A database of integrated molecular and phytochemical interactions of the foxm1 pathway for lung cancer

The FoxM1 pathway is an oncogenic signaling pathway involved in essential mechanisms including control cell-cycle progression, apoptosis and cell growth which are the common hallmarks of various cancers. Although its biological functions in the tumor development and progression are known, the mechanism by which it participates in those processes is not understood. The present work reveals images of the oncogenic FoxM1 pathway controlling the cell cycle process with alternative treatment options via phytochemical substances in the lung cancer study. The downstream significant protein modules of the FoxM1 pathway were extracted by the Molecular Complex Detection (MCODE) and the maximal clique (Mclique) algorithms. Furthermore, the effects of post-transcriptional modification by microRNA, transcription factor binding and the phytochemical compounds are observed through their interactions with the lung cancer protein modules. We provided two case studies to demonstrate the usefulness of our database. Our results suggested that the combination of various phytochemicals is effective in the treatment of lung cancer. The ultimate goal of the present work is to partly support the discovery of plant-derived compounds in combination treatment of classical chemotherapeutic agents to increase the efficacy of lung cancer method probably with minor side effects. Furthermore, a web-based system displaying results of the present work is set up for investigators posing queries at http://sit.mfu.ac.th/lcgdb/index_FoxM1.php.Communicated by Ramaswamy H. Sarma.

Efficient Deployment of Key Nodes for Optimal Coverage of Industrial Mobile Wireless Networks

In recent years, industrial wireless networks (IWNs) have been transformed by the introduction of mobile nodes, and they now offer increased extensibility, mobility, and flexibility. Nevertheless, mobile nodes pose efficiency and reliability challenges. Efficient node deployment and management of channel interference directly affect network system performance, particularly for key node placement in clustered wireless networks. This study analyzes this system model, considering both industrial properties of wireless networks and their mobility. Then, static and mobile node coverage problems are unified and simplified to target coverage problems. We propose a novel strategy for the deployment of clustered heads in grouped industrial mobile wireless networks (IMWNs) based on the improved maximal clique model and the iterative computation of new candidate cluster head positions. The maximal cliques are obtained via a double-layer Tabu search. Each cluster head updates its new position via an improved virtual force while moving with full coverage to find the minimal inter-cluster interference. Finally, we develop a simulation environment. The simulation results, based on a performance comparison, show the efficacy of the proposed strategies and their superiority over current approaches.

A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer

Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. We propose a new graph-based algorithm for identifying recurrent CNVs using the maximal clique detection technique. The algorithm has an optimal solution, which means all maximal cliques can be identified, and guarantees that the identified CNV regions are the most frequent and that the minimal regions have been delineated among tumor samples. The algorithm has successfully been applied to analyze a large cohort of breast cancer samples and identified some breast cancer-associated genes and pathways.

Network-Based Analysis on Orthogonal Separation of Human Plasma Uncovers Distinct High Density Lipoprotein Complexes

High density lipoprotein (HDL) particles are blood-borne complexes whose plasma levels have been associated with protection from cardiovascular disease (CVD). Recent studies have demonstrated the existence of distinct HDL subspecies; however, these have been difficult to isolate and characterize biochemically. Here, we present the first report that employs a network-based approach to systematically infer HDL subspecies. Healthy human plasma was separated into 58 fractions using our previously published three orthogonal chromatography techniques. Similar local migration patterns among HDL proteins were captured with a novel similarity score, and individual comigration networks were constructed for each fraction. By employing a graph mining algorithm, we identified 183 overlapped cliques, among which 38 were further selected as candidate HDL subparticles. Each of these 38 subparticles had at least two literature supports. In addition, GO function enrichment analysis showed that they were enriched with fundamental biological and CVD protective functions. Furthermore, gene knockout experiments in mouse model supported the validity of these subparticles related to three apolipoproteins. Finally, analysis of an apoA-I deficient human patient's plasma provided additional support for apoA-I related complexes. Further biochemical characterization of these putative subspecies may facilitate the mechanistic research of CVD and guide targeted therapeutics aimed at its mitigation.