Investigation of the Genomic and Transcriptomic Variations Underlying Tamoxifen Resistance in Breast Cancer

doi:10.21203/rs.3.rs-4053257/v1

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Investigation of the Genomic and Transcriptomic Variations Underlying Tamoxifen Resistance in Breast Cancer

https://doi.org/10.21203/rs.3.rs-4053257/v1

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Background: Breast cancer is a global burden responsible for millions of deaths per year. One of the significant challenges in the treatment of it is due to the emergence of resistance towards certain drugs, including well-known medication, Tamoxifen. With recent advances in technology, many genes have been identified to be involved in the progression of breast cancer and the development of resistance. Studying these genes and their potential pathways in cancer is a vital aspect of treatment that can enhance patients' response to therapeutic agents.

Methods: In the present study, we investigated major genes associated with the risk of breast cancer and the creation of tamoxifen drug resistance within them. We analyzed data from GO datasets (GSE231629, GSE241654, and GSE42568). Differentially expressed genes were studied in the limma package in the R language and TAC software. Enrichr carried out gene ontology, gene set enrichment, and genomic pathway analysis. Gephi, Cytoscape, and STRING databases were employed to build the network of protein-protein interactions and miRNA-lncRNA-mRNA network.

Results: analysis of differentially expressed genes demonstrated several hub genes including POSTN, COL1A2, LUM, COL3A1, BRINP3, TBX2-AS1, ARHGAP36, DSCAM-AS1 and SOX2 involved in breast cancer progression and resistance toward tamoxifen drug in MCF7 cell lines. These genes are associated with various biological processes such as intracellular signal transduction, MAPK Cas cade, gene expression, protein phosphorylation, and regulation of cell population proliferation.

Conclusion: Our study demonstrates protein-protein interaction and significant genes involved in the development of breast cancer and tamoxifen resistance in MCF7 cell lines.

Breast Cancer

Tamoxifen

non-coding RNA

Microarray Analysis

It is irrefutable that breast cancer (BC) is one of the most significant challenges in public health. In 2020, it held first place and defeated lung cancer as the most widespread type of cancer around the world, with nearly 2.3 million new cases(1). It is estimated that the number of newly diagnosed cases will rise to 3 million and death rates to 1 million by 2040(2). Breast cancer is more common in developed countries, while accounts for higher mortality in developing nations(3). the underlying reasons for more incidents in developed countries can be due to reasons such as reproductive and hormonal risk factors, usage of hormone replacement therapy, delayed first pregnancy, lower childbirths, late menopause, and early onset of menstruation. Additionally, it can be related to alcohol consumption and inadequate physical activity(4). On the other hand, higher death rates in developing places are caused by factors such as limited resources, low access to cancer screening, and delays in addressing environmental factors related to BC(5). Breast cancer forms in breast tissue, usually originating from the inner lining of lobules or milk ducts(6). Mutations of DNA and RNA can contribute to the transition of normal cells to cancerous ones. These mutations can be due to various reasons, such as biological factors, radiation, or chemicals(7). Breast cancer can be classified into different subtypes based on the presence of estrogen receptors (ER-positive or negative), progesterone receptors (PR-positive or negative), human epithelial growth factor receptor 2 (HER2-positive or negative), and ki-67, which is a protein known as a prognostic factor involved in survival of breast cancer patients(8). Another way of classification can be done in the following way: luminal-like (A and B), basal-like, normal-like, HER-2 positive, and triple-negative breast cancer. luminal A, which consists of ER positive, PR positive, HER negative, with low ki-67; luminal B, which is ER positive, PR positive, HER2 negative or positive, with high ki-67; HER2- overexpression made of ER positive, PR negative, HER2 positive; and triple-negative breast cancer (TNBCs), which is ER negative, PR negative and HER2 negative(9). If TNBC is positive for CK/6 expression (basal marker), then, it is considered as basal-like(10–12). Among them TNBC has the highest mortality, with a 77% survival rate (13), next one is HR−/HER2 + with 82.7%, then comes HR+/HER2 + with 90.3%, and the last one is HR+/HER2 − subtype with 92.5% survival rate(14). The aggressiveness of the TNBC subtype is due to the absence of ER, PR, and HER2, and since many drugs and hormonal treatments target these receptors, they become unsuccessful in defeating TNBC(15). Approximately 5–10% of breast cancers are caused by hereditary reasons(16), individuals with a hereditary predisposition are at higher risk and demand more screening. Genetic changes impact breast cancer progression and prognosis, making genetic biomarkers a valuable tool for early therapy and ultimately enhancement of patients’ survival rates. Consequently, various studies have been dedicated to exploring differently expressed genes (DEG) and investigating on their function in breast cancer(17–20).

