Machine learning-based single-sample molecular classifier for cancer grading.

ABSTRACT: Tumor subtyping based on morphological grade is used in cancer treatment and management decision-making and to determine a patient's prognosis. While low- and high-grade tumors are predictive of patient survival for many cancers, tumors of intermediate morphological grades are considered unreliable due to interobserver variability and thus do not have clear prognostic significance. To address this issue, we devised a molecular-based classifier that uses gene expression data from RNA sequencing (RNA-seq) or microarray profiling to predict high- and low-grade risk groups for breast, lung, and renal cancers. For this classifier, we developed a preprocessing procedure that only required expression data from a single sample, without the need for any batch correction or cohort scaling. This clas

SUBMITTER: Antysheva Z 

PROVIDER: S-EPMC12307393 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature