Sains Malaysiana 47(12)(2018): 2933–2940

http://dx.doi.org/10.17576/jsm-2018-4712-01

 

Construction and Analysis of Protein-Protein Interaction Network to Identify the Molecular Mechanism in Laryngeal Cancer

(Pembinaan dan Analisis Jaringan Interaksi Protein-Protein untuk Mengenal Pasti Mekanisme Molekul Kanser Larinks)

 

SARAHANI HARUN1 & NURULISA ZULKIFLE2*

 

1Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia

 

2Cluster for Oncological & Radiological Sciences, Advanced Medical & Dental Institute, Universiti Sains Malaysia, 13200 Bertam, Penang, Malaysia

 

Diserahkan: 30 Mei 2018/Diterima: 13 September 2018

 

ABSTRACT

Laryngeal cancer is the most common head and neck cancer in the world and its incidence is on the rise. However, the molecular mechanism underlying laryngeal cancer pathogenesis is poorly understood. The goal of this study was to develop a protein-protein interaction (PPI) network for laryngeal cancer to predict the biological pathways that underlie the molecular complexes in the network. Genes involved in laryngeal cancer were extracted from the OMIM database and their interaction partners were identified via text and data mining using Agilent Literature Search, STRING and GeneMANIA. PPI network was then integrated and visualised using Cytoscape ver3.6.0. Molecular complexes in the network were predicted by MCODE plugin and functional enrichment analyses of the molecular complexes were performed using BiNGO. 28 laryngeal cancer-related genes were present in the OMIM database. The PPI network associated with laryngeal cancer contained 161 nodes, 661 edges and five molecular complexes. Some of the complexes were related to the biological behaviour of cancer, providing the foundation for further understanding of the mechanism of laryngeal cancer development and progression.

 

Keywords: Functional enrichment analysis; laryngeal cancer; protein-protein interaction network; text mining

 

ABSTRAK

Kanser larinks adalah kanser kepala dan leher yang paling biasa di dunia dan kejadiannya semakin meningkat. Walau bagaimanapun, mekanisme molekul yang terlibat dalam patogenesis kanser larinks masih kurang difahami. Tujuan kajian ini dijalankan adalah untuk membangunkan jaringan interaksi protein-protein (IPP) bagi kanser larinks untuk meramal tapak jalan biologi menerusi analisis kompleks molekul daripada dalam jaringan IPP yang dibina. Gen yang terlibat dalam kanser larinks telah diekstrak daripada pangkalan data OMIM dan pasangan interaksinya telah dikenal pasti melalui pencarian teks dan data menggunakan Agilent Literature Search, STRING dan GeneMANIA. Jaringan IPP kemudiannya digabung dan divisualisasikan menggunakan Cytoscape ver3.6.0. Kompleks molekul dalam jaringan diramalkan oleh plugin MCODE dan analisis pengkayaan berfungsi kompleks molekul dilakukan menggunakan BiNGO. 28 gen berkaitan dengan kanser larinks ditemui dalam pangkalan data OMIM. Jaringan IPP yang dikaitkan dengan kanser larinks mengandungi 161 nodus, 661 interaksi dan lima kompleks molekul. Beberapa kompleks didapati berkaitan dengan tingkah laku biologi kanser dan ini telah menyediakan asas untuk memahami lebih lanjut mekanisme dalam pembangunan dan perkembangan kanser larinks.

 

Kata kunci: Analisis pengayaan berfungsi; jaringan interaksi protein-protein; kanser larinks; pencarian teks

RUJUKAN

Bader, G.D. & Hogue, C.W. 2003. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(1): 2.

Chen, C., Shen, H., Zhang, L.G., Liu, J., Cao, X.G., Yao, A.L., Kang, S.S., Gao, W.X., Han, H., Cao, F.H. & Li, Z.G. 2016. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer. International Journal of Molecular Medicine 37(6): 1576- 1586.

Coskunpinar, E., Oltulu, Y.M., Orhan, K.S., Tiryakioglu, N.O., Kanliada, D. & Akbas, F. 2014. Identification of a differential expression signature associated with tumorigenesis and metastasis of laryngeal carcinoma. Gene 534(2): 183-188.

Engin, H.B., Guney, E., Keskin, O., Oliva, B. & Gursoy, A. 2013. Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer. PLoS ONE 8(11): e81035.

Erkul, E., Kucukodaci, Z., Pinar, D., Gungor, A., Alparslan Babayigit, M., Kurt, O. & Cincik, H. 2016. TRAIL and TRAIL receptors in patients with laryngeal cancer. Head & Neck 38: 535-541.

Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D.M., Forman, D. & Bray, F. 2015. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer 136(5): 359-386.

Gogvadze, V., Orrenius, S. & Zhivotovsky, B. 2006. Multiple pathways of cytochrome c release from mitochondria in apoptosis. Biochimica et Biophysica Acta - Bioenergetics 1757(5-6): 639-647.

Harfe, B.D. & Jinks-Robertson, S. 2000. DNA mismatch repair and genetic instability. Annual Review of Genetics 34(1): 359-399.

