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Identification of Adriamycin Resistance Genes in Breast Cancer Based on Microarray Data Analysis

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Identification of Adriamycin Resistance Genes in Breast Cancer Based on Microarray Data Analysis 7494 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: [email protected]. 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 View this article at: http://dx.doi.org/10.21037/tcr-19-2145 Introduction and a mortality rate of about 500,000 people (1). In China, breast cancer can cause 279,000 new cases and 66,000 Breast cancer is a common malignant tumor in clinical practice, which occurs in the breast epithelium. Breast deaths annually (2). Currently, surgery, chemotherapy, and cancer is one of the leading life-threatening diseases in radiotherapy are the therapeutic methods mostly adopted women, with the highest incidence rate of 1 million patients in clinical practice. Early neoadjuvant chemotherapy can © Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(12):7486-7494 | http://dx.doi.org/10.21037/tcr-19-2145 Translational Cancer Research, Vol 9, No 12 December 2020 7487 significantly improve the progression and prognosis (3). annotation of specific genes and gene products, which can However, despite the advantages, chemotherapy has its limits be divided into biological process (BP), molecular function due to the drug resistance emerged in certain patients (4), (MF), and cellular component (CC) analysis (9). KEGG is a and adriamycin (ADR) resistance is the most common high-throughput database that uses molecular experimental one. Therefore, it is of great significance to explore the techniques to explain the advanced biological functions of mechanisms and molecular pathways of ADR resistance in cells and other organisms at the genomic level (10). The breast cancer. GO analysis and KEGG enrichment analysis of DEGs were Nowadays, bioinformatic methods have been widely conducted with the Database for Annotation, Visualization used in various fields of life sciences, especially in the field and Integrated Discovery (DAVID) tool (https://david. of oncology. By using functional genomics and proteomics, ncifcrf.gov/), and P<0.01 and gene counts >10 were researchers can explore the pathogenesis of cancer, as well as considered statistically significant (11,12). the development of screening and targeted drugs, to provide new ideas and theoretical basis for cancer therapy (5). PPI network construction This study adopted bioinformatic techniques, aiming at analyzing gene expression profiles of ADR-resistant breast The relevant nodes and network diagrams of protein cancer with public data sources, and screening differentially interaction were predicted and analyzed by using the Search expressed genes (DEGs) in ADR-resistant and ADR- Tool for the Retrieval of Interacting Genes (STRING) sensitive cases, and also constructing DEGs-encoded database (http://string-db.org/) (13). We predicted the protein-protein interaction (PPI) networks, to analyze and protein information by uploading the selected differential discover the potential genes associated with ADR resistance, genes to the STRING database. The protein interaction and to provide new clues for further researches of the pairs with combine score greater than 0.4 were extracted molecular mechanisms and the development of clinical and imported into the Cytoscape software (www.cytoscape. treatment methods. 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 Methods to evaluate the importance of each protein node and the Data collection overall contribution to the protein network. The 15 top- rated genes selected by degree method were regarded as The gene expression profile dataset was downloaded from hub genes. the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (6) (http:// www.ncbi.nlm.nih.gov/geo). The GSE76540 dataset (7) Survival analysis of hub genes was based on the GPL570 [HG-U133_Plus_2] Affymetrix GEPIA (http://gepia.cancer-pku.cn/detail.php) (15) is a Human Genome U133 Plus 2.0 Array, consisting of 3 public database based on tumor analysis, providing freely chemo-sensitive samples and 3 chemo-resistant samples. published tumor gene transcriptome data [including the Cancer Genome Atlas (TCGA) database], and also Screening for DEGs collecting and summarizing a large number of tumor-related gene expression levels and patient survival information. The GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/, Herein, the impact of key tumor genes on the survival and accessed January 25th, 2019) online tool was employed prognosis were described based on the GEPIA database. 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. Results Identification of DEGs Gene Ontology (GO) analysis and Kyoto Encyclopedia of A total of 1,481 DEGs were achieved after analyzing the Genes and Genomes (KEGG) enrichment analysis of DEGs GSE76540 dataset by using the GEO2R online tool, GO analysis is a tool widely used for biological function including 549 up-regulated genes and 932 down-regulated © Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(12):7486-7494 | http://dx.doi.org/10.21037/tcr-19-2145 7488 Chen et al. Identification of ADR resistance genes in BC 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. 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 genes. The five genes with the most significant up- with the most down-regulated genes included: homophilic regulation were MMP1, TMEM200A, NEFH, KRTAP2-4, cell adhesion via plasma
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