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E-GEOD-43732 - MiRNA Expression Profile Study Reveals a Prognostic Signature for Esophageal Squamous Cell Carcinoma
Released on 1 August 2014, last updated on 9 August 2014
Esophageal cancer is a highly malignant and prevalent cancer worldwide. Current TNM staging system is insufficient for prognosis of esophagus squamous cell carcinoma (ESCC) patients. The aim of this study is to evaluate miRNA expression profile of ESCC and identify a miRNA signature which robustly predict the survival of ESCC patients. MiRNA expression profiles of paired frozen tissues from 119 ESCC patients were assessed by microarray. After normalization of microarray data, the patients were randomly divided into a training set (n=60) and a test set (n=59). From the training set, we identified a four-miRNA prognostic signature (including hsa-miR-218-5p, hsa-miR-142-3p, hsa-miR-150-5p, and hsa-miR-205-5p) using random forest supervised classification algorithm and nearest shrunken centroid algorithm. This signature distinguished the patients into high-risk or low-risk groups whose overall survival differed significantly (5-year survival 7.4% vs. 66.7%, p<0.001). Prognostic value of this signature was validated in the test set (5-year survival 18.8% vs. 46.5%, p=0.025) and further in an independent cohort of 58 patients assessed by a different platform (5-year survival 11.4% vs. 56.7%, p=0.003). Furthermore, multivariable Cox regression analysis revealed that this signature is an independent prognostic factor for ESCC patients. Moreover, stratified analysis showed that this signature was able to predict survival within TNM stages. The expression level of the four miRNAs measured by microarray was verified by qRT-PCR and showed high level of positive correlation (Pearson correlation coefficient>0.75, p<0.001 for all). Our results suggest that the four-miRNA signature can serve as a reliable biomarker to predict the survival of ESCC patients. the miRNA expression profiles of cancer and adjacent normal tissues form 119 ESCC patients were used to identify a miRNA signature that can perdict the survival of ESCC patients.
transcription profiling by array
Jiagen Li, Liqing Tian, Zhaoli Chen