👤 Pang-Chung Perng

The relationship between apolipoprotein C3 (APOC3) gene polymorphisms and nonalcoholic fatty liver disease (NAFLD) risk has been investigated in many studies, with inconclusive findings. This meta-analysis evaluated the effect of APOC3 promoter region polymorphisms (-455T/C and -482C/T) on NAFLD susceptibility. A comprehensive search of eligible studies up to October 2020 was performed on Medline, Embase, Web of Science, and Google Scholar databases. No restriction was imposed on language, publication date, or publication status. Odds ratios (ORs) with their 95% confidence intervals (CIs) were calculated to assess the combined effect sizes. The levels of heterogeneity, sensitivity, subgroup, and publication bias were analyzed subsequently. This meta-analysis included eight studies, consisting of 1,511 patients with NAFLD and 1,900 controls fulfilling the inclusion criteria and exclusion criteria. The pooled analysis showed significant associations between APOC3 -455T/C polymorphism and NAFLD risk in allelic (OR = 1.33; 95% CI = 1.05-1.67), dominant (OR = 1.34; 95% CI = 1.04-1.72), and recessive (OR = 1.60; 95% CI = 1.06-2.40) models. Ethnicity-based stratification showed that -455T/C polymorphism was significantly associated with NAFLD risk in the non-Asian but not in the Asian population. No association was evident between -482C/T polymorphism and NAFLD risk. Our findings suggest that APOC3 promoter region polymorphism -455T/C may be associated with NAFLD risk in the non-Asian but not in the Asian population. Additional studies with other functional polymorphisms are needed to discover APOC3 gene effects on NAFLD. 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