Machine learning identifies exosome features related to hepatocellular carcinoma.

ABSTRACT: Background: Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. There is still a lack of effective biomarkers to predict its prognosis. Exosomes participate in intercellular communication and play an important role in the development and progression of cancers. Methods: In this study, two machine learning methods (univariate feature selection and random forest (RF) algorithm) were used to select 13 exosome-related genes (ERGs) and construct an ERG signature. Based on the ERG signature score and ERG signature-related pathway score, a novel RF signature was generated. The expression of BSG and SFN, members of 13 ERGs, was examined using real-time quantitative polymerase chain reaction and immunohistochemistry. Finally, the effects of the in

SUBMITTER: Zhu K 

PROVIDER: S-EPMC9527306 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature