Evidence map›Paper›PMID 28879530›Full record

ArticleMolecular genetics and genomics : MGG2018

Correlation analyses revealed global microRNA-mRNA expression associations in human peripheral blood mononuclear cells.

Lan Wang, Jiang Zhu, Fei-Yan Deng, Long-Fei Wu, Xing-Bo Mo, Xiao-Wei Zhu, Wei Xia, Fang-Fei Xie, Pei He, Peng-Fei Bing and 7 more

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular genetics and genomics : MGG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

5 citing papers in PubMed, 13 citations in OpenAlex.

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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

17 authors at 2 institutions in 2 countries.

Lan WangCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Jiang ZhuThe Second Affiliated Hospital of Soochow University, Suzhou, 215008, Jiangsu, People's Republic of China.
Fei-Yan DengCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Long-Fei WuCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Xing-Bo MoCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Xiao-Wei ZhuCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Wei XiaCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Fang-Fei XieCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Pei HeCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Peng-Fei BingCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Ying-Hua QiuCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Xiang LinCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Xin LuCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Lei ZhangCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Neng-Jun YiDepartment of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
Yong-Hong ZhangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China.
Shu-Feng LeiCenter for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, Jiangsu, People's Republic of China. leisf@suda.edu.cn.
Soochow University · CNUniversity of Alabama at Birmingham · US

Funding

National Natural Science Foundation of China 31271336National Natural Science Foundation of China 31401079National Natural Science Foundation of China 81372024National Natural Science Foundation of China 81373010National Natural Science Foundation of China 81401343National Natural Science Foundation of China 81473046National Natural Science Foundation of China 81502868National Natural Science Foundation of China 81541068Natural Science Foundation of Jiangsu Province BK20130300Natural Science Foundation of Jiangsu Province BK20150346Natural Science Research Project of Jiangsu Provincial Higher Education 16KJA330001Startup Fund from Soochow University Q413900112Startup Fund from Soochow University Q413900712
6 · The paper itself

Abstract

MicroRNAs (miRNAs) can regulate gene expression through binding to complementary sites in the 3'-untranslated regions of target mRNAs, which will lead to existence of correlation in expression between miRNA and mRNA. However, the miRNA-mRNA correlation patterns are complex and remain largely unclear yet. To establish the global correlation patterns in human peripheral blood mononuclear cells (PBMCs), multiple miRNA-mRNA correlation analyses and expression quantitative trait locus (eQTL) analysis were conducted in this study. We predicted and achieved 861 miRNA-mRNA pairs (65 miRNAs, 412 mRNAs) using multiple bioinformatics programs, and found global negative miRNA-mRNA correlations in PBMC from all 46 study subjects. Among the 861 pairs of correlations, 19.5% were significant (P < 0.05) and ~70% were negative. The correlation network was complex and highlighted key miRNAs/genes in PBMC. Some miRNAs, such as hsa-miR-29a, hsa-miR-148a, regulate a cluster of target genes. Some genes, e.g., TNRC6A, are regulated by multiple miRNAs. The identified genes tend to be enriched in molecular functions of DNA and RNA binding, and biological processes such as protein transport, regulation of translation and chromatin modification. The results provided a global view of the miRNA-mRNA expression correlation profile in human PBMCs, which would facilitate in-depth investigation of biological functions of key miRNAs/mRNAs and better understanding of the pathogenesis underlying PBMC-related diseases.

Indexed as

AutoantigensChromatin Assembly and DisassemblyComputational BiologyGene Expression RegulationGene Regulatory NetworksHumansLeukocytes, MononuclearMicroRNAsQuantitative Trait LociRNA-Binding ProteinsRNA, MessengerAutoantigensMicroRNAsMIRN148 microRNA, humanMIRN29a microRNA, humanRNA-Binding ProteinsRNA, MessengerTNRC6A protein, humanCorrelationmiRNAmRNANetworkPeripheral blood mononuclear cells

Identifiers

PMID28879530
OpenAlexW2752366057

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.