ArticleDiscover oncology2025
Computational analysis of DEHP's oncogenic role in colorectal cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- DC-FusionGNN: A Dual-Channel Framework Integrating Global Self-Attention and Local Topology Learning for Identifying Key Resistance Genes AgainstPlants (Basel, Switzerland) · 2026Article
- Network toxicology and single-cell analysis reveal molecular mechanisms of DEHP-induced colorectal cancer.Bioinformation · 2026Article
- From Mutation to Prognosis: AI-HOPE-PI3K Enables Artificial Intelligence Agent-Driven Integration of PI3K Pathway Data in Colorectal Cancer Precision Medicine.International journal of molecular sciences · 2025Article
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7 authors.
Funding
Abstract
backgroundColorectal cancer (CRC) remains a leading cause of cancer-related mortality globally, with many patients diagnosed at advanced stages. Current treatments, including surgery, chemotherapy, and targeted therapies, face limitations due to tumor metastasis and chemoresistance. Di-(2-ethylhexyl) phthalate (DEHP), a widely-used plasticizer, has been linked to various cancers, including CRC, through mechanisms such as metabolic reprogramming and inflammation. However, the direct relationship between DEHP and CRC requires further elucidation.
methodsWe integrated transcriptomic data from TCGA-COADREAD (41 normal and 476 cancer tissues) and GEO datasets (GSE32323 and GSE21510) to identify differentially expressed genes (DEGs) using the `limma` package. We predicted DEHP molecular targets via SwissTargetPrediction and ChEMBL databases and constructed a protein-protein interaction (PPI) network using STRING. Machine learning methods, including LASSO regression, SVM, and Random Forest, identified key genes. SHAP analysis and ssGSEA were employed to evaluate gene importance and immune cell infiltration, respectively. Molecular docking experiments assessed the binding affinity of DEHP with key proteins.
resultsDifferential expression analysis identified 86 common genes involved in pathways such as PI3K-Akt and p53 signaling. The PPI network highlighted 14 candidate genes, with machine learning methods narrowing down to three key genes: CDK1, CDK4, and BCL2. SHAP analysis showed CDK1 and CDK4 as top contributors, while ssGSEA revealed significant correlations between these genes and immune cell infiltration. Molecular docking experiments demonstrated strong binding affinities of DEHP with BCL2 (- 8.7 kcal/mol), CDK1 (- 7.8 kcal/mol), and CDK4 (- 6.8 kcal/mol).
conclusionThis study provides comprehensive insights into the oncogenic mechanisms of DEHP in CRC, identifying key genes and pathways that may serve as potential therapeutic targets. Our findings highlight the need for further investigation into DEHP's role in CRC and its potential as a target for prevention and treatment strategies.
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