BMB Reports 2021; 54(10): 497-504  https://doi.org/10.5483/BMBRep.2021.54.10.087
Prognostic role of EGR1 in breast cancer: a systematic review
Subbroto Kumar Saha1,$,#,* , S. M. Riazul Islam2,#, Tripti Saha1, Afsana Nishat3, Polash Kumar Biswas1, Minchan Gil1, Lewis Nkenyereye4, Shaker El-Sappagh5, Md. Saiful Islam6 & Ssang-Goo Cho1,*
1Department of Stem Cell and Regenerative Biotechnology, Konkuk University, Seoul 05029, 2Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea, 3Department of Microbiology & Cell Science, University of Florida, Gainesville, FL 32611, USA, 4Department of Computer and Information Security, Sejong University, Seoul 05006, Korea, 5Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain, 6School of Information and Communication Technology, Griffith University, QLD 4222, Australia
Correspondence to: Subbroto Kumar Saha, Tel: +1-410-413-0532; Fax: +1-916-453-2288; E-mail: sksaha@ucdavis.edu; Ssang-Goo Cho, Tel: +82-2-450-4207; Fax: +82-2-444-4207; E-mail: ssangoo@konkuk.ac.kr
#These authors contributed equally to this work.
$Present address: Department of Biochemistry and Molecular Medicine, University of California at Davis, School of Medicine, Sacramento, CA 95817, USA
Received: July 7, 2021; Revised: August 5, 2021; Accepted: August 16, 2021; Published online: October 31, 2021.
© Korean Society for Biochemistry and Molecular Biology. All rights reserved.

cc This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
EGR1 (early growth response 1) is dysregulated in many cancers and exhibits both tumor suppressor and promoter activities, making it an appealing target for cancer therapy. Here, we used a systematic multi-omics analysis to review the expression of EGR1 and its role in regulating clinical outcomes in breast cancer (BC). EGR1 expression, its promoter methylation, and protein expression pattern were assessed using various publicly available tools. COSMIC-based somatic mutations and cBioPortal-based copy number alterations were analyzed, and the prognostic roles of EGR1 in BC were determined using Prognoscan and Kaplan-Meier Plotter. We also used bc-GenEx- Miner to investigate the EGR1 co-expression profile. EGR1 was more often downregulated in BC tissues than in normal breast tissue, and its knockdown was positively correlated with poor survival. Low EGR1 expression levels were also associated with increased risk of ER+, PR+, and HER2- BCs. High positive correlations were observed among EGR1, DUSP1, FOS, FOSB, CYR61, and JUN mRNA expression in BC tissue. This systematic review suggested that EGR1 expression may serve as a prognostic marker for BC patients and that clinicopathological parameters influence its prognostic utility. In addition to EGR1, DUSP1, FOS, FOSB, CYR61, and JUN can jointly be considered prognostic indicators for BC.
Keywords: Breast cancer, Cancer progression, EGR1, Methylation, Microarray, Prognosis, TCGA, Tumor suppressor
INTRODUCTION

Breast cancer (BC) is the most commonly occurring invasive cancer in women worldwide and the second leading cause of cancer-related deaths in women after lung cancer. Although the overall methods for screening, diagnosis, and treatment of BC have improved in recent years, prognosis remains poor (1). More than one million new cases of BC are reported per year, and the risk of an individual dying from this life-threatening disease is 1/35 (2). Therefore, the identification of more effect-ive and specific biomarkers for the prognosis of BC patients is of paramount importance. The development of BC is usually attributed to multi-gene mutations (3). Molecular targeted treatments have recently transformed the therapeutic approach for various tumors. To adopt a targeted therapy for the treatment of BC patients, it is critical to better understand the status of various molecular processes, such as gene expression and methylation of the related genes.

The early growth response 1 (EGR1) gene encodes a protein belonging to the early growth response (EGR) protein family, a family of zinc finger transcription factors. Various cytokines, hormones, and DNA-damaging agents can temporarily activate EGR1, and EGR1 itself functions as a transcriptional regulator (4). Moreover, EGR1 is a direct regulator of several tumor suppressors, such as transforming growth factor beta 1 (TGFβ1), tumor protein P53 (p53), and phosphatase and tensin homolog (PTEN). In addition, EGR1 is highly overexpressed in colorectal, gastric, liver, and uterine cervical cancer, which is associated with distant metastases and poor survival (5-8). On the other hand, EGR1 has been identified as a tumor suppressor in rhabdomyosarcoma, and it was reported that overexpression of EGR1 prevents proliferation, mobility, and anchorage-independent growth of rhabdomyosarcoma cells (9). The up-regulation of EGR1 has also been reported to arrest cell cycle progression in BC cells (10). In molecular targeted therapy approaches, this gene has been suggested as a potential target for prostate cancer (11). In patients with non-small cell lung cancer, suppressed EGR1 expression is directly associated with poor survival through attenuating PTEN expression following surgical resection (12). In another study, knockdown of EGR1 increased lung cancer cell proliferation by directly suppressing cytokeratin 18 (KRT18) expression (13). Suppression of EGR1 has the potential to induce the proliferation of hematopoietic stem cells in mice bone marrow (14) and prevents glioma proliferation via down-regulation of CCND1 (Cyclin D1) promoter activity (15). In addition to these roles in different types of cancers, the association of EGR1 with diagnosis and clinical outcomes in BC patients has attracted much research attention worldwide. Loss of EGR1 expression can potentially prevent the activation of the multidrug resistance protein 1 (MDR1) promoter in paclitaxel-resistant MCF7 cells and thus, can regulate MDR1 expression (16). Overexpression of MDR1 results in multidrug resistance, which leads to failure of BC chemotherapy. Downregulation of EGR1 is, therefore, associated with poor prognosis in BC, labeling EGR1 as a cancer suppressor gene (16-18). A previous study (19) suggested that EGR1 can regulate BC cell metabolism and may be a promising target to prevent endocrine resistance.

