cancer development extends beyond lung disease, with new research revealing how pollutants drive liver cancer through immune system disruption. This comprehensive study analyzed genetic data from 607 hepatocellular carcinoma (HCC) patients across multiple databases to understand the molecular mechanisms linking air pollution exposure to cancer outcomes.
Researchers used network toxicology and machine learning to identify 19 air pollutant-related immune genes (APIGs) from over 16,000 immune-related genes. They tested 101 different machine learning combinations to create an optimal 7-gene prognostic signature (APIGPS) including CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, and GLS. This signature successfully stratified patients into high-risk and low-risk groups with significantly different survival outcomes.
The analysis revealed that air pollutants including PM2.5, nitrogen dioxide, sulfur dioxide, and ozone interact with specific immune genes to create a pro-tumor environment. High-risk patients showed increased macrophage infiltration and altered immune responses. Single-cell analysis confirmed these genes are primarily expressed in macrophages, suggesting air pollution may reprogram these immune cells to promote cancer progression.
Molecular docking studies demonstrated stable binding between the 7 air pollutants and the identified genes, providing mechanistic evidence for direct molecular interactions. The CDC25C gene emerged as a hub target, showing associations with survival across 10 different cancer types and correlating with tumor mutation burden in 21 cancers. This suggests air pollution's cancer-promoting effects may extend far beyond liver cancer through common immune pathways.
Key Findings
- Identified 19 air pollutant-related immune genes from analysis of 607 liver cancer patients across multiple databases
- Created 7-gene signature (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS) that accurately predicts patient survival outcomes
- High-risk patients showed significantly increased macrophage infiltration and altered immune microenvironment
- Molecular docking confirmed stable binding between 7 air pollutants (PM2.5, NO2, SO2, O3) and identified genes
- CDC25C gene associated with survival in 10 cancer types and tumor mutation burden in 21 cancers
- Air pollution exposure linked to pro-tumor immune environment with enhanced inflammatory cytokine secretion
- Single-cell analysis revealed target genes primarily expressed in macrophages, suggesting immune cell reprogramming
Methodology
Multi-omics analysis of 607 HCC patients from TCGA, GEO, and ICGC databases using weighted gene co-expression network analysis (WGCNA), differential expression analysis, and immune infiltration assessment. Applied 101 combinations of 10 machine learning algorithms with 10-fold cross-validation to construct optimal prognostic signature. Validated findings through qRT-PCR, single-cell RNA sequencing, molecular docking, and pan-cancer analysis across multiple datasets.
Study Limitations
Study relies on computational analysis of existing datasets without direct measurement of individual air pollution exposure levels. The prognostic signature requires validation in prospective clinical trials before implementation. Authors note that mechanistic insights are primarily based on bioinformatics analysis and molecular docking predictions rather than experimental validation in laboratory models.
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