Go to JCI Insight
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact
  • Clinical Research and Public Health
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Gastroenterology
    • Immunology
    • Metabolism
    • Nephrology
    • Neuroscience
    • Oncology
    • Pulmonology
    • Vascular biology
    • All ...
  • Videos
    • Conversations with Giants in Medicine
    • Video Abstracts
  • Reviews
    • View all reviews ...
    • Pancreatic Cancer (Jul 2025)
    • Complement Biology and Therapeutics (May 2025)
    • Evolving insights into MASLD and MASH pathogenesis and treatment (Apr 2025)
    • Microbiome in Health and Disease (Feb 2025)
    • Substance Use Disorders (Oct 2024)
    • Clonal Hematopoiesis (Oct 2024)
    • Sex Differences in Medicine (Sep 2024)
    • View all review series ...
  • Viewpoint
  • Collections
    • In-Press Preview
    • Clinical Research and Public Health
    • Research Letters
    • Letters to the Editor
    • Editorials
    • Commentaries
    • Editor's notes
    • Reviews
    • Viewpoints
    • 100th anniversary
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • Reviews
  • Review series
  • Conversations with Giants in Medicine
  • Video Abstracts
  • In-Press Preview
  • Clinical Research and Public Health
  • Research Letters
  • Letters to the Editor
  • Editorials
  • Commentaries
  • Editor's notes
  • Reviews
  • Viewpoints
  • 100th anniversary
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact
A molecular classifier for predicting future graft loss in late kidney transplant biopsies
Gunilla Einecke, … , Bruce Kaplan, Philip F. Halloran
Gunilla Einecke, … , Bruce Kaplan, Philip F. Halloran
Published May 24, 2010
Citation Information: J Clin Invest. 2010;120(6):1862-1872. https://doi.org/10.1172/JCI41789.
View: Text | PDF
Research Article

A molecular classifier for predicting future graft loss in late kidney transplant biopsies

  • Text
  • PDF
Abstract

Kidney transplant recipients that develop signs of renal dysfunction or proteinuria one or more years after transplantation are at considerable risk for progression to renal failure. To assess the kidney at this time, a “for-cause” biopsy is performed, but this provides little indication as to which recipients will go on to organ failure. In an attempt to identify molecules that could provide this information, we used micorarrays to analyze gene expression in 105 for-cause biopsies taken between 1 and 31 years after transplantation. Using supervised principal components analysis, we derived a molecular classifier to predict graft loss. The genes associated with graft failure were related to tissue injury, epithelial dedifferentiation, matrix remodeling, and TGF-β effects and showed little overlap with rejection-associated genes. We assigned a prognostic molecular risk score to each patient, identifying those at high or low risk for graft loss. The molecular risk score was correlated with interstitial fibrosis, tubular atrophy, tubulitis, interstitial inflammation, proteinuria, and glomerular filtration rate. In multivariate analysis, molecular risk score, peritubular capillary basement membrane multilayering, arteriolar hyalinosis, and proteinuria were independent predictors of graft loss. In an independent validation set, the molecular risk score was the only predictor of graft loss. Thus, the molecular risk score reflects active injury and is superior to either scarring or function in predicting graft failure.

Authors

Gunilla Einecke, Jeff Reeve, Banu Sis, Michael Mengel, Luis Hidalgo, Konrad S. Famulski, Arthur Matas, Bert Kasiske, Bruce Kaplan, Philip F. Halloran

×
Problems with a PDF?

This file is in Adobe Acrobat (PDF) format. If you have not installed and configured the Adobe Acrobat Reader on your system.

Having trouble reading a PDF?

PDFs are designed to be printed out and read, but if you prefer to read them online, you may find it easier if you increase the view size to 125%.

Having trouble saving a PDF?

Many versions of the free Acrobat Reader do not allow Save. You must instead save the PDF from the JCI Online page you downloaded it from. PC users: Right-click on the Download link and choose the option that says something like "Save Link As...". Mac users should hold the mouse button down on the link to get these same options.

Having trouble printing a PDF?

  1. Try printing one page at a time or to a newer printer.
  2. Try saving the file to disk before printing rather than opening it "on the fly." This requires that you configure your browser to "Save" rather than "Launch Application" for the file type "application/pdf", and can usually be done in the "Helper Applications" options.
  3. Make sure you are using the latest version of Adobe's Acrobat Reader.

Supplemental data - Download (104.73 KB)

Advertisement

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

Sign up for email alerts