The EGR1 gene is associated with the pathogenesis of various tumors, including breast tumors (10). However, its prognostic value in BC is controversial. Despite the reasonable volume of related research, the application of EGR1-assisted targeted therapy is still in the early stages, and the use of its expression level as a prognostic marker in BC is an area of active investigation. Therefore, in this study, we sought to investigate the roles of EGR1 in BC. In this study, we systematically reviewed the biomarker utility and prognostic significance of EGR1 in human BC using multiomics analysis. We compre-hensively analyzed EGR1 expression pattern, its promoter methylation status, various functions, and different prognostic impacts on BC using all currently available gene expression data. This multiomics analysis ultimately demonstrated that EGR1 expression can be adopted as a biomarker for the prognosis of BC patients.

SEARCH STRATEGY AND METHODS

We performed a PubMed and Scopus literature search until June 2021 using keywords: EGR1 and cancer/breast cancer (BC), signaling pathway, treatment, and therapeutics. In this review, we included English language articles focused on EGR1-related BC progression and prognosis, and its therapeutic applications.

EGR1 mRNA expression in various cancers was analyzed and displayed using the Oncomine platform (https://www.oncomine.org/resource/login.html; accessed February 2021) (20-23). The default threshold parameters were selected, which consisted of p-value, 1E-4; fold-change, 2; and gene ranking in the top 10%. Statistical analysis was performed using an unpaired t-test and P < 0.05 was considered significant. Genes co-expressed with EGR1 were retrieved from the Oncomine database. Co-expression heatmap data for EGR1 were downloaded from the Oncomine database.

TCGA data regarding EGR1 mRNA expression in human BC was analyzed and displayed using the UALCAN web tool (http://ualcan.path.uab.edu/index.html; accessed July 2020) (23, 24). Statistical analysis was performed using a Student’s t-test and P < 0.05 was considered significant.

TCGA data regarding EGR1 mRNA expression in human BC was analyzed using UCSC Xena. The TCGA RNA-seq data of EGR1 mRNA expression was downloaded from UCSC Xena (https://xenabrowser.net/heatmap/; accessed January 2021) (25) for BC subcategories, including PAM50 subtypes, clinical subtypes, and stages. The raw data were reanalyzed and plotted by GraphPad Prism v9.0 (GraphPad, San Diego, CA, USA). Statistical analysis was performed using an unpaired t-test with Welch’s corrections for two groups and one-way ANOVA for multi groups. P < 0.05 was considered significant.

EGR1 protein expression in BC and its normal tissue was analyzed by immunohistochemistry (IHC). The tissue images were downloaded from the human protein atlas web (https://www.proteinatlas.org/; accessed January 2021) (26, 27). The antibody (CAB019427) against EGR1was used for IHC analysis. The intensity of EGR1 expression was measured using ImageJ following Crowe et al.’s protocol (28), then the data were calculated and plotted using Prism 7 (GraphPad).

Median methylation level of the EGR1 gene promoter in human BC was analyzed using TCGA (Methylation 450K) data through the TCGA Wanderer web tool (http://maplab.imppc. org/wanderer/; accessed July 2020) (29, 30). Statistical analysis was performed using an unpaired t-test with Prism 7 software (GraphPad), and P < 0.05 was considered significant.

The Catalog of Somatic Mutations in Cancer (COSMIC) web resource (https://cancer.sanger.ac.uk/cosmic) (31) was used to analyze EGR1 protein somatic mutations in human cancer. A pie-chart was constructed showing the percentage of different EGR1 mutation types in BC. The cBioPortal web tool (http://www.cbioportal.org/; accessed July 2020) (32, 33) was also used to analyze the frequency of mutations and their location in the EGR1 protein in BC.

Survival analysis of BC patients with high or low EGR1 mRNA expression levels was performed using the PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/; accessed July 2020) (34) and Kaplan-Meier Plotter (http://kmplot.com/analysis/; accessed January 2021) (35). Survival plots, log-rank P-values, and hazard ratios (HRs) with 95% confidence inter-vals (CI) were retrieved from the online tools. A log-rank P-value < 0.05 was considered significant.

The co-expression of EGR1, DUSP1, FOS, FOSB, CYR61, and/or JUN genes was analyzed using the UCSC Xena web tool (http://xena.ucsc.edu/; accessed January 2021) (36), with the TCGA BC cohort (TCGA-BRCA). Heatmaps and regression analyses of the co-expressed genes were retrieved from the UCSC Xena tool. Pearson and Spearman correlation analyses were also performed.

Co-expression between EGR1 or other genes was analyzed and displayed using bcGenExMiner v4.1 (http://bcgenex.centregauducheau.fr/BC-GEM/GEM-Accueil.php?js=1 accessed January 2021) (37). Statistical analysis was performed using a Welch’s test with a Dunnett-Tukey-Kramer’s test and P < 0.05 was considered significant.

EGR1 PROTEIN STUCTURE AND ITS BIOLOGICAL ROLES

The EGR1 protein contains 543 amino acids in humans, consisting of three Cysteine 2-Histidine 2 (C2H2) zinc fingers DNA-binding domains (Fig. 1A) (38). It also contains a strong activation domain, repressor domain (also known as NAB binding site), a nuclear localization domain, and a weak activation domain. Protein kinases and phosphatases controls the phosphorylation of the different EGR1 domains (39). The protein activates or represses specific genetic programs based on its “phosphorylation/acetylation pattern”. The T309 and S350 sites are phosphorylated by protein kinase B (PKB, alias AKT); whereas S378, T391, and T526 sites are phosphorylated by casein kinase II (38). Depending on its post-translational modification statues, EGR1 shows various transcriptional activation or repression functions. SUMO1 can be responsible for SUMOylation of EGR1 at K272. Also, the inhibition of Egr1 transcriptional activity can be triggered by transcriptional co-repressors NGFI-A binding proteins NAB1 and NAB2 via binding to the repressor domain.

