Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Abstract

Background: 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: ITM2C; NK cell-related genes; NK subtypes; multiple myeloma; prognostic model.

Figure 1

The detailed workflow chart analysis in our research.

Figure 2

Identification of NK cells using scRNA–seq analysis in TISCH2 database. (A) Thirty–one clusters were identified in MM_GSE161801 dataset. (B) Nine cell types were defined in MM_GSE161801 dataset. (C) The pie chart of cell-type statistics in MM_GSE161801 dataset. (D) Twenty-six clusters were identified in PBMC_60K dataset. (E) Seven cell types were defined in PBMC_60K dataset. (F) The pie chart of cell-type statistics in PBMC_60K dataset. (G) The dot plot showed the relative expression levels of the marker genes in MM_GSE161801 cell type. (H) Venn diagram showing the 15 intersected cell types by the correlation and differential analysis.

Figure 3

Identification of prognostic NK cell-related genes in MM. (A) The Venn plot of the intersection of OS-related genes and EFS-related genes. (B) The PPI network of the NK cell-related prognostic genes using STRING database. (C) The barplot diagram showing the degree of involvement of each key gene in the PPI network. (D) and (E) The gene co-expression analysis of the 32 key genes.

Figure 4

Identification of prognostic NK cell-related genes and consensus clustering. (A) Two NK subtypes of MM were recognized with prognostic NK cell-related genes using consensus clustering. (B) PCA, (C) tSNE and (D) UMAP plot showing the NK subtypes of MM. The survival curve of the patients in the two subgroups for (E) OS and (F) EFS. (G) Heatmap of distribution of clinicopathological variables and NK cell-related genes between different cluster groups. (H) The boxplot illustrating the difference in NK related-cell genes between two clusters. (I) The boxplot visualizing the distribution differences of immune cells between the two clusters.

Figure 5

Construction of NK cell-related genes risk model in the MM cohort. LASSO regression and cross-validation for (A) OS group and (B) EFS group. (C, E and G) OS and (D, F and H) EFS: Kaplan–Meier curves analysis based on risk score, and ROC analysis for predicting the risk of death at 1, 3 and 5 years in MM patients in all cohort, training cohort and testing cohort.

Figure 6

The heatmaps were plotted to show the expression level of (A) 16 genes from OS risk model and (B) 12 genes from EFS risk model in high- and low-risk groups. The boxplot for risk score of (C) OS and (D) EFS comparisons between A and B cluster subgroups. The Sankey diagram revealed the connection between cluster, risk score, and survival status for (E) OS group and (F) EFS group.

Figure 7

Clinical relevance analysis between high- and low-risk groups. (A) and (B) Correlation of NK risk score and clinicopathological characteristics based on the whole MM cohort. Expression of NKscore in different clinical factors in patients with MM patients. (C) Age; (D) Gender; (E) Race; (F) Isotype; (G) β2-MG; (H) CRP; (I) Creatinine; (J) LDH; (K) Albumin; (L) Haemoglobin; (M) BMPC; (N) Cytogenetics.

Figure 8

Independent prognostic value of NKscore and nomogram construction. (A) Univariate and (B) Multivariate analysis to identify independent prognostic risk factors for OS. (C) The established Nomogram integrating risk score and clinical features. (D, E and F) Calibration curves of the NKscore-integrated nomogram in 1-, 3-, and 5-year in the all cohort.

Figure 9

Identification of NK cell risk model genes in conjunction with WGCNA and DEGs. (A) Volcano plot and heatmap for DEGs in the MM and normal samples. Black: non DEGs in groups; red: upregulated DEGs in MM group; green: downregulated DEGs in in MM group. (B) Analysis of soft thresholds. (C) Gene dendrogram and module colors. (D) Module trait relationships. (E) The venn diagram of the DEGs, Black modules genes of WGCNA and NK risk model genes for OS.

Figure 10

Expression of ITM2C in different clinical factors in patients with MM. (C) Age; (D) Gender; (E) Race; (F) Isotype; (G) β2-MG; (H) CRP; (I) Creatinine; (J) LDH; (I) Albumin; (K) Haemoglobin; (L) BMPC, (M) Albumin; (N) Haemoglobin.