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Asadzadeh, A., Ghorbani, N., & Dastan, K. (2023). Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study. International Journal of Reproductive BioMedicine (IJRM), 21(10), 809–818. https://doi.org/10.18502/ijrm.v21i10.14536
https://doi.org/10.18502/ijrm.v21i10.14536
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authors
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Azizeh Asadzadeh
Azizeh Asadzadeh
Department of Biology, Faculty of Science, Nour Danesh Institute of Higher Education, Meymeh, Isfahan, Iran.
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Nafiseh Ghorbani
Nafiseh Ghorbani
Department of Microbiology, Faculty of Basic Sciences, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
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Katayoun Dastan
Katayoun Dastan
Department of Microbiology, Faculty of Basic Sciences, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
Published Date
- Nov 21, 2023
Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study
Abstract
Background: The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), the fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary to discover the hub gene, key pathways, and the exact underlying mechanisms involved in developing this disease.
Objective: This study aims to use gene expression patterns and protein-protein interaction (PPI) network analysis to identify key pathways and druggable hub genes in CC.
Materials and Methods: In this in silico analysis, 2 microarray gene expression datasets; GSE63514 (104 cancer and 24 normal samples), and GSE9750 (42 cancer and 24 normal samples) were extracted from gene expression omnibus to identify common differentially expressed genes between them. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed via the Enrichr database. STRING 12.0 database and CytoHubba plugin in Cytoscape 3.9.1 software were implemented to create and analyze the PPI network. Finally, druggable hub genes were screened.
Results: Based on the degree method, 10 key genes were known as the hub genes after the screening of PPI networks by the CytoHubba plugin. NCAPG, KIF11, TTK, PBK, MELK, ASPM, TPX2, BUB1, TOP2A, and KIF2C are the key genes, of which 5 genes (KIF11, TTK, PBK, MELK, and TOP2A) were druggable.
Conclusion: This research provides a novel vision for designing therapeutic targets in patients with CC. However, these findings should be verified through additional experiments.
Key words: Protein interactions, Cervical cancer, Hub genes, Gene expression, DEGs.
References
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[19] Naderi M, Asadzadeh A, Heidaryan Naeini F. [Study the effect of thymus vulgaris in inhibiting acetylcholinesterase enzyme in order to treat Alzheimer’s disease]. J Sabzevar Univ Med Sci 2020; 27: 594–602. (in Persian)
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[24] Xiao C, Gong J, Jie Y, Cao J, Chen Zh, Li R, et al. NCAPG is a promising therapeutic target across different tumor types. Front Pharmacol 2020; 11: 387.
[25] Klimaszewska-Wisniewska A, Neska-Dlugosz I, Buchholz K, Durslewicz J, Grzanka D, Kasperska A, et al. Prognostic significance of KIF11 and KIF14 expression in pancreatic adenocarcinoma. Cancers 2021; 13: 3017.
[26] Zhou Y, Chen X, Li B, Li Y, Zhang B. KIF11 is a potential prognostic biomarker and therapeutic target for adrenocortical carcinoma. Transl Androl Urol 2023; 12: 594–611.
[27] Mishra D, Mishra A, Rai SN, Vamanu E, Singh MP. Identification of prognostic biomarkers for suppressing tumorigenesis and metastasis of Hepatocellular carcinoma through transcriptome analysis. Diagnostics 2023; 13: 965.
[28] Hsu W-H, Wang W-J, Lin W-Y, Huang Y-M, Lai Ch-Ch, Liao J-Ch, et al. Adducin-1 is essential for spindle pole integrity through its interaction with TPX2. EMBO Rep 2018; 19: e45607.
[29] Neumayer G, Belzil C, Gruss OJ, Nguyen MD. TPX2: of spindle assembly, DNA damage response, and cancer. Cell Mol Life Sci 2014; 71: 3027–3047.
[30] Li T-F, Zeng H-J, Shan Z, Ye R-Y, Cheang T-Y, Zhang Y-J, et al. Overexpression of kinesin superfamily members as prognostic biomarkers of breast cancer. Cancer Cell Int 2020; 20: 123.
[31] Infante JR, Patnaik A, Verschraegen CF, Olszanski AJ, Shaheen M, Burris HA, et al. Two phase 1 dose-escalation studies exploring multiple regimens of litronesib (LY2523355), an Eg5 inhibitor, in patients with advanced cancer. Cancer Chemother Pharmacol 2017; 79: 315–326.
