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Genome-wide transcriptomics and copy number profiling identify patient-specific CNV-lncRNA-mRNA regulatory triplets in colorectal cancer9.94 MB
Published VersionCC BY-NC-ND V4.0, Open Access
Journal article
Genome-wide transcriptomics and copy number profiling identify patient-specific CNV-lncRNA-mRNA regulatory triplets in colorectal cancer
Computers in Biology and Medicine, Vol.153, pp.1-13
2023
PMID: 36646024
Appears in Cancer Research Cluster Research Collection
Abstract
Screening cancer genomes has provided an in depth characterization of genetic variants such as copy number variations (CNVs) and gene expression changes of non coding transcripts. Single dimensional experiments are often designed to differentiate a patient cohort into various sets with the aim of identifying molecular changes among groups; however, this may be inadequate to decipher the causal relationship between molecular signatures in individual patients. To overcome this challenge with respect to personalized medicine, we implemented a patient specific multi dimensional integrative approach to uncover coherent signals from multiple independent platforms. In particular, we focused on the consistent gene dosage effects of CNVs for both mRNA and long non coding RNA (lncRNA) expression in nine colorectal cancer patients. We identified 511 CNV lncRNA mRNA regulatory triplets associated with CNVs and aberrant expression of both mRNAs and lncRNAs. By filtering out inconsistent changes among CNVs, mRNAs, and lncRNAs, we further characterized 165 coherent motifs associated with 56 genes. In total, 108 motifs were linked with 31 copy number gains, 44 upregulated lncRNAs, and 45 upregulated mRNAs. Another 57 coherent downregulated motifs were also collected. We discuss how for many of these CNV lncRNA mRNA regulatory triplets, their clinical impact remains to be explored, including survival time, microsatellite instability, tumor stage, and primary tumor sites. By validating two example CNV lncRNA mRNA triplets with up and down regulation, we confirmed that individual variations in multiple dimensions are a robust tool to identify reliable molecular signals for personalized medicine. In summary, we utilized a patient specific computational pipeline to explore the consistent CNV driven motifs consisting of lncRNAs and mRNAs. We also identified LSM14B as a potential promoter in colorectal cancer progression, suggesting that it may serve as a target for colorectal cancer treatment.
Details
- Title
- Genome-wide transcriptomics and copy number profiling identify patient-specific CNV-lncRNA-mRNA regulatory triplets in colorectal cancer
- Authors
- Tianqi Liu (Author) - Peking University Cancer HospitalYining Liu (Author) - Guangzhou Medical UniversityXiangqian Su (Author) - Peking University Cancer HospitalLin Peng (Author) - Peking University Cancer HospitalJiangbo Chen (Author) - Peking University Cancer HospitalPu Xing (Author) - Peking University Cancer HospitalXiaowen Qiao (Author) - Peking University Cancer HospitalZaozao Wang (Author) - Peking University Cancer HospitalJiabo Di (Author) - Peking University Cancer HospitalMin Zhao (Corresponding Author) - University of the Sunshine Coast, Queensland, Centre for BioinnovationBeihai Jiang (Corresponding Author) - Peking University Cancer HospitalHong Qu (Corresponding Author) - Peking University
- Publication details
- Computers in Biology and Medicine, Vol.153, pp.1-13
- Publisher
- Elsevier Ltd
- Date published
- 2023
- DOI
- 10.1016/j.compbiomed.2023.106545
- ISSN
- 1879-0534; 0010-4825
- PMID
- 36646024
- Copyright note
- © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
- Grant note
- 82173218, 31671375, 31871339, 82171720, 81872022/ National Natural Science Foundation of China 5202008/ Beijing Natural Science Foundation 2017YFC1201200/ National Key Research and Development Program of China ZYLX202116/ Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support
- Organisation Unit
- University of the Sunshine Coast, Queensland; GeneCology Research Centre - Legacy; Cancer Research Cluster; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99706996302621
- Output Type
- Journal article
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- Engineering, Biomedical
- Mathematical & Computational Biology
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