Next Article in Journal
Subcutaneous Infusion of DNA-Aptamer Raised against Advanced Glycation End Products Prevents Loss of Skeletal Muscle Mass and Strength in Accelerated-Aging Mice
Previous Article in Journal
Tuberculosis Severity Predictive Model Using Mtb Variants and Serum Biomarkers in a Colombian Cohort of APTB Patients
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 11
Issue 12
10.3390/biomedicines11123111
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Gene Signature Associated with Nervous System in an Experimental Radiation- and Estrogen-Induced Breast Cancer Model

by
Gloria M. Calaf
1,*,
Debasish Roy
2,
Lilian Jara
3,
Francisco Aguayo
4 and
Leodan A. Crispin
1
1
Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
2
Department of Natural Sciences, Hostos College of the City University of New York, Bronx, NY 10451, USA
3
Laboratorio de Genética Humana, Programa de Genética Humana, Instituto de Ciencias Biomédicas (ICBM), Facultad de Medicina, Universidad de Chile, Santiago 8380000, Chile
4
Laboratorio de Oncovirología, Programa de Virología, Instituto de Ciencias Biomédicas (ICBM), Facultad de Medicina, Universidad de Chile, Santiago 8380000, Chile
*
Author to whom correspondence should be addressed.
Biomedicines 2023, 11(12), 3111; https://doi.org/10.3390/biomedicines11123111
Submission received: 6 September 2023 / Revised: 17 October 2023 / Accepted: 16 November 2023 / Published: 22 November 2023
(This article belongs to the Section Cancer Biology and Oncology)
Browse Figures
Versions Notes

Abstract

:
Breast cancer is frequently the most diagnosed female cancer in the world. The experimental studies on cancer seldom focus on the relationship between the central nervous system and cancer. Despite extensive research into the treatment of breast cancer, chemotherapy resistance is an important issue limiting the efficacy of treatment. Novel biomarkers to predict prognosis or sensitivity to chemotherapy are urgently needed. This study examined nervous-system-related genes. The profiling of differentially expressed genes indicated that high-LET radiation, such as that emitted by radon progeny, in the presence of estrogen, induced a cascade of events indicative of tumorigenicity in human breast epithelial cells. Bioinformatic tools allowed us to analyze the genes involved in breast cancer and associated with the nervous system. The results indicated that the gene expression of the Ephrin A1 gene (EFNA1), the roundabout guidance receptor 1 (ROBO1), and the kallikrein-related peptidase 6 (KLK6) was greater in T2 and A5 than in the A3 cell line; the LIM domain kinase 2 gene (LIMK2) was greater in T2 than A3 and A5; the kallikrein-related peptidase 7 (KLK7), the neuroligin 4 X-linked gene (NLGN4X), and myelin basic protein (MBP) were greater than A3 only in T2; and the neural precursor cell expressed, developmentally down-regulated 9 gene (NEDD9) was greater in A5 than in the A3 and E cell lines. Concerning the correlation, it was found a positive correlation between ESR1 and EFNA1 in BRCA-LumA patients; with ROBO1 in BRCA-Basal patients, but this correlation was negative with the kallikrein-related peptidase 6 (KLK6) in BRCA-LumA and –LumB, as well as with LIMK2 and ROBO1 in all BRCA. It was also positive with neuroligin 4 X-linked (NLGN4X) in BRCA-Her2 and BRCA-LumB, and with MBP in BRCA-LumA and –LumB, but negative with KLK7 in all BRCA and BRCA-LumA and NEDD9 in BRCA-Her2. The differential gene expression levels between the tumor and adjacent tissue indicated that the ROBO1, KLK6, LIMK2, KLK7, NLGN4X, MBP, and NEDD9 gene expression levels were higher in normal tissues than in tumors; however, EFNA1 was higher in the tumor than the normal ones. EFNA1, LIMK2, ROBO1, KLK6, KLK7, and MBP gene expression had a negative ER status, whereas NEDD9 and NLGN4X were not significant concerning ER status. In conclusion, important markers have been analyzed concerning genes related to the nervous system, opening up a new avenue of studies in breast cancer therapy.

Graphical Abstract

1. Introduction

Breast cancer is the most prevalent cancer and one of the main causes of cancer death in women, accounting for 685,000 cancer deaths globally in 2020 [1,2]. Chemotherapy resistance is a significant problem affecting the effectiveness of treatment for breast cancer, despite extensive research in the field [3].
Early breast cancer is thought to be treatable if it remains within the breast or has progressed only to the axillary lymph nodes [4]. Nevertheless, a prognosis in the late stages, when cancer has disseminated to distant places, is difficult to treat, despite the advancements in endocrine therapy. A familial connection exists in 10% of cases, and mutations in the tumor suppressor genes BRCA1 and BRCA2 confer a hereditary risk [5]. The genomic amplification of ERBB2, a gene that codes for the epidermal growth factor receptor 2 (HER2), is one of the additional contributing factors [4]. According to studies [6,7,8], humanized anti-HER2 antibodies like trastuzumab and pertuzumab, as well as selective estrogen receptor modulators (SERMs) like tamoxifen and toremifene, represent the gold standard for treating patients with breast cancers that are ER and HER2 positive, respectively.
Until recently, the active interaction between the nervous system and breast cancer cells in the tumor microenvironment was not recognized. The nervous system has long been seen as a bystander in breast cancer [9]. Thus, a study described neurotransmitters and growth factors produced by neurons as signal pathways activators that controlled breast cancer cell proliferation, invasion, metastasis, and treatment resistance [10].
Clinical factors such as tumor size, tumor grade, lymph node status, age, and the immunochemical status of the three tumor receptors—estrogen receptor (ER), progesterone receptor (PR), and HER2—are taken into consideration while deciding on adjuvant therapy. Over the past few decades, breast cancer patients have had an increase in survival thanks to the identification of breast cancer subtypes and relapse risk [4]. However, the role of the nervous system has not been contemplated. On the other hand, the initiation and progression of breast cancer have been associated with the dysregulation of axon guidance molecules (AGMs) in the mammary gland, both through autocrine effects on tumor cells and paracrine effects on endothelial cells that stimulate angiogenesis [11].
For instance, the function of the gene Ephrin (EFNA1) in development has changed significantly over the past ten years compared to how it was initially described where it served as an attracting or repulsive element to direct axonal growth and migration [12]. It was demonstrated that such a gene serves as a mediator not only to tissue morphogenesis but also cell adhesion and proliferation in the mammary gland [11]. Since then, the functions of the Ephrin gene have been expanded to include roles such as regulating tissue morphogenesis and development in a variety of organs, but it can be considered as having potential role in the development of new treatments and diagnostic markers for breast cancer because of such a dual role in carcinogenesis and progression [11].
On the other hand, a conserved ligand-receptor system called slit guidance ligand 2 (Slit2)/roundabout guidance receptor 1 (ROBO1) has a significant impact on the distribution, migration, axon guidance, and branching of neuronal cells. In gliomas, Slit2 and its transmembrane receptor ROBO1 have various distribution patterns; in contrast to ROBO1, which is highly expressed in many grades of gliomas at both the mRNA and protein levels, Slit2 is weakly expressed in pilocytic astrocytoma, fibrillary astrocytoma, and glioblastoma [13].
New putative members of the human kallikrein gene family have been discovered in the last few years. Five additional putative kallikrein genes have been included in this family and are known as KLK-L2, KLK-L3, KLK-L4, KLK-L5, and KLK-L6. Human kallikrein6 (protease M/zyme/neurosin) was initially identified as a biomarker for ovarian cancer based on its aberrant expression in tumor cells [14].
Neuroligin 4 X-linked gene (NLGN4X) encodes a member of the type-B carboxylesterase/lipase protein family. The encoded protein belongs to a family of neuronal cell surface proteins, and members of this family may act as splice-site-specific ligands for beta-neurexins and may be involved in the formation and remodeling of central nervous system synapses. The encoded protein interacts with discs large homolog 4 (DLG4). The neuroligin as Neuroligin 2X (NLGN2) is a well-recognized transmembrane scaffolding protein that functions in synapse development and neuronal signal transduction. However, NLGN 4X has been found abundantly expressed in breast cancer tissues [15].
The human brain myelin basic protein (MBP) is also distributed in the nervous system and extensively in other tissues, where it can be detected in many types of tumor cells, such as neuroglioma, lung cancer, and breast cancer [16].
The neural precursor cell expressed, developmentally downregulated protein 9 (NEDD9) has also been linked to cancer and it is a component of the metastatic signatures of melanoma, breast cancer, glioblastoma, lung cancer, and head and neck squamous cell carcinoma [17]. NEDD9 overexpression has been associated with a poor prognosis [18]. However, its function in the biology of breast tumors is not known. Authors have examined the function of NEDD4 expression and have found that it is linked to the progression of breast cancer and serves as a predictor of a poor prognosis [19]. Thus, the Crk-associated substrate family member NEDD9 is implicated in the adhesion, migration, invasion, and epithelial–mesenchymal transition of cancer cells as E-cadherin [20]. It is known that cellular invasion during the epithelial–mesenchymal transition pathway is largely mediated via E-cadherin [21].
The genes involved in the nervous system have been overlooked. The approaches to study the main causes of treatment resistance in breast cancer have underestimated the role of such genes in clinical outcomes. The characterization of neuronal influence on tumor growth and the identification of new therapy targets will be made easier through the identification of nerve system signatures linked to the treatment response. This study offers nervous-system-related predictive and prognostic markers. A better understanding of the molecular events of such genes might help in developing novel prognostic biomarkers to predict prognosis or sensitivity to chemotherapy. Because experimental studies on cancer rarely concentrate on the relationship between the central nervous system (CNS) and cancer, this work aimed to analyze the main target genes between the CNS and an experimental breast cancer model.

