Submit a comment
Abstract
BACKGROUND Acute graft versus host disease (aGvHD) is a major factor that limits the successful outcomes of allogeneic hematopoietic cell transplantation (alloHSCT). Currently, there are few validated biomarkers that can help predict the risk of aGvHD in clinical settings.METHODS We performed an integrated metabolomics and transcriptomics study and identified biomarkers that distinguish alloHSCT recipients with aGvHD from alloHSCT recipients without aGvHD in 2 separate cohorts.RESULTS Pathway analysis of 38 significantly altered metabolites and 1,148 differentially expressed genes uncovered a distinctly altered glycerophospholipid (GPL) metabolism network. Subsequently, we developed an aGvHD risk score (GRS) based on 5 metabolite markers from GPL metabolism to predict the risk of aGvHD. GRS showed a positive predictive value of 92.2% and 89.6% in the training and validation cohorts, respectively. In addition, high GRS was correlated with poor overall survival. Gene expressions of GPL-related lipases were significantly altered in aGvHD samples, leading to dysregulated GPLs.CONCLUSION Using integrative “Omic” analysis, we unraveled a comprehensive view of the molecular perturbations underlying the pathogenesis of aGvHD. Our work represents an initial investigation of a unique metabolic and transcriptomic network that may help identify aGvHD at an early stage and facilitate preemptive therapy.FUNDING National Natural Science Foundation of China (NSFC; 81530047, 81870143, 81470321, 81770160, 81270567, 81270638, 81573396, 81703674) provided funding for material, processing, metabolomics, and transcriptomics studies. This work was also supported by Shanghai Sailing Program from Science and Technology Commission Shanghai Municipality (17YF1424700), the Scholarship from Shanghai Municipal Health and Family Planning Commission (2017BR012), and the Special Clinical Research in Health Industry in Shanghai (20184Y0054). The projects supporting young scholars are 17YF1424700 (to YL), 2017BR012 (to XH), and 20184Y0054 (to QC).
Authors
Yue Liu, Aijie Huang, Qi Chen, Xiaofei Chen, Yang Fei, Xiaoming Zhao, Weiping Zhang, Zhanying Hong, Zhenyu Zhu, Jianmin Yang, Yifeng Chai, Jianmin Wang, Xiaoxia Hu
Guidelines
The Editorial Board will only consider comments that are deemed relevant and of interest to readers. The Journal will not post data that have not been subjected to peer review; or a comment that is essentially a reiteration of another comment.
- Comments appear on the Journal’s website and are linked from the original article’s web page.
- Authors are notified by email if their comments are posted.
- The Journal reserves the right to edit comments for length and clarity.
- No appeals will be considered.
- Comments are not indexed in PubMed.
Specific requirements
- Maximum length, 400 words
- Entered as plain text or HTML
- Author’s name and email address, to be posted with the comment
- Declaration of all potential conflicts of interest (even if these are not ultimately posted); see the Journal’s conflict-of-interest policy
- Comments may not include figures
