ArticleInternational journal of colorectal disease2011
MicroRNA signature analysis in colorectal cancer: identification of expression profiles in stage II tumors associated with aggressive disease.
Article in International journal of colorectal disease, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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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.
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Who cites it
67 citing papers in PubMed, 112 citations in OpenAlex.
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
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- Research on biomarkers using innovative artificial intelligence systems in breast cancer.International journal of clinical oncology · 2024Review
- Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review.AIMS public health · 2024Review
- Potential Diagnostic Utility of microRNAs in Gastrointestinal Cancers.Cancer management and research · 2023Review
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- A microarray expression profile and bioinformatic analysis of circular RNA in human esophageal carcinoma.Journal of gastrointestinal oncology · 2022Article
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- Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification.Frontiers in oncology · 2022Review
- MicroRNA expression in inflammatory bowel disease-associated colorectal cancer.World journal of gastrointestinal oncology · 2021Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.Current oncology (Toronto, Ont.) · 2021Review
- Clinical significance of a microRNA signature for the identification and predicting prognosis in colorectal cancers with mucinous differentiation.Carcinogenesis · 2020Article
- Upregulation of microRNA-31 is associated with poor prognosis in patients with advanced colorectal cancer.Oncology letters · 2020Article
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
- KRAS Mutation-Responsive miR-139-5p inhibits Colorectal Cancer Progression and is repressed by Wnt Signaling.Theranostics · 2020Article
- MicroRNA-139-5p regulates chronic inflammation by suppressing nuclear factor-κB activity to inhibit cell proliferation and invasion in colorectal cancer.Experimental and therapeutic medicine · 2019Article
- Model based on GA and DNN for prediction of mRNA-Smad7 expression regulated by miRNAs in breast cancer.Theoretical biology & medical modelling · 2018Article
- Deep Learning Methods for Predicting Disease Status Using Genomic Data.Journal of biometrics & biostatistics · 2018Article
- Functional polymorphisms in the promoter region of miR-17-92 cluster are associated with a decreased risk of colorectal cancer.Oncotarget · 2017Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeColorectal cancer (CRC) is a clinically diverse disease whose molecular etiology remains poorly understood. The purpose of this study was to identify miRNA expression patterns predictive of CRC tumor status and to investigate associations between microRNA (miRNA) expression and clinicopathological parameters.
methodsExpression profiling of 380 miRNAs was performed on 20 paired stage II tumor and normal tissues. Artificial neural network (ANN) analysis was applied to identify miRNAs predictive of tumor status. The validation of specific miRNAs was performed on 102 tissue specimens of varying stages.
resultsThirty-three miRNAs were identified as differentially expressed in tumor versus normal tissues. ANN analysis identified three miRNAs (miR-139-5p, miR-31, and miR-17-92 cluster) predictive of tumor status in stage II disease. Elevated expression of miR-31 (p = 0.004) and miR-139-5p (p < 0.001) and reduced expression of miR-143 (p = 0.016) were associated with aggressive mucinous phenotype. Increased expression of miR-10b was also associated with mucinous tumors (p = 0.004). Furthermore, progressively increasing levels of miR-10b expression were observed from T1 to T4 lesions and from stage I to IV disease.
conclusionAssociation of specific miRNAs with clinicopathological features indicates their biological relevance and highlights the power of ANN to reliably predict clinically relevant miRNA biomarkers, which it is hoped will better stratify patients to guide adjuvant therapy.
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