EGR1 plays a significant role in the growth, proliferation, and differentiation of various types of cells (40, 41). Although the detailed mechanisms are not yet well characterized, EGR1 plays diverse biological roles in cell signaling. High expression of EGR1 is involved in the acute phase of IL-4 transcription elevation in response to T cell receptor stimulation (40). Duclot and Kabbaj reviewed that EGR1 also regulates brain plasticity and neuropsychiatric disorders (42). Overexpression of EGR1 induced synaptic plasticity, wound repair, female reproductive capacity, and apoptosis by upregulating downstream genes (43). Several studies have shown that up-regulation of EGR1 contributes to the suppression of various human cancers progression except for prostate and bladder cancers (11, 12, 15, 44, 45). In addition, a study has claimed that knockdown of EGR1 could inhibit prostate cancer invasion by attenuating IL-8 production, while another study revealed nanotechnology-based EGR1-assisted targeted therapies for preventing cancer development (46, 47). However, the prognostic significance of EGR1 varies depending upon the cancer type. For example, EGR1 is considered oncogenic in prostate cancer (48, 49), whereas it is usually regarded as a tumor suppressor in BC (16, 17). Moreover, the diverse roles of EGR1 expression in the growth and metastasis of particular cancer remain largely unknown.

EGR1 MRNA AND PROTEIN EXPRESSION IN BREAST CANCER (BC)

To investigate the expression level of EGR1 in BC and their normal counterparts, we first determined the mRNA expression pattern of EGR1 using oncomine database. A significant low mRNA expression levels of EGR1 in invasive ductal breast cancer (IDBC) were found (Fig. 1B; Curtis Breast ref. (50). To crosscheck EGR1 mRNA expression in normal breast and BC tissues, we analyzed data from the TCGA database using the UALCAN web tool. These results were in agreement with those obtained from Oncomine-based analyses. Compared to normal tissue, EGR1 expression levels were significant underexpressed in cancer tissue (Fig. 1C). We further examined protein expression patterns of EGR1 in BC using immunohistochemical (IHC) staining via the Human Protein Atlas. These results also confirm the underexpression of EGR1 at the protein levels in BC samples relative to normal breast tissue (Fig. 1D). It is worth to note that the results on Oncomine and ULCAN-driven EGR1 expression pattern in BC tissues agreed with previous study (51).

CLINICOPATHOLOGICAL RELEVANCE OF EGR1 EXPRESSION IN BREAST CANCER (BC) PATIENTS

The analysis on EGR1 transcript expression reported in the preceding section considered the entire expression data for all BC subtypes combined. In clinical practice, however, subtypes of BC may be advantageous in planning overall treatment and developing precise therapies. Here, we therefore aimed to explore the relationship of EGR1 mRNA expression with clinicopathological variables of BC patients.

As presented in Fig. 1E-G, we performed a number of between-class mRNA expression comparisons, including both molecular and clinical subtypes using TCGA data through the UCSC Xena web. In PAM50 molecular subtypes, the lowest level of EGR1 expression was noticed in luminal B type BC, whereas the highest level of EGR1 expression was seen in normal like BC. The results show that the mRNA level of ERG1 could not significantly differentiate luminal A from normal-like BC and HER2-E from luminal B and basal-like BC. For all other cases, however, significant differences in EGR1 expression levels among the molecular subtypes exist (Fig. 1E). In clinical subtypes, the EGR1 expression level can significantly differentiate “ER+ or PR+/HER2−”-type BC from “ER−/PR−/HER2+” and “ER−/PR−/HER2−” subtypes (Fig. 1F). As revealed from the overall staging classification, although stage II BC cannot be significantly differentiated from stage III BC in terms of mRNA expression level, significant differentiation between any two of the remaining combinations are prevailed (Fig. 1G). It can be noted that the stage IV BC showed the lowest level of EGR1 expression compared to the other stages (Fig. 1G). Thus, the clinicopathological results altogether suggest that ER, PR, and HER2 receptors can be targeted in EGR1-mediated targeted therapy.

METHYLATION STATUS AND GENETIC ALTERATIONS OF EGR1 IN BREAST CANCER (BC)

Epigenetic alterations in cancers can regulate gene expression. This regulation depends on the methylation on the gene promoter regions, which subsequently regulate the gene transcription. Hypermethylation on gene promoter prevents the transcription factor binding on the promoter, which eventually inhibits the gene’s transcription. It is previously reported that epigenetic alteration on gene promoter modulates the gene transcription and thus regulates carcinogenesis (52-54). Therefore, we investigated the methylation status of the EGR1 promoter in normal breast and BC tissues using TCGA Wanderer. The EGR1 gene promoter was found to be hypermethylated in BC in all available CpG sites, and most of the results were statistically significant (Fig. 2A). Thus, the abundance of methylation level on the EGR1 promoter region in BC might cause the down-regulation of EGR1 mRNA expression, which was detected using the Oncomine, and UALCAN tools (Fig. 1).

