ReviewInternational journal of molecular sciences2022
Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.
Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- miRNAs, Mesenchymal Stromal Cells and Major Neoplastic and Inflammatory Skin Diseases: A Page Being Written: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Article
- Epigenetics of Malignant Melanoma: Mechanisms, Diagnostic Approaches and Therapeutic Applications.Oncology research · 2026Review
- Alterations in Circulating miRNAs and Their Potential Role in Aseptic Loosening After Total Hip Replacement: An Observational, Cross-Sectional Study.Journal of personalized medicine · 2025Article
- Nutrition and Diet Patterns as Key Modulators of Metabolic Reprogramming in Melanoma Immunotherapy.Journal of clinical medicine · 2025Review
- Predictive modeling for metastasis in oncology: current methods and future directions.Annals of medicine and surgery (2012) · 2025Review
- Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis.Journal of cellular and molecular medicine · 2025Article
- Review
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- MiR-5195-3p targets the PCBP2/PI3K/AKT pathway to inhibit melanoma cell proliferation and migration.Heliyon · 2023Article
- Article
- MicroRNA-155 and Disease-Related Immunohistochemical Parameters in Cutaneous Melanoma.Diagnostics (Basel, Switzerland) · 2023Article
- Apprising Diagnostic and Prognostic Biomarkers in Cutaneous Melanoma-Persistent Updating.Journal of personalized medicine · 2022Review
- Intra-Abdominal Malignant Melanoma: Challenging Aspects of Epidemiology, Clinical and Paraclinical Diagnosis and Optimal Treatment-A Literature Review.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.
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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.