Scholarlink

Background <p>Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with limited diagnostic and therapeutic options. APOBEC3B (A3B) has emerged as a mutational driver in several cancers, but its downstream mechanisms remain unclear. We aim to utilize bioinformatics methods, such as machine learning, to reveal the mechanism of A3B in ACC and verify and explore it in depth in vitro.</p> Methods <p>Through the comprehensive analysis of 311 samples, including differential expression analysis and weighted gene co-expression network analysis (WGCNA), we use the hub genes extracted from the key modules as the background for 113 machine learning methods. Genes with potential associations were evaluated using feature importance and SHAP analysis techniques, and in vitro studies included qRT-PCR, Western blotting, siRNA-mediated knockdown, overexpression rescue, scratch assays, and Transwell migration assays to assess effects on gene expression and cell motility.</p> Results <p>Random Forest was selected as the optimal model and identified nine gene features centered on A3B and ANLN (AUC &#xa0;= 0.996). Knockout of the A3B gene significantly reduced the mRNA and protein levels of ANLN (p &#xa0;&lt; 0.001). ANLN overexpression rescued the outcome. Compared with knockout alone, the cell migration distance and the number of migrating cells were restored (p &#xa0;&lt; 0.001).</p> Conclusions <p>Our comprehensive omics and experimental methods have revealed the A3B-ANLN axis as a key mechanism of ACC and also provided predictive models and potential targets for the early diagnosis and therapeutic intervention of ACC.</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

Machine Learning-Driven Identification and In Vitro Validation of the APOBEC3B-ANLN Regulatory Axis in Adrenocortical Carcinoma

  • 期刊论文

  • 发表日期：

    2026年1月16日

查看全文
  • Jiadong Zhang,
  • Xinyu Hu,
  • Cong Wei,
  • Wenyun Dong,
  • Huanrui Hu,
  • Rui Wang,
  • Zhili Xiong,
  • Chengyin Li,
  • Jingling Zhao

摘要

Background

Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with limited diagnostic and therapeutic options. APOBEC3B (A3B) has emerged as a mutational driver in several cancers, but its downstream mechanisms remain unclear. We aim to utilize bioinformatics methods, such as machine learning, to reveal the mechanism of A3B in ACC and verify and explore it in depth in vitro.

Methods

Through the comprehensive analysis of 311 samples, including differential expression analysis and weighted gene co-expression network analysis (WGCNA), we use the hub genes extracted from the key modules as the background for 113 machine learning methods. Genes with potential associations were evaluated using feature importance and SHAP analysis techniques, and in vitro studies included qRT-PCR, Western blotting, siRNA-mediated knockdown, overexpression rescue, scratch assays, and Transwell migration assays to assess effects on gene expression and cell motility.

Results

Random Forest was selected as the optimal model and identified nine gene features centered on A3B and ANLN (AUC  = 0.996). Knockout of the A3B gene significantly reduced the mRNA and protein levels of ANLN (p  < 0.001). ANLN overexpression rescued the outcome. Compared with knockout alone, the cell migration distance and the number of migrating cells were restored (p  < 0.001).

Conclusions

Our comprehensive omics and experimental methods have revealed the A3B-ANLN axis as a key mechanism of ACC and also provided predictive models and potential targets for the early diagnosis and therapeutic intervention of ACC.