• Laboraroty Animal Research
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• v.34 no.4
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• pp.160-165
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• 2018

Breast cancer remains the second leading cause of cancer death among woman, worldwide, despite advances in identifying novel targeted therapies and the development of treating strategies. Classification of clinical subtypes (ER+, PR+, HER2+, and TNBC (Triple-negative)) increases the complexity of breast cancers, which thus necessitates further investigation. Mouse models used in breast cancer research provide an essential approach to examine the mechanisms and genetic pathway in cancer progression and metastasis and to develop and evaluate clinical therapeutics. In this review, we summarize tumor transplantation models and genetically engineered mouse models (GEMMs) of breast cancer and their applications in the field of human breast cancer research and anti-cancer drug development. These models may help to improve the knowledge of underlying mechanisms and genetic pathways, as well as creating approaches for modeling clinical tumor subtypes, and developing innovative cancer therapy.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2735-2738
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• 2012

Background: Our objective was to clarify the clinical and biological characteristics of basal-like breast cancer (BLBC) and non-basal-like breast cancer (TN3BKE) in Heilongjiang. Methods: We examined, by immunohistochemistry, expression of biological markers cytokeratin (CK) 5/6 and epidermal growth factor receptor (EGFR) and B cell specific moloney murine leukemia virus integration site 1( Bmi-1) in triple-negative breast cancer (TNBC). We studied the correlation between BLBC and several factors related to tumor progression, along with its prognostic value. Results: In the 229 cases of operable TNBC, BLBC was detected in 178 (77.7%) and TN3BKE- in 51 (22.2%). There was no significant difference in clinicopathological factors between them, However, BLBC was significantly associated with Bmi-1 expression (P=0.000) and shorter disease-free survival (DFS) (P = 0.045) and overall survival (OS) (P = 0.041). Conclusions: Compared with the non-basal group, patients with BLBC have a high expression of Bmi-1 and a poor prognosis.

• Biomolecules & Therapeutics
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• v.31 no.5
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• pp.473-483
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• 2023

Many cancers arise from sites of chronic inflammation, which creates an inflammatory microenvironment surrounding the tumor. Inflammatory substances secreted by cells in the inflammatory environment can induce the proliferation and survival of cancer cells, thereby promoting cancer metastasis and angiogenesis. Therefore, it is important to identify the role of inflammatory factors in cancer progression. This review summarizes the signaling pathways and roles of C-reactive protein (CRP) in various cancer types, including breast, liver, renal, and pancreatic cancer, and the tumor microenvironment. Mounting evidence suggests the role of CRP in breast cancer, particularly in triple-negative breast cancer (TNBC), which is typically associated with a worse prognosis. Increased CRP in the inflammatory environment contributes to enhanced invasiveness and tumor formation in TNBC cells. CRP promotes endothelial cell formation and angiogenesis and contributes to the initiation and progression of atherosclerosis. In pancreatic and kidney cancers, CRP contributes to tumor progression. In liver cancer, CRP regulates inflammatory responses and lipid metabolism. CRP modulates the activity of various signaling molecules in macrophages and monocytes present in the tumor microenvironment, contributing to tumor development, the immune response, and inflammation. In the present review, we overviewed the role of CRP signaling pathways and the association between inflammation and cancer in various types of cancer. Identifying the interactions between CRP signaling pathways and other inflammatory mediators in cancer progression is crucial for understanding the complex relationship between inflammation and cancer.

• Annals of Surgical Treatment and Research
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• v.95 no.5
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• pp.240-248
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• 2018

