Evidence map›Paper›PMID 28529601›Full record

ArticleOncology letters2017

Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks.

Wan Li, Xue Bai, Erqiang Hu, Hao Huang, Yiran Li, Yuehan He, Junjie Lv, Lina Chen, Weiming He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncology letters, 2017. 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. 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

9 authors.

Wan LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Xue BaiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Erqiang HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Hao HuangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Yiran LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Yuehan HeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Junjie LvCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Lina ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Weiming HeInstitute of Opto-electronics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is one of the leading causes of mortality in females. A number of prognostic markers have been identified, including single genes, multi-gene signatures and network modules; however, the robustness of these prognostic markers is insufficient. Thus, the present study proposed a more robust method to identify breast cancer prognostic modules based on weighted protein-protein interaction networks, by integrating four sets of disease-associated expression profiles. Three identified prognostic modules were closely associated with prognosis-associated functions and survival time, as determined by Cox regression and Kaplan-Meier survival analyses. The robustness of these modules was verified with an independent profile from another platform. Genes from these modules may be useful as breast cancer prognostic markers. The prognostic modules could be used to determine the prognoses of patients with breast cancer and characterize patient recovery.

Indexed as

breast cancerprognosisprognostic modulesrobustnesssurvival analysis

Identifiers

PMID28529601
PMCPMC5434807

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.