Clinical and experimental rheumatology · 2021
One year in review 2021: Sjögren's syndrome.
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Cafaro Giacomo, Bursi Roberto, Chatzis Loukas G, Fulvio Giovanni, Ferro Francesco, Bartoloni Elena, Baldini Chiara
Аннотация
BACKGROUND: Lupus nephritis (LN) is an autoimmune-related kidney disease with a poor prognosis, however the potential pathogenic mechanism remains unclear and there is a lack of precise biomarkers. Therefore, a thorough screening and identification of renal markers in LN are immensely beneficial to the research on its pathogenic mechanisms and treatment strategies.
METHODS: We utilized bioinformatics to analyze the differentially expressed genes (DEGs) at the transcriptome level of three clusters: total renal, glomeruli, and renal tubulointerstitium in the GEO database to discover potential renal biomarkers of LN. We utilized NephroSeq datasets and measured mRNA and protein levels in the kidneys of MRL/lpr mice to confirm the expression of key DEGs.
RESULTS: Seven significantly differential genes (EGR1, MME, PTPRC, RORC, MX1, ZBTB16, FKBP5) were revealed from the transcriptome database of GSE200306, which were mostly enriched in the pathway of the hematopoietic cell lineage and T cell differentiation respectively by KEGG and GO analysis. The seven hot differential genes were verified to have consistent change trends using three datasets from NephroSeq database. The receiver operating characteristic (ROC) curve indicated that five DEGs (PTPRC, MX1, EGR1, MME and RORC) exhibited a higher diagnostic ROC value in both the glomerulus and tubulointerstitium group. Validation of core genes using MRL/lpr mice showed that MME and PTPRC exhibit significantly differential mRNA and protein expression patterns in mouse kidneys like the datasets.
CONCLUSIONS: This study identified seven key renal biomarkers through bioinformatics analysis using the GEO and NephroSeq databases. It was identified that MME and PTPRC may have a high predictive value as renal biomarkers in the pathogenesis of LN, as confirmed by animal validation.
METHODS: We utilized bioinformatics to analyze the differentially expressed genes (DEGs) at the transcriptome level of three clusters: total renal, glomeruli, and renal tubulointerstitium in the GEO database to discover potential renal biomarkers of LN. We utilized NephroSeq datasets and measured mRNA and protein levels in the kidneys of MRL/lpr mice to confirm the expression of key DEGs.
RESULTS: Seven significantly differential genes (EGR1, MME, PTPRC, RORC, MX1, ZBTB16, FKBP5) were revealed from the transcriptome database of GSE200306, which were mostly enriched in the pathway of the hematopoietic cell lineage and T cell differentiation respectively by KEGG and GO analysis. The seven hot differential genes were verified to have consistent change trends using three datasets from NephroSeq database. The receiver operating characteristic (ROC) curve indicated that five DEGs (PTPRC, MX1, EGR1, MME and RORC) exhibited a higher diagnostic ROC value in both the glomerulus and tubulointerstitium group. Validation of core genes using MRL/lpr mice showed that MME and PTPRC exhibit significantly differential mRNA and protein expression patterns in mouse kidneys like the datasets.
CONCLUSIONS: This study identified seven key renal biomarkers through bioinformatics analysis using the GEO and NephroSeq databases. It was identified that MME and PTPRC may have a high predictive value as renal biomarkers in the pathogenesis of LN, as confirmed by animal validation.