Evidence map›Paper›PMID 24643254›Full record

ArticlePloS one2014

Cancer-risk module identification and module-based disease risk evaluation: a case study on lung cancer.

Xu Jia, Zhengqiang Miao, Wan Li, Liangcai Zhang, Chenchen Feng, Yuehan He, Xiaoman Bi, Liqiang Wang, Youwen Du, Min Hou and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer.Journal of Huazhong University of Science and Technology. Medical sciences = Hua zhong ke ji da xue xue bao. Yi xue Ying De wen ban = Huazhong keji daxue xuebao. Yixue Yingdewen ban · 2017
    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

14 authors.

Xu JiaCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Zhengqiang MiaoCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Liangcai ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Chenchen FengCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Yuehan HeCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Xiaoman BiCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Liqiang WangCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Youwen DuCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Min HouCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Dapeng HaoCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Yun XiaoCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.
Kongning LiCollege of Bioinformatics Science and Technology, Harbin Medical University,Harbin,Hei Longjiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms.

Indexed as

TranscriptomeBiomarkers, TumorCase-Control StudiesGene OntologyHumansLung NeoplasmsRisk AssessmentROC CurveBiomarkers, Tumor

Identifiers

PMID24643254
PMCPMC3958511

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.