Spandidos Publications Logo
  • About
    • About Spandidos
    • Aims and Scopes
    • Abstracting and Indexing
    • Editorial Policies
    • Reprints and Permissions
    • Job Opportunities
    • Terms and Conditions
    • Contact
  • Journals
    • All Journals
    • Oncology Letters
      • Oncology Letters
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Oncology
      • International Journal of Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular and Clinical Oncology
      • Molecular and Clinical Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Experimental and Therapeutic Medicine
      • Experimental and Therapeutic Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Molecular Medicine
      • International Journal of Molecular Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Biomedical Reports
      • Biomedical Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Oncology Reports
      • Oncology Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular Medicine Reports
      • Molecular Medicine Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • World Academy of Sciences Journal
      • World Academy of Sciences Journal
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Functional Nutrition
      • International Journal of Functional Nutrition
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Epigenetics
      • International Journal of Epigenetics
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Medicine International
      • Medicine International
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
  • Articles
  • Information
    • Information for Authors
    • Information for Reviewers
    • Information for Librarians
    • Information for Advertisers
    • Conferences
  • Language Editing
Spandidos Publications Logo
  • About
    • About Spandidos
    • Aims and Scopes
    • Abstracting and Indexing
    • Editorial Policies
    • Reprints and Permissions
    • Job Opportunities
    • Terms and Conditions
    • Contact
  • Journals
    • All Journals
    • Biomedical Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Experimental and Therapeutic Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Epigenetics
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Functional Nutrition
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Molecular Medicine
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • International Journal of Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Medicine International
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular and Clinical Oncology
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Molecular Medicine Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Oncology Letters
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • Oncology Reports
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
    • World Academy of Sciences Journal
      • Information for Authors
      • Editorial Policies
      • Editorial Board
      • Aims and Scope
      • Abstracting and Indexing
      • Bibliographic Information
      • Archive
  • Articles
  • Information
    • For Authors
    • For Reviewers
    • For Librarians
    • For Advertisers
    • Conferences
  • Language Editing
Login Register Submit
  • This site uses cookies
  • You can change your cookie settings at any time by following the instructions in our Cookie Policy. To find out more, you may read our Privacy Policy.

    I agree
Search articles by DOI, keyword, author or affiliation
Search
Advanced Search
presentation
Molecular Medicine Reports
Join Editorial Board Propose a Special Issue
Print ISSN: 1791-2997 Online ISSN: 1791-3004
Journal Cover
September-2020 Volume 22 Issue 3

Full Size Image

Sign up for eToc alerts
Recommend to Library

Journals

International Journal of Molecular Medicine

International Journal of Molecular Medicine

International Journal of Molecular Medicine is an international journal devoted to molecular mechanisms of human disease.

International Journal of Oncology

International Journal of Oncology

International Journal of Oncology is an international journal devoted to oncology research and cancer treatment.

Molecular Medicine Reports

Molecular Medicine Reports

Covers molecular medicine topics such as pharmacology, pathology, genetics, neuroscience, infectious diseases, molecular cardiology, and molecular surgery.

Oncology Reports

Oncology Reports

Oncology Reports is an international journal devoted to fundamental and applied research in Oncology.

Experimental and Therapeutic Medicine

Experimental and Therapeutic Medicine

Experimental and Therapeutic Medicine is an international journal devoted to laboratory and clinical medicine.

Oncology Letters

Oncology Letters

Oncology Letters is an international journal devoted to Experimental and Clinical Oncology.

Biomedical Reports

Biomedical Reports

Explores a wide range of biological and medical fields, including pharmacology, genetics, microbiology, neuroscience, and molecular cardiology.

Molecular and Clinical Oncology

Molecular and Clinical Oncology

International journal addressing all aspects of oncology research, from tumorigenesis and oncogenes to chemotherapy and metastasis.

World Academy of Sciences Journal

World Academy of Sciences Journal

Multidisciplinary open-access journal spanning biochemistry, genetics, neuroscience, environmental health, and synthetic biology.

International Journal of Functional Nutrition

International Journal of Functional Nutrition

Open-access journal combining biochemistry, pharmacology, immunology, and genetics to advance health through functional nutrition.

International Journal of Epigenetics

International Journal of Epigenetics

Publishes open-access research on using epigenetics to advance understanding and treatment of human disease.

Medicine International

Medicine International

An International Open Access Journal Devoted to General Medicine.

Journal Cover
September-2020 Volume 22 Issue 3

Full Size Image

Sign up for eToc alerts
Recommend to Library

  • Article
  • Citations
    • Cite This Article
    • Download Citation
    • Create Citation Alert
    • Remove Citation Alert
    • Cited By
  • Similar Articles
    • Related Articles (in Spandidos Publications)
    • Similar Articles (Google Scholar)
    • Similar Articles (PubMed)
  • Download PDF
  • Download XML
  • View XML

  • Supplementary Files
    • Supplementary_Data.pdf
Article Open Access

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis

  • Authors:
    • Yana Lu
    • Yihang Li
    • Guang Li
    • Haitao Lu
  • View Affiliations / Copyright

    Affiliations: Key Laboratory of Dai and Southern Medicine of Xishuangbanna Dai Autonomous Prefecture, Yunnan Branch, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Jinghong, Yunnan 666100, P.R. China, Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
    Copyright: © Lu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 1868-1882
    |
    Published online on: June 26, 2020
       https://doi.org/10.3892/mmr.2020.11281
  • Expand metrics +
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )
Cited By (CrossRef): 0 citations Loading Articles...

