Transcriptomics-based liquid biopsy panel for early non-invasive identification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer

微转移 医学 癌症 活检 鉴定(生物学) 胃活检 肿瘤科 内科学 病理 放射科 转移 生物 胃炎 植物
作者
Pingan Ding,Haotian Wu,Jiaxiang Wu,Tongkun Li,Renjun Gu,Lilong Zhang,Peigang Yang,Honghai Guo,Yuan Tian,Jinchen He,Jiaxuan Yang,Na Meng,Xiaolong Li,Lingjiao Meng,Qun Zhao
出处
期刊:Journal of Experimental & Clinical Cancer Research [BioMed Central]
卷期号:43 (1)
标识
DOI:10.1186/s13046-024-03098-5
摘要

Abstract Background This study aimed to develop a novel six-gene expression biomarker panel to enhance the early detection and risk stratification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer (LAGC). Methods We used genome-wide transcriptome profiling and rigorous bioinformatics to identify a six-gene expression biomarker panel. This panel was validated across multiple clinical cohorts using both tissue and liquid biopsy samples to predict peritoneal recurrence and micrometastasis in patients with LAGC. Results Through genome-wide expression profiling, we identified six mRNAs and developed a risk prediction model using 196 samples from a surgical specimen training cohort. This model, incorporating a 6-mRNA panel with clinical features, demonstrated high predictive accuracy for peritoneal recurrence in gastric cancer patients, with an AUC of 0.966 (95% CI: 0.944–0.988). Transitioning from invasive surgical or endoscopic biopsy to noninvasive liquid biopsy, the model retained its predictive efficacy (AUC = 0.963; 95% CI: 0.926–1.000). Additionally, the 6-mRNA panel effectively differentiated patients with or without peritoneal metastasis in 95 peripheral blood specimens (AUC = 0.970; 95% CI: 0.936–1.000) and identified peritoneal micrometastases with a high efficiency (AUC = 0.941; 95% CI: 0.874–1.000). Conclusions Our study provides a novel gene expression biomarker panel that significantly enhances early detection of peritoneal recurrence and micrometastasis in patients with LAGC. The RSA model's predictive capability offers a promising tool for tailored treatment strategies, underscoring the importance of integrating molecular biomarkers with clinical parameters in precision oncology.
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