[32] Porter K, Fairlie WD, Laczka O, Delebecque F, Wilkinson J. Idronoxil as an anticancer agent: Activity and mechanisms. Curr Cancer Drug Targets 2020; 20: 341–354.
[33] Le-Trung N, Minh TD, Phuong TDT, Kamei K. Sphaerocoryne affinis fruit extract causes DNA damage leading to inhibited cell proliferation and activated apoptosis in cervical cancer cells. Research Square 2023.
[34] Gupta AK, Kumar M. An integrative approach toward identification and analysis of therapeutic targets involved in HPV pathogenesis with a focus on carcinomas. Cancer Biomark 2023; 36: 31–52.
[35] Bannoud N, Stupirski JC, Cagnoni AJ, Hockl PF, Pérez Sáez JM, García PA, et al. Circulating galectin- 1 delineates response to bevacizumab in melanoma patients and reprograms endothelial cell biology. Proc Natl Acad Sci 2023; 120: e2214350120.
[36] Wang S, Iqbal Khan F. Investigation of molecular interactions mechanism of pembrolizumab and PD-1. Int J Mol Sci 2023; 24: 10684.
[2] Alias NA, Mustafa WA, Jamlos MA, Ismail S, Alquran H, Rohani MNKH. Pap smear image analysis based on nucleus segmentation and deep learning- A recent review. J Adv Res Appl Sci Eng Technol 2023; 29: 37– 47.
[3] Cohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. Lancet 2019; 393: 169–182.
[4] Kavitha R, Jothi DK, Saravanan K, Swain MP, Gonzáles JLA, Bhardwaj RJ, et al. Ant colony optimizationenabled CNN deep learning technique for accurate detection of cervical cancer. Biomed Res Int 2023; 2023: 1742891.
[5] Harper DM, Demars LR. Primary strategies for HPV infection and cervical cancer prevention. Clin Obstet Gynecol 2014; 57: 256–278.
[6] Lin H, Ma Y, Wei Y, Shang H. Genome-wide analysis of aberrant gene expression and methylation profiles reveals susceptibility genes and underlying mechanism of cervical cancer. Eur J Obstet Gynecol Reprod Biol 2016; 207: 147–152.
[7] Xue H, Sun Z, Wu W, Du D, Liao S. Identification of hub genes as potential prognostic biomarkers in cervical cancer using comprehensive bioinformatics analysis and validation studies. Cancer Manag Res 2021; 13: 117–131.
[8] Dai F, Chen G, Wang Y, Zhang L, Long Y, Yuan M, et al. Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis. Onco Targets Ther 2019; 12: 4517–4532.
[9] Athanasios A, Charalampos V, Vasileios T, Md Ashraf G. Protein-protein interaction (PPI) network: Recent advances in drug discovery. Curr Drug Metab 2017; 18: 5–10.
[10] Wang Zh-Zh, Shi X-X, Huang G-Y, Hao G-F, Yang G-F. Fragment-based drug discovery supports drugging ‘undruggable’protein-protein interactions. Trends Biochem Sci 2023; 48: 539–552.
[11] Rehman AU, Khurshid B, Ali Y, Rasheed S, Wadood A, Ng H-L, et al. Computational approaches for the design of modulators targeting protein-protein interactions. Exp Opin Drug Discov 2023; 18: 315–333.
[12] Clough E, Barrett T. The gene expression omnibus database. Methods Mol Biol 2016; 1418: 93–110.
[13] Chen L, Zhang Y-H, Wang ShP, Zhang YH, Huang T, Cai Y-D. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PloS One 2017; 12: e0184129.
[14] Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, et al. The STRING database in 2023: Protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 2023; 51: D638–D646.
[15] Cotto KC, Wagner AH, Feng Y-Y, Kiwala S, Coffman AC, Spies G, et al. DGIdb 3.0: A redesign and expansion of the drug-gene interaction database. Nucleic Acids Res 2018; 46: D1068–D1073.
[16] Freshour ShL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, et al. Integration of the Drug- Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res 2021; 49: D1144–D1151.
[17] Shams Moattar F, Asadzadeh A, Heydari M, Zamani M, Esnaashari F, Jeldani F. Designing multi-epitope subunit vaccine candidate for Zika virus utilizing in silico tools. Res Mol Med 2022; 10: 9–18.
[18] Mosalanezhad F, Asadzadeh A, Ghanbariasad A, Naderpoor M, Bordbar R, Ghavamizadeh M, et al. The evaluation of the anti-histone deacetylase, antibacterial, antioxidant and cytotoxic activities of synthetic N, N´-ethylenebis (a methylsalicylideneiminate) schiff base derivatives. Iran J Chem Chem Eng 2022; 41: 1856–1869.
