ABSTRACT
Although many genes and miRNAs have been reported for various cancers, pancreatic cancer's specific genes or miRNAs have not been studied precisely yet. Therefore, we have analyzed the gene and miRNA expression profile of pancreatic cancer data in the gene expression omnibus (GEO) database. The microarray-derived miRNAs and mRNAs were annotated by gene ontology (GO) and signaling pathway analysis. We also recognized mRNAs that were targeted by miRNA through the mirDIP database. An integrated analysis of the microarray revealed that only 6 out of 43 common miRNAs had significant differences in their expression profiles between the tumor and normal groups (P value < 0.05 and |log Fold Changes (logFC)|> 1). The hsa-miR-210 had upregulation, whereas hsa-miR-375, hsa-miR-216a, hsa-miR-217, hsa-miR-216b and hsa-miR-634 had downregulation in pancreatic cancer (PC). The analysis results also revealed 109 common mRNAs by microarray and mirDIP 4.1 databases. Pathway analysis showed that amoebiasis, axon guidance, PI3K-Akt signaling pathway, absorption and focal adhesion, adherens junction, platelet activation, protein digestion, human papillomavirus infection, extracellular matrix (ECM) receptor interaction, and riboflavin metabolism played important roles in pancreatic cancer. GO analysis revealed the significant enrichment in the three terms of biological process, cellular component, and molecular function, which were identified as the most important processes associated strongly with pancreatic cancer. In conclusion, DTL, CDH11, COL5A1, ITGA2, KIF14, SMC4, VCAN, hsa-mir-210, hsa-mir-217, hsa-mir-216a, hsa-mir-216b, hsa-mir-375 and hsa-mir-634 can be reported as the novel diagnostic or even therapeutic markers for the future studies. Also, the hsa-mir-107 and hsa-mir-125a-5p with COL5A1, CDH11 and TGFBR1 genes can be introduced as major miRNA and genes on the miRNA-drug-mRNA network. The new regulatory network created in our study could give a deeper knowledge of the pancreatic cancer.
Subject(s)
Antineoplastic Agents/pharmacology , Biomarkers, Tumor/metabolism , Computational Biology/methods , Gene Expression Regulation, Neoplastic , MicroRNAs/genetics , Pancreatic Neoplasms/pathology , RNA, Messenger/metabolism , Biomarkers, Tumor/genetics , Gene Regulatory Networks , Humans , Pancreatic Neoplasms/drug therapy , Pancreatic Neoplasms/genetics , Pancreatic Neoplasms/metabolism , Prognosis , Protein Interaction Maps , RNA, Messenger/genetics , Survival RateABSTRACT
BACKGROUND: Influenza A is a virus that affects a wide range of animals and also human beings. Avian influenza virus (AIV) subtype H9N2 has the potential to create influenza pandemic and vaccination is a common solution for this problem. The vaccine, used for rapid intervention, should be safe to use and highly effective, after a single administration. Chitosan nanoparticles (CNP) have already been recommended as a new adjuvant for inactivated AIV H9N2 vaccine immunization. OBJECTIVES: This study aimed at the evaluation and better understanding of optimum concentration of CNP preparations and also, assessment of loading capacity of AIV into CNP, as an adjuvant in specific pathogen-free (SPF) chickens. MATERIALS AND METHODS: For measurement of vaccine-antibody response, different types of CNP were injected intramuscularly, in a single dose, to 21-day-old specific pathogen-free chickens. Chickens were monitored for the efficacy of the nanoparticles and, also, their immune response, during a follow up of 7 weeks, by using hemagglutination-inhibition (HI) test. The CNP were prepared according to modified ionic gelation method and inactivated antigen was loaded in four hemagglutinin units (HAU) concentrations. Loading capacity of nanoparticles was determined by hemagglutination (HA) method. Inactivated A/H9N2 AIV was mixed with chitosan of low molecular weight. RESULTS: The CNP did not cause any mortality or side effects, when chickens were administered the prepared vaccine. The results strongly showed that this novel vaccine significantly enhances the immunogenicity of inactivated AIV, comparing with ISA70 (SEPPIC, Puteaux, France) adjuvant that is used routinely in the Razi Serum and Vaccine Research and Production Institute, Karaj, Iran, to reduce ISA70's side effects. CONCLUSIONS: The AIV loaded into CNP vaccines induce appropriate antibody titers, after a single immunization, while requiring a low dose of antigen. The CNP also represent an interesting new platform for antigen delivery and a promising adjuvant candidate for H9N2 inactivated influenza vaccine.
