文章：

利用移植前后骨髓样本的靶向转录组与机器学习预测异基因干细胞移植后急性移植物抗宿主病及总体生存率

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

原文发布日期：29 March 2024

DOI: 10.3390/cancers16071357

类型: Article

开放获取: 是

英文摘要：

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 in a machine learning algorithm to predict the presence or absence of aGvHD using either random forest or extreme gradient boosting algorithms. Patients were randomly divided into training (2/3 of patients) and validation (1/3 of patients) sets. Using post-HSCT RNA data, the machine learning algorithm selected 92 genes for predicting aGvHD that appear to play a role in PI3/AKT, MAPK, and FOXO signaling, as well as microRNA. The algorithm selected 20 genes for predicting survival included genes involved in MAPK and chemokine signaling. Using pre-HSCT RNA data, the machine learning algorithm selected 400 genes and 700 genes predicting aGvHD and overall survival, but candidate signaling pathways could not be specified in this analysis. These data show that NGS analyses of RNA expression using machine learning algorithms may be useful biomarkers of aGvHD and overall survival for patients undergoing HSCT, allowing for the identification of major signaling pathways associated with HSCT outcomes and helping to dissect the complex steps involved in the development of aGvHD. The analysis of pre-HSCT bone marrow samples may lead to pre-HSCT interventions including choice of remission induction regimens and modifications in patient health before HSCT.

摘要翻译： 

急性移植物抗宿主病（aGvHD）仍是异基因造血干细胞移植（HSCT）后发病和死亡的主要原因。我们对167例接受HSCT患者的骨髓样本进行了1408个候选基因的RNA分析。利用随机森林或极端梯度提升算法，将RNA表达数据应用于机器学习算法中，以预测aGvHD的存在与否。患者被随机分为训练集（占患者总数的2/3）和验证集（占患者总数的1/3）。基于移植后RNA数据，机器学习算法筛选出92个与aGvHD预测相关的基因，这些基因可能参与PI3/AKT、MAPK、FOXO信号通路及microRNA调控。算法同时筛选出20个与生存预测相关的基因，涉及MAPK和趋化因子信号通路。基于移植前RNA数据，机器学习算法筛选出400个与aGvHD预测相关的基因及700个与总生存期预测相关的基因，但在此分析中未能明确候选信号通路。这些数据表明，采用机器学习算法进行的RNA表达二代测序分析，可作为HSCT患者aGvHD和总生存期的潜在生物标志物，有助于识别与HSCT结局相关的主要信号通路，并解析aGvHD发生过程中的复杂机制。对移植前骨髓样本的分析可能引导移植前干预措施，包括缓解诱导方案的选择以及移植前患者健康状况的调整。

原文链接：

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