Identification of adriamycin resistance genes in breast cancer based on microarray data analysis
Original Article

Identification of adriamycin resistance genes in breast cancer based on microarray data analysis

Yan Chen#, Yingfeng Lin#, Zhaolei Cui

Laboratory of Biochemistry and Molecular Biology Research, Fujian Provincial Key Laboratory of Tumor Biotherapy, Department of Clinical Laboratory, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, China

Contributions: (I) Conception and design: Y Chen, Y Lin; (II) Administrative support: Z Cui; (III) Provision of study materials or patients: Y Chen, Y Lin; (IV) Collection and assembly of data: Y Lin; (V) Data analysis and interpretation: Y Lin, Z Cui; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#These authors contributed equally to this work.

Correspondence to: Dr. Zhaolei Cui. Department of Clinical Laboratory, Fujian Provincial Key Laboratory of Tumor Biotherapy, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, No. 420 Fuma Road, Jin’an District, Fuzhou 350014, China. Email: cuileidizi@fjmu.edu.cn.

Background: Breast cancer is a common malignant tumor with increasing incidence worldwide. This study aimed to investigate the molecular mechanisms of the adriamycin (ADR) resistance in breast cancer.

Methods: The GSE76540 dataset downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database was adopted for analysis. Differentially expressed genes (DEGs) in chemo-sensitive cases and chemo-resistant cases were identified using the GEO2R online tool respectively. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs were carried out by using the DAVID online tool. The protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized with Cytoscape software. The impact of key tumor genes on the survival and prognosis were described.

Results: A total of 1,481 DEGs were excavated, including 549 up-regulated genes and 932 down-regulated genes. According to the GO analysis, the DEGs were significantly enriched in: extracellular matrix organization, positive regulation of transcription from RNA polymerase II promoter, lung development, positive regulation of gene expression, axon guidance and so on. The results of KEGG pathway enrichment analysis showed that the most enriched DEGs can be detected in: pathways in cancer, PI3K/AKT signaling pathway, focal adhesion, Ras signaling pathway and so on. In the PPI network analysis, hub genes of CDH1, ESR1, SOX2, AR, GATA3, FOXA1, KRT19, CLDN7, AGR2, ESRP1, RAB25, CLDN4, IGF1R, CLDN3 and IRS1 were detected. Finally, there is a correlation filter out these hub genes in resistance of ADR.

Conclusions: Hub genes associated with ADR resistance were identified using bioinformatic techniques. The results of this study may contribute to the development of targeted therapy for breast cancer.

Keywords: Breast cancer; microarray data analysis; adriamycin resistance

Submitted Oct 11, 2019. Accepted for publication Feb 05, 2020.

doi: 10.21037/tcr-19-2145

Introduction

Breast cancer is a common malignant tumor in clinical practice, which occurs in the breast epithelium. Breast cancer is one of the leading life-threatening diseases in women, with the highest incidence rate of 1 million patients and a mortality rate of about 500,000 people (1). In China, breast cancer can cause 279,000 new cases and 66,000 deaths annually (2). Currently, surgery, chemotherapy, and radiotherapy are the therapeutic methods mostly adopted in clinical practice. Early neoadjuvant chemotherapy can significantly improve the progression and prognosis (3). However, despite the advantages, chemotherapy has its limits due to the drug resistance emerged in certain patients (4), and adriamycin (ADR) resistance is the most common one. Therefore, it is of great significance to explore the mechanisms and molecular pathways of ADR resistance in breast cancer.

Nowadays, bioinformatic methods have been widely used in various fields of life sciences, especially in the field of oncology. By using functional genomics and proteomics, researchers can explore the pathogenesis of cancer, as well as the development of screening and targeted drugs, to provide new ideas and theoretical basis for cancer therapy (5). This study adopted bioinformatic techniques, aiming at analyzing gene expression profiles of ADR-resistant breast cancer with public data sources, and screening differentially expressed genes (DEGs) in ADR-resistant and ADR-sensitive cases, and also constructing DEGs-encoded protein-protein interaction (PPI) networks, to analyze and discover the potential genes associated with ADR resistance, and to provide new clues for further researches of the molecular mechanisms and the development of clinical treatment methods.

Methods

Data collection

The gene expression profile dataset was downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (6) (http://www.ncbi.nlm.nih.gov/geo). The GSE76540 dataset (7) was based on the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array, consisting of 3 chemo-sensitive samples and 3 chemo-resistant samples.

Screening for DEGs

The GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/, accessed January 25th, 2019) online tool was employed to detect the DEGs in chemo-sensitive cases and chemo-resistant cases (8), respectively. Adjusted P<0.01, P<0.01 and fold change (FC) ≥2 were considered as the cut-off criterion.

Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs

GO analysis is a tool widely used for biological function annotation of specific genes and gene products, which can be divided into biological process (BP), molecular function (MF), and cellular component (CC) analysis (9). KEGG is a high-throughput database that uses molecular experimental techniques to explain the advanced biological functions of cells and other organisms at the genomic level (10). The GO analysis and KEGG enrichment analysis of DEGs were conducted with the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool (https://david.ncifcrf.gov/), and P<0.01 and gene counts >10 were considered statistically significant (11,12).

PPI network construction

The relevant nodes and network diagrams of protein interaction were predicted and analyzed by using the Search Tool for the Retrieval of Interacting Genes (STRING) database (http://string-db.org/) (13). We predicted the protein information by uploading the selected differential genes to the STRING database. The protein interaction pairs with combine score greater than 0.4 were extracted and imported into the Cytoscape software (www.cytoscape.org/) (14) to achieve a clear visualization of protein interaction network. At the same time, the degree model of plug-in Cytohubba in Cytoscape software was adopted to evaluate the importance of each protein node and the overall contribution to the protein network. The 15 top-rated genes selected by degree method were regarded as hub genes.

Survival analysis of hub genes

GEPIA (http://gepia.cancer-pku.cn/detail.php) (15) is a public database based on tumor analysis, providing freely published tumor gene transcriptome data [including the Cancer Genome Atlas (TCGA) database], and also collecting and summarizing a large number of tumor-related gene expression levels and patient survival information. Herein, the impact of key tumor genes on the survival and prognosis were described based on the GEPIA database.

Results

Identification of DEGs

A total of 1,481 DEGs were achieved after analyzing the GSE76540 dataset by using the GEO2R online tool, including 549 up-regulated genes and 932 down-regulated genes. The five genes with the most significant up-regulation were MMP1, TMEM200A, NEFH, KRTAP2-4, and PPP1R14A, while the most significant down-regulated genes were SYTL5, MAL2, WISP2, GREB1, and FXYD3 (Table 1).

Table 1

The 5 up-regulated or down-regulated genes that were mostly enriched

Expression Genes Adjust P value LogFC
Up-regulation MMP1 1.02E−4 9.9795696
TMEM200A 1.02E−4 9.2300751
NEFH 1.02E−4 9.6770347
KRTAP2-4 1.02E−4 11.1822447
PPP1R14A 1.02E−4 8.9519095
Down-regulation SYTL5 1.02E−4 −10.4814297
MAL2 1.02E−4 −9.3188679
WISP2 1.2E−4 −8.6980133
GREB1 1.48E−4 −6.064777
FXYD3 1.48E−4 −8.8321929

FC, fold change.

