Evidence map›Paper›PMID 30012835›Full record

ArticleGenome research2018

A comprehensive characterization of

Yuichi Shiraishi, Keisuke Kataoka, Kenichi Chiba, Ai Okada, Yasunori Kogure, Hiroko Tanaka, Seishi Ogawa, Satoru Miyano

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed, 77 citations in OpenAlex.

  1. Review
  2. Article
  3. Identification of Intronic Variants inNeurology. Genetics · 2026
    Article
  4. International journal of molecular sciences · 2025
    Article
  5. Article
  6. Article
  7. Epitranscriptomic sculpting: the role of mMedical oncology (Northwood, London, England) · 2025
    Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
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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

8 authors at 2 institutions in 1 country.

Yuichi Shiraishi *Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Keisuke Kataoka *Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan.
Kenichi ChibaLaboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Ai OkadaLaboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Yasunori KogureDepartment of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan.
Hiroko TanakaLaboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Seishi OgawaDepartment of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan.
Satoru MiyanoLaboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
The University of Tokyo · JPKyoto University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although many driver mutations are thought to promote carcinogenesis via abnormal splicing, the landscape of splicing-associated variants (SAVs) remains unknown due to the complexity of splicing abnormalities. Here, we developed a statistical framework to systematically identify SAVs disrupting or newly creating splice site motifs and applied it to matched whole-exome and transcriptome sequencing data from 8976 samples across 31 cancer types, generating a catalog of 14,438 SAVs. Such a large collection of SAVs enabled us to characterize their genomic features, underlying mutational processes, and influence on cancer driver genes. In fact, ∼50% of SAVs identified were those disrupting noncanonical splice sites (non-GT-AG dinucleotides), including the third and fifth intronic bases of donor sites, or newly creating splice sites. Mutation signature analysis revealed that tobacco smoking is more strongly associated with SAVs, whereas ultraviolet exposure has less impact. SAVs showed remarkable enrichment of cancer-related genes, and as many as 14.7% of samples harbored at least one SAVs affecting them, particularly in tumor suppressors. In addition to intron retention, whose association with tumor suppressor inactivation has been previously reported, exon skipping and alternative splice site usage caused by SAVs frequently affected tumor suppressors. Finally, we described high-resolution distributions of SAVs along the gene and their splicing outcomes in commonly disrupted genes, including

Indexed as

Gene Expression Regulation, NeoplasticNeoplasm ProteinsRNA, NeoplasmRNA Splice SitesRNA SplicingFemaleHumansMaleNeoplasm ProteinsRNA, NeoplasmRNA Splice Sites

Identifiers

PMID30012835
PMCPMC6071634
OpenAlexW2952183925

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.