Mutations can happen in specific genes such as STK11, TP53, BRCA1, and BRCA2, and increase one’s risk for developing breast cancer by up to 80%. Furthermore, mutations in genes such as ATM, PALB2, and CHEK2 can raise the risk of cancer by a twofold increase(21). Other genes including BARD1, DIRAS3, NBN, RAD50, RAD51, CHEK2, AR, POSTN, COL1A2 and SOX2 are involved in breast cancer(22–26). BRCA1 and BRCA2 are known tumor suppressors and mutations in them can lead to nearly 25% of hereditary breast cancers(27). BRCA1/2 is involved in control of transcription, genome stability, and DNA damage checkpoints(28–30). POSTN is a gene coding the periostin protein and is associated with an increase in angiogenesis, and a rise in cell migration and proliferation(31–33). It has been discovered that soluble POSTNs expression is up-regulated in breast cancer(34), And It has been shown that that POSTN knockdown can decrease metastasis of breast cancer(35), highlighting its role as a potential biomarker in cancer diagnosis and therapy. Another important gene in the pathogenesis of cancer are Collagen family genes(CFGs)(36). The gene COL1A2, which produces the pro-alpha2 chain of type I collagen, affects cell movement by interacting with the cytoskeleton. It was discovered to have high expression in human gastric cancer(37). Additionally, it was found that COL1A2 can interact with microRNA and directly associate with migration and proliferation of pancreatic cancer cells(38, 39). located on chromosome 3p26.3-q27, sex-determining region Y-box 2 (SOX2) is a transcription factor that binds to target genes and regulates their expression(40), it has a vital role in keeping stem cell phenotype in embryonic stem cells(41). SOX2 is overexpressed in multiple cancers, it plays a role in metastasis, migration, and cell proliferation(42). Furthermore, its expression is related to drug resistance(43, 44). Making it a candidate for targeting in cancer treatment.

Tamoxifen (Tam) is a medication administered to treat ER-positive breast cancer, it does it by binding to estrogen receptors, Tam competes with 17β-estradiol by the receptor region, having estrogen-antagonist function leads to blocking the G1 phase and the slowdown of cell proliferation, resulting in cancer suppression. Additionally, tamoxifen may directly induce programmed cell death(45–47). Unfortunately, Tamoxifen resistance (tamR) occurs in 50% of ER-positive breast cancers, creating major challenges in treatment progress(48). Reasons behind TamR include imbalance in regulation of genes such as BAX and BCL2, which can lead to decreased apoptosis(49). Also, overexpression of HER-2 gene augments anti-apoptotic proteins and reduces the effectiveness of tam(50). The tamR cells display stem-like characteristics which are due to overexpression of genes such as Nanog, Oct3/4, and Sox2(51). Tamoxifen resistance contains complex pathways including, up-regulation of growth factors, alterations in RTK, and deregulation of the PI3K/AKT/mTOR pathway(52). There is an urgent need for more research to elucidate the major genes involved in breast cancer which can lead to the development of better treatment regimes.

In this study, our objective is to employ bioinformatic tools to identify potential breast cancer-related and tamoxifen resistance-related genes. By using gene expression profiling by array, we gathered data from Gene Expression Omnibus (GEO), we studied models consisting of breast cancer biopsy, normal breast biopsy, MCF7 cell lines, and MCF7-derived tamoxifen-resistant, and compared them with the aim of identifying hub genes associated with breast cancer and resistance development towards tamoxifen medication. Additionally, we discovered the miRNA-lncRNA-mRNA network related to the emergence of tamR and progression of BC. Consequently, with the help of recent technological advances in biotechnology such as high-throughput sequencing and microarray analysis it is feasible to identify and detect alternations in gene transcription and translation, improving our understanding of molecular biology and potential pathways in cancer progression.

Obtaining biopsy and cancer cell line microarray data sets from GEO

The data was sourced from GEO (53) at the National Centre for Biotechnology Information Database (NCBI). Rossing M and Jensen MB published the accession number of GSE231629 which included a study associated with breast cancer biopsies.

