Identification of novel alternative splicing biomarkers for breast cancer with LC/MS/MS and RNA-Seq.

Autor: Zhang F; Vermont Biomedical Research Network and Department of Biology, University of Vermont, Burlington, VT, 05405, USA. fan.zhang@uvm.edu.; Institute for Translational Research and Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA. fan.zhang@uvm.edu., Deng CK; School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA., Wang M; Department of Biochemistry and Molecular Biology, IU School of Medicine, Indianapolis, IN, 46202, USA.; Indiana Center for Systems Biology and Personalized Medicine, Indianapolis, IN, 46202, USA., Deng B; Vermont Biomedical Research Network and Department of Biology, University of Vermont, Burlington, VT, 05405, USA.; Institute for Translational Research and Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA., Barber R; Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA., Huang G; Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, People's Republic of China. huangg@sumhs.edu.cn.
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2020 Dec 03; Vol. 21 (Suppl 9), pp. 541. Date of Electronic Publication: 2020 Dec 03.
DOI: 10.1186/s12859-020-03824-8
Abstrakt: Background: Alternative splicing isoforms have been reported as a new and robust class of diagnostic biomarkers. Over 95% of human genes are estimated to be alternatively spliced as a powerful means of producing functionally diverse proteins from a single gene. The emergence of next-generation sequencing technologies, especially RNA-seq, provides novel insights into large-scale detection and analysis of alternative splicing at the transcriptional level. Advances in Proteomic Technologies such as liquid chromatography coupled tandem mass spectrometry (LC-MS/MS), have shown tremendous power for the parallel characterization of large amount of proteins in biological samples. Although poor correspondence has been generally found from previous qualitative comparative analysis between proteomics and microarray data, significantly higher degrees of correlation have been observed at the level of exon. Combining protein and RNA data by searching LC-MS/MS data against a customized protein database from RNA-Seq may produce a subset of alternatively spliced protein isoform candidates that have higher confidence.
Results: We developed a bioinformatics workflow to discover alternative splicing biomarkers from LC-MS/MS using RNA-Seq. First, we retrieved high confident, novel alternative splicing biomarkers from the breast cancer RNA-Seq database. Then, we translated these sequences into in silico Isoform Junction Peptides, and created a customized alternative splicing database for MS searching. Lastly, we ran the Open Mass spectrometry Search Algorithm against the customized alternative splicing database with breast cancer plasma proteome. Twenty six alternative splicing biomarker peptides with one single intron event and one exon skipping event were identified. Further interpretation of biological pathways with our Integrated Pathway Analysis Database showed that these 26 peptides are associated with Cancer, Signaling, Metabolism, Regulation, Immune System and Hemostasis pathways, which are consistent with the 256 alternative splicing biomarkers from the RNA-Seq.
Conclusions: This paper presents a bioinformatics workflow for using RNA-seq data to discover novel alternative splicing biomarkers from the breast cancer proteome. As a complement to synthetic alternative splicing database technique for alternative splicing identification, this method combines the advantages of two platforms: mass spectrometry and next generation sequencing and can help identify potentially highly sample-specific alternative splicing isoform biomarkers at early-stage of cancer.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje