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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Updated: Oct 17, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Characterizing the tumor RBP-ncRNA circuits by integrating transcriptomics, interactomics and clinical data.

Leiming Jiang¹, Qiuyang Chen¹, Mingrong Bei¹

¹Computational Systems Biology Laboratory, Department of Bioinformatics, Shantou University Medical College (SUMC), 515041 Shantou, China.

Computational and Structural Biotechnology Journal

|October 11, 2021

Summary

This summary is machine-generated.

Dysregulated RNA binding protein-non-coding RNA circuits (RNCs) are key in cancer progression. These RNCs can predict patient outcomes and reveal new drug targets, offering insights into cancer mechanisms.

Keywords:

Gene regulation LncRNA MiRNA RNA binding protein RNA binding protein-ncRNA circuits

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Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Area of Science:

Molecular Biology
Genetics
Oncology

Background:

Non-coding RNA (ncRNA) and RNA binding protein (RBP) interactions are fundamental to gene regulation and cancer progression.
Understanding the dynamic regulatory network of ncRNA-RBP interactions, particularly in cancer, remains limited.

Purpose of the Study:

To systematically dissect dysregulated RNA binding protein-non-coding RNA circuits (RNCs) across various cancer types.
To investigate the potential of RNCs as prognostic biomarkers and therapeutic targets in cancer.

Main Methods:

Utilized transcriptomics and interactomics data to identify and analyze aberrant RNCs in 14 tumor types.
Performed pan-cancer analysis to identify common dysregulated RNCs across multiple cancers.
Conducted experimental validation to elucidate the role of a specific RNC in cancer progression.

Main Results:

Identified robust, dysregulated RNCs enriched with cancer-associated genes and drug targets.
Demonstrated that altered RNCs, but not individual nodes, can jointly predict clinical outcomes, serving as prognostic biomarkers.
Discovered 30 pan-cancer RNCs and elucidated the role of the hsa-miR-224-5p_MAGI2-AS3_MBNL2 circuit in epithelial-mesenchymal transition (EMT).

Conclusions:

Dysregulated RNCs are significant in cancer, offering potential as prognostic biomarkers and for drug repurposing.
Pan-cancer RNC analysis provides novel insights into cancer mechanisms and therapeutic strategies.
The study highlights the utility of RNCs in understanding ncRNA function, clinical outcomes, and drug discovery in oncology.