This article is mentioned in:



Abstract

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β‑cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein‑protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in ‘inflammatory response’ and ‘lipid metabolic process’, and 37 downregulated candidate genes were involved in ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. These candidate genes were also involved in different signaling pathways associated with ‘PI3K/Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ and ‘MAPK signaling pathway’, which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)‑target gene regulatory network and a transcription factor‑target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa‑miR‑192‑5p, hsa‑miR‑124‑5p and hsa‑miR‑335‑5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C‑endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C‑C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic‑based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.
View Figures

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

View References

1 

Zheng Y, Ley SH and Hu FB: Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 14:88–98. 2018. View Article : Google Scholar : PubMed/NCBI

2 

International Diabetes Federation: IDF Diabetes Atlas. (8th). IDF. (Brussels, Belgium). 2017.

3 

Ohn JH, Kwak SH, Cho YM, Lim S, Jang HC, Park KS and Cho NH: 10-year trajectory of β-cell function and insulin sensitivity in the development of type 2 diabetes: A community-based prospective cohort study. Lancet Diabetes Endocrinol. 4:27–34. 2016. View Article : Google Scholar : PubMed/NCBI

4 

Ashcroft FM, Rohm M, Clark A and Brereton MF: Is type 2 diabetes a glycogen storage disease of pancreatic β cells? Cell Metab. 26:17–23. 2017. View Article : Google Scholar : PubMed/NCBI

5 

Almaça J, Weitz J, Rodriguez-Diaz R, Pereira E and Caicedo A: The pericyte of the pancreatic islet regulates capillary diameter and local blood flow. Cell Metab. 27:630–644.e4. 2018. View Article : Google Scholar : PubMed/NCBI

6 

Wei FJ, Cai CY, Yu P, Lv J, Ling C, Shi WT, Jiao HX, Chang BC, Yang FH, Tian Y, et al: Quantitative candidate gene association studies of metabolic traits in Han Chinese type 2 diabetes patients. Genet Mol Res. 14:15471–15481. 2015. View Article : Google Scholar : PubMed/NCBI

7 

Chen J, Meng Y, Zhou J, Zhuo M, Ling F, Zhang Y, Du H and Wang X: Identifying candidate genes for type 2 diabetes mellitus and obesity through gene expression profiling in multiple tissues or cells. J Diabetes Res. 2013:9704352013. View Article : Google Scholar : PubMed/NCBI

8 

Lynch CJ and Adams SH: Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. 10:723–736. 2014. View Article : Google Scholar : PubMed/NCBI

9 

Xue A, Wu Y, Zhu Z, Zhang F, Kemper KE, Zheng Z, Yengo L, Lloyd-Jones LR, Sidorenko J, Wu Y, et al: eQTLGen Consortium: Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 9:29412018. View Article : Google Scholar : PubMed/NCBI

10 

Lawlor N, Khetan S, Ucar D and Stitzel ML: Genomics of Islet (Dys)function and Type 2 Diabetes. Trends Genet. 33:244–255. 2017. View Article : Google Scholar : PubMed/NCBI

11 

Lee I, Blom UM, Wang PI, Shim JE and Marcotte EM: Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 21:1109–1121. 2011. View Article : Google Scholar : PubMed/NCBI

12 

Lawlor N, George J, Bolisetty M, Kursawe R, Sun L, Sivakamasundari V, Kycia I, Robson P and Stitzel ML: Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 27:208–222. 2017. View Article : Google Scholar : PubMed/NCBI

13 

Segerstolpe Å, Palasantza A, Eliasson P, Andersson EM, Andréasson AC, Sun X, Picelli S, Sabirsh A, Clausen M, Bjursell MK, et al: Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24:593–607. 2016. View Article : Google Scholar : PubMed/NCBI

14 

Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, de Bakker PI, Abecasis GR, Almgren P, Andersen G, et al Wellcome Trust Case Control Consortium, : Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 40:638–645. 2008. View Article : Google Scholar : PubMed/NCBI

15 

Maruthur NM, Gribble MO, Bennett WL, Bolen S, Wilson LM, Balakrishnan P, Sahu A, Bass E, Kao WH and Clark JM: The pharmacogenetics of type 2 diabetes: A systematic review. Diabetes Care. 37:876–886. 2014. View Article : Google Scholar : PubMed/NCBI

16 

van de Bunt M, Manning Fox JE, Dai X, Barrett A, Grey C, Li L, Bennett AJ, Johnson PR, Rajotte RV, Gaulton KJ, et al: Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet. 11:e10056942015. View Article : Google Scholar : PubMed/NCBI

