In Silico Transcriptomic Analysis of Wound-Healing-Associated Genes in Malignant Pleural Mesothelioma
Abstract
:1. Introduction
2. Materials and Methods
2.1. Transcriptomic Analysis of Wound-Healing-Associated Genes in MPM
2.2. Evaluation of the Significantly Differentially Expressed Wound-Healing-Related Genes for Prognostic Relevance
2.3. Functional Annotation Enrichment Gene Ontology Analysis of the miRNAs That Regulate the Significantly Differentially Expressed Wound-Healing-Related Genes in MPM
2.4. Statistical Analysis
3. Results
3.1. Identification of the Differential Transcriptional Expression Of Wound-Healing-Associated Genes in MPM
3.2. Prognostic Significance of ITGAV Gene Expression in MPM
3.3. Statistical Modeling Reveals A Positive Correlation of ITGAV and COL5A1 Gene Expressions
3.4. Enriched Gene Ontologies (GO) Relative To Regulating miRNAs of the Significantly Differentially Expressed Wound-Healing-Associated Genes In MPM
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Nasreen, N.; Mohammed, K.A.; Mubarak, K.K.; Baz, M.A.; Akindipe, O.A.; Fernandez-Bussy, S.; Antony, V.B. Pleural mesothelial cell transformation into myofibroblasts and haptotactic migration in response to TGF-β1 in vitro. Am. J. Physiol. Cell. Mol. Physiol. 2009, 297, L115–L124. [Google Scholar] [CrossRef] [PubMed]
- Carbone, M.; Yang, H. Molecular pathways: Targeting mechanisms of asbestos and erionite carcinogenesis in mesothelioma. Clin. Cancer. Res. 2012, 18, 598–604. [Google Scholar] [CrossRef] [PubMed]
- Carbone, M.; Ly, B.H.; Dodson, R.F.; Pagano, I.; Morris, P.T.; Dogan, U.A.; Gazdar, A.F.; Pass, H.I.; Yang, H. Malignant mesothelioma: Facts, Myths, and Hypotheses. J. Cell. Physiol. 2012, 227, 44–58. [Google Scholar] [CrossRef]
- Sinis, S.I.; Hatzoglou, C.; Gourgoulianis, K.I.; Zarogiannis, S.G. Carbon Nanotubes and Other Engineered Nanoparticles Induced Pathophysiology on Mesothelial Cells and Mesothelial Membranes. Front. Physiol. 2018, 9, 295. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schultze, F.; Gao, X.; Virzonis, D.; Damiati, S.; Schneider, M.R.; Kodzius, R. Air quality effects on human healthand approaches for its assessment through microfluidic chips. Genes 2017, 8, 10. [Google Scholar]
- Baas, P.; Fennel, D.; Kerr, K.M.; Van Scil, P.E.; Haas, R.L.; Peters, S.; ESMO Guidelines Committee. Malignant pleural mesothelioma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2015, 26, 31–39. [Google Scholar] [CrossRef]
- Imperatori, A.S.; Castiglioni, M.; Mortara, L.; Nardecchia, E.; Rotolo, N. The challenge of prognostic markers in pleural mesothelioma. J. Thorac. Dis. 2013, 5, 205–206. [Google Scholar] [PubMed]
