Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Retrieval
2.2. Screening of Differentially Expressed Genes (DEGs)
2.3. Functional Enrichment of Gene Sets
2.4. PPI Network Construction
2.5. Selection of Central Hub Proteins from the PPI Network
2.6. Hub Gene Survival and Expression Profile Analysis
3. Results and Analysis
3.1. DEG Identification
3.2. Functional Analysis of DEGs
3.3. PPI Network Construction
3.4. Modules and Hub Proteins Identification
3.5. Survival and Expression Level of the Hub Genes
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [Green Version]
- Organization WHO. UN Joint Global Programme on Cervical Cancer Prevention and Control; WHO: Geneva, Switzerland, 2017. [Google Scholar]
- Ferlay, J.; Ervik, M.; Lam, F.; Colombet, M.; Mery, L.; Piñeros, M.; Znaor, A.; Soerjomataram, I.; Bray, F. Global cancer Observatory: Cancer today. Lyon, France: International agency for research on cancer. Cancer Today 2018. [Google Scholar]
- WHO. Available online: https://wwwwhoint/cancer/prevention/diagnosis-screening/cervical-cancer/en/ (accessed on 17 April 2020).
- Zhao, M.; Huang, W.; Zou, S.; Shen, Q.; Zhu, X. A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction. Int. J. Genom. 2020. [Google Scholar] [CrossRef] [PubMed]
- Walboomers, J.M.; Jacobs, M.V.; Manos, M.M.; Bosch, F.X.; Kummer, J.A.; Shah, K.V.; Snijders, P.J.; Peto, J.; Meijer, C.J.; Muñoz, N. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J. Pathol. 1999, 189, 12–19. [Google Scholar] [CrossRef]
- Crosbie, E.J.; Einstein, M.H.; Franceschi, S.; Kitchener, H.C. Human papillomavirus and cervical cancer. Lancet 2013, 382, 889–899. [Google Scholar] [CrossRef]
- Schiffman, M.; Castle, P.E.; Jeronimo, J.; Rodriguez, A.C.; Wacholder, S. Human papillomavirus and cervical cancer. Lancet 2007, 370, 890–907. [Google Scholar] [CrossRef]
- Network CGAR. Integrated genomic and molecular characterization of cervical cancer. Nature 2017, 543, 378–384. [Google Scholar] [CrossRef] [PubMed]
- Crook, T.; Wrede, D.; Tidy, J.; Vousden, K.; Tidy, J.; Mason, W.; Evans, D. Clonal p53 mutation in primary cervical cancer: Association with human-papillomavirus-negative tumours. Lancet 1992, 339, 1070–1073. [Google Scholar] [CrossRef]
- McIntyre, J.B.; Wu, J.S.; Craighead, P.S.; Phan, T.; Köbel, M.; Lees-Miller, S.P.; Ghatage, P.; Magliocco, A.M.; Doll, C.M. PIK3CA mutational status and overall survival in patients with cervical cancer treated with radical chemoradiotherapy. Gynecol. Oncol. 2013, 128, 409–414. [Google Scholar] [CrossRef] [PubMed]
- Lee, M.-S.; Jeong, M.-H.; Lee, H.-W.; Han, H.-J.; Ko, A.; Hewitt, S.M.; Kim, J.-H.; Chun, K.-H.; Chung, J.-Y.; Lee, C. PI3K/AKT activation induces PTEN ubiquitination and destabilization accelerating tumourigenesis. Nat. Commun. 2015, 6, 7769. [Google Scholar] [CrossRef] [PubMed]
- Gadducci, A.; Barsotti, C.; Cosio, S.; Domenici, L.; Riccardo Genazzani, A. Smoking habit, immune suppression, oral contraceptive use, and hormone replacement therapy use and cervical carcinogenesis: A review of the literature. Gynecol. Endocrinol. 2011, 27, 597–604. [Google Scholar] [CrossRef]
- Kim, S.-W.; Chun, M.; Ryu, H.-S.; Chang, S.-J.; Kong, T.W.; Lee, E.J.; Lee, Y.H.; Oh, Y.-T. Salvage radiotherapy with or without concurrent chemotherapy for pelvic recurrence after hysterectomy alone for early-stage uterine cervical cancer. Strahlenther. Onkol. 2017, 193, 534–542. [Google Scholar] [CrossRef]
- Fuller, C.D.; Wang, S.J.; Thomas, C.R., Jr.; Hoffman, H.T.; Weber, R.S.; Rosenthal, D.I. Conditional survival in head and neck squamous cell carcinoma: Results from the SEER dataset 1973–1998. Cancer Interdiscip. Int. J. Am. Cancer Soc. 2007, 109, 1331–1343. [Google Scholar] [CrossRef] [PubMed]
- Waggoner, S.E.J.T.L. Cervical Cancer. Lancet 2003, 361, 2217–2225. [Google Scholar] [CrossRef]
- Dehn, D.; Torkko, K.C.; Shroyer, K.R. Human papillomavirus testing and molecular markers of cervical dysplasia and carcinoma. Cancer Cytopathol. Interdiscip. Int. J. Am. Cancer Soc. 2007, 111, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Baiocchi, G.; de Brot, L.; Faloppa, C.C.; Mantoan, H.; Duque, M.R.; Badiglian-Filho, L.; da Costa, A.A.B.A.; Kumagai, L.Y. Is parametrectomy always necessary in early-stage cervical cancer? Gynecol. Oncol. 2017, 146, 16–19. [Google Scholar] [CrossRef]
- Chen, A.-H.; Qin, Y.-E.; Tang, W.-F.; Tao, J.; Song, H.M. MiR-34a and miR-206 act as novel prognostic and therapy biomarkers in cervical cancer. Cancer Cell Int. 2017, 17, 1–9. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Tian, R.; Gao, H.; Yan, F.; Ying, L.; Yang, Y.; Yang, P.; Gao, Y.E. Identification of significant gene signatures and prognostic biomarkers for patients with cervical cancer by integrated bioinformatic methods. J. Technol. Cancer Res. Treat. 2018, 17, 1533033818767455. [Google Scholar] [CrossRef] [Green Version]
- Huang, L.; Zheng, M.; Zhou, Q.-M.; Zhang, M.-Y.; Yu, Y.-H.; Yun, J.-P.; Wang, H.-Y. Identification of a 7-gene signature that predicts relapse and survival for early stage patients with cervical carcinoma. Med. Oncol. 2012, 29, 2911–2918. [Google Scholar] [CrossRef]
- Lee, Y.-Y.; Kim, T.-J.; Kim, J.-Y.; Choi, C.H.; Do, I.-G.; Song, S.Y.; Sohn, I.; Jung, S.-H.; Bae, D.-S.; Lee, J.-W. Genetic profiling to predict recurrence of early cervical cancer. Gynecol. Oncol. 2013, 131, 650–654. [Google Scholar] [CrossRef]
- Mao, Y.; Dong, L.; Zheng, Y.; Dong, J.; Li, X. Prediction of recurrence in cervical cancer using a nine-lncRNA signature. Front. Genet. 2019, 10, 284. [Google Scholar] [CrossRef] [Green Version]
- Leite, G.G.F.; Scicluna, B.P.; van Der Poll, T.; Salomão, R. Genetic signature related to heme-hemoglobin metabolism pathway in sepsis secondary to pneumonia. NPJ Syst. Biol. Appl. 2019, 5, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rahman, M.R.; Islam, T.; Zaman, T.; Shahjaman, M.; Karim, M.R.; Huq, F.; Quinn, J.M.; Holsinger, R.D.; Gov, E.; Moni, M.A. Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer’s disease: Insights from a systems biomedicine perspective. Genomics 2020, 112, 1290–1299. [Google Scholar] [CrossRef] [PubMed]
- Oany, A.R.; Jyoti, T.P.; Ahmad, S.A.I. An in silico approach for characterization of an aminoglycoside antibiotic-resistant methyltransferase protein from Pyrococcus furiosus (DSM 3638). Bioinform. Biol. Insights 2014, 8, 65–72. [Google Scholar] [CrossRef]
- Oany, A.R.; Pervin, T.; Mia, M.; Hossain, M.; Shahnaij, M.; Mahmud, S.; Kibria, K. Vaccinomics approach for designing potential peptide vaccine by targeting Shigella spp. serine protease autotransporter subfamily protein SigA. J. Immunol. Res. 2017, 2017, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Oany, A.R.; Mia, M.; Pervin, T.; Hasan, M.N.; Hirashima, A. Identification of potential drug targets and inhibitor of the pathogenic bacteria Shigella flexneri 2a through the subtractive genomic approach. Silico Pharmacol. 2018, 6, 11. [Google Scholar] [CrossRef] [PubMed]
- Oany, A.R.; Mia, M.; Pervin, T.; Junaid, M.; Hosen, S.Z.; Moni, M.A. Design of novel viral attachment inhibitors of the spike glycoprotein (S) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) through virtual screening and dynamics. Int. J. Antimicrob. Agents 2020, 56, 106177. [Google Scholar] [CrossRef] [PubMed]
- Oany, A.R.; Pervin, T.; Moni, M.A. Pharmacoinformatics based elucidation and designing of potential inhibitors against Plasmodium falciparum to target importin α/β mediated nuclear importation. Infection Genet. Evol. 2020, 104699. [Google Scholar] [CrossRef]
- Pervin, T.; Oany, A.R. Vaccinomics approach for scheming potential epitope-based peptide vaccine by targeting l-protein of Marburg virus. In Silico Pharmacol. 2020, 9, 1–18. [Google Scholar]
- Clough, E.; Barrett, T. The gene expression omnibus database. In Statistical Genomics; Springer: Berlin/Heidelberg, Germany, 2016; pp. 93–110. [Google Scholar]
- Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef]
- Benjamini, Y.; Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 2001, 29, 1165–1188. [Google Scholar] [CrossRef]
- Aubert, J.; Bar-Hen, A.; Daudin, J.-J.; Robin, S. Determination of the differentially expressed genes in microarray experiments using local FDR. BMC Bioinform. 2004, 5, 125. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pawitan, Y.; Michiels, S.; Koscielny, S.; Gusnanto, A.; Ploner, A. False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 2005, 21, 3017–3024. [Google Scholar] [CrossRef]
- Islam, M.R.; Ahmed, M.L.; Paul, B.K.; Bhuiyan, T.; Ahmed, K.; Moni, M.A. Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): A bioinformatics analysis. Inform. Med. Unlocked 2020, 100304. [Google Scholar] [CrossRef]
- Jiao, X.; Sherman, B.T.; Huang, D.W.; Stephens, R.; Baseler, M.W.; Lane, H.C.; Lempicki, R.A. DAVID-WS: A stateful web service to facilitate gene/protein list analysis. Bioinformatics 2012, 28, 1805–1806. [Google Scholar] [CrossRef] [Green Version]
- Xia, J.; Gill, E.E.; Hancock, R.E. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 2015, 10, 823. [Google Scholar] [CrossRef]
- Ashburner, M.; Ball, C.A.; Blake, J.A.; Botstein, D.; Butler, H.; Cherry, J.M.; Davis, A.P.; Dolinski, K.; Dwight, S.S.; Eppig, J.T. Gene ontology: Tool for the unification of biology. Nat. Genet. 2000, 25, 25–29. [Google Scholar] [CrossRef] [Green Version]
- Hulsegge, I.; Kommadath, A.; Smits, M.A. Globaltest and GOEAST: Two different approaches for Gene Ontology analysis. In BMC Proceedings; Springer: Berlin/Heidelberg, Germany, 2009. [Google Scholar]
- Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
- Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016, 44, D457–D462. [Google Scholar] [CrossRef] [Green Version]
- Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P. STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015, 43, D447–D452. [Google Scholar] [CrossRef]
- Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
- Bader, G.D.; Hogue, C.W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003, 4, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chin, C.-H.; Chen, S.-H.; Wu, H.-H.; Ho, C.-W.; Ko, M.-T.; Lin, C.-Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8, S11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017, 45, W98–W102. [Google Scholar] [CrossRef] [Green Version]
- Gorter, A.; Prins, F.; van Diepen, M.; Punt, S.; van der Burg, S.H. The tumor area occupied by Tbet+ cells in deeply invading cervical cancer predicts clinical outcome. J. Transl. Med. 2015, 13, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Peng, Y.; Wu, D.; Li, F.; Zhang, P.; Feng, Y.; He, A. Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis. Cancer Cell Int. 2020, 20, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Bhairavabhotla, R.K.; Verma, V.; Tongaonkar, H.; Shastri, S.; Dinshaw, K.; Chiplunkar, S. Role of IL-10 in Immune Suppression in Cervical Cancer; CSIR: New Delhi, India, 2007. [Google Scholar]
- Domingos-Pereira, S.; Decrausaz, L.; Derré, L.; Bobst, M.; Romero, P.; Schiller, J.T.; Jichlinski, P.; Nardelli-Haefliger, D. Intravaginal TLR agonists increase local vaccine-specific CD8 T cells and human papillomavirus-associated genital-tumor regression in mice. Mucosal Immunol. 2013, 6, 393–404. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Che, L.-F.; Shao, S.F.; Wang, L. Downregulation of CCR5 inhibits the proliferation and invasion of cervical cancer cells and is regulated by microRNA-107. Exp. Ther. Med. 2016, 11, 503–509. [Google Scholar] [CrossRef] [Green Version]
