RCANE: a deep learning algorithm for whole-genome pan-cancer somatic copy number aberration prediction using RNA-seq data.

ABSTRACT: Transcriptome sequencing (RNA-seq) of cancers is widely employed in cancer research to investigate gene expression patterns and their role in disease progression. Somatic copy-number aberrations (SCNAs)-critical genomic drivers of tumorigenesis-can also be inferred directly from RNA-seq, yielding a "two-for-one" return of quantitative expression measures plus structural-variation calls at a fraction of the cost of separate DNA assays. Here, we present RCANE, a deep-learning framework that predicts genome-wide SCNAs across diverse cancer types using only RNA-seq data. Trained on The Cancer Genome Atlas (TCGA) and DepMap cell-line cohorts, RCANE consistently outperforms existing approaches, delivering a scalable, robust solution for improving somatic copy-number aberration profiling in cance

SUBMITTER: Ge C 

PROVIDER: S-EPMC12460768 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature