Evidence map›Paper›PMID 27279334›Full record

ArticleNature communications2016

SURVIV for survival analysis of mRNA isoform variation.

Shihao Shen, Yuanyuan Wang, Chengyang Wang, Ying Nian Wu, Yi Xing

Abstract read
In one paragraph

Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
–field-weighted citation impact
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

56 citing papers in PubMed.

  1. SRSF3 plays a key role in liver cancer sex disparity by facilitating hepatocyte expression of ERα and FOXA.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  8. Sources of Cancer Neoantigens beyond Single-Nucleotide Variants.International journal of molecular sciences · 2022
    Review
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  15. Genome-Wide Profiling of Alternative Splicing Signatures Associated with Prognosis and Immune Microenvironment of Hepatocellular Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2021
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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

5 authors.

Shihao ShenDepartment of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, California 90095, USA.
Yuanyuan WangDepartment of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, California 90095, USA.
Chengyang WangBioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, California 90095, USA.
Ying Nian WuDepartment of Statistics, University of California, Los Angeles, Los Angeles, California 90095, USA.
Yi XingDepartment of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, California 90095, USA.

Funding

VARIATION AND REGULATION OF ALTERNATIVE SPLICING IN HUMAN TRANSCRIPTOMESR01GM088342 · NIGMS · UNIVERSITY OF IOWA · PI XING, YI · 2010 to 2020
$2.9M
Integrative Analysis of Public RNA-seq RepositoriesR01GM105431 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ZHOU, XIANGHONG JASMINE · 2013 to 2016
$1.3M
NIGMS NIH HHS R01 GM088342NIGMS NIH HHS R01 GM105431
6 · The paper itself

Abstract

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Indexed as

Alternative SplicingRNA IsoformsSurvival AnalysisBreast NeoplasmsCarcinoma, Ductal, BreastHumansModels, StatisticalRNA Isoforms

Identifiers

PMID27279334
PMCPMC4906168

What OpenQuestion holds

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Registered trials

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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.