Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/061085

Characterization of kinase gene expression and splicing profile in prostate cancer with RNA-Seq data

Huijuan Feng·Tingting Li·Xuegong Zhang
View original source ↗Export metadata ↓

Abstract

Background: Alternative splicing is a ubiquitous post-transcriptional process in most eukaryotic genes. Aberrant splicing isoforms and abnormal isoform ratios can contribute to cancer development. Kinase genes are key regulators of various cellular processes. Many kinases are found to be oncogenic and have been intensively investigated in the study of cancer and drugs. RNA-Seq provides a powerful technology for genome-wide study of alternative splicing in cancer besides the conventional gene expression profiling. But this potential has not been fully demonstrated yet. Methods: Here we characterized the transcriptome profile of prostate cancer using RNA-Seq data from viewpoints of both differential expression and differential splicing, with an emphasis on kinase genes and their splicing variations. We built up a pipeline to conduct differential expression and differential splicing analysis. Further functional enrichment analysis was performed to explore functional interpretation of the genes. With focus on kinase genes, we performed kinase domain analysis to identify the functionally important candidate kinase gene in prostate cancer. We further calculated the expression level of isoforms to explore the function of isoform switching of kinase genes in prostate cancer. Results: We identified distinct gene groups from differential expression and splicing analysis, which suggested that alternative splicing adds another level to gene expression regulation. Enriched GO terms of differentially expressed and spliced kinase genes were found to play different roles in regulation of cellular metabolism. Function analysis on differentially spliced kinase genes showed that differentially spliced exons of these genes are significantly enriched in protein kinase domains. Among them, we found that gene CDK5 has isoform switching between prostate cancer and benign tissues, which may affect cancer development by changing androgen receptor (AR) phosphorylation. The observation was validated in another RNA-Seq dataset of prostate cancer cell lines. Conclusions: Our work characterized the expression and splicing profile of kinase genes in prostate cancer and proposed a hypothetical model on isoform switching of CDK5 and AR phosphorylation in prostate cancer. These findings bring new understanding to the role of alternatively spliced kinases in prostate cancer and demonstrate the use of RNA-Seq data in studying alternative splicing in cancer.

Source connections

Explore related subjects

BioinformaticsPreprint

Keep this discovery

BibTeXRIS

Huijuan Feng, Tingting Li, Xuegong Zhang. 2016-06-29. Characterization of kinase gene expression and splicing profile in prostate cancer with RNA-Seq data. https://doi.org/10.1101/061085

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

iCARE: An R Package to Build and Apply Absolute Risk Models

This report describes a R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individuals risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Bioinformatics↗
bioRxiv2016

deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

SummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.\n\nAvailabilitythe C++ source code of deSPI is available at https://github.com/hitbc/deSPI\n\nContactydwang@hit.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2016

De novo assembly of viral quasispecies using overlap graphs

A viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

Bioinformatics↗