Functional enrichment analysis of DEGs

The results of GO analysis indicated that the five BPs with the most up-regulated genes were: extracellular matrix organization, positive regulation of transcription from RNA polymerase II promoter, lung development, positive regulation of gene expression, axon guidance, while the BPs with the most down-regulated genes included: homophilic cell adhesion via plasma membrane adhesion molecules, positive regulation of transcription from RNA polymerase II promoter, signal transduction, cell-cell signaling, and angiogenesis (Table 2). The CC analysis showed that the up-regulated genes were mostly enriched in plasma membrane, extracellular space, extracellular region, basement membrane, and cell surface, in contrast of which, the down-regulated genes were mainly in extracellular exosome, bicellular tight junction, plasma membrane, integral component plasma membrane, and extracellular space (Table 3). According to the MF analysis, the most up-regulated genes could be detected in heparin binding, extracellular matrix structural constituent, signal transducer activity, transcription factor activity, sequence-specific DNA binding, and RNA polymerase II core promoter proximal region sequence-specific binding, whereas the down-regulated genes were enriched significantly in calcium ion binding, transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding, and sequence-specific binding (Table 4).

Table 2

The biological processes with enriched up-regulated or down-regulated genes

Expression Term Count P value Benjamin
Up-regulation Extracellular matrix organization 24 6.9E−8 1.8E−4
Positive regulation of transcription from RNA polymerase II promoter 57 1.4E−5 1.8E−2
Lung development 12 2.6E−5 2.2E−2
Positive regulation of gene expression 22 9.9E−5 6.2E−2
Axon guidance 15 5.5E−4 2.1E−1
Down-regulation Homophilic cell adhesion via plasma membrane adhesion molecules 15 2.9E−6 5.8E−3
Positive regulation of transcription from RNA polymerase II promoter 36 4.8E−4 1.5E−1
Signal transduction 38 2.5E−3 4.3E−1
Cell-cell signaling 13 4.6E−3 4.8E−1
Angiogenesis 12 4.7E−3 4.7E−1

Table 3

The cellular components with enriched up-regulated or down-regulated genes

Expression Term Count P value Benjamin
Up-regulation Plasma membrane 179 3.2E−7 1.2E−4
Extracellular space 75 8.1E−7 1.5E−4
Extracellular region 84 2.1E−6 2.5E−4
Basement membrane 13 5.1E−6 4.6E−4
Cell surface 36 3.3E−5 2.4E−3
Down-regulation Extracellular exosome 83 5.4E−5 1.4E−2
Bicellular tight junction 11 6.6E−5 8.7E−3
Plasma membrane 109 2.3E−4 1.5E−2
Integral component plasma membrane 44 2.0E−4 9.8E−3
Extracellular space 41 4.2E−3 1.5E−1

Table 4

The molecular functions with enriched up-regulated or down-regulated genes

Expression Term Count P value Benjamin
Up-regulation Heparin binding 16 1.3E−4 8.6E−2
Extracellular matrix structural constituent 11 2.1E−4 6.7E−2
Signal transducer activity 18 2.1E−4 4.5E−2
Transcription factor activity sequence-specific DNA binding 51 2.2E−4 3.6E−2
RNA polymerase II core promoter proximal region sequence-specific binding 18 1.1E−3 1.2E−1
Down-regulation Calcium ion binding 38 6.7E−8 3.4E−5
RNA polymerase II core promoter proximal region sequence-specific binding 20 2.0E−7 4.9E−5
RNA polymerase II core promoter proximal region sequence-specific binding 16 4.3E−3 4.1E−1
Sequence-specific binding 20 6.5E−3 4.8E−1

In addition, the KEGG pathway enrichment analysis suggested that the DEGs were enriched in: cancer pathways, PI3K AKT signaling pathway, focal adhesion, Ras signaling pathway, cytokine-cytokine receptor interaction, MAPA signaling pathway, hematopoietic cell lineage, amoebiasis, calcium signaling pathway, oxytocin signaling pathway, and proteoglycans in cancer (Table 5).

