Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers.

ABSTRACT: A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparatively analyzed the circRNA landscape in pan-cancer and pan-normal tissues. Using co-expression and LASSO regularization analyses, as well as a support vector machine, we analyzed 265 pan-cancer and 319 pan-normal tissues in order to identify the circRNAs with the highest ability to distinguish between pan-cancer and pan-normal tissues. We further studied their expression in plasma exosomes from patients with cancer and their relation with cancer mutations and tumor microenvironment landscape. We di

SUBMITTER: Wang X 

PROVIDER: S-EPMC8479194 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature