ArticleMolecular cancer2023
An exosome-based liquid biopsy signature for pre-operative identification of lymph node metastasis in patients with pathological high-risk T1 colorectal cancer.
Article in Molecular cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Predicting Recurrence After Curative-Intent Resection of T1 Colorectal Cancer With Transcriptomics
Early Detection of Stomach Cancer With a Liquid Biopsy Based on Exosomal Micro-RNA
An Exosome-Based Liquid Biopsy Assay to Detect Molecular Residual Disease for the Identification of High-Risk Patients With Stage II-III Colorectal Cancer
Biobank for Validating Liquid Biopsy in Predicting the Prognosis of Superficial Colonic Lesions
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Encapsulation and assessment of therapeutic cargo in engineered exosomes: a systematic review.Journal of nanobiotechnology · 2024Pooled it
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- Decoding the Spatiotemporal Logic of Cellular Senescence through Multimodal Exosomal miRNA Integration.JACS Au · 2026Article
- Circulating tumor DNA methylation-based method for noninvasive detection and stage stratification of colorectal tumor.Clinical epigenetics · 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
- Review
- A machine learning-based transcriptomic signature for predicting tumor recurrence after curative resection in T1 colorectal cancer: a retrospective multicenter cohort study (The Tw1CE trial).International journal of surgery (London, England) · 2026Article
- Astragalus polysaccharide promotes latent HIV-1 reactivation via exosome-mediated modulation of the PI3K/AKT/NF-κB axis.Extracellular vesicles and circulating nucleic acids · 2026Article
- Extracellular vesicle microRNA signature as a highly accurate diagnostic biomarker for human brucellosis.Frontiers in cellular and infection microbiology · 2026Article
- Synergistic terahertz platforms for precision oncology.Theranostics · 2026Review
- A Liquid Biopsy Assay of Exosomal miRNA for Non-invasive Identification of Lymph Node Metastasis in Early Gastric Cancer.Annals of surgical oncology · 2025Article
- Diagnostic Performance of [World journal of nuclear medicine · 2025Review
- Development and explanation of a machine learning model for identifying non-localized early-onset T1 colorectal cancer.Discover oncology · 2025Article
- Characteristics of extracellular vesicle-derived lncRNAs during the progression of HBV-related hepatocellular carcinoma.BMC cancer · 2025Article
- Implication of an exosome-based gene signature for estimating clinical outcomes of triple-negative breast cancer and assisting in individualized therapy.Scientific reports · 2025Article
- A promising frontier of circulating messenger RNA in liquid biopsy: From mechanisms to clinical applications.International journal of cancer · 2025Review
- Extracellular vesicles in colorectal cancer.Annals of coloproctology · 2025Review
- MicroRNA 195, lactate dehydrogenase 5, phosphatase and tensin homologue in colorectal cancer: Clinicopathology and prognosis.World journal of gastrointestinal surgery · 2025Article
- Clinical implications of miR-195 in cancer: mechanisms, potential applications, and therapeutic strategies.Journal of cancer research and clinical oncology · 2025Review
- Lymphatic system is the mainstream for breast cancer dissemination and metastasis revealed by single-cell lineage tracing.Molecular cancer · 2025Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundAccording to current guidelines, more than 70% of patients with invasive submucosal colorectal cancer (T1 CRC) undergo a radical operation with lymph node dissection, even though only ~ 10% have lymph node metastasis (LNM). Hence, there is imperative to develop biomarkers that can help robustly identify LNM-positive patients to prevent such overtreatments. Given the emerging interest in exosomal cargo as a source for biomarker development in cancer, we examined the potential of exosomal miRNAs as LNM prediction biomarkers in T1 CRC.
methodsWe analyzed 200 patients with high-risk T1 CRC from two independent cohorts, including a training (n = 58) and a validation cohort (n = 142). Cell-free and exosomal RNAs from pre-operative serum were extracted, followed by quantitative reverse-transcription polymerase chain reactions for a panel of miRNAs.
resultsA panel of four miRNAs (miR-181b, miR-193b, miR-195, and miR-411) exhibited robust ability for detecting LNM in the exosomal vs. cell-free component. We subsequently established a cell-free and exosomal combination signature, successfully validated in two independent clinical cohorts (AUC, 0.84; 95% CI 0.70-0.98). Finally, we developed a risk-stratification model by including key pathological features, which reduced the false positive rates for LNM by 76% without missing any true LNM-positive patients.
conclusionsOur novel exosomal miRNA-based liquid biopsy signature robustly identifies T1 CRC patients at risk of LNM in a preoperative setting. This could be clinically transformative in reducing the significant overtreatment burden of this malignancy.
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Registered trials
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.