Kaipparettu reported accession number GSE241654, consisting of 5 samples of MCF7-derived tamoxifen-resistant and 3 samples of MCF7 control. Additionally, accession number GSE42568 was offered by Clarke C (54), which included 16 samples of normal breast cancer biopsies and 105 samples of breast cancer biopsies. These datasets were based on the ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array [transcript (gene) version, Affymetrix, Inc., USA]) platform(55), and were selected from a range of others, including GSE228601, GSE144113, GSE158779, and GSE171445 as part of the study.

Microarray data preprocessing

By employing the affy R-package (Bioconductor version 3.17), original CEL profiles were brought into R (56) for background correction and normalization. The mas5calls method of Affybatch was used to access expression sets for specific genes through multiple probes. Moreover, the original CEL profiles were also moved into the Transcriptome Analysis Console (TAC) (version 4.0.1), and differentially expressed genes (DEGs) based on criteria of |log2 fold change| > 2 and P < 0.05, were identified. To preprocess Affy platform microarray data (GSE231629, GSE42568, and GSE241654), Bioconductor-developed packages, such as Affy and sva, were used. Furthermore, the Affy package's robust multiarray average technique was utilized for preprocessing Affy platform microarray data, which involved quantile normalization, background correction, and computation of gene average expression values.

Creation of PPI network

The STRING database(57), was utilized to create protein-protein interaction network (PPI), consisting of up-regulated and down-regulated DEGs, using a cutoff score of greater than 0.4. Important modules were discovered from the PPI network in Cytoscape (58) using the clustering add-in, with a statistical significance threshold of P < 0.05. To discover the most important nodes and modules in the network, K-core analysis and assessment of betweenness, degree, and closeness centrality were performed using the CentiScaPe and Molecular Complex Detection (MCODE) add-ins in Cytoscape. Additionally, Gephi (version 0.9.2) was employed to analyze protein-protein interaction networks and filter genes based on betweenness, degree, and closeness centrality.

Gene Set Enrichment Analysis

To understand the mechanisms of differentially expressed genes (DEGs) involved in breast cancer and tamoxifen resistance, we used the cluster profiler R-package to assess the enrichment of specific pathways and processes. A significance threshold of P < 0.05 was applied. The biological functions of the genes and their links with diseases in the protein-protein interaction (PPI) network were presented. Moreover, we directed functional gene enrichment analysis using different tools including STRING Enrichment analysis in Cytoscape, Bioinformatics SRplots (59), Gene Ontology (60), and Enrichr (Jansen-Tissues, Wikipathway-2023, KEGG-Human-2021, OMIM-Diseases, GO-Molecular-Function-2023, GO-Biological-Process-2023, GO-Cellular-Component-2023, Elsevier-Pathway, Reactome-2022, Disease-Perturbations-from-GEO, GeDiPNet-2023, Gene-Perturbations-from-GEO, lncHUB-lncRNA-Co-Expression, miRTarBase-2017, OMIM-Disease, TargetScan-microRNA-2017, and WikiPathway-2023), offering a wide range of databases and resources. A great number of relevant functional enrichments were identified concerning our data, and these findings are provided in the supplementary file, without accounting for a false discovery rate (FDR).

Identification of predicted miRNAs and lncRNAs

We employed databases including Target scan(61), miRDB (62), RNAInter(63), and NPInter (64) with the aim of discovering miRNAs involved in tamoxifen-resistant breast cancer cells. Cytoscape was utilized to create the lncRNA-miRNA-mRNA interaction network according to hub genes and high interactions between nodes.

Differentially expressed genes (DEGs)

We downloaded datasets GSE231629 and GSE241654 from NCBI (GEO). GSE231629 including 109 breast cancer biopsies, and GSE241654 including 6 MCF7-derived tamoxifen-resistant and 3 MCF7-control. Additionally, downloaded GSE42568 contained 121 samples which consisted of 105 Breast cancer biopsies and 16 Normal breast biopsies. Differentially expressed genes (DEG) of groups MCF7-derived tamoxifen resistant were compared with breast cancer biopsy. Moreover, we compared DEG between MCF7-derived tamoxifen-resistant samples and normal breast biopsies, breast cancer biopsy, and normal breast biopsy, breast cancer biopsy and MCF7 control samples. We merged the results to identify genes with significant expression differences (Figs. 1 and 2). In DEG between MCF7-derived tamoxifen-resistant samples and normal breast biopsies a total number of 907 genes were downregulated and 1825 of them were upregulated from 2732 genes. Genes such as POSTN, COL1A2, SLC40A1, GJA1, and HLA-DPA1 were most upregulated and genes like SOX2, ASCL1, DSCAM-AS1, WDR72, FREM2, and RIMS2 were highly downregulated.

