AUTHOR=Wang Chenji , Pu Weilin , Zhao Dunmei , Zhou Yinghui , Lu Ting , Chen Sidi , He Zhenglei , Feng Xulong , Wang Ying , Li Caihua , Li Shilin , Jin Li , Guo Shicheng , Wang Jiucun , Wang Minghua 

TITLE=Identification of Hyper-Methylated Tumor Suppressor Genes-Based Diagnostic Panel for Esophageal Squamous Cell Carcinoma (ESCC) in a Chinese Han Population

JOURNAL=Frontiers in Genetics

VOLUME=9

YEAR=2018

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2018.00356

DOI=10.3389/fgene.2018.00356

ISSN=1664-8021

ABSTRACT=<p>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 (<italic>N</italic> = 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. <italic>ADHFE1, EOMES, SALL1</italic> and <italic>TFPI2</italic> 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 (<italic>ADHFE1</italic>, <italic>P</italic> = 1.7 × 10<sup>-3</sup>; <italic>EOMES</italic>, <italic>P</italic> = 2.9 × 10<sup>-9</sup>; <italic>SALL1</italic>, <italic>P</italic> = 3.9 × 10<sup>-7</sup>; <italic>TFPI2</italic>, <italic>p</italic> = 3.4 × 10<sup>-6</sup>). 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 <italic>ADHFE1</italic>, <italic>EOMES</italic>, <italic>SALL1</italic> and <italic>TFPI2</italic> could be an effective methylation-based diagnostic assay for ESCC.</p>