Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data - Kutlay, Ayşegül; Aydin Son, Yeşim (Frontiers in Molecular Biosciences, 2021)
Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation. Method: We developed multiple machine learning models to distinguish the metastasis by integrating miRNA, mRNA, and DNA methylation markers. We used the TCGA melanoma dataset to differentiate metastatic melanoma samples by assessing a set of predictive models. For this purpose, machine learning models using support vector machine with different kernels, artificial neural networks, random forest, AdaBoost, and Naïve Bayes are compared. An iterative combination of differentially expressed miRNA, mRNA, and methylation signatures is used as candidate markers to reveal each new biomarker category's impact. In each iteration, the performances of the combined models are calculated. During all comparisons, the choice of feature selection method and under and oversampling approaches are analyzed. Selected biomarkers of the highest performing models are further analyzed for the biological interpretation of functional enrichment. Results: In the initial model, miRNA biomarkers can identify metastatic melanoma with the 81% F-score. The addition of mRNA markers upon miRNA increased F-score to 92 %. In the final integrated model, the addition of the methylation data result in a similar F-score of 92% but produced a stable model with low variance across multiple trials. Conclusion: Our results support the role of miRNA regulation in metastatic melanoma as miRNA markers models metastasis outcomes with high accuracy. Moreover, the integrated evaluation of miRNA with mRNA and Methylation biomarkers increases the model's power. It populates selected biomarkers on the metastasis-associated pathways of melanoma, such as "Osteoclast," "Rap1 Signaling" "and "Chemokine Signaling" Pathways.