17 

Bonnefond A and Froguel P: Rare and common genetic events in type 2 diabetes: What should biologists know? Cell Metab. 21:357–368. 2015. View Article : Google Scholar : PubMed/NCBI

18 

Ndiaye FK, Ortalli A, Canouil M, Huyvaert M, Salazar-Cardozo C, Lecoeur C, Verbanck M, Pawlowski V, Boutry R, Durand E, et al: Expression and functional assessment of candidate type 2 diabetes susceptibility genes identify four new genes contributing to human insulin secretion. Mol Metab. 6:459–470. 2017. View Article : Google Scholar : PubMed/NCBI

19 

Pellegrino M, Sciambi A, Treusch S, Durruthy-Durruthy R, Gokhale K, Jacob J, Chen TX, Geis JA, Oldham W, Matthews J, et al: High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics. Genome Res. 28:1345–1352. 2018. View Article : Google Scholar : PubMed/NCBI

20 

Zhang P, Xia JH, Zhu J, Gao P, Tian YJ, Du M, Guo YC, Suleman S, Zhang Q, Kohli M, et al: High-throughput screening of prostate cancer risk loci by single nucleotide polymorphisms sequencing. Nat Commun. 9:20222018. View Article : Google Scholar : PubMed/NCBI

21 

Nepal C, O'Rourke CJ, Oliveira DVNP, Taranta A, Shema S, Gautam P, Calderaro J, Barbour A, Raggi C, Wennerberg K, et al: Genomic perturbations reveal distinct regulatory networks in intrahepatic cholangiocarcinoma. Hepatology. 68:949–963. 2018. View Article : Google Scholar : PubMed/NCBI

22 

Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, Ma C, Fontanillas P, Moutsianas L, McCarthy DJ, et al: The genetic architecture of type 2 diabetes. Nature. 536:41–47. 2016. View Article : Google Scholar : PubMed/NCBI

23 

Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, et al: A subcellular map of the human proteome. Science. 356:8062017. View Article : Google Scholar : PubMed/NCBI

24 

Zhu H, Bilgin M and Snyder M: Proteomics. Annu Rev Biochem. 72:783–812. 2003. View Article : Google Scholar : PubMed/NCBI

25 

Larance M and Lamond AI: Multidimensional proteomics for cell biology. Nat Rev Mol Cell Biol. 16:269–280. 2015. View Article : Google Scholar : PubMed/NCBI

26 

Stuart LM, Boulais J, Charriere GM, Hennessy EJ, Brunet S, Jutras I, Goyette G, Rondeau C, Letarte S, Huang H, et al: A systems biology analysis of the Drosophila phagosome. Nature. 445:95–101. 2007. View Article : Google Scholar : PubMed/NCBI

27 

Zhao L, Chen Y, Bajaj AO, Eblimit A, Xu M, Soens ZT, Wang F, Ge Z, Jung SY, He F, et al: Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes. Genome Res. 26:660–669. 2016. View Article : Google Scholar : PubMed/NCBI

28 

Hunt KK, Karakas C, Ha MJ, Biernacka A, Yi M, Sahin AA, Adjapong O, Hortobagyi GN, Bondy M, Thompson P, et al: Cytoplasmic Cyclin E Predicts Recurrence in Patients with Breast Cancer. Clin Cancer Res. 23:2991–3002. 2017. View Article : Google Scholar : PubMed/NCBI

29 

Mezzanzanica D, Fabbi M, Bagnoli M, Staurengo S, Losa M, Balladore E, Alberti P, Lusa L, Ditto A, Ferrini S, et al: Subcellular localization of activated leukocyte cell adhesion molecule is a molecular predictor of survival in ovarian carcinoma patients. Clin Cancer Res. 14:1726–1733. 2008. View Article : Google Scholar : PubMed/NCBI

30 

Zhu W, Yang L and Du Z: Layered functional network analysis of gene expression in human heart failure. PLoS One. 4:e62882009. View Article : Google Scholar : PubMed/NCBI

31 

Du ZP, Wu BL, Wang SH, Shen JH, Lin XH, Zheng CP, Wu ZY, Qiu XY, Zhan XF, Xu LY, et al: Shortest path analyses in the protein-protein interaction network of NGAL (neutrophil gelatinase-associated lipocalin) overexpression in esophageal squamous cell carcinoma. Asian Pac J Cancer Prev. 15:6899–6904. 2014. View Article : Google Scholar : PubMed/NCBI

32 

Diao B, Liu Y, Zhang Y, Liu Q, Lu WJ and Xu G: Functional network analysis with the subcellular location and gene ontology information in human allergic asthma. Genet Test Mol Biomarkers. 16:1287–1292. 2012. View Article : Google Scholar : PubMed/NCBI

33 

Lee S, Zhang C, Kilicarslan M, Piening BD, Bjornson E, Hallström BM, Groen AK, Ferrannini E, Laakso M, Snyder M, et al: Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance. Cell Metab. 24:172–184. 2016. View Article : Google Scholar : PubMed/NCBI

