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2 results on '"Heng‐Fa Ge"'

1. MicroRNA‐26a‐5p inhibits breast cancer cell growth by suppressing RNF6 expression

Author

Zi‐Ming Huang, Heng‐Fa Ge, Chen‐Chen Yang, Yong Cai, Zhen Chen, Wen‐Ze Tian, and Jia‐Li Tao

Subjects
Bcl‐xL, breast cancer, ERα, microRNA‐26a‐5p, RNF6, Medicine (General), R5-920
Abstract

Abstract MicroRNA‐26a‐5p (miR‐26a‐5p) has been reported to be involved in the tumorigenesis of several tumors, but its function in breast cancer is still unknown. In this study, miR‐26a‐5p was found significantly downregulated in both of the breast cancer tissues and cell lines, and low expression of miR‐26a‐5p predicted a poor prognosis for breast cancer patients. Overexpression of miR‐26a‐5p could significantly inhibit breast cancer cell growth. Further studies revealed that overexpression of miR‐26a‐5p downregulated the protein levels of Cyclin D1, CDK4, and CDK6, but upregulated the expression levels of p21, p27, and p53. In mechanism, miR‐26a‐5p targeted the 3′UTR of ring finger protein 6 (RNF6) mRNA and inhibited RNF6 expression in breast cancer cells. Moreover, overexpression of miR‐26a‐5p inhibited RNF6/ERα/Bcl‐xL axis in breast cancer cells. In contrast, inhibiting miR‐26a‐5p upregulated RNF6/ERα/Bcl‐xL axis. Further studies indicated that miR‐26a‐5p mediated RNF6/ERα/Bcl‐xL axis through regulating the stability of ERα protein. Collectively, downregulation of miR‐26a‐5p plays essential roles in breast cancer by mediating RNF6/ERα/Bcl‐xL axis, which might provide important implications for the therapeutics of breast cancer.

Published
2019
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2. Identification of MSX1 and DCLK1 as mRNA Biomarkers for Colorectal Cancer Detection Through DNA Methylation Information

Author

Ai-Jun, Sun, Hai-Bo, Gao, Gao, Liu, Heng-Fa, Ge, Zun-Ping, Ke, and Sen, Li

Subjects
MSX1 Transcription Factor, Decision Trees, Intracellular Signaling Peptides and Proteins, DNA Methylation, Protein Serine-Threonine Kinases, Gene Expression Regulation, Neoplastic, Doublecortin-Like Kinases, Logistic Models, ROC Curve, Cell Line, Tumor, Biomarkers, Tumor, Humans, CpG Islands, RNA, Messenger, Colorectal Neoplasms
Abstract

Colorectal cancer is the second most deadly malignancy in the United States. However, the currently screening options had their limitation. Novel biomarkers for colorectal cancer detections are necessary to reduce the mortality. The clinical information, mRNA expression levels and DNA methylation information of colorectal cancer were downloaded from TCGA. The patients were separated into training group and testing group based on their platforms for DNA methylation. Beta values of DNA methylation from tumor tissues and normal tissues were utilized to figure out the position that were differentially methylated. The expression levels of mRNA of thirteen genes, whose CpG islands were differentially methylated, were extracted from the RNA-Seq results from TCGA. The probabilities whether the mRNA was differentially expressed between tumor and normal samples were calculated using Student's t-test. Logistic regression and decision tree were built for cancer detection and their performances were evaluated by the area under the curve (AUC). Twenty-four genomic locations were differentially methylated, which could be mapped to eleven genes. Nine out of eleven genes had differentially expressed mRNA levels, which were used to build the model for cancer detection. The final detection models consisting of mRNA expression levels of these nine genes had great performances on both training group and testing group. The model that constructed in this study suggested MSX1 and DCLK1 might be used in colorectal cancer detection or as target of cancer therapies. J. Cell. Physiol. 232: 1879-1884, 2017. © 2016 Wiley Periodicals, Inc.

Published
2016

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