"Exploring Differentially Expressed Genes to Identify Biomarkers of Cer" by Dwi Anita Suryandari, Luluk Yunaini et al.
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Indonesian Journal of Medical Chemistry and Bioinformatics

Abstract

This study explores the molecular landscape of cervical cancer through the identification and analysis of differentially expressed genes (DEGs) from the GSE63514 dataset. A high-confidence protein–protein interaction (PPI) network was constructed using the STRING database (v11.5) and visualized via Cytoscape, identifying 178 nodes and 1,052 edges. Using the CytoHubba plugin, the top 10 hub genes—TOP2A, MKI67, CDK1, BUB1, CCNB1, CCNA2, AURKA, CDC20, PLK1, and RFC4—were highlighted based on degree centrality. These genes are predominantly associated with cell cycle regulation, DNA replication, and mitotic division, and are potentially valuable as biomarkers or therapeutic targets for cervical cancer. Functional enrichment using DAVID and Enrichr tools revealed significant involvement of DEGs in ATP binding, spindle microtubule formation, and protein kinase activity, particularly within the chromosome centromeric region and nucleoplasm. KEGG pathway analysis identified key associations with the cell cycle, DNA replication, p53 signaling, and complement and coagulation cascades. Further heatmap analysis of treatment responders versus non-responders demonstrated distinct gene expression profiles, particularly of immune-related genes like C1QA, C3, and SERPING1, and proliferative markers such as TOP2A and MKI67. These findings underscore the dual role of immune and proliferative pathways in cervical cancer progression and suggest their utility in developing predictive biomarkers and personalized treatment strategies.

