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

Yan Chen; Yingfeng Lin; Zhaolei Cui

doi:10.21037/tcr-19-2145

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

Transl Cancer Res. 2020 Dec;9(12):7486-7494. doi: 10.21037/tcr-19-2145.

Authors

Yan Chen^#¹, Yingfeng Lin^#¹, Zhaolei Cui¹

Affiliation

¹ 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.

^# Contributed equally.

Abstract

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; adriamycin resistance; microarray data analysis.