Evidence map›Paper›PMID 33272210›Full record

ArticleBMC bioinformatics2020

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

Fan Zhang, Chris K Deng, Mu Wang, Bin Deng, Robert Barber, Gang Huang

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 2 countries.

Fan ZhangVermont Biomedical Research Network and Department of Biology, University of Vermont, Burlington, VT, 05405, USA. fan.zhang@uvm.edu.ORCID http://orcid.org/0000-0003-3502-1808
Chris K DengSchool of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.
Mu WangDepartment of Biochemistry and Molecular Biology, IU School of Medicine, Indianapolis, IN, 46202, USA.
Bin DengVermont Biomedical Research Network and Department of Biology, University of Vermont, Burlington, VT, 05405, USA.
Robert BarberDepartment of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA.
Gang HuangShanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, People's Republic of China. huangg@sumhs.edu.cn.
University of Vermont · USCenter for Systems Biology · USShanghai University of Medicine and Health Sciences · CNUniversity of Illinois Urbana-Champaign · USUniversity of North Texas · US

Funding

Vermont INBRE Administrative Supplement 2024 AWD 118P20GM103449 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI CHRISTOPHER S FRANCKLYN · 2012 to 2026
$58.4M
NIGMS NIH HHS P20 GM103449NIH HHS P20GM103449
6 · The paper itself

Abstract

backgroundAlternative 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.

resultsWe 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.

conclusionsThis 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.

Indexed as

RNA-SeqAlgorithmsAlternative SplicingAmino Acid SequenceBiomarkers, TumorBreast NeoplasmsChromatography, LiquidDatabases, ProteinFemaleHumansPeptidesProtein IsoformsProteomeTandem Mass SpectrometryBiomarkers, TumorPeptidesProtein IsoformsProteomeAlternative splicingBiomarker discoveryBreast cancerMass spectrometryPathway analysis

Identifiers

PMID33272210
PMCPMC7713335
OpenAlexW3106710884

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.