Screening Molecular Biomarkers For Two Autoimmune Dieases SLE And RA At MRNA Level

Autoimmune diease was listed as the third human killer after cardiovascular diease and cancer by the World Health Organization in 1999. It is charactered by high prevalence, high disability rate and high cost for treatment. For most autoimmune dieases, the pathogenesis was not clear, but it was accepted widely that they were induced by the genetic factors and environmental factors at the same time. Recent years, the microarray technology was widely used for the pathogenesis research of complex dieases, and it was fruitful to diease-related gene marker. In this study, we focused on two common autoimmune diease: systemic lupus erythematosus and rheumatoid arthritis, we tried to screen out their marker at m RNA level. This article had two parts: at the first part, we aimed to identify the common marker genes across various types for SLE, utilizing the public gene expression datasets; for the second part, we used the samples of RA and healthy individuals, with gene microarray technology, got the m RNA gene expression datasets, conducted diffential gene expression analysis, functional annotation analysis, protein protein interaction analysis and at last, with experimental vertifaicaion, we selected the marker at m RNA and protein level.Part IObjective: Systemic lupus erythematosus(SLE) is a complex auto-immune disease. Gene expression studies have been conducted to identify SLE-related genes in various types of samples. It is unknown whether there are common marker genes significant for SLE but independent of sample types, which may have potentials for follow-up translational research. In this study, we aimed to identify the common marker genes across various types for SLE.Methods: Based on four public microarray gene expression datasets for SLE covering three representative types of blood-born samples(monocyteperipheral blood mononuclear cell, PBMC whole blood), we utilized three statistics(fold-change, FC;t-test p value;false discovery rate adjusted p value) to scrutinize genes simultaneously regulated with SLE across various sample types. For common marker genes, we conducted the Gene Ontology enrichment analysis and Protein-Protein Interaction analysis to gain insights into their functions.Results: We identified 10 common marker genes associated with SLE(IFI6, IFI27, IFI44 L, OAS1, OAS2, EIF2AK2, PLSCR1, STAT1, RNASE2, and GSTO1). Significant up-regulation of IFI6, IFI27, and IFI44 L with SLE was observed in all the studied sample types, though the FC was most striking in monocyte, compared with PBMC and whole blood(8.82-251.66 vs. 3.73-74.05 vs. 1.19-1.87). Eight of the above 10 genes, except RNASE2 and GSTO1, interact with each other and with known SLE susceptibility genes, participate in immune response, RNA and protein catabolism, and cell death.Conclusion: Our data suggest that there exist common marker genes across various sample types for SLE. The 10 common marker genes, identified herein, deserve follow-up studies to dissert their potentials as diagnostic or therapeutic markers to predict SLE or treatment response.Part IIObjective: Rheumatoid arthritis is an autoimmune diaease, which can make people diaabled. This suty aimed at the identification of the marker for rheumatoid arthritis(RA) at m RNA and protein level.Materials and Methods: By collecting age- and sex-matched RA patients(28) and healthy individuals(18), we extracted the peripheral blood mononuclear cell( PBMC) of cases and controls, then got the RNA and made it into m RNA microarray to get the gene expression. Then, we tried to conduct functional annatation,cluster and protein protein interaction analysis. For the gene encoded secreted protein, we validated the cellular gene expreession by RT-PCR and plasma protein level by ELISA. For the validated genes and protein they encoded, we conducted ROC analysis.Results: We got 181 differential expressed gene(DEG) with fold-change>2 or <0.5 and bonferroni adjusted p-value<0.05 simultaneously.We selected 7 secreted the gene/protein(CRKL,GSN,ITGAM,CKLF,GUSB,FAM3 C and ANXA7) from the DEG, 2(CKLF and GUSB) of which were validated by RT-PCR(FC=1.9 and 2.3, p-value<0.05)with the same direction to gene expression, notely, FAM3 C was also validated by RT-PCR and ELISA simultaneously(p-value=0.025 and 0.026), but with the adverse direction. With the above 3 genes or the protein they encoded in finding group and validation group, we made ROC analysis,the AUC were 0.933 and 0.748,respectively.Conclusion: The PBMC transcriptome expression profiles differ significantly between RA cases and controls. With validation and evaluation, three genes( CKLF,GUSB and FAM3C) which encode secreted protein have a good ability to distinguish cases and controls. The three plasma proteins, especially FAM3 C, may serve as a potential biomarker and deserve validation in indepenent large samples for its dignostic value.