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Objective This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD). Methods Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes. Results A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes ( CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers. Conclusion Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.
Objective This study aimed to identify the potential biomarkers associated with pyroptosis in diabetic kidney disease (DKD). Methods Three datasets from the Gene Expression Omnibus (GEO) were downloaded and merged into an integrated dataset. Differentially expressed genes (DEGs) were filtered and intersected with pyroptosis-related genes (PRGs). Pyroptosis-related DEGs (PRDEGs) were obtained and analyzed using functional enrichment analysis. Random forest, Least Absolute Shrinkage and Selection Operator, and logistic regression analyses were used to select the features of PRDEGs. These feature genes were used to build a diagnostic prediction model, identify the subtypes of the disease, and analyze their interactions with transcription factors (TFs)/miRNAs/drugs and small molecules. We conducted a comparative analysis of immune cell infiltration at different risk levels of pyroptosis. qRT-PCR was used to validate the expression of the feature genes. Results A total of 25 PRDEGs were obtained. These genes were coenriched in biological processes and pathways, such as the regulation of inflammatory responses. Five key genes ( CASP1, CITED2, HTRA1, PTGS2, S100A12) were identified and verified using qRT-PCR. The diagnostic model based on key genes has a good diagnostic prediction ability. Five key genes interacted with TFs and miRNAs in 67 and 80 pairs, respectively, and interacted with 113 types of drugs or molecules. Immune infiltration of samples with different pyroptosis risk levels showed significant differences. Thus, CASP1, CITED2, HTRA1, PTGS2 and S100A12 are potential DKD biomarkers. Conclusion Genes that regulate pyroptosis can be used as predictors of DKD. Early diagnosis of DKD can aid in its effective treatment.
Background/aim Familial Mediterranean fever (FMF) is an autoinflammatory disease, with a high prevalence in the Mediterranean region. It is brought out by variants in the MEFV gene. The present goal is to describe the demographic, clinical features, and MEFV gene variants among Egyptian FMF patients and to explore the relation of MEFV variants with clinical features and selected laboratory markers. Patients and methods The present study enrolled 302 patients with FMF from both sexes with a mean age 18.01±8.73 years. Patients were recruited from the Clinical Genetic Clinic, Medical Research Centre of Excellence, National Research Centre, Cairo, Egypt, during the period from 2021 to 2023. All patients were subjected to complete history taking, clinical evaluation, and laboratory investigations. C-reactive protein, serum amyloid A (SAA) protein and vitamin D were measured using enzyme-linked immuno-sorbent assay technique, while erythrocyte sedimentation rate was measured by Westergren method. In addition, MEFV genetic variants were investigated using a real-time PCR genotyping assay and direct sequencing of exon 2 and exon 10 of the MEFV gene. Results The average age of FMF cases was 18.01±8.73 years (with a range between 2 and 34 years), and the female/male ratio was 1.07. The most prevalent symptoms were abdominal pain, fever, and arthritis. Genotyping of the MEFV gene demonstrated that 215 (71.2%) patients were heterozygotes, 26 (8.6%) patients were compound heterozygotes and 12 (4.0%) patients were homozygotes, while 49 (16.2%) patients had no detected mutation. p. Met 694Ile was the most common MEFV variant (36.7%), followed by p. Met680Ile (21.5%), p.Val726Ala (9.6%), p.Glu148Gln (8.94%), and p.Met694Val (7.94%). There was no significant variation in clinical manifestations between different MEFV gene variants. The level of SAA protein was higher in FMF patients carrying the Met694Val variant, while carriers of the p. Glu148Gln variant showed lower erythrocyte sedimentation rate, SAA, and higher serum vitamin D. Conclusion The most commonly encountered MEFV gene variants among our Egyptian FMF cases were p. Met694Ile followed by p. Met680Ile. No phenotype-genotype association was observed. The p. Met694Val variant could be a possible risk factor for developing amyloidosis. Investigating the whole MEFV gene is recommended to fully understand the molecular background of FMF cases and properly establish a good correlation with the variable phenotypes.
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