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Related Experiment Video

Updated: May 13, 2025

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

High tissue specificity of lncRNAs maximises the prediction of tissue of origin of circulating DNA.

Elyas Mouhou¹, Fabien Genty², Walid El M'selmi³

¹Laboratoire GBCM (EA7528), Conservatoire national des arts et métiers (CNAM), Paris, France.

Scientific Reports

|April 15, 2025

Summary

This summary is machine-generated.

This study enhances circulating tumor DNA (ctDNA) detection by analyzing cell-free DNA (cfDNA) fragmentation patterns near transcription start sites. Long non-coding RNAs (lncRNAs) show superior tissue specificity, improving cancer diagnosis accuracy.

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RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

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Published on: May 19, 2019

Area of Science:

Genomics
Biomarkers
Cancer Research

Background:

Plasma DNA sequencing holds promise for cancer diagnosis and monitoring.
Low concentrations of circulating tumor DNA (ctDNA) and underdeveloped statistical models present significant challenges.
Cell-free DNA (cfDNA) fragmentation patterns offer potential biomarkers.

Purpose of the Study:

To evaluate binary classification models using cfDNA fragmentation features around transcription start sites (TSSs).
To compare the utility of long non-coding RNA (lncRNA) genes versus coding genes for cancer detection.
To determine optimal gene class selection criteria for predictive models.

Main Methods:

Analysis of cfDNA fragmentation patterns around TSSs in healthy patient datasets.
Investigated fragment density, gene types (lncRNA vs. coding), and gene class selection criteria.
Developed and validated binary classification models for predictive accuracy.

Main Results:

The definition of tissue-specific gene classes significantly impacts model performance.
Long non-coding RNAs (lncRNAs) demonstrate higher tissue specificity compared to coding genes.
lncRNAs exhibit superior sensitivity and specificity in classifying samples.

Conclusions:

Optimizing gene class definitions is crucial for accurate cfDNA-based cancer detection.
lncRNA fragmentation patterns are highly promising biomarkers for sensitive and specific cancer diagnosis.
This approach advances the translational application of cfDNA sequencing in oncology.