Identification of significant genes and pathways associated with tenascin-C in cancer progression by bioinformatics analysis

Rahimmanesh, Ilnaz; Fatehi, Razieh; Khanahmad, Hossein

Identification of significant genes and pathways associated with tenascin-C in cancer progression by bioinformatics analysis

Document Type : Original Article

Authors

¹ Applied Physiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences; Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

² Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

³ Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences; Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Tenascin-C (TNC) is a large glycoprotein of the extracellular matrix which associated with poor clinical outcomes in several malignancies. TNC over-expression is repeatedly observed in several cancer tissues and promotes several processes in tumor progression. Until quite recently, more needs to be known about the potential mechanisms of TNC as a key player in cancer progression and metastasis. Materials and Methods: In the present study, we performed a bioinformatics analysis of breast and colorectal cancer expression microarray data to survey TNC role and function with holistic view. Gene expression profiles were analyzed to identify differentially expressed genes (DEGs) between normal samples and cancer biopsy samples. The protein-protein interaction (PPI) networks of the DEGs with CluePedia plugin of Cytoscape software were constructed. Furthermore, after PPI network construction, gene-regulatory networks analysis was performed to predict long noncoding RNAs and microRNAs associated with TNC and cluster analysis was performed. Using the Clue gene ontology (GO) plugin of Cytoscape software, the GO and pathway enrichment analysis were performed. Results: PPI and DEGs-miRNA-lncRNA regulatory networks showed TNC is a significant node in a huge network, and one of the main gene with high centrality parameters. Furthermore, from the regulatory level perspective, TNC could be significantly impressed by miR-335-5p. GO analysis results showed that TNC was significantly enriched in cancer-related biological processes. Conclusions: It is important to identify the TNC underlying molecular mechanisms in cancer progression, which may be clinically useful for tumor-targeting strategies. Bioinformatics analysis provides an insight into the significant roles that TNC plays in cancer progression scenarios.

