method have become a research trend. The ultimate biomarkers must be easily detectable with fine sensitivity and specificity and also must discriminate PC from other benign pancreatic diseases 9. Blood is a simply reachable and rather steady sample to find alerting biomarkers. Technological advances in the recent years have provided possibilities to detect circulating biomarkers based on "omics" research, relying on proteins, cell-free DNAs, non-coding RNAs, circulating tumor cells (CTCs), and exosomes molecular contents 10. MicroRNAs (miRs) are small (~22 nucleotides) non-coding RNAs that have gene regulatory roles via targeting the 3′-untranslated region (3′-UTR) of their target mRNA and finally cause either translational repression or mRNA degradation 11,12. Up to now, a number of biomarkers have been introduced as PC biomarkers such CEA, CA19-9, CA125 and CA72-4. Nonetheless, none of these tumor markers has shown efficient sensitivity or specificity for diagnosing PC at primary stages and have been used for post resection monitoring rather than earlier detection purposes. MiRNAs seem to be truly stable in blood and several authors reported that miRNAs show dysregulation in pancreatic diseases being able to differentiate PC from pancreatitis, pancreatic benign masses as well as normal subjects 13. Furthermore, owing to advanced technologies in high-throughput molecular methods the understanding of the pathophysiology of pancreatic cancer have been improved. Various genome-wide mRNA and miRNA expression profiling studies using microarray-based and NGS approaches have provided important insights into the phenotypic characteristics of pancreatic cancer 14. In this study, using multiple bioinformatics tools, we integrated various serum expression profiles of miRNAs to find the most significant potential miRNA signatures helpful in the diagnosis of PC and constructed a novel miRNA-mRNA regulatory network in PC using bioinformatics approaches. Next, we investigated the molecular mechanisms downstream of the captured miRNA signatures and their predicted target genes correlated to PC progression and analyzed them in a logistic model. Material and method Microarray datasets search. In order to find proper miRNA expression profiles in microarray datasets, we conducted a systematic search in Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/ geo/) 15. Using the keywords "Pancreatic cancer" and "Serum", at the first step we reached 900 datasets. Then, we limited the results using 'Homo sapiens' and 'Non-coding RNA profiling by array' filters, so we reached to 16 datasets. Finally, by setting the sample count on more than 200 samples, 4 final datasets were achieved. Differentially expressed miRNAs (DEMs) detection. The DEMs were obtained using the online tool GEO2R in the GEO database 15 , which makes evaluations using the GEOquery and limma R packages from the Bioconductor project to compare two or more groups of samples in a GEO dataset. Normalization has been carried out using the RMA algorithm. To ke...

Background and Aims:  Helicobacter pylori is a highly diverse pathogen, which encounters epithelial cells as the initial defense barrier during its lifelong infection. The structure of epithelial cells can be disrupted through cleavage of microfilaments. Cytokeratin 18 (CK18) is an intermediate filament, the cleavage of which is considered an early event during apoptosis following activation of effector caspases. Methods:  Helicobacter pylori strains were isolated from 76 dyspeptic patients. cagA 3’ variable region and CagA protein status were analyzed by PCR and western blotting, respectively. Eight hours post‐co‐culture of AGS cells with different H. pylori strains, flow cytometric analysis was performed using M30 monoclonal antibody specific to CK18 cleavage‐induced neo‐epitope. Results:  Higher rates of CK18 cleavage were detected during co‐culture of AGS cells with H. pylori strains bearing greater numbers of cagA EPIYA‐C and multimerization (CM) motifs. On the other hand, H. pylori strains with greater numbers of EPIYA‐B relative to EPIYA‐C demonstrated a decrease in CK18 cleavage rate. Thus, H. pylori‐mediated cleavage of CK18 appeared proportional to the number of CagA EPIYA‐C and CM motifs, which seemed to be downplayed in the presence of EPIYA‐B motifs. Conclusions:  Our observation associating the heterogeneity of cagA variants with the potential of H. pylori strains in the induction of CK18 cleavage as an early indication of apoptosis in gastric epithelial cells supports the fact that apoptosis may be a type‐specific trait. However, additional cagA‐targeted experiments are required to clearly identify the role of EPIYA and CM motifs in apoptosis and/or the responsible effector molecules.