Genetic association of CCL18 and EGF genes elevate the risk of breast cancer

doi:10.21203/rs.3.rs-3089850/v1

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Genetic association of CCL18 and EGF genes elevate the risk of breast cancer

https://doi.org/10.21203/rs.3.rs-3089850/v1

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Background:

CCL18 and EGF, two biologically plausible cancer biomarkers, are responsible for the emergence and progression of breast invasive carcinoma (BRCA). We aimed to study the expression of the CCL18 and EGF genes and how CCL18 and EGF affect immune function and prognosis in breast cancer.

Subjects and methods:

To evaluate the relationship between CCL18 and EGF and breast cancer risks, web-based bioinformatics tools were used. The Kaplan-Meier plotter was used to assess the predictive importance of CCL18 and EGF mRNA expression in breast cancer. In order to find whether CCL18 and EGF are independent risk factors for overall survival (OS) and relapse-free survival (RFS) of breast cancer patients, single- and multivariate Cox proportional hazards regression analysis was used. Additionally, STRING database was used to analyze protein-protein interactions.

Result:

Our findings demonstrated that both CCL18 and EGF exhibit considerable expression and are linked to an elevated risk of breast cancer. In addition, increased expression of CCL18 might indicate poor OS and RFS. Moreover, disease stage and expression level of CCL18 and EGF were correlated with relapse-free survival and overall survival in breast cancer. Analysis of protein-protein interaction based on STRING database gained 8 top genes which could interact with ZWINT, including PMF1, MIS12, DSN1, ZW10, BUB1, BUB1B, CASC5, NDC80, NSL1 and NUF2.

Conclusion:

Our findings indicate that CCL18 and EGF have high levels of expression in breast tumor tissues and may play a crucial role in the etiology of breast cancer risk.

Breast cancer

CCL18

EGF

Overall survival

and relapse-free survival

Female breast Cancer (BRCA) is one of the most common forms of cancer and the fifth leading cause of cancer mortality worldwide. BRCA has now exceeded lung cancer as the leading cause of cancer incidence in 2020 with an estimated 2,261,419 (11.7% of all sites) new cases and 684,966 (6.9% of all sites) deaths globally. It accounts for 1 in 4 cancer cases and 1 in 6 cancer deaths, ranking first for incidence in the vast majority of countries (159 of 185 countries) and mortality in 110 countries among females [1]. Incidence rates are 88% higher in developed countries than in developing countries (55.9 and 29.7 per 100,000, respectively), with the highest incidence rates (> 80 per 100,000) in Australia/New Zealand, Western Europe (Belgium has the world's highest incidence), Northern America, and Northern Europe and the lowest rates (< 40 per 100,000) in Central America, Eastern and Middle Africa, and South Central Asia. However, women living in developing countries have 17% higher mortality rates compared with women in developed countries (15.0 and 12.8 per 100,000, respectively) because of high fatality rates, with the highest mortality rates found in Melanesia, Western Africa, Micronesia/Polynesia, and the Caribbean (Barbados has the world's highest mortality) [1, 2].

According to the WHO, a proper definition of biomarkers encompasses "seemingly any data indicating a connection between a biological system and a possible danger, which may be chemical, physical, or biological". It is a biological molecule that can be discovered in blood, other bodily fluids, or tissues and is a marker of a healthy or unhealthy process. The observed reaction can be physiological, functional, or cellular biological [3]. Particularly, the term "cancer biomarker" refers to a biological compound produced by a tumor cell or by human tissues in response to cancer. Cancer biomarkers (CBs) might be proteins, peptides, DNA (germline or somatic), RNA, metabolites, hormones, or even in biological processes like apoptosis, proliferation, or angiogenesis, depending on their biochemical composition. It can be found in secretions such as urine, feces, sputum, or nipple discharge as well as in the circulation (whole blood, plasma, or serum). In addition, CBs are primarily employed in three different capacities in cancer research and treatment: diagnostic, prognostic, and predictive [4].

The CC chemokine family includes the small cytokine known as chemokine (C-C motif) ligand 18 (CCL18) which is predominantly produced by monocyte-derived cells with M2 phenotype [5]. Its production can be upregulated by interleukins (IL-4, IL-10, and IL-13 cells), which are cytokines that favor a 2 type response. Besides other consequences, higher concentrations of CCL18 in the serum or the tumor are linked to a worse prognosis for cancer patients[6]. Abundant CCL18 production in M2 macrophages has been observed in a variety of chronic inflammations and fibrotic illnesses, such as Gaucher's disease and rheumatoid arthritis [7]. Furthermore, CCL18 was found to be expressed constitutively in macrophages infiltrating ovarian cancer, gastric cancer, breast cancer, and glioma [8]. In particular, breast cancer is the condition in which CCL18 is most evidently involved. CCL18 may function as an immunosuppressive cytokine in breast cancers by generating immunosuppressive dendritic cells, macrophages, and T-regulatory cells, as well as driving effector T-cells to abolish their anti-cancer capabilities and immune system [9]. Several studies have shown that breast TAMs (tumor-associated macrophages) release CCL18, which reduces the expression of miR98 and miR27b but promotes angiogenesis, adherence, and migration of breast cancer cells to the extracellular matrix. By triggering the epithelial-mesenchymal transition (EMT) via the PI3K/Akt/GSK3/Snail signaling pathway in breast cancer cells, CCL18 binds to its receptor and promotes tumor growth and metastasis. It is known that when carcinoma-associated fibroblasts and breast cancer cells are co-cultured, CCL18 production increases, promoting the invasion and metastasis of breast cancer cells [10].

A 53-amino acid polypeptide known as epidermal growth factor (EGF), has the ability to affect the differentiation and proliferation of a wide range of cells. Numerous breast cancer cell lines, particularly ER-negative tumor cells, as well as primary tumors and metastases in humans, have been shown to express EGF receptors [11]. Both healthy and cancerous mammary epithelial cells coexpress a variety of related peptides to epidermal growth factor (EGF), including transforming growth factor c (TGFc), amphiregulin (AR), heregulin (HRG), and cripto-1 (CR-1). Among these, the EGF-related peptides may be crucial in the etiology of breast cancer. Generally, malignant breast epithelial cells express TGFc, AR, and CR-1 more frequently and at a higher level than normal mammary epithelium [12]. It has been demonstrated that activating this receptor promotes the migration, proliferation, and protection against apoptosis of breast cancer cells. Metaplastic breast cancer has been linked to the EGFR gene in 25% of patients. Activating mutations of EGFR, which have been shown in lung cancer and central nervous system tumors but are uncommon in breast cancer, are another method of EGFR overexpression [13].

Several in vitro and in vivo studies have revealed that the release of cytokines and chemokines under the control of EGF and CCL18 or the protease-dependent remodeling of the extracellular matrix are two ways that macrophages promote chemoattractant bioavailability, invasion, and migration [14, 15]. In addition, a study by Li et al., 2018 suggests that after being stimulated for 1 hour with tumor-associated macrophages produced by cytokines such as CCL18, CXCL12, IL-beta, IL-6, and EGF, only CCL18 significantly increased the phosphorylation of AMAP1 at Tyr782 in MCF-7 cells, whereas EGF only marginally increased it [16].

There is no such type of research concerning the CCL18 and EGF correlation with breast cancer carried. Here we investigated the expression, correlation, and survival analysis of both CCL18 and EGF with BRCA risks using publicly available computational biology tools. Therefore, this study aims to disclose the risk of breast cancer associated with CCL18 and EGF.

Analysis of gene expression and immune infiltration profile with TCGA cancer data.

TIMER2.0 (http://timer.cistrome.org/) allows the visualization of the immune infiltration levels for The Cancer Genome Atlas (TCGA), which provides comprehensive analysis and visualization functions of tumor infiltrating immune cells. Gene_DE module in TIMER2.0 allows the study of differential expression between tumor and adjacent normal tissues for any gene of interest across all TCGA tumors. Several types of cancer and normal data available in the TCGA cancer data set [17].

Assessment of CCL18 and EGF expression and correlation level.