- Greillier, L.; Baas, P.; Welch, J.J.; Hasan, B.; Passioukov, A. Biomarkers for malignant pleural mesothelioma: Current status. Mol. Diagn. Ther. 2008, 12, 375–390. [Google Scholar] [CrossRef]
- Linton, A.; Van Zandwijk, N.; Reid, G.; Clarke, S.; Cao, C.; Kao, S. Inflammation in malignant mesothelioma–friend or foe? Ann. Cardiothorac. Surg. 2012, 1, 516–522. [Google Scholar]
- Arnold, K.M.; Opdenaker, L.M.; Flynn, D.; Sims-Mourtada, J. Wound Healing and Cancer Stem Cells: Inflammation as a Driver of Treatment Resistance in Breast Cancer. Cancer Growth Metastasis 2015, 8, 1–13. [Google Scholar] [CrossRef]
- Matsuzaki, H.; Maeda, M.; Lee, S.; Nishimura, Y.; Kumagai-Takei, N.; Hayashi, H.; Yamamoto, S.; Hatayama, T.; Kojima, Y.; Tabata, R.; et al. Asbestos-Induced Cellular and Molecular Alteration of Immunocompetent Cells and Their Relationship with Chronic Inflammation and Carcinogenesis. J. Biomed. Biotechnol. 2012, 2012, 1–9. [Google Scholar] [CrossRef] [PubMed]
- Mutsaers, S.E. The mesothelial cell. Int. J. Biochem. Cell. Biol. 2004, 36, 9–16. [Google Scholar] [CrossRef]
- Mutsaers, S.E.; Wilkosz, S. Structure and Function of Mesothelial Cells. Periton. Carcinomat. 2007, 134, 1–19. [Google Scholar]
- Nagai, H.; Chew, S.H.; Okazaki, Y.; Funahashi, S.; Namba, T.; Kato, T.; Enomoto, A.; Jiang, L.; Akatsuka, S.; Toyokuni, S. Metamorphosis of mesothelial cells with active horizontal motility in tissue culture. Sci. Rep. 2013, 3, 1144. [Google Scholar] [CrossRef] [PubMed]
- Mutsaers, S.E.; Birnie, K.; Lansley, S.; Herrick, S.E.; Lim, C.-B.; Prele, C.M.; PrêLe, C.M. Mesothelial cells in tissue repair and fibrosis. Front. Pharmacol. 2015, 6, 113. [Google Scholar] [CrossRef] [Green Version]
- Schramm, A.; Opitz, I.; Thies, S.; Seifert, B.; Moch, H.; Weder, W.; Soltermann, A. Prognostic significance of epithelial–mesenchymal transition in malignant pleural mesothelioma. Eur. J. Cardio-Thoracic Surg. 2010, 37, 566–572. [Google Scholar] [CrossRef] [PubMed]
- Jaurand, M.-C.F.; Renier, A.; Daubriac, J. Mesothelioma: Do asbestos and carbon nanotubes pose the same health risk? Part. Fibre Toxicol. 2009, 6, 16. [Google Scholar] [CrossRef] [PubMed]
- Nagai, H.; Toyokuni, S. Biopersistent fiber-induced inflammation and carcinogenesis: Lessons learned from asbestos toward safety of fibrous nanomaterials. Arch. Biochem. Biophys. 2010, 502, 1–7. [Google Scholar] [CrossRef]
- Jagirdar, R.; Solenov, E.I.; Hatzoglou, C.; Molyvdas, P.-A.; Gourgoulianis, K.I.; Zarogiannis, S.G. Gene expression profile of aquaporin 1 and associated interactors in malignant pleural mesothelioma. Gene 2013, 517, 99–105. [Google Scholar] [CrossRef]
- Tasiopoulou, V.; Magouliotis, D.; Solenov, E.I.; Vavougios, G.; Molyvdas, P.-A.; Gourgoulianis, K.I.; Hatzoglou, C.; Zarogiannis, S.G. Transcriptional over-expression of chloride intracellular channels 3 and 4 in malignant pleural mesothelioma. Comput. Boil. Chem. 2015, 59, 111–116. [Google Scholar] [CrossRef]
- Vavougios, G.D.; I Solenov, E.; Hatzoglou, C.; Baturina, G.S.; E Katkova, L.; Molyvdas, P.A.; Gourgoulianis, K.I.; Zarogiannis, S.G. Computational genomic analysis of PARK7 interactome reveals high BBS1 gene expression as a prognostic factor favoring survival in malignant pleural mesothelioma. Am. J. Physiol. Cell. Mol. Physiol. 2015, 309, 677–686. [Google Scholar] [CrossRef] [PubMed]
- Rouka, E.; Vavougios, G.D.; Solenov, E.I.; Gourgoulianis, K.I.; Hatzoglou, C.; Zarogiannis, S.G. Transcriptomic Analysis of the Claudin Interactome in Malignant Pleural Mesothelioma: Evaluation of the Effect of Disease Phenotype, Asbestos Exposure, and CDKN2A Deletion Status. Front. Physiol. 2017, 8, 4969. [Google Scholar] [CrossRef] [PubMed]
- Gordon, G.J.; Rockwell, G.N.; Jensen, R.V.; Rheinwald, J.G.; Glickman, J.N.; Aronson, J.P.; Pottorf, B.J.; Nitz, M.D.; Richards, W.G.; Sugarbaker, D.J.; et al. Identification of Novel Candidate Oncogenes and Tumor Suppressors in Malignant Pleural Mesothelioma Using Large-Scale Transcriptional Profiling. Am. J. Pathol. 2005, 166, 1827–1840. [Google Scholar] [CrossRef]
- Rhodes, D.R.; Yu, J.; Shanker, K.; Deshpande, N.; Varambally, R.; Ghosh, D.; Barrette, T.; Pander, A.; Chinnaiyan, A.M. ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform. Neoplasia 2004, 6, 1–6. [Google Scholar] [CrossRef] [Green Version]
- Goswami, C.P.; Nakshatri, H. PROGgeneV2: Enhancements on the existing database. BMC Cancer 2014, 14, 970. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.; Bardes, E.E.; Aronow, B.J.; Jegga, A.G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009, 37, W305–W311. [Google Scholar] [CrossRef] [PubMed]
- R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, 2013. Available online: http://www.R-project.org/ (accessed on 20 May 2018).
- Frei, C.; Opitz, I.; Soltermann, A.; Fischer, B.; Moura, U.; Rehrauer, H.; Weder, W.; Stahel, R.; Felley-Bosco, E. Pleural mesothelioma side populations have a precursor phenotype. Carcinogenesis 2011, 32, 1324–1332. [Google Scholar] [CrossRef]
- López-Novoa, J.M.; Nieto, M.A. Inflammation and EMT: An alliance towards organ fibrosis and cancer progression. EMBO Mol. Med. 2009, 1, 303–314. [Google Scholar] [CrossRef]
- Makrilia, N.; Kollias, A.; Manolopoulos, L.; Syrigos, K. Cell Adhesion Molecules: Role and Clinical Significance in Cancer. Cancer Investig. 2009, 27, 1023–1037. [Google Scholar] [CrossRef]
- Lu, P.; Weaver, V.M.; Werb, Z. The extracelular matrix: A dynamic niche in cancer progression. J. Cell. Biol. 2012, 196, 395–406. [Google Scholar] [CrossRef]
