Filtering of the Gene Signature as the Predictors of Cisplatin-Resistance in Ovarian Cancer.

BACKGROUND: Gene expression profiling and prediction of drug responses based on the molecular signature indicate new molecular biomarkers which help to find the most effective drugs according to the tumor characteristics. OBJECTIVES: In this study two independent datasets, GSE28646 and GSE15372 were subjected to meta-analysis based on Affymetrix microarrays. MATERIAL AND METHODS: In-silico methods were used to determine differentially expressed genes (DEGs) in the previously reported sensitive and resistant A2780 cell lines to Cisplatin. Gene Fuzzy Scoring (GFS) and Principle Component Analysis (PCA) were then used to eliminate batch effects and reduce data dimension, respectively. Moreover, SVM method was performed to classify sensitive and resistant data samples. Furthermore, Wilcoxon Rank sum test was performed to determine DEGs. Following the selection of drug resistance markers, several networks including transcription factor-target regulatory network and miRNA-target network were constructed and Differential correlation analysis was performed on these networks. RESULTS: The trained SVM successfully classified sensitive and resistant data samples. Moreover, Performing DiffCorr analysis on the sensitive and resistant samples resulted in detection of 27 and 25 significant (with correlation ≥|0.9|) pairs of genes that respectively correspond to newly constructed correlations and loss of correlations in the resistant samples. CONCLUSIONS: Our results indicated the functional genes and networks in Cisplatin resistance of ovarian cancer cells and support the importance of differential expression studies in ovarian cancer chemotherapeutic agent responsiveness.

Topological Analysis of Regulatory Networks Reveals Functionally Key Genes and miRNAs Involved in the Differentiation of Mesenchymal Stem Cells.

BACKGROUND: The details of molecular mechanisms underlying the differentiation of Mesenchymal Stem Cells (MSCs) into specific lineages are not well understood. OBJECTIVES: We aimed to construct the interactome network and topology analysis of bone marrow mesenchymal stem cell of CAGE data. Applying the enrichment results, we wanted to introduce the common genes and hub-microRNA and hub-genes of these giant network. MATERIALS AND METHODS: In this study, we constructed gene regulatory networks for each non-mesenchymal cell lineage according to their gene expression profiles obtained from FANTOM5 database. The putative interactions of TF-gene and protein-protein were determined using TRED, STRING, HPRD and GeneMANIA servers. In parallel, a regulatory network including corresponding miRNAs and total differentially expressed genes (DEGs) was constructed for each cell lineage. RESULTS: The results indicated that analysis of networks' topology can significantly distinguish the hub regulatory genes and miRNAs involved in the differentiation of MSCs. The functional annotation of identified hub genes and miRNAs revealed that several signal transduction pathways i.e. AKT, WNT and TGFß and cell proliferation related pathways play a pivotal role in the regulation of MSCs differentiation. We also classified cell lineages into two groups based on their predicted miRNA profiles. CONCLUSIONS: In conclusion, we found a number of hub genes and miRNAs which seem to have key regulatory functions during differentiation of MSCs. Our results also introduce a number of new regulatory genes and miRNAs which can be considered as the new candidates for genetic manipulation of MSCs in vitro.

Key genes and regulatory networks involved in the initiation, progression and invasion of colorectal cancer.

AIM: Until now, identification of drug targets for treatment of patients with specific stages of colorectal cancer (CRC) has remained a challenging field of research. Herein, we aimed to identify the key genes and regulatory networks involved in each stage of CRC. RESULTS: The results of gene expression profiles were integrated with protein-protein interaction networks, and topologically analyzed. The most important regulatory genes (e.g., CDK1, UBC, ESR1 and ATXN1) and signaling pathways (e.g., Wnt, MAPK and JAK-STAT) in CRC initiation, progression and metastasis were identified. In vitro analysis confirmed some in silico findings. CONCLUSION: Our study introduces functional hub genes, subnetworks, prioritizes signaling pathways and novel biomarkers in CRC that may guide further development of targeted therapy programs.

Regulatory network analysis of Epstein-Barr virus identifies functional modules and hub genes involved in infectious mononucleosis.

Epstein-Barr virus (EBV) is the most common cause of infectious mononucleosis (IM) and establishes lifetime infection associated with a variety of cancers and autoimmune diseases. The aim of this study was to develop an integrative gene regulatory network (GRN) approach and overlying gene expression data to identify the representative subnetworks for IM and EBV latent infection (LI). After identifying differentially expressed genes (DEGs) in both IM and LI gene expression profiles, functional annotations were applied using gene ontology (GO) and BiNGO tools, and construction of GRNs, topological analysis and identification of modules were carried out using several plugins of Cytoscape. In parallel, a human-EBV GRN was generated using the Hu-Vir database for further analyses. Our analysis revealed that the majority of DEGs in both IM and LI were involved in cell-cycle and DNA repair processes. However, these genes showed a significant negative correlation in the IM and LI states. Furthermore, cyclin-dependent kinase 2 (CDK2) - a hub gene with the highest centrality score - appeared to be the key player in cell cycle regulation in IM disease. The most significant functional modules in the IM and LI states were involved in the regulation of the cell cycle and apoptosis, respectively. Human-EBV network analysis revealed several direct targets of EBV proteins during IM disease. Our study provides an important first report on the response to IM/LI EBV infection in humans. An important aspect of our data was the upregulation of genes associated with cell cycle progression and proliferation.