Background: The aim of the study was to suggest a high specific and sensitive blood biomarker for early GC diagnosis. Methods: the expression data of miRNAs and mRNAs were collected from the blood samples of the GC patients based on literature mining. Bioinformatics tools and databases (PANTHER, TargetScan, miRTarBase, miRDB, STRING, and Cytoscape) were used to predict the Regulatory relationship. Subsequently, expression level of the selected miRNA was evaluated in the blood samples of gastritis patients to recognize the common miRNA between the GC and gastritis patients. Results: Analysis of 40 target genes by MCODE (installed in Cytoscape software) indicated 4 hub genes (WWP1, SKP2, KLHL42, and FBXO11) as a significant cluster in the PPI network related to miR-21, with Node Score Cutoff: 0. 2, Degree Cutoff: 2 and K-Core: 2. In addition, the miRNA RT-qPCR results showed that, the expression level of miR-21 was significantly higher in gastritis group compared to the healthy group (p< 0. 05). Conclusions: the present study clearly demonstrated the increasing level of blood miR-21 among the gastritis patients infected by H. pylori. Therefore, the altered miRNAs, especially overexpression of onco-miRs, may identify a potential link between miRNAs and pathogenesis of the H. pylori–, related complications.

Introduction: In clinical practice, distinguishing invasive lung tumors from primary tumors remains a challenge. With recent advances in understanding biological alterations of tumorigenesis and molecular analytic technologies, using these molecular alterations can be sensitive and tumor-specific as biomarker for the stratification of patients. In this study, the molecular network of miRNA-mRNA contributing to primary lung cancer has been assessed by bioinformatics approaches. Methods: In this analytical-observational study gene expression profiles of patients with primary lung cancer were collected from the RNASeq data of the Cancer Genome Atlas (TCGA) database by TCGAbiolinks package. With edgeR and limma packages in R, non-specific expression genes were filtered and the significant differentially expressed mRNAs and miRNAs between tumor tissues and normal tissues were saved and their targets were predicted by 2 databases,miRWalk, and Targetscan. Subsequently, the interaction Regulatory network of miRNA-mRNA was visualized using Cytoscape software. Results: By miRNA-mRNA network analysis revealed that, 7 miRNAs included,hsa-miR-373-3p, hsa-let-7a-5p, hsa-miR-23b-3p, hsa-miR-152-3p, hsa-miR-216a-3p, hsa-miR-106-5p, hsa-let-7i-5p and 6 miRNAs including,has-miR-107, has-miR-17-5p, has-185-5p, has-miR-34a-5p, has-miR-130a-5p and has-96-5p, mediated regulation of up-regulated and down-regulated mRNAs in primary lung cancer patients, respectively. Conclusion: This bioinformatics study proposes a miRNA–, mRNA network associated with primary lung cancer, which may help to screening and new therapeutic targets for primary lung cancer as prognostic marker.

Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption. On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications. This is not an unrealistic goal since genes which are regulated by gene Regulatory networks (GRNs) are the core governors of life processes at the molecular level. In fact, manipulation of GRNs would be the ultimate strategy for optimal purposeful control of cell’ s life. GRNs are in charge of regulating the amounts of all the inter-cellular as well as intra-cellular molecular species produced all the time in all living organisms. Manipulation of a GRN requires comprehensive knowledge about nodes and interconnections. This paper deals with both aspects in networks having more than fifty nodes. In the first part of the paper, restrictions of probabilistic models in modeling node behavior are discussed, i. e.: 1) unfeasibility of reliably predicting the next state of GRN based on its current state, 2) impossibility of modelling logical relations among genes, and 3) scarcity of biological data needed for model identification. These findings which are supported by arguments from probability theory suggest that probabilistic models should not be used for analysis and prediction of node behavior in GRNs. Next part of the paper focuses on models of GRN structure. It is shown that the use of multi-tree models for structure for GRN poses severe limitations on network behavior, i. e. 1) increase in signal entropy while passing through the network, 2) decrease in signal bandwidth while passing through the network, and 3) lack of feedback as a key element for oscillatory and/or autonomous behavior (a requirement for any biological network). To demonstrate that, these restrictions are consequences of model selection, we use information theoretic arguments. At the last and the most important part of the paper we look into the gene perturbation experiments from a network-theoretic perspective to show that multi-perturbation experiments are not as informative as assumed so far. A generally accepted belief among researches states that multi-perturbation experiments are more informative than single-perturbation ones, i. e., multiple simultaneously applied perturbations provide more information than a single perturbation. It is shown that single-perturbation experiments are optimal for identification of network structure, provided the ultimate goal is to discover correct subnet structures.