TNEAtlas: A Pan-cancer Database to Identify and Characterize Transcribed Non-coding Elements.
TL;DR
It is revealed that TNEs act as molecular switches that drive tumor evolution through conserved regulatory functions, tissue-specific transcription factor recruitment, and epigenetic modification crosstalk, and it is demonstrated that these TNEs exhibit structural motif preferences, especially G-quadruplexes, and tumor heterogeneity patterns consistent with cancer subtype classifications.
Abstract
Advances in precision oncology have underscored the need to move beyond regulatory frameworks centered on protein-coding regions and better understand regulatory mechanisms within the non-coding genome. To systematically characterize transcribed non-coding elements (TNEs) in cancer, we developed an automated computational framework that integrates 130 RNA-seq datasets from 26 cancer types and 16 human tissues and identifies more than 2 million intergenic TNEs. In parallel, we established the first pan-cancer TNE database, TNEAtlas, which annotates TNEs with epigenetic signatures and functional features. Our analyses revealed that TNEs act as molecular switches that drive tumor evolution through conserved regulatory functions, tissue-specific transcription factor recruitment, and epigenetic modification crosstalk. We also demonstrated that these TNEs exhibit structural motif preferences, especially G-quadruplexes, and tumor heterogeneity patterns consistent with cancer subtype classifications. Our database implemented an interactive platform comprising dynamic visualization tools, integrated analysis modules, and structured data resources to enable the exploration of TNE regulatory networks across multi-omics data. Our database provides a systematic framework for decoding non-coding genome regulation in carcinogenesis by combining transcriptional activity, chromatin architecture, tumor microenvironment interactions, and comprehensive pharmacological data. Overall, our database not only advances precision medicine by facilitating the identification of functional TNEs but also offers comprehensive analysis frameworks of the non-coding cancer genome, providing the scientific community with an open-access platform that bridges fragmented TNE studies with systematic exploration of genomic machinery and remains scalable for future discoveries in the non-coding cancer genome. TNEAtlas is publicly accessible at https://www.seubioinfo.cn/tneatlas.