Combination Analysis of PCDHGA12and CDO1 DNA Methylation in Bronchial Washing Fluid for Lung Cancer Diagnosis

Background@#When suspicious lesions are observed on computer-tomography (CT), invasive tests are needed to confirm lung cancer. Compared with other procedures, bronchoscopy has fewer complications. However, the sensitivity of peripheral lesion through bronchoscopy including washing cytology is low. A new test with higher sensitivity through bronchoscopy is needed. In our previous study, DNA methylation of PCDHGA12 in bronchial washing cytology has a diagnostic value for lung cancer. In this study, combination of PCDHGA12 and CDO1 methylation obtained through bronchial washing cytology was evaluated as a diagnostic tool for lung cancer. @*Methods@#A total of 187 patients who had suspicious lesions in CT were enrolled. PCDHGA12methylation test, CDO1 methylation test, and cytological examination were performed using 3-plex LTE-qMSP test. @*Results@#Sixty-two patients were diagnosed with benign diseases and 125 patients were diagnosed with lung cancer. The sensitivity of PCDHGA12 was 74.4% and the specificity of PCDHGA12 was 91.9% respectively. CDO1 methylation test had a sensitivity of 57.6% and a specificity of 96.8%. The combination of both PCDHGA12 methylation test and CDO1 methylation test showed a sensitivity of 77.6% and a specificity of 90.3%. The sensitivity of lung cancer diagnosis was increased by combining both PCDHGA12 and CDO1 methylation tests. @*Conclusion@#Checking DNA methylation of both PCDHGA12 and CDO1 genes using bronchial washing fluid can reduce the invasive procedure to diagnose lung cancer.

Analysis of Syndecan-2 Methylation in Bowel Lavage Fluid for the Detection of Colorectal Neoplasm

BACKGROUND/AIMS: Syndecan-2 (SDC2) methylation was previously reported as a sensitive serologic biomarker for the early detection of colorectal cancer (CRC). The purpose of this study was to investigate whether SDC2 methylation is detectable in precancerous lesions and to determine the feasibility of using SDC2 methylation for the detection of CRC and precancerous lesions in bowel lavage fluid (BLF). METHODS: A total of 190 BLF samples were collected from the rectum at the beginning of colonoscopy from patients with colorectal neoplasm and healthy normal individuals. Fourteen polypectomy specimens were obtained during colonoscopy. A bisulfite pyrosequencing assay and quantitative methylation-specific polymerase chain reaction were conducted to measure SDC2 methylation in tissues and BLF DNA. RESULTS: SDC2 methylation was positive in 100% of villous adenoma (VA) and high-grade dysplasia, and hyperplastic polyp samples; 88.9% of tubular adenoma samples; and 0% of normal mucosa samples. In the BLF DNA test forSDC2 methylation, the sensitivity for detecting CRC and VA was 80.0% and 64.7%, respectively, at a specificity of 88.9%. The BLF of patients with multiple tubular adenomas, single tubular adenoma and hyperplastic polyps showed 62.8%, 26.7% and 28.6% rates of methylation-positive SDC2, respectively. CONCLUSIONS: Our results demonstrated that SDC2 methylation was a frequent event in precancerous lesions and showed high potential in BLF for detecting patients with colorectal neoplasm.

Asthma-Predictive Genetic Markers in Gene Expression Profiling of Peripheral Blood Mononuclear Cells

PURPOSE: We sought to identify asthma-related genes and to examine the potential of these genes to predict asthma, based on expression levels. METHODS: The subjects were 42 asthmatics and 10 normal healthy controls. PBMC RNA was subjected to microarray analysis using a 35K array; t-tests were used to identify genes that were expressed differentially between the two groups. A multiple logistic regression analysis was applied to the differentially expressed genes, and area under the curve (AUC) values from receiver operating characteristic (ROC) curves were obtained. RESULTS: In total, 170 genes were selected using the following criteria: P or =2-fold change. Among these genes, 57 were up-regulated and 113 were down-regulated in asthmatics versus normal controls. A multiple logistic regression analysis was done using more stringent criteria (P or =5-fold change), and eight genes were selected as candidate asthma biomarkers. Using these genes, 255 models (2(8)-1) were generated. Among them, only 85 showed P< or =0.05 by multiple logistic regression analysis. Based on the AUCs from ROC curves for the 85 models, we found that the best model consisted of the genes MEPE, MLSTD1, and TRIM37. The model showed 0.9928 of the AUC with 98% sensitivity and 80% specificity. CONCLUSIONS: MEPE, MLSTD1, and TRIM37 may be useful biomarkers for asthma.