The gene expression data cohort with Breast Invasive Carcinoma (BRCA) was measured using Gene Expression Profiling Interactive analysis (GEPIA2) online tool, a recently developed interactive database for analysing the expression and correlation of genes in TCGA data. (http://gepia2.cancer-pku.cn)[18].

Survival Analysis

The prognostic significance of mRNA expression of CCL18 and EGF in breast cancer were evaluated using the Kaplan–Meier plotter (www.kmplot.com), an online database including gene expression data and clinical data [19]. The overall survival (OS) of 1879 patients and the relapse free survival (FP) of 4929 patients were collected in breast cancer database. Briefly, the genes CCL18 and EGF were uploaded into the database, and samples were divided into two cohorts according to the median expression of CCL18 and EGF (high vs low expression) to obtain the Kaplan–Meier survival plots, in which the number-at-risk were shown below the main plot. Log-rank P-value and hazard ratio (HR) with 95% confidence intervals were calculated and displayed on the web page. The p values < 0.05 were considered statistically significant. In this study, “array quality control” was selected to “exclude biased arrays”. Survival probability of breast cancer patients were also analysed based on the expression of CCL18 and EGF with menopause status of patient’s using the survival of TCGA data conducted by UALCAN (http://ualcan.path.uab.edu/) tool [20].

Assessment of promoter methylation profile with CCL18 and EGF in breast invasive cancer

Analysis of DNA methylation level of CCL18 and EGF promoter in breast cancers from TCGA were conducted by UALCAN (http://ualcan.path.uab.edu/), tool [20].

Gene correlation analyses

Correlation analysis of genes using RNA expression values helps to identify interacting or co-expressing genes. Thus, we performed Pearson correlation analysis using expression values of all protein-coding genes using UALCAN. Gene pairs showing Pearson correlation coefficients of 0.3 or above were considered as positive correlations, and those showing Pearson correlation coefficients of -0.3 or below were considered as negative correlations.

Protein–protein interaction network construction

The Search Tool for the Retrieval of Interacting Genes (STRING) database aims to construct functional protein association networks by consolidating known and predicted protein–protein association data for a large number of organisms [21]. The STRING resource is available at http://string-db.org/. The corresponding protein–protein interaction network of CCL18 and EGF were constructed when we selected the interactions pertaining to Homo sapiens and showed minimum interactions with a confidence score 0.9.

Statistical analysis

The significance denoted by ns: not significant; *: p-value < 0.05; **: p-value < 0.01; ***: p-value < 0.001. Log-rank P-value and hazard ratio (HR) with 95% confidence intervals were calculated and the p values < 0.05 were considered statistically significant.

CCL18 and EGF genes expression with different TCGA cancer data

We analysed the expression of CCL18 and EGF genes with differential expression between tumor and adjacent normal tissues for any gene of interest across all TCGA tumors utilising the Gene_DE module in TIMER2.0 tool. The statistical significance calculated by the Wilcoxon test is noted by the number of stars (*: p-value < 0.05; **: p-value < 0.01; ***: p-value < 0.001). It can identify genes that are up-regulated or down-regulated in the tumors compared to normal tissues for each cancer type, as displayed in gray columns when normal data are available. After proper analysing, we found that both CCL18 and EGF genes expression was statistically significant with the development and progression of breast cancer (Fig. 1A and 1B).

Correlation of gene expression with immune infiltration profile in breast cancer

The correlation of CCL18 and EGF genes expression with immune infiltration profile in breast cancer were measured using the TIMER2.0 online tools. A scatter plot showed Spearman’s correlation in different immune cells. In case of CCL18, CD8 + T cell (Fig. 2A), regulatory T cell (Fig. 2B), macrophages M1 (Fig. 2C), macrophages M2 (Fig. 2D), Myeloid dendritic cell (Fig. 2E), Monocyte (2F), memory B cell (Fig. 2G) and Neutrophil (Fig. 2H) showed positive correlation with CCL18 expression in breast cancer patient’s sequencing data. And the result was statistically significant. In case of EGF, CD8 + T cell (Fig. 2I), regulatory T cell (Fig. 2J), macrophages M2 (Fig. 2L), Myeloid dendritic cell (Fig. 2M), Monocyte (2N), memory B cell (Fig. 2O) and Neutrophil (Fig. 2P) showed positive correlation whereas, macrophages M1 (Fig. 2K) showed negative correlation with EGF expression in breast cancer patient’s sequencing data. And the result was statistically significant. There are many other potential immune mediators are also associated with CCL18 and EGF genes in breast cancer proliferation (Supplementary Fig. S1A, S1B, S1C, S1D, S1E, S1E, S1F, S1G, S1H, S1I, and S1J)

Clinical outcome of CCL18 and EGF with breast cancer

We explored the prognostic value of the expression of ZWINT using Kaplan–Meier plotter (www.kmplot.com). The desired Affymetrix ID was valid: 32128_at (CCL18) for relapse-free survival (RFS), and patients were split into low- and high-expression groups by the median of CCL18 expression values. A low expression of CCL18 mRNA was related to significantly shorter RFS for all breast cancer patients (n = 4929, HR 1.2 [1.08–1.32], P = 0.0005) (Fig. 3A). However, patient’s high expression of CCL18 mRNA was related to shorter overall survival (OS) for all breast cancer patients (n = 1879, HR 1.12 [0.93–1.35], P = 0.23) (Fig. 3B) and the result was not statistically significant. In case of EGF, patient’s with high expression of EGF mRNA was related to significantly shorter RFS for all breast cancer patients (n = 4929, HR 0.83 [0.75–0.91], P = 0.0002) (Fig. 3C), whereas, patient’s low expression of EGF mRNA was related to shorter overall survival (OS) for all breast cancer patients (n = 1879, HR 0.9 [0.75–1.09], P = 0.27) (Fig. 3D) and the result was not statistically significant. Survival probability of breast cancer patients was also analysed using the survival of TCGA datasets. The association of gene expression levels combined with patient’s menopause status and the survival are illustrated in Fig. 4. Patients with high expression of both CCL18 and EGF with post menopause status had decreased survival probability than those with other groups (Fig. 4A and 4B) and the results were statistically significant (p < 0.05).

CCL18 and EGF expression and correlation with breast cancer

CCL18 and EGF gene expression levels were analysed using GEPIA2 online tool and it was found that CCL18 and EGF were negatively correlated (r= -0.083) with breast invasive cancer whereas, positively correlated in prostate cancer (r = 0.0065) and pancreatic carcinoma (r = 0.0052) using Pearson’s correlation (Supplementary Fig. S2A, S2B and S2C). We then checked the expression pattern between CCL18 and EGF genes in breast cancer pathological stages and found that there were no significant different between the CCL18 and EGF gene expression in TCGA breast cancer (Supplementary Fig. S2D and S2E).

Promoter DNA methylation analysis

Epigenetic modifications are involved in the regulation of gene expression and controlling many cellular processes in both normal and cancer cells. To evaluate the potential role of DNA promoter methylation and histone modifications in regulating the expression of cancer genes so that we then checked the DNA promoter methylation analysis into UALCAN. Considering that DNA hypermethylation is a potential mechanism of transcriptomic CCL18 and EGF abnormality in breast cancer [22], we next analyzed the DNA methylation level of CCL18 and EGF promoter in TCGA lung cancers into UALCAN. As shown in Fig. 5, with the beta value ranging from 0 (un-methylated) to 1 (fully-methylated), significantly decreased DNA methylation level of CCL18 and EGF was detected in breast tumors compared with their paired normal breast tissues (Normal = 97, primary tumor = 793, p < 0.05). However, we again checked the DNA methylation with different breast subclasses and statuses and found that all stages and subclasses were significantly decreased DNA methylation levels of CCL18, and EGF was detected in breast cancer compared with their paired normal breast tissues. The DNA methylation level of CCL18 and EGF based on patient’s nodal metastasis stages, normal: 97, N0 = 230, N1 = 242, N2 = 97 and n3 = 20, p < 0.05, based on major sub-classes, normal: 97, Luminal = 393, HER2 positive = 17, and TNBC = 84, p < 0.05, and based on menopause stage, normal: 97, pre-menopause = 171, peri-menopause = 23, and post-menopause = 506, p < 0.05 and the result were tabulated (Supplementary Fig. S3A, S3B, S3C, S3D, S3E, and S3E). This result suggests that DNA methylation might be one possible mechanism leading to the up-regulation of CCL18 and EGF expression in breast cancer.

