Browsing by Author "Huang, Steve G."

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A bio-inspired computing model for ovarian carcinoma classification and oncogene detection
(2015-04-01)
Tsai, Meng-Hsiun
;
Chen, Mu-Yen
;
Huang, Steve G.
;
Hung, Yao-Ching
;
Wang, Hsin-Chieh
Motivation: Ovarian cancer is the fifth leading cause of cancer deaths in women in the western world for 2013. In ovarian cancer, benign tumors turn malignant, but the point of transition is difficult to predict and diagnose. The 5-year survival rate of all types of ovarian cancer is 44%, but this can be improved to 92% if the cancer is found and treated before it spreads beyond the ovary. However, only 15% of all ovarian cancers are found at this early stage. Therefore, the ability to automatically identify and diagnose ovarian cancer precisely and efficiently as the tissue changes from benign to invasive is important for clinical treatment and for increasing the cure rate. This study proposes a new ovarian carcinoma classification model using two algorithms: a novel discretization of food sources for an artificial bee colony (DfABC), and a support vector machine (SVM). For the first time in the literature, oncogene detection using this method is also investigated. Results: A novel bio-inspired computing model and hybrid algorithms combining DfABC and SVM was applied to ovarian carcinoma and oncogene classification. This study used the human ovarian cDNA expression database to collect 41 patient samples and 9600 genes in each pathological stage. Feature selection methods were used to detect and extract 15 notable oncogenes. We then used the DfABC-SVM model to examine these 15 oncogenes, dividing them into eight different classifications according to their gene expressions of various pathological stages. The average accuracyof the eight classification experiments was 94.76%. This research also found some oncogenes that had not been discovered or indicated in previous scientific studies. The main contribution of this research is the proof that these newly discovered oncogenes are highly related to ovarian or other cancers.
CLINICAL GENOMICS IDENTIFIES THE EXPRESSION OF HUMAN OVARIAN CARCINOMA
(2009-09-01)
Tsai, Meng-Hsiun
;
Huang, Steve G.
;
Tsaur, Shyh-Chang
;
Hung, Yao-Ching
;
Chiu, Sheng-Hsiung
Background: Human ovarian carcinoma is a silent killer. Most patients with human ovarian carcinoma are diagnosed in the advanced stages. Currently, no effective means of early detection are available. identifying genes that distinguish early cancers from normal tissue may provide insight to the initiation and progression of ovarian cancer. Methods: A large-scale gene expression profiling using 9,600 genes-complementary DNA array to study ovarian tumors was conducted. We analyzed 13 benign tumors, 22 invasive cancers (seven stage I and 15 stage III), and 16 normal ovaries. Cluster analysis revealed two major groups: the early-stage cluster that included 13 benign tumors and seven stage I cancers; and the late-stage cluster that included all stage III cancers. Results: There were 433 upregulated and 60 downregulated genes from the early-stage cluster, and 395 upregulated and 101 downregulated genes from the late-stage cluster. These genes showed altered functions for tumor/cancers, functions such as cell growth, cell cycle control, differentiation, signaling, apoptosis, ion transport, and morphogenesis. of most interest is the downregulation of nuclear receptor proteins. Differentially expressed genes between early-stage cluster and late-stage clusters were identified. We found 90 genes preferentially overly expressed and 165 genes relatively downregulated at late-stage cancers. The advanced cancer appears differently from early-stage cancer in cell growth, cell adhesion, hormone activity, morphology, and cell-cell signaling compared with benign or early-stage cancer. of particular interest are enzymes corresponding to pteridine biosynthesis, reported for the first time in this study. Real-time reverse transcription polymerase chain reaction showed that GCH1, KRT18, H3F3A, and MYBL2 were overly expressed in all cancer tissues, while MDFI, ARP3BETA, COL16A1, and MLN discriminated late-stage from early-stage cancers. Conclusion: The microarray provides valuable resources for comparing expression profiles of cancers at various stages and helps research the diagnosis and mechanism of ovarian cancer. [International journal of Gerontology 2009; 3(3): 163-169]