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RNA Splicing

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

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

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Systematic Characterization of Splicing Dysregulation in Pan Solid Tumor Transcriptome.

Jingru Sui^1,2,3, Dan Guo^1,2, Xiao Wen^1,2

¹China National Center for Bioinformation, Beijing, 100101, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|December 6, 2024

Summary

Splicing dysregulation is common in solid tumors, promoting cancer. This study reveals six pan-cancer splicing patterns, uncovering shared and distinct mechanisms across tumor types.

Keywords:

alternative splicing mathematical modeling pan cancer splicing factor

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Engineering Artificial Factors to Specifically Manipulate Alternative Splicing in Human Cells

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Published on: April 26, 2017

Area of Science:

Genomics
Molecular Biology
Computational Biology

Background:

Splicing dysregulation, often from spliceosomal mutations, drives disease and treatment resistance, particularly in hematologic cancers.
While less common in solid tumors, splicing disorders are pervasive and contribute to tumorigenesis.
A systematic understanding of splicing dysregulation patterns across solid tumors is lacking.

Purpose of the Study:

To computationally uncover the pan-cancer splicing dysregulation landscape.
To identify joint modular patterns of splicing factors (SFs) and alternative splicing events (ASEs).
To characterize the full spectrum of splicing dysregulation patterns across 31 human solid tumors.

Main Methods:

Development of a computational method, SMNPLS (Sparse Multi-Network Regularized Partial Least Squares).
Extraction of joint modular patterns from paired SF expression and ASE matrices.
Analysis of splicing dysregulation patterns across 40% of TCGA solid tumors.

Main Results:

Six unique ASE-SF co-module patterns were identified, involving 1,570 ASEs and 170 SFs.
Common splicing dysregulation patterns were observed in digestive, renal, and urogenital tumors.
Brain tumors exhibited a distinct splicing pattern with high ASE-SF correlation, and novel potentially oncogenic regulatory relationships were identified.

Conclusions:

The study characterizes the complete spectrum of splicing dysregulation in solid tumors.
It highlights both similarities and specificities in splicing-derived pathogenesis across different cancer types.
Findings provide insights into splicing-driven tumorigenesis and potential therapeutic targets.