We then focused on the mutations and copy number alterations (CNAs) of EGR1 in BC. Somatic cells can be mutated spontaneously throughout a person’s lifetime. We analyzed somatic mutations in EGR1 in BC using COSMIC. The results of the different types of mutations are presented in Fig. 2B. Of the queried samples, 9 samples were associated with somatic mutations. Most of the somatic mutations cannot show any obvious effect, while few of them can change the key molecular functions in cancer cells (55). The major mutation types were synonymous substitution, missense substitution, frameshift insertion, and nonsense substitution, with rates of 33.33%, 33.33%, 22.22%, and 11.11% of the mutant samples, respectively (Fig. 2B). Of the EGR1 mutations detected in BC tissues, 42.86% were G>C mutations (Fig. 2B). Moreover, we determined the EGR1 mutation frequency in BC using cBioPortal. These results showed that BRCA (INSERM 2016) had the most genetic alterations, accounting for approximately 1% of all samples (Fig. 2C). The mutation sites for EGR1 in BC tissues were located between amino acids 0 and 543, with a hotspot at H334Pfs*13, suggesting that mutations in EGR1 may possess a potential role in BC progression. Moreover, we analyzed the copy number alterations (CNAs) for EGR1 in BC. The results showed that the alterations (due to amplification and deep deletions) occurred mostly in the Breast (TCGA 2015), accounting for approximately 0.6% of all samples (Fig. 2D).

PROGNOSTIC RELEVANCE OF EGR1 EXPRESSION IN BREAST CANCER (BC) PATIENTS

We next investigated whether EGR1 mRNA expression has any potential role on BC prognosis. To find the prognostic relevance of EGR1 in BC, we performed survival analysis using PrognoScan and Kaplan-Meier (KM) Plotter webs. In each type of survival pattern, including overall survival (OS), relapse-free survival (RFS), distant metastasis-free survival (DMFS), and disease-specific survival, low levels of EGR1 expression correlated with poor survival, whereas high levels of EGR1 expression were associated with high survival rates. The Prognoscan-based survival analysis showed a positive correlation between EGR1 down-regulation and poor OS rates in patients with BC (Fig. 3A). Similar correlation characteristics for EGR1 expression were found for other survival types, including DMFS, RFS, disease specific survival, and DFS (Fig. 3B-E). Next, to confirm the relevance of EGR1 expression in BC to patient survival, we performed survival analysis using Kaplan-Meier (KM) Plotter. Like Prognoscan, KM Plotter-based survival analysis also showed that low levels of EGR1 expression were positively correlated with poor survival for RFS and DMFS but not OS (Fig. 3F-H). Also, previous studies reported that EGR1 expression can regulate clinical outcomes in various cancers including gastric and ovarian (56, 57). Furthermore, we analyzed patient survival based on clinical subtypes. Both univariate and multivariate regression analyses confirmed that various clinicopathological parameters further regulate EGR1 expression in BC and thus the clinical outcomes of the patients. Overexpression of EGR1 in ER+/PR+ or ER+/PR+/HER2− BC was positively correlated with high survival rates (Fig. 3I, K), whereas EGR1 up-regulation in ER−/PR− or ER−/PR−/HER2+ BC was associated with poor survival (Fig. 3J, L). The ER−/PR−/HER2− BC patient was not shown any significant difference in patient survival (Fig. 3M). The opposite outcomes of BC with EGR1 expression in terms of clinical subtypes (ER/PR/HER2 status) may be explained as follows. From clinicopathological studies, we observed that individual PR+, ER+, and HER2− BC tissues showed high levels of EGR1 expression, which might intuitively associate with better clinical outcomes in BC patients. In contrast, high EGR1 expression in PR−/ER−/HER2+ type BC should, therefore, naturally be related to poor out-comes. The relationship between EGR1 expression and translational clinical relevance is further highlighted by meta-analysis (Fig. 3N-Q) using KM Plotter. Hazard ratio (HR) of RFS and DMFS in GSE20685 were significantly higher than 1, showing that elevated EGR1 expression in BC is correlated with poor clinical outcomes, while HR of RFS in GSE16391, GSE1456, GSE17705 were significantly lower than 1, showing that attenuated EGR1 expression in BC is correlated with poor clinical outcomes (Fig. 3O, P). These findings suggested that various clinicopathological parameters in general and ER, PR, and HER2 receptor status in particular, should be considered when designing EGR1-mediated targeted therapy for BC patients.

EGR1 AND CO-EXPRESSED GENES AND THEIR ASSOCIATION IN BREAST CANCER (BC) PROGNOSIS

As EGR1 expression contributes to BC progression and prognosis, we further aimed to find the possible underlying signaling mechanism involved in EGR1-mediated BC progression and prognosis. For that, we first used the Oncomine platform to analyze the co-expression pattern of EGR1 with its correlated genes in BC. In Fig. 4A, we present the top 20 genes (total count 17), ranked based on correlation coefficient values, that correlated with EGR1, after analyzing 53 BC and 140 normal breast samples. Based on a threshold correlation coefficient of around 0.75, the highly correlated genes were DUSP1, FOS, FOSB, CYR61, and JUN. To confirm the co-expression status of EGR1 with the correlated genes, we also performed a correlation heatmap and various regression analyses. The heat maps of EGR1, DUSP1, FOS, FOSB, CYR61, and JUN showed similar expression patterns across each PAM50 BC subtype, including HER2+, luminal B, basal-like, and luminal A (TCGA data; Fig. 4B), thus supporting the Oncomine result showing that DUSP1, FOS, FOSB, CYR61, and JUN were highly co-expressed with EGR1 (Fig. 4A). We plotted scatter diagrams for DUSP1 vs. EGR1 and FOS vs. EGR1 expression using UCSC Xena (Fig. 4C, D). We constructed a correlation matrix of the expression of EGR1 and the five most highly correlated genes by performing data mining in bc-GenExMiner 4.0 that includes DNA microarrays and RNA-seq data. The results showed that all the cross-correlation coefficients between any pair of genes selected from the possible combinations were highly positive (Fig. 4E). Finally, bc-GenExMiner 4.0-based regression analysis further confirmed the positive correlation of EGR1 vs. DUSP1 and EGR1 vs. FOS mRNA expression (Fig. 4F, G). In fact, it has been reported that DUSP1 regulates the epithelial-to-mesenchymal transition (EMT) process, affecting various signaling pathways involved in BC, such as wnt, notch, and mitogen-activated protein kinase (MAPK) pathways (58). A significant reduction in DUSP1 mRNA expression has been reported in BC tissue compared with that in normal breast tissue (59). Another study reported that FOS expression is associated with intracellular signaling events affecting BC cell growth (60, 61) and the overexpression of FOS has been associated with improved clinical outcome (62). This association between higher FOS expression and improved clinical outcome was also seen in our analysis, as we showed that the prognostic significance of EGR1 co-expression with DUSP1 and FOS. It is worth noting that DUSP1 has previously been reported to be overexpressed in BC (63, 64). FOS, FOSB, and JUN expression has also been associated with BC and correlates with various clinicopathological parameters (65-67). Likewise, a number of researchers had reported that increased expression of CYR61 is associated with BC progression (68, 69). Finally, we also analyzed the prognostic relevance of the co-expression of EGR1 with the set of highly correlated genes. High levels of co-expression of these genes were associated with a good prognosis of both OS and RFS (Fig. 4H, I), suggesting that the co-expression of EGR1 with DUSP1, FOS, FOSB, CYR61, and JUN can also regulate the clinical outcomes of patients with BC.

