Identifying Prostate Cancer Biomarkers | Multi-omics
A recent study using multi-omics in PCa showed that proteomics features can predict whether the disease will recur, saying this method is more informative than other omics. Integration of transcriptomics and metabolomics revealed changes in metabolic pathways. The authors aim to use high-quality multi-omics datasets to better understand prostate cancer progression, subtyping, and individual variation in drug response and present a simplified protocol for routine integration of multi-omics data.
Prostate cancer (PCa) is a common and dangerous disease in men that can affect anyone—in fact, this type of cancer is the most common malignancy in men. The exact cause of prostate cancer is not known, but several risk factors have been identified, including age, family history, genetic ancestry, and lifestyle factors such as diet and physical activity. Other possible factors include hormonal imbalances, exposure to certain chemicals, and conditions such as obesity. However, more research is needed to fully understand the causes of prostate cancer.
Current clinicopathologic indicators are not useful for predicting treatment success, so early intervention and precise treatment are critical. Multi-omic technologies such as genomics, transcriptomics, epigenomics, proteomics, and metabolomics have advanced biomedical research and helped unlock biological relationships for improved diagnosis and treatment. A more comprehensive understanding of prostate cancer mechanisms and biomarkers requires a systems biology approach that utilizes the integration of multi-omics data.
A recent study using multi-omics in PCa showed that proteomics features can predict whether the disease will recur, saying this method is more informative than other omics. Integration of transcriptomics and metabolomics revealed changes in metabolic pathways. The authors aim to use high-quality multi-omics datasets to better understand prostate cancer progression, subtyping, and individual variation in drug response. Therefore, they present a simplified protocol for routine integration of multi-omics data.
How to get an insight into the processes in the body
Let's take a quick look at the methods the researchers used: The study used a systems biology approach to analyze genomic, transcriptomic, and metabolomic data from publicly available databases. Quality data were selected through a screening process followed by performance evaluation. The study included:
- Genome analysis: data retrieval, quality control, read mapping and variant calling, and SNP set enrichment analysis.
- Transcriptome analysis: data retrieval and quality control, read sequence mapping and differential gene expression analysis.
- Metabolome analysis: data retrieval, inspection, standardization of metabolite names, and creation of a Pathway Deregulation Score Matrix.
Integrated multi-omics analysis was also performed. Tools used include MultiQC, Trimmomatic-Qc, FastQC, BWA-MEM, Samtools, vcftools, snpEff, HISAT2, DESeq2, and various pathway analysis algorithms. If you want to learn more about the tools, check out the study—we'll keep it short here.
E2F, Chk1, and MCM genes involved in tumor growth
So, in the study, publicly available genome, transcriptome, and metabolome data from patients with prostate cancer were analyzed. In doing so, their analysis provides an exciting insight into what is happening in the body. The results showed a similar metabolic pathway in the RNA-Seq and metabolic data sets of people at risk. The study found that the glycolytic metabolic pathway is highly regulated in PCa as it provides the building blocks for the synthesis of precursors needed for cell growth.
Glucose and pyruvate were the major metabolites associated with glycolysis and the Warburg effect. The upregulated cell cycle pathway showed that the E2F, Chk1, and MCM genes were involved in promoting cell proliferation. In contrast, the downregulated gene TGFR inhibited cell growth.
From the up-regulated cell cycle pathway, E2 factor (E2F), Checkpoint kinase 1 (Chk1), and
Minichromosome Maintenance Complex (MCM) are the up-regulated gene and Transforming
Growth Factor (TGFR) is down-regulated. The up-regulated genes are majorly used by cancer cells
to promote tumour growth, cell proliferation and control of cell cycle DNA replication.
Up-regulation of the gene RAB38 was related to the increase in mitochondrial respiration and enhanced cell growth. Pyruvate-related metabolic pathways were directed to oxidative phosphorylation to increase cell growth. The study also found that the GLYATL1 gene is highly expressed in PCa and associated with early stage PCa.