34 

Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH and Califano A: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet. 48:838–847. 2016. View Article : Google Scholar : PubMed/NCBI

35 

Krell J, Stebbing J, Frampton AE, Carissimi C, Harding V, De Giorgio A, Fulci V, Macino G, Colombo T and Castellano L: The role of TP53 in miRNA loading onto AGO2 and in remodelling the miRNA-mRNA interaction network. Lancet. 385 (Suppl 1):S152015. View Article : Google Scholar : PubMed/NCBI

36 

Marselli L, Thorne J, Dahiya S, Sgroi DC, Sharma A, Bonner-Weir S, Marchetti P and Weir GC: Gene expression profiles of Beta-cell enriched tissue obtained by laser capture microdissection from subjects with type 2 diabetes. PLoS One. 5:e114992010. View Article : Google Scholar : PubMed/NCBI

37 

Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, et al: Human Protein Reference Database--2009 update. Nucleic Acids Res 37 (Database). D767–D772. 2009. View Article : Google Scholar

38 

Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A, Kolas N, O'Donnell L, et al: The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43(D1): D470–D478. 2015. View Article : Google Scholar : PubMed/NCBI

39 

Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, et al: The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40(D1): D841–D846. 2012. View Article : Google Scholar : PubMed/NCBI

40 

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43(D1): D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI

41 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

42 

Pontén F, Jirström K and Uhlen M: The Human Protein Atlas--a tool for pathology. J Pathol. 216:387–393. 2008. View Article : Google Scholar : PubMed/NCBI

43 

Barsky A, Gardy JL, Hancock RE and Munzner T: Cerebral: A Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics. 23:1040–1042. 2007. View Article : Google Scholar : PubMed/NCBI

44 

Bindea G, Galon J and Mlecnik B: CluePedia Cytoscape plugin: Pathway insights using integrated experimental and in silico data. Bioinformatics. 29:661–663. 2013. View Article : Google Scholar : PubMed/NCBI

45 

Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z and Galon J: ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 25:1091–1093. 2009. View Article : Google Scholar : PubMed/NCBI

46 

Gene Ontology Consortium: Gene Ontology Consortium: Going forward. Nucleic Acids Res. 43(D1): D1049–D1056. 2015. View Article : Google Scholar : PubMed/NCBI

47 

Kanehisa M, Furumichi M, Tanabe M, Sato Y and Morishima K: KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45(D1): D353–D361. 2017. View Article : Google Scholar : PubMed/NCBI

48 

Fan Y, Siklenka K, Arora SK, Ribeiro P, Kimmins S and Xia J: miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Res 44 (W1). W135–41. 2016. View Article : Google Scholar

49 

Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, Yang CD, Hong HC, Wei TY, Tu SJ, et al: miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res. 44(D1): D239–D247. 2016. View Article : Google Scholar : PubMed/NCBI

50 

Xiao F, Zuo Z, Cai G, Kang S, Gao X and Li T: miRecords: An integrated resource for microRNA-target interactions. Nucleic Acids Res 37 (Database). D105–D110. 2009. View Article : Google Scholar

51 

Xia J, Gill EE and Hancock RE: NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 10:823–844. 2015. View Article : Google Scholar : PubMed/NCBI

52 

Wang S, Sun H, Ma J, Zang C, Wang C, Wang J, Tang Q, Meyer CA, Zhang Y and Liu XS: Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc. 8:2502–2515. 2013. View Article : Google Scholar : PubMed/NCBI

53 

Yoshikawa A, Imagawa A, Nakata S, Fukui K, Kuroda Y, Miyata Y, Sato Y, Hanafusa T, Matsuoka TA, Kaneto H, et al: Interferon stimulated gene 15 has an anti-apoptotic effect on MIN6 cells. Endocr J. 61:883–890. 2014. View Article : Google Scholar : PubMed/NCBI

54 

Rees SD, Britten AC, Bellary S, O'Hare JP, Kumar S, Barnett AH and Kelly MA: The promoter polymorphism −232C/G of the PCK1 gene is associated with type 2 diabetes in a UK-resident South Asian population. BMC Med Genet. 10:832009. View Article : Google Scholar : PubMed/NCBI

55 

Gustavsson N and Han W: Calcium-sensing beyond neurotransmitters: Functions of synaptotagmins in neuroendocrine and endocrine secretion. Biosci Rep. 29:245–259. 2009. View Article : Google Scholar : PubMed/NCBI

56 

Marshall C, Hitman GA, Partridge CJ, Clark A, Ma H, Shearer TR and Turner MD: Evidence that an isoform of calpain-10 is a regulator of exocytosis in pancreatic beta-cells. Mol Endocrinol. 19:213–224. 2005. View Article : Google Scholar : PubMed/NCBI

57 

Rajpathak SN, He M, Sun Q, Kaplan RC, Muzumdar R, Rohan TE, Gunter MJ, Pollak M, Kim M, Pessin JE, et al: Insulin-like growth factor axis and risk of type 2 diabetes in women. Diabetes. 61:2248–2254. 2012. View Article : Google Scholar : PubMed/NCBI

