Analysis of Viability of TCGA and GTEx Gene Expression for Gleason Grade Identification

Abstract

Gleason grade is a critical indicator for determining patient treatment for prostate cancer. In this paper, we analyze the viability of RNA sequencing gene expression data for Gleason grade identification. We combine datasets from the TCGA (sampled from cancer patients) and GTEx (sampled from healthy patients) databases. Using mutual information techniques, we reduce the dimensionality from 19046 genes to only the 20 most predictive genes. Then, we apply an unsupervised approach to analyze the separability of the grades of cancer. We use the t-SNE algorithm to map features into two dimensions and apply a Gaussian Mixture Model (GMM) for clustering. The result shows a clear visual separability between cancer and healthy samples. However, the grades of cancer themselves are not visually separable. Also, we apply the Mann-Whitney U test to compare the statistical similarity of the different Gleason grades and find that most grades are similar to each other. We further apply a random forest model to estimate the Gleason grade. The results show that the model accurately predicts whether a sample comes from healthy or cancer tissue. However, the model is weak in classifying the Gleason grade. The best performing model has a weighted macro-averaged F1 score of 0.66, improving on a baseline score of 0.22 obtained by random guessing. Our results indicate that the difference in gene expression among Gleason grades is relatively small compared to the difference between healthy and cancer samples. Thus, gene expression alone cannot be used for Gleason grade identification.