ArticleAging2020
Identification of an exosomal long non-coding RNAs panel for predicting recurrence risk in patients with colorectal cancer.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Prognostic value of extracellular vesicles in colorectal cancer: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- The CCAT2 enigma: pioneering insights into colorectal cancer pathophysiology and therapeutic innovation.Cancer cell international · 2025Review
- Comprehensive profiling of lncRNAs and mRNAs enriched in small extracellular vesicles for early noninvasive detection of colorectal cancer: diagnostic panel assembly and extensive validation.Molecular oncology · 2025Article
- Exosomes: a potential tool in the diagnosis, prognosis and treatment of patients with colorectal cancer.Future oncology (London, England) · 2025Review
- Harnessing the potential of small extracellular vesicle biomarkers for cancer diagnosis and prognosis with advanced analytical technologies.Journal of translational internal medicine · 2025Article
- M2 exosomes modified by hydrogen sulfide promoted bone regeneration by moesin mediated endocytosis.Bioactive materials · 2024Article
- A panel of necroptosis-related genes predicts the prognosis of pancreatic adenocarcinoma.Translational oncology · 2022Article
- Isolation and characterization of extracellular vesicles for clinical applications in cancer - time for standardization?Nanoscale advances · 2021Review
- LINC01116 facilitates colorectal cancer cell proliferation and angiogenesis through targeting EZH2-regulated TPM1.Journal of translational medicine · 2021Article
- Exosomal Cripto-1 Serves as a Potential Biomarker for Perihilar Cholangiocarcinoma.Frontiers in oncology · 2021Article
- Identification of a Genome Instability-Associated LncRNA Signature for Prognosis Prediction in Colon Cancer.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recurrence is a major cause of cancer-related deaths in colorectal cancer (CRC) patients, but the current strategies are limited to predict this clinical behavior. Our aim is to develop a recurrence prediction model based on long non-coding RNAs (lncRNAs) in exosomes of serum to improve the prediction accuracy. In discovery phase, 11 lncRNAs were found to be associated with CRC recurrence in tissues using high-throughput lncRNAs microarray and reverse transcription quantitative real-time PCR. And, 9 of them were correlated with their expression levels of serum exosomes. In training phase, a model based on 5-exosomal lncRNAs (exolncRNAs) panel was constructed, and showed high distinguish capability for recurrent CRC patients. ROC showed the panel was superior to serum CEA and CA19-9 in prediction of CRC recurrence. In both training and test sets, high-risk patients defined by the 5-exolncRNAs panel had poor recurrence free and overall survival. And, COX model showed it was an independent factor for CRC prognosis. Moreover, there was a significant relationship in detection of 5-exolncRNAs between plasma samples and paired serum samples. In summary, the 5-exolncRNAs panel robustly stratifies CRC patients' risk of recurrence, enabling more accurate prediction of prognosis.
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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.