Md Zubbair Malik1, Keilash Chirom2, Shahnawaz Ali3, Romana Ishrat3, Pallavi Somvanshi2, R K Brojen Singh4. 1. School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India. 2. Department of Biotechnology, TERI University, New Delhi, 110070, India. 3. Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India. 4. School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India. brojen@jnu.ac.in.
Abstract
BACKGROUND: Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. METHODS: The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. RESULTS: The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. CONCLUSIONS: The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.
BACKGROUND: Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. METHODS: The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. RESULTS: The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. CONCLUSIONS: The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.
Authors: Hagen Kulbe; Francesco Iorio; Probir Chakravarty; Carla S Milagre; Robert Moore; Richard G Thompson; Gemma Everitt; Monica Canosa; Alexander Montoya; Denis Drygin; Ioana Braicu; Jalid Sehouli; Julio Saez-Rodriguez; Pedro R Cutillas; Frances R Balkwill Journal: Oncotarget Date: 2016-03-29
Authors: Eleonora A Braga; Marina V Fridman; Alexey A Moscovtsev; Elena A Filippova; Alexey A Dmitriev; Nikolay E Kushlinskii Journal: Int J Mol Sci Date: 2020-11-23 Impact factor: 5.923
Authors: Safia Tazyeen; Mohd Murshad Ahmed; Anam Farooqui; Aftab Alam; Md Zubbair Malik; Mohd Saeed; Irfan Ahmad; Mohammed Abohashrh; R K Brojen Singh; Romana Ishrat Journal: Sci Rep Date: 2022-01-24 Impact factor: 4.379