Keywords

References

1.	Insua-Rodríguez J, Oskarsson T. The extracellular matrix in breast cancer. Adv Drug Deliv Rev 2016;97:41-55.
2.	Jena MK, Janjanam J. Role of extracellular matrix in breast cancer development: A brief update. F1000Res 2018;7:274.
3.	Walker C, Mojares E, Del Río Hernández A. Role of extracellular matrix in development and cancer progression. Int J Mol Sci 2018;19:3028.
4.	Nagaharu K, Zhang X, Yoshida T, Katoh D, Hanamura N, Kozuka Y, et al. Tenascin C induces epithelial-mesenchymal transition-like change accompanied by SRC activation and focal adhesion kinase phosphorylation in human breast cancer cells. Am J Pathol 2011;178:754-63.
5.	Sofat N, Robertson SD, Hermansson M, Jones J, Mitchell P, Wait R. Tenascin-C fragments are endogenous inducers of cartilage matrix degradation. Rheumatol Int 2012;32:2809-17.
6.	Tsunoda T, Inada H, Kalembeyi I, Imanaka-Yoshida K, Sakakibara M, Okada R, et al. Involvement of large tenascin-C splice variants in breast cancer progression. Am J Pathol 2003;162:1857-67.
7.	Yoshida T, Akatsuka T, Imanaka-Yoshida K. Tenascin-C and integrins in cancer. Cell Adh Migr 2015;9:96-104.
8.	Wickham H. Elegant Graphics for Data Analysis (ggplot2). In: New York, NY: Springer-Verlag; 2009.
9.	Core Team R. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.
10.	Bindea G, Galon J, Mlecnik B. Cluepedia cytoscape plugin: Pathway insights using integrated experimental and in silico data. Bioinformatics 2013;29:661-3.
11.	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-504.
12.	Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. Network analyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 2019;47:W234-41.
13.	Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. Clue GO: A cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009;25:1091-3.
14.	Wawrzyniak D, Grabowska M, Głodowicz P, Kuczyński K, Kuczyńska B, Fedoruk-Wyszomirska A, et al. Down-regulation of tenascin-C inhibits breast cancer cells development by cell growth, migration, and adhesion impairment. PLoS One 2020;15:e0237889.
15.	Katoh D, Nagaharu K, Shimojo N, Hanamura N, Yamashita M, Kozuka Y, et al. Binding of αvβ1 and αvβ6 integrins to tenascin-C induces epithelial-mesenchymal transition-like change of breast cancer cells. Oncogenesis 2013;2:e65.
16.	Deng JL, Xu YH, Wang G. Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front Genet 2019;10:695.
17.	Kubo Y, Ishikawa K, Mori K, Kobayashi Y, Nakama T, Arima M, et al. Periostin and tenascin-C interaction promotes angiogenesis in ischemic proliferative retinopathy. Sci Rep 2020;10:9299.
18.	Mitsui Y, Shiina H, Kato T, Maekawa S, Hashimoto Y, Shiina M, et al. Versican promotes tumor progression, metastasis and predicts poor prognosis in renal carcinoma. Mol Cancer Res 2017;15:884-95.
19.	Przemyslaw L, Boguslaw HA, Elzbieta S, Malgorzata SM. ADAM and ADAMTS family proteins and their role in the colorectal cancer etiopathogenesis. BMB Rep 2013;46:139-50.
20.	Singhai R, Patil VW, Jaiswal SR, Patil SD, Tayade MB, Patil AV. E-Cadherin as a diagnostic biomarker in breast cancer. N Am J Med Sci 2011;3:227-33.
21.	Chaudhary B, Khaled YS, Ammori BJ, Elkord E. Neuropilin 1: Function and therapeutic potential in cancer. Cancer Immunol Immunother 2014;63:81-99.
22.	Chu W, Song X, Yang X, Ma L, Zhu J, He M, et al. Neuropilin-1 promotes epithelial-to-mesenchymal transition by stimulating nuclear factor-kappa B and is associated with poor prognosis in human oral squamous cell carcinoma. PLoS One 2014;9:e101931.
23.	Naik A, Al-Yahyaee A, Abdullah N, Sam JE, Al-Zeheimi N, Yaish MW, et al. Neuropilin-1 promotes the oncogenic tenascin-C/integrin β3 pathway and modulates chemo resistance in breast cancer cells. BMC Cancer 2018;18:533.
24.	Lange K, Kammerer M, Hegi ME, Grotegut S, Dittmann A, Huang W, et al. Endothelin receptor type B counteracts tenascin-C-induced endothelin receptor type A-dependent focal adhesion and actin stress fiber disorganization. Cancer Res 2007;67:6163-73.
25.	Williams SA, Schwarzbauer JE. A shared mechanism of adhesion modulation for tenascin-C and fibulin-1. Mol Biol Cell 2009;20:1141-9.
26.	Du W, Tang H, Lei Z, Zhu J, Zeng Y, Liu Z, et al. miR-335-5p inhibits TGF-β1-induced epithelial-mesenchymal transition in non-small cell lung cancer via ROCK1. Respir Res 2019;20:225.
27.	Chen L, Song Y, Lu Y, Zheng W, Ma W, Zhang C. miR-335 inhibits cell proliferation, migration and invasion in HeLa cervical cancer cells. Int J Clin Exp Pathol 2016;9:10351-62.
28.	Sandoval-Bórquez A, Polakovicova I, Carrasco-Véliz N, Lobos-González L, Riquelme I, Carrasco-Avino G, et al. MicroRNA-335-5p is a potential suppressor of metastasis and invasion in gastric cancer. Clin Epigenetics 2017;9:114.
29.	Gao Y, Zeng F, Wu JY, Li HY, Fan JJ, Mai L, et al. MiR-335 inhibits migration of breast cancer cells through targeting oncoprotein c-Met. Tumour Biol 2015;36:2875-83.

Advanced Biomedical Research