2. Materials and Methods

2.1. Cell Lines

DMEM/F-12 (1:1) medium supplemented with antibiotics [100 U/mL penicillin, 100 g/mL streptomycin, and 2.5 g/mL amphotericin B (all from Life Technologies, Grand Island, NY, USA)] was used to culture MCF-10F cells, a negative estrogen receptor cell line. Moreover, 0.02 g/mL of epidermal growth factor (Collaborative Research, Bedford, MA, USA), 0.5 g/mL of hydrocortisone (Sigma, St. Louis, MO, USA), and 10 g/mL and 5% equine serum (Biofluids, Rockville, MD, USA) were added [22,23,24,25,26]. The cell lines from the Alpha model included (i) a control cell line (MCF-10F); (ii) an estrogen cell line (E), a negative estrogen receptor cell line; (iii) a malignant cell line (Alpha3), (iv) a malignant and tumorigenic cell line (Alpha5), positive for estrogen receptors, and (v) the Tumor2 cell line, positive for estrogen receptors [23].

2.2. Irradiation

Three days before radiation, exponentially expanding MCF-10F cells were plated in 60 mm diameter stainless steel rings with a 6 µm mylar bottom at a density of 3 × 105 cells per ring. At the Columbia University Radiological Research Facilities, cells were exposed to graded doses of 150 KeV/µm 4He ions that were accelerated to 4 MeV using the van de Graaff accelerator, as previously mentioned [27]. These high-energy particles have a LET value similar to the radon progeny particles. The MCF-10F cells were subcultured for 12–14 weeks in between doses and either a single or double dose of 30, 60, or 100 cGy of 4He ions was administered. Irradiated cultures were subcultured right away to establish growth kinetics and grown further in culture to test for changed phenotypes. At the same time, samples were frozen as future stock. The remaining cells were then subsequently passaged for additional treatment with radiation and sampled for different altered phenotypes. After that, cells were grown with or without estrogen. Cell growth kinetics, anchorage-independent growth, invasiveness, tumorigenicity, and the expression of the BRCA1, BRCA2, and RAD51 proteins were assessed in irradiated cultures.

2.3. Alpha-Model

The immortalized human breast epithelial cell line MCF-10F was exposed to low doses of high-linear-energy-transfer (LET) particle radiation (150 keV/m) and subsequent growth in the presence or absence of 17ß-estradiol to create an in vitro experimental breast cancer model. After receiving either a single 60 cGy dose or two 60 cGy doses of alpha particles, the MCF-10F cell line underwent various stages of transformation that included altered morphology, increased cell proliferation compared to the control, anchorage-independent growth, and invasive capability before becoming tumorigenic in nude mice, as previously described [23]. Briefly, the experimental cell lines were: (a) the parental non-irradiated MCF-10F cell line (Control); (b) a non-irradiated MCF-l0F cell line continuously treated with estradiol at 10−8 M (E); (c) MCF-10F cells irradiated with a double dose of 60/60 cGy α particles, named Alpha3 (A3), a malignant and non-tumorigenic cell line; (d) MCF-10F cells irradiated with a double dose of 60/60 cGy plus estrogen, named Alpha5 (A5), a tumorigenic cell line; and (e) the Tumor2 cell line (T2) derived from the mammary tumors formed in nude mice after they were injected with the A5 cell line. The A5 and T2 cell lines were tumorigenic in the model and SCID animal, whereas the E and A3 cell lines did not cause mammary tumors. These cell lines were grown either with or without estrogen for up to 10 months.

2.4. Preparation of Fluorescence-Labeled Probes for Microarray Analysis

The QIA-Direct-mRNA Isolation kit (Qiagen, Valencia, CA, USA) was used to isolate the poly(A) mRNA from normal, radiation-treated, and estrogen-treated breast cell lines. Following the standard procedure as previously described [28], fluorescent-labeled cDNA was generated from 1 µg of each of these poly(A)mRNAs using oligo dT-primed polymerization and the Superscript II reverse transcriptase kit (Life Technologies, Grand Island, NY, USA) in the presence of either Cy3- or Cy5-labeled dCTP. For analysis, the relevant Cy3- and Cy5-labeled probes were combined, hybridized to the microarray on glass coverslips for 16 h at 65 °C, and then thoroughly washed.

2.5. Analysis of Microarray Gene Expression Using the Affymetrix HG-U133A Plus 2.0 GeneChip

The Affymetrix U133A oligonucleotide microarray (Affymetrix, Santa Clara, CA, USA), which comprises 14,500 genes, was used to assess gene expression in the breast cancer model (Alpha-model), which included the cell lines MCF-10F, Estrogen, Alpha3, Alpha5, and Tumor2. The Affymetrix GeneChip Operating Software (GCOS) v1.0 ST, the Genes@Work software platform, and the discovery algorithm SPLASH (structural pattern localization analysis by sequential histograms), with a false discovery rate of 0.05, were used to quantitatively analyze arrays for gene expression [29].

2.6. Bioinformatic Gene Expression Analysis and Statistical Analysis

Through its three main components of Immune Association, Cancer Exploration, and Immune Estimation, TIMER2.0 is a web tool that systematically analyzes immune infiltrates across various cancer types. The Cancer Exploration component included the Gene_DE module that provided the differential gene expression between tumor and normal tissues, displaying the distribution of gene expression levels using box plots; the statistical significance computed via the Wilcoxon test was annotated with the number of stars (*: p < 0.05, **: p < 0.01, ***: p < 0.001); the Gene_Corr module provided the correlation between genes with the statistical analysis carried out using Spearman’s test; and the Gene_Outcome module used the Cox proportional hazard model to evaluate the outcome significance of gene expression adjusted via the stage clinical factor [30].
UCSC Xena, another web resource of information, provided the ER status of the genes involved in this study; the statistical significance was computed via a one-way ANOVA test [31]. p < 0.05 was considered significant.

3. Results

The approach behind this work was to find the marker genes related to the nervous system, and the marker genes were chosen based on data in the literature. Then, gene expression was measured in the cell lines developed in this experimental cancer model, produced from MCF-10F, exposed to LET radiation, and/or incubated in the presence of estradiol and cell lines, derived from a xenograft of irradiated and estradiol-incubated cells, inoculated in nude mice. Among differentially expressed genes (Table 1), analyzed via Affymetrix HG U-133A oligonucleotide microarray, the expression level of preselected genes was assessed and compared between the model cell lines. The correlation between estrogen receptor α ESR1 and nervous system genes in BRCA patients was estimated using the web platform TIMER2.0, as well as the differential gene expression levels between tumor and normal tissues, and the clinical relevance of gene expression across various breast cancer subtypes was analyzed using the disease stage factor. Gene expression and estrogen receptor status in TCGA breast cancer were provided via the UCSC Xena resource.

