Evidence map›Paper›PMID 28436302›Full record

ArticleThe Journal of international medical research2018

Gene set enrichment analysis: A genome-wide expression profile-based strategy for discovering functional microRNA-disease relationships.

Yin Ni, Caiyun Song, Shuqing Jin, Zhoufeng Chen, Ming Ni, Lu Han, Jiansheng Wu, Yin Jin

Abstract read
In one paragraph

Article in The Journal of international medical research, 2018. 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

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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
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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

8 authors.

Yin Ni1 Department of Intensive Care Unit, Zhejiang Provincial People's Hospital, Hangzhou, China.
Caiyun Song2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Shuqing Jin2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Zhoufeng Chen2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Ming Ni3 Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
Lu Han3 Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
Jiansheng Wu2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Yin Jin2 Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective To explore stable and functional microRNA (miRNA)-disease relationships using a genome-wide expression profile pattern matching strategy. Methods We applied the ranked microarray pattern matching strategy Gene Set Enrichment Analysis to identify miRNA permutations with similar expression patterns to diseases. We also used quantitative reverse transcription PCR to validate the predicted expression levels of miRNAs in three diseases: inflammatory bowel disease (IBD), oesophageal cancer, and colorectal cancer. Results We found that hsa-miR-200 c was upregulated more than 40-fold in oesophageal cancer. The expression of miR-16 and miR-124 was not consistently upregulated in IBD or colorectal cancer. Conclusions Our results suggest that this expression profile matching strategy can be used to identify functional miRNA-disease relationships.

Indexed as

Genome, HumanAdultAgedColorectal NeoplasmsDatasets as TopicEsophageal NeoplasmsFemaleGene Expression ProfilingGene Expression RegulationGenome-Wide Association StudyHumansInflammatory Bowel DiseasesMaleMicroRNAsMiddle AgedMultigene FamilyMicroRNAsMIRN124 microRNA, humanMIRN16 microRNA, humanMIRN200 microRNA, humancancerGene Set Enrichment Analysisgenome-wide expression patterninflammatory bowel diseasemicroRNA

Identifiers

PMID28436302
PMCPMC5971487

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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.