A novel model used to detect differential splice junctions as biomarkers in prostate cancer from RNA-Seq data

In cancer alternative RNA splicing represents one mechanism for flexible gene regulation, whereby protein isoforms can be created to promote cell growth, division and survival. Detecting novel splice junctions in the cancer transcriptome may reveal pathways driving tumorigenic events. In this regard, RNA-Seq, a high-throughput sequencing technology, has expanded the study of cancer transcriptomics in the areas of gene expression, chimeric events and alternative splicing in search of novel biomarkers for the disease.

In this study, we propose a new two-dimensional peak finding method for detecting differential splice junctions in prostate cancer using RNA-Seq data. We have designed an integrative process that involves a new two-dimensional peak finding algorithm to combine junctions and then remove irrelevant introns across different samples within a population. We have also designed a scoring mechanism to select the most common junctions.

Our computational analysis on three independent datasets collected from patients diagnosed with prostate cancer reveals a small subset of junctions that may potentially serve as biomarkers for prostate cancer.

The pipeline, along with their corresponding algorithms, are available upon request.