The identification of lung cancer gene-drug module based on multiplex networks algorithm / 生物医学工程学杂志

Using modular identification methods in gene-drug multiplex networks to infer new gene-drug associations can identify new therapeutic target genes for known drugs. In this paper, based on the gene expression data and drug response data of lung cancer in the genomics of drug sensitivity in cancer (GDSC) database, a multiple network algorithm is proposed. First, a heterogeneous network of genes of lung cancer and drugs in different cell lines is constructed, and then a network module identification method based on graph entropy is used. In this heterogeneous network, network modules are identified, and five lung cancer gene-drug association modules are identified through iterative convergence. Compared with other methods, the algorithm has better results in terms of running time, accuracy and robustness, and the identified modules have obvious biological significance. The research results in this article have guiding significance for the medication and treatment of lung cancer, and can provide references for the treatment of other diseases with the same targeted genes.

Analysis of Modular Network Mechanism of Danhong Injection in Treatment of Coronary Angina Pectoris / 中国实验方剂学杂志

Objective:To explore the pharmacological mechanism of Danhong injection (DHI) in the treatment of coronary heart disease with angina pectoris from the level of functional modules by modular pharmacological analysis framework. Method:The targets of drug components in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the angina-related genes in DisGeNET, OMIM and CTD databases were combined to construct the target network of DHI for the treatment of coronary angina pectoris by STRING version 11.0. Functional modules were identified by the molecular complex detection (MCODE), Markov cluster (MCL) and GLay algorithms, and the results were optimized by the minimum network structure entropy algorithm. The Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis was performed on the modules by DAVID version 6.8 bioinformatics analysis platform. Result:By integrating 262 genes related to DHI and 192 genes related to angina pectoris, the target network of DHI for angina pectoris was constructed, including 414 nodes and 6 621 edges. After optimization of the minimum network structure entropy, 12 functional modules (number of nodes>3) were identified by MCODE algorithm, of which the largest module (module 1) has 47 nodes and 962 edges, MCODE score=41.826. KEGG pathway enrichment analysis was conducted on the gene network of DHI for angina pectoris and the modules divided by MCODE, and 37 and 58 KEGG signaling pathways were obtained respectively, with the coverage rate of 86.5%. The pathways enriched by the modules could be roughly divided into 11 categories, among which human diseases (45%), signal transduction (17%), and amino acid metabolism (14%) were involved in a large proportion. Module 1 was enriched into 39 pathways, which was signal transduction-related module. Module 3 was amino acid metabolism-related module. Conclusion:The therapeutic effect of DHI on coronary heart disease with angina pectoris is achieved through multiple modules, multiple pathways and multiple functions, mainly by regulating modules related to signal transduction, amino acid metabolism, neuroactive ligand-receptor interaction, Ca2+ and p53 signaling.