A microRNA expression profile for vascular invasion can predict overall survival in hepatocellular carcinoma
| Autor: | Yu-Qun Wang, Mengtao Zhou, Bicheng Chen, Ke-Qing Shi, Mei Song, Liang Zhao, Shi-Hao Xu, Xiao-Dong Wang, Yi-Jing Cai, Rui-Cong Chen, Jianmin Wu, Zhuo Lin |
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| Rok vydání: | 2017 |
| Předmět: |
Male
0301 basic medicine Oncology medicine.medical_specialty Carcinoma Hepatocellular Clinical Biochemistry Bioinformatics Biochemistry 03 medical and health sciences 0302 clinical medicine Cell Line Tumor Internal medicine microRNA Biomarkers Tumor medicine Humans Neoplasm Invasiveness Survival analysis Aged Receiver operating characteristic business.industry Gene Expression Profiling Liver Neoplasms Biochemistry (medical) General Medicine MicroRNA Expression Profile Middle Aged Prognosis medicine.disease Survival Analysis Gene expression profiling MicroRNAs 030104 developmental biology 030220 oncology & carcinogenesis Hepatocellular carcinoma Cohort Blood Vessels Female business Classifier (UML) |
| Zdroj: | Clinica Chimica Acta. 469:171-179 |
| ISSN: | 0009-8981 |
| DOI: | 10.1016/j.cca.2017.03.026 |
| Popis: | Background The presence of vascular invasion (VI) in pathology specimens is a well-known unfavorable prognostic factor of hepatocellular carcinoma (HCC) recurrence and overall survival (OS). We investigated the vascular invasion related microRNA (miRNA) expression profiles and potential of prognostic value in HCC. Methods MiRNA and mRNA expression data for HCC were accessed from The Cancer Genome Atlas (TCGA). LASSO logistic regression models were used to develop a miRNA-based classifier for predicting VI. The predictive capability was accessed by area under receiver operating characteristics (AUC). Concordance index (C-index) and time-dependent receiver operating characteristic (td-ROC) were used to determine its prognostic value. We validated the predictive and prognostic accuracy of this classifier in an external independent cohort of 127 patients. Functionally relevant targets of miRNAs were determined using miRNA target prediction, experimental validation and correlation of miRNA and mRNA expression data. Results A 16-miRNA-based classifier was developed which identified VI accurately, with AUC of 0.731 and 0.727 in TCGA set and validation cohort, respectively. C-index and td-ROC showed that the classifier was able to stratify patients into risk groups strongly associated with OS. When stratified by tumor characteristics, the classifier was still a clinically and statistically significant prognostic model. The predictive and prognostic accuracy of the classifier was confirmed in validation cohort. Vascular invasion related miRNA/target pairs were identified by integrating expression patterns of predicted targets, which were validated in cell lines. Conclusions A multi-miRNA-based classifier developed based on the presence of VI, which could effectively predict OS in HCC. |
| Databáze: | OpenAIRE |
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