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Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

1
Department of Medical Physics, School of Medicine, University of Patras, 26504 Patras, Greece
2
Department of Dermatology, School of Medicine, University of Patras, 26504 Patras, Greece
3
Department of Biochemistry, School of Medicine, University of Patras, 26504 Patras, Greece
4
Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Ekaterini Chatzaki
Int. J. Mol. Sci. 2022, 23(3), 1299; https://doi.org/10.3390/ijms23031299
Received: 7 January 2022 / Revised: 20 January 2022 / Accepted: 20 January 2022 / Published: 24 January 2022
(This article belongs to the Special Issue Artificial Intelligence in Biomarker Discovery)
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. View Full-Text
Keywords: miRNAs; gene targets; cutaneous melanoma; artificial intelligence; metastasis; recurrence; NGS analysis; precision medicine miRNAs; gene targets; cutaneous melanoma; artificial intelligence; metastasis; recurrence; NGS analysis; precision medicine
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MDPI and ACS Style

Korfiati, A.; Grafanaki, K.; Kyriakopoulos, G.C.; Skeparnias, I.; Georgiou, S.; Sakellaropoulos, G.; Stathopoulos, C. Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View. Int. J. Mol. Sci. 2022, 23, 1299. https://doi.org/10.3390/ijms23031299

AMA Style

Korfiati A, Grafanaki K, Kyriakopoulos GC, Skeparnias I, Georgiou S, Sakellaropoulos G, Stathopoulos C. Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View. International Journal of Molecular Sciences. 2022; 23(3):1299. https://doi.org/10.3390/ijms23031299

Chicago/Turabian Style

Korfiati, Aigli, Katerina Grafanaki, George C. Kyriakopoulos, Ilias Skeparnias, Sophia Georgiou, George Sakellaropoulos, and Constantinos Stathopoulos. 2022. "Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View" International Journal of Molecular Sciences 23, no. 3: 1299. https://doi.org/10.3390/ijms23031299

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