3.1. Gene Expression Induced via Radiation and Estrogen

High-LET radiation, such as that emitted by radon progeny, in combination with estrogen, caused a series of events that were indicative of cell transformation and tumorigenicity in human breast epithelial cells, according to the profiling of differentially expressed genes obtained using an Affymetrix array U133A (Figure 1).
The results in Figure 2A,B show that T2 and A5 had greater EFNA1 and ROBO1 gene expression than A3. The cell line T2 had greater KLK6 and LIMK2 expression than the A3 and A5 cell lines (Figure 2C,D). One of the cell lines, T2, had greater KLK7, NLGN4X, and MBP expression than A3 (Figure 2E,G) and, finally, the A5 cell line had higher NEDD9 gene expression than the A3 cell line (Figure 2H).

3.2. Correlation between ERα and Nervous System Genes in BRCA Patients

Figure 3 shows there was a positive significant (p < 0.05) correlation between the ESR1 gene expression level and EFNA1 expression in BRCA-LumA cancer patients as well as the ROBO1 expression level in BRCA-Basal patients, but a negative significant (p < 0.05) correlation between the ESR1 and KLK6 gene expression levels in BRCA-LumA and BRCA-LumB patients, as well as between the KLK6, LIMK2, and ROBO1 expression levels in all BRCA patients.
Figure 4 shows there was a negative significant (p < 0.05) correlation between ESR1 expression and KLK7 levels in all BRCA and BRCA-LumA cancer patients as well as in NEDD9 in BRCA-Her2 breast cancer patients. We also observed a positive significant (p < 0.05) correlation between the ESR1 gene expression level and NLGN4X expression in BRCA-Her2 and BRCA-LumB cancer patients and MBP expression in BRCA-LumA and -LumB patients.

3.3. Differential Gene Expression Levels between Tumor and Normal Tissues across Various Cancer Types

The differential gene expression levels between the tumor and adjacent tissues displayed in the box plot (Figure 5A–H) show that the EFNA1 expression level was significantly (p < 0.001) higher in the tumor tissue than in the normal ones. However, the ROBO1, KLK6, LIMK2, KLK7, NLGN4X, MBP, and NEDD9 gene expression levels were significantly (p < 0.001) higher in normal tissues than in tumors.

3.4. Gene Expression and Estrogen Receptor Status in TCGA Breast Cancer

UCSC Xena is an online tool for exploring multiomic, clinical, and phenotypic data [31], including data from The Cancer Genome Atlas (TCGA), which has completed the most thorough cross-cancer investigation to date by creating detailed, multidimensional maps of the main genetic alterations in 33 different forms of cancer [32].
The results in Figure 6A–H from the UCSC Xena online exploration tool [31] indicated that the patients’ samples having higher EFNA1, ROBO1, KLK6, LIMK2, KLK7, and MBP gene expression had negative ER, whereas the NLGN4X and NEDD9 gene expressions were not significantly different with regard to ER status.

3.5. Disease Stage Factor Analysis of Gene Expression across Various Breast Cancer Subtypes

Numerous genetic analysis studies have shown the molecular components of several pathways, and the discovery of genes linked to particular tissues has helped elucidate their biological role and put disease states like breast cancer, and subtypes like BRCA-Basal, BRCA-Her2, BRCA-LumA, and BRCA-LumB, into context [33,34,35].
The Cox proportional hazard model was used to evaluate the survival difference of gene expression in BRCA (n = 1100) patients adjusted by clinical factors. Overall, when the clinical stages of patients were analyzed in breast invasive carcinoma (Table 2), the results indicated that the genes EFNA1, ROBO1, KLK6, LIMK2, KLK7, NLGN4X, MBP, and NEDD9 showed no significance at any stage in BRCA-Basal patients but were significantly (p < 0.001) higher in stages 3 and 4 for all BRCA patients and stage 4 for BRCA-LumA patients than in other clinical stages. Additionally, there was a significant (p < 0.05 or p < 0.01) difference at stage 4 in BRCA-Her2 and BRCA-LumB patients.

