Transcriptomic Analysis of Primary Breast Cancer Utilizing Gene Expression Datasets from Middle-aged Caucasian Women

doi:10.21203/rs.3.rs-4701356/v1

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Transcriptomic Analysis of Primary Breast Cancer Utilizing Gene Expression Datasets from Middle-aged Caucasian Women

https://doi.org/10.21203/rs.3.rs-4701356/v1

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Breast cancer due to its genetic and clinical diversity across various subtypes often presents overlapping molecular profiles. Despite evolving classification methods, it still poses challenges for accurate prognosis. The present study addresses this challenge by identifying genes associated with each clinical subtype through a comprehensive differential expression analysis. As a representative example of breast cancer, the expression dataset (SRA Project: SRP375823) was sourced from middle-aged Caucasian/European women. First, samples were categorized according to histological types, stratified into Luminal-A, -B, and HER2 enriched tumours, as well as based on EMT properties (presence or absence of circulating tumour cells (CTCs)) and p53 mutation status (presence or absence of P53 mutation). The entire differential expression analysis was conducted utilizing GATK and Tuxedo II pipelines, followed by functional enrichment analysis. Upon comparison with healthy controls, genes like mTOR, BARD, MYH10, APC exhibited significant associations with breast cancer. Within immunohistological subtypes, all the primary tumours regardless of immunohistological subtypes exhibited significant upregulation in gonadotrophin-releasing hormone (GnRH) pathways. Further, upon differential gene expression across tumours within p53 mutation statuses, HSPA1A (chaperone Hsp70) showed significantly lower expression in wild p53-negative (p53-) tumours compared to mutated p53-positive (p53+) tumours.In examining the differential expression in tumours across EMT characteristics, in CTC-positive (CTC+) tumours, various viral oncogenes like Epstein-Barr Virus, hepatitis B, and Kaposi-Sarcoma Associated Herpes Virus were upregulated. Overall present finding strives to identify unique molecular signatures for each breast cancer subtype/class providing a primary insight into identifying potential prognostic markers, particularly within the Caucasian demographic.

Biological sciences/Biological techniques/Gene expression analysis

Biological sciences/Biological techniques/Genomic analysis

Biological sciences/Biological techniques/Software

Health sciences/Oncology

Biological sciences/Computational biology and bioinformatics/Gene ontology

Biological sciences/Molecular biology/Transcriptomics

Biological sciences/Biotechnology/Genomics

Differential gene expression analysis

Next-generation sequencing

NGS

Primary breast cancer tumour

PBC

RNA-Seq

Tuxedo

Genome analysis toolkit

GATK

Breast cancer stands as the most prevalent form of cancer among women worldwide, and the second-leading cause of mortality after lung cancer [1, 2]. According to the International Agency for Research on Cancer (IARC), approximately 2.3 million cases and more than 680,000 deaths were reported due to breast cancer worldwide in 2020 [3]. According to the American Cancer Society (Breast Cancer Facts & Figures, 2019-2020, the incidence of breast cancer is highest among non-Hispanic whites in the United States, with rates reaching 130.8 per 100,000 [4]. Moreover, in 2008, a staggering 115,000 new cases of breast cancer were reported, with over 47,000 deaths among women in Western, Central, and Eastern Europe combined. Interestingly, the higher incidence of breast cancer among women of European origin in the United States has been attributed to an elevated heritability associated with greater European ancestry [5].

Breast cancer can be classified into various molecular subtypes. The primary and widely accepted molecular subtyping is the immunohistological assessment which classifies breast cancer into the following 4 subtypes: Triple Negative Breast Cancer (TNBC), Luminal A, Luminal B, and HER2+ tumours. Triple-negative breast cancer (TNBC) is characterized by a lack of all three receptors (Estrogen receptor-ER, Progesterone Receptor-PR, and HER2 receptor), Luminal A is characterized by ER+ and/or PR+, and HER2- enriched and luminal B are characterized by ER+ and/or PR- and HER2+/- [6, 7]the clinical features of the subtypes and its common breast tumorigenesis pathways are shown in Figure 1.

One of the major hallmarks of cancers, including breast cancer, is epithelial-to-mesenchymal transition (EMT) which plays a critical role in the formation of circulating tumour cells (CTCs) in epithelial cancers. It enhances tumour cell invasiveness, facilitates tumour cell entry into blood vessels (intravasation), and ensures the survival of these cells in the peripheral circulation [8, 9]. Subsequently, being readily accessible from blood specimens, and as decent a representative of tumour heterogeneity, CTCs have immense prognostic importance in the prediction of metastatic and primary breast cancer. In fact, epithelial cells, which are usually characterized by strong cell-cell adhesion and apical-basal polarity, lose their characteristics and become mesenchymal, acquiring highly invasive and migratory properties [10-12]. Therefore, identifying EMT-associated genes presenting with CTCs can serve as a prognostic marker for treatment decisions in breast cancer.

Considering the various subtypes of primary breast cancers including immunohistological classifications and EMT characteristics, identifying distinct genetic signatures for a given subtype facilitates personalized selection of treatments. Furthermore, genetic signatures hold significant value by contributing to prognostic insights and serving as crucial biomarkers for early detection that could proactively mitigate the disease progression and ensure extended survival[13, 14]. Given the recent developments in rapid next-generation sequencing, especially RNA-seq methods, which involve quantitative profiling of genes and identification of structural variations such as gene fusions and alternative splicing events, have paved the way for biomarker stratification-based prognoses in breast cancer diagnosis and treatment [15-17].

In this view, our study aims to identify genetic signatures associated with specific breast cancer subtypes. To achieve this, we utilized an expression dataset (SRA ID: SRP375823) obtained from middle-aged Caucasian/European women. The entire dataset was first stratified categorized according to histological types, stratified into LuminalA, -B, and HER-2 enriched tumors, as well as based on EMT properties (presence or absence of circulating tumour cells (CTCs)) and p53 mutation status (presence or absence of P53 mutation). Following stratification, we conducted differential expression analysis across the statuses of each subtype and strived to identify genes associated with each subtype/class.

2.1 Data collection

All expression datasets of human breast cancer primary cancer (PBC) (N=18) and healthy controls (N=5), from middle-aged Caucasian/European women, were obtained from the SRA database. (SRA ID: SRP375823, under bio project: PRJNA839244 by Institute of Molecular Biology SAS- Translational research unit, Faculty of Medicine, Comenius University and National Cancer Institute, Bratislava, Slovak Republic, Project ID: SRP375823) (https://www.ncbi.nlm.nih.gov/sra?term=SRP375823&cmd=DetailsSearch).

The age of the subjects at the time of sample collection ranged from 27 to 79 years (with a median age of 59.5 years for the PBC samples and 44 years for the healthy controls). The respective SRR accession IDs of the healthy and PBC tumour samples are represented as SRR192624XX (where XX ϵ [11, 33]). The distribution of different molecular subtypes of breast cancer in the datasets is represented by a Venn diagram in Figure 2. Samples with the XX ϵ {12,16,17,19,20,21,22,32,33} contain circulating tumour cells (CTC+), while the remaining PBC samples with the XX ϵ {11,13,14,15,24,25,29,30,31} are non-metastasizing (CTC-) samples. In addition, samples {11,12,17,19,20,21,22,24,25,29,30,31,33} were positive for hormone receptor (HR) (labelled HR+), and depending on their relative Ki67 levels, they were classified as either luminal A or luminal B. Samples numbers- 14,15,19 and 24 were HER2-enriched. Samples - 13,16 and 32 were TNBC samples (lacking both HR and HER2 receptors). The details of the sampling and other properties of the samples are provided in Supplementary Table 1.