58 

Drogan D, Schulze MB, Boeing H and Pischon T: Insulin-like growth factor 1 and insulin-like growth factor-binding protein 3 in relation to the risk of type 2 diabetes mellitus: Results from the EPIC-Potsdam study. Am J Epidemiol. 183:553–560. 2016. View Article : Google Scholar : PubMed/NCBI

59 

Wang Y, Katayama A, Terami T, Han X, Nunoue T, Zhang D, Teshigawara S, Eguchi J, Nakatsuka A, Murakami K, et al: Translocase of inner mitochondrial membrane 44 alters the mitochondrial fusion and fission dynamics and protects from type 2 diabetes. Metabolism. 64:677–688. 2015. View Article : Google Scholar : PubMed/NCBI

60 

Venkatesan B, Valente AJ, Das NA, Carpenter AJ, Yoshida T, Delafontaine JL, Siebenlist U and Chandrasekar B: CIKS (Act1 or TRAF3IP2) mediates high glucose-induced endothelial dysfunction. Cell Signal. 25:359–371. 2013. View Article : Google Scholar : PubMed/NCBI

61 

Bergholdt R, Brorsson C, Palleja A, Berchtold LA, Fløyel T, Bang-Berthelsen CH, Frederiksen KS, Jensen LJ, Størling J and Pociot F: Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression. Diabetes. 61:954–962. 2012. View Article : Google Scholar : PubMed/NCBI

62 

Kokkola T, Suuronen T, Molnár F, Määttä J, Salminen A, Jarho EM and Lahtela-Kakkonen M: AROS has a context-dependent effect on SIRT1. FEBS Lett. 588:1523–1528. 2014. View Article : Google Scholar : PubMed/NCBI

63 

Haigis MC and Sinclair DA: Mammalian sirtuins: Biological insights and disease relevance. Annu Rev Pathol. 5:253–295. 2010. View Article : Google Scholar : PubMed/NCBI

64 

Zhou CH, Zhang MX, Zhou SS, Li H, Gao J, Du L and Yin XX: SIRT1 attenuates neuropathic pain by epigenetic regulation of mGluR1/5 expressions in type 2 diabetic rats. Pain. 158:130–139. 2017. View Article : Google Scholar : PubMed/NCBI

65 

Ortega FJ, Pueyo N, Moreno-Navarrete JM, Sabater M, Rodriguez-Hermosa JI, Ricart W, Tinahones FJ and Fernandez-Real JM: The lung innate immune gene surfactant protein-D is expressed in adipose tissue and linked to obesity status. Int J Obes (Lond) (2005). 37:1532–1538. 2013. View Article : Google Scholar

66 

Crook M: Type 2 diabetes mellitus: A disease of the innate immune system? An update. Diabet Med. 21:203–207. 2004. View Article : Google Scholar : PubMed/NCBI

67 

Yang W, Li Y, Wang JY, Han R and Wang L: Circulating levels of adipose tissue-derived inflammatory factors in elderly diabetes patients with carotid atherosclerosis: A retrospective study. Cardiovasc Diabetol. 17:752018. View Article : Google Scholar : PubMed/NCBI

68 

Prattichizzo F, De Nigris V, Spiga R, Mancuso E, La Sala L, Antonicelli R, Testa R, Procopio AD, Olivieri F and Ceriello A: Inflammageing and metaflammation: The yin and yang of type 2 diabetes. Ageing Res Rev. 41:1–17. 2018. View Article : Google Scholar : PubMed/NCBI

69 

Naidoo V, Naidoo M and Ghai M: Cell- and tissue-specific epigenetic changes associated with chronic inflammation in insulin resistance and type 2 diabetes mellitus. Scand J Immunol. 88:e127232018. View Article : Google Scholar : PubMed/NCBI

70 

Ehses JA, Perren A, Eppler E, Ribaux P, Pospisilik JA, Maor-Cahn R, Gueripel X, Ellingsgaard H, Schneider MK, Biollaz G, et al: Increased number of islet-associated macrophages in type 2 diabetes. Diabetes. 56:2356–2370. 2007. View Article : Google Scholar : PubMed/NCBI

71 

Böni-Schnetzler M, Thorne J, Parnaud G, Marselli L, Ehses JA, Kerr-Conte J, Pattou F, Halban PA, Weir GC and Donath MY: Increased interleukin (IL)-1beta messenger ribonucleic acid expression in beta -cells of individuals with type 2 diabetes and regulation of IL-1beta in human islets by glucose and autostimulation. J Clin Endocrinol Metab. 93:4065–4074. 2008. View Article : Google Scholar : PubMed/NCBI

72 

Donath MY, Böni-Schnetzler M, Ellingsgaard H and Ehses JA: Islet inflammation impairs the pancreatic beta-cell in type 2 diabetes. Physiology (Bethesda). 24:325–331. 2009.PubMed/NCBI

