Background/Objectives: Myeloid-derived suppressor cells (MDSCs) play an essential role in tumor immune evasion from the immune system; however, the common molecular signatures associated with MDSCs across different cancer types remain limited. This study aimed to identify and validate common MDSC-associated hub genes as potential biomarkers across breast, liver, and lung cancers through integrated bioinformatics analysis and experimental validation by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Methods: Integrated bioinformatics analyses were conducted using The Cancer Genome Atlas (TCGA) transcriptomic datasets to identify differentially expressed genes (DEGs), followed by functional enrichment analysis, protein–protein interaction (PPI) network construction, and TIMER2.0 analysis. Peripheral blood samples were obtained from twenty-four participants, including cancer patients before chemotherapy and healthy controls. Hematological and biochemical data were assessed. A CD11b+CD33+ myeloid-enriched population was isolated, and the expression levels of the detected hub genes were validated by qRT-PCR. Results: The bioinformatics analysis identified a common differentially expressed gene signature enriched in cell cycle and mitotic pathways, highlighting four correlated hub genes, including cell division cycle 20 (CDC20), cyclin-dependent kinase 1 (CDK1), kinesin family member 20A (KIF20A), and PDZ-binding kinase (PBK). TIMER2.0 demonstrated significant positive correlations between the expression of these hub genes and estimated MDSC infiltration across the three cancer types. The analysis by qRT-PCR showed that CDC20, CDK1, KIF20A and PBK were significantly upregulated in the CD11b+CD33+ myeloid-enriched population of cancer patients compared with healthy controls. Conclusions: The upregulation of CDC20, CDK1, KIF20A and PBK suggests a significant association between MDSC-related immune suppression and cell-cycle dysregulation in different solid tumors. These hub genes may also represent candidate biomarkers across different cancer types and require further validation.
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Abosenna et al. (2026) studied this question.
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