Bioinformatic characterization of STING expression in hematological malignancies reveals association with prognosis and anti-tumor immunity

Abstract

Introduction: Stimulator of interferon response cGAMP interactor (STING) is essential for both innate and adaptive immunity. However, a comprehensive molecular characterization of STING expression across hematological malignancies is lacking.

Methods: In this study, the pan-blood-cancer landscape related to STING expression was identified using the GTEx, CCLE, Hemap, and TCGA databases, and the potential value for predicting prognosis was investigated. The relationship between STING expression and immune cell enrichment was assessed in the Hemap database. Moreover, the value of STING in predicting the efficacy of immunotherapy was validated using tumor immune dysfunction and exclusion (TIDE) biomarkers and real-world immunotherapy datasets.

Results and discussion: STING was found to be relatively highly expressed in acute myeloid leukemia (AML) and chronic myeloid leukemia, with higher STING expression correlated with poorer prognosis in AML. STING expression was positively correlated with immune-related pathways such as IFN-gamma response, IFN-alpha response, and inflammatory response. Cytolytic score and STING expression were positively correlated in some hematological tumors, especially chronic lymphocytic leukemia and mantle cell lymphoma. Interestingly, STING expression was negatively correlated with TIDE biomarkers in AML, suggesting that AML patients with a high STING expression level may benefit from immunologic treatment. Our findings contribute a molecular characterization of STING across hematological malignancies, facilitating the development of individualized prognosis and treatment strategies.

Keywords: STING; hematological malignancies; immunotherapy; prognosis; tumor immune microenvironment.

Figure 1

Variation in expression of STING. (A) Utilizing the HPA database, STING exhibited high expression levels in respiratory system tissues, bone marrow, and lymphoid tissues. (B) STING expression was most pronounced in CD4+ T cells, followed by HSCs and T/NK cells in the Hemap database. (C) Analysis of the CCLE database indicated that STING expression levels were comparatively elevated in AML and CML compared with other tumor cell lines. (D) RT-qPCR analysis demonstrated a markedly increased expression of STING in the THP-1, NOMO-1, and K562 cell lines from our laboratory. (E) STING was overexpressed in AML compared with other hematological malignancies when exploring STING expression in the Hemap database. (F) The expression status of the STING gene in various types of cancers in TCGA and GTEx databases. (G) Differential expression of STING between hematological malignancies and normal tissues using the Pan-Hem-Diff data. (*P < 0.05, **P < 0.01, ***P < 0.001).

Figure 2

The relationship between STING expression and patient prognosis. (A-C) Forest plots of the relationship between STING expression and survival prognosis in DLBCL patients (A), AML patients (B) and MM patients (C) through a meta-analysis of Cox regression values, where high-risk genes have HR>1 and low-risk genes have HR<1. (D-G) Kaplan-Meier curves for EFS and OS of AML patients in GSE10358. (H-K) Kaplan-Meier curves for EFS and OS of AML patients in GSE6891. EFS = event-free survival; OS = overall survival; NK = normal karyotype. Orange and blue indicate high and low STING expression groups, respectively.

Figure 3

Correlations between STING expression and hallmark signaling pathways. (A) Bubble plot of the correlations between STING expression and hallmark gene sets among blood cancer samples in the Hemap dataset. The color indicates the direction of the correlation, positive correlations are shown in red, while negative correlations are represented in blue. The size of the circles indicates the correlation significance level, with larger circles representing lower P-values. (B) GSVA enrichment analysis using scRNA-seq data from AML patients with low and high STING expression (GSE116256). The signaling pathways on the left were down-regulated, whereas those on the right were up-regulated.

Figure 4

Correlations between the tumor immune microenvironment and STING expression. (A) The bubble chart showed the relationship between STING expression and 29 TME signatures across hematological malignancies in the Hemap database. (B) Each pie chart showed the correlation between STING expression and immune cell distribution in each hematologic malignancy based on CIBERSORT or MCP-counter analysis. Positive and negative associations are shown on a scale from orange to blue. (C) The bubble chart showed the relationship between STING expression and immune genes across hematological malignancies. (D-I) Association of cytolytic score (D), HLA I score (E), HLA II score (F), immune score (G), stromal score (H), and tumor purity (I) with STING expression in hematological neoplasms. (*p < 0.05, **p < 0.01, ***p < 0.001).

Figure 5

Investigation of the response of STING expression to immunotherapy. (A-D) Correlations between STING expression and other immune checkpoints in BeatAML (A), TCGA AML (B), MDS (GSE58831) (C), CLL (GSE22762) (D). (E-H) The differences in STING expression profile between the non-responder and responder group in BeatAML (E), TCGA AML (F), MDS (GSE58831) (G), and CLL (GSE22762) (H).

Figure 6

Stemness and response of STING expression to immunotherapy. Correlations of (A, B) LSC17 (A) and mRNAsi (B) score with STING expression in hematological neoplasms. (C-F) Patients with higher STING expression exhibited stronger responses to ICB in real-world immunotherapy cohorts encompassing four cancer types. (*p < 0.05, **p < 0.01, ***p < 0.001).