Design of a Clinical Decision Support System for Predicting Erectile Dysfunction in Men Using NHIRD Dataset.

Erectile dysfunction (ED) affects millions of men worldwide. Men with ED generally complain failure to attain or maintain an adequate erection during sexual activity. The prevalence of ED is strongly correlated with age, affecting about 40% of men at age 40 and nearly 70% at age 70. A variety of chronic diseases, including diabetes, ischemic heart disease, congestive heart failure, hypertension, depression, chronic renal failure, obstructive sleep apnea, prostate disease, gout, and sleep disorder, were reported to be associated with ED. In this study, data retrieved from a subset of the National Health Insurance Research Database of Taiwan were used for designing the clinical decision support system (CDSS) for predicting ED incidences in men. The positive cases were male patients aged 20-65 who were diagnosed with ED between January 2000 and December 2010 confirmed by at least three outpatient visits or at least one inpatient visit, while the negative cases were randomly selected from the database without a history of ED and were frequency (1:1), age, and index year matched with the ED patients. Data of a total of 2832 ED patients and 2832 non-ED patients, each consisting of 41 features including index age, 10 comorbidities, and 30 other comorbidity-related variables, were retrieved for designing the predictive models. Integrated genetic algorithm and support vector machine was adopted to design the CDSSs with two experiments of independent training and testing (ITT) conducted to verify their effectiveness. In the 1st ITT experiment, data extracted from January 2000 till December 2005 (61.51%, 1742 positive cases and 1742 negative cases) were used for training and validating and the data retrieved from January 2006 till December 2010 were used for testing (38.49%), whereas in the 2nd ITT experiment, data in the training set (77.78%) were extracted from January 2000 till Deceber 2007 and those in the testing set (22.22%) were retrieved afterward. Tenfold cross validation and three different objective functions were adopted for obtaining the optimal models with best predictive performance in the training phase. The testing results show that the CDSSs achieved a predictive performance with accuracy, sensitivity, specificity, g-mean, and area under ROC curve of 74.72%-76.65%, 72.33%-83.76%, 69.54%-77.10%, 0.7468-0.7632, and 0.766-0.817, respectively. In conclusion, the CDSSs designed based on cost-sensitive objective functions as well as salient comorbidity-related features achieve satisfactory predictive performance for predicting ED incidences.

Construction of diagnosis system and gene regulatory networks based on microarray analysis.

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.

Upregulation of Wnt signaling under hypoxia promotes lung cancer progression.

The hypoxic tumor microenvironment induces epithelial-mesenchymal transition (EMT) in tumor cells and increases tumor cell malignancy. Previous studies indicated that malfunction of Wnt signaling is observed in some lung cancer patients. Athough crosstalk between hypoxia and Wnt signaling in tumor cells has recently been revealed, the detailed underlying mechanisms have not been well defined. In the present study, we demonstrated that hypoxia in lung adenocarcinoma cells can enhance Wnt signaling activity by stabilizing ß-catenin and altering its localization into the nucleus. Overexpression of HIF-2α increased ß-catenin expression, promoted cell mobility, and induced morphological changes to a greater degree than HIF-1α overexpression. Knockdown of HIF-2α decreased ß-catenin expression and inhibited hypoxia-induced cell mobility. Moreover, we identified that phosphorylational activation of AKT1 by hypoxia and HIF-2α was required for Wnt activation upon hypoxia treatment. Downregulation of HIF-2α and ß-catenin reduced colony formation when cells were exposed to long-term hypoxia treatment. Taken together, our data support that hypoxia activates PI3K/AKT as well as Wnt signaling in a HIF-2α-dependent manner, thus elevating the resistance of lung cancer cells to chronic hypoxia-induced stress.

MicroRNA-7 Compromises p53 Protein-dependent Apoptosis by Controlling the Expression of the Chromatin Remodeling Factor SMARCD1.

We previously demonstrated that the epidermal growth factor receptor (EGFR) up-regulated miR-7 to promote tumor growth during lung cancer oncogenesis. Several lines of evidence have suggested that alterations in chromatin remodeling components contribute to cancer initiation and progression. In this study, we identified SMARCD1 (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily d, member 1) as a novel target gene of miR-7. miR-7 expression reduced SMARCD1 protein expression in lung cancer cell lines. We used luciferase reporters carrying wild type or mutated 3'UTR of SMARCD1 and found that miR-7 blocked SMARCD1 expression by binding to two seed regions in the 3'UTR of SMARCD1 and down-regulated SMARCD1 mRNA expression. Additionally, upon chemotherapy drug treatment, miR-7 down-regulated p53-dependent apoptosis-related gene BAX (BCL2-associated X protein) and p21 expression by interfering with the interaction between SMARCD1 and p53, thereby reducing caspase3 cleavage and the downstream apoptosis cascades. We found that although SMARCD1 sensitized lung cancer cells to chemotherapy drug-induced apoptosis, miR-7 enhanced the drug resistance potential of lung cancer cells against chemotherapy drugs. SMARCD1 was down-regulated in patients with non-small cell lung cancer and lung adenocarcinoma cell lines, and SMARCD1 and miR-7 expression levels were negatively correlated in clinical samples. Our investigation into the involvement of the EGFR-regulated microRNA pathway in the SWI/SNF chromatin remodeling complex suggests that EGFR-mediated miR-7 suppresses the coupling of the chromatin remodeling factor SMARCD1 with p53, resulting in increased chemo-resistance of lung cancer cells.

