Blood and Lymphatic Cancer: Targets and Therapy (Jun 2024)
Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis
Abstract
Nan Mei,1,* Sha Gong,1,* Lizhao Wang,2 Lu Wang,1 Jincheng Wang,1 Jianpeng Li,3 Yingying Bao,4 Huanming Zhang,1 Huaiyu Wang1 1Department of Hematology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China; 2Department of Breast Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China; 3Department of Urology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China; 4Institute of Gene and Cell Therapy, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaiyu Wang, Department of Hematology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an, Shannxi, 710061, People’s Republic of China, Email [email protected]; [email protected]: Multiple myeloma (MM), an incurable plasma cell malignancy. The significance of the relationship between natural killer (NK) cell-related genes and clinical factors in MM remains unclear.Methods: Initially, we extracted NK cell-related genes from peripheral blood mononuclear cells (PBMC) of healthy donors and MM samples by employing single-cell transcriptome data analysis in TISCH2. Subsequently, we screened NK cell-related genes with prognostic significance through univariate Cox regression analysis and protein-protein interaction (PPI) network analysis. Following the initial analyses, we developed potential subtypes and prognostic models for MM using consensus clustering and lasso regression analysis. Additionally, we conducted a correlation analysis to explore the relationship between clinical features and risk scores. Finally, we constructed a weighted gene co-expression network analysis (WGCNA) and identified differentially expressed genes (DEGs) within the MM cohort.Results: We discovered that 153 NK cell-related genes were significantly associated with the prognosisof MM patients (P < 0.05). Patients in NK cluster A exhibited poorer survival outcomes compared to those in cluster B. Furthermore, our NK cell-related genes risk model revealed that patients with a high risk score had significantly worse prognoses (P < 0.05). Patients with a high risk score were more likely to exhibit adverse clinical markers. Additionally, the nomogram based on NK cell-related genes demonstrated strong prognostic performance. The enrichment analysis indicated that immune-related pathways were significantly correlated with both the NK subtypes and the NK cell-related genes risk model. Ultimately, through the combined use of WGCNA and DEGs analysis, and by employing Venn diagrams, we determined that ITM2C is an independent prognostic marker for MM patients.Conclusion: In this study, we developed a novel model based on NK cell-related genes to stratify the prognosis of MM patients. Notably, higher expression levels of ITM2C were associated with more favorable survival outcomes in these patients.Keywords: multiple myeloma, NK cell-related genes, NK subtypes, prognostic model, ITM2C
