Novel miRNA signature for predicting the stage of hepatocellular carcinoma

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.

Identification of the miRNA signature associated with survival in patients with ovarian cancer

“…Various machine learning methods have been developed for cancer typing and diagnosis, including subtyping of liver cancer [ 24 ], breast cancer [ 25 ], lung cancer [ 26 ], and ovarian cancer [ 27 ]. These studies have used different machine learning models, such as support vector machines (SVMs) and artificial neural networks, along with various validation methods.…”

A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients

“…In this study, we showed an efficient strategy to identify miRNA signatures that can stratify high risk and low risk patients as well as classify early and late tumor stages. Several computational and machine leering algorithms have been developed to explore miRNA-associated diseases [ 17 , 29 ]. Previous studies also used miRNA profiles to identify biomarkers for risk stratification [ 11 , 30 ]; however, only a few research works have been conducted to explore miRNA signatures for early tumor stage of ccRCC.…”

“…Ng and Taguchi employed the tensor decomposition method to identify miRNA signature in ccRCC [ 15 ]. Previously, studies were used miRNA expression profiles of liver and breast cancer patients, followed by a support vector machine (SVM) with genetic algorithm, to predict the early and advanced stages [ 16 , 17 ]. Recently, miRNA profiles were used to detect lung cancer subtypes [ 18 ].…”

Predicting the Risk Genes of Autism Spectrum Disorders

“…The IBCGA uses an intelligent evolutionary algorithm ( Ho et al, 2004a ), which is good at deriving an optimized SVM with feature selection. The IBCGA has been successfully applied in solving several biological problems ( Yerukala Sathipati et al, 2016 , 2019 ; Yerukala Sathipati and Ho, 2017 , 2018 , 2020 , 2021 ).…”