73 

Marchetti P, Lupi R, Del Guerra S, Bugliani M, D'Aleo V, Occhipinti M, Boggi U, Marselli L and Masini M: Goals of treatment for type 2 diabetes: Beta-cell preservation for glycemic control. Diabetes Care. 32 (Suppl 2):S178–S183. 2009. View Article : Google Scholar : PubMed/NCBI

74 

Cnop M: Fatty acids and glucolipotoxicity in the pathogenesis of Type 2 diabetes. Biochem Soc Trans. 36:348–352. 2008. View Article : Google Scholar : PubMed/NCBI

75 

Saltiel AR and Kahn CR: Insulin signalling and the regulation of glucose and lipid metabolism. Nature. 414:799–806. 2001. View Article : Google Scholar : PubMed/NCBI

76 

Wajchenberg BL: beta-cell failure in diabetes and preservation by clinical treatment. Endocr Rev. 28:187–218. 2007. View Article : Google Scholar : PubMed/NCBI

77 

Takahashi E, Unoki-Kubota H, Shimizu Y, Okamura T, Iwata W, Kajio H, Yamamoto-Honda R, Shiga T, Yamashita S, Tobe K, et al: Proteomic analysis of serum biomarkers for prediabetes using the Long-Evans Agouti rat, a spontaneous animal model of type 2 diabetes mellitus. J Diabetes Investig. 8:661–671. 2017. View Article : Google Scholar : PubMed/NCBI

78 

Hart LM, Fritsche A, Nijpels G, van Leeuwen N, Donnelly LA, Dekker JM, Alssema M, Fadista J, Carlotti F, Gjesing AP, et al: The CTRB1/2 locus affects diabetes susceptibility and treatment via the incretin pathway. Diabetes. 62:3275–3281. 2013. View Article : Google Scholar : PubMed/NCBI

79 

Xue M and Jackson CJ: Activated protein C and its potential applications in prevention of islet β-cell damage and diabetes. Vitam Horm. 95:323–363. 2014. View Article : Google Scholar : PubMed/NCBI

80 

Matsumoto K, Yano Y, Gabazza EC, Araki R, Bruno NE, Suematsu M, Akatsuka H, Katsuki A, Taguchi O, Adachi Y, et al: Inverse correlation between activated protein C generation and carotid atherosclerosis in Type 2 diabetic patients. Diabet Med. 24:1322–1328. 2007. View Article : Google Scholar : PubMed/NCBI

81 

Wang JY, Lu Q, Tao Y, Jiang YR and Jonas JB: Intraocular expression of thymosin β4 in proliferative diabetic retinopathy. Acta Ophthalmol. 89:e396–e403. 2011. View Article : Google Scholar : PubMed/NCBI

82 

Sidarala V and Kowluru A: The regulatory roles of mitogen-activated protein kinase (MAPK) pathways in health and diabetes: Lessons learned from the pancreatic β-cell. Recent Pat Endocr Metab Immune Drug Discov. 10:76–84. 2017. View Article : Google Scholar : PubMed/NCBI

83 

Pang Y, Zhu H, Xu J, Yang L, Liu L and Li J: β-arrestin-2 is involved in irisin induced glucose metabolism in type 2 diabetes via p38 MAPK signaling. Exp Cell Res. 360:199–204. 2017. View Article : Google Scholar : PubMed/NCBI

84 

Stewart AF, Hussain MA, García-Ocaña A, Vasavada RC, Bhushan A, Bernal-Mizrachi E and Kulkarni RN: Human β-cell proliferation and intracellular signaling: Part 3. Diabetes. 64:1872–1885. 2015. View Article : Google Scholar : PubMed/NCBI

85 

Fraenkel M, Ketzinel-Gilad M, Ariav Y, Pappo O, Karaca M, Castel J, Berthault MF, Magnan C, Cerasi E, Kaiser N, et al: mTOR inhibition by rapamycin prevents beta-cell adaptation to hyperglycemia and exacerbates the metabolic state in type 2 diabetes. Diabetes. 57:945–957. 2008. View Article : Google Scholar : PubMed/NCBI

86 

Baeder WL, Sredy J, Sehgal SN, Chang JY and Adams LM: Rapamycin prevents the onset of insulin-dependent diabetes mellitus (IDDM) in NOD mice. Clin Exp Immunol. 89:174–178. 1992. View Article : Google Scholar : PubMed/NCBI

87 

Zhang CL, Katoh M, Shibasaki T, Minami K, Sunaga Y, Takahashi H, Yokoi N, Iwasaki M, Miki T and Seino S: The cAMP sensor Epac2 is a direct target of antidiabetic sulfonylurea drugs. Science. 325:607–610. 2009. View Article : Google Scholar : PubMed/NCBI

88 

Sabbatini ME, Chen X, Ernst SA and Williams JA: Rap1 activation plays a regulatory role in pancreatic amylase secretion. J Biol Chem. 283:23884–23894. 2008. View Article : Google Scholar : PubMed/NCBI

89 

Manyes L, Arribas M, Gomez C, Calzada N, Fernandez-Medarde A and Santos E: Transcriptional profiling reveals functional links between RasGrf1 and Pttg1 in pancreatic beta cells. BMC Genomics. 15:10192014. View Article : Google Scholar : PubMed/NCBI

90 

Hoffmann A and Spengler D: Transient neonatal diabetes mellitus gene Zac1 impairs insulin secretion in mice through Rasgrf1. Mol Cell Biol. 32:2549–2560. 2012. View Article : Google Scholar : PubMed/NCBI

91 

Yang Y, Fu Q, Wang X, Liu Y, Zeng Q, Li Y, Gao S, Bao L, Liu S, Gao D, et al: Comparative transcriptome analysis of the swimbladder reveals expression signatures in response to low oxygen stress in channel catfish, Ictalurus punctatus. Physiol Genomics. 50:636–647. 2018. View Article : Google Scholar : PubMed/NCBI

92 

Dong Q, Ginsberg HN and Erlanger BF: Overexpression of the A1 adenosine receptor in adipose tissue protects mice from obesity-related insulin resistance. Diabetes Obes Metab. 3:360–366. 2001. View Article : Google Scholar : PubMed/NCBI

93 

Rampersaud E, Damcott CM, Fu M, Shen H, McArdle P, Shi X, Shelton J, Yin J, Chang YP, Ott SH, et al: Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the Old Order Amish: Evidence for replication from diabetes-related quantitative traits and from independent populations. Diabetes. 56:3053–3062. 2007. View Article : Google Scholar : PubMed/NCBI

94 

Straub SG and Sharp GW: Glucose-stimulated signaling pathways in biphasic insulin secretion. Diabetes Metab Res Rev. 18:451–463. 2002. View Article : Google Scholar : PubMed/NCBI

95 

Ma X, Lu C, Lv C, Wu C and Wang Q: The expression of miR-192 and its significance in diabetic nephropathy patients with different urine albumin creatinine ratio. J Diabetes Res. 2016:67894022016. View Article : Google Scholar : PubMed/NCBI

96 

Wu C, Chen X, Shu J and Lee CT: Whole-genome expression analyses of type 2 diabetes in human skin reveal altered immune function and burden of infection. Oncotarget. 8:34601–34609. 2017. View Article : Google Scholar : PubMed/NCBI

97 

Zhu Z, Yin J, Li DC and Mao ZQ: Role of microRNAs in the treatment of type 2 diabetes mellitus with Roux-en-Y gastric bypass. Braz J Med Biol Res. 50:e58172017. View Article : Google Scholar : PubMed/NCBI

98 

Pivovarova O, Fisher E, Dudziak K, Ilkavets I, Dooley S, Slominsky P, Limborska S, Weickert MO, Spranger J, Fritsche A, et al: A polymorphism within the connective tissue growth factor (CTGF) gene has no effect on non-invasive markers of beta-cell area and risk of type 2 diabetes. Dis Markers. 31:241–246. 2011. View Article : Google Scholar : PubMed/NCBI

99 

Salunkhe VA, Ofori JK, Gandasi NR, Salo SA, Hansson S, Andersson ME, Wendt A, Barg S, Esguerra JLS and Eliasson L: MiR-335 overexpression impairs insulin secretion through defective priming of insulin vesicles. Physiol Rep. 52017. PubMed/NCBI

100 

Taneera J, Fadista J, Ahlqvist E, Atac D, Ottosson-Laakso E, Wollheim CB and Groop L: Identification of novel genes for glucose metabolism based upon expression pattern in human islets and effect on insulin secretion and glycemia. Hum Mol Genet. 24:1945–1955. 2015. View Article : Google Scholar : PubMed/NCBI

101 

Aso Y, Matsuura H, Momobayashi A, Inukai Y, Sugawara N, Nakano T, Yamamoto R, Wakabayashi S, Takebayashi K and Inukai T: Profound reduction in T-helper (Th) 1 lymphocytes in peripheral blood from patients with concurrent type 1 diabetes and Graves' disease. Endocr J. 53:377–385. 2006. View Article : Google Scholar : PubMed/NCBI

102 

Voss M, Paterson J, Kelsall IR, Martín-Granados C, Hastie CJ, Peggie MW and Cohen PT: Ppm1E is an in cellulo AMP-activated protein kinase phosphatase. Cell Signal. 23:114–124. 2011. View Article : Google Scholar : PubMed/NCBI

103 

Torsvik J, Johansson S, Johansen A, Ek J, Minton J, Raeder H, Ellard S, Hattersley A, Pedersen O, Hansen T, et al: Mutations in the VNTR of the carboxyl-ester lipase gene (CEL) are a rare cause of monogenic diabetes. Hum Genet. 127:55–64. 2010. View Article : Google Scholar : PubMed/NCBI

104 

Taneera J, Lang S, Sharma A, Fadista J, Zhou Y, Ahlqvist E, Jonsson A, Lyssenko V, Vikman P, Hansson O, et al: A systems genetics approach identifies genes and pathways for type 2 diabetes in human islets. Cell Metab. 16:122–134. 2012. View Article : Google Scholar : PubMed/NCBI

