Evidence map›Paper›PMID 27234029›Full record

ArticleBMC genomics2016

Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

Zhilong Jia, Ying Liu, Naiyang Guan, Xiaochen Bo, Zhigang Luo, Michael R Barnes

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
3.8field-weighted citation impact, top 6% 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

32 citing papers in PubMed, 71 citations in OpenAlex.

  1. Review
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  5. A drug repurposing method based on inhibition effect on gene regulatory network.Computational and structural biotechnology journal · 2023
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  15. Scoring functions for drug-effect similarity.Briefings in bioinformatics · 2021
    Review
  16. Article
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  18. Major Vault Protein (MVP) Associated WithFrontiers in cell and developmental biology · 2021
    Article
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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

6 authors at 4 institutions in 2 countries.

Zhilong JiaDepartment of Chemistry and Biology, College of Science, National University of Defense Technology, Changsha, Hunan, 410073, People's Republic of China.
Ying LiuHunan Key Laboratory of Medical Epigenomics, Department of Dermatology, Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, People's Republic of China.
Naiyang GuanCollege of Computer, National University of Defense Technology, Changsha, 410073, People's Republic of China.
Xiaochen BoBeijing Institute of Radiation Medicine, Beijing, 100850, People's Republic of China.
Zhigang LuoCollege of Computer, National University of Defense Technology, Changsha, 410073, People's Republic of China. zgluo@nudt.edu.cn.
Michael R BarnesWilliam Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK. m.r.barnes@qmul.ac.uk.
National University of Defense Technology · CNCentral South University · CNQueen Mary University of London · GBWilliam Harvey Research Institute · GB

Funding

Medical Research Council G1001516Medical Research Council G1001518Medical Research Council JID-2015-0339Medical Research Council MR/K006584/1Medical Research Council MR/L011808/1
6 · The paper itself

Abstract

backgroundDrug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context.

resultsWe demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena .

conclusionsIn conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and repositioning, allowing the grouping and prioritisation of drug repositioning candidates on the basis of putative mode of action.

Indexed as

Drug RepositioningSoftwareAlgorithmsCluster AnalysisDrug DiscoveryGene Expression ProfilingGene Regulatory NetworksWeb BrowserWorkflowDrug repositioningMode of actionPathway analysisPsoriasis

Identifiers

PMID27234029
PMCPMC4884357
OpenAlexW2398816925

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.