Biomedical subjects

Chun-Chi Liu

Publications and source records attributed to Chun-Chi Liu.

4 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

Topology-based cancer classification and related pathway mining using microarray data.

Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.

Gene Expression Profiling↗

PubMed2006

CRSD: a comprehensive web server for composite regulatory signature discovery.

Transcription factors (TFs) and microRNAs play important roles in the regulation of human gene expression, and the study of their combinatory regulations of gene expression is a new research field. We constructed a comprehensive web server, the composite regulatory signature database (CRSD), that can be applied in investigating complex regulatory behaviors involving gene expression signatures (GESs), microRNA regulatory signatures (MRSs) and TF regulatory signatures (TRSs). Six well-known and large-scale databases, including the human UniGene, mature microRNAs, putative promoter, TRANSFAC, pathway and Gene Ontology (GO) databases, were integrated to provide the comprehensive analysis in CRSD. Two new genome-wide databases, of MRSs and TRSs, were also constructed and further integrated into CRSD. To accomplish the microarray data analysis at one go, several methods, including microarray data pretreatment, statistical and clustering analysis, iterative enrichment analysis and motif discovery, were closely integrated in the web server, which has not been the case in previous studies. Our implementation showed that the published literature could demonstrate the results of genome-wide enrichment analysis. We conclude that CRSD is a powerful and useful bioinformatic web server and may provide new insights into gene regulation networks. CRSD and the online tutorial are publicly available at http://biochip.nchu.edu.tw/crsd1/.

3' Untranslated Regions↗

PubMed2006

A new tumor suppressor DnaJ-like heat shock protein, HLJ1, and survival of patients with non-small-cell lung carcinoma.

BACKGROUND: We previously identified DnaJ-like heat shock protein (HLJ1) as a gene associated with tumor invasion. Here, we investigated the clinical significance of HLJ1 expression in non-small-cell lung cancer (NSCLC) patients and its role in cancer progression. METHODS: We induced HLJ1 overexpression or knockdown in human lung adenocarcinoma CL1-5 cells and analyzed cell proliferation, anchorage-independent growth, in vivo tumorigenesis, cell motility, invasion, and cell cycle progression. Expression of genes that act downstream of HLJ1 was examined by DNA microarray analysis, pathway analysis, and western blotting. We measured HLJ1 expression in tumors and adjacent normal tissues of 71 NSCLC patients by quantitative reverse transcription-polymerase chain reaction. Associations between HLJ1 expression and disease-free and overall survival were determined using the log-rank test and multivariable Cox proportional hazards regression analysis. Validation was performed in an independent cohort of 56 NSCLC patients. Loss of heterozygosity (LOH) mapping of the HLJ1 locus was analyzed in 48 paired microdissected NSCLC tumors. All statistical tests were two-sided. RESULTS: HLJ1 expression inhibited lung cancer cell proliferation, anchorage-independent growth, tumorigenesis, cell motility, and invasion, and slowed cell cycle progression through a novel STAT1/P21(WAF1) pathway that is independent of P53 and interferon. HLJ1 expression was lower in tumors than in adjacent normal tissue in 55 of 71 patients studied. NSCLC patients with high HLJI expressing tumors had reduced cancer recurrence (hazard ratio [HR] = 0.47; 95% confidence interval [CI] = 0.23 to 0.93; P = .03) and longer overall survival (HR = 0.38; 95% CI = 0.16 to 0.89; P = .03) than those with low-expressing tumors. Validation in the independent patient cohort confirmed the association between HLJ1 expression and patient outcome. LOH mapping revealed high frequencies (66.7% and 70.8%) of allelic loss and microsatellite instability (87.5% and 95.2%) of the HLJ1 locus at chromosome 1p31.1. CONCLUSIONS: HLJ1 is a novel tumor suppressor in NSCLC, and high HLJ1 expression is associated with reduced cancer recurrence and prolonged survival of NSCLC patients.

Biomarkers, Tumor↗