Utilizing multiple pathway cross-talk networks reveals hub pathways in primary mediastinal B-cell lymphoma

Second malignant neoplasms in lymphomas, secondary lymphomas and lymphomas in metabolic disorders/diseases

With inconsistent findings, evidence has been obtained in recent years that metabolic disorders are closely associated with the development of lymphomas. Studies and multiple analyses have been published also indicating that some solid tumor survivors develop a secondary lymphoma, whereas some lymphoma survivors subsequently develop a second malignant neoplasm (SMN), particularly solid tumors. An interaction between the multiple etiologic factors such as genetic factors and late effects of cancer therapy may play an important role contributing to the carcinogenesis in patients with metabolic diseases or with a primary cancer. In this review, we summarize the current knowledge of the multiple etiologic factors for lymphomagenesis, focusing on the SMN in lymphoma, secondary lymphomas in primary cancers, and the lymphomas associated to metabolic disorders/diseases, which have been received less attention previously. Further, we also review the data of coexistence of lymphomas and hepatocellular carcinoma (HCC) in patients with infection of hepatitis C virus and hepatitis B virus.

Orienting Conflicted Graph Edges Using Genetic Algorithms to Discover Pathways in Protein-Protein Interaction Networks

Advanced computational techniques of the current era help to identify proteins from the complex biological network that interact with each other and with the cell's environment. Biological pathways are a chain of molecular actions that leads to a new molecular product creation or alters the cellular state. These pathways are helpful in the predication of many real-world issues. Rebuilding these pathways is a challenging task due to the fact that protein interactions are undirected, whereas pathways are directed. To discover these pathways in protein-protein interaction data from specified source and target, it is essential to orient protein interactions. Unfortunately, the edge orientation problem is NP-hard, which makes it challenging to develop effective algorithms. This work rebuilds biologically important pathways in a weighted network of protein interactions of yeast species. The proposed algorithm, pseudo-guided multi-objective genetic algorithm (PGMOGA) rebuilds pathways by assigning orientation to the edges of the weighted network. Extending the past research, mathematical modeling of single-objective and multi-objective functions is performed. The PGMOGA is compared with four state-of-the-art approaches, namely, random orientation plus local search (ROLS), single-objective genetic algorithm (SOGA), multi-objective genetic algorithm (MOGA), and multi random search (MRS). The comparison is based on three general and four path specific metrics. Results show that the current proposal performs better.