# Deciphering the molecular networks of 3-methylcholanthrene-induced clear cell renal cell carcinoma through multi-omics integration

**Authors:** Yuzhe Su, Peihuang Chen, Yaoan Wen, Jiangbin Yang, Yeda Chen, Shaoxing Zhu, Shuyuan Zhan, Song Zheng

PMC · DOI: 10.1038/s41598-025-34526-x · Scientific Reports · 2026-01-07

## TL;DR

This study explores how 3-Methylcholanthrene causes clear cell renal cell carcinoma by analyzing gene interactions and using machine learning.

## Contribution

The study introduces a novel multi-omics approach combining SHAP and machine learning to identify key genes in 3-MC-induced ccRCC.

## Key findings

- 99 potential target genes were identified in 3-MC-induced ccRCC pathogenesis.
- Four core genes (GPC3, PIK3C2G, PPARA, TRPA1) were found to significantly influence the disease.
- Molecular simulations confirmed the interaction between 3-MC and PPARA.

## Abstract

Exposure to 3-Methylcholanthrene (3-MC) may be associated with the development and progression of clear cell renal cell carcinoma (ccRCC); however, its underlying molecular mechanisms remain unclear. We conducted differential expression analyses across multiple datasets to identify target genes linked to ccRCC. By integrating SHapley Additive exPlanations (SHAP) with machine learning algorithms, network toxicology and molecular docking, we investigated the binding interactions between 3-MC and these target proteins. A total of 99 genes were identified as potential targets involved in 3-MC-induced ccRCC pathogenesis. Four core genes (GPC3, PIK3C2G, PPARA, and TRPA1) were selected through machine learning approaches. SHAP analysis demonstrated the combined contribution of these genes to the model’s predictive performance. GPC3, PIK3C2G, and PPARA were significantly downregulated, whereas TRPA1 was upregulated (P < 0.05). Survival analysis using data from The Cancer Genome Atlas (TCGA) revealed significant differences in patient survival based on the expression levels of these genes. Molecular dynamics simulations validated the structural stability of the interaction between 3-MC and PPARA. These findings suggest that 3-MC promotes ccRCC pathogenesis by targeting specific genes.

The online version contains supplementary material available at 10.1038/s41598-025-34526-x.

## Linked entities

- **Genes:** GPC3 (glypican 3) [NCBI Gene 2719], PIK3C2G (phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 gamma) [NCBI Gene 5288], PPARA (peroxisome proliferator activated receptor alpha) [NCBI Gene 5465], TRPA1 (transient receptor potential cation channel subfamily A member 1) [NCBI Gene 8989]
- **Chemicals:** 3-Methylcholanthrene (PubChem CID 1674), 3-MC (PubChem CID 1674)
- **Diseases:** clear cell renal cell carcinoma (MONDO:0005005), ccRCC (MONDO:0007763)

## Full-text entities

- **Genes:** GPC3 (glypican 3) [NCBI Gene 2719] {aka DGSX, GTR2-2, MXR7, OCI-5, SDYS, SGB}, PIK3C2G (phosphatidylinositol-4-phosphate 3-kinase catalytic subunit type 2 gamma) [NCBI Gene 5288] {aka PI3K-C2-gamma, PI3K-C2GAMMA}, TRPA1 (transient receptor potential cation channel subfamily A member 1) [NCBI Gene 8989] {aka ANKTM1, FEPS, FEPS1, p120}, PPARA (peroxisome proliferator activated receptor alpha) [NCBI Gene 5465] {aka NR1C1, PPAR, PPAR-alpha, PPARalpha, hPPAR}
- **Diseases:** Cancer (MESH:D009369), ccRCC (MESH:D002292)
- **Chemicals:** 3-MC (MESH:D008748)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12865013/full.md

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Source: https://tomesphere.com/paper/PMC12865013