[19] Naderi M, Asadzadeh A, Heidaryan Naeini F. [Study the effect of thymus vulgaris in inhibiting acetylcholinesterase enzyme in order to treat Alzheimer’s disease]. J Sabzevar Univ Med Sci 2020; 27: 594–602. (in Persian)
[20] Sholehvar F, Asadzadeh A, Seyedhosseini Ghaheh H. [Molecular docking studies of some hydroxy nitrodiphenyl ether analogues as tyrosinase inhibitors]. J Fasa Univ Med Sci 2017; 6: 548–555. (in Persian)
[21] Ghaffari S, Asadzadeh A, Seyedhosseini Ghaheh H, Sholehvar F. [Docking study on salicylaldehyde derivatives as anti-melanogenesis agents]. J Fasa Univ Med Sci 2018; 8: 618–627. (in Persian)
[22] Graham SV. Keratinocyte differentiation-dependent human papillomavirus gene regulation. Viruses 2017; 9: 245.
[23] Yarla NS, Bishayee A, Sethi G, Reddanna P, Kalle AM, Dhananjaya BL, et al. Targeting arachidonic acid pathway by natural products for cancer prevention and therapy. Semin Cancer Biol 2016; 40: 48–81.
[24] Xiao C, Gong J, Jie Y, Cao J, Chen Zh, Li R, et al. NCAPG is a promising therapeutic target across different tumor types. Front Pharmacol 2020; 11: 387.
[25] Klimaszewska-Wisniewska A, Neska-Dlugosz I, Buchholz K, Durslewicz J, Grzanka D, Kasperska A, et al. Prognostic significance of KIF11 and KIF14 expression in pancreatic adenocarcinoma. Cancers 2021; 13: 3017.
[26] Zhou Y, Chen X, Li B, Li Y, Zhang B. KIF11 is a potential prognostic biomarker and therapeutic target for adrenocortical carcinoma. Transl Androl Urol 2023; 12: 594–611.
[27] Mishra D, Mishra A, Rai SN, Vamanu E, Singh MP. Identification of prognostic biomarkers for suppressing tumorigenesis and metastasis of Hepatocellular carcinoma through transcriptome analysis. Diagnostics 2023; 13: 965.
[28] Hsu W-H, Wang W-J, Lin W-Y, Huang Y-M, Lai Ch-Ch, Liao J-Ch, et al. Adducin-1 is essential for spindle pole integrity through its interaction with TPX2. EMBO Rep 2018; 19: e45607.
[29] Neumayer G, Belzil C, Gruss OJ, Nguyen MD. TPX2: of spindle assembly, DNA damage response, and cancer. Cell Mol Life Sci 2014; 71: 3027–3047.
[30] Li T-F, Zeng H-J, Shan Z, Ye R-Y, Cheang T-Y, Zhang Y-J, et al. Overexpression of kinesin superfamily members as prognostic biomarkers of breast cancer. Cancer Cell Int 2020; 20: 123.
[31] Infante JR, Patnaik A, Verschraegen CF, Olszanski AJ, Shaheen M, Burris HA, et al. Two phase 1 dose-escalation studies exploring multiple regimens of litronesib (LY2523355), an Eg5 inhibitor, in patients with advanced cancer. Cancer Chemother Pharmacol 2017; 79: 315–326.
[32] Porter K, Fairlie WD, Laczka O, Delebecque F, Wilkinson J. Idronoxil as an anticancer agent: Activity and mechanisms. Curr Cancer Drug Targets 2020; 20: 341–354.
[33] Le-Trung N, Minh TD, Phuong TDT, Kamei K. Sphaerocoryne affinis fruit extract causes DNA damage leading to inhibited cell proliferation and activated apoptosis in cervical cancer cells. Research Square 2023.
[34] Gupta AK, Kumar M. An integrative approach toward identification and analysis of therapeutic targets involved in HPV pathogenesis with a focus on carcinomas. Cancer Biomark 2023; 36: 31–52.
[35] Bannoud N, Stupirski JC, Cagnoni AJ, Hockl PF, Pérez Sáez JM, García PA, et al. Circulating galectin- 1 delineates response to bevacizumab in melanoma patients and reprograms endothelial cell biology. Proc Natl Acad Sci 2023; 120: e2214350120.
[36] Wang S, Iqbal Khan F. Investigation of molecular interactions mechanism of pembrolizumab and PD-1. Int J Mol Sci 2023; 24: 10684.