CONCLUDING REMARKS

In this study, we used various web-based bioinformatics tools to perform a multiomics analysis of EGR1 mRNA expression, promoter methylation, somatic mutation, and clinical outcome data to investigate the impact of EGR1 on human breast cancer (BC). Based on EGR1 expression, promoter methylation, protein expression pattern, and prognosis status, our analysis showed that this gene was more often underexpressed in BC tissues especially in Ductal breast carcinoma, invasive ductal breast carcinoma, and medullary breast carcinoma subtypes and its down-regulation was positively correlated with poorer prognosis. Moreover, different clinicopathological parameters, such as ER, PR, and HER2 status play important roles in regulating the expression pattern of EGR1 in patients with BC, which event-ually modulates patient survival. Furthermore, we found that EGR1 expression was highly positively correlated with DUSP1, FOS, FOSB, CYR61, and JUN expression. The results of this multiomics analysis suggested that EGR1 can be targeted for the treatment of patients with BC and its co-expression with DUSP1, FOS, FOSB, CYR61, and JUN can be considered as a prognostic indicator. The present findings also reveal the significance of EGR1 expression and possible EGR1-related pathways in BC progression.

AVAILABILITY OF DATA AND MATERIALS

The data that support the findings of this study are available from the corresponding author upon reasonable request.

ACKNOWLEDGEMENTS

This study was supported by grants from the National Research Foundation (NRF) funded by the Korean government (grant no. 2015R1A5A1009701 and 2019M3A9H1030682); and, in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2017R1A2B2012337. In addition, this paper was written as part of Konkuk University's research support program for its faculty on sabbatical leave in 2019-2020.

CONFLICTS OF INTEREST

The authors have no conflicting interests. The sponsors had no role in the design, execution, interpretation, or writing of the study.

