Down-regulation of ECRG4, a candidate tumor suppressor gene, in human breast cancer

Abstract

Introduction: ECRG4/C2ORF40 is a potential tumor suppressor gene (TSG) recently identified in esophageal carcinoma. Its expression, gene copy number and prognostic value have never been explored in breast cancer.

Methods: Using DNA microarray and array-based comparative genomic hybridization (aCGH), we examined ECRG4 mRNA expression and copy number alterations in 353 invasive breast cancer samples and normal breast (NB) samples. A meta-analysis was done on a large public retrospective gene expression dataset (n = 1,387) in search of correlations between ECRG4 expression and histo-clinical features including survival.

Results: ECRG4 was underexpressed in 94.3% of cancers when compared to NB. aCGH data revealed ECRG4 loss in 18% of tumors, suggesting that DNA loss is not the main mechanism of underexpression. Meta-analysis showed that ECRG4 expression was significantly higher in tumors displaying earlier stage, smaller size, negative axillary lymph node status, lower grade, and normal-like subtype. Higher expression was also associated with disease-free survival (DFS; HR = 0.84 [0.76-0.92], p = 0.0002) and overall survival (OS; HR = 0.72 [0.63-0.83], p = 5.0E-06). In multivariate analysis including the other histo-clinical prognostic features, ECRG4 expression remained the only prognostic factor for DFS and OS.

Conclusions: Our data suggest that ECRG4 is a candidate TSG in breast cancer, the expression of which may help improve the prognostication. If functional analyses confirm this TSG role, restoring ECRG4 expression in the tumor may represent a promising therapeutic approach.

Figure 1. mRNA expression of ECRG4 in breast cancer.

(A) Thumbnail of the hierarchical clustering of the 353 breast cancers and 4 NB samples (columns) and the 12,304 most variable genes (rows). According to a log₂ pseudocolor scale (bottom), red indicates a high level of mRNA expression compared to the median value across all samples, whereas green indicates a low level of expression. The magnitude of deviation from the median is represented by the colour saturation. The dendrogram of samples (above matrixes) represents overall similarities in gene expression profiles and is zoomed in B. Green branches indicate the 4 NB samples. To the right of the color matrix, are represented some biologically relevant gene clusters. The extra-cellular matrix (ECM)-related cluster, which includes ECRG4, is detailed in C. (B) Samples dendogram. Green branches indicate the 4 NB samples. Under the dendogram are reported some histo-clinical tumor features colored as below: ER IHC status (white, negative, and black, positive); ERBB2 IHC status (white, negative, and black, positive); SBR Grade (white, 1, grey, 2; and black, 3molecular subtypes (dark blue, luminal A, light blue, Luminal B, pink, ERBB2, red, basal-like, and green, normal-like). Some molecular features regarding ECRG4 are represented below: mRNA expression level (median-centered and color-coded as in A), expression status as compared to NB (overexpression, black, neutral, grey, and underexpression, white), and aCGH-based copy number alteration (CNA: gain, black, neutral, grey, and loss, white). (C) Details of the genes belonging to the ECM gene cluster.

Figure 2. mRNA expression of ECRG4 according to breast cancer molecular subtypes.

ECRG4 expression across 1,387 breast cancer samples was examined according to molecular subtypes. Box plots of ECRG4 expression are shown according to basal, ERBB2, luminal A, luminal B, and normal-like subtypes. Expression values are NB-centered. The horizontal black line represents the level of expression of ECRG4 in normal breast (NB) tissue. Differences in ECRG4 expression levels between the subtypes were tested for significance using one-way ANOVA. For each box plot, median and ranges are indicated.

Figure 3. Disease-free and overall survivals according to ERCG4 mRNA expression.

(A) Kaplan-Meier DFS curves in patients with high and low expression (cut-off defined with Cox proportional-hazards regression model built on the IPC data). The respective 5-year DFS are 73 and 63%. (B) Kaplan-Meier OS curves (the legend is similar to A). The respective 5-year DFS are 88 and 74%.