Interpretable AI models for predicting distant metastasis development based on genetic data: Kidney cancer example

Abstract

Kidney cancer has a high metastatic potential with up to 30% of patients developing distant metastasis after surgery. We assessed the value of AI models in predicting the metastatic potential of clear cell renal cell carcinoma (ccRCC), based on the genetic data. Tissue samples from patients with both metastatic and non-metastatic squamous cell carcinoma were analyzed, focusing on the expression and methylation levels of specific protein-coding (PC) and microRNA (miRNA) genes. Using quantitative PCR and data classification techniques, we found a correlation between metastasis and reduced expression of PC-genes CA9, NDUFA4L2, EGLN3, and BHLHE41, as well as increased methylation in miRNA genes MIR125B-1, MIR137, MIR375, MIR193A, and MIR34B. AI models were built for predicting distant metastases based on the expression values and methylation status of selected genes. One model is based on solving a regression problem and is non-interpretable, while another one is based on proposed decision rules and is interpretable. The quality of the models was assessed using sensitivity and specificity metrics, and cross-validation technology was used to ensure the reliability of the results.