差异表达基因（DEGs）生物标志物可用于帮助诊断和监测疾病，以及确定哪种治疗方法最有效。因此，考虑到骨髓增殖性疾病（MDS）的复杂性，难以确定CD34 + HSC（造血干细胞）或原始骨髓细胞（PBMC）中DEGs的影响和差异在MDS发病机制中的作用，因此仍然很大程度上不为人知。在这里，我们对来自1092名MDS患者的CD34 + HSC和PBMC进行了体外转录组分析，分析了这两种细胞类型之间差异基因表达模式的差异作为MDS潜在致病生物标志物。 最初，我们观察到PBMC（n = 40165）和CD34 + HSC（n = 33048）之间存在7117个表达转录本的差异。另外，我们发现CD34 + HSC和PBMC样本分别显示了240和2948个DEGs。总之，我们确定了CD34 + HSC和PBMC细胞类型中的DEGs差异。然而，基于基因本体论和KEGG通路富集分析，两种细胞样本中的激活通路存在某种相似性。我们的结果为MDS发病机制提供了新的DEGs生物标志物洞见，具有临床意义。数据和材料的可用性：所有微阵列数据库都来自Gene Expression Omnibus（https://www.ncbi.nlm.nih.gov/geo/）。为了评估差异表达基因的生物功能，使用了DAVID（注释，可视化和整合发现工具数据库）（https://david.ncifcrf.gov/）。版权所有©2023 Elsevier Ltd。保留所有权利。

Differentially expressed genes (DEGs) biomarkers can be used to help diagnose and monitor the disease, as well as to determine which treatments are most effective. So, given the complexity of Myelodysplastic neoplasm (MDS), it is difficult to determine the impact and disparities of DEGs between CD34+ HSC (hematopoietic stem cells) or primary bone marrow cells (PBMC) in MDS pathogenesis, and therefore it remains largely unknown. Here, we performed an in-silico transcriptome analysis on CD34+ HSC and PBMC from 1092 MDS patients analyzing the divergences between differential gene expression patterns in these two cell types as potential pathogenic biomarkers for MDS. Initially, we observed a difference of 7117 expressed transcripts between PBMC (n = 40,165) and CD34 +HSC (n = 33,048). Also, we identified that CD34+ HSC and PBMC samples showed 240 and 2948 DEGs, respectively. In summary, we identified DEGs disparities in CD34+ HSC and PBMC cell types. However, there was a certain similarity of the activated pathways in both cellular samples based on Gene Ontology and KEGG pathways enrichment analyses. Our results provide novel insights into novel DEGs biomarkers to MDS pathogenesis with clinical significance. AVAILABILITY OF DATA AND MATERIALS: All microarray databases were obtained from Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). To evaluate the biological function of differentially expressed genes, the DAVID (Database for Annotation, Visualization and Integrated Discovery tool was used) (https://david.ncifcrf.gov/).Copyright © 2023 Elsevier Ltd. All rights reserved.