Identification of cancer-specific correlated genes

Most genes do not function in isolation but have networks that facilitate gene function. There are master regulatory genes and the expression of some genes regulates numerous other genes. Thus, for the identification of associated gene expression, we then examine which genes are positively correlated with CCL 18 and EGF. The expression-based gene correlations are then very useful in constructing and understanding gene interaction/regulatory networks. After proper analyze we found top 25 positively correlated genes with CCL18 (Fig. 6A) and the top 3 genes are positively correlated with EGF (Fig. 6B). Scatter plots for the top one gene pair in breast cancers can be visualized and downloaded (Fig. 4C, 4D, 4E and 4F).

Interaction networks of CCL18 and EGF.

In case of CCL18: The STRING database was used to consolidate known and predicted protein–protein association with CCL18. As shown in Fig. 7A, the top 10 predicted functional partners were as follows: PITPNM3 (score = 0.993), CCR8 (score = 0.988), CXCR5 (score = 0.871), CCR5 (score = 0.830), CCL19 (score = 0.830), CCR3 (score = 0.830), CCR1 (score = 0.825), CCR2 (score = 0.817), CCR7 (score = 0.789) and CD163 (score = 0.787). Function enrichment analysis against gene ontology in this network showed that for biological processes, this network is most enriched in cell division, small GTPase-mediated signal transduction, and chromosome segregation. However, strong interacting functional partners were found in the case of EGF compare to CCL18. The STRING database was used to consolidate known and predicted protein–protein association with EGF. As shown in Fig. 7B, the top 10 predicted functional partners were as follows: EGFR (score = 0.999), ERBB2 (score = 0.993), ERBB3 (score = 0.992), PCSK9 (score = 0.989), P4HB (score = 0.989), ERBB4 (score = 0.987), GRB2 (score = 0.984), SHB (score = 0.969), F3 (score = 0.966) and FGF2 (score = 0.959). Function enrichment analysis against gene ontology in this network showed that for biological processes, this network is most enriched in cell division, small GTPase-mediated signal transduction and chromosome segregation.

We investigated the relationship between the expression of the CCL18 and EGF genes using information from the Cancer Genome Atlas (TCGA). The approach taken here is intended to examine substantial malignancies all across the complete genome in order to find genetic interconnections and linkages between solid carcinomas at different levels in contrast to normal surrounding tissues.

Prior to recently, many scientists did not use data from computational biology or "in silico" techniques. The acceptance of these techniques has substantially expanded due to the quick development of machine-learning strategies and bioinformatics tools. For logical drug design, 3D (three-dimensional) structures are also exceptionally valuable [23]. Moreover, a number of sequence-based databases have been created as a result of the exponential increase in biological sequences found in the post-genomic age and their successful application in drug discovery [24]. Pharmaceutical science has undergone a spectacular transformation in a consequence of swift discoveries in RNA sequencing and molecular bioinformatics, in which computational biology is increasingly being used to promote the development of novel drugs [25]. Because of this, we analyzed the genetic correlation between the onset and progression of breast cancer in this study using computational techniques and bioinformatics tools.

According to our computational investigation, there is a statistically significant association between the expression of the CCL18 and EGF genes and the genesis, progress, and prevalence of breast cancer. Additionally, our analysis revealed that patients with post-menopausal status and high levels of both CCL18 and EGF expression showed lower survival probabilities than those in other groups, as evaluated by analyzing the survival of TCGA datasets. In contrast, the survival ability of prostate cancer patients indicated that CCL18 is more potential than the EGF as shown by Figs. 4(A) and 4(B). Therefore, EGF has overall less survival ability in prostate cancer but is high in breast cancer compared to CCL18. We also evaluated DNA methylation in several breast subtypes and found that all stages and subclasses of breast cancer had significantly lower levels of DNA methylation in their CCL18 and EGF-expressing tissues as compared to their corresponding normal breast tissues (p < 0.05). For the nodal metastatic stage, major subtypes, and menopause status, the supplemental data also revealed similar outcomes. This finding implies that DNA methylation may be one potential mechanism underlying the increased production of CCL18 and EGF in breast cancer.

