Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets

Author:

Hu Congxue1,Xu Yingqi1,Li Feng1,Mi Wanqi1,Yu He1,Wang Xinran1,Wen Xin1,Chen Shuaijun1,Li Xia12ORCID,Xu Yanjun1,Zhang Yunpeng1ORCID

Affiliation:

1. College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081 , China

2. Key Laboratory of Tropical Translational Medicine of Ministry of Education , College of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199 , China

Abstract

AbstractRecently, 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.

Funder

Heilongjiang Postdoctoral Science Foundation

National Natural Science Foundation of China

National Key R&D Program of China

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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