ArticleBritish journal of cancer2023
A liquid biopsy signature for predicting early recurrence in patients with gastric cancer.
Article in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Liquid Biopsy for Molecular Residual Disease Detection and Postoperative Surveillance in Gastric Cancer: Current Evidence and Future Directions.International journal of molecular sciences · 2026Review
- Lymph Node Recurrence after Endoscopic Resection of Early Gastric Cancer Previously Classified as eCura B.Internal medicine (Tokyo, Japan) · 2026Article
- An exosome-based liquid biopsy to predict molecular residual disease for the identification of high-risk patients with stage II-III colorectal cancer: the CLEAR study.Molecular cancer · 2026Observational
- A nomogram model based on CT-assessed body composition parameters for predicting postoperative recurrence in advanced gastric cancer.Abdominal radiology (New York) · 2026Article
- A machine-learning powered liquid biopsy predicts response to paclitaxel plus ramucirumab in advanced gastric cancer: results from the prospective IVY trial.Molecular cancer · 2025Observational
- Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Non-invasive liquid biopsy based on transcriptomic profiling for early diagnosis of occult peritoneal metastases in locally advanced gastric cancer.NPJ precision oncology · 2025Article
- Transcriptomics-Based Liquid Biopsy for Early Detection of Recurrence in Locally Advanced Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Nomogram for predicting 10-year postoperative recurrence of stage I gastric cancer.Translational cancer research · 2024Article
- The exploration of surgery and survival prediction in patients with peritoneal metastasis from gastric adenocarcinoma based on the SEER database.Journal of gastrointestinal oncology · 2024Article
- cfOmics: a cell-free multi-Omics database for diseases.Nucleic acids research · 2024Article
- Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients.American journal of translational research · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundGastric cancer (GC) patients who experience recurrence within the first year following surgery (early recurrence [ER]) exhibit worse prognosis. Herein, we established a microRNA-based liquid biopsy assay to predict ER in GC patients.
methodsA comprehensive biomarker discovery was performed by analysing miRNA expression profiling in 271 primary GC tumours. Thereafter, the expression of these biomarkers was validated in 290 GC cases, which included 218 tissues and 72 pre-treatment sera, from two independent institutions.
resultsA panel of 8 miRNAs was identified during the initial biomarker discovery, and this panel could robustly predict ER in a tissue-based clinical cohort (area under the curve [AUC]: 0.81). Furthermore, a model combining the miRNA panel, microsatellite instability (MSI) status and tumour size exhibited superior predictive performance (AUC: 0.86), and was defined as a Prediction of Early Recurrence in GC (PERGC) signature, which was successfully validated in another independent cohort (AUC: 0.82). Finally, the PERGC signature was translated into a liquid biopsy assay (AUC: 0.81), and a multivariate regression analysis revealed this signature to be an independent predictor for ER (odds ratio: 11.20).
conclusionWe successfully established a miRNA-based liquid biopsy signature that robustly predicts the risk of ER in GC patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.