Prediction of tissue-of-origin of early stage cancers using serum miRNomes.

ABSTRACT:

Background

Noninvasive detection of early stage cancers with accurate prediction of tumor tissue-of-origin could improve patient prognosis. Because miRNA profiles differ between organs, circulating miRNomics represent a promising method for early detection of cancers, but this has not been shown conclusively.

Methods

A serum miRNA profile (miRNomes)-based classifier was evaluated for its ability to discriminate cancer types using advanced machine learning. The training set comprised 7931 serum samples from patients with 13 types of solid cancers and 5013 noncancer samples. The validation set consisted of 1990 cancer and 1256 noncancer samples. The contribution of each miRNA to the cancer-type classification was evaluated, and those with a high contribution were identified.

SUBMITTER: Matsuzaki J 

PROVIDER: S-EPMC9825310 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature