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Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
Article in English | WPRIM (Western Pacific) | ID: wpr-927021
Responsible library: WPRO
ABSTRACT
Kidney transplantation is the preferred treatment for patients with end-stage kidney disease, because it prolongs survival and improves quality of life. Allograft biopsy is the gold standard for diagnosing allograft rejection. However, it is invasive and reactive, and continuous monitoring is unrealistic. Various biomarkers for diagnosing allograft rejection have been developed over the last two decades based on omics technologies to overcome these limitations. Omics technologies are based on a holistic view of the molecules that constitute an individual. They include genomics, transcriptomics, proteomics, and metabolomics. The omics approach has dramatically accelerated biomarker discovery and enhanced our understanding of multifactorial biological processes in the field of transplantation. However, clinical application of omics-based biomarkers is limited by several issues. First, no large-scale prospective randomized controlled trial has been conducted to compare omics-based biomarkers with traditional biomarkers for rejection. Second, given the variety and complexity of injuries that a kidney allograft may experience, it is likely that no single omics approach will suffice to predict rejection or outcome. Therefore, integrated methods using multiomics technologies are needed. Herein, we introduce omics technologies and review the latest literature on omics biomarkers predictive of allograft rejection in kidney transplant recipients.
Full text: Available Database: WPRIM (Western Pacific) Type of study: Controlled clinical trial / Diagnostic study / Prognostic study Aspects: Patient-preference Language: English Journal: The Korean Journal of Internal Medicine Year: 2022 Document type: Article
Full text: Available Database: WPRIM (Western Pacific) Type of study: Controlled clinical trial / Diagnostic study / Prognostic study Aspects: Patient-preference Language: English Journal: The Korean Journal of Internal Medicine Year: 2022 Document type: Article
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