Transcriptomic signature predicts the distant relapse in patients with ER+ breast cancer treated with tamoxifen for five years

Zhou, Hao; Lv, Qingfu; Guo, Zhaoji

doi:10.3892/mmr.2017.8234

Cited by 10 publications

(13 citation statements)

References 18 publications

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“…Among the top DEGs, up-regulated expression of genes such as COL10A1 , MMP1 , MMP11 , and BUB1 ; down-regulated expression of genes such as ADH1B , CIDEC , FABP4 , AQP7 , RBP4 , CDO1 , FIGF , and LPL were reported to be differentially expressed in breast and or other cancers by various authors using microarray profiling in western population 26–38 , showing concordance with the present study. In the present study, we found up-regulation of cell cycle genes such as BUB1 , CCNA2 , CCNB2 , and CDC2 ; up-regulated expression of BUB1 , CCNA2 , CCNB2 and CDC2 has also been reported in breast 39,40 and several other cancers 41–46 using microarray and were found to be associated with poor prognosis of the disease 44,47–49 . Overexpression of the above cell cycle genes may be contributing to the uncontrolled proliferation of the tumour cells and hence may serve as biomarkers and targets for therapy.…”

Section: Discussionsupporting

confidence: 75%

Study of Gene Expression Profiles of Breast Cancers in Indian Women

Malvia

Bagadi

Pradhan

et al. 2019

Sci Rep

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Breast cancer is the most common cancer among women globally. In India, the incidence of breast cancer has increased significantly during the last two decades with a higher proportion of the disease at a young age compared to the west. To understand the molecular processes underlying breast cancer in Indian women, we analysed gene expression profiles of 29 tumours and 9 controls using microarray. In the present study, we obtained 2413 differentially expressed genes, consisting of overexpressed genes such as COL10A1 , COL11A1 , MMP1 , MMP13 , MMP11 , GJB2 , and CST1 and underexpressed genes such as PLIN1 , FABP4 , LIPE , AQP7 , LEP , ADH1A , ADH1B , and CIDEC . The deregulated pathways include cell cycle, focal adhesion and metastasis, DNA replication, PPAR signaling, and lipid metabolism. Using PAM50 classifier, we demonstrated the existence of molecular subtypes in Indian women. In addition, qPCR validation of expression of metalloproteinase genes, MMP1 , MMP3 , MMP11 , MMP13 , MMP14 , ADAMTS1 , and ADAMTS5 showed concordance with that of the microarray data; wherein we found a significant association of ADAMTS5 down-regulation with older age (≥55 years) of patients. Together, this study reports gene expression profiles of breast tumours from the Indian subcontinent, throwing light on the pathways and genes associated with the breast tumourigenesis in Indian women.

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Section: Discussionsupporting

confidence: 75%

Study of Gene Expression Profiles of Breast Cancers in Indian Women

Malvia

Bagadi

Pradhan

et al. 2019

Sci Rep

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Section: Discussionmentioning

confidence: 99%

“…The protein binds to and activates cyclin-dependent kinase 2, thereby facilitating the G 1 /S and G 2 /M transitions. 39 CCNA2 is also a prognostic biomarker for ER-positive breast cancer, 40 and overexpression of CCNA2 in tumor tissue predicts poor survival in patients with pancreatic ductal adenocarcinoma. 41 DNA topoisomerase II alpha, encoded by the TOP2A gene, modulates DNA topology during transcription by catalyzing the breakage and recombination of double-stranded and is also involved in DNA transcription and replication, chromatid separation, and chromosome condensation.…”

Section: Discussionmentioning

confidence: 99%

Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis

Gong

Zhou

Liu

et al. 2021

Technol Cancer Res Treat

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Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan–Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which ( CDC20, CCNB2, BUB1, CCNB1, CCNA2, KIF11, TOP2A, NDC80, and ASPM) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion: CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.

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“…29 , 30 It was reported that ER-positive patients who have high expression of CCNA2 or CCNB2 have inferior survival. 31 , 32 KIF4A, KIF20A and KIF23 are all mitotic kinesins and have a highly conserved motor domain involving ATP-binding and microtubule-binding sequences. 33 They all promote the proliferation of breast cancers, and treatment with tamoxifen reduced the expression of these three proteins.…”

Section: Discussionmentioning

confidence: 99%

Identification of Ten Mitosis Genes Associated with Tamoxifen Resistance in Breast Cancer

Sun

Ding

et al. 2021

OTT

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Background Endocrine 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 Methods In 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. Results A 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. Conclusion Our 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.

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Transcriptomic signature predicts the distant relapse in patients with ER+ breast cancer treated with tamoxifen for five years

Cited by 10 publications

References 18 publications

Study of Gene Expression Profiles of Breast Cancers in Indian Women

Study of Gene Expression Profiles of Breast Cancers in Indian Women

Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis

Identification of Ten Mitosis Genes Associated with Tamoxifen Resistance in Breast Cancer

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