Single nucleotide polymorphisms within HLA region are associated with the outcomes of unrelated cord blood transplantation

Prediction of Acute Cardiac Rejection Based on Gene Expression Profiles

Acute cardiac rejection remains a significant challenge in the post-transplant period, necessitating meticulous monitoring and timely intervention to prevent graft failure. Thus, the goal of the present study was to identify novel biomarkers involved in acute cardiac rejection, paving the way for personalized diagnostic, preventive, and treatment strategies. A total of 809 differentially expressed genes were identified in the GSE150059 dataset. We intersected genes selected by analysis of variance, recursive feature elimination, least absolute shrinkage and selection operator, and random forest classifier to identify the most relevant genes involved in acute cardiac rejection. Thus, HCP5, KLRD1, GZMB, PLA1A, GNLY, and KLRB1 were used to train eight machine learning models: random forest, logistic regression, decision trees, support vector machines, gradient boosting machines, K-nearest neighbors, XGBoost, and neural networks. Models were trained, tested, and validated on the GSE150059 dataset (MMDx-based diagnosis of rejection). Eight algorithms achieved great performance in predicting acute cardiac rejection. However, all machine learning models demonstrated poor performance in two external validation sets that had rejection diagnosis based on histology: merged GSE2596 and GSE4470 dataset and GSE9377 dataset, thus highlighting differences between these two methods. According to SHAP and LIME, KLRD1 and HCP5 were the most impactful genes.

Screening of single nucleotide polymorphisms within HLA region related to hematopoietic stem cell transplantation using MassARRAY technology

A growing number of studies showed that single nucleotide polymorphisms (SNPs) in the human leukocyte antigen (HLA)-related genes were associated with the outcome of hematopoietic stem cell transplantation (HSCT). Thus, other SNPs located nearby the classical HLA genes must be considered in HSCT. We evaluated the clinical feasibility of MassARRAY by comparing to Sanger sequencing. The PCR amplicons with each one of the 17 loci that were related to the outcomes of HSCT published by our previous study were transferred onto a SpectroCHIP Array for genotyping by mass spectrometry. The sensitivity of MassARRAY was 97.9% (614/627) and the specificity was 100% (1281/1281), where the positive predictive value (PPV) was 100% (614/614) and the negative predictive value (NPV) was 99.0% (1281/1294). MassARRAY is high-throughput, which can accurately analyze multiple SNPs at the same time. Based on these properties, we proposed that it could be an efficient method to match the genotype between the graft and the recipient before transplantation.

Single-Nucleotide Polymorphisms Within Non-HLA Regions Are Associated With Engraftment Effectiveness for Patients With Unrelated Cord Blood Transplantation

Clinically, stem cells with matched human leukocyte antigens (HLAs) must be selected for allogeneic transplantation to avoid graft rejection. However, adverse reactions still occur after cord blood transplantation (CBT). It was inferred that the HLA system is not the only regulatory factor that may influence CBT outcomes. Therefore, we plan to investigate whether the single-nucleotide polymorphisms (SNPs) located in non-HLA genes are associated with the effectiveness of CBT. In this study, the samples of 65 donors from CBT cases were collected for testing. DNA sequencing was focused on the SNPs of non-HLA genes, cytotoxic T-lymphocyte-associated protein 4 (CTLA4), CD28, tumor necrosis factor ligand superfamily 4 (TNFSF4), and programmed cell death protein 1 (PDCD1), which were selected in regard to the literatures published in 2017 and 2018, which indicated that they were related to stem cell transplantation. Then, in combination with the detailed follow-up transplantation tracking database, these SNPs were analyzed with the risk of mortality, relapse, cytomegalovirus (CMV) infection, and graft-versus-host disease (GVHD). We found that there were 2 SNPs of CTLA4, 1 SNP of TNFSF4, and 2 SNPs of PDCD1 associated with the effectiveness of unrelated CBT. These statistically significant SNPs and haplotypes would be used in clinical to choose the best donor for the patient receiving CBT. Moreover, the polygenic risk scores (PRSs) with these SNPs could be used to predict the risk of CBT adverse reactions with an area under the receiver operating characteristic curve (AUC) of 0.7692. Furthermore, these SNPs were associated with several immune-related diseases or cancer susceptibility, which implied that SNPs play an important role in immune regulation.