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. 2023 Jan 5:13:1104338.
doi: 10.3389/fgene.2022.1104338. eCollection 2022.

A circadian rhythm-related gene signature for prognosis, invasion and immune microenvironment of breast cancer

Mei-Huan Wang  1 , Xiao Liu  1 , Qian Wang  1   2 , Hua-Wei Zhang  1   2
Affiliations

A circadian rhythm-related gene signature for prognosis, invasion and immune microenvironment of breast cancer

Mei-Huan Wang et al. Front Genet. .

Abstract

Background: Circadian dysregulation is linked to the onset and progression of cancer, but current knowledge of the role of circadian rhythm-related genes (CRRGs) in breast cancer (BC) is limited and incomplete. The purpose of this study was to investigate the potential role and immune-related prognostic significance of CRRGs in BC. Methods: The Cancer Genome Atlas breast cancer (TCGA-BRCA) genetic data were combined with 1369 CRRGs to create a model of BC prognosis-related CRRGs. To validate the model's predictive power in TCGA and other external datasets, the Kaplan-Meier survival curve and receptor operation characteristic curve were plotted. The relationship between CRRGs model and gene enrichment pathways, immune cell infiltration, and differences in patient response to immune checkpoint inhibitors (ICIs) therapy was then discussed. Results: A CRRG-based eighteen-gene model was developed that accurately predicted the survival time of BC patients. Based on this model, BC patients can be classified as high or low risk. The high-risk group has negative immune cell infiltration (such as macrophages M0 and M2) and a poor therapeutic response to ICIs due to lower immune checkpoint gene expression. Furthermore, TCF7 and IFNG were found to be strongly associated with immune checkpoints in CRRGs model. Conclusion: The 18 CRRGs may be useful in assessing the prognosis of BC patients, studying immune infiltration, and developing more effective immunotherapy strategies.

Keywords: TCGA; breast cancer; circadian rhythm; immunity; prognosis.

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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
Flow diagram of this study.
FIGURE 2
FIGURE 2
(A) The LASSO Cox regression model was utilized to identify CRRGs. (B) Select range of the optimal parameter (lambda) in the LASSO Cox regression model. (C) The coefficient of the selected CRRGs. (D) Expression of 18 CRRGs in the high and low risk groups. (E) The distribution of the CRRGs risk score in different TNM stages of TCGA-BRCA dataset.
FIGURE 3
FIGURE 3
CRRGs signature associated with BC patient’s overall survival (OS). (A) The predictive value for the 1-y, 2-y and 3-y OS in TCGA-BRCA dataset. (B) The predictive value for the 1-y, 2-y and 3-y OS in GSE20685 dataset. (C) The OS between the CRRGs high- and low-risk groups in TCGA-BRCA dataset. (D) The OS between the CRRGs high- and low-risk groups in GSE20685 dataset. (E) The risk plot of the CRRGs signature in TCGA-BRCA dataset. (F) The risk plot of the CRRGs signature in GSE20685 dataset.
FIGURE 4
FIGURE 4
CRRGs signature associated with BC patient’s disease-free survival (DFS) and metastasis-free survival (MFS). (A) The predictive value for the 1-y, 2-y and 3-y DFS in GSE21653 dataset. (B) The predictive value for the 1-y, 2-y and 3-y MFS in GSE58812 dataset. (C) The DFS between the CRRGs high- and low-risk groups in GSE21653 dataset. (D) The MFS between the CRRGs high- and low-risk groups in GSE58812 dataset. (E) The risk plot of the CRRGs signature in GSE21653 dataset. (F) The risk plot of the CRRGs signature in GSE58812 dataset.
FIGURE 5
FIGURE 5
(A) The construction of OS predictive nomogram for TCGA-BRCA patients. (B–D) 1-year, 2-year, and 3-year calibration curves of the nomogram combined model in TCGA-BRCA dataset.
FIGURE 6
FIGURE 6
Enrichment analysis based on CRRGs risk model (A) Bubble plot for GO enrichment analysis based on CRRGs. (B) Bar chart of KEGG enrichment analysis based on CRRGs. (C) Gene enrichment analysis of CRRGs in the TCGA-BRCA dataset.
FIGURE 7
FIGURE 7
Correlation analysis of the expression of eighteen CRRGs in BC with the level of complex immune infiltration. (A) Differences of the abundance of 22 immune cells between high- and low-risk groups in the TCGA-BRCA dataset. (B) Correlation heat map between immune checkpoints and eighteen CRRGs. (C) Chord plot of the correlation between immune checkpoints and risk scores. (D) Correlation between TCF7 expression levels in BC and three immune checkpoints. (E) Correlation between IFNG expression levels in BC and three immune checkpoints.
FIGURE 8
FIGURE 8
(A) Relative probability of response to CTLA-4 treatment in low-risk and high-risk groups. (B) Relative probability of response to PD-1/PD-L2 treatment in low-risk and high-risk groups. Expression of (C) CTLA-4, (D) PD-L1 and (E) pdcd1LG2(PD-L2) in low- and high-risk groups.
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