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A better understanding of incidences at the cellular level in uterine cancer is necessary for its effective treatment and favourable prognosis. Till date, it lacks appropriate molecular target-based treatment because of unknown molecular mechanisms that proceed to cancer and no drug has shown the required results of treatment with less severe side effects. Uterine Cancer is one of the top five cancer diagnoses and among the ten most common death-causing cancer in the United States of America. There is no FDA-approved drug for it yet. Therefore, it became necessary to identify the molecular targets for molecular targeted therapy of this widely prevalent cancer type. For this study, we used a network-based approach to the list of the deregulated (both up and down-regulated) genes taking adjacent p-Value ≤ 0.05 as significance cut off for the mRNA data of uterine cancer. We constructed the protein-protein interaction (PPI) network and analyzed the degree, closeness, and betweenness centrality-like topological properties of the PPI network. Then we traced the top 30 genes listed from each topological property to find the key regulators involved in the endometrial cancer (ECa) network. We then detected the communities and sub-communities from the PPI network using the Cytoscape network analyzer and Louvain modularity optimization method. A set of 26 (TOP2A, CENPE, RAD51, BUB1, BUB1B, KIF2C, KIF23, KIF11, KIF20A, ASPM, AURKA, AURKB, PLK1, CDC20, CDKN2A, EZH2, CCNA2, CCNB1, CDK1, FGF2, PRKCA, PGR, CAMK2A, HPGDS, and CDCA8) genes were found to be key genes of ECa regulatory network altered in disease state and might be playing the regulatory role in complex ECa network. Our study suggests that among these genes, KIF11 and H PGDS appeared to be novel key genes identified in our research. We also identified these key genes interactions with miRNAs.

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Cardiorenal syndromes constellate primary dysfunction of either heart or kidney whereby one organ dysfunction leads to the dysfunction of another. The role of several microRNAs (miRNAs) has been implicated in number of diseases, including hypertension, heart failure, and kidney diseases. Wide range of miRNAs has been identified as ideal candidate biomarkers due to their stable expression. Current study was aimed to identify crucial miRNAs and their target genes associated with cardiorenal syndrome and to explore their interaction analysis. Three differentially expressed microRNAs (DEMs), namely, hsa-miR-4476, hsa-miR-345-3p, and hsa-miR-371a-5p, were obtained from GSE89699 and GSE87885 microRNA data sets, using R/GEO2R tools. Furthermore, literature mining resulted in the retrieval of 15 miRNAs from scientific research and review articles. The miRNAs-gene networks were constructed using miRNet (a Web platform of miRNA-centric network visual analytics). CytoHubba (Cytoscape plugin) was adopted to identify the modules and the top-ranked nodes in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. The overlapped miRNAs were further used in pathway enrichment analysis. We found that hsa-miR-21-5p was common in 8 pathways out of the top 10. Based on the degree, 5 miRNAs, namely, hsa-mir-122-5p, hsa-mir-222-3p, hsa-mir-21-5p, hsa-mir-146a-5p, and hsa-mir-29b-3p, are considered as key influencing nodes in a network. We suggest that the identified miRNAs and their target genes may have pathological relevance in cardiorenal syndrome (CRS) and may emerge as potential diagnostic biomarkers.

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Cross-talk among coupled stochastic Hindmarsh-Rose (HR) neurons is significantly affected by the topology of the neurons organization. If the coupled stochastic HR neurons are arranged in the form of ring topology with odd number of neurons, the neurons are in anti-phase synchronization with homogeneous distribution of phase ordering of the oscillators. On the other hand, if the coupled HR oscillators are arranged in the ring topology with even number of oscillators, the oscillators are formed into two groups which are anti-phase synchronized, but all the oscillators in each group are in in-phase synchronization.Synchronization of the HR oscillators due to coupling in all topological arrangements is affected by the noise.However, noise can induce optimal coherence of the cross-talked oscillators at a particular value at which signal processing is the most favorable with amplified signal, the phenomenon known as stochastic resonance.

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We present the mechanism of interaction of Wnt network module, which is responsible for periodic somitogenesis, with p53 regulatory network, which is one of the main regulators of various cellular functions, and switching of various oscillating states by investigating p53-Wnt model. The variation in Nutlin concentration in p53 regulating network drives the Wnt network module to different states, stabilized, damped and sustain oscillation states, and even to cycle arrest. Similarly, the change in Axin2 concentration in Wnt could able to modulate the p53 dynamics at these states. We then solve the set of coupled ordinary differential equations of the model using quasi steady state approximation. We, further, demonstrate the change of p53 and GSK3 interaction rate, due to hypothetical catalytic reaction or external stimuli, can able to regulate the dynamics of the two network modules, and even can control their dynamics to protect the system from cycle arrest (apoptosis).

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