Genomic Biomarker Discovery in Disease Progression and Therapy Response in Bladder Cancer Utilizing Machine Learning

SIMPLE SUMMARY: Cancer in all its forms of expression is a major cause of death. The bladder cancer is also causes the same. finding the biomarkers responsible for the cancer is a challenging task and in certain cases, such as disease progression and therapy response, it become more challenging. The advancements in technology provides latest machine learning methods that help to identify the genomic biomarkers computationally. In this paper, the genomic biomarkers are tracked for bladder cancer from Univeristy of Calgary cohort and different bioinformatics methods, such as differential gene expression, survival rate estimation, consensus gene selection approaches were optimally used. The elastic-net based regression method has been utilized as a machine learning method which shows satisfactory results. ABSTRACT: Cancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan–Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner.