Hypofractionated Radiation Versus Conventional Fractionated Radiation: A Network Analysis

Introduction: Conventional fractionation (CF) and hypofractionation (HF) are two radiotherapy methods against cancer, which are applied in medicine. Understanding the efficacy and molecular mechanism of the two methods implies more investigations. In the present study, proteomic findings about the mentioned methods relative to the controls were analyzed via network analysis. Methods: The significant differentially expressed proteins (DEPs) of prostate cancer (PCa) cell line DU145 in response to CF and HF radiation therapy versus controls were extracted from the literature. The protein-protein interaction (PPI) networks were constructed via the STRING database via Cytoscape software. The networks were analyzed by "NetworkAnalyzer" to determine hub DEPs. Results: 126 and 63 significant DEPs were identified for treated DU145 with CF and HF radiation respectively. The PPI networks were constructed by the queried DEPs plus 100 first neighbors. ALB, CD44, THBS1, EPCAM, F2, KRT19, and MCAM were highlighted as common hubs. VTM, OCLN, HSPB1, FLNA, AHSG, and SERPINC1 appeared as the discriminator hub between the studied cells. Conclusion: 70% of the hubs were common between CF and HF conditions, and they induced radio-resistance activity in the survived cells. Six central proteins which discriminate the function of the two groups of the irradiated cells were introduced. On the basis of these findings, it seems that DU145-CF cells, relative to the DU145-UF cells, are more radio-resistant.

Bayesian inference of hub nodes across multiple networks

Hub nodes within biological networks play a pivotal role in determining phenotypes and disease outcomes. In the multiple network setting, we are interested in understanding network similarities and differences across different experimental conditions or subtypes of disease. The majority of proposed approaches for joint modeling of multiple networks focus on the sharing of edges across graphs. Rather than assuming the network similarities are driven by individual edges, we instead focus on the presence of common hub nodes, which are more likely to be preserved across settings. Specifically, we formulate a Bayesian approach to the problem of multiple network inference which allows direct inference on shared and differential hub nodes. The proposed method not only allows a more intuitive interpretation of the resulting networks and clearer guidance on potential targets for treatment, but also improves power for identifying the edges of highly connected nodes. Through simulations, we demonstrate the utility of our method and compare its performance to current popular methods that do not borrow information regarding hub nodes across networks. We illustrate the applicability of our method to inference of co-expression networks from The Cancer Genome Atlas ovarian carcinoma dataset.