- Pickup, M.W.; Mouw, J.K.; Weaver, V.M. The extracellular matrix modulates the hallmarks of cancer. EMBO Rep. 2014, 15, 1243–1253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Thompson, J.K.; MacPherson, M.B.; Beuschel, S.L.; Shukla, A. Asbestos-Induced Mesothelial to Fibroblastic Transition Is Modulated by the Inflammasome. Am. J. Pathol. 2017, 187, 665–678. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sayan, M.; Mossman, B.T. The NLRP3 inflammasome in pathogenic particle and fibre-associated lung inflammation and diseases. Part. Fibre Toxicol. 2016, 13, 674. [Google Scholar] [CrossRef] [PubMed]
- Fassina, A.; Cappellesco, R.; Guzzardo, V.; Della Via, L.; Piccolo, S.; Ventura, L.; Fassan, M. Epithelial-mesenchymal transition in malignant mesothelioma. Mod. Pathol. 2012, 25, 86–99. [Google Scholar] [CrossRef] [PubMed]
- Balduyck, B.; Trousse, D.; Nakas, A.; Martin-Ucar, A.E.; Edwards, J.; Waller, D.A. Therapeutic Surgery for Nonepithelioid Malignant Pleural Mesothelioma: Is it Really Worthwhile? Ann. Thorac. Surg. 2010, 89, 907–911. [Google Scholar] [CrossRef] [PubMed]
- Waisberg, J.; Viana, L.D.S.; Junior, R.J.A.; Silva, S.R.M.; Denadai, M.V.A.; Margeotto, F.B.; De Souza, C.S.; Matos, D. Overexpression of the ITGAV gene is associated with progression and spread of colorectal cancer. Anticancer Res. 2014, 34, 5599–5607. [Google Scholar] [PubMed]
- Ding, Y.; Pan, Y.; Liu, S.; Jiang, F.; Jiao, J. Elevation of MiR-9–3p suppresses the epithelial-mesenchymal transition of nasopharyngeal carcinoma cells via down-regulating FN1, ITGB1 and ITGAV. Cancer Boil. Ther. 2017, 18, 414–424. [Google Scholar] [CrossRef]
- Linhares, M.M.; Affonso, R.J.; Viana, L.D.S.; Silva, S.R.M.; Denadai, M.V.A.; De Toledo, S.R.C.; Matos, D. Genetic and Immunohistochemical Expression of Integrins ITGAV, ITGA6, and ITGA3 As Prognostic Factor for Colorectal Cancer: Models for Global and Disease-Free Survival. PLoS ONE 2015, 10, e0144333. [Google Scholar] [CrossRef]
- Zhang, J.J.; Yano, H.; Sasaki, T.; Matsuo, N.; Yoshioka, H. The pro-α1(V) collagen gene (Col5a1) is coordinately regulated by miR-29b with core promoter in cultured cells. Connect. Tissue Res. 2018, 59, 263–273. [Google Scholar] [CrossRef]
- Gelse, K. Collagens—structure, function, and biosynthesis. Adv. Drug Deliv. Rev. 2003, 55, 1531–1546. [Google Scholar] [CrossRef]
- Cheon, D.J.; Tong, Y.; Sim, M.S.; Dering, J.; Berel, D.; Cui, X.; Lester, J.; Beach, J.A.; Tighiouart, M.; Walts, A.E.; et al. A collagen-remodeling gene signature regulated by TGF-β signaling is associated with metastasis and poor survival in serous ovarian cancer. Clin. Cancer Res. 2014, 20, 711–723. [Google Scholar] [CrossRef] [PubMed]
