Correlation Between Depression-Associated Genes and Cancer Types: Predicting Cancer Based on Mutation Frequencies

Abstract

This study examines the potential interconnection between genetic mutations frequently observed in various types of cancer and depression, aiming to elucidate shared genetic factors that may influence the etiology and treatment of both conditions. Genomic profiles from cancer patients were extracted using cBioPortal to perform comparisons with a subset of individuals carrying genetic variants linked to depression. The analysis employed advanced methodologies, including HJ biplot K-means and DBSCAN clustering algorithms for pattern grouping in 2D. This process generated a dataset, enabling the training and testing of machine learning and deep learning classification models. Clustering results highlighted genes associated with depression and mutation patterns across different cancer types, such as skin, uterine, cervical, stomach, and prostate cancer. Furthermore, in classifying cancers with similar characteristics potentially related to depression, an F1 score of 0.95 or higher was achieved using random forest, KNN, and neural network models. The implementation details and source code for this research are available in https://github.com/Aptroide/Genes_Protocol, providing transparency and facilitating further studies.

Keywords

• Artificial intelligence
• cancer
• data synthesis
• depression