Dataset Information

Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer.

ABSTRACT:

Background

Tamoxifen resistance in breast cancer is an unsolved problem in clinical practice. The aim of this study was to determine the potential mechanisms of tamoxifen resistance through bioinformatics analysis.

Methods

Gene expression profiles of tamoxifen-resistant MCF-7/TR and MCF-7 cells were acquired from the Gene Expression Omnibus dataset GSE26459, and differentially expressed genes (DEGs) were detected with R software. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was generated, and we analyzed hub genes in the network with the Search Tool for the Retrieval of Interacting Genes database. Finally, we used siRNAs to silence the target genes and conducted the MTS assay.

Results

We identified 865 DEGs, 399 of which were upregulated. GO analysis indicated that most genes are related to telomere organization, extracellular exosomes, and binding-related items for protein heterodimerization. PPI network construction revealed that the top 10 hub genes-ACLY, HSPD1, PFAS, GART, TXN, HSPH1, HSPE1, IRAS, TRAP1, and ATIC-might be associated with tamoxifen resistance. Consistently, RT-qPCR analysis indicated that the expression of these 10 genes was increased in MCF-7/TR cells comparing with MCF-7 cells. Four hub genes (TXN, HSPD1, HSPH1 and ATIC) were related to overall survival in patients who accepted tamoxifen. In addition, knockdown of HSPH1 by siRNA may lead to reduced growth of MCF-7/TR cell with a trend close to significance (P = 0.07), indicating that upregulation of HSPH1 may play a role in tamoxifen resistance.

Conclusion

This study revealed a number of critical hub genes that might serve as therapeutic targets in breast cancer resistant to tamoxifen and provided potential directions for uncovering the mechanisms of tamoxifen resistance.

SUBMITTER: Zhang K

PROVIDER: S-EPMC7720728 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer.

Zhang Kai K Jiang Kuikui K Hong Ruoxi R Xu Fei F Xia Wen W Qin Ge G Lee Kaping K Zheng Qiufan Q Lu Qianyi Q Zhai Qinglian Q Wang Shusen S

PeerJ 20201204

<h4>Background</h4>Tamoxifen resistance in breast cancer is an unsolved problem in clinical practice. The aim of this study was to determine the potential mechanisms of tamoxifen resistance through bioinformatics analysis.<h4>Methods</h4>Gene expression profiles of tamoxifen-resistant MCF-7/TR and MCF-7 cells were acquired from the Gene Expression Omnibus dataset GSE26459, and differentially expressed genes (DEGs) were detected with R software. We conducted Gene Ontology (GO) and Kyoto Encyclope ...[more]

PMID: 33335811

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Project description:BackgroundEndocrine therapy is the backbone therapy in estrogen receptor α (ER)-positive breast cancer, and tamoxifen resistance is a great challenge for endocrine therapy. Tamoxifen-resistant and sensitive samples from the international public repository, the Gene Expression Omnibus (GEO) database, were used to identify therapeutic biomarkers associated with tamoxifen resistance.Materials and methodsIn this study, integrated analysis was used to identify tamoxifen resistance-associated genes. Differentially expressed genes (DEGs) were identified. Gene ontology and pathway analysis were then analyzed. Weighted correlation network analysis (WGCNA) was performed to find modules correlated with tamoxifen resistance. Protein-protein interaction (PPI) network was used to find hub genes. Genes of prognostic significance were further validated in another GEO dataset and cohort from Shanghai Ruijin Hospital using RT-PCR.ResultsA total of 441 genes were down-regulated and 123 genes were up-regulated in tamoxifen-resistant samples. Those up-regulated genes were mostly enriched in the cell cycle pathway. Then, WGCNA was performed, and the brown module was correlated with tamoxifen resistance. An overlap of 81 genes was identified between differentially expressed genes (DEGs) and genes in the brown module. These genes were also enriched in the cell cycle. Twelve hub genes were identified using PPI network, which were involved in the mitosis phase of the cell cycle. Finally, 10 of these 12 genes were validated to be up-regulated in tamoxifen-resistant patients and were associated with poor prognosis in ER-positive patients.ConclusionOur study suggested mitosis-related genes are mainly involved in tamoxifen resistance, and high expression of these genes could predict poor prognosis of patients receiving tamoxifen. These genes may be potential targets to improve efficacy of endocrine therapy in breast cancer, and inhibitors targeted these genes could be used in endocrine-resistant patients.