- Boguslawska, J.; Kedzierska, H.; Poplawski, P.; Rybicka, B.; Tanski, Z.; Piekielko-Witkowska, A.; Information, P.E.K.F.C. Expression of Genes Involved in Cellular Adhesion and Extracellular Matrix Remodeling Correlates with Poor Survival of Patients with Renal Cancer. J. Urol. 2016, 195, 1892–1902. [Google Scholar] [CrossRef] [PubMed]
- Reid, G. MicroRNAs in mesothelioma: From tumor suppressors and biomarkers to therapeutic targets. J. Thorac. Dis. 2015, 7, 1031–1040. [Google Scholar] [PubMed]
- Pass, H.I.; Goparaju, C.; Ivanov, S.; Donington, J.; Carbone, M.; Hoshen, M.; Cohen, D.; Chajut, A.; Rosenwald, S.; Dan, J.; et al. has-miR-29c is linked to the prognosis of malignant pleural mesothelioma. Cancer Res. 2010, 70, 1916–1924. [Google Scholar] [CrossRef] [PubMed]
- Andersen, M.; Grauslund, M.; Ravn, J.; Sorensen, J.B.; Andersen, C.B.; Santoni-Rugiu, E. Diagnostic potential of miR126, miR143, miR145, miR-652 in malignant pleural mesothelioma. J. Mol. Diagn. 2014, 16, 418–430. [Google Scholar] [CrossRef] [PubMed]
- He, M.; Zhan, M.; Chen, W.; Xu, S.; Long, M.; Shen, H.; Shi, Y.; Liu, Q.; Mohan, M.; Wang, J. MiR-143-5p Deficiency Triggers EMT and Metastasis by Targeting HIF-1α in Gallbladder Cancer. Cell. Physiol. Biochem. 2017, 42, 2078–2092. [Google Scholar] [CrossRef] [PubMed]
- Zhou, L.; Dong, J.; Huang, G.; Sun, Z.; Wu, J. MicroRNA-143 inhibits cell growth by targeting ERK5 and MAP3K7 in breast cancer. Braz. J. Med. Boil. Res. 2017, 50, 5891. [Google Scholar] [CrossRef]
Hugo Gene Nomenclature Committee Gene Name. | Gene Description |
---|---|
ITGA3 | Integrin Subunit Alpha 3 |
ITGAV | Integrin Subunit Alpha V |
ITGB6 | Integrin Subunit Beta 6 |
RAC1 | Ras-Related C3 Botulinum Toxin Substrate 1 (Rho Family, Small GTP Binding Protein Rac1) |
COL5A1 | Collagen Type V Alpha 1 Chain |
COL5A2 | Collagen Type V Alpha 2 Chain |
ANGPT1 | Angiopoietin 1 |
COL1A1 | Collagen Type I Alpha 1 Chain |
COL3A1 | Collagen Type III Alpha 1 Chain |
CSF3 | Colony Stimulating Factor 3 |
HBEGF | Heparin Binding EGF Like Growth Factor |
MIF | Macrophage Migration Inhibitory Factor (Glycosylation-Inhibiting Factor) |
TGFA | Transforming Growth Factor Alpha |
TNF | Tumor Necrosis Factor |
VTN | Vitronectin |
CXCL2 | C-X-C Motif Chemokine Ligand 2 |
CXCL5 | C-X-C Motif Chemokine Ligand 5 |
IL6 | Interleukin 6 |
IL10 | Interleukin 10 |
PTGS2 | Prostaglandin-Endoperoxide Synthase 2 |
MAPK3 | Mitogen-Activated Protein Kinase 3 |
PTEN | Phosphatase And Tensin Homolog |
IL6ST | Interleukin 6 Signal Transducer |
STAT3 | Signal Transducer And Activator Of Transcription 3 |
TGFBR3 | Transforming Growth Factor Beta Receptor 3 |
CTSG | Cathepsin G |
F3 | Coagulation Factor III, Tissue Factor |
F13A1 | Coagulation Factor XIII A Chain |
PLAUR | Plasminogen Activator, Urokinase Receptor |
