Karya
Judul/Title Curcumin Inhibit Vascular Endothelial Growth Factor (VEGF) Expression on Triple Negative Breast Cancer
Penulis/Author drh. Retno Murwanti, M.P., Ph.D. (1) ; Prof. Dr.rer.nat. apt. Adam Hermawan, S.Farm., M.Sc. (2); Dr. B.S. Ari Sudarmanto, M.Si. (3)
Tanggal/Date 22 2019
Kata Kunci/Keyword
Abstrak/Abstract VEGF is the most important angiogenic factor with proven significance in breast cancer. Triple- negative breast cancer (TNBC) is the most malignant type of breast cancer with high vascular endothelial growth factor (VEGF) expression. Therefore, VEGF can be one of the target to treat TNBC. The purpose of this study is to investigate the effect of curcumin on VEGF expression in triple negative breast cancer using 4T1 cell line. Each concentration of curcumin was added in triplicate into 96-wellplate containing the 4T1 cells. Cells without treatment served as a control group. The number of viable cells after 24 hrs incubation at 37°C and 5% CO2 was determined by the 3-(4,5-dimethyl-thiazol-2-yl) -2,5-diphenyl-tetrazolium bromide (MTT) assay. Tissue total RNA mini kit (Favorgen) was used to extract the RNA. Expression of VEGF was studied by reverse transcription-polymerase chain reaction (RT-PCR). Lower doses of curcumin enhanced the viability of the cultured cells, MTT assay .higher doses decreased viability of the cells by 50% or more. Curcumin significantly inhibit the viability of 4T1 breast cancer cells with an IC50 value of 34,34 ?g/mL. 150,32 ng/?L was obtained from RNA extraction with a ratio 260/280 of 2,1. This shows that the results of the RNA produced was pure The VEGF mRNA expression was significantly lowered upon curcumin treatment. Curcumin can promotes viability and inhibit VEGF expression in triple negative breast cancer. It can be concluded that curcumin has the potential to be developed as triple negative breast cancer treatment.
Level Internasional
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Dokumen Karya
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1Proceeding ICBS2.pdfBukti Accepted
2Additional Article 7.pdf[PAK] Full Dokumen
3Similarity Additional Article 7.pdfCek Similarity