Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma

Simple Summary Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. HCC is often diagnosed at a late stage when treatment effectiveness is limited and its prognosis remains dire. Exosomes are secreted by all living cells, including cancer cells, and contain biological material with potential to highlight disease conditions or dysregulated pathways involved in tumourigenesis. This study employs artificial intelligence methods to identify a signature of exosomal RNAs from 114,602 exosomal RNAs in 118 HCC patients and 112 healthy individuals that can predict HCC. A signature of nine exosomal RNAs, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, was identified to predict HCC with an accuracy of ~85%. Hence, these nine exosomal RNAs have potential to be developed as clinically useful minimally invasive biomarkers for HCC. Abstract Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79–0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.