PLG | Plasminogen |
SERPINE1 | Serpin Family E Member 1 |
TIMP1 | IMP Metallopeptidase Inhibitor 1 |
COL1A2 | Collagen Type I Alpha 2 Chain |
ITGB3 | Integrin Subunit Beta 3 |
ITGB5 | Integrin Subunit Beta 5 |
COL5A3 | Collagen Type V Alpha 3 Chain |
VEGFA | Vascular Endothelial Growth Factor A |
WNT5A | Wnt Family Member 5A |
WISP1 | WNT1 Inducible Signaling Pathway Protein 1 |
CTNNB1 | Catenin Beta 1 |
MAPK1 | Mitogen-Activated Protein Kinase 1 |
EGFR | Epidermal Growth Factor Receptor |
TGFB1 | Transforming Growth Factor Beta 1 |
CDH1 | Cadherin 1 |
ITGA1 | Integrin Subunit Alpha 1 |
ITGA2 | Integrin Subunit Alpha 2 |
ITGA4 | Integrin Subunit Alpha 4 |
ITGA5 | Integrin Subunit Alpha 5 |
ITGA6 | Integrin Subunit Alpha 6 |
ITGB1 | Integrin Subunit Beta 1 |
ACTA2 | Actin, Alpha 2, Smooth Muscle, Aorta |
ACTA1 | Actin, Alpha 1, Skeletal Muscle |
RHOA | Ras Homolog Family Member A |
TAGLN | Transgelin |
COL4A1 | Collagen Type IV Alpha 1 Chain |
COL4A3 | Collagen Type IV Alpha 3 Chain |
COL14A1 | Collagen Type XIV Alpha 1 Chain |
CTSK | Cathepsin K |
CTSL2 | cathepsin L2 |
FGA | Fibrinogen Alpha Chain |
MMP1 | Matrix Metallopeptidase 1 |
MMP2 | Matrix Metallopeptidase 2 |
MMP7 | Matrix Metallopeptidase 7 |
MMP9 | Matrix Metallopeptidase 9 |
PLAT | Plasminogen Activator, Tissue Type |
PLAU | Plasminogen Activator, Urokinase |
CSF2 | Colony Stimulating Factor 2 |
CTGF | Connective Tissue Growth Factor |
EGF | Epidermal Growth Factor |
FGF2 | Fibroblast Growth Factor 2 |
FGF7 | Fibroblast Growth Factor 7 |
HGF | Hepatocyte Growth Factor |
IGF1 | Insulin Like Growth Factor 1 |
PDGFA | Platelet Derived Growth Factor Subunit A |
CCL2 | C-C Motif Chemokine Ligand 2 |
CCL7 | C-C Motif Chemokine Ligand 7 |
CD40LG | CD40 Ligand |
CXCL1 | C-X-C Motif Chemokine Ligand 1 |
CXCL11 | C-X-C Motif Chemokine Ligand 11 |
IL1B | Interleukin 1 Beta |
IL2 | Interleukin 2 |
IL4 | Interleukin 4 |
Q Value | FC | |
---|---|---|
Up-Regulated | ||
ITGA3 | 0.0075 | 3.65983 |
ITGAV | 0.019 | 1.62136 |
RAC1 | 0.0075 | 1.437 |
COL5A1 | 1.29 × 10−3 | 3.4584 |
COL5A2 | 1.06 × 10−3 | 3.7013 |
COL1A1 | 1.24 × 10−3 | 2.6257 |
COL3A1 | 2.79 × 10−3 | 4.0285 |
MIF | 8.36 × 10−3 | 2.8341 |
VTN | 4.55 × 10−3 | 7.1654 |
MAPK3 | 1.24 × 10−3 | 2.4605 |
IL6ST | 0.0149 | 1.6038 |
STAT3 | 2.51 × 10−3 | 2.9051 |
SERPINE1 | 0.02 | 3.1149 |
COL1A2 | 0.0255 | 2.7937 |
Down-Regulated | ||
ITGB6 | 9.76 × 10−4 | −2.5496 |
ANGPT1 | 0.011 | −2.3463 |
CSF3 | 0.012 | −3.5254 |
HBEGF | 0.02 | −1.5826 |
TGFA | 0.0126 | −1.4865 |
TNF | 0.04208 | −4.0594 |
CXCL2 | 3.86 × 10−3 | −4.6691 |
CXCL5 | 8.40 × 10−4 | −2.1452 |
IL6 | 0.01932 | −6.2047 |
IL10 | 0.039 | −1.3583 |
PTGS2 | 7.29 × 10−4 | −4.408 |
PTEN | 0.033 | −1.407 |
TGFBR3 | 2.14 × 10−3 | −2.0823 |
