👤 Chih-Feng Chian

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

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