2.2 Data analysis

The downloaded datasets (.sra files) were split into raw RNA-Seq NGS (.fastq files) using the sra toolkit (https://hpc.nih.gov/apps/sratoolkit.html). The human reference genome (.fasta file), gene annotation (.gtf file), and known variants (SNPs and INDELs) (.vcf file) were obtained from the ENSEMBL database (https://asia.ensembl.org/Homo_sapiens/Info/Index) and Broad Institute Resource Bundle (https://gatk.broadinstitute.org/hc/en-us/articles/360035890811-Resource-bundle). The quality of the fastq files was assessed using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Following the GATK Best Practices Pipeline (https://gatk.broadinstitute.org/hc/en-us/sections/360007226651-Best-Practices-Workflows)[18-20], the raw fastq files were trimmed with TrimGalore software (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)to remove adaptor contamination and GC bias from the 5’ and 3’ ends of the reads. The trimmed reads were aligned to the human reference genome using HISAT2.0 [21] (https://daehwankimlab.github.io/hisat2/). The Picard tool (http://broadinstitute.github.io/picard/)was used to compress the SAM files to BAM files, followed by coordinate sorting. BAM files were processed by flagging the duplicate reads, and the reads with Cigar score of ‘N’ were split using the –SplitNCigarReads command from the GATK package. The base quality scores of the reads were recalibrated using the –BaseRecalibrator and -ApplyBQSR functions of GATK. As an optional step, to validate the recalibrated base quality score, the BAM files were converted to .fastq files using Picard -Sam to Fastq function, which were subsequently assessed using FastQC to check for improved base quality scores. The details of the FastQC output report are tabulated in Supplementary Table 2. The data analysis pipeline is shown in Figure 3. The entire analysis pipeline was executed on a 64-bit PC running a Linux (Ubuntu 20.04) environment using 16 GB of RAM.

2.3 Transcript assembly and differential gene expression analysis

Transcript assembly for each sample was carried out using the Stringtie program (https://ccb.jhu.edu/software/stringtie/) from its processed coordinate-sorted BAM file. Subsequently, the individual .gtf files were merged by employing the -stringtie-merge function. The resulting merged .gtf files were subjected to differential gene expression analysis, comparing healthy vs. PBC samples. Further, within PBC samples, differential expression analysis was carried out across the classes of each category viz., immunohistology subtype (Luminal A vs. Luminal B vs. HER-2 enriched), EMT subtype (CTC+ vs. CTC-), p53 mutation status (p53+ vs. p53-) samples. Differential gene expression analysis was performed using the Ballgown package (https://www.bioconductor.org/packages/release/bioc/html/ballgown.html) with the stringtie-eB command. For additional details about the phenotype information, please refer to Supplementary Table 1.

2.4 Pathway and functional enrichment analysis

Gene Ontology (GO) and functional enrichment analyses were carried out by using KOBAS 3.0 annotation and enrichment modules (http://bioinfo.org/kobas) [22] to elucidate signaling pathways and molecular functions predominant in each subtype (and/or given the status of the category) of the PBC.

2.5 Gene association analysis

The genes showing significant differential expression (differentially expressed genes -DEGs) across the classes in the given category were identified (p<0.05 and FDR≤1), and their prognostic significance of the DEGs, were evaluated against the clinical pathology database of the Human Protein Atlas (https://www.proteinatlas.org/) [23]. In addition, the mutations analysed from the study were mapped onto the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/)specifying PBC histology[24]. The genes that were specifically implicated previously in breast cancer were shortlisted from the DGE list, and their expression estimates (FPKM values) were obtained from the ballgown package.

3.1 Gene Expression analysis between healthy and primary breast cancer samples

Upon differential expression analysis between healthy and primary breast cancer samples, we found that genes like mTOR, SDHB, MYH, BARD1, APC, SPEN, ARID5B, RAD50, and PMS2, IKBKGP had significant differences in the expression between PBC tumour and healthy samples. The findings were widely anticipated, given deregulation of these genes is found not just in PBC, but also in many other malignancies such as colorectal, endometrial, and gastric cancers. Figure 4A shows a heatmap of genes differentially expressed between healthy and breast cancer samples across all 23 samples. Figure 4B shows the average differences in the expression patterns of these genes between tumour and healthy samples.

3.2 Differential expression analysis across immunohistology subtype

The subsequent analysis followed differential expression within distinct classes of immunohistological subtypes. First, we observed the expression levels of Estrogen receptor-α (ESR1), progesterone receptor (PGR) and HER2 in immunohistological classes (Figure 5). We found that Luminal B-type cancers exhibit higher levels of ESR1 expression than luminal A-or HER2 enriched tumours, whereas both luminal A- and B-type tumours exhibit higher levels of PGR than HER2 enriched tumours. Not surprisingly, HER2-enriched tumours showed significantly elevated ERBB2 (a receptor tyrosine kinase protein) transcript levels, compared to luminal A-type and luminal B-type tumours. In contrast to other classes within the immunohistological subtype, triple-negative breast cancer (TNBC) tumours showed decreased expression levels of the three proteins, ESR1, PGR, and HER2. In fact, a significant association of ESR1, and progesterone receptor (PGR) mutations and their respective expression have been reported in hormonotherapy-resistant breast cancers [25, 26].In addition, all the primary tumour samples regardless of immunohistological subtypes exhibited significant upregulation in the gonadotrophin-releasing hormone (GnRH) signaling pathways.