4. Discussion

The analysis of differentially expressed genes from an Affymetrix array revealed that estrogen and high-LET radiation caused a series of events in human breast epithelial cells that are suggestive of cell transformation and tumorigenicity in the tumorigenic cell lines, where alterations were uncontrolled in comparison to the normal, non-tumorigenic MCF-10F cell line.
Experimental investigations need to focus on the connection between the central nervous system (CNS) and cancer to comprehend the molecular processes required to create new prognostic biomarkers for cancer. Therefore, this study examined the primary CNS-related genes in an experimental breast cancer model.
EFNA1 had greater expression in T2 and A5 than other cell lines of the model, indicating that the tumorigenic effect induced by radiation and estrogen affected EFNA expression. There was a positive correlation between ESR1 and EFNA1 gene expression in BRCA-Lum-A patients, corroborated via the EFNA1 differential gene expression level being higher in the tumor than the normal tissues and a negative ER status as well as significantly higher EFNA levels in stages 3 and 4 in all BRCA patients and stage 4 in BRCA-LumA patients, and a significant difference in stage 4 in BRCA-Her2 and BRCA-LumB patients, but not significance in any stage in BRCA-Basal patients.
ROBO1 had a greater gene expression level in the A5 and T2 cell lines than A3, as with the previously mentioned genes. There was a negative correlation between ESR1 and ROBO1 in all BRCA, but positive in BRCA-Basal patients, indicating that ROBO1 could be a good complementary gene as a biomarker for breast cancer since there are few markers for BRCA-Basal cancer patients. Patients’ samples with a high ROBO1 gene expression had a negative ER status, and the differential gene expression levels showed that ROBO1 expression levels were significantly higher in normal tissues than in tumors. However, ROBO1 gene expression levels were higher in stages 3 and 4 in all BRCA patients, and in stage 4 in BRCA-LumA patients, than in other clinical stages, and also showed a significant difference in stage 4 in BRCA-Her2 and BRCA-LumB patients. However, this gene showed no significance in any stage in BRCA-Basal patients.
LIMK2 showed greater expression in T2 than A5 and A3 cell lines of the model, demonstrating that it is a good marker for carcinogenicity. A negative correlation was observed between ESR1 and LIMK2 in all BRCA patients. Furthermore, the presence of a positive correlation in BRCA-Basal patients indicates that it can be a good marker for patients who are negative for the present therapy options, such as hormonal responses. The LIMK2 differential gene expression levels were significantly higher in normal tissues than in tumors, demonstrating that it is a possible tumor suppressor gene. However, other results have shown that LIMK2 was overexpressed in triple-negative breast cancer (TNBC) and was necessary for facilitating metastasis. To understand the role of LIMK2 in breast cancer, we first asked whether LIMK2 was overexpressed in breast cancer. Authors [36] analyzed a breast cancer tissue microarray that included 100 cases of invasive breast cancer and 10 adjacent normal breast tissues with information on ER, PR, and HER2 status. They observed that patients’ samples with high LIMK2 gene expression had a negative ER status, which was also common in advanced stages of this disease.
The present work indicates that LIMK2 gene expression levels were very high in stages 3 and 4 for all BRCA patients, and stage 4 for BRCA-LumA patients, compared to other clinical stages. Moreover, stage 4 showed a significant difference in patients with BRCA-Her2 and BRCA-LumB. However, in BRCA-Basal patients, this gene displayed no significance at any stage.
A possible target for glioma prevention and treatment is Slit2/ROBO1 signaling. In humans, 4% to 50% of cancers of the nervous system are gliomas, which develop from neural mesenchymal cells [37]. The infiltrative nature of gliomas, which leads to inadequate surgical excision, is one of the main barriers to their effective treatment [38]. Authors reported that malignant glioma cells developed insidious invasiveness through a variety of genetic changes to signaling pathways [39]. It was demonstrated that Slit2/ROBO1 signaling prevented glioma cell motility and invasion by inactivating Cdc42-GTP, even though the precise mechanisms behind this tumor-suppressive gene impact of Slit2/ROBO1 remain unknown [13].
KLK6 and KLK7 showed greater expression in T2 than in the A3 and A5 cell lines in the model. There was a negative correlation between ESR1 and KLK6 in all BRCA, BRCA-LumA, and LumB patients, and KLK7 in all BRCA and BRCA-LumA. The differential gene expression levels between the tumor and adjacent tissue showed that KLK6 and KLK7 expression levels were higher in normal tissues than in tumors. According to a study [40], the value of the kallikrein-related peptidase 5 gene KLK5 and KLK7 in the diagnosis and prognosis prediction of breast cancer patients was far from clear. To further determine their role and clinical significance in breast cancer and to explore the relationship between KLK5 and KLK7, the mRNA levels of KLK5 and KLK7 in normal breast tissues, benign breast tissues, primary tumors, and lymph node metastases were detected using a real-time reverse transcription polymerase chain reaction and microarray. The relationship between KLK5 and KLK7 expression and clinicopathological parameters and the correlation between the mRNA levels of KLK5 and KLK7, as well as the 5′-noncoding regions of KLK5 and KLK7, were analyzed. The mRNA levels of both KLK5 and KLK7 were downregulated in breast cancers relative to normal and benign tissues, and downregulated in metastases compared to primary cancers.
The present study shows that patient samples with higher KLK6 and KLK7 gene expression had a negative ER status. KLK6 and KLK7 gene expression levels were higher in stages 3 and 4 for all BRCA patients, and in stage 4 for BRCA-LumA patients, than in other clinical stages. Additionally, stage 4 showed a significant difference in patients with BRCA-Her2 and BRCA-LumB. However, in BRCA-Basal patients, this gene displayed no significance at any stage.
Experimental data suggest that KLK6 may have a potential function in the CNS; the human kallikrein gene family includes kallikrein 6 (hK6), also called KLK6 (zyme/protease M/neurosin), and other related proteins [41]. Zyme appears to play a role in breast cancer, and the cDNA for the KLK6 gene has just been discovered [42]. A study [43] indicated that differential proteomic profiling revealed that KLK6 re-expression resulted in significant down-regulation of vimentin, which represented an established marker of epithelial-to-mesenchymal transition of tumor cells and the concomitant up-regulation of calreticulin and epithelial markers cytokeratin 8 and 19, indicating that KLK6 might play a protective role against tumor progression that was likely mediated via the inhibition of epithelial-to-mesenchymal transition. Such a study suggested that KLK6 was an epigenetically regulated tumor suppressor in human breast cancer and provided methods for pharmacologic modulation. The overexpression of the mRNA in breast and ovarian primary cells was demonstrated by authors who earlier cloned the cDNA for this gene via differential display [44].
The NLGN4X gene expression level showed greater expression in T2 than the A3 and A5 cell lines of the model. There was a positive correlation between ESR1 and the gene expression of NLGN4X in BRCA Her2 and BRCA-LumB patients. Authors [15] showed that neuroligins were neural cell adhesion molecules that were implicated in hetero-topic cell adhesion and were highly expressed in blood vessels and implicated in the growth of glioma cells; they reported an increased expression of NLGN4X in breast cancer tissues. However, our analysis indicated that the differential gene expression of NLGN4X levels was higher in normal tissues than in tumors, indicating that other factors could be involved in such a difference. On the other hand, NLGN4X expression levels were also higher in stages 3 and 4 for all BRCA patients, as well as in stage 4 for BRCA-LumA patients, compared to other clinical stages. Additionally, stage 4 showed a substantial difference between patients with BRCA-Her2 and with BRCA-LumB. However, in BRCA-Basal patients, this gene displayed no significance at any stage. Furthermore, this gene expression was not significant concerning ER status. It has been reported that the NLGN gene family has great importance in mediating synapse formation in the central nervous system and displays a strong and selective synapse formation, which promotes activity between neurons in vitro.
Even though the MBP gene expression was greater in T2 than in the A3 and A5 cell lines of the model, the differential gene expression levels between the tumor and adjacent normal tissue showed that MBP expression levels were significantly higher in normal tissues than in tumors, indicating that T2 was greater due to another factor not present in the normal tissues.
On the other hand, the ESR1 and MBP correlation was positive in BRCA-LumA and Lum-B cancer patients. Patient samples with higher MBP gene expression had a negative ER status corroborated via higher MBP expression levels in stages 3 and 4 for all BRCA patients, as well as in stage 4 for BRCA-LumA patients, than in other clinical stages. Moreover, stage 4 showed a significant difference between patients with BRCA-Her2 and with BRCA-LumB. But, in BRCA-Basal patients, this gene displayed no significance at any stage.
Studies have shown that MBP plays a role in immunity: it was serially measured in 177 CSF samples from 33 patients with leptomeningeal metastases and compared to 34 cancer controls to determine the degree of autosensitization to MBP [45].
The NEDD9 gene expression level showed greater expression in the A5 than the E cell line of the model, indicating that radiation was involved in the transformation process. The differential gene expression levels showed that NEDD9 expression levels were significantly higher in normal tissues than in tumors, corroborating that NEDD9 gene expression is activated before tumor formation. Otherwise, T2 would have been higher than the A5 cell line. A negative correlation was observed between ESR1 and NEDD9 in BRCA-Her2 patients. This gene expression was not significant concerning ER status. However, the gene expression levels were significantly different at stage 4 for BRCA-Her2 and BRCA-LumB patients but, for BRCA-Basal patients, this gene showed no significant difference at any stage.
The T2 cell line had greater EFNA1, ROBO1, KLK7, NLGN4X, and MBP gene expression when compared to A3, a non-malignant cell line treated with alpha particles without estrogen, or the A5, treated with both. Such results suggest that the heterogeneity of the tumor microenvironment after tumor formation in the SCID mouse could affect the changes in many genes, influencing resistance in breast cancer (Scheme 1). Among such components, the nervous system seems to be affected, as seen by the list of genes above mentioned. However, such a factor has been underestimated in clinical outcomes. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers has been analyzed on merged transcriptomic datasets from public databases to obtain potential gene signatures.
Authors [46] presented prognostic and predictive signatures pertaining to the neurological system. Such a work indicated that through a better understanding of the molecular processes behind these genes, new prognostic biomarkers for prognosis prediction or chemotherapy sensitivity could be developed. They suggested that the generated signatures showed prognostic and predictive potential after analysis using Cox models. The signatures generated from the first and external cohorts were compared to 14 other published signatures. As a result, the T pipeline produced the 24 and 97 gene signatures (NervSign24 and NervSign97) for the nervous system. Such signals were not chemotherapy predictive, but they were predictive for prognosis and hormone therapy. NervSign97 and NervSign24 were the two top performers when their predictive performance was compared to 14 documented risk signatures in six hormonotherapy-treated groups. Brain neural progenitor presence and perineural invasion were found to be nervous system-related mechanisms that were positively associated with NervSign97 and a poor clinical prognosis in patients receiving hormone therapy, according to pathway enrichment scores and deconvolution analysis. Further investigation into the role of neuronal components in tumor progression is warranted in light of the identification of two nervous-system-related signatures via transcriptomic profiling that were verified in clinical samples as hormonotherapy-predictive signatures [46]. Such a study provided two hormonotherapy-predictive and prognostic signatures related to the nervous system. It can highlight tumor neuronal components as potential new targets for breast cancer therapy. Then, the development of personalized medicine could be the future of cancer therapy.
Another factor to be considered for the many genes affected in the T2 cell line is the innervation, another hallmark of cancer, that also includes neurons and not only fibroblasts, adipocytes, endothelial cells, and lymphocytes [47]. The neurons are now considered important in breast cancer progression.
The T2 cell line is derived from a solid tumor formed by factors present in the microenvironment; as nerve fibers usually originate from the peripheral nervous system [48,49]. Many cancers, including breast cancer [50,51], can progress due to tumor innervation [52]. It has been shown that innervation is linked to a poor prognosis (overall and disease-free survival) and that nerves stimulate the carcinogenesis of breast cancer [53]. Sympathetic, parasympathetic, and sensory nerves comprise the autonomic nervous system, which is the primary source of innervation for tumors. It is commonly recognized that, in physiological settings, the sympathetic and parasympathetic/vagal nerves regulate the activity of practically every organ [54].
The SLIT and ROBO signaling pathways regulate axon guidance, neuronal migration, and axonal remnants in the nervous system by acting as a neural targeting factor. Recent studies have shown that distinct tumor cells display varying expression patterns at different stages of tumor angiogenesis, cell invasion, metastasis, and infiltration, in addition to varying levels of SLIT/ROBO signaling. The SLIT and ROBO axon-guidance molecules are now known to perform new functions in the onset of liver fibrosis and cancer [55]. ROBO1 has been implicated as a tumor suppressor for various types of cancer, including breast cancer, and the transmembrane proline-rich γ-carboxyglutamic acid protein 4 (PRRG4) may be a novel target for treating metastatic breast cancer; however, the regulation of ROBO1 expression during tumorigenesis remains poorly understood. However, it was found that via downregulating Robo1, PRRG4 promoted breast cancer metastasis; thus, PRRG4 expression was found to be higher in breast tumors than in normal breast tissues. PRRG4 was found to boost metastasis in an experimental metastasis model and to promote the migration and invasion of breast cancer cells in experiments including PRRG4 overexpression and knockdown [56].

