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Alternative RNA Splicing

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

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Updated: Aug 6, 2025

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

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Published on: December 9, 2016

Splicing signature database development to delineate cancer pathways using literature mining and transcriptome

Kyubin Lee^1,2, Daejin Hyung¹, Soo Young Cho³

¹Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea.

Computational and Structural Biotechnology Journal

|March 21, 2023

Summary

This study introduces a novel database of alternative splicing (AS) signatures to understand cancer pathway regulation. The random-forest model identified reliable AS events, aiding cancer research and clinical applications.

Keywords:

AS, Alternative splicing AUCPR, the area under the precision-recall curve AUROC, the area under the receiver operating characteristic Alternative splicing DAS, differential alternative splicing Database EMT, epithelial mesenchymal transition Gene signature ML, machine learning Machine-learning NER, named entity recognition NLP, natural language process PCA, principal component analysis PSI, percent spliced in index RF, random-forest SF, splicing factor TCGA, The Cancer Genome Atlas Text-mining Tumor transcriptome

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Area of Science:

Genomics
Bioinformatics
Cancer Biology

Background:

Alternative splicing (AS) significantly impacts cancer pathways and cellular adaptability.
Identifying reliable AS events for functional analysis remains a computational challenge.

Purpose of the Study:

To develop a comprehensive database of AS event signatures for assessing pathway regulation in cancer.
To integrate knowledge-based and machine learning-based approaches for robust AS signature identification.

Main Methods:

Collected and processed two datasets: literature-mined splicing signatures (202 pathways) and cancer transcriptome profiles (16 cancer types, 42 pathways).
Employed six machine learning models, including random-forest, to identify top-ranked AS events as pathway signatures.
Validated signatures using performance metrics, external differential AS sets, and knowledge-based signatures.

Main Results:

The random-forest model demonstrated superior performance in identifying reliable AS signatures compared to other models and validation sets.
The developed database includes clinical data (survival, molecular subtype, tumor microenvironment) for AS signatures.
Investigated the regulatory roles of splicing factors and provided a retrieval and visualization system.

Conclusions:

The curated database provides essential AS signatures for pathway activity assessment in cancer.
The identified signatures offer valuable insights into cancer biology and potential clinical applications.
The random-forest-derived signatures are robust and suitable for further investigation in cancer research.