Back
For best output, select "Paper Size" as "A4" and "Margin" as "0" or "None".
To save or print to PDF, please select Print Destination > Save as PDF, enable Background Graphics under "More Settings", then click "Save".
During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Acute kidney injury (AKI) is a common and severe complication following renal transplantation, driven largely by ischemia–reperfusion injury (IRI) that occurs during organ procurement and implantation. Post-transplant AKI often occurs concomitantly with delayed graft function, early hospital readmission, inferior long-term graft survival, and increased mortality. Current diagnostic approaches, mainly relying on serum creatinine level, are limited by low sensitivity for early detection. However, reliable molecular biomarkers to predict post-transplant AKI remain unavailable.
We integrated transcriptomic datasets from multiple cohorts of renal transplantation to identify robust gene signatures associated with transplant-related AKI. Utilizing weighted gene co-expression network analysis alongside differential expression analysis, we initially identified candidate genes, then, refined them using LASSO regression and support vector machine–recursive feature elimination to yield a consistent gene set. The candidate gene set was validated in external datasets, tested using a mouse model of renal IRI and human AKI biopsies.
We initially identified 222 candidate genes in renal allograft, which showed significantly altered expression at early stage of allograft IRI, associated with transplant-related AKI. These candidate genes were refined to a consistent four-gene set—SOCS3, MYC, TGIF1, and LETM2 based on LASSO regression and support vector machine analyses. Incorporation of the four-gene set into a logistic regression framework produced strong performance in the training cohort (AUC = 0.969), which was corroborated in external validation datasets, including a large independent cohort (AUC = 0.942). Decision curve analyses confirmed potential clinical utility across a broad threshold-probability range, with net benefit exceeding established biomarkers such as neutrophil gelatinase associated lipocalin. single-cell transcriptomic profiling revealed that SOCS3, MYC, TGIF1, and LETM2 presented cell type–specific expression patterns across renal compartments. In a mouse model of IRI, these four proteins were significantly upregulated in kidney at 24 hours after ischemia reperfusion. Furthermore, in human kidneys of acute tubular necrosis, immunohistochemistry staining showed a marked elevation of the four proteins.
This study identified a four-gene signature that may predict the onset of AKI after transplantation, and serve as a potential biomarker panel for post-transplant AKI.
