Prostate Cancer
Prostate cancer is the most common noncutaneous cancer in men worldwide. Metastatic disease is the leading cause of prostate cancer-associated deaths. Many model systems have been developed to study the genetics and biology of prostate cancer. Cataloging the genetic drivers of prostate cancer has been foundational to defining disease subtypes and associated therapeutic strategies.
Since the introduction of microarrays, there has been considerable interest in using whole-genome expression profiling to gain insight into cancer and to identify key genetic mediators. The rapid development in computational approaches has identified and will continue to identify novel driver genes in prostate cancer.
Network-based Approach
There is a need to identify genetic mediators of prostate cancer, where invasion and distant metastases determine the clinical outcome of the disease. A network biology approach can be used advantageously to identify the genetic mediators and mediating pathways associated with a disease. Whole-genome expression profiling offers promise in this regard. Reverse-engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease.
- Computational reconstruction of interaction mechanisms of pathways in prostate cancer.
- Identify novel roles for hub genes in this pathway.
- Infer molecular mechanisms of interaction among pathway members.
Data Resources
- NIH Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)
- ONCOMINE Database (www.oncomine.org)
- EBI ArrayExpress (https://www.ebi.ac.uk/arrayexpress/)
- Broad Institute Cancer Datasets (https://www.broadinstitute.org/datasets)
- St Jude Research Datasets (https://www.stjude.org/research/why-st-jude/data-tools.html)
- ProCanBio (https://webs.iiitd.edu.in/raghava/procanbio/)
Computational Tools
- Gene Set Enrichment Analysis (GSEA, http://www.broad.mit.edu/gsea/) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).
- MNI algorithm uses a training data set of hundreds of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue.
- RMAExpress is a standalone GUI program to compute gene expression summary values for data and to carry out quality assessment using probe-level metrics.
- Bayesian data integration is a method to to predict functional relationships between proteins under a variety of conditions.
Molecular research in cancer is one of the largest areas of bioinformatic investigation. Computational biology methods are generalizable to other tissue types, cancers, and organisms, and new information about pathways will allow us to further understand prostate cancer and to develop more effective prevention and treatment strategies.
Why Choose Us?
CD ComputaBio utilizes microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.
Moreover, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.
Reference:
- Ayla Ergün, et al. A network biology approach to prostate cancer. Molecular Systems Biology (2007)3:82.
