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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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Immune-Related Long Non-Coding RNA Signatures for Tongue Squamous Cell Carcinoma.

Daniel Hu1, Diana V Messadi1,2

  • 1School of Dentistry, University of California, Los Angeles, CA 90095-1668, USA.

Current Oncology (Toronto, Ont.)
|May 26, 2023
PubMed
Summary

This study identified six immune-related long non-coding RNAs (lncRNAs) as key prognostic biomarkers for tongue squamous cell carcinoma (TSCC). These lncRNAs form a model that can predict patient survival and guide personalized immunotherapy strategies for TSCC.

Keywords:
immune-related geneslncRNAprognosissurvival analysistongue squamous cell carcinoma

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Area of Science:

  • Oncology
  • Genomics
  • Immunology

Background:

  • Tongue squamous cell carcinoma (TSCC) is a significant head and neck cancer subtype with poor prognosis and high mortality.
  • The underlying molecular mechanisms of tongue tumorigenesis are not fully understood.
  • Identifying reliable prognostic biomarkers is crucial for improving TSCC patient outcomes.

Purpose of the Study:

  • To identify and evaluate immune-related long non-coding RNAs (lncRNAs) as potential prognostic biomarkers for TSCC.
  • To develop a prognostic model based on these identified lncRNAs.
  • To assess the clinical utility of the prognostic model in predicting patient survival and guiding immunotherapy.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) for TSCC lncRNA expression data and the Immunology Database and Analysis Portal (ImmPort) for immune-related genes.
  • Employed Pearson correlation analysis to identify immune-related lncRNAs.
  • Applied univariate and multivariate Cox regression, Kaplan-Meier survival analysis, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) analysis for model development and validation.

Main Results:

  • Identified six immune-related lncRNAs (MIR4713HG, AC104088.1, LINC00534, NAALADL2-AS2, AC083967.1, FNDC1-IT1) with significant prognostic value in TSCC.
  • Developed a six-lncRNA prognostic model demonstrating superior predictive power compared to established clinicopathological factors.
  • Kaplan-Meier analysis revealed significantly better overall survival in the low-risk group versus the high-risk group; ROC analysis showed good predictive accuracy (AUCs ranging from 0.691 to 0.790).

Conclusions:

  • Established a robust prognostic model based on six immune-related lncRNAs for TSCC.
  • The developed model holds clinical significance for predicting patient survival.
  • This prognostic model may facilitate the development of personalized immunotherapy strategies for TSCC patients.