Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation

Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation

Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used...

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Bibliographic Details
Main Authors: Scott D. Rowley, Thomas S. Gunning, Michael Pelliccia, Alexandra Della Pia, Albert Lee, James Behrmann, Ayrton Bangolo, Parul Jandir, Hong Zhang, Sukhdeep Kaur, Hyung C. Suh, Michele Donato, Maher Albitar, Andrew Ip
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Cancers
Subjects:
allogeneic transplantation
acute GvHD
post-transplant survival
transcriptomics
machine learning
signaling pathways
Online Access:https://www.mdpi.com/2072-6694/16/7/1357

Internet

https://www.mdpi.com/2072-6694/16/7/1357

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