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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: Nov 3, 2025

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method

Published on: October 7, 2025

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers.

Wenwen Min^1,2,3, Tsung-Hui Chang^1,2, Shihua Zhang^4,5,6,7

¹Shenzhen Research Institute of Big Data, Shenzhen, China.

Plos Computational Biology

|June 1, 2021

Summary

This summary is machine-generated.

Researchers developed a new method, tensor sparse canonical correlation analysis (TSCCA), to identify microRNA-gene patterns across multiple cancers. This approach reveals shared and specific cancer-related modules, offering insights into cancer mechanisms and treatments.

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Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

MicroRNAs (miRNAs) play a crucial role in cancer development and progression.
miRNAs are being investigated as biomarkers for cancer diagnosis, prognosis, and therapy.
Large-scale miRNA expression data from multiple cancers are now available, necessitating advanced analytical approaches.

Purpose of the Study:

To develop a novel method for identifying cancer-related miRNA-gene modules across multiple cancer types.
To capture both shared and cancer-specific miRNA-gene co-expression patterns.
To improve biological interpretation of miRNA regulatory mechanisms in cancer.

Main Methods:

Development and application of a tensor sparse canonical correlation analysis (TSCCA) method.
Integration of multi-omics data in a pan-cancer framework.
Evaluation using simulated data and The Cancer Genome Atlas (TCGA) miRNA/gene expression data from 33 cancer types.

Main Results:

Identification of several statistically significant and biologically relevant miRNA-gene modules.
Demonstration of TSCCA's ability to uncover both shared and specific cancer-related modules.
Uncovering dysfunctional miRNA-gene modules linked to cancer biology.

Conclusions:

The developed TSCCA method effectively identifies complex miRNA-gene patterns across diverse cancers.
These findings enhance the understanding of miRNA's role in cancer regulation.
The identified modules offer potential insights for developing novel miRNA-based cancer diagnostics and therapeutics.