Scholarlink

Background <p>The molecular features determining the risk of metachronous metastases in clear cell renal cell carcinoma (ccRCC) are poorly defined. This study aimed to identify molecular factors associated with the risk of metachronous metastasis.</p> Methods <p>Using a systematic tumor transcriptome deconvolution approach, we investigated the genomic and transcriptomic profiles of 192 ccRCC primary tumors with extended clinical follow-up to identify cancer- and stromal cell-specific molecular features associated with metastatic risk. Based on these features, we applied multivariate Cox regression to develop a compact 5-gene predictive model for metachronous metastasis.</p> Results <p>At the genomic level, we identify a significantly higher frequency of copy number loss at 1p31-36 in primary tumors that later progress with metastases. Tumor transcriptome deconvolution identifies significant down-regulation of epithelial cell polarity, including <i>PATJ</i> (1p31), and fatty acid metabolism, including <i>CYP4A11</i> (1p33), in cancer cells of tumors that develop metastatic progression. We develop and benchmark a compact 5-gene predictive model (5G) that demonstrates improved accuracy over existing ccRCC gene signatures in the prediction of metachronous metastasis risk.</p> Conclusions <p>Overall, our study highlights convergent genomic and transcriptomic alterations in chromosome 1p, driving dysregulation of epithelial cell polarity and fatty acid metabolism, as putative risk factors of metachronous metastasis in ccRCC.</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

Convergent genomic and molecular features predict risk of metachronous metastasis in clear cell renal cell carcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月25日

查看全文
  • Marjan M. Naeini,
  • Mengyuan Pang,
  • Neha Rohatgi,
  • Sinem Kadioglu,
  • Umesh Ghoshdastider,
  • Renzo G. DiNatale,
  • Roy Mano,
  • A. Ari Hakimi,
  • Anders Jacobsen Skanderup

摘要

Background

The molecular features determining the risk of metachronous metastases in clear cell renal cell carcinoma (ccRCC) are poorly defined. This study aimed to identify molecular factors associated with the risk of metachronous metastasis.

Methods

Using a systematic tumor transcriptome deconvolution approach, we investigated the genomic and transcriptomic profiles of 192 ccRCC primary tumors with extended clinical follow-up to identify cancer- and stromal cell-specific molecular features associated with metastatic risk. Based on these features, we applied multivariate Cox regression to develop a compact 5-gene predictive model for metachronous metastasis.

Results

At the genomic level, we identify a significantly higher frequency of copy number loss at 1p31-36 in primary tumors that later progress with metastases. Tumor transcriptome deconvolution identifies significant down-regulation of epithelial cell polarity, including PATJ (1p31), and fatty acid metabolism, including CYP4A11 (1p33), in cancer cells of tumors that develop metastatic progression. We develop and benchmark a compact 5-gene predictive model (5G) that demonstrates improved accuracy over existing ccRCC gene signatures in the prediction of metachronous metastasis risk.

Conclusions

Overall, our study highlights convergent genomic and transcriptomic alterations in chromosome 1p, driving dysregulation of epithelial cell polarity and fatty acid metabolism, as putative risk factors of metachronous metastasis in ccRCC.