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Updated: Jan 9, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
Published on: December 9, 2016
Transcriptome-wide RNA Stability Across Cancers Reveals Therapeutic Vulnerabilities
Yang Tong1, Yuting Wang2, Gerui Liu3
1Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
RNA stability, governed by RNA decay, plays a crucial role in cancer. This study reveals RNA stability profiles can predict drug response and identify new therapeutic targets in cancer precision oncology.
Area of Science:
- Molecular Biology
- Cancer Biology
- Genomics
Background:
- Steady-state mRNA levels depend on transcription and degradation.
- The role of RNA stability in cancer remains unclear.
- Post-transcriptional regulation is key in cancer development.
Purpose of the Study:
- Investigate RNA decay dynamics across diverse cancer types.
- Identify molecular subtypes based on RNA stability.
- Explore the role of RNA-binding proteins (RBPs) and microRNAs (miRNAs) in cancer RNA stability.
Main Methods:
- Systematic analysis of RNA-seq data from the Cancer Cell Line Encyclopedia.
- Inference of transcriptome-wide RNA stability profiles.
- Integrative analyses of RBPs, miRNAs, and somatic mutations.
- Machine learning models to predict drug sensitivity.
Main Results:
- Identified distinct cancer subtypes defined by RNA stability.
- RBPs (e.g., SNRPA, RBMX) and miRNAs are key modulators of RNA stability and cancer cell survival.
- Somatic mutations affecting miRNA binding sites disrupt RNA decay.
- RNA stability profiles predict sensitivity to 24 anticancer drugs.
Conclusions:
- RNA stability is a critical regulatory layer in cancer.
- RNA stability profiles have implications for molecular stratification.
- RBPs are potential biomarkers for predicting therapeutic response in precision oncology.
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