5. Conclusions

The study described in this article involves genes related to the nervous system and changes due to the effect of estrogen and radiation. The expression of genes related to the nervous system, including EFNA1, ROBO1, KLK6, LIMK2, KLK7, NLGN4X, MBP, and NEDD9 was higher in the tumorigenic T2 than in the control cell line. There was a positive correlation between ESR1 and ROBO1 gene expression in BRCA-Basal patients, and EGFR showed a positive correlation with LIMK2, ROBO1, NEDD9, and MBP expression levels in BRCA-Basal patients, as well, demonstrating to be a good marker for carcinogenicity, especially for Basal breast cancer patients. ROBO1, KLK6, LIMK2, KLK7, NLGN4X, MBP, and NEDD9 expression levels were significantly higher in normal tissues than in tumors, demonstrating the possibility of being a tumor suppressor gene. Thus, experimental investigations need to be focused on the connection between the central nervous system and cancer to comprehend the molecular processes required to create new prognostic biomarkers.

Author Contributions

Formal analysis, G.M.C., D.R., L.J. and L.A.C.; Writing—original draft, G.M.C.; Writing—review and editing, G.M.C., D.R., L.J., F.A. and L.A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT), grant numbers FONDECYT 1200656 (G.M.C.) and FONDECYT 1231537 (G.M.C.).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

TIMER2.0 is freely available at http://timer.cistrome.org (accessed on 6 August 2021); UCSC Xena online exploration tools are freely available at http://xena.ucsc.edu/ (accessed on 20 August 2021). The data generated in the present study may be requested from the corresponding author.

Acknowledgments

The authors want to thank The Agencia Nacional de Investigación y Desarrollo (ANID) of the Government of Chile for its financial support.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Al-Khater, K.M.; Almofty, S.; Ravinayagam, V.; Alrushaid, N.; Rehman, S. Role of a metastatic suppressor gene KAI1/CD82 in the diagnosis and prognosis of breast cancer. Saudi J. Biol. Sci. 2021, 28, 3391–3398. [Google Scholar] [CrossRef]
  2. Lei, S.; Zheng, R.; Zhang, S.; Wang, S.; Chen, R.; Sun, K.; Zeng, H.; Zhou, J.; Wei, W. Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020. Cancer Commun. 2021, 41, 1183–1194. [Google Scholar] [CrossRef] [PubMed]
  3. Vasan, N.; Baselga, J.; Hyman, D.M. A view on drug resistance in cancer. Nature 2019, 575, 299–309. [Google Scholar] [CrossRef] [PubMed]
  4. Harbeck, N.; Penault-Llorca, F.; Cortes, J.; Gnant, M.; Houssami, N.; Poortmans, P.; Ruddy, K.; Tsang, J.; Cardoso, F. Breast cancer. Nat. Rev. Dis. Primers 2019, 5, 66. [Google Scholar] [CrossRef]
  5. Kuchenbaecker, K.B.; Hopper, J.L.; Barnes, D.R.; Phillips, K.A.; Mooij, T.M.; Roos-Blom, M.J.; Jervis, S.; van Leeuwen, F.E.; Milne, R.L.; Andrieu, N.; et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers. JAMA 2017, 317, 2402–2416. [Google Scholar] [CrossRef] [PubMed]
  6. Korde, L.A.; Somerfield, M.R.; Carey, L.A.; Crews, J.R.; Denduluri, N.; Hwang, E.S.; Khan, S.A.; Loibl, S.; Morris, E.A.; Perez, A.; et al. Neoadjuvant Chemotherapy, Endocrine Therapy, and Targeted Therapy for Breast Cancer: ASCO Guideline. J. Clin. Oncol. 2021, 39, 1485–1505. [Google Scholar] [CrossRef]
  7. Robson, M.E.; Tung, N.; Conte, P.; Im, S.A.; Senkus, E.; Xu, B.; Masuda, N.; Delaloge, S.; Li, W.; Armstrong, A.; et al. OlympiAD final overall survival and tolerability results: Olaparib versus chemotherapy treatment of physician’s choice in patients with a germline BRCA mutation and HER2-negative metastatic breast cancer. Ann. Oncol. 2019, 30, 558–566. [Google Scholar] [CrossRef]
  8. Litton, J.K.; Hurvitz, S.A.; Mina, L.A.; Rugo, H.S.; Lee, K.H.; Goncalves, A.; Diab, S.; Woodward, N.; Goodwin, A.; Yerushalmi, R.; et al. Talazoparib versus chemotherapy in patients with germline BRCA1/2-mutated HER2-negative advanced breast cancer: Final overall survival results from the EMBRACA trial. Ann. Oncol. 2020, 31, 1526–1535. [Google Scholar] [CrossRef]
  9. Boilly, B.; Faulkner, S.; Jobling, P.; Hondermarck, H. Nerve Dependence: From Regeneration to Cancer. Cancer Cell 2017, 31, 342–354. [Google Scholar] [CrossRef]
  10. Faulkner, S.; Jobling, P.; March, B.; Jiang, C.C.; Hondermarck, H. Tumor Neurobiology and the War of Nerves in Cancer. Cancer Discov. 2019, 9, 702–710. [Google Scholar] [CrossRef]
  11. Harburg, G.C.; Hinck, L. Navigating breast cancer: Axon guidance molecules as breast cancer tumor suppressors and oncogenes. J. Mammary Gland. Biol. Neoplasia 2011, 16, 257–270. [Google Scholar] [CrossRef] [PubMed]
  12. Cramer, K.S.; Miko, I.J. Eph-ephrin signaling in nervous system development. F1000Research 2016, 5, 413. [Google Scholar] [CrossRef] [PubMed]
  13. Jiang, Z.; Liang, G.; Xiao, Y.; Qin, T.; Chen, X.; Wu, E.; Ma, Q.; Wang, Z. Targeting the SLIT/ROBO pathway in tumor progression: Molecular mechanisms and therapeutic perspectives. Ther. Adv. Med. Oncol. 2019, 11, 1758835919855238. [Google Scholar] [CrossRef] [PubMed]
  14. Pampalakis, G.; Kurlender, L.; Diamandis, E.P.; Sotiropoulou, G. Cloning and characterization of novel isoforms of the human kallikrein 6 gene. Biochem. Biophys. Res. Commun. 2004, 320, 54–61. [Google Scholar] [CrossRef] [PubMed]
  15. Henderson, H.J.; Karanam, B.; Samant, R.; Vig, K.; Singh, S.R.; Yates, C.; Bedi, D. Neuroligin 4X overexpression in human breast cancer is associated with poor relapse-free survival. PLoS ONE 2017, 12, e0189662. [Google Scholar] [CrossRef]
  16. Chen, J.Y.; Wang, X.M.; Liu, J.; Chen, J.X.; Wang, R.H.; Peng, W.Z.; Cheng, H.H.; Chen, J.J. Inhibitory effect of human brain myelin basic protein on H2O2-induced apoptosis of human lung cancer cell line YTLMC-90. Ai Zheng 2006, 25, 170–174. [Google Scholar]
  17. Grauzam, S.; Brock, A.M.; Holmes, C.O.; Tiedeken, J.A.; Boniface, S.G.; Pierson, B.N.; Patterson, D.G.; Coaxum, S.D.; Neskey, D.M.; Rosenzweig, S.A. NEDD9 stimulated MMP9 secretion is required for invadopodia formation in oral squamous cell carcinoma. Oncotarget 2018, 9, 25503–25516. [Google Scholar] [CrossRef]
  18. Arslan, E.; Aral, H.; Aksoy, T.; Afsar, C.U.; Karabulut, S.; Trabulus, F.D.C.; Gursu, R.U.; Cermik, T.F. Comparison of serum NEDD-9, CA 15-3, and CEA levels and PET metabolic parameters in breast cancer patients with 18 F-FDG PET/CT. Rev. Da Assoc. Medica Bras. 2020, 66, 673–679. [Google Scholar] [CrossRef] [PubMed]
  19. Wan, L.; Liu, T.; Hong, Z.; Pan, Y.; Sizemore, S.T.; Zhang, J.; Ma, Z. NEDD4 expression is associated with breast cancer progression and is predictive of a poor prognosis. Breast Cancer Res. BCR 2019, 21, 148. [Google Scholar] [CrossRef]
  20. Li, H.; Li, J.; Han, R.; Deng, X.; Shi, J.; Huang, H.; Hamad, N.; McCaughley, A.; Liu, J.; Wang, C.; et al. Deletion of tetraspanin CD151 alters the Wnt oncogene-induced mammary tumorigenesis: A cell type-linked function and signaling. Neoplasia 2019, 21, 1151–1163. [Google Scholar] [CrossRef]
  21. Lamouille, S.; Xu, J.; Derynck, R. Molecular mechanisms of epithelial-mesenchymal transition. Nat. Rev. Mol. Cell Biol. 2014, 15, 178–196. [Google Scholar] [CrossRef] [PubMed]
  22. Calaf, G.; Russo, J. Transformation of human breast epithelial cells by chemical carcinogens. Carcinogenesis 1993, 14, 483–492. [Google Scholar] [CrossRef]
  23. Calaf, G.M.; Hei, T.K. Establishment of a radiation- and estrogen-induced breast cancer model. Carcinogenesis 2000, 21, 769–776. [Google Scholar] [CrossRef] [PubMed]
  24. Soule, H.D.; Maloney, T.M.; Wolman, S.R.; Peterson, W.D., Jr.; Brenz, R.; McGrath, C.M.; Russo, J.; Pauley, R.J.; Jones, R.F.; Brooks, S.C. Isolation and characterization of a spontaneously immortalized human breast epithelial cell line, MCF-10. Cancer Res. 1990, 50, 6075–6086. [Google Scholar] [PubMed]
  25. Calaf, G.M.; Roy, D. Gene and protein expressions induced by 17beta-estradiol and parathion in cultured breast epithelial cells. Mol. Med. 2007, 13, 255–265. [Google Scholar] [CrossRef] [PubMed]
  26. Calaf, G.M.; Roy, D. Cell adhesion proteins altered by 17beta estradiol and parathion in breast epithelial cells. Oncol. Rep. 2008, 19, 165–169. [Google Scholar] [PubMed]
  27. Hei, T.K.; Piao, C.Q.; Willey, J.C.; Thomas, S.; Hall, E.J. Malignant transformation of human bronchial epithelial cells by radon-simulated alpha-particles. Carcinogenesis 1994, 15, 431–437. [Google Scholar] [CrossRef] [PubMed]
  28. Calaf, G.M.; Roy, D.; Narayan, G.; Balajee, A.S. Differential expression of cell adhesion molecules in an ionizing radiation-induced breast cancer model system. Oncol. Rep. 2013, 30, 285–291. [Google Scholar] [CrossRef] [PubMed]
  29. Calaf, G.M.; Crispin, L.A.; Munoz, J.P.; Aguayo, F.; Roy, D.; Narayan, G. Ionizing Radiation and Estrogen Affecting Growth Factor Genes in an Experimental Breast Cancer Model. Int. J. Mol. Sci. 2022, 23, 14284. [Google Scholar] [CrossRef]
  30. Li, T.; Fu, J.; Zeng, Z.; Cohen, D.; Li, J.; Chen, Q.; Li, B.; Liu, X.S. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020, 48, W509–W514. [Google Scholar] [CrossRef]
  31. Goldman, M.; Craft, B.; Hastie, M.; Repecka, K.; McDade, F.; Kamath, A.; Banerjee, A.; Luo, Y.; Rogers, D.; Brooks, A.N.; et al. The UCSC Xena platform for public and private cancer genomics data visualization and interpretation. bioRxiv 2019, 326470, 1–16. [Google Scholar] [CrossRef]
  32. NIH. The Cancer Genome Atlas Program. National Cancer Institute. Available online: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga (accessed on 17 June 2022).
  33. Toda, S.; Frankel, N.W.; Lim, W.A. Engineering cell-cell communication networks: Programming multicellular behaviors. Curr. Opin. Chem. Biol. 2019, 52, 31–38. [Google Scholar] [CrossRef] [PubMed]
  34. Testa, U.; Castelli, G.; Pelosi, E. Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments. Med. Sci. 2020, 8, 18. [Google Scholar] [CrossRef] [PubMed]
  35. Cava, C.; Pisati, M.; Frasca, M.; Castiglioni, I. Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles. Medicina 2021, 57, 261. [Google Scholar] [CrossRef]
  36. Malvi, P.; Janostiak, R.; Chava, S.; Manrai, P.; Yoon, E.; Singh, K.; Harigopal, M.; Gupta, R.; Wajapeyee, N. LIMK2 promotes the metastatic progression of triple-negative breast cancer by activating SRPK1. Oncogenesis 2020, 9, 77. [Google Scholar] [CrossRef] [PubMed]
  37. Xu, Y.; Li, W.L.; Fu, L.; Gu, F.; Ma, Y.J. Slit2/Robo1 signaling in glioma migration and invasion. Neurosci. Bull. 2010, 26, 474–478. [Google Scholar] [CrossRef]
  38. Osuka, S.; Van Meir, E.G. Overcoming therapeutic resistance in glioblastoma: The way forward. J. Clin. Investig. 2017, 127, 415–426. [Google Scholar] [CrossRef]
  39. Yiin, J.J.; Hu, B.; Jarzynka, M.J.; Feng, H.; Liu, K.W.; Wu, J.Y.; Ma, H.I.; Cheng, S.Y. Slit2 inhibits glioma cell invasion in the brain by suppression of Cdc42 activity. Neuro Oncol. 2009, 11, 779–789. [Google Scholar] [CrossRef]
  40. Li, X.; Liu, J.; Wang, Y.; Zhang, L.; Ning, L.; Feng, Y. Parallel underexpression of kallikrein 5 and kallikrein 7 mRNA in breast malignancies. Cancer Sci. 2009, 100, 601–607. [Google Scholar] [CrossRef]
  41. Yousef, G.M.; Kishi, T.; Diamandis, E.P. Role of kallikrein enzymes in the central nervous system. Clin. Chim. Acta 2003, 329, 1–8. [Google Scholar] [CrossRef]
  42. Yousef, G.M.; Luo, L.Y.; Scherer, S.W.; Sotiropoulou, G.; Diamandis, E.P. Molecular characterization of zyme/protease M/neurosin (PRSS9), a hormonally regulated kallikrein-like serine protease. Genomics 1999, 62, 251–259. [Google Scholar] [CrossRef] [PubMed]
  43. Pampalakis, G.; Prosnikli, E.; Agalioti, T.; Vlahou, A.; Zoumpourlis, V.; Sotiropoulou, G. A tumor-protective role for human kallikrein-related peptidase 6 in breast cancer mediated by inhibition of epithelial-to-mesenchymal transition. Cancer Res. 2009, 69, 3779–3787. [Google Scholar] [CrossRef] [PubMed]
  44. Ni, X.; Zhang, W.; Huang, K.C.; Wang, Y.; Ng, S.K.; Mok, S.C.; Berkowitz, R.S.; Ng, S.W. Characterisation of human kallikrein 6/protease M expression in ovarian cancer. Br. J. Cancer 2004, 91, 725–731. [Google Scholar] [CrossRef] [PubMed]
  45. Gouda, Z.; Thomson, D.M. Measurement by leukocyte adherence inhibition of autosensitization of cancer patients to myelin basic protein. Jpn. J. Cancer Res. 1988, 79, 529–537. [Google Scholar] [CrossRef]
  46. Basseville, A.; Cordier, C.; Ben Azzouz, F.; Gouraud, W.; Lasla, H.; Panloup, F.; Campone, M.; Jezequel, P. Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy. Cancer Res. Commun. 2022, 2, 857–869. [Google Scholar] [CrossRef]
  47. Cui, Q.; Jiang, D.; Zhang, Y.; Chen, C. The tumor-nerve circuit in breast cancer. Cancer Metastasis Rev. 2023, 42, 543–574. [Google Scholar] [CrossRef]
  48. Magnon, C.; Hall, S.J.; Lin, J.; Xue, X.; Gerber, L.; Freedland, S.J.; Frenette, P.S. Autonomic nerve development contributes to prostate cancer progression. Science 2013, 341, 1236361. [Google Scholar] [CrossRef]
  49. Ayala, G.E.; Dai, H.; Powell, M.; Li, R.; Ding, Y.; Wheeler, T.M.; Shine, D.; Kadmon, D.; Thompson, T.; Miles, B.J.; et al. Cancer-related axonogenesis and neurogenesis in prostate cancer. Clin. Cancer Res. 2008, 14, 7593–7603. [Google Scholar] [CrossRef]
  50. Hondermarck, H. Neurotrophins and their receptors in breast cancer. Cytokine Growth Factor. Rev. 2012, 23, 357–365. [Google Scholar] [CrossRef]
  51. Kamiya, A.; Hayama, Y.; Kato, S.; Shimomura, A.; Shimomura, T.; Irie, K.; Kaneko, R.; Yanagawa, Y.; Kobayashi, K.; Ochiya, T. Genetic manipulation of autonomic nerve fiber innervation and activity and its effect on breast cancer progression. Nat. Neurosci. 2019, 22, 1289–1305. [Google Scholar] [CrossRef]
  52. Jin, M.Z.; Jin, W.L. The updated landscape of tumor microenvironment and drug repurposing. Signal Transduct. Target. Ther. 2020, 5, 166. [Google Scholar] [CrossRef]
  53. Gruet, M.; Cotton, D.; Coveney, C.; Boocock, D.J.; Wagner, S.; Komorowski, L.; Rees, R.C.; Pockley, A.G.; Garner, A.C.; Wallis, J.D.; et al. beta2-Adrenergic Signalling Promotes Cell Migration by Upregulating Expression of the Metastasis-Associated Molecule LYPD3. Biology 2020, 9, 39. [Google Scholar] [CrossRef]
  54. Cole, S.W.; Nagaraja, A.S.; Lutgendorf, S.K.; Green, P.A.; Sood, A.K. Sympathetic nervous system regulation of the tumour microenvironment. Nat. Rev. Cancer 2015, 15, 563–572. [Google Scholar] [CrossRef] [PubMed]
  55. Basha, S.; Jin-Smith, B.; Sun, C.; Pi, L. The SLIT/ROBO Pathway in Liver Fibrosis and Cancer. Biomolecules 2023, 13, 785. [Google Scholar] [CrossRef] [PubMed]
  56. Zhang, L.; Qin, Y.; Wu, G.; Wang, J.; Cao, J.; Wang, Y.; Wu, D.; Yang, K.; Zhao, Z.; He, L.; et al. PRRG4 promotes breast cancer metastasis through the recruitment of NEDD4 and downregulation of Robo1. Oncogene 2020, 39, 7196–7208. [Google Scholar] [CrossRef] [PubMed]
Biomedicines 11 03111 g001
Figure 1. (A) Heatmap of Affymetrix array (U133A) data to allow the comparison of the gene expression of the cell lines derived from the model: MCF-10F/estrogen (Ct/E); control/Alpha3 (Ct/A3); estrogen/Alpha5 (E/A5); Alpha3/Alpha5 (A3/A5); Alpha5/Tumor2 (A5/T2); and Alpha3/Tumor2 (A3/T2) (reprinted/adapted with permission from Ref. [28], Spandidos Publications, 2013). The red color indicates a higher expression; blue indicates a lower expression; and yellow indicates an equal expression (left panel). (B) The gene box plot summarizes the range of differential gene expression in the same pairwise cell line comparisons (right panel).
Figure 1. (A) Heatmap of Affymetrix array (U133A) data to allow the comparison of the gene expression of the cell lines derived from the model: MCF-10F/estrogen (Ct/E); control/Alpha3 (Ct/A3); estrogen/Alpha5 (E/A5); Alpha3/Alpha5 (A3/A5); Alpha5/Tumor2 (A5/T2); and Alpha3/Tumor2 (A3/T2) (reprinted/adapted with permission from Ref. [28], Spandidos Publications, 2013). The red color indicates a higher expression; blue indicates a lower expression; and yellow indicates an equal expression (left panel). (B) The gene box plot summarizes the range of differential gene expression in the same pairwise cell line comparisons (right panel).
Biomedicines 11 03111 g001
Biomedicines 11 03111 g002
Figure 2. The graphs show the profiling of differentially expressed genes obtained through an Affymetrix array (U133A), with data comparing genes such as (A) the ephrin A1 gene, EFNA1, (B) the roundabout guidance receptor 1 gene, ROBO1, (C) the kallikrein-related peptidase 6 gene, KLK6, (D) the LIM domain kinase 2 gene, LIMK2, (E) the kallikrein-related peptidase 7 gene, KLK7, (F) the neuroligin 4 X−linked gene, NLGN4X, (G) the myelin basic protein gene, MBP, and (H) the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 in the following cell lines: MCF-10F/Estrogen (Ct/E), Control/Alpha3 (Ct/A3), Estrogen/Alpha5 (E/A5), Alpha3/Alpha5 (A3/A5), Alpha5/Tumor2 (A5/T2), and Alpha3/Tumor2 (A3/T2). The graphs were obtained from a cluster dendrogram repository of gene expression from our laboratory for this article.
Figure 2. The graphs show the profiling of differentially expressed genes obtained through an Affymetrix array (U133A), with data comparing genes such as (A) the ephrin A1 gene, EFNA1, (B) the roundabout guidance receptor 1 gene, ROBO1, (C) the kallikrein-related peptidase 6 gene, KLK6, (D) the LIM domain kinase 2 gene, LIMK2, (E) the kallikrein-related peptidase 7 gene, KLK7, (F) the neuroligin 4 X−linked gene, NLGN4X, (G) the myelin basic protein gene, MBP, and (H) the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 in the following cell lines: MCF-10F/Estrogen (Ct/E), Control/Alpha3 (Ct/A3), Estrogen/Alpha5 (E/A5), Alpha3/Alpha5 (A3/A5), Alpha5/Tumor2 (A5/T2), and Alpha3/Tumor2 (A3/T2). The graphs were obtained from a cluster dendrogram repository of gene expression from our laboratory for this article.
Biomedicines 11 03111 g002
Biomedicines 11 03111 g003
Figure 3. (A) Heatmap table and (B) box plots representing the correlation between ESR1 expression with purity adjustment (left) and the ephrin A1 gene, EFNA1; the roundabout guidance receptor 1 gene, ROBO1; the kallikrein-related peptidase 6 gene, KLK6, and the LIM domain kinase 2 gene, LIMK2 expression levels (right) in breast invasive carcinoma (BRCA) with tumor purity. Heatmap table: the red color indicates a statistically significant positive correlation, the blue color indicates a statistically significant negative correlation, and the gray denotes a non-significant result. Box plots: the correlation values for each analysis are stated in red in the top right of the box. The statistical significance (Spearman’s, p < 0.05) was estimated using TIMER2.0, accessed on 17 June 2022 [30].
Figure 3. (A) Heatmap table and (B) box plots representing the correlation between ESR1 expression with purity adjustment (left) and the ephrin A1 gene, EFNA1; the roundabout guidance receptor 1 gene, ROBO1; the kallikrein-related peptidase 6 gene, KLK6, and the LIM domain kinase 2 gene, LIMK2 expression levels (right) in breast invasive carcinoma (BRCA) with tumor purity. Heatmap table: the red color indicates a statistically significant positive correlation, the blue color indicates a statistically significant negative correlation, and the gray denotes a non-significant result. Box plots: the correlation values for each analysis are stated in red in the top right of the box. The statistical significance (Spearman’s, p < 0.05) was estimated using TIMER2.0, accessed on 17 June 2022 [30].
Biomedicines 11 03111 g003
Biomedicines 11 03111 g004
Figure 4. (A) Heatmap table and (B) box plots representing the correlation between ESR1 expression with purity adjustment (left) and the kallikrein-related peptidase 7 gene, KLK7; the neuroligin 4 X−linked gene, NLGN4X; the myelin basic protein gene, MBP, and the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 expression levels (right) in breast invasive carcinoma (BRCA) with tumor purity. Heatmap table: the red color indicates a statistically significant positive correlation, the blue color indicates a statistically significant negative correlation, and the gray denotes a non-significant result. Box plots: the correlation values for each analysis are stated in red in the top right of the box. The statistical significance (Spearman’s, p < 0.05) was estimated using TIMER2.0, accessed on 17 June 2022 [30].
Figure 4. (A) Heatmap table and (B) box plots representing the correlation between ESR1 expression with purity adjustment (left) and the kallikrein-related peptidase 7 gene, KLK7; the neuroligin 4 X−linked gene, NLGN4X; the myelin basic protein gene, MBP, and the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 expression levels (right) in breast invasive carcinoma (BRCA) with tumor purity. Heatmap table: the red color indicates a statistically significant positive correlation, the blue color indicates a statistically significant negative correlation, and the gray denotes a non-significant result. Box plots: the correlation values for each analysis are stated in red in the top right of the box. The statistical significance (Spearman’s, p < 0.05) was estimated using TIMER2.0, accessed on 17 June 2022 [30].
Biomedicines 11 03111 g004
Biomedicines 11 03111 g005
Figure 5. The differential gene expression levels between tumor and normal tissues across various cancer types. The box plots show the distribution of gene expression levels of (A) the ephrin A1 gene, EFNA1, (B) the roundabout guidance receptor 1 gene, ROBO1 (C), the kallikrein-related peptidase 6 gene, KLK6, (D) the LIM domain kinase 2 gene, LIMK2, (E) the kallikrein-related peptidase 7 gene, KLK7, (F) the neuroligin 4 X-linked gene, NLGN4X, (G) the myelin basic protein gene, MBP, and (H) the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 in tumors versus normal tissues (Wilcoxon test, ***: p < 0.001) estimated using TIMER2.0 in breast invasive carcinoma, accessed on 17 June 2022 [30]. (1) BRCA.Tumor (n = 1093), (2) BRCA.Normal (n = 112), (3) BRCA—Basal.Tumor (n = 190), (4) BRCA—Her2.Tumor (n = 82), (5) BRCA—LumA.Tumor (n = 564), (6) BRCA—LumB.Tumor (n = 217).
Figure 5. The differential gene expression levels between tumor and normal tissues across various cancer types. The box plots show the distribution of gene expression levels of (A) the ephrin A1 gene, EFNA1, (B) the roundabout guidance receptor 1 gene, ROBO1 (C), the kallikrein-related peptidase 6 gene, KLK6, (D) the LIM domain kinase 2 gene, LIMK2, (E) the kallikrein-related peptidase 7 gene, KLK7, (F) the neuroligin 4 X-linked gene, NLGN4X, (G) the myelin basic protein gene, MBP, and (H) the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 in tumors versus normal tissues (Wilcoxon test, ***: p < 0.001) estimated using TIMER2.0 in breast invasive carcinoma, accessed on 17 June 2022 [30]. (1) BRCA.Tumor (n = 1093), (2) BRCA.Normal (n = 112), (3) BRCA—Basal.Tumor (n = 190), (4) BRCA—Her2.Tumor (n = 82), (5) BRCA—LumA.Tumor (n = 564), (6) BRCA—LumB.Tumor (n = 217).
Biomedicines 11 03111 g005
Biomedicines 11 03111 g006
Figure 6. Box plot transcript expression of (A) the ephrin A1 gene, EFNA1; (B) the roundabout guidance receptor 1 gene, ROBO1; (C) the kallikrein-related peptidase 6 gene, KLK6; (D) the LIM domain kinase 2 gene, LIMK2; (E) the kallikrein-related peptidase 7 gene, KLK7; (F) the neuroligin 4 X-linked gene, NLGN4X; (G) the myelin basic protein gene, MBP; and (H) the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 in breast cancer (TCGA, n = 782) stratified by nature2012 for estrogen receptor status (one-way ANOVA, p < 0.05). Raw data were extracted from the University of California, Santa Cruz (ena.ucsc.edu). UCSC Xena functional genomics explorer (https://xenabrowser.net) was accessed on 17 June 2022 [31].
Figure 6. Box plot transcript expression of (A) the ephrin A1 gene, EFNA1; (B) the roundabout guidance receptor 1 gene, ROBO1; (C) the kallikrein-related peptidase 6 gene, KLK6; (D) the LIM domain kinase 2 gene, LIMK2; (E) the kallikrein-related peptidase 7 gene, KLK7; (F) the neuroligin 4 X-linked gene, NLGN4X; (G) the myelin basic protein gene, MBP; and (H) the neural precursor cell expressed, developmentally down-regulated 9 gene, NEDD9 in breast cancer (TCGA, n = 782) stratified by nature2012 for estrogen receptor status (one-way ANOVA, p < 0.05). Raw data were extracted from the University of California, Santa Cruz (ena.ucsc.edu). UCSC Xena functional genomics explorer (https://xenabrowser.net) was accessed on 17 June 2022 [31].
Biomedicines 11 03111 g006
Biomedicines 11 03111 sch001
Scheme 1. Summary of the alpha model and main findings observed in this study. (1) EFNA1, ROBO1, KLK6, KLK7, LIMK2, NLGN4X, MBP, and NEDD9 gene expression in the estrogen (E), the Alpha3 (A3), the Alpha5, and the Tumor2 (T2) cell lines; (2) estrogen receptor α gene ESR1 expression in BRCA patients; (3) differential gene expression between tumor and normal tissues; (4) estrogen receptor status.
Scheme 1. Summary of the alpha model and main findings observed in this study. (1) EFNA1, ROBO1, KLK6, KLK7, LIMK2, NLGN4X, MBP, and NEDD9 gene expression in the estrogen (E), the Alpha3 (A3), the Alpha5, and the Tumor2 (T2) cell lines; (2) estrogen receptor α gene ESR1 expression in BRCA patients; (3) differential gene expression between tumor and normal tissues; (4) estrogen receptor status.
Biomedicines 11 03111 sch001
Table 1. Gene selection raw data from the Affymetrix array U133A.
Table 1. Gene selection raw data from the Affymetrix array U133A.
Probe IDCt/ECt/A3E/A5A3/A5A5/T2A3/T2Gene Symbol
202023_at1.2−6.2−1.45.2−1.05.1EFNA1
213194_at1.2−30.71.110.91.144ROBO1
204733_at−1.4−6.9−3.01.77.412.3KLK6
210582_s−1.2−1.4−1.2−1.03.73.7LIMK2
205778_at−2.0−59.4−4.86.211.369.5KLK7
221933_at2.4−21.8−11.84.529.6134.1NLGN4X
210136_at2.2−2.2−1.33.72.28.0MBP
202150_s_at1.1−6.81.611.7−3.73.2NEDD9
C/E: MCF-10F/estrogen; C/A3: MCF-10F/Alpha3; E/A5: estrogen/Alpha5; A3/A5: Alpha3/Alpha5; A5/T2: Alpha5/Tumor2; A3/T2: Alpha3/Tumor2.
Table 2. Clinical relevance of gene expressions across various cancer types analyzed according to the disease stage factor.
Table 2. Clinical relevance of gene expressions across various cancer types analyzed according to the disease stage factor.
Breast CancerEFNA1ROBO1KLK6LIMK2KLK7NLGN4XMBPNEDD9
BRCA (n = 1100)3, 4 ***3, 4 ***3, 4 ***3, 4 ***3, 4 ***3, 4 ***3, 4 ***3, 4 ***
BRCA-Basal (n = 191)N.S.N.S.N.S.N.S.N.S.N.S.N.S.N.S.
BRCA-Her2 (n = 82)4 *4 **4 **4 **4 *4 **4 *4 *
BRCA-LumA (n = 568)4 ***4 ***4 ***4 ***4 ***4 ***4 ***4 ***
BRCA-LumB (n = 219)4 **4 **4 **4 **4 **4 *4 *4 **
The statistical significance is annotated via the number of stars (*: p < 0.05; **: p < 0.01; ***: p < 0.001); 3, 4: clinical stage factor; N.S.: not significant. Data were estimated using TIMER2.0 in breast invasive carcinoma [30].
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Calaf, G.M.; Roy, D.; Jara, L.; Aguayo, F.; Crispin, L.A. Gene Signature Associated with Nervous System in an Experimental Radiation- and Estrogen-Induced Breast Cancer Model. Biomedicines 2023, 11, 3111. https://doi.org/10.3390/biomedicines11123111

AMA Style

Calaf GM, Roy D, Jara L, Aguayo F, Crispin LA. Gene Signature Associated with Nervous System in an Experimental Radiation- and Estrogen-Induced Breast Cancer Model. Biomedicines. 2023; 11(12):3111. https://doi.org/10.3390/biomedicines11123111

Chicago/Turabian Style

Calaf, Gloria M., Debasish Roy, Lilian Jara, Francisco Aguayo, and Leodan A. Crispin. 2023. "Gene Signature Associated with Nervous System in an Experimental Radiation- and Estrogen-Induced Breast Cancer Model" Biomedicines 11, no. 12: 3111. https://doi.org/10.3390/biomedicines11123111

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop