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. 2021 May 7:8:613359.
doi: 10.3389/fmolb.2021.613359. eCollection 2021.

Identification of a Hypoxia-Related Signature for Predicting Prognosis and the Immune Microenvironment in Bladder Cancer

Minxiao Jiang  1 , Liangliang Ren  1 , Yuanlei Chen  1 , Huan Wang  1 , Haiyang Wu  1 , Sheng Cheng  1 , Gonghui Li  1 , Shicheng Yu  1
Affiliations

Identification of a Hypoxia-Related Signature for Predicting Prognosis and the Immune Microenvironment in Bladder Cancer

Minxiao Jiang et al. Front Mol Biosci. .

Abstract

Accumulating evidence indicates that hypoxia is highly associated with bladder cancer genesis, progression, and immune microenvironment. Nevertheless, few studies have identified the role of hypoxia-related genes as a prognostic signature in bladder cancer. This study aimed to establish a hypoxia-related signature with high accuracy for prognosis and immune microenvironment prediction in bladder cancer. We obtained expression profiles and clinical information from Gene Expression Omnibus and The Cancer Genome Atlas. Then the univariate Cox regression, random survival forest algorithm, and multivariate Cox regression analysis were conducted to identify the core genes and four hypoxia-related genes (ANXA2, GALK1, COL5A1, and HS3ST1) were selected to construct the signature. Kaplan-Meier survival analysis demonstrated that patients with a low-risk score had a higher disease-specific survival rate (p < 0.0001). The areas under the curve of the signature were 0.829 at 1 year, 0.869 at 3 years, and 0.848 at 5 years, respectively. Additionally, we found this hypoxia-related signature was highly correlated with tumor immune microenvironment and had the potential to predict the efficacy of immunotherapy. In summary, our study developed a hypoxia-related signature, which had high accuracy for prognosis prediction and the potential to guide the immunotherapy for bladder cancer patients.

Keywords: bladder cancer; disease-specific survival; hypoxia; immune microenvironment; signature.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The workflow chart of the study.
FIGURE 2
FIGURE 2
Validation of the expression of hub genes after culturation in hypoxia environment (A) J82 cell line (B) TCC cell line.
FIGURE 3
FIGURE 3
Assessment of the prognostic ability of the hypoxia-related signature in the discovery cohort (A) DSS status and core genes expression heatmap in two risk groups (B) Kaplan-Meier survival analysis according to the signature (C) Time-dependent ROC analysis for DSS prediction.
FIGURE 4
FIGURE 4
Validation of the prognostic ability of the hypoxia-related signature for DSS prediction in validation cohorts. (A) Kaplan-Meier curves for patients in GSE32548 cohort; (B) Time-dependent ROC curves for patients in GSE32548 cohort; (C) Kaplan-Meier curves for patients in GSE13507 cohort; (D) Time-dependent ROC curves for patients in GSE13507 cohort; (E) Kaplan-Meier curves for patients in GSE48075 cohort; (F) Time-dependent ROC curves for patients in GSE48075 cohort.
FIGURE 5
FIGURE 5
Validation of the prognostic ability of the hypoxia-related signature for OS prediction in validation cohorts. (A) Kaplan-Meier curves for patients in GSE13507 cohort; (B) Time-dependent ROC curves for patients in GSE13507 cohort; (C) Kaplan-Meier curves for patients in GSE48075 cohort; (D) Time-dependent ROC curves for patients in GSE48075 cohort; (E) Kaplan-Meier curves for patients in TCGA-BLCA cohort; (F) Time-dependent ROC curves for patients in TCGA-BLCA cohort.
FIGURE 6
FIGURE 6
The relationship between risk score and clinical characteristics. (A) The relationship between risk score and gender; (B) Kaplan-Meier curves for patients in GSE32894 cohort based on gender; (C) The relationship between risk score and tumor grade; (D) Kaplan-Meier curves for patients in GSE32894 cohort based on tumor grade; (E) The relationship between risk score and tumor stage; (F) Kaplan-Meier curves for patients in GSE32894 cohort based on tumor stage.
FIGURE 7
FIGURE 7
Establishment and assessment of the nomogram in the discovery cohort (A) The signature is the only independent fact in GSE32894 cohort (B) Nomogram based the signature and clinical characteristics (C) The 3-years and 5-years calibration plots for the nomogram (D) Time-dependent ROC analysis for the nomogram.
FIGURE 8
FIGURE 8
GSEA analysis between two risk groups (A) GO analysis (B) KEGG analysis.
FIGURE 9
FIGURE 9
The relationship between hypoxia-related signature and TME (A) The heatmap of 28 immune cells infiltration (B–D) The relationships between the risk score and ESTIMATEScore, ImmuneScore, and StromalScore.
FIGURE 10
FIGURE 10
The relationship between hypoxia-related signature and immune checkpoints (A) The chord diagram used to visualize the relationship between risk score and immune checkpoints expression (B) The expression characteristics of immune checkpoints in different risk groups (C) Immunohistochemistry detection of CTLA4 in bladder cancer tissues (D) Immunohistochemistry detection of LAG3 in bladder cancer tissues.
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