105 

Anhê FF, Lellis-Santos C, Leite AR, Hirabara SM, Boschero AC, Curi R, Anhê GF and Bordin S: Smad5 regulates Akt2 expression and insulin-induced glucose uptake in L6 myotubes. Mol Cell Endocrinol. 319:30–38. 2010. View Article : Google Scholar : PubMed/NCBI

106 

Glauser DA and Schlegel W: The FoxO/Bcl-6/cyclin D2 pathway mediates metabolic and growth factor stimulation of proliferation in Min6 pancreatic beta-cells. J Recept Signal Transduct Res. 29:293–298. 2009. View Article : Google Scholar : PubMed/NCBI

107 

Rechsteiner MP, Floros X, Boehm BO, Marselli L, Marchetti P, Stoffel M, Moch H and Spinas GA: Automated assessment of β-cell area and density per islet and patient using TMEM27 and BACE2 immunofluorescence staining in human pancreatic β-cells. PLoS One. 9:e989322014. View Article : Google Scholar : PubMed/NCBI

108 

Ikeda K, Emoto N, Matsuo M and Yokoyama M: Molecular identification and characterization of a novel nuclear protein whose expression is up-regulated in insulin-resistant animals. J Biol Chem. 278:3514–3520. 2003. View Article : Google Scholar : PubMed/NCBI

109 

Solimena M, Schulte AM, Marselli L, Ehehalt F, Richter D, Kleeberg M, Mziaut H, Knoch KP, Parnis J, Bugliani M, et al: Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes. Diabetologia. 61:641–657. 2018. View Article : Google Scholar : PubMed/NCBI

110 

Scott EM, Carter AM and Findlay JB: The application of proteomics to diabetes. Diab Vasc Dis Res. 2:54–60. 2005. View Article : Google Scholar : PubMed/NCBI

111 

Khan AR and Awan FR: Mining of protein based biomarkers for type 2 diabetes mellitus. Pak J Pharm Sci. 25:889–901. 2012.PubMed/NCBI

112 

Okada S, List EO, Sankaran S and Kopchick JJ: Plasma protein biomarkers correlated with the development of diet-induced Type 2 diabetes in mice. Clin Proteomics. 6:6–17. 2010. View Article : Google Scholar : PubMed/NCBI

113 

Bustin SA and Mueller R: Real-time reverse transcription PCR (qRT-PCR) and its potential use in clinical diagnosis. Clin Sci (Lond). 109:365–379. 2005. View Article : Google Scholar : PubMed/NCBI

114 

Guénin S, Mauriat M, Pelloux J, Van Wuytswinkel O, Bellini C and Gutierrez L: Normalization of qRT-PCR data: The necessity of adopting a systematic, experimental conditions-specific, validation of references. J Exp Bot. 60:487–493. 2009. View Article : Google Scholar : PubMed/NCBI

115 

Kim B: Western Blot Techniques. Methods Mol Biol. 1606:133–139. 2017. View Article : Google Scholar : PubMed/NCBI

116 

Ule J, Hwang HW and Darnell RB: The future of cross-linking and immunoprecipitation (CLIP). Cold Spring Harb Perspect Biol. 10:a0322432018. View Article : Google Scholar : PubMed/NCBI

Related Articles

  • Abstract
  • View
  • Download
  • Twitter
Copy and paste a formatted citation
Spandidos Publications style
Lu Y, Li Y, Li G and Lu H: Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis. Mol Med Rep 22: 1868-1882, 2020.
APA
Lu, Y., Li, Y., Li, G., & Lu, H. (2020). Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis. Molecular Medicine Reports, 22, 1868-1882. https://doi.org/10.3892/mmr.2020.11281
MLA
Lu, Y., Li, Y., Li, G., Lu, H."Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis". Molecular Medicine Reports 22.3 (2020): 1868-1882.
Chicago
Lu, Y., Li, Y., Li, G., Lu, H."Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis". Molecular Medicine Reports 22, no. 3 (2020): 1868-1882. https://doi.org/10.3892/mmr.2020.11281
Copy and paste a formatted citation
x
Spandidos Publications style
Lu Y, Li Y, Li G and Lu H: Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis. Mol Med Rep 22: 1868-1882, 2020.
APA
Lu, Y., Li, Y., Li, G., & Lu, H. (2020). Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis. Molecular Medicine Reports, 22, 1868-1882. https://doi.org/10.3892/mmr.2020.11281
MLA
Lu, Y., Li, Y., Li, G., Lu, H."Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis". Molecular Medicine Reports 22.3 (2020): 1868-1882.
Chicago
Lu, Y., Li, Y., Li, G., Lu, H."Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis". Molecular Medicine Reports 22, no. 3 (2020): 1868-1882. https://doi.org/10.3892/mmr.2020.11281
Follow us
  • Twitter
  • LinkedIn
  • Facebook
About
  • Spandidos Publications
  • Careers
  • Cookie Policy
  • Privacy Policy
How can we help?
  • Help
  • Live Chat
  • Contact
  • Email to our Support Team