Abstract | BACKGROUND: 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.
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Authors | Weiwei Wang, Di Zhu, Zhihua Zhao, Miaomiao Sun, Feng Wang, Wencai Li, Jianying Zhang, Guozhong Jiang |
Journal | Cancer cell international
(Cancer Cell Int)
Vol. 21
Issue 1
Pg. 151
(Mar 04 2021)
ISSN: 1475-2867 [Print] England |
PMID | 33663506
(Publication Type: Journal Article)
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