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CN 34-1304/RISSN 1674-3679

Volume 27 Issue 2
Feb.  2023
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ZHAO Xin, WEI Yi-fang, LI Ling-mei, SHI Guo-jing, FANG Rui-ling, CAO Hong-yan. Multi-omics data integration molecular subtyping of lower-grade gliomas based on MOVICS clustering ensemble[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(2): 216-223. doi: 10.16462/j.cnki.zhjbkz.2023.02.015
Citation: ZHAO Xin, WEI Yi-fang, LI Ling-mei, SHI Guo-jing, FANG Rui-ling, CAO Hong-yan. Multi-omics data integration molecular subtyping of lower-grade gliomas based on MOVICS clustering ensemble[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(2): 216-223. doi: 10.16462/j.cnki.zhjbkz.2023.02.015

Multi-omics data integration molecular subtyping of lower-grade gliomas based on MOVICS clustering ensemble

doi: 10.16462/j.cnki.zhjbkz.2023.02.015
Funds:

National Natural Science Foundation of China 71403156

Applied Basic Research Program of Shanxi Province 201901D111204

Shanxi Medical University Doctoral Initiation Fund Project BS201722

More Information
  • Corresponding author: CAO Hong-yan, E-mail: caohy@sxmu.edu.cn
  • Received Date: 2022-04-05
  • Rev Recd Date: 2022-07-03
  • Available Online: 2023-02-20
  • Publish Date: 2023-02-10
  •   Objective  To investigate the application of multi-omics integration and visualization in cancer subtyping (MOVICS) clustering ensemble method in multi-omics data integration for lower-grade gliomas (LGG) subtyping, and high risk LGG group identification, and further screen potential biomarkers and important pathways.  Methods  The MOVICS method was used to integrate the subtyping results of 10 integration methods based on the LGG multi-omics data, to obtain a robust molecular subtyping of LGG patients. Cox regression analysis was carried out to evaluate the mortality risk of different patients. Differentially expressed mRNA (DEmRNAs), miRNA (DEmiRNAs) and differential methylation genes (DMGs) analyses were conducted between different subtypes. Overlapping genes among the three omics data types were used for GO term and KEGG pathway enrichment analysis. Additional analysis was conducted to identify hub genes and further evaluate their influence on patients survival outcome. Finally, pathway activity analysis between different subtypes was performed.  Results  LGG patients were divided into three subtypes. Patients in subtype 3 were 2.794 times more likely to die than patients in subtype 1. A total of 1 569 DEmRNAs, 140 DEmiRNAs and 337 DMGs were screened, the combined analysis genes yielded 119 genes which are regulated by mRNA, miRNA and DNA methylation and enriched 26 GO items and 7 KEGG pathways with statistical differences. Survival analysis showed that DNAJB14 and MTUS1 were significantly associated with survival outcome. Pathway activity analysis indicated that activities of Androgen, EGFR, Trail and VEGF showed significant difference between subtypes.  Conclusions  MOVICS classified LGG patients into three subtypes with distinct survival outcomes. Potential biomarkers, hub genes and important pathways were identified, which provided novel insights into the underlying differences between subtypes in molecular levels. These molecular signatures could offer new opportunities for individualized treatment and prevention of LGG patients.
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