B. X. Li, W. H. Yang1 and Wenfang Liu*

Surgery, Teaching and Research Office, Cangzhou Medical College,1 Department of Thyroid and Breast Surgery, Cangzhou Central Hospital, Cangzhou, Hebei 061001, China

*Corresponding Author:
Wenfang Liu
Department of Thyroid and Breast Surgery, Cangzhou Central Hospital, Cangzhou, Hebei 061001, China
E-mail: 164442488@qq.com
This article was originally published in a special issue, “Role of Biomedicine in Pharmaceutical Sciences”
Sci 2023:85(2) Spl Issue “159-165”

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms

DOI: 10.36468/pharmaceutical-sciences.spl.656

Abstract

Breast cancer is the most common global malignancy and the leading cause of cancer deaths for women. Aromatase inhibitors are clinically recommended therapy for the disorders. However, resistance to aromatase inhibitors therapies still occurs and the underlined molecular mechanisms are still poorly understood. The tumour specimens consisted of aromatase inhibitor-sensitive and aromatase inhibitor-resistant breast cancer patients, who were compared and the differentially expressed genes for aromatase inhibitor-sensitive patients were generated and related genetic network was explored. The enriched cellular process and signaling cassettes for aromatase inhibitor-sensitive patients were also in-depth investigated. Compared with the aromatase inhibitor-resistant patients, 896 genes showed a significant differential expression. 621 were significantly up-regulated and 275 were remarkably downregulated. From the interaction network, the differentially expressed genes including signal recognition particle 72, B-cell lymphoma-2 associated transcription factor 1, cathelicidin antimicrobial peptide and defensin alpha 1B could be considered as the key target genes for aromatase inhibitors treatment in breast cancer patients. These differentially expressed genes were enriched in processes involving immunoglobulin complex, cell division and others while, drug metabolism and other signalling mechanisms were enriched in aromatase inhibitors treatment of breast cancer patients. The study speculated several novel gene targets for aromatase inhibitors therapeutic treatment of breast cancer patients and initiated a beneficial direction for future aromatase inhibitors drug resistance research.

Keywords

Breast cancer, aromatase inhibitors, differentially expressed genes, protein-protein interaction

Breast cancer is the leading cause of cancer death in women and the second most common cancer overall after lung cancer. It is also the fifth most common cause of cancer death[1], so it makes sense that breast cancer is one of the most studied malignancies in the world [2]. Many factors have been shown to be directly related to breast cancer, including those related to hormones, pregnancy, anthropometric indices, physical condition, diet, environmental exposures, etc.[3]. With currently available treatments, including surgery, chemotherapy, endocrine therapy, radiotherapy and targeted therapy, the clinical outcomes of breast cancer have improved over the past 20 y. However, the treatment of breast cancer remains challenging due to its significant heterogeneity[4]. In the clinic, breast cancer patients can be classified into a number of subtypes. To date, classifications have been based on different expression patterns of the Progesterone Receptor (PR), the Estrogen Receptor (ER) and the Human Epidermal Growth Factor Receptor-2 (HER-2)[5]. These breast cancers that have a significant number of receptors for either estrogen or progesterone are called Hormone Receptor (HR) positive breast cancers[6].

Aromatase is a Cytochrome P450 (CYP450) enzyme encoded by the Cytochrome P450 Family 19 Subfamily A Member 1 (CYP19A1) gene that can convert androgens to estrogens, specifically testosterone and androstenedione to the aromatic estrogens, estradiol and estrone[7]. Aromatase is expressed in many tissues including the placenta, central nervous system, bone, muscle, testes, prostate, adrenal glands and skin[8]. Aromatase is often overexpressed in breast endothelial cells and the surrounding stroma in HR+ breast cancer, leading to local estrogen production in the tumour microenvironment and promoting cancer progression by activating the ER[9]. Therefore, Aromatase Inhibitors (AIs) play a key role in the treatment of HR+ breast cancer. AIs include both steroidal and non-steroidal agents and have been shown to be useful in the treatment of breast cancer[10]. In clinical practice, AIs are the most commonly used first-line endocrine therapy options for postmenopausal women with ER-positive/PR- positive locally advanced or metastatic breast cancer, while tamoxifen or an AI plus ovarian suppression is the recommended therapy for premenopausal women[11]. However, resistance to AI therapies still occurs[12]. The molecular mechanism of resistance to AI therapy in breast cancer is still unclear.

Based on the uncertain molecular mechanism behind AI resistance in breast cancer patients, we compared tumour tissues between AI-sensitive and AI-resistant ER-positive breast cancer patients. At the same time, the potential target genes and genetic network were also investigated in detail. All these promising findings shed light on future breast cancer research.

Materials and Methods

Specimens and Differentially Expressed Gene (DEG) analysis:

The Ribonucleic Acid (RNA) sequencing data and complete clinical information in this study were obtained from the Gene Expression Omnibus (GEO) database with accession number GSE206199 (https://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc=GSE206199). Of these, 14 tumour samples were obtained from ER-positive breast cancer patients receiving adjuvant AI treatment. The 14 tumour samples consisted of 7 AI-sensitive (S1-S7) and 7 AI-resistant (R1-R7) patients.

Differential expression analysis:

The edgeR package in the R language was used to analyze DEGs between AI-sensitive (S1-S7) and AI-resistant (R1-R7) patients. The absolute values of logarithmically transformed differential expression multiples (Log2Fold Change (FC))>1 and p-value<0.05 were used as screening criteria[13].

Functional enrichment analysis:

The clusterProfiler package in R language was processed for Gene Ontology (GO) analysis (including Biological Process (BP), Molecular Function (MF) and Cellular Component (CC)) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis[14], p-value<0.05 was considered statistically significant.

Protein-Protein Interaction (PPI) networks:

The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database is a database that analyses and predicts functional protein-protein connections. We used STRING (https://string- db.org/, version 11.0) to analyze the functional connections and interactions of candidate proteins[15], from which the interaction pairs with a combined score greater than or equal to 0.4 (confidence score≥0.4) were retained. Cytoscape (https:// cytoscape.org/, version 3.7.2) was used to visualize the PPI network[16]. The network modules reserve specific biological meanings, which are usually the core of protein networks. We process the Molecular Complex Detection (MCODE) method plug-in in Cytoscape software to identify significant clustering modules, using MCODE score>2 as a threshold.

Statistical analysis:

The GraphPad Prism 9 was established for data collection and analysis in this study. The student t-test was used to compare the differences between two groups. The p-value<0.05 was considered as a statistical significant.

Results and Discussion

DEG profiling establishment for AI-sensitive breast cancer patients were shown here. In the study, we first analysed the differentially expressed messenger RNAs (mRNAs) using the online GEO chip GSEGSE206199, which contains RNA sequencing information for 7 AI-sensitive (S1-S7) and AI- resistant (R1-R7) breast cancer patients. Compared to the AI-resistant patients, 896 genes showed a significant differential expression (fig. 1A and fig. 1B).

IJPS-profiling

Fig 1: DEG profiling establishment for AI-sensitive breast cancer patients
Note: (A) The volcano diagram of DEGs. The horizontal axis indicates the log2FC value and the vertical axis indicates -log10 (p-value),Eqaution Up-regulation;Eqaution Down-regulation andEqaution No significant difference; (B) The heat map of the DEGs expression for AI-sensitive breast cancer patients, Eqaution Control and Eqaution Tumour

Of the 896 DEGs, 621 were significantly up-regulated (Table 1). At the same time, 275 were remarkably downregulated in 7 AI-sensitive (S1-S7) breast cancer patients compared to AI-resistant patients (Table 2).

Gene name Results Adjusted p value p value logFC
SRP72 31670_s_at 6.71E-16 1.98E-18 5.73919
BCLAF1 38050_at 4.94E-21 2.09E-24 4.664181
LUC7L3 34397_at 9.47E-12 6.80E-14 4.536923
DNTTIP2 37938_at 2.31E-18 1.95E-21 4.416025
TRAPPC8 36002_at 2.36E-09 3.54E-11 4.310544
CLEC2B 40698_at 0.0125 0.00233 4.251532
EXOSC8 36968_s_at 4.14E-10 5.08E-12 4.191484
RECQL 34684_at 1.09E-12 6.21E-15 4.027176
RBM6 40869_at 7.22E-10 9.15E-12 3.81235
PRPF40A 37506_at 2.33E-17 3.94E-20 3.764995
SRSF11 32183_at 6.93E-18 8.62E-21 3.703981
ITGA4 2061_at 7.46E-08 1.81E-09 3.675505
ZFR 40610_at 2.79E-09 4.42E-11 3.634965
SMARCA5 39132_at 1.54E-14 6.52E-17 3.592693
ZC3H15 35750_at 2.39E-21 5.05E-25 3.554028
RPS6KB1 2037_s_at 1.07E-10 1.04E-12 3.505803
SECISBP2L 41634_at 1.43E-09 1.97E-11 3.474673
SMC4 34878_at 8.15E-09 1.53E-10 3.462946
PPIG 37385_at 1.59E-18 1.01E-21 3.443338
KTN1 32846_s_at 6.93E-18 8.78E-21 3.396649

Table 1: Top 20 Gene List of Enhanced Expression for AI-Sensitive Breast Cancer Patients Compared With AI-Resistant Breast Cancer Patients

Gene name Results Adjusted p value p value logFC
NR2F1 39294_at 0.0518 0.0144 -1.00729
KAZN 39615_at 0.00221 0.00028 -1.00736
PTPRU 33750_at 0.0897 0.0303 -1.00861
HIST2H2BE 33352_at 0.207 0.0937 -1.01303
NOP2 1979_s_at 0.00966 0.0017 -1.01368
IGHMBP2 31861_at 0.00544 0.000835 -1.01482
LBR 288_s_at 0.0326 0.00774 -1.02196
GNA11 564_at 0.00278 0.000368 -1.02255
FAM214B 33841_at 0.00259 0.000337 -1.02311
ARL2-SNX15 37570_at 0.00327 0.000451 -1.02462
CDH6 37784_at 0.124 0.0465 -1.02556
CCND3 1794_at 0.0293 0.00684 -1.03604
SOD3 692_s_at 0.0421 0.0108 -1.03861
KRT31 36310_at 0.0111 0.00201 -1.03882
AURKB 33266_at 0.00838 0.00141 -1.03884
HLA-F 37420_i_at 0.116 0.0428 -1.04081
TGFA 160025_at 0.215 0.0988 -1.044
HEXA 39340_at 0.0598 0.0173 -1.04657
CDKN2D 1797_at 0.00879 0.00151 -1.05586
CXCR4 649_s_at 0.165 0.0687 -1.06204

Table 2: Top 20 Gene List of Decreased Expression For AI-Sensitive Breast Cancer Patients Compared with AI-Resistant Breast Cancer Patients

Construction of PPI network was explained here. A STRING database was used to construct a PPI network for the 896 DEGs and the gene interactions with a confidence score≥0.4 were selected for visualization using Cytoscape software. The MCODE plug-ins were run to identify significant clustering modules (fig. 2). Within these, there are 9 prominent MCODEs. The primary up-regulated genes (Signal Recognition Particle 72 (SRP72) and B-Cell Lymphoma-2 Associated Transcription Factor 1 (BCLAF1)) were localized in MCODE1, while the primary down- regulated genes (Cathelicidin Antimicrobial Peptide (CAMP) and Defensin Alpha 1B (DEFA1B)) were localized in MCODE3. MCODE1 and MCODE3 were also the leading MCODEs for AI-sensitive breast cancer patients (with the highest nodal grade). Thus, the DEGs including SRP72, BCLAF1, CAMP and DEFA1B could be considered as the key target genes for AI treatment in breast cancer patients. As shown in fig. 2, in the network, each point represents a node. The more line segments connected to the node, the higher the degree of the node. The size of the node directly reflects the degree; the thicker the connection between the nodes, the stronger the interaction between the two nodes.

IJPS-construction

Fig 2: Construction of PPI network Note:Eqaution Eqaution

GO and KEGG functional enrichment analysis was explained here. For the 896 DEGs, we performed both GO and KEGG analysis for AI treatment in breast cancer patients. The GO enrichment analysis included BPs and CCs as well as MFs. As shown in fig. 3A, DEGs were enriched in processes involving immunoglobulin complex, cell division and others. At the same time, the KEGG analysis reflected the enriched signalling pathways. As shown in fig. 3B, drug metabolism and other signalling mechanisms were enriched in AI treatment of breast cancer patients.

