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Updated: Jul 10, 2026

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

TNEAtlas: A Pan-cancer Database to Identify and Characterize Transcribed Non-coding Elements.

Wenyong Zhu^1,2, Rongxin Zhang^2,3, Xiao Sun²

¹Institute of Innovative Drug, China Pharmaceutical University, Nanjing 211198, China.

Genomics, Proteomics & Bioinformatics

|July 9, 2026

Summary

Researchers identified over 2 million transcribed non-coding elements (TNEs) in cancer, revealing their role as molecular switches driving tumor evolution. The TNEAtlas database offers a platform for exploring these crucial non-coding cancer genome elements.

Keywords:

Cancer genome Database Epigenetic signature Functional profile Transcribed non-coding element

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Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

Genomics
Cancer Biology
Bioinformatics

Background:

Precision oncology requires understanding the non-coding genome beyond protein-coding regions.
Transcribed non-coding elements (TNEs) play critical regulatory roles in cancer.

Purpose of the Study:

To systematically characterize TNEs across diverse cancer types and human tissues.
To establish a comprehensive, pan-cancer TNE database (TNEAtlas) with functional annotations.

Main Methods:

Developed an automated computational framework integrating 130 RNA-seq datasets from 26 cancer types and 16 human tissues.
Identified over 2 million intergenic TNEs and annotated them with epigenetic signatures and functional features in TNEAtlas.
Analyzed TNE regulatory functions, transcription factor binding, epigenetic crosstalk, structural motifs (e.g., G-quadruplexes), and tumor heterogeneity patterns.

Main Results:

Identified >2 million intergenic TNEs, revealing their function as molecular switches in tumor evolution.
Demonstrated conserved regulatory functions, tissue-specific transcription factor recruitment, and epigenetic crosstalk mediated by TNEs.
Observed TNEs exhibit structural motif preferences and patterns consistent with cancer subtypes.

Conclusions:

TNEAtlas provides a systematic framework for decoding non-coding genome regulation in carcinogenesis.
The database facilitates the identification of functional TNEs, advancing precision medicine.
Offers an open-access platform for exploring the non-coding cancer genome and TNE regulatory networks.