Evidence map›Paper›PMID 27899621›Full record

ArticleNucleic acids research2017

Cooperative genomic alteration network reveals molecular classification across 12 major cancer types.

Hongyi Zhang, Yulan Deng, Yong Zhang, Yanyan Ping, Hongying Zhao, Lin Pang, Xinxin Zhang, Li Wang, Chaohan Xu, Yun Xiao and 1 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
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

11 authors.

Hongyi ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Yulan DengCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Yong ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Yanyan PingCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Hongying ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Lin PangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Xinxin ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Li WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Chaohan XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Yun XiaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China xiaoyun@ems.hrbmu.edu.cn.
Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China lixia@hrbmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification.

Indexed as

Gene Regulatory NetworksGenetic VariationGenomicsAlgorithmsBreast NeoplasmsComputational BiologyDNA Copy Number VariationsGlioblastomaHumansKaplan-Meier EstimateMutationNeoplasmsPrognosis

Identifiers

PMID27899621
PMCPMC5314758

What OpenQuestion holds

Textmetadata
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.