CoChat
Sign Up
Cited by 25

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Characterization and predicted role of microRNA expression profiles associated with early childhood obesity

Ouyang, Shengrong, Tang, Renqiao, Liu, Zhuo, Ma, Feifei, Li, Yuanyuan, Wu, Jianxin·2017·Molecular Medicine Reports
DOI 10.3892/mmr.2017.7118PMC PMC5646957
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3892/mmr.2017.7118

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1. Analysis of differentially-expressed miRNAs. A total of 76 miRNAs with an expression level greater than 100 code counts are presented. 1–6, lean samples; 7–12, obese samples; miRNA, microRNA.
Figure 1. Analysis of differentially-expressed miRNAs. A total of 76 miRNAs with an expression level greater than 100 code counts are presented. 1–6, lean samples; 7–12, obese samples; miRNA, microRNA.CC BY-NC-ND · PubMed Central
Figure 2. Identification of miRNAs that were differentially expressed between groups (obese vs. lean). Among these miRNAs, miR-199a-3p/miR-199b-3p and miR-4454 exhibited at least a 1.5-fold change in expression. (A) Volcano plot of statistical significance against fold-change of differentially-expressed miRNAs. P<0.05 was considered to indicate a statistically-significant difference. (B) Fold-change of differentially-expressed miRNAs. miRNA, microRNA.
Figure 2. Identification of miRNAs that were differentially expressed between groups (obese vs. lean). Among these miRNAs, miR-199a-3p/miR-199b-3p and miR-4454 exhibited at least a 1.5-fold change in expression. (A) Volcano plot of statistical significance against fold-change of differentially-expressed miRNAs. P<0.05 was considered to indicate a statistically-significant difference. (B) Fold-change of differentially-expressed miRNAs. miRNA, microRNA.CC BY-NC-ND · PubMed Central
Figure 3. Protein-protein interaction network of predicted miRNA-targeted genes. The 14 hub genes with a degree of >10 are indicated in red. The size of the node indicates the number of interactions with other nodes within the network. miRNA, microRNA.
Figure 3. Protein-protein interaction network of predicted miRNA-targeted genes. The 14 hub genes with a degree of >10 are indicated in red. The size of the node indicates the number of interactions with other nodes within the network. miRNA, microRNA.CC BY-NC-ND · PubMed Central
Figure 4. miRNA-GO network analysis. Red box nodes represent miRNAs, and green circle nodes represent GO terms. Edges indicate the inhibitory effect of miRNAs on target genes. GO, gene ontology; miRNA, microRNA.
Figure 4. miRNA-GO network analysis. Red box nodes represent miRNAs, and green circle nodes represent GO terms. Edges indicate the inhibitory effect of miRNAs on target genes. GO, gene ontology; miRNA, microRNA.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 37