Additionally, except for macrophage M1 of EGF, the immune infiltration profile in breast cancer disclosed a positive connection in all T cell CD4+, regulatory T cell, monocyte, memory B cell, and neutrophil cells that expressed by CCL18 and EGF. Furthermore, CCL18 demonstrated a positive association with activated NK cells and the others in contrast to EGF (S1). This correlation undoubtedly indicates that CCL18 is more susceptible to breast cancer than EGF. The STRING database shows that the interaction network is most enriched in chromosome segregation, small GTPase-mediated signal transduction, and cell division in the cases of CCL18 and EGF, respectively. In contrast to CCL18, EGF was shown to have a considerable functional associate interaction. The majority of genes are part of networks that help them function rather than acting independently. In order to build and comprehend gene interaction/regulatory networks, expression-based gene correlations are particularly helpful.

After thorough analysis, we discovered the top 25 genes that are positively correlated with CCL18 (Fig. 6A), but only three genes are positively correlated with EGF. The GEPIA2 online tool was used to analyze the gene expression levels of CCL18 and EGF in support of this finding. Pearson's correlation analysis revealed that CCL18 and EGF were positively correlated in prostate cancer (r = 0.0065) and pancreatic carcinoma (r = 0.0052) but negatively correlated with breast invasive cancer (r= -0.083) (Supplementary Fig. S2A, S2B, and S2C). The additional Figure of S2D & S2E, however, did not reveal any appreciable differences in the expression of CCL18 and EGF in TCGA breast cancer at the pathological phases. With this, we became able to determine the pattern of CCL18 and EGF gene expression at various phases of breast cancer pathology and establish that there was no discernible difference between the two in the TCGA breast cancer samples. Our overall findings suggested that the CCL18 and EGF genes were significantly correlated with prostate cancer and pancreatic carcinoma instead of breast cancer.

Most importantly, The Pearson Correlation Coefficient value between CCL18 and CCL13 gene expression is 0.67 in Fig. 6(C, D, E, &F), whereas it is only 0.41 for EGF and MYNN gene expression. CCL18, therefore, is more favorably connected to BRCA than EGF is. CCL13 is a member of the CC chemokine family that regulates the chemotaxis of several immune cells. A comprehensive analysis of CCL13 is not yet available despite substantial research into its role in several illnesses. In particular, CCL13's role in cancer is comparatively well known. A number of studies suggest that CCL13 is associated with 2-microglobulin (2-MG) levels in multiple myeloma, and cell proliferation in breast cancer [26, 27]. If it is possible to associate CCL13 and CCL18 genes on breast cancer in future research, scientists may able to establish a strong linkage between these two genes in BRCA.

Acknowledgments

We thank members of our groups for insightful discussion during the course of this study.

Author Contributions

Sm Faysal Bellah: Conceptualization, Methodology, Formal analysis, Writing- original draft, Supervision. Fatema Akter Sonia: Methodology, Writing - review & editing. Md. Razowanul Ferdous: Methodology, Writing - review & editing. Sayed Ala Moududee: Conceptualization, Methodology, Writing - review & editing. All authors reviewed the manuscript.

Disclosure statement: The authors have no conflicts of interest.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Supplementary Material

Supplemental materials are available for this article.

Data Availability Statement

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

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No competing interests reported.

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Genetic association of CCL18 and EGF genes elevate the risk of breast cancer

Status:

Version 1

Abstract

Figures

Introduction

Material and methods

Survival Analysis

Assessment of promoter methylation profile with CCL18 and EGF in breast invasive cancer

Gene correlation analyses

Protein–protein interaction network construction

Statistical analysis

Result

CCL18 and EGF genes expression with different TCGA cancer data

Correlation of gene expression with immune infiltration profile in breast cancer

Clinical outcome of CCL18 and EGF with breast cancer

CCL18 and EGF expression and correlation with breast cancer

Promoter DNA methylation analysis

Identification of cancer-specific correlated genes

Discussions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1