Biomedical subjects

Chun-Houh Chen

Publications and source records attributed to Chun-Houh Chen.

12 recordsLinked to original sources

PubMed2007

A five-gene signature and clinical outcome in non-small-cell lung cancer.

BACKGROUND: Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC. METHODS: We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients. RESULTS: Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC. CONCLUSIONS: Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

Aged↗

PubMed2006

Human kallikrein 8 protease confers a favorable clinical outcome in non-small cell lung cancer by suppressing tumor cell invasiveness.

The human kallikrein 8 (KLK8) gene, a member of the human tissue kallikrein gene family, encodes a serine protease. The KLK8 protein (hK8) is known to be a favorable prognostic marker in ovarian cancer, but the biological basis of this is not understood. We found that overexpressing the KLK8 gene in highly invasive lung cancer cell lines suppresses their invasiveness. This role in invasiveness was further confirmed by the fact that inhibition of endogenous KLK8 expression with a specific short hairpin RNA reduced cancer cell invasiveness. In situ degradation and cell adhesion assays showed that proteins produced from KLK8 splice variants modify the extracellular microenvironment by cleaving fibronectin. DNA microarray experiments and staining of cells for actin filaments revealed that the degradation of fibronectin by hK8 suppresses integrin signaling and retards cancer cell motility by inhibiting actin polymerization. In addition, studies in a mouse model coupled with the detection of circulating tumor cells by quantitative PCR for the human Alu sequence showed that KLK8 suppresses tumor growth and invasion in vivo. Finally, studies of clinical specimens from patients with non-small cell lung cancer showed that the time to postoperative recurrence was longer for early-stage patients (stages I and II) with high KLK8 expression (mean, 49.9 months) than for patients with low KLK8 expression (mean, 22.9 months). Collectively, these findings show that KLK8 expression confers a favorable clinical outcome in non-small cell lung cancer by suppressing tumor cell invasiveness.

Base Sequence↗

PubMed2006

Brain responses of explicit and implicit memory: An event-related potential study.

Implicit memory is acquired by an unintentional or unconscious learning. Recognition memory involves either automatic knowing or consciously controlled remembering. We provided an event-related potential paradigm capable of differentiating memory for the explicitly learned, implicitly learned and unstudied materials. In the explicit memory, we obtained both frontal (controlled retrieval) and parietal (recollection) old/new effects. In the implicit memory, we found persistent occipitotemporal activation (visual priming) and late attenuation in the temporoparietooccipital (repetition suppression). Event-related potential provides an insight into the dissociable mechanism of memory function that supports the dual process model with an enhanced temporal resolution on the dynamic process of both explicit perceptual learning and implicit perceptual priming.

Adult↗

PubMed2006

A single nucleotide polymorphism fine mapping study of chromosome 1q42.1 reveals the vulnerability genes for schizophrenia, GNPAT and DISC1: Association with impairment of sustained attention.

BACKGROUND: The marker D1S251 of chromosome 1q42.1 showed significant association with schizophrenia in a Taiwanese sample. We used single nucleotide polymorphism (SNP) fine mapping to search for the vulnerability genes of schizophrenia. METHODS: We selected 120 SNPs covering 1 Mb around D1S251 from the public database. These selected SNPs were initially validated if allele frequency was >10%. Forty-seven validated SNPs were genotyped in 102 families with at least 2 siblings affected with schizophrenia. RESULTS: Two SNP blocks showed significant association with schizophrenia. Block 1 (five-SNP), located between intron 2 and intron 13 of the glyceronephosphate O-acyltransferase (GNPAT) gene, showed the most significant associations using single-locus TDT (z = -2.07, p = .038, df = 1) and haplotype association analyses (z = -1.99, p = .046, df = 1). Block 2 (two-SNP), located between intron 4 and intron 5 of the disrupted-in-schizophrenia 1 (DISC1) gene, also showed the most significant results in both the single-locus (z = -3.22, p = .0013, df = 1) and haplotype association analyses (z = 3.35, p = .0008, df = 1). The association of the DISC1 gene with schizophrenia was mainly in the patient group with sustained attention deficits as assessed by the Continuous Performance Test. CONCLUSIONS: Chromosome 1q42.1 harbors GNPAT and DISC1 as candidate genes for schizophrenia, and DISC1 is associated with sustained attention deficits.

Acyltransferases↗

PubMed2006

Design of microarray probes for virus identification and detection of emerging viruses at the genus level.

BACKGROUND: Most virus detection methods are geared towards the detection of specific single viruses or just a few known targets, and lack the capability to uncover the novel viruses that cause emerging viral infections. To address this issue, we developed a computational method that identifies the conserved viral sequences at the genus level for all viral genomes available in GenBank, and established a virus probe library. The virus probes are used not only to identify known viruses but also for discerning the genera of emerging or uncharacterized ones. RESULTS: Using the microarray approach, the identity of the virus in a test sample is determined by the signals of both genus and species-specific probes. The genera of emerging and uncharacterized viruses are determined based on hybridization of the viral sequences to the conserved probes for the existing viral genera. A detection and classification procedure to determine the identity of a virus directly from detection signals results in the rapid identification of the virus. CONCLUSION: We have demonstrated the validity and feasibility of the above strategy with a small number of viral samples. The probe design algorithm can be applied to any publicly available viral sequence database. The strategy of using separate genus and species probe sets enables the use of a straightforward virus identity calculation directly based on the hybridization signals. Our virus identification strategy has great potential in the diagnosis of viral infections. The virus genus and specific probe database and the associated summary tables are available at http://genestamp.sinica.edu.tw/virus/index.htm.

Animals↗

PubMed2006

Using endophenotypes for pathway clusters to map complex disease genes.

Nature determines the complexity of disease etiology and the likelihood of revealing disease genes. While culprit genes for many monogenic diseases have been successfully unraveled, efforts to map major complex disease genes have not been as productive as hoped. The conceptual framework currently adopted to deal with the heterogeneous nature of complex diseases focuses on using homogeneous internal features of the disease phenotype for mapping. However, phenotypic homogeneity does not equal genotypic homogeneity. In this report, we advocate working with well-measured phenotypes portrayed by amounts of transcripts and activities of gene products or their metabolites, which are pertinent to relatively small pathway clusters. Reliable and controlled measures for oligogenic traits resulting from proper dissection efforts may enhance statistical power. The large amounts of information obtained on gene and protein expression from technological advances can add to the power of gene finding, particularly for diseases with unclear etiology. Data-mining tools for dimension reduction can assist biologists to reveal novel molecular endophenotypes. However, there are still hurdles to overcome, including high cost, relatively poor reproducibility and comparability among platforms, the cross-sectional nature of the information, and the accessibility of human tissues. Concerted efforts are required to carry out large-scale prospective studies that are integrated at the levels of phenotype characterization, high throughput experimental techniques, data analyses, and beyond.

Chromosome Mapping↗

PubMed2005

Molecular signature of clinical severity in recovering patients with severe acute respiratory syndrome coronavirus (SARS-CoV).

BACKGROUND: Severe acute respiratory syndrome (SARS), a recent epidemic human disease, is caused by a novel coronavirus (SARS-CoV). First reported in Asia, SARS quickly spread worldwide through international travelling. As of July 2003, the World Health Organization reported a total of 8,437 people afflicted with SARS with a 9.6% mortality rate. Although immunopathological damages may account for the severity of respiratory distress, little is known about how the genome-wide gene expression of the host changes under the attack of SARS-CoV. RESULTS: Based on changes in gene expression of peripheral blood, we identified 52 signature genes that accurately discriminated acute SARS patients from non-SARS controls. While a general suppression of gene expression predominated in SARS-infected blood, several genes including those involved in innate immunity, such as defensins and eosinophil-derived neurotoxin, were upregulated. Instead of employing clustering methods, we ranked the severity of recovering SARS patients by generalized associate plots (GAP) according to the expression profiles of 52 signature genes. Through this method, we discovered a smooth transition pattern of severity from normal controls to acute SARS patients. The rank of SARS severity was significantly correlated with the recovery period (in days) and with the clinical pulmonary infection score. CONCLUSION: The use of the GAP approach has proved useful in analyzing the complexity and continuity of biological systems. The severity rank derived from the global expression profile of significantly regulated genes in patients may be useful for further elucidating the pathophysiology of their disease.

Algorithms↗

PubMed2004

Multi-class clustering and prediction in the analysis of microarray data.

DNA microarray technology provides tools for studying the expression profiles of a large number of distinct genes simultaneously. This technology has been applied to sample clustering and sample prediction. Because of a large number of genes measured, many of the genes in the original data set are irrelevant to the analysis. Selection of discriminatory genes is critical to the accuracy of clustering and prediction. This paper considers statistical significance testing approach to selecting discriminatory gene sets for multi-class clustering and prediction of experimental samples. A toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV with a total of 55 samples) is used to illustrate a general framework of the approach. Among four selected gene sets, a gene set omega(I) formed by the intersection of the F-test and the set of the union of one-versus-all t-tests performs the best in terms of clustering as well as prediction. Hierarchical and two modified partition (k-means) methods all show that the set omega(I) is able to group the 55 samples into seven clusters reasonably well, in which the As and AsV samples are considered as one cluster (the same group) as are the Cd and Cu samples. With respect to prediction, the overall accuracy for the gene set omega(I) using the nearest neighbors algorithm to predict 55 samples into one of the nine treatments is 85%.

Algorithms↗

PubMed2004

Tumor-associated macrophages: the double-edged sword in cancer progression.

PURPOSE: Inflammation plays a critical role in cancer progression. In this study we investigate the pro-tumorigenic activities and gene expression profiles of lung cancer cells after interaction with macrophages. MATERIALS AND METHODS: We measured intratumoral microvessel counts and macrophage density in 41 lung cancer tumor specimens and correlated these with the patients' clinical outcome. The interaction between macrophages and cancer cell lines was assessed using a transwell coculture system. The invasive potential was evaluated by in vitro invasion assay. The matrix-degrading activity was assayed by gelatin zymography. The microarray was applied to a large-scale analysis of the genes involved in the interaction, as well as to monitor the gene expression profiles of lung cancer cells responding to anti-inflammatory drugs in cocultures. RESULTS: The macrophage density positively correlated with microvessel counts and negatively correlated with patient relapse-free survival (P < .05). After coculture with macrophages, lung cancer cell lines exhibited higher invasive potentials and matrix-degrading activities. We identified 50 genes by microarray that were upregulated more than two-fold in cancer cells after coculture. Northern blot analyses confirmed some gene expression such as interleukin-6, interleukin-8, and matrix metalloproteinase 9. The two-dimensional hierarchical clustering also demonstrated that the gene expression profiles of lung cancer cells responding to various anti-inflammatory drugs in cocultures are distinct. CONCLUSION: The interaction of lung cancer cells and macrophages can promote the invasiveness and matrix-degrading activity of cancer cells. Our results also suggest that a great diversity of gene expression occurs in this interaction, which may assist us in understanding the process of cancer metastasis.

Adenocarcinoma↗

PubMed2004

Optimization of probe length and the number of probes per gene for optimal microarray analysis of gene expression.

Gene-specific oligonucleotide probes are currently used in microarrays to avoid cross-hybridization of highly similar sequences. We developed an approach to determine the optimal number and length of gene-specific probes for accurate transcriptional profiling studies. The study surveyed probe lengths from 25 to 1000 nt. Long probes yield better signal intensity than short probes. The signal intensity of short probes can be improved by addition of spacers or using higher probe concentration for spotting. We also found that accurate gene expression measurement can be achieved with multiple probes per gene and fewer probes are needed if longer probes rather than shorter probes are used. Based on theoretical considerations that were confirmed experimentally, our results showed that 150mer is the optimal probe length for expression measurement. Gene-specific probes can be identified using a computational approach for 150mer probes and they can be treated like long cDNA probes in terms of the hybridization reaction for high sensitivity detection. Our experimental data also show that probes which do not generate good signal intensity give erroneous expression ratio measurement results. To use microarray probes without experimental validation, gene-specific probes approximately 150mer in length are necessary. However, shorter oligonucleotide probes also work well in gene expression analysis if the probes are validated by experimental selection or if multiple probes per gene are used for expression measurement.

Escherichia coli↗

PubMed2004

Gene selection for sample classifications in microarray experiments.

DNA microarray technology provides useful tools for profiling global gene expression patterns in different cell/tissue samples. One major challenge is the large number of genes relative to the number of samples. The use of all genes can suppress or reduce the performance of a classification rule due to the noise of nondiscriminatory genes. Selection of an optimal subset from the original gene set becomes an important prestep in sample classification. In this study, we propose a family-wise error (FWE) rate approach to selection of discriminatory genes for two-sample or multiple-sample classification. The FWE approach controls the probability of the number of one or more false positives at a prespecified level. A public colon cancer data set is used to evaluate the performance of the proposed approach for the two classification methods: k nearest neighbors (k-NN) and support vector machine (SVM). The selected gene sets from the proposed procedure appears to perform better than or comparable to several results reported in the literature using the univariate analysis without performing multivariate search. In addition, we apply the FWE approach to a toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV) for a total of 55 samples for a multisample classification. Two gene sets are considered: the gene set omegaF formed by the ANOVA F-test, and a gene set omegaT formed by the union of one-versus-all t-tests. The predicted accuracies are evaluated using the internal and external crossvalidation. Using the SVM classification, the overall accuracies to predict 55 samples into one of the nine treatments are above 80% for internal crossvalidation. OmegaF has slightly higher accuracy rates than omegaT. The overall predicted accuracies are above 70% for the external crossvalidation; the two gene sets omegaT and omegaF performed equally well.

Colonic Neoplasms↗

PubMed2002

Symptom patterns and subgrouping of schizophrenic patients: significance of negative symptoms assessed on admission.

This study subgroups schizophrenic patients based on symptoms assessed on admission and examines the validity of the subgrouping using follow-up data and other clinical outcome variables. Schizophrenic patients (n=163) from consecutive admission received ratings on the positive and negative syndrome scale (PANSS) on admission and during a 1-year follow-up course. An exploratory graphic analysis on the admission PANSS derived four symptom dimensions: negative symptoms, disorganized thought, hostility/excitement and delusions/hallucinations. This yielded two subgroups of patients on admission, a group with marked negative (GWNEG) and a group without marked negative (GONEG) symptoms. Compared with the GONEG, the GWNEG had a poorer recovery rate, more impairment in attention, a slower response of the delusion/hallucination symptoms to neuroleptic treatment and a longer duration of index hospitalization. At a one-year follow-up, the GWNEG assessed on admission had persistently higher scores on the negative symptom and disorganized thought syndromes, less relapse rate, a shorter duration on job, as well as worse social functioning than the GONEG. Thus, the GONEG might comprise patients having a pure paranoid syndrome with quick and better treatment response, while the GWNEG comprises patients with the blunt-disorganization syndrome having a poorer outcome.

Adult↗