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Background <p>Endocrine-disrupting chemicals (EDCs) are common environmental pollutants that affect human health. These chemicals have been linked to the development and progression of many types of cancer. However, the mechanism by which EDCs affect the development of lung adenocarcinoma (LUAD) remains unclear.</p> Methods <p>Bioinformatics and machine learning approaches were used to explore the connection between 13 EDCs and LUAD. Target genes for the 13 EDCs were retrieved from the ChEMBL, STITCH, and SwissTargetPrediction databases. Differentially expressed genes (DEGs) in LUAD were identified from GEO datasets. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks were used to identify key gene modules related to LUAD. Machine learning methods were employed to select important genes associated with both LUAD and EDC exposure. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to assess relevant biological pathways. Immune cell infiltration was analyzed using CIBERSORT to explore changes in the immune microenvironment of LUAD.</p> Results <p>A total of 1,818 putative target genes for the 13 EDCs were initially identified. WGCNA refined this list to 93 candidate genes, among which COL1A1 was prioritized as a robust feature gene via machine learning algorithms. Functional enrichment analysis indicated that COL1A1 is involved in critical pathways, including cytokine-cytokine receptor interaction, ECM-receptor interaction, and fatty acid metabolism. Furthermore, immune infiltration profiling revealed distinct correlation patterns: COL1A1 expression was positively correlated with the abundance of M0 and M2 macrophages, while showing a negative correlation with monocytes and eosinophils.</p> Conclusion <p>This study identifies COL1A1 as a key feature gene associated with EDC-targeted pathways in LUAD. The findings characterize its involvement in ECM-related signaling and its correlation with specific immune cell infiltration patterns, providing a computational framework for understanding EDC-associated molecular changes in LUAD.</p>

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Exploratory identification of COL1A1 as a potential gene linking endocrine-disrupting chemicals and lung adenocarcinoma: a bioinformatics and machine learning analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月16日

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  • Tianyu She,
  • Fanting Sun,
  • Zhen Xie,
  • Yuze Cao,
  • Haitao Xu

摘要

Background

Endocrine-disrupting chemicals (EDCs) are common environmental pollutants that affect human health. These chemicals have been linked to the development and progression of many types of cancer. However, the mechanism by which EDCs affect the development of lung adenocarcinoma (LUAD) remains unclear.

Methods

Bioinformatics and machine learning approaches were used to explore the connection between 13 EDCs and LUAD. Target genes for the 13 EDCs were retrieved from the ChEMBL, STITCH, and SwissTargetPrediction databases. Differentially expressed genes (DEGs) in LUAD were identified from GEO datasets. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks were used to identify key gene modules related to LUAD. Machine learning methods were employed to select important genes associated with both LUAD and EDC exposure. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to assess relevant biological pathways. Immune cell infiltration was analyzed using CIBERSORT to explore changes in the immune microenvironment of LUAD.

Results

A total of 1,818 putative target genes for the 13 EDCs were initially identified. WGCNA refined this list to 93 candidate genes, among which COL1A1 was prioritized as a robust feature gene via machine learning algorithms. Functional enrichment analysis indicated that COL1A1 is involved in critical pathways, including cytokine-cytokine receptor interaction, ECM-receptor interaction, and fatty acid metabolism. Furthermore, immune infiltration profiling revealed distinct correlation patterns: COL1A1 expression was positively correlated with the abundance of M0 and M2 macrophages, while showing a negative correlation with monocytes and eosinophils.

Conclusion

This study identifies COL1A1 as a key feature gene associated with EDC-targeted pathways in LUAD. The findings characterize its involvement in ECM-related signaling and its correlation with specific immune cell infiltration patterns, providing a computational framework for understanding EDC-associated molecular changes in LUAD.