3.2 Differential expression analysis across wild p53-negative (p53-)and p53-positive (p53+) phenotypes

Since p53 is a known tumour suppressor implicated in a variety of cancers, differential gene expression analysis was performed between p53+ and p53- tumours samples (Figure 6a). The p53 interactome comprises the nearest neighbouring interacting partners from the literature, several of which are involved in cancer tumours metastasis and progression (Figure 6b). A heatmap representation of the FPKM values of certain candidate genes in all the samples (Figure 6c).

Differential expression analysis revealed significantly higher BReastCAncer 2 (BRCA2) expression in the p53+ than in p53- samples. Indeed, mutations in BRCA2 and its ortholog BRCA1 have been linked to the over expression of mutated p53 (p53+), correlating with a poor prognosis in breast cancers [27-29]. Further, we observed that TP53 inducible nuclear protein 2 (TP53INP2) which regulates EMT in cancer metastases [28], surprisingly had markedly lower expression in p53+ samples than in p53- samples. Due to the sampling method, the majority of p53+ samples were found to be CTC. Consequently, this would likely lead to a decrease in the expression of TP53INP2 in the p53+ samples. Further, we observed that the YWHAG gene (that stabilizes Snail thereby promoting EMT in breast cancer) which is a direct interacting partner of p53, had an increased YWHAG expression in p53+ cancers compared to p53- tumours [29-34]. The UBC genes, critical for preserving ubiquitin balance and homeostasis, have been implicated in various cancers, however, within our dataset, we did not detect a significant difference in UBC gene expression between p53+ and p53- samples. Moreover, the expression levels of TNF-receptor Associated Factor 1 (TRAF1), a common interacting partner of p53, showed no significant difference between the p53- and p53+ samples [35, 36]. Further, HSPA1A (aka. Hsp70) which encourages proper folding of the p53-DNA-binding domain[37], had lower expression in p53- samples than in their p53+ counterparts. Table 1 lists 20 transcripts that were significantly differentially expressed between the p53- and p53+ tumour samples.

Table 1. List of the top 20 transcripts that were significantly differentially expressed between the p53- and p53+ tumour samples.

GeneNames	geneIDs	fc	pval	qval
BTN2A1	MSTRG.139719	0.307241	5.50E-06	0.117846
ANKHD1	MSTRG.135585	0.334479	1.39E-05	0.149321
GRINA	MSTRG.166165	0.245895	0.000117	0.834394
SEPTIN6	MSTRG.180041	0.440208	0.000209	0.999784
<none>	MSTRG.60458	3.591075	0.001085	0.999784
GPANK1	MSTRG.140188	0.483044	0.002043	0.999784
RABGAP1	MSTRG.172656	0.828765	0.002138	0.999784
MTREX	MSTRG.129326	0.710346	0.002143	0.999784
<none>	MSTRG.110887	0.472226	0.002245	0.999784
<none>	MSTRG.62309	1.644774	0.002454	0.999784
<none>	MSTRG.12459	0.584573	0.0029	0.999784
ELOA	MSTRG.2120	0.484198	0.002984	0.999784
UFSP2	MSTRG.126214	0.723889	0.003036	0.999784
TRANK1	MSTRG.107059	2.022271	0.003318	0.999784
ITGA6	MSTRG.90910	0.308721	0.003367	0.999784
NR3C1	MSTRG.135883	0.6114	0.003547	0.999784
<none>	MSTRG.110878	0.53199	0.00375	0.999784
TM9SF4	MSTRG.97196	1.831184	0.003906	0.999784
NUCB1	MSTRG.79998	0.471756	0.004307	0.999784
RNF34	MSTRG.41634	0.5993	0.004368	0.999784

3.3 Differential expression analysis across EMT phenotypes by comparing localized tumours (CTC+) and metastatic tumours (CTC-)

Upondifferential expression across localized tumours (CTC+) and metastatic tumours (CTC), we found significant differential expression of certain genes, such as Cytokeratin 19 (KRT19), CDH1, EpCAM etc. In previous studies, KRT19 was reported to regulate epithelial-mesenchymal transition in breast cancer metastases however, its reduced expression of KRT19 was associated with poor prognosis [38]. In the present study, KRT19 expression was reduced in CTC+ tumours than in CTC- tumours. CDH1, which encodes E-cadherin, (a protein responsible for cell-to-cell attachment, previously implicated in hereditary lobular breast cancer (LBC)), and diffuse gastric carcinoma (DGC)) demonstrated significantly high expression in CTC+ tumours than in non-CTC+ tumours (Figure 6c) [39]. Likewise, the epithelial cell adhesion molecule (EpCAM), was also found to have elevated expression in CTC+ tumours, in fact, previous studies showed that increased EpCAM expression is associated with poor prognosis in breast cancer patients [40]. Further, conserved serine protease HTRA1 which is involved in the suppression of EMT in tumour metastases [41] showed lower expression of HTRA1 in CTC+ tumours than in CTC- tumours in the present study especially in the “BC12” and “BC20” samples(Figure 6c). In previous studies, Fibrillin-1 (FBN1) was shown to be implicated in TGF-β-induced breast cancer metastasis [42], however, for the present dataset, we did not observe a significant difference in the expression of FBN1 between CTC+ and CTC- tumours. Several breast cancer examples revealed increased expression of interleukin 2 receptor-γ (IL2RG)[39], in the present dataset however, only a single CTC+ sample (“BC14”) exhibited the highest expression of this receptor (Figure 6c). Likewise, c-Myc which is often overexpressed in breast cancer patients, sometimes in conjunction with BRCA1 suppression [40], in the samples from the present dataset, only “BC19”¾a CTC+ sample showed elevated levels of c-Myc. In further analysis, increased expression of Mitogen-activated protein kinases 3 (MAPK3) was observed in 2 CTC+ samples (“BC17” and “BC17”). However, the average expression of MAPK1 was significantly lower in CTC+ samples than in CTC- samples. In assertion to our findings, previous studies have also reported MAPK1&3 to have reduced expression in metastatic (CTC+) tumours[43].

3.4 Functional enrichment analysis and pathway analysis

Considering a q value <0.05, we identified 138 genes differentially expressed between PBC patients and healthy individuals. The transcript levels of these genes were cross-referenced with those in the Human Protein Atlas database to explore their associations with various cancers. Apart from breast cancer, most of the genes on the list exhibited implications in multiple cancer types, such as hepatocarcinoma, renal carcinoma, glioma, colorectal cancer, and lung cancer. From this pool, only 8 genes exclusively linked to breast cancer were selected, and their corresponding expression values (FPKM values) were extracted from the transcript list. The compiled list is provided in Table 2 and Figure 7. The list of significant genes thus obtained was used for functional enrichment studies utilizing a hypergeometric distribution. Functional enrichment analysis revealed that several pathways, including signalling pathways (such as RAS, RAP1, and MAPK) and cellular metabolic pathways (including glycolysis/gluconeogenesis, pyruvate metabolism, and the pentose phosphate pathway), exhibited significant prominence in tumour samples compared to healthy samples. (Figure 8).

Table 2. List of breast cancer-specific genes obtained upon differential expression comparing PBC samples to healthy controls.

Gene Names	geneIDS	fc	pval	qval
FGF10	MSTRG.128492	5.635777	1.47E-07	0.00077
ADIPOQ	MSTRG.115804	21.45618	2.40E-05	0.021864
LIPE	MSTRG.79410	7.523439	3.36E-05	0.024938
EDNRB	MSTRG.45346	3.549042	4.01E-05	0.024938
TRARG1	MSTRG.65217	11.74944	4.89E-05	0.026304
LEP	MSTRG.156541	9.036603	7.99E-05	0.031626
SLC9A3R1	MSTRG.70633	0.23404	0.00014	0.037534
CIDEA	MSTRG.72178	4.153306	0.000224	0.040077

We further conducted functional enrichment analysis across the EMT subtype, comparing CTC+ and CTC- samples. Analysis of differentially expressed genes (DEGs) revealed that SMAD1, TGFBR2, TWIST1, ZEB1, and SNAIL2/SLUG were significantly upregulated in CTC+ samples relative to CTC- samples. In fact, several of these genes have been shown to pathologically elevate epithelial-mesenchymal transition (EMT) for wide scale metastases (Figure 9a).In addition to the aforementioned upregulated genes, our study observed an increase in metastatic traits within tumours induced by viral oncogenes. CTC+ tumours displayed upregulation of various oncoviral induced pathways, including those associated with the Epstein-Barr Virus (EBV), hepatitis B, hepatitis C, and Kaposi-Sarcoma Associated Herpes Virus. Table 3 shows the list of differentially expressed genes between the CTC+ and CTC- groups. A bar plot of the functional enrichment and circular interactome analysis is provided in Figure 9b.

Table 3. Differentially expressed genes identified between CTC+ and CTC- samples.

Gene Names	Gene IDs	fc	pval	qval
JUN	MSTRG.4863	2.206242	0.000757	0.962418
SFT2D1	MSTRG.147328	0.42453	0.000787	0.962418
KRT5	MSTRG.36783	5.604195	0.001414	0.962418
ERH	MSTRG.51032	0.644329	0.001514	0.962418
ICAM3	MSTRG.76604	0.553084	0.002609	0.962418
KIT	MSTRG.119767	2.796855	0.002647	0.962418
DTX3L	MSTRG.111625	0.609323	0.003237	0.962418
CCDC8	MSTRG.79741	1.994054	0.003368	0.962418
IRX1	MSTRG.126571	2.359517	0.003429	0.962418
CD109	MSTRG.142410	1.73033	0.003874	0.962418
DSC3	MSTRG.72875	2.414973	0.004219	0.962418
ITIH5	MSTRG.16602	1.616171	0.004565	0.962418
SCRN2	MSTRG.68752	1.360452	0.00488	0.962418
RBM15	MSTRG.8354	0.555303	0.006089	0.962418
ALDH1A3	MSTRG.60095	1.670188	0.006102	0.962418
KRT17	MSTRG.68202	5.030419	0.006735	0.962418
FAM53B	MSTRG.24085	0.634872	0.00699	0.962418
PRNP	MSTRG.95627	1.601053	0.007342	0.962418
BECN1	MSTRG.68319	1.326989	0.007732	0.962418
TDP1	MSTRG.52593	0.531036	0.007836	0.962418
AASDH	MSTRG.119954	1.894606	0.008083	0.962418
TRIM29	MSTRG.31870	2.191798	0.008108	0.962418
TSPYL2	MSTRG.176994	1.393778	0.008511	0.962418
PITX1	MSTRG.135257	0.411268	0.009034	0.962418
MTCL1	MSTRG.72021	1.59995	0.009062	0.962418
MPHOSPH9	MSTRG.41732	0.688438	0.009918	0.962418

Caucasian women have one of the highest rates of new breast cancer diagnoses compared to other racial/ethnic groups [44]. In 2022, the American Cancer Society reported that the incidence rate of breast cancer was highest in White women (133.7 per 100,000) and close behind in Black women (127.8 per 100,000) [National Cancer Institute. The Breast Cancer Risk Assessment Tool. https://bcrisktool.cancer.gov/about.html,2024]. Although, in recent times, younger Caucasian women (aged 20-49) have benefited from discussing their risk factors with healthcare providers [45]. Despite these clinical and molecular interventions, it still stands unclear why Caucasian women may be more susceptible to specific breast cancer subtypes compared to other populations. Given the higher incidence, the present study is an effort directed toward understanding the molecular basis of primary breast cancer subtypes from the available transcriptome datasets of Caucasian women from public repositories.

In this study, we first compared the transcriptomes of primary breast tumours to healthy controls. We found that various genes, including mTOR, BARD, MYH10, APC, SPEN, ARID5B, RAD50, and PMS2, exhibited significant associations with breast tumours across different subtypes. Our findings are in coherence with various studies emphasizing the aforementioned genes with breast cancer. For example, mTOR, which showed a significant association in our study with breast cancer, is a common pathway central to cell survival metabolism and proliferation [46]. The role of the mTOR pathway in tumorigenesis is so significant that Everolimus, an mTOR inhibitor, has been approved for the treatment of hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Likewise, the current study reveals an association between breast cancer and MYH10, a protein previously demonstrated to interact with FOXM1 in the formation of a FOXM1/MYH10 protein complex influencing the self-renewal capacity of MCF breast cancer stem-like cells [47]. The association of BARD with breast cancer in the present study further substantiates the finding of prior studies which highlighted the regulatory role of BARD1β and BARD1δ isoforms regulating BRCA pathway and their essential involvement in breast cancer pathogenesis. [48]. In our investigation, we observed a downregulation of Adenomatous Polyposis Coli (APC) gene in breast cancer samples. However, we did not conduct functional studies to elucidate the underlying cause of this downregulation. Nonetheless, existing research indicates that transcriptional silencing by hypermethylation is prevalent in APC genes in almost 70% of sporadic breast cancers, particularly within the aggressive triple-negative breast cancer (TNBC) subtype [49].

Further, the genes SPEN, ARID5B, RAD50, and PMS2, which exhibited a significant association with primary breast tumors in our study, have been previously implicated in detectable Molecular Residual Disease (MRD). This phenomenon represents the primary cause of postoperative recurrence in breast cancer cases, thereby suggesting the likelihood of recurrence among the patients from whom the samples were obtained [50]. Adiponectin/ADIPOQ and leptin/LEP, prominent members of the adipocytokine signaling pathway and fatty acid biosynthesis, were observed to be downregulated in tumour samples in the present analysis. This downregulation and its association with tumors can likely be attributed to the fact that enhanced secretion of Adiponectin and Leptin has been shown to induce an unfavorable microenvironment for cell growth within tumours, which mitigates the risk of several obesity-associated cancers such as colorectal, prostate, and breast cancers [51].

Subsequently, we conducted expression analysis across the primary breast tumors stratified by their immunohistological classification. As expected, Luminal B-type cancers exhibited elevated ESR1 expression compared to luminal A- and HER2-enriched tumours. Moreover, both luminal A and B types demonstrated higher levels of PGR compared to HER2-enriched tumours. Furthermore, we found a high expression of ERBB2 transcripts in HER2-enriched tumours compared to luminal A and B-type tumours. Our findings in a way align with the study by Osborne et al. (2011), which demonstrated that the interplay between ER and growth factor receptor signaling pathways (including crosstalk with HER2) can modulate ER activity leading to alterations in its nuclear function, ultimately contributing to the development of endocrine resistance. [52]. Hence, from the present observation, it can be speculated that a strategic combination of hormone therapy and immunotherapy may offer a promising approach for enhancing patient outcomes, particularly in individuals with endocrine resistance.

In our analysis, all the primary tumour samples regardless of immunohistological subtypes exhibited significant upregulation in the gonadotrophin-releasing hormone (GnRH) signaling pathways, which establishes the FSH and LH levels in the blood. Previous studies have demonstrated significant expression of GnRH and its receptor in breast tissues as well as in other tissues like the placenta [53], prostate [54], and ovary [55]. Indeed, the significant involvement of GnRH in breast cancers is further underscored by the findings of Lattrich et al. (2012) which demonstrated a significant association between GnRH polymorphisms and breast cancer susceptibility [56].

In our subsequent analysis, we compared the differential gene expression between breast tumors with p53 mutations (p53+) and those without the mutation (p53-). We found that HSPA1A, a chaperone protein Hsp70, had significantly lower expression in p53- tumors. The rationale behind the elevated expression of Hsp70 in p53+ tumors observed in our study can be attributed to previous research that showed p53+ genotypes overproduce Hsp70, which contributes to the conformational stabilization of mutant p53. [57]. This stabilization has been shown to promote tumor progression, correlating positively with the poor prognosis of breast cancers. [58,59].

Intending to identify genes associated with the EMT characteristics of the tumor in the current study datasets, in our next approach, we performed differential expression analysis across the classes of EMT subtype, comparing tumours with metastatic characteristics (CTC+ type) to localized tumours (CTC- type). In this view, we sought to first evaluate the differential expression of FGF10 across the EMT subtype, given that FGF10, is known for its role in eliciting paracrine signalling to facilitate epithelial-to-mesenchymal transition. In contrast to our expectation, we observed no significant difference in FGF10 transcripts between CTC+ and CTC- tumours. Our study is in quite a contrast to the preliminary survey by Theodorou et al., 2004, which showed that FGF10 is expressed in 92% of the primary breast tumours with metastatic characteristics [60]. Despite its significant association with metastatic phenotypes, the lack of association of FGF10 with metastatic (CTC+) tumours in the present study dataset still requires further elucidations focusing on performing a somatic variant analysis to explore potential mutations that may result in the loss of function of the FGF10 gene in CTC+ tumours.

The lack of association of FGF10 expression with CTC+ tumours in the present study datasets prompted us to analyse the expression of other genes involved in EMT. Interestingly, our analysis revealed that in CTC+ tumors, several oncoviral pathways were upregulated, including contagious infections of Epstein-Barr Virus (EBV), hepatitis B, hepatitis C, and Kaposi-Sarcoma Associated Herpes Virus. Consistent with our current findings, Mazouni et al. (2011) documented that breast cancers infected with EBV displayed heightened aggressiveness and were linked to unfavorable prognostic outcomes. [61]. Indeed, EBV-associated malignancy has been widely known for its EMT characteristics across various cancers. For example, a recent study by Morris et al., (2018) revealed that the EBV-encoded oncoprotein, LMP1, triggers EMT programming through Integrin-Mediated ERK-MAPK signaling. [62]. In addition, viral oncogenes have been demonstrated to augment the survival of circulating tumor cells (CTCs) by reconstructing the cytoskeleton [63, 64]. This accounts for the notable predominance of viral oncogene expression observed in metastatic CTC+ tumours with EMT characteristics in the present study compared to localized CTC- tumours. Moreover, the crucial role of viral oncogenesis is reflected in contemporary therapies, which are focused on engineering monoclonal antibodies such as Rituximab, aimed at targeting signaling pathways to inhibit viral oncogenesis.

In conclusion, the present study attempts to identify the association of genes for a specific class of molecular subtype of breast cancer within the Caucasian demographic. Although the study warrants additional function studies to validate the expression association of genes, nevertheless, the study provides a primary insight into identifying potential biomarkers specific to the subtype. Further, these findings may contribute to informed treatment decisions and improve therapeutic outcomes for patients.

AUTHOR CONTRIBUTION

A.N.: Principal investigator and chief project administrator. D.G.: Conceptualized the idea, performed RNA seq analysis. S.B.: Co-investigated the project and prepared the manuscript. I.C, R.C, D.B, A.K, T.D, A.K.: Visualized the data and performed statistical analysis. A.B, K.S, M.K, and U.P.: Validated and curated the data and additional programmer of the project. S.K.S: Co-supervisor of the study.

CONFLICTS OF INTEREST/COMPETING INTERESTS:

The authors declare that they have no conflicts of interest.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE:

Not applicable.

HUMAN AND ANIMAL RIGHTS:

No animals or humans were used in the studies that were the basis of this research.

ACKNOWLEDGMENTS:

AN, AB, AK, KS, UP, and SKS thankfully acknowledge the MHRD-RUSA Phase 2.0 under [grant sanctioned wide letter no. F.24-51/ 2014-U, Policy (TN Multi-Gen), Department of Education, Government of India, dated 09.10.2018], Tamil Nadu State Council for Higher Education (TANSCHE) under [grant sanctioned wide letter no. AU: S.O. (P&D): TANSCHE Projects: 117/2021, dated 31.03.2021, File No. RGP/2019-20/ALU/HECP-0048 dated 27.04.2021], Department of Biotechnology, Ministry of Science and Technology, New Delhi, under Grant/Award [No. BT/PR40154/BTIS/137/ 34/2021, dated 07.03.2022], DST-PURSE 2nd Phase Program [Order No. SR/PURSE Phase 2/38 (G dated 21.02.2017)& DST-FIST (SR/FST/LSI—667/2016)], DBT-NNP Project, New Delhi, under Grant/Award [No. BT/PR40156/BTIS/54/2023 dated 06.02.2023] for providing financial assistance, infrastructure facilities in the lab. AB thanks DBT-NNP for the Fellowship.

FUNDING

We do not receive any funding for this study.

AVAILABILITY OF DATA AND MATERIALS

Most of the data generated or analyzed during this study are included in this article. We have included additional data in the supplementary material. Moreover, queries can be directed to the corresponding author for any clarifications about the study if needed.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Not applicable.

CONSENT FOR PUBLICATION

Not applicable.

COMPETING INTERESTS

The authors declare no competing interests.

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Transcriptomic Analysis of Primary Breast Cancer Utilizing Gene Expression Datasets from Middle-aged Caucasian Women

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