Cargando…

Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection

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 unreal...

Descripción completa

Detalles Bibliográficos
Autores principales: Lim, Jeong-Hoon, Chung, Byung Ha, Lee, Sang-Ho, Jung, Hee-Yeon, Choi, Ji-Young, Cho, Jang-Hee, Park, Sun-Hee, Kim, Yong-Lim, Kim, Chan-Duck
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Association of Internal Medicine 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082440/
https://www.ncbi.nlm.nih.gov/pubmed/35417937
http://dx.doi.org/10.3904/kjim.2021.518
_version_ 1784703206094274560
author Lim, Jeong-Hoon
Chung, Byung Ha
Lee, Sang-Ho
Jung, Hee-Yeon
Choi, Ji-Young
Cho, Jang-Hee
Park, Sun-Hee
Kim, Yong-Lim
Kim, Chan-Duck
author_facet Lim, Jeong-Hoon
Chung, Byung Ha
Lee, Sang-Ho
Jung, Hee-Yeon
Choi, Ji-Young
Cho, Jang-Hee
Park, Sun-Hee
Kim, Yong-Lim
Kim, Chan-Duck
author_sort Lim, Jeong-Hoon
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9082440
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Korean Association of Internal Medicine
record_format MEDLINE/PubMed
spelling pubmed-90824402022-05-17 Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection Lim, Jeong-Hoon Chung, Byung Ha Lee, Sang-Ho Jung, Hee-Yeon Choi, Ji-Young Cho, Jang-Hee Park, Sun-Hee Kim, Yong-Lim Kim, Chan-Duck Korean J Intern Med Review 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. Korean Association of Internal Medicine 2022-05 2022-04-15 /pmc/articles/PMC9082440/ /pubmed/35417937 http://dx.doi.org/10.3904/kjim.2021.518 Text en Copyright © 2022 The Korean Association of Internal Medicine https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Lim, Jeong-Hoon
Chung, Byung Ha
Lee, Sang-Ho
Jung, Hee-Yeon
Choi, Ji-Young
Cho, Jang-Hee
Park, Sun-Hee
Kim, Yong-Lim
Kim, Chan-Duck
Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
title Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
title_full Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
title_fullStr Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
title_full_unstemmed Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
title_short Omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
title_sort omics-based biomarkers for diagnosis and prediction of kidney allograft rejection
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9082440/
https://www.ncbi.nlm.nih.gov/pubmed/35417937
http://dx.doi.org/10.3904/kjim.2021.518
work_keys_str_mv AT limjeonghoon omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT chungbyungha omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT leesangho omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT jungheeyeon omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT choijiyoung omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT chojanghee omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT parksunhee omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT kimyonglim omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection
AT kimchanduck omicsbasedbiomarkersfordiagnosisandpredictionofkidneyallograftrejection