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Identification of RNA-binding protein genes associated with renal rejection and graft survival.
Zhong, Zhaozhong; Ye, Yongrong; Xia, Liubing; Na, Ning.
Afiliación
  • Zhong Z; Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Ye Y; Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Xia L; Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Na N; Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Ren Fail ; 46(2): 2360173, 2024 Dec.
Article en En | MEDLINE | ID: mdl-38874084
ABSTRACT
Rejection is one of the major factors affecting the long-term prognosis of kidney transplantation, and timely recognition and aggressive treatment of rejection is essential to prevent disease progression. RBPs are proteins that bind to RNA to form ribonucleoprotein complexes, thereby affecting RNA stability, processing, splicing, localization, transport, and translation, which play a key role in post-transcriptional gene regulation. However, their role in renal transplant rejection and long-term graft survival is unclear. The aim of this study was to comprehensively analyze the expression of RPBs in renal rejection and use it to construct a robust prediction strategy for long-term graft survival. The microarray expression profiles used in this study were obtained from GEO database. In this study, a total of eight hub RBPs were identified, all of which were upregulated in renal rejection samples. Based on these RBPs, the renal rejection samples could be categorized into two different clusters (cluster A and cluster B). Inflammatory activation in cluster B and functional enrichment analysis showed a strong association with rejection-related pathways. The diagnostic prediction model had a high diagnostic accuracy for T cell mediated rejection (TCMR) in renal grafts (area under the curve = 0.86). The prognostic prediction model effectively predicts the prognosis and survival of renal grafts (p < .001) and applies to both rejection and non-rejection situations. Finally, we validated the expression of hub genes, and patient prognosis in clinical samples, respectively, and the results were consistent with the above analysis.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Trasplante de Riñón / Proteínas de Unión al ARN / Rechazo de Injerto / Supervivencia de Injerto Límite: Humans Idioma: En Revista: Ren Fail Asunto de la revista: NEFROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Trasplante de Riñón / Proteínas de Unión al ARN / Rechazo de Injerto / Supervivencia de Injerto Límite: Humans Idioma: En Revista: Ren Fail Asunto de la revista: NEFROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China