Weiwei Wang1,2,3, Di Zhu1,2,3, Zhihua Zhao1,2,3, Miaomiao Sun1,2,3, Feng Wang4, Wencai Li1,2,3, Jianying Zhang5, Guozhong Jiang6,7,8. 1. Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan, China. 2. Department of Pathology, School of Basic Medicine, Zhengzhou University, Zhengzhou, 450002, China. 3. Henan Key Laboratory for Tumor Pathology, Zhengzhou University, Zhengzhou, 450052, China. 4. Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450052, China. 5. State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, 450052, Henan, China. jianyingzhang@hotmail.com. 6. Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan, China. guozhongjiang@zzu.edu.cn. 7. Department of Pathology, School of Basic Medicine, Zhengzhou University, Zhengzhou, 450002, China. guozhongjiang@zzu.edu.cn. 8. Henan Key Laboratory for Tumor Pathology, Zhengzhou University, Zhengzhou, 450052, China. guozhongjiang@zzu.edu.cn.
Abstract
BACKGROUND: CircRNAs with tissue-specific expression and stable structure may be good tumor prognostic markers. However, the expression of circRNAs in esophageal squamous cell carcinoma (ESCC) remain unknown. We aim to identify prognostic circRNAs and construct a circRNA-related signature in ESCC. METHODS: RNA sequencing was used to test the circRNA expression profiles of 73 paired ESCC tumor and normal tissues after RNase R enrichment. Bioinformatics methods, such as principal component analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm, unsupervised clustering and hierarchical clustering were performed to analyze the circRNA expression characteristics. Univariate cox regression analysis, random survival forests-variable hunting (RSFVH), Kaplan-Meier analysis, multivariable Cox regression and ROC (receiver operating characteristic) curve analysis were used to screen the prognostic circRNA signature. Real-time quantitative PCR (qPCR) and fluorescence in situ hybridization(FISH) in 125 ESCC tissues were performed. RESULTS: Compared with normal tissues, there were 11651 differentially expressed circRNAs in cancer tissues. A total of 1202 circRNAs associated with ESCC prognosis (P < 0.05) were identified. Through bioinformatics analysis, we screened a circRNA signature including four circRNAs (hsa_circ_0000005, hsa_circ_0007541, hsa_circ_0008199, hsa_circ_0077536) which can classify the ESCC patients into two groups with significantly different survival (log rank P < 0.001), and found its predictive performance was better than that of the TNM stage(0.84 vs. 0.66; 0.65 vs. 0.62). Through qPCR and FISH experiment, we validated the existence of the screened circRNAs and the predictive power of the circRNA signature. CONCLUSION: The prognostic four-circRNA signature could be a new prognostic biomarker for ESCC, which has high clinical application value.
BACKGROUND: CircRNAs with tissue-specific expression and stable structure may be good tumor prognostic markers. However, the expression of circRNAs in esophageal squamous cell carcinoma (ESCC) remain unknown. We aim to identify prognostic circRNAs and construct a circRNA-related signature in ESCC. METHODS: RNA sequencing was used to test the circRNA expression profiles of 73 paired ESCC tumor and normal tissues after RNase R enrichment. Bioinformatics methods, such as principal component analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm, unsupervised clustering and hierarchical clustering were performed to analyze the circRNA expression characteristics. Univariate cox regression analysis, random survival forests-variable hunting (RSFVH), Kaplan-Meier analysis, multivariable Cox regression and ROC (receiver operating characteristic) curve analysis were used to screen the prognostic circRNA signature. Real-time quantitative PCR (qPCR) and fluorescence in situ hybridization(FISH) in 125 ESCC tissues were performed. RESULTS: Compared with normal tissues, there were 11651 differentially expressed circRNAs in cancer tissues. A total of 1202 circRNAs associated with ESCC prognosis (P < 0.05) were identified. Through bioinformatics analysis, we screened a circRNA signature including four circRNAs (hsa_circ_0000005, hsa_circ_0007541, hsa_circ_0008199, hsa_circ_0077536) which can classify the ESCC patients into two groups with significantly different survival (log rank P < 0.001), and found its predictive performance was better than that of the TNM stage(0.84 vs. 0.66; 0.65 vs. 0.62). Through qPCR and FISH experiment, we validated the existence of the screened circRNAs and the predictive power of the circRNA signature. CONCLUSION: The prognostic four-circRNA signature could be a new prognostic biomarker for ESCC, which has high clinical application value.
Authors: Thomas B Hansen; Trine I Jensen; Bettina H Clausen; Jesper B Bramsen; Bente Finsen; Christian K Damgaard; Jørgen Kjems Journal: Nature Date: 2013-02-27 Impact factor: 49.962