Table 5

KEGG pathways with enriched differentially expressed genes (DEGs)

Term Count P value Benjamin
Pathways in cancer 31 1.7E−6 3.8E−4
Focal adhesion 19 4.0E−5 4.6E−3
Hematopoietic cell lineage 11 2.5E−4 1.9E−2
Amoebiasis 11 1.2E−3 6.8E−2
Calcium signaling pathway 14 2.7E−3 1.2E−1
Ras signaling pathway 16 3.1E−3 1.1E−1
PI3K AKT signaling pathway 21 3.3E−3 1.0E−1
Oxytocin signaling pathway 12 5.2E−3 1.4E−1
Cytokine-cytokine receptor interaction 16 6.1E−3 1.3E−1
Proteoglycans in cancer 14 6.8E−3 1.3E−1
MAPA signaling pathway 16 8.7E−3 1.4E−1

KEGG, Kyoto Encyclopedia of Genes and Genomes.

PPI network and hub genes identification

A total of 635 pairs of interacting proteins were achieved after screening and the network structure was constructed (Figure 1). Fifteen key genes were obtained by using the degree model of plug-in Cytohubba in Cytoscape software, which were considered as hub genes, including: CDH1, ESR1, SOX2, AR, GATA3, FOXA1, KRT19, CLDN7, AGR2, ESRP1, RAB25, CLDN4, IGF1R, CLDN3, and IRS1 (Table 6, Figure 2).

Figure 1 The visualization of differentially expressed genes in the protein-protein interaction (PPI) network predicted by the Cytoscape software.

Table 6

Degree assessment of hub genes

Gene Degree MCC MNC
CDH1 50 1,817 43
ESR1 41 1,142 37
AR 24 430 20
SOX2 24 309 21
FOXA1 23 1,028 23
GATA3 23 762 23
AGR2 18 229 17
CLDN7 18 1,314 18
KRT19 18 372 15
ESRP1 16 922 14
IGF1R 15 114 13
CLDN4 15 1,256 13
RAB25 15 766 11
IRS1 14 98 12
CLDN3 14 1,284 14
Figure 2 The predicted 15 hub genes (adriamycin resistance) in the PPI network with high degree of association in breast cancer.

Survival analysis of hub genes

After analyzing influences of the 15 hub genes on the overall survival (OS) and disease-free survival (DFS) prognosis, the results suggested that only the insulin like growth factor 1 receptor (IGF1R) gene had significant impact on both OS and DFS (log-rank P=0.047, 0.038 respectively), and the epithelial splicing regulatory protein 1 (ESRP1) gene showed significant effect on the OS only (log-rank P=0.0019). The rest of hub genes did not have significant influence on the survival prognosis (Figure 3).

Figure 3 Survival analysis of the identified genes, insulin like growth factor 1 receptor (IGF1R) and epithelial splicing regulatory protein 1 (ESRP1), in breast cancer cases. High expression level of IGF1R was associated with prolonged overall survival (A) and disease-free survival (B) in patients with breast cancer; elevated ESRP1 expression was associated with worse overall survival (C), but was not significant in disease free survival (D) in breast cancer cases.

Discussion

ADR is a first-line neoadjuvant chemotherapy drug for breast cancer (16,17). Although it is effective, long-term use of ADR can always cause drug resistance. The mechanisms of drug resistance in tumor cells have not been completely clarified. However, theories have been established that tumor chemotherapy resistance is caused by a combination of multi-drug resistance pump and enzymes. In addition, genetic differences between individuals may also contribute to the drug resistance to some extent. In order to improve the efficacy of chemotherapy for breast cancer, it is necessary to explore the potential mechanisms of drug resistance.

It is widely believed that glycoprotein P-gp and multi-drug resistance protein MRP1 are involved in the resistance to several drugs in tumor cells. The catalytic ATP pumps can generate energy to expel the drugs out of cells, thus reducing the effective intracellular concentration, and reducing the inhibition effect on tumor cells, which is manifested as drug resistance in clinical practice (18-23). In addition, a series of genes associated with ADR resistance were detected, such as Nrf2, SOX2, SPIN1, COP1, Mdr1 and so on (24-29). In this study, we explored ADR resistance genes in breast cancer with the GSE76540 dataset. By analyzing 3 drug-resistant samples and 3 drug-sensitive samples, overall 1,481 DEGs were detected, including 932 down-regulated genes and 549 up-regulated genes. GO functional analysis and KEGG pathway analysis were conducted to obtain biological characteristics of the selected genes for further comprehensive analysis.

