Developing a novel medulloblastoma diagnostic with miRNA biomarkers and machine learning.
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BACKGROUND: Medulloblastoma (MB) is the most common malignant brain tumor in children. Current diagnostic methods, such as MRI and lumbar puncture, are invasive and not sensitive enough, making early diagnosis challenging. MicroRNAs (miRNAs) have emerged as promising biomarkers for cancer diagnosis due to their dysregulated expression in tumors. This study aims to develop a novel machine learning (ML)-based diagnostic tool for MB using miRNA biomarkers. METHODS: We collected miRNAs associated with MB and random controls, generating sequence- and target gene-based descriptors. We employed the WEKA software to evaluate several ML models, including logistic regression, naïve Bayes, and multilayer perceptron (MLP). Attribute selection reduced noise by selecting the most significant 24 features. Model performance was evaluated using 10-fold cross-validation and independent test datasets. RESULTS: Logistic regression achieved the highest training accuracy (96.2%), while the MLP model was selected for further testing due to its ability to capture complex nonlinear relationships in biological data. The MLP model showed 78.6% accuracy on an independent MB dataset and successfully distinguished MB miRNAs from those associated with chronic myeloid leukemia (CML), further validating its specificity. CONCLUSION: The ML-based diagnostic tool using miRNA biomarkers shows promise for improving MB diagnosis, offering a non-invasive alternative to traditional methods. Further validation with larger datasets and diverse control groups is needed to refine the model.