Protein-protein interaction networks and hub genes

Based on the expression of upregulated and downregulated genes, protein-protein interactions were discovered in the STRING, Cytoscape, and Gephi databases. By interaction pairs, a protein-protein interaction network consisting of 1816 nodes and 26203 edges was established. The key genes in the network, are identified as hub genes. Some of them include CNNB1, GAPDH, BCL2, ALB, EGFR, STAT3, MYC, CASP3, MYC, PTEN, MAPK3, PECAM1, HIF1A, SRC, JUN, IL6, TLR4, FN1, MMP9, ICAM1, CCL2, NFKB1, CD4, ITGB1, PTPRC, INS, CCND1, CD34, FGF2, EGF, IL1B, SP90AA1, CDH1, APOE, CXCL8, KRAS, ACTB, SOX2, CDKN2A, FOS, COL1A1, CD8A, and IL2. The protein-protein interaction networks were visualized based on various parameters such as degree, betweenness, and centrality (Fig. 3).

Analysis of functional and pathway enrichment

Taken from gene set enrichment, DEGs were enriched in biological processes like intracellular signal transduction, MAPK Cas cade, gene expression, protein phosphorylation, and cell population proliferation. They were likewise enriched in cellular components such as focal adhesion, membrane raft, cell substrate junction, platelet alpha granule, and intracellular organelle lumen. In addition, the genes were enriched in Disease-Perturbations-from-GEO including breast cancer in human, pancreatic ductal adenocarcinoma in mouse, and anaplastic thyroid carcinoma in human. In KEGG-2021: proteoglycans in cancer, cancer pathways, and fluid shear stress and atherosclerosis. In Jensen-TISSUES: stromal cell, adult mesenchymal stem cell, immune system, mesenchyme, and HUVEC cell. Results from Reactome-2022 showed: interleukin − 4 and interleukin-13 signaling, signaling by interleukins, cytokine signaling in immune system, immune system, and signal transduction. In GO-molecular-function: protease binding, protein kinase binding, growth factor receptor binding, kinase binding, and receptor ligand activity. In Elsevier-pathway-collection: proteins involved in protein nephropathy, proteins involved in myocardial ischemia, proteins involved in melanoma, proteins involved in pancreatic cancer, and proteins involved in atherosclerosis. In GeDiPNet-2023: Mammary neoplasms, breast carcinoma, carcinoma, and breast cancer. In OMIM-Disease: breast cancer, ovarian cancer, and colorectal cancer. In TargetScan-microRNA-2017: APP; POSTN; STAT1; and PLEK genes are associated with mmu-miR-5116. ITGB1, POSTN, JUN, and ARF1 with mmu-miR-92a. ITGB1, POSTN, JUN, and ARF1 with mmu-miR-32. In miRTarBase-2017: genes such as CSF1R, CXCL8, PTEN, and FGF2 were associated with hsa-miR-155-5p. TGFB1, CXCL8, STAT1, and ITGB2 related to hsa-miR-146a-5p. Genes such as PDGFRB, ITGB1, TGFB1, and MMP2 are linked to hsa-miR-29b-3p. In lncHUB-lncRNA-Co-Expression: genes like PDGFRB, COL1A1, ITGB1, and POSTN are associated with LINC01614. PDGFRB, COL1A1, POSTN, and COL1A2 linked with FNDC1-IT1. PDGFRB, COL1A1, ITGB1, and POSTN are related to VCAN-AS1 (Fig. 4).

Development of the miRNA-lncRNA-mRNA network

We discovered long non-coding RNAs such as H19 and HOTAIR, and miRNAs such as miR 448, hsa-miR 6510-5p and hsa-miR 4433a-3p associated with breast cancer and tamoxifen resistance by using different databases including (miRWalk, miRTarBase, LncRNAWiki, TargetScan, NPInter, miRDB and RNAInter) and visualized them. In all datasets, a total number of 1628 microRNA and 390 long-non coding RNA have been seen (Fig. 5).

