Scholarlink

<p>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 (<i>P</i> &lt; 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

查看全文
  • Yuzhe Su,
  • Peihuang Chen,
  • Yaoan Wen,
  • Jiangbin Yang,
  • Yeda Chen,
  • Shaoxing Zhu,
  • Shuyuan Zhan,
  • Song Zheng

摘要

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.