Evidence map›Paper›PMID 26208504›Full record

ArticleJournal of molecular medicine (Berlin, Germany)2015

Integrated systems approach identifies risk regulatory pathways and key regulators in coronary artery disease.

Yan Zhang, Dianming Liu, Lihong Wang, Shuyuan Wang, Xuexin Yu, Enyu Dai, Xinyi Liu, Shanshun Luo, Wei Jiang

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of molecular medicine (Berlin, Germany), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Translational Bioinformatics: Past, Present, and Future.Genomics, proteomics & bioinformatics · 2016
    Review
  8. 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.

Yan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Dianming LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Lihong WangInstitute of Cancer Prevention and Treatment, Harbin Medical University, Harbin, China.
Shuyuan WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xuexin YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Enyu DaiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Xinyi LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Shanshun LuoDepartment of Gerontology, The First Hospital of Harbin Medical University, Harbin Medical University, Harbin, China. Luoss63@126.com.
Wei JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. jiangwei@hrbmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledCoronary artery disease (CAD) is the most common type of heart disease. However, the molecular mechanisms of CAD remain elusive. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, inferring risk regulatory pathways is an important step toward elucidating the mechanisms underlying CAD. With advances in high-throughput data, we developed an integrated systems approach to identify CAD risk regulatory pathways and key regulators. Firstly, a CAD-related core subnetwork was identified from a curated transcription factor (TF) and microRNA (miRNA) regulatory network based on a random walk algorithm. Secondly, candidate risk regulatory pathways were extracted from the subnetwork by applying a breadth-first search (BFS) algorithm. Then, risk regulatory pathways were prioritized based on multiple CAD-associated data sources. Finally, we also proposed a new measure to prioritize upstream regulators. We inferred that phosphatase and tensin homolog (PTEN) may be a key regulator in the dysregulation of risk regulatory pathways. This study takes a closer step than the identification of disease subnetworks or modules. From the risk regulatory pathways, we could understand the flow of regulatory information in the initiation and progression of the disease. Our approach helps to uncover its potential etiology. KEY MESSAGES: We developed an integrated systems approach to identify risk regulatory pathways. We proposed a new measure to prioritize the key regulators in CAD. PTEN may be a key regulator in dysregulation of the risk regulatory pathways.

Indexed as

Gene Expression RegulationSignal TransductionAlgorithmsCluster AnalysisComputational BiologyCoronary Artery DiseaseDatasets as TopicGene Expression ProfilingGene Regulatory NetworksHumansMicroRNAsProtein Interaction MappingProtein Interaction MapsPTEN PhosphohydrolaseSystems BiologyTranscription FactorsMicroRNAsPTEN PhosphohydrolaseTranscription FactorsCoronary artery diseasemiRNARegulatory pathwaySystems biologyTranscription factor

Identifiers

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.