DDOST is associated with tumor immunosuppressive microenvironment in cervical cancer

Abstract

Evidence has revealed that DDOST plays an important role in cancer development and progression. However, there are no reports on functions of DDOST in cervical tumorigenesis. Hence, we investigated the relationship of DDOST with prognosis, mutation, promoter methylation, immune cell infiltration, and drug sensitivity using bioinformatics techniques. Our results demonstrated that DDOST was significantly upregulated in a variety of tumor types and correlated with poor prognosis, including cervical cancer. Cox regression analysis dissected that high DDOST expression was associated with poor survival in cervical cancer patients. Immune infiltration analysis defined that DDOST was negatively correlated with CD8 T cells and NK cells. Strikingly, the sensitivity to multiple drugs was negatively correlated with the expression of DDOST. Therefore, our findings uncovered that DDOST could play an essential role in the tumor microenvironment and tumor immune regulation in cervical cancer, which indicated that DDOST could be a useful biomarker for prognosis and a potential therapeutic target for cancer treatment.

Keywords: Biomarker; Cervical cancer; DDOST; TME; Treatment.

Fig. 1

DDOST expression analysis in Pan-cancer. A, Pan-cancer differential expression of DDOST between tumor tissues from TCGA and normal tissues from TCGA and GTEx database is illustrated. B, DDOST expression in tumor tissues from TCGA database. C, DDOST expression in normal tissues from GTEx database. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Fig. 2

DDOST expression in single cell sequence data. A. The differential expression of DDOST between tumor tissues and normal tissues was presented. B. DDOST expression in single cell sequence data of cervical cancer using TISCH database. *P < 0.05, **P < 0.01, ***P < 0.001

Fig. 3

The gene alteration of DDOST in pan-cancer. A, CNA and mutation frequency of DDOST in TCGA pan-cancer were accessed from cBioPortal database. B, The correlation of DDOST mRNA expression and linear copy-number value in indicated tumor types from TCGA. C, The correlation of DDOST mRNA expression and promoter methylation level in indicated tumor types from TCGA

Fig. 4

Univariate regression analysis of DDOST in pan-cancer. A–D, Forest map shows the univariate cox regression analysis results of DDOST in TCGA pan-cancer, including OS (A), DSS (B), DFI (C), and PFI (D)

Fig. 5

Overall survival analysis of DDOST in pan-cancer. Kaplan–Meier survival analysis results of DDOST in indicated tumor types from TCGA pan-cancer

Fig. 6

GSVA of DDOST. The GSVA results of DDOST based on 50 HALLMARK pathways in pan-cancer. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Fig. 7

GSEA of DDOST. A–B, The expression of top 50 genes positively or negatively associated with DDOST expression in cervical cancer. C–E The top 20 GSEA results of DDOST were showed based on GO (C), KEGG (D), and Reactome (E) pathways in cervical cancer

Fig. 8

TME analysis of DDOST. A. The correlation of DDOST with indicated TME-related pathways in pan-cancer. B. The score of indicated TME-related pathways in high- and low-DDOST expression group in cervical cancer. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Fig. 9

Immune infiltration analysis. A–B, The relationship between DDOST expression and infiltration levels of indicated immune cells in cervical cancer. C. The infiltration levels of indicated immune cells in high- and low-DDOST expression group in cervical cancer. *P < 0.05, **P < 0.01, ****P < 0.0001. P = 0 means p < 0.0001

Fig. 10

Drug resistance analysis of DDOST. The correlation between DDOST expression and IC50 values of indicated anti-cancer drugs. P = 0 means p < 0.0001

Fig. 11

Experimental verification of DDOST expression in cervical cancer A, The expression of DDOST in indicated cell lines via qRT-PCR assay. B–C, The protein expression of DDOST in cervical and tumor tissues via immunohistochemical assay