Network topologies decoding cervical cancer

• Verfasst von: Jalan, Sarika [VerfasserIn]
• Verfasst von: Bandapalli, Obul Reddy [VerfasserIn]
• Titel: Network topologies decoding cervical cancer
• Verf.angabe: Sarika Jalan, Krishna Kanhaiya, Aparna Rai, Obul Reddy Bandapalli, Alok Yadav
• E-Jahr: 2015
• Jahr: August 26, 2015
• Umfang: 19 S.
• Fussnoten: Gesehen am 23.06.2020
• Titel Quelle: Enthalten in: PLOS ONE
• Ort Quelle: San Francisco, California, US : PLOS, 2006
• Jahr Quelle: 2015
• Band/Heft Quelle: 10(2015,8) Artikel-Nummer e0135183, 19 Seiten
• ISSN Quelle: 1932-6203
• DOI: doi:10.1371/journal.pone.0135183
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Carcinogenesis
• Sach-SW: Carcinomas
• Sach-SW: Centrality
• Sach-SW: Cervical cancer
• Sach-SW: Cyclins
• Sach-SW: Network analysis
• Sach-SW: Protein interaction networks
• Sach-SW: Transcription factors
• K10plus-PPN: 1700120433

Abstract

According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.