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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
Published on: July 9, 2021
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Network-Based Coexpression Analysis Identifies Functional and Prognostic Long Noncoding RNAs in Hepatocellular
Jianguo Li1, Jin Zhou1, Shuangshuang Kai1
1Schools of Basic Medicine and Pharmacy, Weifang Medical University, 7166 Baotong West Street, Weifang, 261053 Shandong Province, China.
Biomed Research International
|October 21, 2020
Summary
This study reveals key gene expression changes in hepatocellular carcinoma (HCC), identifying cell cycle and metabolism pathways involved in liver cancer progression. Network analysis pinpointed crucial long noncoding RNAs (lncRNAs) linked to patient outcomes.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Hepatocellular carcinoma (HCC) presents a significant global health challenge due to rising incidence and high mortality rates.
- The intricate pathogenic mechanisms underlying HCC remain incompletely elucidated.
- Understanding gene expression alterations is crucial for deciphering HCC development.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) in HCC.
- To explore the biological functions and pathways dysregulated in HCC using weighted gene coexpression network analysis (WGCNA).
- To uncover potential diagnostic or prognostic biomarkers, including long noncoding RNAs (lncRNAs).
Main Methods:
- Differential gene expression analysis identified upregulated and downregulated genes in HCC.
- WGCNA was employed to construct gene coexpression networks and identify functional modules.
- Gene Ontology (GO) enrichment analysis was performed to determine the biological significance of identified modules.
- Hub lncRNAs associated with HCC prognosis were identified within the WGCNA network.
Main Results:
- 1,631 genes were found to be upregulated, and 1,515 were downregulated in HCC.
- Cell cycle and metabolism-related pathways were significantly dysregulated.
- Five key modules were identified, enriched in processes like metabolism, cell proliferation, and tumor microenvironment components.
- Specific immune cells (cytotoxic cells, macrophages, Th2 cells) were enriched in distinct modules.
- Four hub lncRNAs were identified, correlating with HCC patient prognostic outcomes.
Conclusions:
- Network-based analysis effectively identifies functional modules critical for HCC pathogenesis and progression.
- Hub lncRNAs uncovered through WGCNA hold potential as prognostic biomarkers for HCC.
- This study provides valuable insights into the molecular mechanisms driving HCC and suggests novel therapeutic targets.
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