Breast cancer is a major challenge happening on a global scale and affecting women of different ages. While there are various reasons involved in its occurrence, genetic elements such as variants, mutations, and polymorphisms play a crucial role in its development and increase its risk(65). There has been a great endeavor to combat breast cancer by developing effective medications. Tamoxifen is a well-known drug that reduces mortality by 31% in ER-positive breast cancer, but over half of advanced cases are resistant, and about 40% gain resistance during treatment(66). Several mechanisms have been suggested for tamoxifen resistance, it can be due to the loss of estrogen receptor (ER) function or expression, the existence of ER-negative cancer stem cells, changing reactions to oxidative stress, and changes in the communication between ER and growth factor-related signaling pathways(67), but there is still a considerable gap in our understanding of molecular mechanisms related to tamR. Carcinogenesis and occurrence of drug resistance is a complex process with intricate pathways, triggered via both environmental and genetic causes. Since studying the genes and their expression is an important step in understanding the underlying mechanisms of cancer and designing the right drugs to defeat it; many studies focused on the genetic aspect of breast cancer progression, but there are still numerous genes and corresponding mechanisms remaining that need attention. With the development of technologies, bioinformatic analysis can be employed as a valuable tool in identifying vital genes involved in tamoxifen resistance. We discovered various DEG genes between breast cancer biopsy and two groups of MCF7 treated with tamoxifen drug which developed resistance towards it. The scatter plot, heatmap, and volcano plot showed us 2732 differentially expressed genes, With 1824 of up-regulated and 908 down-regulated genes. DEGs were mainly enriched in biological processes such as intracellular signal transduction, MAPK Cas cade, gene expression, protein phosphorylation, and regulation of cell population proliferation. Following that, we will discuss a few of the important genes. For instance, ITGB1 is a protein-coding gene and is illustrated as a hub gene in our study. In recent research by Chen Su et al, its role in signal transduction pathways in normal physiological functions was highlighted (68), and it was reported that in tumor situations they are linked with stem-like properties and increasing metastasis. Additionally, they promote resistance to radiotherapy. Through GO enrichment analysis genes such as APP, SRC, TNF, FGF2, THBS1 EGFR, INS, SOX2, GJA1, KDR, and HMOX1 were mentioned in intracellular signal transduction of biological process.

APP is another protein-coding gene that is involved in the regulation of a signaling pathway known as MAPK in breast cancer. Xiong Wu et al demonstrated that over expression of APP can lead to increased expression of EMT genes in breast cancer and it does it via the MAPK signaling pathway (69). Other hub genes including, CSF1R, CXCL8, PTEN, FGF2, THBS1, ACTB, EGFR, INS, and MYC were linked with protein phosphorylation in biological processes from GO enrichment analysis.

SOX2 is another of our hub genes and it was found before that its expression is controlled via Ras/MAPK and PI3K signaling pathways (70). The PI3K-AKT-mTOR pathway activates SOX2 through PI3K, which activates PKB (Akt). After that, Akt stimulates mTOR, which moves into the nucleus and controls the expression of genes associated with survival, cell growth, and proliferation(70, 71). Additionally, APP, CSF1R, LRRK2, TNF, FGF2, THBS1, EGFR, ICAM1, and INS were reported in GO enrichment analysis and were involved in MAPK Cascade, in our study.

CXCL8, also known as IL-8, is a chemokine that is expressed by epithelial cells and macrophages to recruit neutrophils to sites of inflammation, injury, or infection (72). CXCL8 is one of the identified hub genes in our study, and its expression is associated with tamR and is involved in the regulation of cell population proliferation, from GO enrichment analysis. In consistence with our results, previous research by Liu. Q., demonstrates the link between this gene and inhibition of apoptosis in addition to cell proliferation in various cancers including breast cancer(73). Other genes involved in two biological processes of gene expression and regulation of cell population proliferation are APP, TNF, FGF2, HIF1A, THBS1, CDH5, GJA1, IFI16, MYC, AKT1, CD36, CD34, IL10, CD74, TGFB1, CSF1R, PTEN, INS, AKT1, and JAK2.

Additionally, the data obtained from GeDiPNet-2023, Disease-Perturbations, and OMIM-Disease suggest that our hub genes are significantly associated with breast cancer. GO-Molecular-Function-2023 shows the relation of our genes in several important functions such as protease binding, protein kinase binding, and growth factor receptor binding. Also, hub genes such as COL1A1, ITGB1, COL1A2, VWF, BIN1, FN1, BCL2, PTEN, TNF, TP53, THBS1, and INS were reported to be involved in protease binding.

Epidermal growth factor receptor (EGFR) is over expressed in 50% of TNBC and inflammatory breast cancer (IBC)(74). Jinkyoung Kim, reports that in breast cancer, epidermal growth factor (EGF) triggers activation of Smad2/3 which down-regulates E-cadherin and promotes EMT(75). EGF was a hub gene in our study and by GO molecular function enrichment analysis it was involved in growth factor receptor binding.

We identified PDGFRB, ITGB1, HSP90AA1, CDKN2A, CAV1, PLEK, ITGB2, STAT3, HIF1A, ESR1, and ACTB hub genes associated with protein kinase binding. A study by Yuchen Gu et al shows that this connection stimulates c-myc and augments cyclin D1(76). C-myc and cyclin D1 are associated with control of the cell cycle and these results are in consistence with our findings.

KEGG results demonstrated the relation of our hub genes with pathways in cancer, some of these genes include JUN, TGFB1, CXCL8, STAT1, MMP2, STAT3, FN1, TNF, NFKB1, and AGT. In wiki pathways, hub genes such as CXCL8, SRC, PTEN, CXCR4, TNF, EGFR, ICAM1, SOX2, CCNB1, CDH1, MYC, and CASP3 were associated with IL 24 Signaling Pathway. Interleukin (IL-24) has been mentioned as a tumor suppressor before. During endoplasmic reticulum (ER) stress in cancer, it regulates apoptosis through phosphorylated eukaryotic initiation factor 2 alpha (eIF2α)(77). Mda-7/IL-24 hinders the activity of miR-221 which itself targets estrogen receptors in breast cancer, influences tamoxifen, and decreases its effects(78). In addition, Reactome analysis indicated that our hub genes were mainly enriched in signaling by interleukins, in Cytokine Signaling in Immune System, and signal transduction. ITGB1, APP, CSF1R, CXCL8, ITGB2, TNF, HIF1A, ICAM1, and STAT3 are some of the hub genes, mentioned in these pathways.

According to Differently expressed genes, POSTN, COL1A2, SLC40A1, GJA1, and HLA-DPA1 were upregulated and SOX2, ASCL1, DSCAM-AS1, WDR72, FREM2, and RIMS2 were downregulated. Katarzyna Ratajczak-Wielgomas et all, demonstrates that POSTN is linked with cancer progression and metastasis(24). And its expression is associated with more aggressive types of breast cancer(79). While there are not many studies on the role of POSTN in tamoxifen-resistant breast cancer cells, our study shows a significant upregulation in it in MCF7-derived tamoxifen-resistant samples in comparison to breast cancer biopsies. Interestingly, when DEGs were compared between MCF7-derived tamoxifen-resistant samples and normal breast biopsies POSTN faced downregulation. Guorong Yao et al identified 136 DEGs associated with radiation in breast cancer(80). They reported an upregulation for COL1A2, additionally, they showed the correlation between two RNAs, hsa-miR-29a/c and hsa-miR-29a/c with COL1A2. While some studies suggest the oncogenic function of it some count it as a potential tumor suppressor. These findings highlight the complexity of the tumor environment and how it can alter the role of a gene. The results of our study demonstrate a notable upregulation in COL1A2 between MCF7-derived tamoxifen-resistant samples and breast cancer biopsies. On the other hand, it had downregulation when compared between MCF7-derived tamoxifen-resistant samples and normal breast biopsies.

It was found that Breast cancer cells upregulate the iron exporter SLC40A1 (ferroprotein) and satisfy their need, and suppressing the transferrin receptor can be a potential approach to inhibit tumor growth, which they proved in the mouse model(81). Our findings suggested that this specific gene had more amount of expression in MCF7-derived tamoxifen-resistant samples compared with breast cancer biopsies and less expression from normal breast biopsies.

Previous studies showed the role of the SOX2 gene in breast cancer. Peng Liu et al demonstrate SOX2 as an oncogene in triple-negative breast cancer (TNBC) cell lines and its inhibition can decrease cancer cell growth(82). While Kuancan Liu et al, found that it does it by influencing miR-30e-5p and miR-181a-5p(83). In addition, SOX2 is mostly considered as an oncogenic protein and is linked with lower survival in patients, it promotes proliferation, and cancer stemness(44). J Yu, W Sun, demonstrated that By blocking FOXO1 degradation and stimulating SOX2 transcription, the pseudokinase Tribble 3 (TRIB3) promotes breast cancer stemness(84). Based on the study by Ankita Dey et al, high levels of SOX2 were observed in breast cancer tissues, and its expression was correlated with resistance to therapy (85). Additionally, it has been seen that SOX2 can increase tamoxifen resistance in breast cancer cells(86). In our results, SOX2 in MCF7-derived tamoxifen resistant samples was downregulated when compared with cancer biopsy samples and upregulated when compared with normal breast biopsy. Also, the levels of ASCL1 were downregulated when cells were treated via drug and developed resistance. Elaine Zhong et al, showed that ASCL1 is not frequently seen in breast cancer and therefore, not a necessary factor for checkups(87). While in small cell lung cancer, it was found that ASCL1, is a target of the TGF-β signaling pathway and it can promote survival of small cell lung cancer cells(88).

The role of ncRNA in various human cancers has been investigated before, they have a dual function of both tumor suppressors and oncogenes(89). In our study, we discovered several ncRNAs linked with breast cancer and potential drug resistance development. Some of these ncRNAs include Long non-coding RNAs include H19, HOTAIR, MALAT1, MIAT, and NEAT1 which themselves have multiple targets including microRNAs such as miR-200b, miR-148a, miR-448, miR-150, and miR-218, respectively. Additionally, TLR4, TP53, PTEN, NFKB1, Sox2, MYC, and CD44 are several protein-coding genes involved in a wider network linked to ncRNAs which can also result in the progression of breast cancer and the emergence of tamoxifen resistance. Yashar S. Niknafs et al, have shown that lncRNA, DSCAM-AS1, plays an important role in the progression of breast cancer and makes them resistant to tamoxifen. They discovered a protein called hnRNPL which interacts with DSCAM-AS1 and contributes to its mechanism of action(90). The findings of Wen-Hui Liang are consistent with it. They demonstrated that DSCAM-AS1 inhibits miR-204-5p and promotes RRM2 and by that, decreases apoptosis and increases tumor progression in breast cancer cells(91). We discovered that in MCF7-derived tamoxifen-resistant samples, DSCAM-AS1 has lower expression when compared with breast cancer biopsies, and higher expression when compared with normal breast biopsies.

Over expression of long non-coding H19 has been seen in breast cancer and has been linked to various processes required in cancer progression(92) and our results align with these findings. A study done by Hong Sun et al demonstrated the role of H19 in breast cancer. Their results indicate that the expression of H19 is augmented in ER-positive MCF7 cells and lower when compared with ER-negative MDA-MB-231 cells. In MCF7 cells, H19 was increased via the usage of 17β-estradiol, and this effect was facilitated by the Erα receptor. Additionally, cell survival and growth were hindered when H19 was knocked out(93). In another study by Yang Li, levels of H19 were significantly higher in TNBC cells, and it was found that H19 acts as a competitive inhibitor of p53 leading to metastasis and proliferation(94).

HOX transcript antisense intergenic RNA which is also known as HOTAIR, is a well-known long non-coding RNA involved in Breast Cancer(95). This lncRNA, has been the focus of many research investigations. Yuanyuan Wang et al, reported that with a reduction of HOTAIR, migration and proliferation of cancer cells decreased (96). Additionally, the activity of the AKT signaling pathway was hindered. Results of their study suggest that via miR-601/ZEB1 axis, HOTAIR augments cancer progression. X Xue et al, demonstrate a link between HOTAIR expression levels and resistance towards tamoxifen (97), and increased survival and proliferation and development of tamoxifen resistance happen through a higher amount of HOTAIR which enhances ligand-independent estrogen receptor (ER) activity. Based on our findings, HOTAIR is overexpressed in breast cancer and tamoxifen-resistant circumstances.

Metastasis associated lung adenocarcinoma transcript 1 (MALAT1) has been associated with cancer. Mariam Naveed et al, indicate that by targeting MALAT1, breast cancer progression can be harnessed (98). Their results suggest that the PI3K/AKT/mTOR axis is associated with MALAT1 and upon its inhibition the given axis will decrease. In a study by Hibah Shaath et al, on TNBC cells, a link between MALAT1 and cancer cells’ resistance to treatment was shown. By employing CRISPR/Cas9, MALAT1 was deleted and cells became sensitive to chemotherapy. They also found that this certain long ncRNA changes the activity of other lncRNAs such as LINC-PINT, NEAT1, and USP3-AS1(99). Our results show the upregulation of MALAT1 in breast cancer and its relation with miR-448, miR-129-5p, and miR-145.

The expression levels of another lncRNA, known as myocardial infarction-associated transcripts (MIAT), has been linked to cancer. A research by Jintao Mi et al was done on luminal B breast cancer and demonstrated that through interaction with miR-150-5p, MIAT acts as a competitive endogenous RNA (CeRNA), controls the expression of programmed cell death ligand 1 (PDL1), and impacts invasion and growth of cancer cells(100). Chao Yan et al, reported that by silencing MIAT in breast cancer cells the expression of Bcl-2, which is involved in the promotion of growth and survival, is hindered. MIAT is linked with the expression of apoptosis proteins such as caspase-3 and Bax. Additionally, they discovered binding between miR-378a-5p and MIAT and suggested that by silencing MIAT, the proliferation of breast cancer cells can be repressed through the elevation of miR-378a-5p(101). Our findings demonstrate higher expression of MIAT in breast cancer cells and tamoxifen resistance, MIAT targets microRNAs such as mir-302, miR-29c, and miR-150.

Nuclear enriched abundant transcript 1 (NEAT1) is involved in the metastasis and growth of breast cancer(102). M. Azadeh et al show a connection between MTRNR2L8, XIST, and hsa-miR-612 and link decreased survival rates with augmented NEAT1 expression, in breast cancer(103). A recent study was done by Dongling Quan lon et al and their findings showed upregulation in amount of NEAT1, miR-21, and RRM2 in breast cancer cells (104). It was found that higher expression of small non-coding RNA miR-21 associates with the migration and proliferation of cancer cells. Using bioinformatic analysis Xiaopeng Wang et al discovered genes that had different expressions between tamoxifen-prone and tamoxifen-resistant samples. Their results demonstrated an upregulation in NEAT1 as one of the differentially expressed genes (DEG)(105). Our findings align with previous research and show an upregulation in the expression of NEAT1 in relation to breast cancer and tamoxifen resistance, and some of its targets include miR-548, miR-129-5p, and miR-218.

At last, its vital to acknowledge that the field of breast cancer and development of drug resistance are dynamic areas that require constant updates. Therefore, this research provides information on the gene expression status at a specific point in time.

The network of interactions and analysis of key genes related to tamoxifen-resistance in MCF7 cell lines revealed their crucial roles in different pathways. Our results indicated that genes such as POSTN, COL1A2, LUM, COL3A1, PPM1E, BRINP3, FREM2, TBX2-AS1, ARHGAP36, WDR72, DSCAM-AS1 and SOX2, non-coding RNAs including H19, HOTAIR, MALAT1, MIAT, NEAT1, miR-200b, miR-148a, miR-448, miR-150, and miR-218 have a potential impact on breast cancer, and development of resistance. Further research is needed to validate their significance.

breast cancer

TamR

tamoxifen resistance

estrogen receptor

progesterone receptor

HER2

human epithelial growth factor receptor 2

NcRNA

non-coding RNA

lncRNA

long non-coding RNA

Ethics declaration

Not applicable.

Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Competing interests

The research was carried out without any commercial or financial ties that may be seen as a possible conflict of interest. The authors disclose that they have no competing interests.

Funding

We declare that there was no external funding received for this study. All costs associated with the research were covered by the authors.

Authors' Contributions

MS and ER: Wrote the manuscript and designed the bioinformatics data, and performed data analysis. MR: Assembly of data, and data analysis. The authors read and approved the final manuscript for publication.

Acknowledgments

The faculty of biotechnology of Amol University of Special Modern Technologies, Islamic Republic of Iran.

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No competing interests reported.

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Investigation of the Genomic and Transcriptomic Variations Underlying Tamoxifen Resistance in Breast Cancer

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Abstract

Figures

Background

Methods

Obtaining biopsy and cancer cell line microarray data sets from GEO

Microarray data preprocessing

Creation of PPI network

Gene Set Enrichment Analysis

Identification of predicted miRNAs and lncRNAs

Results

Differentially expressed genes (DEGs)

Protein-protein interaction networks and hub genes

Analysis of functional and pathway enrichment

Development of the miRNA-lncRNA-mRNA network

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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