Renal failure · 2025
Identification and validation of parthanatos-related genes in end-stage renal disease.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Dai Xuan, Yuan Lianfang
Аннотация
BACKGROUND: End-Stage Renal Disease (ESRD) is a severe chronic kidney disease with a rising global incidence, often accompanied by various complications, severely impacting patients' quality of life. Parthanatos plays a crucial role in the pathogenesis of multiple diseases. This study aims to explore the role of parthanatos-related genes in ESRD through bioinformatics analysis.
METHODS: In this study, blood samples from ESRD patients and healthy controls were analyzed using public transcriptomic data. Two machine learning algorithms identified candidate genes, refined through ROC analysis. A nomogram assessed their predictive potential for ESRD prevalence. Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis confirmed their roles in immune functions. The relationship between the two identified biomarkers and ESRD was investigated through molecular and disease networks, enhancing understanding of their association. Clinical validation of biomarker expression was conducted using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
RESULTS: The 65 candidate genes were refined by PPI network, screened by two algorithms, and then determined by ROC analysis to obtain HBM and MYL4 as biomarkers. The nomogram constructed for these two biomarkers demonstrated their effectiveness in predicting survival outcomes among ESRD patients. Notably, there is a strong correlation between HBM and MYL4 with Type 17 T helper cells and central memory CD4 T cells RT-qPCR validation showed that the expression of biomarkers in ESRD patients was significantly higher than that in controls (p < 0.05).
CONCLUSION: This study identified two biomarkers (HBM, MYL4) through transcriptome analysis, investigating their functions and mechanisms, offering new therapeutic insights for ESRD.
METHODS: In this study, blood samples from ESRD patients and healthy controls were analyzed using public transcriptomic data. Two machine learning algorithms identified candidate genes, refined through ROC analysis. A nomogram assessed their predictive potential for ESRD prevalence. Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis confirmed their roles in immune functions. The relationship between the two identified biomarkers and ESRD was investigated through molecular and disease networks, enhancing understanding of their association. Clinical validation of biomarker expression was conducted using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
RESULTS: The 65 candidate genes were refined by PPI network, screened by two algorithms, and then determined by ROC analysis to obtain HBM and MYL4 as biomarkers. The nomogram constructed for these two biomarkers demonstrated their effectiveness in predicting survival outcomes among ESRD patients. Notably, there is a strong correlation between HBM and MYL4 with Type 17 T helper cells and central memory CD4 T cells RT-qPCR validation showed that the expression of biomarkers in ESRD patients was significantly higher than that in controls (p < 0.05).
CONCLUSION: This study identified two biomarkers (HBM, MYL4) through transcriptome analysis, investigating their functions and mechanisms, offering new therapeutic insights for ESRD.