Epigenetic alteration of tumor suppression gene is one of the most significant indicators in human esophageal squamous cell carcinoma (ESCC). In this study, we identified a novel ESCC hypermethylation biomarker ZNF132 by integrative computational analysis to comprehensive genome-wide DNA methylation microarray dataset. We validated the hypermethylation status of ZNF132 in 91 Chinese Han ESCC patients and adjacent normal tissues with methylation target bisulfite sequencing (MTBS) assay. Meanwhile, ZNF132 gene silencing mediated by hypermethylation was confirmed in both solid tissues and cancer cell lines. What is more, we found that in vitro overexpression of ZNF132 in ESCC cells could significantly reduce the abilities of the cell in growth, migration and invasion, and tumorigenicity of cells in a nude mouse model. We validated the Sp1-binding site in the ZNF132 promoter region with chromatin immunoprecipitation assay and demonstrated that the hypermethylation status could reduce the Sp1 transcript factor activity. Our results suggest that ZNF132 plays an important role in the development of ESCC as a tumor suppressor gene and support the underlying mechanism caused by the DNA hypermethylation-mediated Sp1-binding decay and gene silencing.

DNA methylation-based biomarkers were suggested to be promising for early cancer diagnosis. However, DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC), especially in Chinese Han populations have not been identified and evaluated quantitatively. Candidate tumor suppressor genes (N = 65) were selected through literature searching and four public high-throughput DNA methylation microarray datasets including 136 samples totally were collected for initial confirmation. Targeted bisulfite sequencing was applied in an independent cohort of 94 pairs of ESCC and normal tissues from a Chinese Han population for eventual validation. We applied nine different classification algorithms for the prediction to evaluate to the prediction performance. ADHFE1, EOMES, SALL1 and TFPI2 were identified and validated in the ESCC samples from a Chinese Han population. All four candidate regions were validated to be significantly hyper-methylated in ESCC samples through Wilcoxon rank-sum test (ADHFE1, P = 1.7 × 10-3; EOMES, P = 2.9 × 10-9; SALL1, P = 3.9 × 10-7; TFPI2, p = 3.4 × 10-6). Logistic regression based prediction model shown a moderately ESCC classification performance (Sensitivity = 66%, Specificity = 87%, AUC = 0.81). Moreover, advanced classification method had better performances (random forest and naive Bayes). Interestingly, the diagnostic performance could be improved in non-alcohol use subgroup (AUC = 0.84). In conclusion, our data demonstrate the methylation panel of ADHFE1, EOMES, SALL1 and TFPI2 could be an effective methylation-based diagnostic assay for ESCC.

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Textile products with a faded effect achieved via ozonation are increasingly popular nowadays. In order to better understand and apply this process, the complex factors and effects of color fading ozonation are investigated via process modeling in terms of pH, temperature, water pick-up, time (of process) and original color (of textile) affecting the color performance ( K/ S, L*, a*, b* values) of reactive-dyed cotton using the Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Random Forest (RF), respectively. It is found that the RF and SVR perform better than the ELM as the latter were very unstable in the case of predicting a certain single output. Both the RF and SVR are potentially applicable, but SVR would be more recommended to be used in the real application due to its balancer predicting performance and lower training time cost.