👤 Harn-Jing Terng

Malignant pleural effusions (MPE) commonly result from malignant tumors and represent advanced-stage cancers. Thus, in clinical practice, early recognition of MPE is valuable. However, the current diagnosis of MPE is based on pleural fluid cytology or histologic analysis of pleural biopsies with a low diagnostic rate. This research aimed to assess the diagnostic ability of eight previously identified Non-Small Cell Lung Cancer (NSCLC)-associated genes for MPE. In the study, eighty-two individuals with pleural effusion were recruited. There were thirty-three patients with MPE and forty-nine patients with benign transudate. mRNA was isolated from the pleural effusion and amplified by Quantitative real-time PCR. The logistic models were further applied to evaluate the diagnostic performance of those genes. Four significant MPE-associated genes were discovered in our study, including Dual-specificity phosphatase 6 (DUSP6), MDM2 proto-oncogene (MDM2), Ring finger protein 4 (RNF4), and WEE1 G2 Checkpoint Kinase (WEE1). Pleural effusion with higher expression levels of MDM2 and WEE1 and lower expression levels of RNF4 and DUSP6 had a higher possibility of being MPE. The four-gene model had an excellent performance distinguishing MPE and benign pleural effusion, especially for pathologically negative effusions. Therefore, the gene combination is a suitable candidate for MPE screening in patients with pleural effusion. We also identified three survival-associated genes, WEE1, Neurofibromin 1 (NF1), and DNA polymerase delta interacting protein 2 (POLDIP2), which could predict the overall survival of patients with MPE. Show less

Colorectal cancer (CRC) is a complex disease characterized by dynamically deregulated gene expression and crosstalk between signaling pathways. In this study, a new approach based on gene-function-based clusters was introduced to explore the CRC-associated networks of gene expression. Each cluster contained genes involved in coordinated regulatory activity, such as RAS signaling, the cell cycle process, transcription, or translation. A retrospective case-control study was conducted with the inclusion of 119 patients with histologically confirmed colorectal cancer and 308 controls. The quantitative expression data of 15 genes were obtained from the peripheral blood samples of all participants to investigate cluster-gene and gene-gene interactions. Show less

Lung cancer is a heterogeneous disease with varied outcomes. Molecular markers are eagerly investigated to predict a patient's treatment response or outcome. Previous studies used frozen biopsy tissues to identify crucial genes as prognostic markers. We explored the prognostic value of peripheral blood (PB) molecular signatures in patients with advanced non-small cell lung cancer (NSCLC). Peripheral blood mononuclear cell (PBMC) fractions from patients with advanced NSCLC were applied for RNA extraction, cDNA synthesis, and real-time polymerase chain reaction (PCR) for the expression profiling of eight genes: DUSP6, MMD, CPEB4, RNF4, STAT2, NF1, IRF4, and ZNF264. Proportional hazard (PH) models were constructed to evaluate the association of the eight expressing genes and multiple clinical factors [e.g., sex, smoking status, and Charlson comorbidity index (CCI)] with overall survival. One hundred and forty-one patients with advanced NSCLC were enrolled. They included 109 (77.30%) patients with adenocarcinoma, 12 (8.51%) patients with squamous cell carcinoma, and 20 (14.18%) patients with other pathological lung cancer types. A PH model containing two significant survival-associated genes, CPEB4 and IRF4, could help in predicting the overall survival of patients with advanced stage NSCLC [hazard ratio (HR) = 0.48, p < 0.0001). Adding multiple clinical factors further improved the prediction power of prognosis (HR = 0.33; p < 0.0001). Molecular signatures in PB can stratify the prognosis in patients with advanced NSCLC. Further prospective, interventional clinical trials should be performed to test if gene profiling also predicts resistance to chemotherapy. Show less

Peripheral blood mononuclear cell (PBMC)-derived gene signatures were investigated for their potential use in the early detection of non-small cell lung cancer (NSCLC). In our study, 187 patients with NSCLC and 310 age- and gender-matched controls, and an independent set containing 29 patients for validation were included. Eight significant NSCLC-associated genes were identified, including DUSP6, EIF2S3, GRB2, MDM2, NF1, POLDIP2, RNF4, and WEE1. The logistic model containing these significant markers was able to distinguish subjects with NSCLC from controls with an excellent performance, 80.7% sensitivity, 90.6% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.924. Repeated random sub-sampling for 100 times was used to validate the performance of classification training models with an average AUC of 0.92. Additional cross-validation using the independent set resulted in the sensitivity 75.86%. Furthermore, six age/gender-dependent genes: CPEB4, EIF2S3, GRB2, MCM4, RNF4, and STAT2 were identified using age and gender stratification approach. STAT2 and WEE1 were explored as stage-dependent using stage-stratified subpopulation. We conclude that these logistic models using different signatures for total and stratified samples are potential complementary tools for assessing the risk of NSCLC. Show less

Optimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated. A matched (by variables of age and sex) case-control design (111 CRC and 227 non-cancer samples) was applied. Total RNAs isolated from the 338 blood samples were reverse-transcribed, and the relative transcript levels of candidate genes were analyzed. The training set was made of 162 random samples of the total 338 samples. A logistic regression analysis was performed, and odds ratios for each gene were determined between CRC and non-cancer. The samples (n = 176) in the testing set were used to validate the logistic model, and an inferred performance (generality) was verified. By pooling 12 public microarray datasets(GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105), which included 519 cases of adenocarcinoma and 88 controls of normal mucosa, we were able to verify the selected genes from logistic models and estimate their external generality. The logistic regression analysis resulted in the selection of five significant genes (P < 0.05; MDM2, DUSP6, CPEB4, MMD, and EIF2S3), with odds ratios of 2.978, 6.029, 3.776, 0.538 and 0.138, respectively. The five-gene model performed stably for the discrimination of CRC cases from controls in the training set, with accuracies ranging from 73.9% to 87.0%, a sensitivity of 95% and a specificity of 95%. In addition, a good performance in the test set was obtained using the discrimination model, providing 83.5% accuracy, 66.0% sensitivity, 92.0% specificity, a positive predictive value of 89.2% and a negative predictive value of 73.0%. Multivariate logistic regressions analyzed 12 pooled public microarray data sets as an external validation. Models that provided similar expected and observed event rates in subgroups were termed well calibrated. A model in which MDM2, DUSP6, CPEB4, MMD, and EIF2S3 were selected showed the result in logistic regression analysis (H-L P = 0.460, R2= 0.853, AUC = 0.978, accuracy = 0.949, specificity = 0.818 and sensitivity = 0.971). A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays. Show less

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. 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. 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. Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC. Show less