FIGURES
Fig. 1. Analysis of EGR1 protein structure, post-translational modification, and expression in breast cancer (BC). (A) Schematic diagram of EGR1 protein structure and post-translational modifications. (B) mRNA expression of EGR1 in normal and BC tissue (IDBC, invasive ductal breast carcinoma) was derived from Oncomine database. (C) mRNA expression of EGR1 in breast normal and cancer tissues was derived from UALCAN web using TCGA database. (D) Protein expression of EGR1 in breast normal and cancer tissues by immunohistochemistry (IHC) was derived from Human Protein Atlas web. The intensity of EGR1 expression was quantified by ImageJ and plotted by GraphPad Prism 7 software (right panel). (E-G) mRNA expression of EGR1 in BC clinicopathological subtypes was analyzed using the BRCA TCGA datasets through UCSC Xena web. Box plots showing the EGR1 mRNA expression in BC subcategories including PAM50 subtypes (E), clinical subtypes (F), and stages (G). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Fig. 2. Methylation status and genetic alterations of EGR1 in BC. (A) Methylation level of the EGR1 gene promoter in BC (TCGA Wanderer web tool). Median methylation level of the EGR1 gene promoter in BC. The box plot comparing specific CpG sites of EGR1 promoter methylation in normal (blue plot) and cancer tissue (red plot) was derived from the TCGA database (Methylation 450K) through the TCGA Wanderer web tool. The P values were obtained after an unpaired t-test using GraphPad Prism 7 software. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). (B) EGR1 mutation in human BC. Table showed the percentage of the mutation type of EGR1 in BC according to COSMIC database. (C) Alteration frequency of EGR1 mutation in BC was analyzed by using cBioPortal web. (D) Alteration frequency of EGR1 copy number in BC was analyzed by using cBioPortal web.
Fig. 3. Relationship between EGR1 mRNA expression and clinical outcomes in BC patients (PrognoScan and Kaplan Meier plotter Database). (A-E) The survival curve comparing the patient with high (red) and low (blue) expression of EGR1 (probe: 201693_s_at) was plotted from PrognoScan database in BC patients. (F-M) The survival curve comparing the patient with high (red) and low (blue) expression of EGR1 (probe: 201693_s_at) was plotted from Kaplan Meier plotter in BC patients. The threshold of cox P-value < 0.05. Meta-Analysis of Studies of BC studies with EGR1 mRNA expression. Forest plots of GEO datasets evaluating association of EGR1 mRNA expression with OS (N), RFS (O), DMFS (P), and PPS (Q) in BC. Hazard ratio (HR) with 95% confidential interval (CI) and p-value were labeled in the right column of each forest plot.
Fig. 4. EGR1 mRNA expression is correlated to DUSP1, FOS, FOSB, CYR61, and JUN mRNA expression in BC. (A) Top 20 genes positively correlated with EGR1 mRNA expression based on 2, 136 BC samples in Curtis Breast (PMID: 22522925). Analysis was performed using Oncomine database. (B) The heat map of EGR1, DUSP1, FOSB, CYR61, and JUN mRNA expression across PAM50 BC subtypes in TCGA database. Data was analyzed using UCSC Xena (http://xena.ucsc.edu/). (C, D) Regression analysis showed that EGR1, DUSP1, and FOS had positively high correlation coefficients. Data was analyzed using UCSC Xena (http://xena.ucsc.edu/). (E) Data mining in bc-GenExMiner 4.0 confirmed the positive correlation between EGR1, DUSP1, FOSB, and JUN mRNA expression across DNA microarray data. (F, G) Regression analysis confirmed that EGR1, DUSP1, and FOS had positively high correlation coefficients across DNA microarray data. Data was analyzed using bc-GenExMiner 4.0 web. (H, I) The survival curve comparing the patient with high (red) and low (blue) expression of EGR1, DUSP1, FOS, FOSB, CYR61, and JUN was plotted from Kaplan Meier plotter in BC patients. The threshold of cox P-value < 0.05.
REFERENCES
  1. Shah R, Rosso K and Nathanson SD (2014) Pathogenesis, prevention, diagnosis and treatment of breast cancer. World J Clin Oncol 5, 283-298
    Pubmed KoreaMed CrossRef
  2. Ataollahi MR, Sharifi J, Paknahad MR and Paknahad A (2015) Breast cancer and associated factors: a review. J Med Life 8(Spec Iss 4), 6-11
    Pubmed KoreaMed
  3. Nunziato M, Esposito MV, Starnone F et al (2019) A multi-gene panel beyond BRCA1/BRCA2 to identify new breast cancer-predisposing mutations by a picodroplet PCR followed by a next-generation sequencing strategy: a pilot study. Anal Chim Acta 1046, 154-162
    Pubmed CrossRef
  4. Blaschke F, Bruemmer D and Law RE (2004) Egr-1 is a major vascular pathogenic transcription factor in atherosclerosis and restenosis. Rev Endocr Metab Dis 5, 249-254
    Pubmed CrossRef
  5. Myung DS, Park YL, Kim N et al (2014) Expression of early growth response-1 in colorectal cancer and its relation to tumor cell proliferation and apoptosis. Oncol Rep 31, 788-794
    Pubmed CrossRef
  6. Ma Z, Gao X, Shuai Y et al (2021) EGR1‐mediated linc01503 promotes cell cycle progression and tumorigenesis in gastric cancer. Cell Proliferation 54, e12922
    KoreaMed CrossRef
  7. Zhao J, Li H and Yuan M (2021) EGR1 promotes stemness and predicts a poor outcome of uterine cervical cancer by inducing SOX9 expression. Genes Genomics 43, 459-470
    Pubmed CrossRef
  8. Ma S, Cheng J, Wang H et al (2021) A novel regulatory loop miR-101/ANXA2/EGR1 mediates malignant characteristics of liver cancer stem cells. Carcinogenesis 42, 93-104
    Pubmed CrossRef
  9. Mohamad T, Kazim N, Adhikari A and Davie JK (2018) EGR1 interacts with TBX2 and functions as a tumor suppressor in rhabdomyosarcoma. Oncotarget 9, 18084-18098
    Pubmed KoreaMed CrossRef
  10. Wei LL, Wu XJ, Gong CC and Pei DS (2017) Egr-1 suppresses breast cancer cells proliferation by arresting cell cycle progression via down-regulating CyclinDs. Int J Clin Exp Patho 10, 10212-10222