Purpose: This study aimed to validate the synergistic effect of ABT-737 on docetaxel using MDA-MB-231, a triple negative breast cancer (TNBC) cell line overexpressing B-cell lymphoma-2 (Bcl-2). Methods: Western blot analysis was performed to assess expression levels of Bcl-2 family proteins and caspase-related molecules. Cell viability was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell cycle distribution was determined by flow cytometry analysis. Benzyloxycarbonyl-Val-Ala-Asp(OMe)-fluoromethylketone (z-VAD-fmk) was used for pretreatment to assess the role of caspases. Results: Cell viability of MDA-MB-231 after combination treatment with ABT-737 and docetaxel was significantly lower than that after docetaxel or ABT-737 monotherapy based on MTT assay (both P < 0.001), with a combination index of 0.41. The proportion of sub-G1 population after combination treatment was significantly higher than that after docetaxel or ABT-737 monotherapy (P = 0.001, P = 0.003, respectively). Pretreatment with z-VAD-fmk completely restored cell viability of MDA-MB-231 from apoptotic cell death induced by combination therapy (P = 0.001). Although pro-caspase-8 or Bid did not show significant change in expression level, pro-casepase-9 showed significantly decreased expression after combination treatment. Cleaved caspase-3 showed increased expression while poly (ADP-ribose) polymerase cleavage was induced after combination treatment. However, hypoxia-inducible factor 1-alpha and aldehyde dehydrogenase 1 totally lost their expression after combination treatment. Conclusion: Combination of ABT-737 with docetaxel elicits synergistic therapeutic effect on MDA-MB-231, a TNBC cell line overexpressing Bcl-2, mainly by activating the intrinsic pathway of apoptosis. Therefore, adjunct of ABT-737 to docetaxel might be a new therapeutic option to overcome docetaxel resistance of TNBCs overexpressing Bcl-2.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3223-3228
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• 2013

Gene expression profiling (GEP) has identified several molecular subtypes of breast cancer, with different clinico-pathologic features and exhibiting different responses to chemotherapy. However, GEP is expensive and not available in the developing countries where the majority of patients present at advanced stage. The St Gallen Consensus in 2011 proposed use of a simplified, four immunohistochemical (IHC) biomarker panel (ER, PR, HER2, Ki67/Tumor Grade) for molecular classification. The present study was conducted in 75 newly diagnosed patients of breast cancer with large (>5cm) tumors to evaluate the association of IHC surrogate molecular subtype with the clinical response to presurgical chemotherapy, evaluated by the WHO criteria, 3 weeks after the third cycle of 5 flourouracil, adriamycin, cyclophosphamide (FAC regimen). The subtypes of luminal, basal-like and HER2 enriched were found to account for 36.0 % (27/75), 34.7 % (26/75) and 29.3% (22/75) of patients respectively. Ten were luminal A and 14 luminal B (8 HER2 negative and 6HER2 positive). The triple negative breast cancer (TNBC) was most sensitive to chemotherapy with 19% achieving clinical-complete-response (cCR) followed by HER2 enriched (2/22 (9%) cCR), luminal B (1/6 (7%) cCR) and luminal A (0/10 (0%) cCR). Heterogeneity was observed within each subgroup, being most marked in the TNBC although the most responding tumors, 8% developing clinical-progressive-disease. The study supports association of molecular subtypes with response to chemotherapy in patients with advanced breast cancer and the existence of further heterogeneity within subtypes.

• BMB Reports
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• v.56 no.2
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• pp.120-125
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• 2023

Karyopherin-α3 (KPNA3), a karyopherin-α isoform, is intimately associated with metastatic progression via epithelial-mesenchymal transition (EMT). However, the molecular mechanism underlying how KPNA3 acts as an EMT inducer remains to be elucidated. In this report, we identified that KPNA3 was significantly upregulated in cancer cells, particularly in triple-negative breast cancer, and its knockdown resulted in the suppression of cell proliferation and metastasis. The comprehensive transcriptome analysis from KPNA3 knockdown cells indicated that KPNA3 is involved in the regulation of numerous EMT-related genes, including the downregulation of GATA3 and E-cadherin and the up-regulation of HAS2. Moreover, it was found that KPNA3 EMT-mediated metastasis can be achieved by TGF-β or AKT signaling pathways; this suggests that the novel independent signaling pathways KPNA3-TGF-β-GATA3-HAS2/E-cadherin and KPNA3-AKT-HAS2/E-cadherin are involved in the EMT-mediated progress of TNBC MDA-MB-231 cells. These findings provide new insights into the divergent EMT inducibility of KPNA3 according to cell and cancer type.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.