Systematic expression profiling analysis mines dys-regulated modules in active tuberculosis based on re-weighted protein-protein interaction network and attract algorithm

Identification of potential biomarkers of lung adenocarcinoma brain metastases via microarray analysis of cDNA expression profiles

Brain metastases originating from lung adenocarcinoma (LAD) occur frequently. The aim of the current study was to assess potential biomarkers for the prognosis of lung adenocarcinoma brain metastasis (LAD-BM) through the analysis of gene expression microarrays. The current study downloaded two gene expression datasets, GSE14108 and GSE10245, from the Gene Expression Omnibus database. From GSE14108 and GSE10245, 19 LAD-BM samples and 40 primary LAD samples were selected for analysis. To identify the differentially expressed genes (DEGs), the current study compared the two sample groups, using the limma R package. Subsequently, pathway enrichment analysis was conducted using the Cluster Profiler R package, and the construction of the protein-protein interaction (PPI) network was executed utilizing the Search Tool for the Retrieval of Interacting Genes database. The microRNA-target network was built using the TargetScore R package. Then, these networks were established and visualized using Cytoscape software. An array of 463 DEGs was identified in the LAD-BM samples, including 256 upregulated and 207 downregulated genes. Based on functional term enrichment analysis using the Gene Ontology database and signaling pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes database, it was identified that the overlapping DEGs were primarily involved in chemokine-associated signal transduction, which may mediate lung cancer cell metastasis to the brain. Chemokine ligand 2, lysozyme, matrix metalloproteinase-2 (MMP-2), lysyl oxidase (LOX) and granzyme B were identified as potential biomarkers according to a topological analysis of the PPI networks. Two notable nodes, MMP-2 and LOX, appeared in the PPI network and were key points in the microRNA-target network, as they were regulated by hsa-let-7d. Many DEGs and microRNAs were regarded as prognostic biomarkers for lung adenocarcinoma metastasis in the current study. These DEGs were primarily associated with chemokine-mediated signaling pathways. In addition, MMP-2 and LOX were predicted to be targets of hsa-let-7d.

The integration of weighted gene association networks based on information entropy

Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.