The immunosuppressive tumor microenvironment (TME) supports the development of tumors and limits tumor immunotherapy, including hematological malignancies. Hematological malignancies remain a major public health issue with high morbidity and mortality worldwide. As an important component of immunosuppressive regulators, the phenotypic characteristics and prognostic value of myeloid-derived suppressor cells (MDSCs) have received much attention. A variety of MDSC-targeting therapeutic approaches have produced encouraging outcomes. However, the use of various MDSC-targeted treatment strategies in hematologic malignancies is still difficult due to the heterogeneity of hematologic malignancies and the complexity of the immune system. In this review, we summarize the biological functions of MDSCs and further provide a summary of the phenotypes and suppressive mechanisms of MDSC populations expanded in various types of hematological malignancy contexts. Moreover, we discussed the clinical correlation between MDSCs and the diagnosis of malignant hematological disease, as well as the drugs targeting MDSCs, and focused on summarizing the therapeutic strategies in combination with other immunotherapies, such as various immune checkpoint inhibitors (ICIs), that are under active investigation. We highlight the new direction of targeting MDSCs to improve the therapeutic efficacy of tumors.
Background
In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.
Methods
SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time polymerase chain reaction (PCR) assay.
Results
Five core genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, the nomogram model showed that the five optimal key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic (ROC) curves of the five genes also revealed that they had critical roles in the pathogenesis of SLE.
Conclusion
Overall, five key genes were obtained and validated through machine-learning analysis of the databases, which might offer a new perspective for the molecular mechanism and potential therapeutic targets for SLE.
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