Meta analysis of MRNA and MİRNA molecules in autoinflammatory diseases

Autoinflammatory diseases are the group of diseases in which inflammatory responses are increased as a result of the innate immune system being affected. These diseases can be grouped as monogenic and polygenic in terms of their genetic characteristics. Among the autoinflammatory diseases examined within the scope of the thesis; PFAPA is polygenic and AAA, CAPS, TRAPS, MKD diseases are caused by mutations in different genes, but the similar phenotype seen in these diseases suggests that similar pathways and molecules are affected. In the literature, there are omic-based studies in which autoinflammatory diseases are included Within the scope of this thesis, it was aimed to evaluate these data together, and microarray data sets of mRNA and miRNA-based omic studies were analyzed using programs such as R (4.0.5), BRB-ArrayTools and MeV. Differentially expressed genes were analyzed using DAVID program and InnateDB database and those related to inflammation were determined. Then, the binding possibilities of differentially expressed common mRNAs in these diseases and miRNAs obtained from data sets were shown using the TargetScan database. Finally, the proteins targeted by differentially expressed mRNAs and miRNAs significantly were identified and the protein-protein relationships between these proteins were determined by STRING analysis. As a result of the analyzes, STAT1, NAIP, FAS, MAPK14 proteins, which are important proteins in terms of inflammation, were mainly demonstrated and it was shown that they were interacted with the proteins encoded by the genes that cause the selected diseases. This preliminary data supported the hypothesis that common molecules affect the pathogenesis in selected diseases, and it became important to examine these findings in the laboratory. This thesis will make important contributions to the better classification of diseases by elucidating the pathways that cause inflammation in autoinflammatory diseases and to the identification of molecules that may be the target of treatment in the long term.