Coexpression of Oct4 and Nanog enhances malignancy in lung adenocarcinoma by inducing cancer stem cell-like properties and epithelial-mesenchymal transdifferentiation.

Epithelial-mesenchymal transition (EMT), a critical process of cancer invasion and metastasis, is associated with stemness property of cancer cells. Though Oct4 and Nanog are homebox transcription factors essential to the self-renewal of stem cells and are expressed in several cancers, the role of Oct4/Nanog signaling in tumorigenesis is still elusive. Here microarray and quantitative real-time PCR analysis showed a parallel, elevated expression of Oct4 and Nanog in lung adenocarcinoma (LAC). Ectopic expressions of Oct4 and Nanog in LACs increased the percentage of CD133-expressing subpopulation and sphere formation, enhanced drug resistance, and promoted EMT. Ectopic expressions of Oct4 and Nanog activated Slug and enhanced the tumor-initiating capability of LAC. Furthermore, double knockdown of Oct4 and Nanog suppressed the expression of Slug, reversed the EMT process, blocked the tumorigenic and metastatic ability, and greatly improved the mean survival time of transplanted immunocompromised mice. The immunohistochemical analysis demonstrated that expressions of Oct4, Nanog, and Slug were present in high-grade LAC, and triple positivity of Oct4/Nanog/Slug indicated a worse prognostic value of LAC patients. Our results support the notion that the Oct4/Nanog signaling controls epithelial-mesenchymal transdifferentiation, regulates tumor-initiating ability, and promotes metastasis of LAC.

EGFR promotes lung tumorigenesis by activating miR-7 through a Ras/ERK/Myc pathway that targets the Ets2 transcriptional repressor ERF.

MicroRNAs (miRNA) mediate distinct gene regulatory pathways triggered by epidermal growth factor receptor (EGFR) activation, which occurs commonly in lung cancers with poor prognosis. In this study, we report the discovery and mechanistic characterization of the miRNA miR-7 as an oncogenic "oncomiR" and its role as a key mediator of EGFR signaling in lung cancer cells. EGFR activation or ectopic expression of Ras as well as c-Myc stimulated miR-7 expression in an extracellular signal-regulated kinase (ERK)-dependent manner, suggesting that EGFR induces miR-7 expression through a Ras/ERK/Myc pathway. In support of this likelihood, c-Myc bound to the miR-7 promoter and enhanced its activity. Ectopic miR-7 promoted cell growth and tumor formation in lung cancer cells, significantly increasing the mortality of nude mice hosts, which were orthotopically implanted with lung cancers. Quantitative proteomic analysis revealed that miR-7 decreased levels of the Ets2 transcriptional repression factor ERF, the coding sequence of which was found to contain a miR-7 complementary sequence. Indeed, ectopic miR-7 inhibited production of ERF messages with a wild-type but not a silently mutated coding sequence, and ectopic miR-7 rescued growth arrest produced by wild-type but not mutated ERF. Together, these results identified that ERF is a direct target of miR-7 in lung cancer. Our findings suggest that miR-7 may act as an important modulator of EGFR-mediated oncogenesis, with potential applications as a novel prognostic biomarker and therapeutic target in lung cancer.

MAD2B, a novel TCF4-binding protein, modulates TCF4-mediated epithelial-mesenchymal transdifferentiation.

T cell factor 4 (TCF4) interacts with beta-catenin in the WNT signaling pathway and transactivates downstream target genes involved in cancer progression. To identify proteins that regulate TCF4-mediated biological responses, we performed a yeast two-hybrid screen to search for a TCF4-binding protein(s) and found that MAD2B interacts with TCF4. We confirmed that MAD2B is a TCF4-binding protein by co-immunoprecipitation. Using the TOPFLASH reporter assay, we found that MAD2B blocks TCF4-mediated transactivation. The MAD2B binding regions of TCF4 were identified by TCF4 deletion mapping and electrophoretic mobility shift assay analysis. TCF4 and MAD2B interactions abolished the DNA binding ability of TCF4. Knockdown of MAD2B in SW480 colorectal cancer cells led to the conversion of epithelial cells to a mesenchymal fibroblastoid phenotype (epithelial-mesenchymal transdifferentiation). An E-cadherin promoter reporter analysis showed that MAD2B modulates TCF4-mediated E-cadherin expression. MAD2B knockdown blocked E-cadherin expression and significantly induced mesenchymal markers, such as N-cadherin and vimentin. Mesenchymal induction was accompanied by F-actin redistribution and the appearance of a fibroblastoid phenotype. MAD2B knockdown also increased both mRNA and protein levels of Slug, a known TCF4-induced E-cadherin transcriptional repressor. A chromatin immunoprecipitation assay showed that MAD2B silencing enhances the ability of TCF4 to bind the Slug promoter. Thus, MAD2B is a novel TCF4-interacting protein. This study provides the first evidence for the involvement of MAD2B in TCF4-mediated epithelial-mesenchymal transdifferentiation.