Browsing by Author "Chiew, Men-Yee"

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Integrated microRNA and mRNA expression profile analysis of tumor-associated macrophages after exposure to single-dose irradiation
(2018-06-01)
Kung, Wei-Hsiang
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Lee, Chi-Lung
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Yang, Chi-Dung
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Yu, Ching-Fang
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Chiew, Men-Yee
Background: Radiotherapy (RT) is a common approach that accounts for nearly 50% of cancer patient treatment and has the potential for long-term tumor control Recently, we published a research article on gene expression profiling of tumor-associated macrophages (TAM) that were exposed to ionizing radiation (IR). Single-dose irradiation of tumors could initiate differentially expressed genes in TAM as a time series from irradiated tumors that are associated with the immune response. It is also well known that human cancers are associated with microRNA (miRNA) alterations that are involved in cancer progression. However, the role of miRNA on TAM after exposure to irradiation remains unclear. Results: In this study, miRNA expression profiles from microarrays were used to identify the key miRNAs and correlating pathways involved in the role of TAMs in tumor progression and recurrence after RT. Using a mouse tumor model, we identified miRNA pattern changes over time in response to irradiation. Based on our results, we hypothesize that miRNA expression in the irradiated tumor may be used as a distinguishing marker to indicate the best time for therapeutic intervention to prevent tumor recurrence after RT. Conclusions: We established a murine model irradiated with a single dose of 25 Gy that could initiate temporal changes in the expression of miRNAs associated with cell proliferation and the immune response, as evidenced by macrophages directly extracted from irradiated tumors after two weeks of IR. Statistical analyses were conducted by comparing the miRNA expression in macrophages from non-lrradiated versus irradiated tumors. Thus, our study could lead to a better understanding of the function of miRNA expressions, which changed temporally in an irradiated tumor microenvironment. (C) 2018 Elsevier Ltd. All rights reserved.
Integrated MicroRNA-mRNA Analysis Reveals miR-204 Inhibits Cell Proliferation in Gastric Cancer by Targeting CKS1B, CXCL1 and GPRC5A
(2018-01-01)
Shrestha, Sirjana
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Yang, Chi-Dung
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Hong, Hsiao-Chin
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Chou, Chih-Hung
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Tai, Chun-San
Gastric cancer (GC) is the second most frequent cause of cancer-related deaths worldwide. MicroRNAs are single-stranded RNA molecules of 21-23 nucleotides that regulate target gene expression through specific base-pairing interactions between miRNA and untranslated regions of targeted mRNAs. In this study, we generated a multistep approach for the integrated analysis of miRNA and mRNA expression. First, both miRNA and mRNA expression profiling datasets in gastric cancer from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) identified 79 and 1042 differentially expressed miRNAs and mRNAs, respectively, in gastric cancer. Second, inverse correlations between miRNA and mRNA expression levels identified 3206 miRNA-mRNA pairs combined with 79 dysregulated miRNAs and their 774 target mRNAs predicted by three prediction tools, miRanda, PITA, and RNAhybrid. Additionally, miR-204, which was found to be down-regulated in gastric cancer, was ectopically over-expressed in the AGS gastric cancer cell line and all down-regulated targets were identified by RNA sequencing (RNA-seq) analysis. Over-expression of miR-204 reduced the gastric cancer cell proliferation and suppressed the expression of three targets which were validated by qRT-PCR and luciferase assays. For the first time, we identified that CKS1B, CXCL1, and GPRC5A are putative targets of miR-204 and elucidated that miR-204 acted as potential tumor suppressor and, therefore, are useful as a promising therapeutic target for gastric cancer.
miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions
(2018-01-04)
Chou, Chih-Hung
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Shrestha, Sirjana
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Yang, Chi-Dung
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Chang, Nai-Wen
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Lin, Yu-Ling
MicroRNAs (miRNAs) are small non-coding RNAs of similar to 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased similar to 1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.
Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer
(2020-01-21)
Huang, Chia-Yen
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Liao, Kuang-Wen
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Chou, Chih-Hung
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Shrestha, Sirjana
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Yang, Chi-Dung
Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC. Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established. Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively. Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.
SVAD: A genetic database curates non-ischemic sudden cardiac death-associated variants
(2020-08-19)
Huang, Wei-Chih
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Huang, Hsin-Tzu
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Chen, Po-Yuan
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Wang, Wei-Chi
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Ko, Tai-Ming
Sudden cardiac death (SCD) is an important cause of mortality worldwide. It accounts for approximately half of all deaths from cardiovascular disease. While coronary artery disease and acute myocardial infarction account for the majority of SCD in the elderly population, inherited cardiac diseases (inherited CDs) comprise a substantial proportion of younger SCD victims with a significant genetic component. Currently, the use of next-generation sequencing enables the rapid analysis to investigate relationships between genetic variants and inherited CDs causing SCD. Genetic contribution to risk has been considered an alternate predictor of SCD. In the past years, large numbers of SCD susceptibility variants were reported, but these results are scattered in numerous publications. Here, we present the SCD-associated Variants Annotation Database (SVAD) to facilitate the interpretation of variants and to meet the needs of data integration. SVAD contains data from a broad screening of scientific literature. It was constructed to provide a comprehensive collection of genetic variants along with integrated information regarding their effects. At present, SVAD has accumulated 2,292 entries within 1,239 variants by manually surveying pertinent literature, and approximately one-third of the collected variants are pathogenic/likely-pathogenic following the ACMG guidelines. To the best of our knowledge, SVAD is the most comprehensive database that can provide integrated information on the associated variants in various types of inherited CDs. SVAD represents a valuable source of variant information based on scientific literature and benefits clinicians and researchers, and it is now available on.