Abstract:
Prostate cancer (PCa) is one of the most common malignancies, and many studies have shown
that PCa has a poor prognosis, which varies across different ethnicities. This variability is caused
by genetic diversity. High-throughput omics technologies have identified and shed some light on
the mechanisms of its progression and finding new biomarkers. Still, a systems biology approach
is needed for a holistic molecular perspective. In this study, we applied a multi-omics approach
to data analysis using different publicly available omics data sets from diverse populations to
better understand the PCa disease etiology. Our study used multiple omic datasets, which
included genomic, transcriptomic and metabolomic datasets, to identify drivers for PCa better.
Individual omics datasets were analysed separately based on the standard pipeline for each
dataset. Furthermore, we applied a novel multi-omics pathways algorithm to integrate all the
individual omics datasets. This algorithm applies the p-values of enriched pathways from unique
omics data types, which are then combined using the MiniMax statistic of the
PathwayMultiomics tool to prioritise pathways dysregulated in the omics datasets. The single
omics result indicated an association between up-regulated genes in RNA-Seq data and the
metabolomics data. Glucose and pyruvate are the primary metabolites, and the associatedpathways are glycolysis, gluconeogenesis, pyruvate kinase deficiency, and the Warburg effect
pathway. From the interim result, the identified genes in RNA-Seq single omics analysis are linked
with the significant pathways from the metabolomics analysis. The multi-omics pathway analysis
will eventually enable the identification of biomarkers shared amongst these different omics
datasets to ease prostate cancer prognosis.
