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.