    Pubmed KoreaMed
  11. Baron V, Adamson ED, Calogero A, Ragona G and Mercola D (2006) The transcription factor Egr1 is a direct regulator of multiple tumor suppressors including TGFbeta1, PTEN, p53, and fibronectin. Cancer Gene Ther 13, 115-124
    Pubmed KoreaMed CrossRef
  12. Ferraro B, Bepler G, Sharma S, Cantor A and Haura EB (2005) EGR1 predicts PTEN and survival in patients with non-small-cell lung cancer. J Clin Oncol 23, 1921-1926
    Pubmed CrossRef
  13. Zhang HH, Chen XJ, Wang JK et al (2014) EGR1 decreases the malignancy of human non-small cell lung carcinoma by regulating KRT18 expression. Sci Rep 4, 5416
    Pubmed KoreaMed CrossRef
  14. Min IM, Pietramaggiori G, Kim FS, Passegue E, Stevenson KE and Wagers AJ (2008) The transcription factor EGR1 controls both the proliferation and localization of hematopoietic stem cells. Cell Stem Cell 2, 380-391
    Pubmed CrossRef
  15. Chen DG, Zhu B, Lv SQl et al (2017) Inhibition of EGR1 inhibits glioma proliferation by targeting CCND1 promoter. J Exp Clin Canc Res 36, 186
    Pubmed KoreaMed CrossRef
  16. Tao WW, Shi JF, Zhang Q, Xue B, Sun YJ and Li CJ (2013) Egr-1 enhances drug resistance of breast cancer by modulating MDR1 expression in a GGPPS-independent manner. Biomed Pharmacother 67, 197-202
    Pubmed CrossRef
  17. Wong KM, Song J and Wong YH (2021) CTCF and EGR1 suppress breast cancer cell migration through transcriptional control of Nm23-H1. Sci Rep 11, 491
    Pubmed KoreaMed CrossRef
  18. Hao L, Huang F, Yu X et al (2021) The role of early growth response family members 1-4 in prognostic value of breast cancer. Front Genet 12, 809
    Pubmed KoreaMed CrossRef
  19. Shajahan-Haq AN, Boca SM, Jin L et al (2017) EGR1 regulates cellular metabolism and survival in endocrine resistant breast cancer. Oncotarget 8, 96865-96884
    Pubmed KoreaMed CrossRef
  20. Rhodes DR, Kalyana-Sundaram S, Mahavisno V et al (2007) Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 9, 166-180
    Pubmed KoreaMed CrossRef
  21. Rhodes DR, Yu J, Shanker K et al (2004) ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6, 1-6
    Pubmed KoreaMed CrossRef
  22. Saha SK, Jeong Y, Cho S and Cho SG (2018) Systematic expression alteration analysis of master reprogramming factor OCT4 and its three pseudogenes in human cancer and their prognostic outcomes. Sci Rep 8, 14806
    Pubmed KoreaMed CrossRef
  23. Saha SK, Kim KE, Islam SMR, Cho SG and Gil M (2019) Systematic multiomics analysis of alterations in C1QBP mRNA expression and relevance for clinical outcomes in cancers. J Clin Med 8, 513
    Pubmed KoreaMed CrossRef
  24. Chandrashekar DS, Bashel B, Balasubramanya SAH et al (2017) UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 19, 649-658
    Pubmed KoreaMed CrossRef
  25. Goldman MJ, Craft B, Hastie M et al (2020) Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 38, 675-678
    Pubmed KoreaMed CrossRef
  26. Uhlen M, Zhang C, Lee S et al (2017) A pathology atlas of the human cancer transcriptome. Science 357, eaan2507
    Pubmed CrossRef
  27. Uhlen M, Fagerberg L, Hallstrom BM et al (2015) Proteo-mics. Tissue-based map of the human proteome. Science 347, 1260419
    Pubmed CrossRef
  28. Crowe AR and Yue W (2019) Semi-quantitative determination of protein expression using immunohistochemistry staining and analysis: an integrated protocol. Bio Protoc 9, e3465
    Pubmed KoreaMed CrossRef
  29. Diez-Villanueva A, Mallona I and Peinado MA (2015) Wanderer, an interactive viewer to explore DNA methylation and gene expression data in human cancer. Epigenetics Chromatin 8, 22
    Pubmed KoreaMed CrossRef
  30. Saha SK, Islam S, Abdullah-AL-Wadud M, Islam S, Ali F and Park KS (2019) Multiomics analysis reveals that GLS and GLS2 differentially modulate the clinical outcomes of cancer. J Clin Med 8, 355
    Pubmed KoreaMed CrossRef
  31. Tate JG, Bamford S, Jubb HC et al (2019) COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res 47, D941-D947
    Pubmed KoreaMed CrossRef
  32. Cerami E, Gao J, Dogrusoz U et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2, 401-404
    Pubmed KoreaMed CrossRef
  33. Gao J, Aksoy BA, Dogrusoz U et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6, p11
    Pubmed KoreaMed CrossRef
  34. Mizuno H, Kitada K, Nakai K and Sarai A (2009) Progno-Scan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genomics 2, 18
    Pubmed KoreaMed CrossRef
  35. Nagy A, Lanczky A, Menyhart O and Gyorffy B (2018) Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep 8, 9227
    Pubmed KoreaMed CrossRef
  36. Goldman M, Craft B, Hastie M, Repečka K, Kamath A and McDade F (2019) The UCSC Xena platform for cancer genomics data visualization and interpretation. BioRxiv 326470. doi: https://doi.org/10.1101/326470
    CrossRef
  37. Jézéquel P, Frénel JS, Campion L et al (2013) bc-GenEx-Miner 3.0: new mining module computes breast cancer gene expression correlation analyses. Database 2013, bas060
    Pubmed KoreaMed CrossRef
  38. Havis E and Duprez D (2020) EGR1 transcription factor is a multifaceted regulator of matrix production in tendons and other connective tissues. Int J Mol Sci 21, 1664
    Pubmed KoreaMed CrossRef
  39. Magee N and Zhang Y (2017) Role of early growth re-sponse 1 in liver metabolism and liver cancer. Hepatoma Res 3, 268
    Pubmed KoreaMed CrossRef
  40. Lohoff M, Giaisi M, Kohler R, Casper B, Krammer PH and Li-Weber M (2010) Early growth response protein-1 (Egr-1) is preferentially expressed in T helper type 2 (Th2) cells and is involved in acute transcription of the Th2 cytokine interleukin-4. J Biol Chem 285, 1643-1652
    Pubmed KoreaMed CrossRef
  41. Milbrandt J (1987) A nerve growth factor-induced gene encodes a possible transcriptional regulatory factor. Science 238, 797-799