CTSG | 2.08 × 10−3 | −2.5715 |
F3 | 0.03 | −2.3331 |
PLG | 0.027 | −1.5597 |
Coefficients | Estimate | Std. Error | T Value | Pr (>|t|) |
---|---|---|---|---|
(Intercept) | 1.89725 | 0.20885 | 9.084 | 4.59 × 10−11 *** |
COL5A1 | 0.41537 | 0.07451 | 5.574 | 2.18 × 10−6 *** |
ID | Name | P-Value | FDR B&H | FDR B&Y | Bonferroni | Genes From Input | Genes in Annotation | |
---|---|---|---|---|---|---|---|---|
1 | GO:0040011 | locomotion | 2.531 × 10−23 | 7.177 × 10−20 | 6.120 × 10−19 | 7.177 × 10−20 | 26 | 1735 |
2 | GO:0016477 | cell migration | 5.798 × 10−23 | 1.644 × 10−19 | 24 | 1300 | ||
3 | GO:0051674 | localization of cell | 5.395 × 10−22 | 3.825 × 10−19 | 3.262 × 10−18 | 1.530 × 10−18 | 24 | 1428 |
4 | GO:0048870 | cell motility | 5.395 × 10−22 | 3.825 × 10−19 | 3.262 × 10−18 | 1.530 × 10−18 | 24 | 1428 |
5 | GO:0030334 | regulation of cell migration | 1.635 × 10−21 | 9.274 × 10−19 | 7.909 × 10−18 | 4.637 × 10−18 | 20 | 742 |
ID | Name | Source | P-Value | FDR B&H | FDR B&Y | Bonferroni | Genes From Input | Genes in Annotation | |
---|---|---|---|---|---|---|---|---|---|
1 | hsa-miR-143-3p:Functional MTI | Functional MTI | miRTarbase | 5.442 × 10−12 | 1.459 × 10−8 | 1.236 × 10−7 | 1.459 × 10−8 | 7 | 228 |
2 | hsa-miR-223-3p:Functional MTI | Functional MTI | miRTarbase | 5.747 × 10−10 | 7.704 × 10−7 | 6.526 × 10−6 | 1.541 × 10−6 | 5 | 98 |
3 | hsa-miR-29b-3p:Functional MTI | Functional MTI | miRTarbase | 1.159 × 10−9 | 1.036 × 10−6 | 8.776 × 10−6 | 3.108 × 10−6 | 6 | 261 |
4 | hsa-miR-29a:PITA | hsa-miR-29a:PITA TOP | PITA | 3.728 × 10−9 | 1.666 × 10−6 | 1.411 × 10−5 | 9.995 × 10−6 | 7 | 583 |
5 | hsa-miR-29c:PITA | hsa-miR-29c:PITA TOP | PITA | 3.728 × 10−9 | 1.666 × 10−6 | 1.411 × 10−5 | 9.995 × 10−6 | 7 | 583 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rouka, E.; Beltsios, E.; Goundaroulis, D.; Vavougios, G.D.; Solenov, E.I.; Hatzoglou, C.; Gourgoulianis, K.I.; Zarogiannis, S.G. In Silico Transcriptomic Analysis of Wound-Healing-Associated Genes in Malignant Pleural Mesothelioma. Medicina 2019, 55, 267. https://doi.org/10.3390/medicina55060267
Rouka E, Beltsios E, Goundaroulis D, Vavougios GD, Solenov EI, Hatzoglou C, Gourgoulianis KI, Zarogiannis SG. In Silico Transcriptomic Analysis of Wound-Healing-Associated Genes in Malignant Pleural Mesothelioma. Medicina. 2019; 55(6):267. https://doi.org/10.3390/medicina55060267
Chicago/Turabian StyleRouka, Erasmia, Eleftherios Beltsios, Dimos Goundaroulis, Georgios D. Vavougios, Evgeniy I. Solenov, Chrissi Hatzoglou, Konstantinos I. Gourgoulianis, and Sotirios G. Zarogiannis. 2019. "In Silico Transcriptomic Analysis of Wound-Healing-Associated Genes in Malignant Pleural Mesothelioma" Medicina 55, no. 6: 267. https://doi.org/10.3390/medicina55060267