The GO analysis suggested that the BPs with the most enriched DEGs included: extracellular matrix organization, positive regulation of transcription from RNA polymerase II promoter, lung development, positive regulation of gene expression, axon guidance, homophilic cell adhesion via plasma membrane adhesion molecules, positive regulation of transcription from RNA polymerase II promoter, signal transduction, cell-cell signaling, and angiogenesis. The MF analysis showed that enriched DEGs can be detected in certain MFs, such as heparin binding, extracellular matrix structural constituent, signal transducer activity, transcription factor activity, sequence-specific DNA binding, RNA polymerase II core promoter proximal region sequence-specific binding, calcium ion binding, transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding, RNA polymerase II core promoter proximal region sequence-specific binding, sequence-specific binding and so on. The CC analysis indicated that the DEGs were mostly enriched in plasma membrane, extracellular space, extracellular region, basement membrane, cell surface, extracellular exosome, bicellular tight junction, plasma membrane, integral component plasma membrane, and extracellular space.

The KEGG pathway analysis showed that DEGs were mainly enriched in cancer-associated pathways such as pathways in cancer, PI3K AKT signaling pathway, focal adhesion, Ras signaling pathway, cytokine-cytokine receptor interaction, MAPA signaling pathway, hematopoietic cell lineage, amoebiasis, calcium signaling pathway, oxytocin signaling pathway, and proteoglycans in cancer.

According to the PPI network and the analysis with the degree model, 15 genes with a high degree of association were selected as hub genes, among which CDH1, ESR1, SOX2, AR, and GATA3 were the top five genes. The CDH1 gene is a member of the cadherin superfamily, which has been demonstrated to be correlated with a series cancer occurring in different parts, such as gastric cancer, breast cancer, colorectal cancer, thyroid cancer, ovarian cancer and so on. Mutation of the CDH1 gene can promote the progression and growth of tumor tissues, as a result of which, it may be related to drug resistance in tumor cells (30). The ESR1 gene encodes an estrogen receptor involved in the pathological process of breast cancer. The SOX2 gene plays an important role in the regulation of stem cell growth and development. The AR gene encodes an androgen receptor that promotes androgen binding, and its mutation can lead to loss of control of tumor cells (31). The GATA3 gene is a transcription factor-regulated protein whose mutation can lead to developmental disorders of immune cells thus damaging to the immune system (32).

In the prognostic survival analysis, only two genes were related with prognostic significance. The IGF1R gene had influence on both OS and DFS, while the ESRP1 gene only affected the OS. Therefore, patients screened for these two genes may have worse prognosis and quality of life. It is necessary to modify the chemotherapeutic drugs in advance and redesign the chemotherapeutic regimen.

Conclusions

In summary, this study contributed to a better understanding of the molecular screening and potential mechanisms of drug resistance in breast cancer. By using bioinformatic techniques, 15 hub genes were selected among a total of 1,481 DEGs. The IGF1R and ESRP1 genes might be options as prognostic biomarkers for breast cancer. Further studies are needed to verify the results.

Acknowledgments

Funding: This study was supported by the National Natural Science Foundation of China (grant number: 81802631), Fujian Provincial Health Technology Project (grant number: 2016-ZQN-17), and Joint Funds for the Innovation of Science and Technology, Fujian province (grant number: 2017Y9073), and Science and Technology Program of Fujian Province, China (grant number: 2018Y2003).

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr-19-2145). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Chen Y, Lin Y, Cui Z. Identification of adriamycin resistance genes in breast cancer based on microarray data analysis. Transl Cancer Res 2020;9(12):7486-7494. doi: 10.21037/tcr-19-2145

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