    Pubmed CrossRef
  42. Duclot F and Kabbaj M (2017) The role of early growth response 1 (EGR1) in brain plasticity and neuropsychiatric disorders. Front Behav Neurosci 11, 35
    Pubmed KoreaMed CrossRef
  43. Thiel G and Cibelli G (2002) Regulation of life and death by the zinc finger transcription factor Egr-1. J Cell Physiol 193, 287-292
    Pubmed CrossRef
  44. Egerod FL, Bartels A, Fristrup N et al (2009) High frequency of tumor cells with nuclear Egr-1 protein expression in human bladder cancer is associated with disease progression. BMC Cancer 9, 385
    Pubmed KoreaMed CrossRef
  45. Gitenay D and Baron VT (2009) Is EGR1 a potential target for prostate cancer therapy?. Future Oncol 5, 993-1003
    Pubmed KoreaMed CrossRef
  46. Lin M, Huang J, Jiang X et al (2016) A combination hepa-toma-targeted therapy based on nanotechnology: pHRE-Egr1-HSV-TK/131 I-antiAFPMcAb-GCV/MFH. Sci Rep 6, 33524
    Pubmed KoreaMed CrossRef
  47. Ma J, Ren Z, Ma Y et al (2009) Targeted knockdown of EGR-1 inhibits IL-8 production and IL-8-mediated invasion of prostate cancer cells through suppressing EGR-1/NF-κB synergy. J Biol Chem 284, 34600-34606
    Pubmed KoreaMed CrossRef
  48. Maegawa M, Arao T, Yokote H et al (2009) EGFR mutation up-regulates EGR1 expression through the ERK pathway. Anticancer Res 29, 1111-1117
    Pubmed
  49. Adamson E, de Belle I, Mittal S et al (2003) Egr1 signaling in prostate cancer. Cancer Biol Ther 2, 617-622
    Pubmed CrossRef
  50. Curtis C, Shah SP, Chin S-F et al (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346-352
    Pubmed KoreaMed CrossRef
  51. Liu J, Liu YG, Huang R et al (2007) Concurrent down-regulation of Egr-1 and gelsolin in the majority of human breast cancer cells. Cancer Genomics Proteomics 4, 377-385
    Pubmed
  52. Yamashita K, Upadhyay S, Osada M et al (2002) Pharmacologic unmasking of epigenetically silenced tumor suppressor genes in esophageal squamous cell carcinoma. Cancer Cell 2, 485-495
    Pubmed CrossRef
  53. Ng JMK and Yu J (2015) Promoter hypermethylation of tumour suppressor genes as potential biomarkers in colorectal cancer. Int J Mol Sci 16, 2472-2496
    Pubmed KoreaMed CrossRef
  54. Richter AM, Walesch SK and Dammann RH (2016) Aberrant promoter methylation of the tumour suppressor RASSF10 and its growth inhibitory function in breast cancer. Cancers 8, 26
    Pubmed KoreaMed CrossRef
  55. Martincorena I and Campbell PJ (2015) Somatic mutation in cancer and normal cells. Science 349, 1483-1489
    Pubmed CrossRef
  56. Myung E, Park YL, Kim N et al (2013) Expression of early growth response-1 in human gastric cancer and its relationship with tumor cell behaviors and prognosis. Pathol Res Pract 209, 692-699
    Pubmed CrossRef
  57. Kataoka F, Tsuda H, Arao T et al (2012) EGRI and FOSB gene expressions in cancer stroma are independent prognostic indicators for epithelial ovarian cancer receiving standard therapy. Genes Chromosomes Cancer 51, 300-312
    Pubmed CrossRef
  58. Boulding T, Wu F, McCuaig R et al (2016) Differential roles for DUSP family members in epithelial-to-mesenchymal transition and cancer stem cell regulation in breast cancer. PLoS One 11, e0148065
    Pubmed KoreaMed CrossRef
  59. Chen FM, Chang HW, Yang SF et al (2012) The mitogen-activated protein kinase phosphatase-1 (MKP-1) gene is a potential methylation biomarker for malignancy of breast cancer. Exp Mol Med 44, 356-362
    Pubmed KoreaMed CrossRef
  60. Shen Q and Brown PH (2003) Novel agents for the prevention of breast cancer: targeting transcription factors and signal transduction pathways. J Mammary Gland Biol Neoplasia 8, 45-73
    Pubmed CrossRef
  61. Wagstaff SC, Bowler WB, Gallagher JA and Hipskind RA (2000) Extracellular ATP activates multiple signalling pathways and potentiates growth factor-induced c-fos gene expression in MCF-7 breast cancer cells. Carcinogenesis 21, 2175-2181
    Pubmed CrossRef
  62. Fisler DA, Sikaria D, Yavorski JM, Tu YPN and Blanck G (2018) Elucidating feed-forward apoptosis signatures in breast cancer datasets: Higher FOS expression associated with a better outcome. Oncol Lett 16, 2757-2763
    Pubmed KoreaMed CrossRef
  63. Shen JL, Zhang YP, Yu H et al (2016) Role of DUSP1/MKP1 in tumorigenesis, tumor progression and therapy. Cancer Med 5, 2061-2068
    Pubmed KoreaMed CrossRef
  64. Fang J, Ye ZM, Gu FY et al (2018) DUSP1 enhances the chemoresistance of gallbladder cancer via the modulation of the p38 pathway and DNA damage/repair system. Oncol Lett 16, 1869-1875
    Pubmed KoreaMed CrossRef
  65. Lu CH, Shen Q, DuPre E, Kim H, Hilsenbeck S and Brown PH (2005) cFos is critical for MCF-7 breast cancer cell growth. Oncogene 24, 6516-6524
    Pubmed CrossRef
  66. Langer S, Singer CF, Hudelist G et al (2006) Jun and Fos family protein expression in human breast cancer: Correlation of protein expression and clinicopathological parameters. Eur J Gynaecol Oncol 27, 345-352
    Pubmed
  67. Park JA, Na HH, Jin HO and Kim KC (2019) Increased expression of fosb through reactive oxygen species accumulation functions as pro-apoptotic protein in Piperlongumine Treated MCF7 breast cancer cells. Mol Cells 42, 884-892
    Pubmed KoreaMed CrossRef
  68. Huang Y-T, Lan Q, Lorusso G, Duffey N and Rüegg C (2017) The matricellular protein CYR61 promotes breast cancer lung metastasis by facilitating tumor cell extravasation and suppressing anoikis. Oncotarget 8, 9200-9215
    Pubmed KoreaMed CrossRef
  69. Hellinger JW, Hüchel S, Goetz L, Bauerschmitz G, Emons G and Gründker C (2019) Inhibition of CYR61-S100A4 axis limits breast cancer invasion. Front Oncol 9, 1074
    Pubmed KoreaMed CrossRef


This Article


Cited By Articles
  • CrossRef (0)

Author ORCID Information

Funding Information

Collections

Services
Social Network Service

e-submission

Archives