Multimodal Gene Expression and Methylation Profiling Reveals Misclassified Tumors Beyond Histological Diagnosis

Accurate tumor classification is essential for guiding treatment, yet histology alone may overlook key molecular differences or result in misclassification. We present a multimodal strategy that integrates gene expression (mRNA) and DNA methylation data to improve classification accuracy and detect misclassified tumors. Using 6216 samples from The Cancer Genome Atlas (TCGA), we applied Support Vector Machines (SVMs) and hierarchical clustering to evaluate classification accuracy across single and integrated platforms. mRNA and methylation data alone achieved accuracies of 97% and 95.4%, respectively. Their integration further reduced false positives and improved the identification of outliers, including histologically misclassified cases such as papillary renal cell carcinoma samples clustering with bladder cancer. The integrated approach also revealed molecular subtypes correlated with somatic mutations and patient survival, offering clinically relevant insights. Our findings highlight the value of combining genetic and epigenetic profiles to refine cancer diagnostics. This framework enhances diagnostic precision, supports treatment decisions, and provides a scalable quality control tool for molecular oncology.