Abstract
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer deaths worldwide. Recently, microRNAs (miRNAs) are reported to be altered and act as potential biomarkers in various cancers. However, miRNA biomarkers for predicting the stage of HCC are limitedly discovered. Hence, we sought to identify a novel miRNA signature associated with cancer stage in HCC. We proposed a support vector machine (SVM)-based cancer stage prediction method, SVM-HCC, which uses an inheritable bi-objective combinatorial genetic algorithm for selecting a minimal set of miRNA biomarkers while maximizing the accuracy of predicting the early and advanced stages of HCC. SVM-HCC identified a 23-miRNA signature that is associated with cancer stages in patients with HCC and achieved a 10-fold cross-validation accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve (AUC) of 92.59%, 0.98, 0.74, 0.80, and 0.86, respectively; and test accuracy and test AUC of 74.28% and 0.73, respectively. We prioritized the miRNAs in the signature based on their contributions to predictive performance, and validated the prognostic power of the prioritized miRNAs using Kaplan–Meier survival curves. The results showed that seven miRNAs were significantly associated with prognosis in HCC patients. Correlation analysis of the miRNA signature and its co-expressed miRNAs revealed that hsa-let-7i and its 13 co-expressed miRNAs are significantly involved in the hepatitis B pathway. In clinical practice, a prediction model using the identified 23-miRNA signature could be valuable for early-stage detection, and could also help to develop miRNA-based therapeutic strategies for HCC.
Highlights
Profiling of 89 Hepatocellular carcinoma (HCC) patients, followed by unsupervised hierarchical clustering, to categorize HCC into three sub classes[20]
The proposed method, support vector machine (SVM)-HCC, distinguished patients with HCC into early-stage and advanced-stage groups based on their miRNA expression profiles
SVM-HCC performed well relative to these machine learning methods in terms of training accuracy
Summary
Profiling of 89 HCC patients, followed by unsupervised hierarchical clustering, to categorize HCC into three sub classes[20]. Few studies have attempted to predict the stage of HCC using the genomic profiling. This study aims to identify a miRNA signature consisting of a small set of miRNA biomarkers that can predict the cancer stage of patients with HCC, so that this miRNA signature can be useful for developing gene-based target therapies in HCC. We proposed a method for predicting the early and advanced stages of HCC using miRNA expression profiles. We utilized a support vector machine (SVM)-based classifier[29], SVM-HCC, which incorporated with an inheritable bi-objective combinatorial genetic algorithm (IBCGA)[30] to identify a miRNA signature capable of distinguishing early-stage patients from advanced-stage HCC. The main purpose of this study is to identify a miRNA signature associated with cancer stage of patients with HCC.
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