ArticleClinical cancer research : an official journal of the American Association for Cancer Research2021
Transcriptomic Profiling Identifies a Risk Stratification Signature for Predicting Peritoneal Recurrence and Micrometastasis in Gastric Cancer.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 46 citations in OpenAlex.
- Biomaterial-driven remove-remodel-repair (3R) strategy for full-cycle gastric cancer management: bibliometric mapping, preclinical evidence, and a translational roadmap.Nanoscale advances · 2026Review
- CT semantic features of primary gastric cancer: a preoperative predictive model for peritoneal metastasis with superior efficacy to conventional direct CT assessment.BMC gastroenterology · 2026Article
- ITPG: an immune-related transcriptomic predictive model for gastric cancer prognosis.Translational cancer research · 2026Article
- A novel multimodal combining radiomics and tumor-stroma ratio (TSR) improves diagnosis of gastric cancer peritoneal metastasis.Clinical & experimental metastasis · 2026Article
- CHCHD3(MIC19): mitochondrial cristae structure regulation and disease associations.Frontiers in molecular biosciences · 2026Review
- Molecular mechanisms of metastatic peritoneal dissemination in gastric adenocarcinoma.Cancer metastasis reviews · 2025Review
- Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancer.NPJ precision oncology · 2025Article
- Tumor-Stroma Ratio is a Critical Indicator of Peritoneal Metastasis in Gastric Cancer.Clinical and experimental gastroenterology · 2025Article
- Transcriptional landscape and predictive potential of long noncoding RNAs in peritoneal recurrence of gastric cancer.Molecular cancer · 2024Article
- Layer Analysis Based on RNA-Seq Reveals Molecular Complexity of Gastric Cancer.International journal of molecular sciences · 2024Article
- Transcriptomics-based liquid biopsy panel for early non-invasive identification of peritoneal recurrence and micrometastasis in locally advanced gastric cancer.Journal of experimental & clinical cancer research : CR · 2024Article
- Gastric cancer peritoneal metastasis related signature predicts prognosis and sensitivity to immunotherapy in gastric cancer.Journal of cellular and molecular medicine · 2023Article
- Review
- A thirty-three gene-based signature predicts lymph node metastasis and prognosis in patients with gastric cancer.Heliyon · 2023Article
- Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer.Nature communications · 2023Article
- A liquid biopsy signature for predicting early recurrence in patients with gastric cancer.British journal of cancer · 2023Article
- Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.iScience · 2023Article
- Discovery of CDK signature and CDK5 as potential biomarkers for predicting prognosis and immunotherapeutic response in gastric cancer peritoneal metastases.American journal of cancer research · 2023Article
- FMO family may serve as novel marker and potential therapeutic target for the peritoneal metastasis in gastric cancer.Frontiers in oncology · 2023Article
- Integration of Genomic Biology Into Therapeutic Strategies of Gastric Cancer Peritoneal Metastasis.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2022Review
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
Abstract
purposeGastric cancer peritoneal carcinomatosis is fatal. Delay in detection of peritoneal metastases contributes to high mortality, highlighting the need to develop biomarkers that can help identify patients at high risk for peritoneal recurrence or metastasis. EXPERIMENTAL
designWe performed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers by analyzing expression profiling datasets from 249 patients with gastric cancer, followed by analysis of 426 patients from three cohorts for clinical validation.
resultsGenome-wide expression profiling identified a 12-gene panel for robust prediction of peritoneal recurrence in patients with gastric cancer (AUC = 0.95), which was successfully validated in a second dataset (AUC = 0.86). Examination of 216 specimens from a training cohort allowed us to establish a six gene-based risk-prediction model [AUC = 0.72; 95% confidence interval (CI): 0.66-0.78], which was subsequently validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95% CI: 0.67-0.83). In both cohorts, combining tumor morphology and depth of invasion further improved the predictive accuracy of the prediction model (AUC = 0.84). Thereafter, we evaluated the performance of the identical six-gene panel for its ability to detect peritoneal metastasis by analyzing 210 gastric cancer specimens (prior 111 patients plus additional 99 cases), which discriminated patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was also remarkably effective for identifying peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was significantly enhanced when depth of invasion was included in the model (AUC = 0.85).
conclusionsOur novel transcriptomic signature for risk stratification and identification of high-risk patients with peritoneal carcinomatosis might serve as an important clinical decision making in patients with gastric cancer.
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