Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method

Abstract

Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.

XPNPEP2 is associated with lymph node metastasis in prostate cancer patients

“…In addition, we analyzed XPNPEP2 gene alterations using data extracted from the cBioPortal online analysis tool and found that metastatic prostate cancer has the highest frequency of XPNPEP2 gene amplification 17,18 . Using a gene prioritization method (GP-MIDAS-VXEF) to compare prostate cancer and lymph node metastasis, Carlos Roberto Arias et.al demonstrated that XPNPEP2 is a metastatic gene candidate 19 . Additionally, Cheng te.al revealed that XPNPEP2 facilitated cervical cancer cell invasion and migration by inducing epithelial-mesenchymal transition 20 .…”

Feature selection and survival modeling in The Cancer Genome Atlas

“…Protein interaction networks in combination with gene expression data have been used to identify biomarkers associated with cancer metastases. 2 Another approach is to identify subcategories of a cancer and the associated biomarkers for each category, so as to allow treating a patient based on the cosegregation of her/his cancer profile within one cancer subcategory. Recently, subgroup-specific biomarker networks have been shown to predict glioblastoma prognosis.…”

Screening Biomarkers of Prostate Cancer by Integrating microRNA and mRNA Microarrays

“…Arias et al (2012) identified biomarkers for prostate cancer and lymph node metastasis from microarray data and the protein interaction network using the gene prioritization method. A protein-protein interaction network of established miRNA targets confirm that these proteins are highly connected and essential to the cell, affecting tumorigenesis, cell growth/proliferation, cellular death, cell assembly, and maintenance pathways (Budd et al, 2012).…”