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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Related Experiment Video

Updated: Aug 29, 2025

Mass Cytometry Analysis of Systemic and Local Immune Responses in Hepatocellular Carcinoma
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Mutational signatures representative transcriptomic perturbations in hepatocellular carcinoma.

Qiong Wu1,2, Lingyi Wang1, Stephen Kwok-Wing Tsui1

  • 1School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.

Frontiers in Genetics
|September 9, 2022
PubMed
Summary

Genomic instability in hepatocellular carcinoma (HCC) drives heterogeneity. This study links specific mutations to RNA changes, identifying prognostic markers and potential therapies for personalized HCC treatment.

Keywords:
ceRNA networklncRNAmiRNAmultiomicsmutational signature

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Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • Hepatocellular carcinoma (HCC) presents increasing incidence and poor prognosis, largely due to genomic instability and resulting tumor heterogeneity.
  • Understanding the molecular mechanisms and pathways affected by mutations is crucial for improving patient outcomes and developing targeted therapies.

Purpose of the Study:

  • To investigate the impact of specific mutational signatures on coding and non-coding RNAs in HCC using multiomics data.
  • To identify RNA-based prognostic markers and potential therapeutic targets for mutational signature-specific HCC.
  • To develop a projection approach linking genomic mutations to transcriptomic alterations for simplified therapeutic strategies.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) genomic and transcriptomic data for Hepatocellular Carcinoma (HCC).
  • Performed integrative analysis to identify differentially expressed coding RNAs, non-coding RNAs, and microRNAs (miRNAs) associated with specific mutational signatures.
  • Investigated the competing endogenous RNA (ceRNA) regulatory network and assessed the prognostic value of identified RNAs.

Main Results:

  • Specific mutational signatures in HCC correlate with distinct RNA expression profiles, including lipid metabolism-associated upregulated coding RNAs and axonogenesis-related downregulated coding RNAs.
  • Differentially expressed miRNAs are enriched in cancer-related signaling pathways, and some RNAs serve as significant prognostic factors for patient survival.
  • Deregulation of the ceRNA network was identified, suggesting miRNA-targeted therapies for specific HCC subtypes.

Conclusions:

  • Integrative multiomics analysis reveals critical RNA alterations driven by HCC mutational signatures.
  • Identified novel prognostic markers and potential therapeutic strategies, including miRNA-based interventions, for precision medicine in HCC.
  • The proposed projection approach simplifies the link between genomic mutations and transcriptomic changes, aiding in the identification of key genes and pathways for HCC treatment.