Project description:BackgroundHonokiol (HON) inhibits epidermal growth factor receptor (EGFR) signaling and increases the activity of erlotinib, an EGFR inhibitor, in human head and neck cancers. In this study, using a bioinformatics approach and in vitro experiments, we assessed the target genes of HON against breast cancer resistance to tamoxifen (TAM).Materials and methodsMicroarray data were obtained from GSE67916 and GSE85871 datasets to identify differentially expressed genes (DEGs). DEGs common between HON-treated and TAM-resistant cells were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and protein-protein interaction (PPI) networks were constructed. Selected genes were analyzed for genetic alterations, expression, prognostic value, and receiver operating characteristics (ROC). TAM-resistant MCF-7 (MCF-7 TAM-R) cells were generated and characterized for their resistance toward TAM. A combination of HON and TAM was used for cytotoxicity and gene expression analyses. Molecular docking was performed using the Molecular Operating Environment software.ResultsPPI network analysis revealed that FN1, FGFR2, and RET were the top three genes with the highest scores. A genetic alteration study of potential target genes revealed MMP16 and ERBB4 as the genes with the highest alterations among the breast cancer samples. Pathway enrichment analysis of FGFR2, RET, ERBB4, SOX2, FN1, and MMP16 showed that the genetic alterations herein were likely to impact the RTK-Ras pathway. The expression levels of RET, MMP16, and SOX2 were strongly correlated with prognostic power, with areas under the ROC curves (AUC) of 1, 0.8, and 0.8, respectively. The HON and TAM combination increased TAM cytotoxicity in MCF-7 TAM-R cells by regulating the expression of potential target genes ret, ERBB4, SOX2, and FN1, as well as the TAM resistance regulatory genes including HES1, VIM, PCNA, TP53, and CASP7. Molecular docking results indicated that HON tended to bind RET, ErbB4, and the receptor protein Notch1 ankyrin domain more robustly than its native ligand.ConclusionHON could overcome breast cancer resistance to TAM, potentially by targeting FGFR2, RET, ERBB4, MMP16, FN1, and SOX2. However, further studies are required to validate these results.

Project description:Tamoxifen resistance is a major cause of death in patients with recurrent breast cancer. Current clinical factors can correctly predict therapy response in only half of the treated patients. Identification of proteins that are associated with tamoxifen resistance is a first step toward better response prediction and tailored treatment of patients. In the present study we intended to identify putative protein biomarkers indicative of tamoxifen therapy resistance in breast cancer using nano-LC coupled with FTICR MS. Comparative proteome analysis was performed on approximately 5,500 pooled tumor cells (corresponding to approximately 550 ng of protein lysate/analysis) obtained through laser capture microdissection (LCM) from two independently processed data sets (n = 24 and n = 27) containing both tamoxifen therapy-sensitive and therapy-resistant tumors. Peptides and proteins were identified by matching mass and elution time of newly acquired LC-MS features to information in previously generated accurate mass and time tag reference databases. A total of 17,263 unique peptides were identified that corresponded to 2,556 non-redundant proteins identified with > or = 2 peptides. 1,713 overlapping proteins between the two data sets were used for further analysis. Comparative proteome analysis revealed 100 putatively differentially abundant proteins between tamoxifen-sensitive and tamoxifen-resistant tumors. The presence and relative abundance for 47 differentially abundant proteins were verified by targeted nano-LC-MS/MS in a selection of unpooled, non-microdissected discovery set tumor tissue extracts. ENPP1, EIF3E, and GNB4 were significantly associated with progression-free survival upon tamoxifen treatment for recurrent disease. Differential abundance of our top discriminating protein, extracellular matrix metalloproteinase inducer, was validated by tissue microarray in an independent patient cohort (n = 156). Extracellular matrix metalloproteinase inducer levels were higher in therapy-resistant tumors and significantly associated with an earlier tumor progression following first line tamoxifen treatment (hazard ratio, 1.87; 95% confidence interval, 1.25-2.80; p = 0.002). In summary, comparative proteomics performed on laser capture microdissection-derived breast tumor cells using nano-LC-FTICR MS technology revealed a set of putative biomarkers associated with tamoxifen therapy resistance in recurrent breast cancer.

Dataset Information

Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer.

Background

Methods

Results

Conclusion

Publications

Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer.

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