Ideker, T. & Sharan, R. 2008. Protein networks in disease. Genome Research 18(4): 644-652.

Jeřábková, B., Marek, J., Bučková, H., Kopečková, L., Veselý, K., Valíčková, J., Fajkus, J. & Fajkusová, L. 2010. Keratin mutations in patients with epidermolysis bullosa simplex: Correlations between phenotype severity and disturbance of intermediate filament molecular structure. British Journal of Dermatology 162(5): 1004-1013.

Kunkel, T.A. & Erie, D.A. 2005. DNA mismatch repair. Annual Review of Biochemistry 74(1): 681-710.

Maere, S., Heymans, K. & Kuiper, M. 2005. BiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21(16): 3448-3449.

Mehrotra, R. & Yadav, S. 2006. Oral squamous cell carcinoma: Etiology, pathogenesis and prognostic value of genomic alterations. Indian Journal of Cancer 43(2): 60-66.

Mohamed-Hussein, Z.A. & Harun, S. 2009. Construction of a polycystic ovarian syndrome (PCOS) pathway based on the interactions of PCOS-related proteins retrieved from bibliomic data. Theoretical Biology and Medical Modelling 6(18): 1-7.

Sasiadek, M.M., Smigiel, R., Stembalska, A., Ramsey, D. & Blin, N. 2006. Cyclin D1 and MLH1 levels in laryngeal cancer are linked to chromosomal imbalance. Anticancer Research 26(6 B): 4597-4601.

Schabath, M.B., Giuliano, A.R., Thompson, Z.J., Amankwah, E.K., Gray, J.E., Fenstermacher, D.A., Jonathan, K.A., Beg, A.A. & Haura, E.B. 2013. TNFRSF10B polymorphisms and haplotypes associated with increased risk of death in non-small cell lung cancer. Carcinogenesis 34(11): 2525-2530.

Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B. & Ideker, T. 2003. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research 13(11): 2498-2504.

Siegel, R.L., Miller, K.D. & Jemal, A. 2017. Cancer statistics. 2017. CA Cancer J. Clin. 67(1): 7-30.

Szklarczyk, D., Morris, J.H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N.T., Roth, A., Bork, P., Jensen, L.J. & von Mering, C. 2017. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Research 45: D362-D368.

Tahir, R.A., Sehgal, S.A., Khattak, N.A., Khan Khattak, J.Z. & Mir, A. 2013. Tumor necrosis factor receptor superfamily 10B (TNFRSF10B): An insight from structure modeling to virtual screening for designing drug against head and neck cancer. Theoretical Biology and Medical Modelling 10(1): 1.

Verim, A., Turan, S., Farooqi, A.A., Kahraman, O.T., Tepe- Karaca, C., Yildiz, Y., Naiboglu, B., Ozkan, N.E., Ergen, A., Isitmangil, G.A. & Yaylim, I. 2014. Association between laryngeal squamous cell carcinoma and polymorphisms in tumor necrosis factor related apoptosis induce ligand (TRAIL), TRAIL receptor and sTRAIL levels. Asian Pacific Journal of Cancer Prevention 15(24): 10697-10703.

Vyas, R., Bapat, S., Jain, E., Karthikeyan, M., Tambe, S. & Kulkarni, B.D. 2016. Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis. Computational Biology and Chemistry 65: 37-44.

Wei, M.C., Zong, W.X., Cheng, E.H., Lindsten, T., Panoutsakopoulou, V., Ross, A.J., Roth, K.A., MacGregor, G.R., Thompson, C.B. & Korsmeyer, S.J. 2001. Proapoptotic BAX and BAK: A requisite gateway to mitochondrial dysfunction and death. Science 292(5517): 727-730.

Wu, C.H., Hsu, C.L., Lu, P.C., Lin, W.C., Juan, H.F. & Huang, H.C. 2016. Identification of lncRNA functions in lung cancer based on associated protein-protein interaction modules. Scientific Reports 6(1): 35939.

Yoldas, B., Ozer, C., Ozen, O., Canpolat, T., Dogan, I., Griffith, T.S., Sanlioglu, S. & Ozluoglu, L.N. 2011. Clinical significance of TRAIL and TRAIL receptors in patients with head and neck cancer. Head & Neck 33(9): 1278-1284.

Yu, H., Kim, P.M., Sprecher, E., Trifonov, V. & Gerstein, M. 2007. The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics. PLoS Computational Biology 3(4): e59.

Zhang, Y., Chen, Y., Yu, J., Liu, G. & Huang, Z. 2014. Integrated transcriptome analysis reveals miRNA-mRNA crosstalk in laryngeal squamous cell carcinoma. Genomics 104(4): 249-256.

Zuberi, K., Franz, M., Rodriguez, H., Montojo, J., Lopes, C.T., Bader, G.D. & Morris, Q. 2013. GeneMANIA prediction server 2013 update. Nucleic Acids Research 41: 115-122.

*Pengarang untuk surat-menyurat; email: nurulisa@usm.my