Related Experiment Video
Updated: Aug 31, 2025
Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
Published on: March 27, 2020
Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets
Congxue Hu1, Yingqi Xu1, Feng Li1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Abstract:
Recently, many studies have shown that lncRNA can mediate the regulation of TF-gene in drug sensitivity. However, there is still a lack of systematic identification of lncRNA-TF-gene regulatory triplets for drug sensitivity. In this study, we propose a novel analytic approach to systematically identify the lncRNA-TF-gene regulatory triplets related to the drug sensitivity by integrating transcriptome data and drug sensitivity data. Totally, 1570 drug sensitivity-related lncRNA-TF-gene triplets were identified, and 16 307 relationships were formed between drugs and triplets. Then, a comprehensive characterization was performed. Drug sensitivity-related triplets affect a variety of biological functions including drug response-related pathways. Phenotypic similarity analysis showed that the drugs with many shared triplets had high similarity in their two-dimensional structures and indications. In addition, Network analysis revealed the diverse regulation mechanism of lncRNAs in different drugs. Also, survival analysis indicated that lncRNA-TF-gene triplets related to the drug sensitivity could be candidate prognostic biomarkers for clinical applications. Next, using the random walk algorithm, the results of which we screen therapeutic drugs for patients across three cancer types showed high accuracy in the drug-cell line heterogeneity network based on the identified triplets. Besides, we developed a user-friendly web interface-DrugSETs (http://bio-bigdata.hrbmu.edu.cn/DrugSETs/) available to explore 1570 lncRNA-TF-gene triplets relevant with 282 drugs. It can also submit a patient's expression profile to predict therapeutic drugs conveniently. In summary, our research may promote the study of lncRNAs in the drug resistance mechanism and improve the effectiveness of treatment.
Insights
This study systematically identifies long non-coding RNA (lncRNA)-transcription factor (TF)-gene triplets regulating drug sensitivity, revealing their potential as prognostic biomarkers and therapeutic targets.
Area of Science:
- Genomics and Bioinformatics
- Molecular Biology
- Pharmacology
Background:
- Long non-coding RNAs (lncRNAs) are increasingly recognized for their role in regulating gene expression.
- lncRNAs mediate transcription factor (TF)-gene regulation impacting drug sensitivity.
- A systematic identification of lncRNA-TF-gene regulatory networks for drug sensitivity is lacking.
Purpose of the Study:
- To systematically identify lncRNA-TF-gene regulatory triplets associated with drug sensitivity.
- To characterize these triplets' biological functions and regulatory mechanisms.
- To develop a predictive tool for therapeutic drug screening.
Main Methods:
- Integration of transcriptome and drug sensitivity data.
- Identification and characterization of lncRNA-TF-gene triplets.
- Network analysis, survival analysis, and random walk algorithm.
- Development of a web interface (DrugSETs).
Main Results:
- 1570 drug sensitivity-related lncRNA-TF-gene triplets and 16,307 drug-triplet relationships were identified.
- These triplets are involved in drug response pathways and exhibit phenotypic similarity among drugs.
- lncRNA-TF-gene triplets serve as potential prognostic biomarkers and enable accurate drug screening.
- A web tool, DrugSETs, was developed for exploring triplets and predicting therapeutic drugs.
Conclusions:
- The identified lncRNA-TF-gene triplets provide insights into drug resistance mechanisms.
- These triplets have potential as prognostic biomarkers for clinical applications.
- The developed approach and tool can improve personalized treatment strategies.