  1. Djuranovic, 2011, A parsimonious model for gene regulation by miRNAs, Science, 331, 550, 10.1126/science.1191138 · DOI
  2. Bartel, 2004, MicroRNAs: Genomics, biogenesis, mechanism, and function, Cell, 116, 281, 10.1016/S0092-8674(04)00045-5 · DOI
  3. Hulsmans, 2012, Decrease of miR-146b-5p in monocytes during obesity is associated with loss of the anti-inflammatory but not insulin signaling action of adiponectin, PLoS One, 7, e32794, 10.1371/journal.pone.0032794 · DOI
  4. Carolan, 2014, The impact of childhood obesity on inflammation, innate immune cell frequency, and metabolic microRNA expression, J Clin Endocrinol Metab, 99, E474, 10.1210/jc.2013-3529 · DOI
  5. Milagro, 2013, High-throughput sequencing of microRNAs in peripheral blood mononuclear cells: Identification of potential weight loss biomarkers, PLoS One, 8, e54319, 10.1371/journal.pone.0054319 · DOI
  6. Prats-Puig, 2013, Changes in circulating microRNAs are associated with childhood obesity, J Clin Endocrinol Metab, 98, E1655, 10.1210/jc.2013-1496 · DOI
  7. Hulsmans, 2012, Decreased miR-181a expression in monocytes of obese patients is associated with the occurrence of metabolic syndrome and coronary artery disease, J Clin Endocrinol Metab, 97, E1213, 10.1210/jc.2012-1008 · DOI
  8. Tam, 2010, Obesity and low-grade inflammation: A paediatric perspective, Obes Rev, 11, 118, 10.1111/j.1467-789X.2009.00674.x · DOI
  9. Ouyang, 2016, Association between serum soluble tumor necrosis factor-α receptors and early childhood obesity, Endocr J, 63, 581, 10.1507/endocrj.EJ15-0628 · DOI
  10. Li, 2010, Body mass index cut-offs for overweight and obesity in Chinese children and adolescents aged 2–18 years, Zhonghua Liu Xing Bing Xue Za Zhi, 31, 616
  11. Vlachos, 2015, DIANA-TarBase v7.0: Indexing more than half a million experimentally supported miRNA: MRNA interactions, Nucleic Acids Res, 43, D153, 10.1093/nar/gku1215 · DOI
  12. Franceschini, 2013, STRING v9.1: Protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Res, 41, D808, 10.1093/nar/gks1094 · DOI · Paper page
  13. Smoot, 2011, Cytoscape 2.8: New features for data integration and network visualization, Bioinformatics, 27, 431, 10.1093/bioinformatics/btq675 · DOI
  14. da Huang, 2009, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat Protoc, 4, 44, 10.1038/nprot.2008.211 · DOI · Paper page
  15. Ghanim, 2004, Circulating mononuclear cells in the obese are in a proinflammatory state, Circulation, 110, 1564, 10.1161/01.CIR.0000142055.53122.FA · DOI
  16. Sayed, 2011, MicroRNAs in development and disease, Physiol Rev, 91, 827, 10.1152/physrev.00006.2010 · DOI
  17. Lu, 2009, MicroRNA in the immune system, microRNA as an immune system, Immunology, 127, 291, 10.1111/j.1365-2567.2009.03092.x · DOI
  18. McGregor, 2011, MicroRNAs in the regulation of adipogenesis and obesity, Curr Mol Med, 11, 304, 10.2174/156652411795677990 · DOI
  19. Peng, 2014, MicroRNAs: Emerging roles in adipogenesis and obesity, Cell Signal, 26, 1888, 10.1016/j.cellsig.2014.05.006 · DOI
  20. Koh, 2010, Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of hepatic nuclear factor 4 alpha, BMC Genomics, 11, S6, 10.1186/1471-2164-11-S1-S6 · DOI
  21. Nesca, 2013, Identification of particular groups of microRNAs that positively or negatively impact on beta cell function in obese models of type 2 diabetes, Diabetologia, 56, 2203, 10.1007/s00125-013-2993-y · DOI
  22. Shi, 2015, MiR-148a is associated with obesity and modulates adipocyte differentiation of mesenchymal stem cells through Wnt signaling, Sci Rep, 5, 9930, 10.1038/srep09930 · DOI
  23. Ortega, 2013, Targeting the circulating microRNA signature of obesity, Clin Chem, 59, 781, 10.1373/clinchem.2012.195776 · DOI
  24. Jima, 2010, Deep sequencing of the small RNA transcriptome of normal and malignant human B cells identifies hundreds of novel microRNAs, Blood, 116, e118, 10.1182/blood-2010-05-285403 · DOI
  25. Zhu, 2015, Identification of microRNA biomarkers in type 2 diabetes: A meta-analysis of controlled profiling studies, Diabetologia, 58, 900, 10.1007/s00125-015-3510-2 · DOI
  26. Wang, 2012, Association of perfluorooctanoic acid with HDL cholesterol and circulating miR-26b and miR-199-3p in workers of a fluorochemical plant and nearby residents, Environ Sci Technol, 46, 9274, 10.1021/es300906q · DOI
  27. Zhang, 2012, Expression of microRNAs during chondrogenesis of human adipose-derived stem cells, Osteoarthritis Cartilage, 20, 1638, 10.1016/j.joca.2012.08.024 · DOI
  28. Wojcicka, 2014, Next generation sequencing reveals microRNA isoforms in liver cirrhosis and hepatocellular carcinoma, Int J Biochem Cell Biol, 53, 208, 10.1016/j.biocel.2014.05.020 · DOI
  29. Fornari, 2010, MiR-199a-3p regulates mTOR and c-Met to influence the doxorubicin sensitivity of human hepatocarcinoma cells, Cancer Res, 70, 5184, 10.1158/0008-5472.CAN-10-0145 · DOI
  30. Yin, 2015, Circulating miR-375 and miR-199a-3p as potential biomarkers for the diagnosis of hepatocellular carcinoma, Tumour Biol, 36, 4501, 10.1007/s13277-015-3092-0 · DOI
  31. Ji, 2014, Deep sequencing of RNA from three different extracellular vesicle (EV) subtypes released from the human LIM1863 colon cancer cell line uncovers distinct miRNA-enrichment signatures, PLoS One, 9, e110314, 10.1371/journal.pone.0110314 · DOI
  32. Weisberg, 2003, Obesity is associated with macrophage accumulation in adipose tissue, J Clin Invest, 112, 1796, 10.1172/JCI200319246 · DOI · Paper page
  33. He, 2006, Why do hubs tend to be essential in protein networks?, PLoS Genet, 2, e88, 10.1371/journal.pgen.0020088 · DOI
  34. Jeong, 2001, Lethality and centrality in protein networks, Nature, 411, 41, 10.1038/35075138 · DOI
  35. Tabur, 2015, Association of Rho/Rho-kinase gene polymorphisms and expressions with obesity-related metabolic syndrome, Eur Rev Med Pharmacol Sci, 19, 1680
  36. Huang, 2015, MiR-378a-3p enhances adipogenesis by targeting mitogen-activated protein kinase 1, Biochem Biophys Res Commun, 457, 37, 10.1016/j.bbrc.2014.12.055 · DOI
  37. Qiu, 2010, Gene expression profiles of adipose tissue of high-fat diet-induced obese rats by cDNA microarrays, Mol Biol Rep, 37, 3691, 10.1007/s11033-010-0021-6 · DOI

Cited by

Sorted by citations

See all 25

Metadata

Authors
Ouyang, S., Tang, R., Liu, Z., Ma, F., Li, Y., & Wu, J.
Venue
Molecular Medicine Reports
Publisher
Spandidos Publications
Published
2017-07-31
Volume
16
Issue
4
Pages
3799-3806
ISSN
  • 1791-2997
  • 1791-3004
DOI
10.3892/mmr.2017.7118
PMC
PMC5646957
Cited by
25
References
2
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review