Identification and validation of parthanatos-related genes in end-stage renal disease
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...
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Taylor & Francis Group
2025-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2519834 |
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| _version_ | 1849425580283396096 |
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| author | Xuan Dai Lianfang Yuan |
| author_facet | Xuan Dai Lianfang Yuan |
| author_sort | Xuan Dai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0569e69af4b1455a9db386d0d1f5cbc0 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-0569e69af4b1455a9db386d0d1f5cbc02025-08-20T03:29:44ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492025-12-0147110.1080/0886022X.2025.2519834Identification and validation of parthanatos-related genes in end-stage renal diseaseXuan Dai0Lianfang Yuan1Tianjin Key Laboratory of Acute Abdomen Disease Associated Organ Injury and ITCWM Repair, Institute of Integrative Medicine for Acute Abdominal Diseases, Tianjin NanKai Hospital, Tianjin Medical University, Tianjin, ChinaNHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, ChinaBackground 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.https://www.tandfonline.com/doi/10.1080/0886022X.2025.2519834End stage renal diseaseparthanatosimmune regulationmachine learningdiagnostic |
| spellingShingle | Xuan Dai Lianfang Yuan Identification and validation of parthanatos-related genes in end-stage renal disease Renal Failure End stage renal disease parthanatos immune regulation machine learning diagnostic |
| title | Identification and validation of parthanatos-related genes in end-stage renal disease |
| title_full | Identification and validation of parthanatos-related genes in end-stage renal disease |
| title_fullStr | Identification and validation of parthanatos-related genes in end-stage renal disease |
| title_full_unstemmed | Identification and validation of parthanatos-related genes in end-stage renal disease |
| title_short | Identification and validation of parthanatos-related genes in end-stage renal disease |
| title_sort | identification and validation of parthanatos related genes in end stage renal disease |
| topic | End stage renal disease parthanatos immune regulation machine learning diagnostic |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2519834 |
| work_keys_str_mv | AT xuandai identificationandvalidationofparthanatosrelatedgenesinendstagerenaldisease AT lianfangyuan identificationandvalidationofparthanatosrelatedgenesinendstagerenaldisease |