Scholarlink

<p>This study proposes a novel non-invasive diagnostic approach utilizing transcriptomic profiling of liquid biopsy samples for the early detection of occult peritoneal metastases in locally advanced gastric cancer (LAGC). By analyzing RNA expression patterns of cancer cells, this method identifies specific gene signatures associated with peritoneal spread, potentially offering a more sensitive and comprehensive diagnostic tool compared to conventional imaging techniques. A 4-mRNA panel (BUB1, SPC25, CT83, MMP3) integrated with clinical features was developed into a Risk Stratification Assessment (RSA) model, demonstrating superior predictive accuracy in multiple cohorts with an area under the curve (AUC) of 0.836 in training and 0.882 in validation. This approach offers a promising alternative for early diagnosis, improving treatment decisions and clinical outcomes for gastric cancer patients, while enabling a shift from tissue-based testing to non-invasive blood-based diagnostics.</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

Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月15日

查看全文
  • Ping’an Ding,
  • Haotian Wu,
  • Jiaxiang Wu,
  • Tongkun Li,
  • Renjun Gu,
  • Lilong Zhang,
  • Xiaoman Niu,
  • Jinchen He,
  • Jiaxuan Yang,
  • Peigang Yang,
  • Honghai Guo,
  • Yuan Tian,
  • Ning Meng,
  • Xiaolong Li,
  • Zhenjiang Guo,
  • Lingjiao Meng,
  • Qun Zhao

摘要

This study proposes a novel non-invasive diagnostic approach utilizing transcriptomic profiling of liquid biopsy samples for the early detection of occult peritoneal metastases in locally advanced gastric cancer (LAGC). By analyzing RNA expression patterns of cancer cells, this method identifies specific gene signatures associated with peritoneal spread, potentially offering a more sensitive and comprehensive diagnostic tool compared to conventional imaging techniques. A 4-mRNA panel (BUB1, SPC25, CT83, MMP3) integrated with clinical features was developed into a Risk Stratification Assessment (RSA) model, demonstrating superior predictive accuracy in multiple cohorts with an area under the curve (AUC) of 0.836 in training and 0.882 in validation. This approach offers a promising alternative for early diagnosis, improving treatment decisions and clinical outcomes for gastric cancer patients, while enabling a shift from tissue-based testing to non-invasive blood-based diagnostics.