Bahasa Abstract

Penelitian ini mengeksplorasi bentang molekuler kanker serviks melalui identifikasi dan analisis Differentially Expressed Genes (DEG) dari kumpulan data GSE63514. Protein-Protein Interaction Network (PPI) berkeyakinan tinggi dibangun menggunakan basis data STRING (v11.5) dan divisualisasikan melalui Cytoscape, mengidentifikasi 178 node dan 1.052 edge. Menggunakan plugin CytoHubba, 10 gen hub teratas—TOP2A, MKI67, CDK1, BUB1, CCNB1, CCNA2, AURKA, CDC20, PLK1, dan RFC4—disorot berdasarkan derajat sentralitas. Gen-gen ini sebagian besar terkait dengan regulasi siklus sel, replikasi DNA, dan pembelahan mitosis, dan berpotensi berharga sebagai biomarker atau target terapeutik untuk kanker serviks. Pengayaan fungsional menggunakan tools DAVID dan Enrichr menunjukkan keterlibatan signifikan DEG dalam pengikatan ATP, pembentukan mikrotubulus spindel, dan aktivitas protein kinase, khususnya dalam wilayah sentromerik kromosom dan nukleoplasma. Analisis jalur KEGG mengidentifikasi hubungan utama dengan siklus sel, replikasi DNA, pensinyalan p53, dan kaskade komplemen dan koagulasi. Analisis peta panas lebih lanjut dari responden pengobatan versus non-responden menunjukkan profil ekspresi gen yang berbeda, khususnya gen yang berhubungan dengan imun seperti C1QA, C3, dan SERPING1, dan penanda proliferatif seperti TOP2A dan MKI67. Temuan ini menggarisbawahi peran ganda jalur imun dan proliferatif dalam perkembangan kanker serviks dan menunjukkan kegunaannya dalam mengembangkan biomarker prediktif dan strategi personalized treatment.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71, 209–249, doi:10.3322/caac.21660.
  2. Yim, E.-K.; Park, J.-S. Biomarkers in Cervical Cancer. Biomark Insights 2007, 1, 215–225.
  3. Wentzensen, N.; von Knebel Doeberitz, M. Biomarkers in Cervical Cancer Screening. Dis Markers 2007, 23, 315–330, doi:10.1155/2007/678793.
  4. Clifford, G.M.; Smith, J.S.; Plummer, M.; Muñoz, N.; Franceschi, S. Human Papillomavirus Types in Invasive Cervical Cancer Worldwide: A Meta-Analysis. Br J Cancer 2003, 88, 63–73, doi:10.1038/sj.bjc.6600688.
  5. Doorbar, J.; Quint, W.; Banks, L.; Bravo, I.G.; Stoler, M.; Broker, T.R.; Stanley, M.A. The Biology and Life-Cycle of Human Papillomaviruses. Vaccine 2012, 30, F55–F70, doi:10.1016/j.vaccine.2012.06.083.
  6. Cuschieri, K.; Wentzensen, N. Human Papillomavirus MRNA and P16 Detection as Biomarkers for the Improved Diagnosis of Cervical Neoplasia. Cancer Epidemiol Biomarkers Prev 2008, 17, 2536–2545, doi:10.1158/1055-9965.EPI-08-0306.
  7. Wu, X.; Peng, L.; Zhang, Y.; Chen, S.; Lei, Q.; Li, G.; Zhang, C. Identification of Key Genes and Pathways in Cervical Cancer by Bioinformatics Analysis. Int J Med Sci 2019, 16, 800–812, doi:10.7150/ijms.34172.
  8. Zhang, B.; Wang, J.; Wang, X.; Zhu, J.; Liu, Q.; Shi, Z.; Chambers, M.C.; Zimmerman, L.J.; Shaddox, K.F.; Kim, S.; et al. Proteogenomic Characterization of Human Colon and Rectal Cancer. Nature 2014, 513, 382–387, doi:10.1038/nature13438.
  9. Su, H.-C.; Wu, S.-C.; Yen, L.-C.; Chiao, L.-K.; Wang, J.-K.; Chiu, Y.-L.; Ho, C.-L.; Huang, S.-M. Gene Expression Profiling Identifies the Role of Zac1 in Cervical Cancer Metastasis. Sci Rep 2020, 10, 11837, doi:10.1038/s41598-020-68835-0.
  10. Wu, S.F.; Qian, W.Y.; Zhang, J.W.; Yang, Y. Bin; Liu, Y.; Dong, Y.; Zhang, Z.B.; Zhu, Y.P.; Feng, Y.J. Network Motifs in the Transcriptional Regulation Network of Cervical Carcinoma Cells Respond to EGF. Arch Gynecol Obstet 2013, 287, 771–777, doi:10.1007/s00404-012-2608-8.
  11. Tang, Y.; Zhang, Y.; Hu, X. Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus‐Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis. Biomed Res Int 2020, 2020, doi:10.1155/2020/4251761.
  12. Lin, Y.; Wang, S.; Yang, Q. Identification of Hub Genes and Diagnostic Efficacy for Triple-Negative Breast Cancer through WGCNA and Mendelian Randomization. Discover oncology 2024, 15, 117, doi:10.1007/s12672-024-00970-w.
  13. Hossen, M.A.; Reza, Md.S.; Rana, Md.M.; Hossen, Md.B.; Shoaib, M.; Mollah, Md.N.H.; Han, C. Identification of Most Representative Hub-Genes for Diagnosis, Prognosis, and Therapies of Hepatocellular Carcinoma. Chin Clin Oncol 2024, 13, 32–32, doi:10.21037/cco-23-151.
  14. Guo, L.; Hua, K. Cervical Cancer: Emerging Immune Landscape and Treatment. Onco Targets Ther 2020, 13, 8037–8047, doi:10.2147/OTT.S264312.
  15. Afshar-Kharghan, V. The Role of the Complement System in Cancer. J Clin Invest 2017, 127, 780–789, doi:10.1172/JCI90962.
  16. Kanehisa, M.; Goto, S.; Furumichi, M.; Tanabe, M.; Hirakawa, M. KEGG for Representation and Analysis of Molecular Networks Involving Diseases and Drugs. Nucleic Acids Res 2010, 38, D355-60, doi:10.1093/nar/gkp896.
  17. Kori, M.; Yalcin Arga, K. Potential Biomarkers and Therapeutic Targets in Cervical Cancer: Insights from the Meta-Analysis of Transcriptomics Data within Network Biomedicine Perspective. PLoS One 2018, 13, e0200717, doi:10.1371/journal.pone.0200717.
  18. Martin, C.M.; Astbury, K.; McEvoy, L.; O’Toole, S.; Sheils, O.; O’Leary, J.J. Gene Expression Profiling in Cervical Cancer: Identification of Novel Markers for Disease Diagnosis and Therapy. Methods Mol Biol 2009, 511, 333–359, doi:10.1007/978-1-59745-447-6_15.
  19. GEO Accession Viewer.
  20. Xie, Z.; Bailey, A.; Kuleshov, M. V.; Clarke, D.J.B.; Evangelista, J.E.; Jenkins, S.L.; Lachmann, A.; Wojciechowicz, M.L.; Kropiwnicki, E.; Jagodnik, K.M.; et al. Gene Set Knowledge Discovery with Enrichr. Curr Protoc 2021, 1, doi:10.1002/cpz1.90.
  21. 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–e47, doi:10.1093/nar/gkv007.

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