ARNTL2 is a Prognostic Biomarker and Correlates with Immune Cell Infiltration in Triple-Negative Breast Cancer

Abstract

Background: Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and is associated with poor prognosis. The aberrant expression of circadian genes contributes to the origin and progression of breast cancer. The present study was designed to explore the potential function and prognosis value of circadian genes in TNBC.

Methods: The transcriptome data of circadian genes were downloaded from The Cancer Genomic Atlas (TCGA), GSE25066 and GSE31448 datasets. The differential expressed circadian genes between non-TNBC and TNBC patients were analysed by Wilcoxon test. Univariate and multivariate Cox regression analyses were employed to identify the prognostic circadian genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were performed to study the biological functions of ARNTL2. The composition of 22 immune cells in the tumour samples was estimated with CIBERSORT algorithm. The correlations between ARNTL2 expression and tumour-infiltrating immune cells were evaluated by Spearman correlation coefficient.

Results: A total of 8 circadian genes were found to be differentially expressed between non-TNBC and TNBC, but only ARNTL2 has prognostic value. Multivariate Cox analysis identified that ARNTL2 was an independent prognosis factor for overall survival and relapse-free survival in TNBC patients. Functionally, ARNTL2 was mainly involved in immune response processes such as positive regulation of cytokine production, regulation of innate immune response, and cellular responses to molecules of bacterial origin. High expression of ARNTL2 was positively correlated with activated CD4 memory T cells, activated mast cells, and neutrophil infiltration and the expression of markers of neutrophils (ITGAM), dendritic cells (HLA-DRA, HLA-DPA1, ITGAM), Th1 (IL1B, STAT1), Th2 (IL13), Th17 (STAT3) and mast cells (TPSB2, TPSAB1).

Conclusion: ARNTL2 may be linked with the functional modulation of the tumour immune microenvironment and serve as a potential biomarker for predicting the prognosis of TNBC patients.

Keywords: ARNTL2; biomarker; circadian rhythm; immune cell infiltration; triple-negative breast cancer.

Figure 1

The differential expression circadian genes between non-TNBC and TNBC patients. (A–C) Heat maps showed the differential expression of circadian genes between non-TNBC and TNBC in TCGA (A), GSE25066 (B) and GSE31448 (C) dataset, respectively. (D) The Venn diagram showed the common differential expression genes among the three datasets. *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 2

The prognostic value of ARNTL2 in TNBC patients. (A–C) Univariate Cox regression analysis showed the association between ARNTL2 expression and overall survival in patients with TNBC from the TCGA dataset. (A), Lum (B) and HER-2 (C) subtype. (D–F) KM survival analysis was performed to evaluate the difference of overall survival time between high and low groups of ARNTL2 expression in patients with TNBC from the TCGA dataset. (D), Lum (E) and HER-2 (F) subtype. (G) ARNTL2 expression distribution and survival status in TNBC patients. Upper panel: The y-axis shows the ARNTL2 expression levels, and the x-axis shows the different patients. The green dots indicate the patients of ARNTL2 low expression, and the red dots represent the patients of ARNTL2 high expression. Lower panel: The y-axis shows the survival time, and the x-axis shows the different patients. The green dots indicate the patients alive, and the red dots represent the patients of the dead. (H) ROC curves of ARNTL2.

Figure 3

The prognostic value of ARNTL2 in the SYSUCC cohort. (A) The representative pictures of low and high ARNTL2 expressions were shown. (B and C) KM survival curve showed the differential overall survival (B) and relapse-free survival (C) between the high and low group of ARNTL2 expression in the SYSUCC cohort.

Figure 4

Functional characterize of ARNTL2 in TNBC. (A) Heatmap showed the top 50 co-expression genes of ARNTL2 in the TCGA dataset. The criteria for co-expression genes is Pearson correlation coefficient ≥ 0.3 or ≤ −0.3. (B) The circular plot indicated the top five genes positively and negatively correlated with ARNTL2. Green means negative correlation, and red means positive correlation. (C) GO enrichment analysis of the co-expression genes of ARNTL2. The y-axis shows the enriched GO term, and the x-axis shows the numbers of the core genes. The numbers inside the dots indicate the numbers of core genes, and the colour of the dots represent the adjusted p-value. (D) KEGG analysis of the co-expression genes of ARNTL2. The y-axis indicates the enriched KEGG pathways, and the x-axis shows the numbers of the core genes. The colour of the bars represents the adjusted p-value.

Figure 5

Correlation between ARNTL2 expression and immune cell infiltration in data from TIMER. The scatter plot showed the association between ARNTL2 expression and Purity and the immune cell infiltration of B cell, CD8+ T cell, CD4+ T cell, Microphage, Neutrophil, and Dendritic cell in different breast cancer subtypes (basal, luminal, and HER-2).

Figure 6

CIBERSORT analysis of the relationship between ARNTL2 expression and 22 immune cell infiltration in data from TCGA. (A) The stacked bar chart shows the relative fiction of 22 immune cells in each sample. (B–D) The correlation between ARNTL2 expression and activated CD4 memory T cells (B), activated Mast cells (C), and Neutrophils (D). (E) The correlation among different immune cells. The red colour indicates a positive correlation and the blue colour shows a negative correlation.

Figure 7

ARNTL2 expression correlates with activated CD4 memory T cell and neutrophil infiltration in multiple cancer types. (A) Heatmap showed the association between ARNTL2 expression and activated CD4 memory T cell infiltration in multiple tumor types. (B) Heatmap showed the association between ARNTL2 expression and Neutrophil and Mast cell infiltration in multiple cancer types. The row name represents the abbreviation and the sample number of 32 cancer types, the column name represents the method used to calculate the infiltration levels of immune cells.