Machine learning and Bioinformatics Models to Identify Gene Expression Patterns of Ovarian Cancer Associated with Disease Progression and Mortality

Publication date: Available online 23 October 2019Source: Journal of Biomedical InformaticsAuthor(s): Md. Ali Hossain, Sheikh Muhammad Saiful Islam, Julian M.W. Quinn, Fazlul Huq, Mohammad Ali MoniAbstractOvarian cancer (OC) is a common cause of cancer death among women worldwide, so there is a pressing need to identify factors influencing OC mortality. Much OC patient clinical data is publicly accessible via the Broad Institute Cancer Genome Atlas (TCGA) datasets which include patient age, cancer site, stage and subtype and patient survival, as well as OC gene transcription profiles. These allow studies correlation of OC patient survival (and other clinical variables) with gene expression to identify new OC biomarkers to predict patient mortality. We integrated clinical and tissue transcriptome data from patients available from the TCGA portal. We determined OC mRNA expression levels (compared to normal ovarian tissue) of 41 genes already implicated in OC progression, and assessed their ability to use their OC tissue expression levels predicts patient survival. We employed Cox Proportional Hazard regression models to analyse clinical factors and transcriptomic information to determine relative effects on survival that is associated with each factor. Multivariate analysis of combined data (clinical and gene mRNA expression) found age and ovary tumour site significantly correlated with patient survival. The univariate analysis also confirmed significant differences in patient ...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research