IJPS-enrichment

Fig 3: GO and KEGG functional enrichment analysis
Note: (A) The top 20 most enriched GO entries for DEGs in AI-sensitive breast cancer patients and (B) The top 10 enriched KEGG signaling pathways for DEGs in AI-sensitive breast cancer patients

Like other types of cancer, breast cancer is a truly complex disease and is the second leading cause of cancer-related death in women worldwide[17-19]. Accumulating evidence has suggested that breast cancer is closely associated with a variety of internal (genetic) as well as external (environmental) factors. Due to the characteristics of breast cancer, different therapies have emerged in the era of breast cancer in recent years[20]. For example, Antibody- Drug Conjugation systems (ADCs), nanoparticles (albumin, metal, lipid, polymer and micelle-based nanoparticles) and Breast Cancer Stem Cells (BCSCs)-based therapies as well as AI treatment in this study. However, even with the breakthrough of these novel therapies, resistance to treatment has severely hampered the overall effect.

There are several AIs available in clinical practice, including exemestane, formestane, anastrozole, letrozole, fadrozole and vorozole, etc. Currently, AI is recommended in clinical practice for postmenopausal women. In fact, postmenopausal women are at high risk of developing breast cancer due to estrogen production in peripheral tissues of the body other than the ovaries[21]. However, aromatase has been identified in breast tissue, leading to local estrogen accumulation that can be effectively suppressed by AIs. A previous study by the Early Breast Cancer Trialists Collaborative Group (EBCTCG) compared the clinical outcomes between AI and a common anticancer drug (tamoxifen)[22].

They claimed that for women with early-stage ER- positive breast cancer, adjuvant tamoxifen reduces 15 y risk of breast cancer mortality by one third. While AIs were more effective than tamoxifen in postmenopausal women, they are ineffective in premenopausal women when used without ovarian suppression. In addition, using an AI instead of tamoxifen in premenopausal women with ovarian suppression could significantly reduce the risk of breast cancer recurrence. However, the most promising challenge for AIs in breast cancer therapy is resistance and toxicity, especially for the steroid class of drugs. All of this could be due to the fact that the molecular mechanism behind AIs is unclear.

In the study, we compared 7 AI-sensitive (S1-S7) and AI-resistant (R1-R7) breast cancer patients in detail. Compared with AI-resistant patients, 896 genes showed significantly different expression. With further PPI analysis, we found that the DEGs such as SRP72, BCLAF1, CAMP and DEFA1B could be considered as central target genes for AI treatment in breast cancer patients. SRP72 stands for stress response (heat shock) protein (SRP) 72. A previous study by Prevo et al. suggested SRP72 as a novel gene involved in radioresistance for tumour progression[23]. Deletion of SRP72 resulted in significant radiosensitization of immortal Human cell line (HeLa, cervical), Pancreatic Adenocarcinoma cell line (PSN-1, pancreatic), Urinary Bladder Carcinoma cell line (T24, bladder), BT-549 (breast) and MCF7 (breast) tumour lines as measured by colony formation assays. The identification of SRP72s that underpin tumour radioresistance could help us to better understand the development of targeted radiosensitizers or help to personalize radiotherapy treatment. BCLAF1 has been shown to manipulate Programmed Cell Death Ligand 1 (PD-L1) in response to Ionizing Radiation (IR)[24]. Knockdown of BCLAF1 greatly reduced PD-L1 expression by promoting PD-L1 ubiquitination. Previously, using a similar method, a study focused on the expression profile of dysregulated proteins in sublethally heat- treated breast cancer cells[25]. The overall expression of BCLAF1 was inhibited and the proliferation and invasion ability of breast cancer cells were activated in the sublethal heat-treated group. The CAMP is a classical G-protein dependent signaling pathway. A large body of evidence has shown that hormone- bound steroid receptors in breast cancer cells activate complex signaling networks that include the Mitogen-Activated Protein Kinase (MAPK) and cyclic Adenosine Monophosphate (cAMP) pathways. Collectively, these pathways lead to breast cancer progression and resistance to hormone therapy[26].

Breast cancer is a common cancer with a rapidly increasing prevalence and its aetiology is still poorly understood. AIs are the recommended therapy for breast cancer with high frequency of drug resistance. In this study, we systematically investigated the differentially expressed mRNAs for AI-treated breast cancer patients. The DEGs were approved and the associated genetic network was established. Overall, several novel genes were initiated and the innovative signaling cascade was established.

Conflict of Interest

The authors declared no conflict of interest.

References