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

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 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. Finally, aging subtypes were established, and the correlation between MDS and the 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 expressions were identified based on ARG, illustrating that different subtypes have different biological and immune functions. The cell clustering results obtained from manual annotation were validated using single cell analysis, and the expression of 7 pivotal genes in MDS was verified by flow cytometry and RT-PCR. Discussion: 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 also show that aging plays an important function in the immunological microenvironment of MDS, giving new insights into the pathogenesis of MDS and possible immunotherapy.

Keywords

• myelodysplastic syndromes
• aging-related genes
• immune signature
• classification models
• bioinformatics