Identification and validation of aging-related genes and their classification models based on myelodysplastic syndromes

Abstract

Abstract Background Myelodysplastic syndrome is a malignant clonal disorder of hematopoietic stem cells (HSC) with both myelodysplastic problems and hematopoietic disorders. The greatest risk factor for the development of MDS is advanced age, and aging causes dysregulation and decreased function of the immune and hematopoietic systems. However, the mechanisms by which this occurs remain to be explored. Therefore, we would like to explore the association between MDS and aging genes through a classification model and use bioinformatics analysis tools to explore the relationship between MDS aging subtypes and the immune microenvironment.Methods The dataset of MDS in the paper was obtained from the GEO database and aging-related genes were taken from HAGR. specific genes were screened by three machine learning algorithms. Then, artificial neural network (ANN) models and Nomogram models were developed to validate the effectiveness of the methods by subject operating characteristic (ROC) and decision curve analysis (DCA). Finally, aging subtypes were established and the correlation between MDS and immune microenvironment was analyzed using bioinformatics analysis tools. Weighted correlation network analysis (WGCNA) and single cell analysis were also added to validate the consistency of the result analysis.Results Seven core genes associated with ARG were screened by differential analysis, enrichment analysis and machine learning algorithms for accurate diagnosis of MDS. subsequently, two subtypes of senescent expression were identified based on ARG, illustrating that different subtypes have different biological and immune functions. And the cell clustering results obtained from manual annotation were validated using single cell analysis.Discussion Our findings emphasize the crucial role of senescence-related genes in the regulation of the immunological microenvironment. Seven hub ARGs (FOS,IKBKB,HDAC3,JUN,PTK2B,CEBPA,BAX) create a categorization model, and the findings demonstrate a key role of senescence in the immunological milieu of MDS, giving new insights into MDS pathogenesis and potential treatments. The findings show that agingplays an important function in the immunological microenvironment of MDS, giving new insights into the pathogenesis of MDS and possible immunotherapy.