Transcriptional patterns of cancer-related genes in primary and metastatic tumours revealed by machine learning.

ABSTRACT:

Background

A key to understanding cancer is to determine the impact on the cellular pathways caused by the repertoire of DNA changes accrued in a cancer cell. Exploring the interactions between genomic aberrations and the expressed transcriptome can not only improve our understanding of the disease but also identify potential therapeutic approaches.

Results

Using random forest models, we successfully identified transcriptional patterns associated with the loss of wild-type activity in cancer-related genes across various tumour types. While genes like TP53 and CDKN2A exhibited unique pan-cancer transcriptional patterns, others like ATRX, BRAF, and NRAS showed tumour-type-specific expression patterns. We also observed that genes like AR and ERBB4 did not lead to strong detecta

SUBMITTER: Keshavarz-Rahaghi F 

PROVIDER: S-EPMC12329921 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature