TRANSCRIPTOMIC PUZZLE IN MYELODYSPLASTIC NEOPLASMS: UNRAVELING THE DISCORDANCE BETWEEN CD34+ STEM CELLS AND PRIMARY BONE MARROW CELLS

Abstract

Introduction: Differentially expressed genes (DEGs) biomarkers have the potential to aid in the diagnosis and monitoring of diseases, as well as determining the most effective treatments. However, due to the complex nature of Myelodysplastic neoplasm (MDS), it is challenging to assess the impact and variations of DEGs between CD34+ HSC (hematopoietic stem cells) and primary bone marrow cells (PBMC) in MDS pathogenesis. Consequently, this aspect remains largely unknown. Objective: The aim was to investigate the divergences in differential gene expression patterns between these two cell types as potential pathogenic biomarkers for MDS. Materials and Methods: To understand the transcriptome differential expression in MDS pathogenesis, we employed two groups: first, four datasets (GSE18366, GSE19429, GSE30195 and GSE58831) from CD34+ HSC samples were obtained to analyze expression profiles of 392 MDS patients and 44 healthy individuals; second, four other datasets (GSE41130, GSE97064, GSE107400 and GSE114869) from PBMC samples were obtained to analyze expression profiles of 700 MDS patients and 47 healthy individuals. All databases were based on Affymetrix GeneChip Human Transcriptome Array. We employed bioinformatic analysis of microarray to investigate significant abnormal differentially expressed genes (DEGs) in MDS and health individuals on every microarray dataset for both types of samples based on Transcriptome Analysis Console (TAC) Software algorithm. Heatmaps, Volcano and Scatter plots were performed in TAC software based in R algorithm. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were conducted to clarify the functional roles of DEGs. Enriched functional categories of gene–ontology associations were performed using the R software and the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Results: Our analysis initially revealed a disparity of 7117 expressed transcripts between PBMC (n = 40,165) and CD34+ HSC (n = 33,048). Furthermore, we identified 240 DEGs in CD34+ HSC samples and 2948 DEGs in PBMC samples. Among them, it was notable that there is no common pattern of gene expression between upregulated or downregulated transcripts in both samples. In this study, we conducted GO enrichment and KEGG pathway analyses to investigate the functional roles of DEGs in Myelodysplastic Syndrome (MDS) pathobiology. We categorized the DEGs of CD34+ HSC and PBMC into functional categories using GO analysis. In CD34+ HSC, the most enriched pathways were signal transduction, negative regulation of transcription, and immune response. In PBMC, the immune response, signal transduction, and innate immune response pathways were significantly activated. We also performed KEGG pathway analysis for upregulated and downregulated DEGs in both CD34+ HSC and PBMC samples. In CD34+ HSC, the top pathways were involved in hematopoietic cell lineage, cell receptor signaling, and primary immunodeficiency. These pathways were also identified in PBMC, along with cancer and cell cycle pathways. Discussion and Conclusion: To summarize, our study uncovered disparities in DEGs between CD34+ HSC and PBMC cell types in MDS. However, we also observed a certain degree of similarity in the activated pathways of both cell samples based on Gene Ontology and KEGG pathways enrichment analyses. Of particular importance in our study was the role of the immune system and the activation of hematopoietic cell lineage pathways as the most activated in MDS, regardless of the cell type evaluated. These findings provide novel insights into DEGs biomarkers associated with MDS pathogenesis, holding clinical significance.