- Chen, C.-L.; Hsieh, F.-C.; Lieblein, J.; Brown, J.; Chan, C.; Wallace, J.; Cheng, G.; Hall, B.; Lin, J. Stat3 activation in human endometrial and cervical cancers. Br. J. Cancer 2007, 96, 591–599. [Google Scholar] [CrossRef]
- Huang, Y.; Zhang, J.; Cui, Z.-M.; Zhao, J.; Zheng, Y. Expression of the CXCL12/CXCR4 and CXCL16/CXCR6 axes in cervical intraepithelial neoplasia and cervical cancer. Chin. J. Cancer 2013, 32, 289. [Google Scholar] [CrossRef] [Green Version]
- Sekuła, M.; Miekus, K.; Majka, M. Downregulation of the CXCR4 receptor inhibits cervical carcinoma metastatic behavior in vitro and in vivo. Int. J. Oncol. 2014, 44, 1853–1860. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yadav, S.S.; Prasad, S.B.; Das, M.; Kumari, S.; Pandey, L.K.; Singh, S.; Pradhan, S.; Narayan, G. Epigenetic silencing of CXCR4 promotes loss of cell adhesion in cervical cancer. BioMed Res. Int. 2014, 2014. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.-P.; Lu, W.-G.; Ye, F.; Chen, H.-Z.; Zhou, C.-Y.; Xie, X. Study on CXCR4/SDF-1α axis in lymph node metastasis of cervical squamous cell carcinoma. Int. J. Gynecol. Cancer 2007, 17. [Google Scholar] [CrossRef] [PubMed]
- Smith, M.C.; Luker, K.E.; Garbow, J.R.; Prior, J.L.; Jackson, E.; Piwnica-Worms, D.; Luker, G.D. CXCR4 regulates growth of both primary and metastatic breast cancer. Cancer Res. 2004, 64, 8604–8612. [Google Scholar] [CrossRef] [Green Version]
- Wang, S.; Chen, X. Identification of potential biomarkers in cervical cancer with combined public mRNA and miRNA expression microarray data analysis. Oncol. Lett. 2018, 16, 5200–5208. [Google Scholar] [CrossRef] [Green Version]
- Pan, X.-B.; Lu, Y.; Huang, J.-L.; Long, Y.; Yao, D.-S.J.A. Prognostic genes in the tumor microenvironment in cervical squamous cell carcinoma. Aging 2019, 11, 10154. [Google Scholar] [CrossRef]
Group | Accession | Organism | Disease State | Cell Type |
---|---|---|---|---|
Normal | GSM4478163 | Homo sapiens | Normal | Normal fibroblast |
GSM4478166 | Homo sapiens | Normal | Normal fibroblast | |
GSM4478167 | Homo sapiens | Normal | Normal fibroblast | |
GSM4478168 | Homo sapiens | Normal | Normal fibroblast | |
GSM4478170 | Homo sapiens | Normal | Normal fibroblast | |
Tumor | GSM4478164 | Homo sapiens | Cervical Cancer | Tumor-associated cervix fibroblasts |
GSM4478165 | Homo sapiens | Cervical Cancer | Tumor-associated cervix fibroblasts | |
GSM4478169 | Homo sapiens | Cervical Cancer | Tumor-associated cervix fibroblasts |
Gene Signature Name | Degree | Betweenness Centrality | Clustering Coefficient | Closeness Centrality | Stress |
---|---|---|---|---|---|
PTPRC | 80 | 18,133.72223 | 0.23196 | 244.0667 | 167,664 |
ITGAM | 79 | 14,447.28068 | 0.23337 | 242.4833 | 140,924 |
IL10 | 70 | 15,227.82113 | 0.18841 | 234.9833 | 145,074 |
TYROBP | 69 | 6407.99793 | 0.29113 | 227.9833 | 78,988 |
ITGB2 | 66 | 9280.28036 | 0.28858 | 226.5595 | 95,454 |
CCR5 | 61 | 8309.33294 | 0.29836 | 225.8 | 98,026 |
ITGAX | 60 | 4722.34853 | 0.29492 | 222.35 | 59,082 |
CSF1R | 55 | 7708.90149 | 0.32727 | 221.5333 | 87,030 |
LILRB2 | 55 | 5622.88217 | 0.34007 | 217.1333 | 57,172 |
CXCR4 | 55 | 10,433.80294 | 0.24108 | 225.8333 | 95,538 |
STAT3 | 53 | 15,091.4144 | 0.20682 | 225.2833 | 126,744 |
CYBB | 50 | 4529.93215 | 0.37469 | 218.95 | 54,562 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Oany, A.R.; Mia, M.; Pervin, T.; Alyami, S.A.; Moni, M.A. Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer. J. Pers. Med. 2021, 11, 363. https://doi.org/10.3390/jpm11050363
Oany AR, Mia M, Pervin T, Alyami SA, Moni MA. Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer. Journal of Personalized Medicine. 2021; 11(5):363. https://doi.org/10.3390/jpm11050363
Chicago/Turabian StyleOany, Arafat Rahman, Mamun Mia, Tahmina Pervin, Salem Ali Alyami, and Mohammad Ali Moni. 2021. "Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer" Journal of Personalized Medicine 11, no. 5: 363. https://doi.org/10.3390/jpm11050363