Evidence map›Paper›PMID 30625331›Full record

ArticleCell reports2019

Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types.

Peilin Jia, Zhongming Zhao

Open access · goldAbstract read
In one paragraph

Article in Cell reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

20 citing papers in PubMed, 32 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Identify Non-mutational p53 Functional Deficiency in Human Cancers.Genomics, proteomics & bioinformatics · 2024
    Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Hereditary retinoblastoma iPSC model reveals aberrant spliceosome function driving bone malignancies.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  12. Review
  13. Article
  14. Review
  15. Article
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  17. Article
  18. Observational
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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

2 authors at 2 institutions in 1 country.

Peilin JiaCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Zhongming ZhaoCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. Electronic address: zhongming.zhao@uth.tmc.edu.
The University of Texas Health Science Center at Houston · USThe University of Texas MD Anderson Cancer Center · US

Funding

Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
NLM NIH HHS R01 LM012806
6 · The paper itself

Abstract

We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confidence 337 TSG × cancer events in 30 cancer types, of which 277 were unique events. The majority (91.0%) of these events had a significant downstream impact measured by reduced expression of the TSG itself (cis-effect), disturbance of the transcriptome (trans-effect), or combinatorial effects. Importantly, the transcriptomic changes associated with TSG inactivation events were stronger than the cancer lineage difference, and the same TSGs inactivated in different cancer types tended to cluster together. Several TSGs (e.g., RB1, TP53, and CDKN2A) involved in the regulation of the cell-cycle-formed clusters. Finally, we constructed subnetworks of the TSG × cancer inactivation events, including the local genes frequently disturbed upon the inactivation events.

Indexed as

Gene Expression Regulation, NeoplasticGene SilencingGenes, Tumor SuppressorGene Regulatory NetworksHumansNeoplasmsTranscriptomeCDKN2Acell cycle pathwayRB1The Cancer Genome Atlastranscriptome impacttumor suppressor genetwo-hit model

Identifiers

PMID30625331
PMCPMC7375892
OpenAlexW2910709941

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.