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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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A prognostic signature of Glutathione metabolism-associated long non-coding RNAs for lung adenocarcinoma with immune

Junxi Hu1,2, Shuyu Tian2,3, Qingwen Liu2,3

  • 1Clinical Medical College, Yangzhou University, Yangzhou, China.

Frontiers in Immunology
|February 25, 2025
PubMed
Summary

This study identifies glutathione (GSH) metabolism-related long non-coding RNAs (lncRNAs) as key prognostic factors for lung adenocarcinoma (LUAD). A new model predicts patient survival and guides personalized immunotherapy strategies for improved outcomes.

Keywords:
Glutathione metabolismimmune microenvironmentlncRNAlung adenocarcinomaprognostic prediction

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Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Glutathione (GSH) metabolism is crucial for tumor redox balance and drug resistance in lung adenocarcinoma (LUAD).
  • Long non-coding RNAs (lncRNAs) play a significant role in the progression of LUAD.
  • Understanding the interplay between GSH metabolism and lncRNAs is vital for predicting LUAD outcomes.

Purpose of the Study:

  • To develop a prognostic model utilizing GSH metabolism-related lncRNAs for predicting LUAD patient survival.
  • To assess the impact of these lncRNAs on tumor immunity and drug sensitivity.
  • To identify potential therapeutic targets within LUAD based on lncRNA expression.

Main Methods:

  • Analysis of The Cancer Genome Atlas (TCGA) survival data to identify GSH metabolism-related lncRNAs.
  • Construction and validation of a prognostic model using Cox and LASSO regression, Kaplan-Meier analysis, ROC curves, and PCA.
  • Functional enrichment analysis to investigate immune infiltration and drug sensitivity.
  • Quantitative PCR and in vitro experiments to validate the role of specific lncRNAs (e.g., lnc-AL162632.3).

Main Results:

  • A prognostic model comprising nine lncRNAs (AL162632.3, AL360270.1, LINC00707, DEPDC1-AS1, GSEC, LINC01711, AL078590.2, AC026355.2, AL096701.4) was developed.
  • The model accurately predicted patient survival and, when combined with clinical factors, proved clinically useful.
  • Stratification by model scores revealed significant differences in immune cell composition, functionality, mutations, and drug sensitivity (Cisplatin, Docetaxel, Paclitaxel).
  • lnc-AL162632.3 was upregulated and its suppression inhibited LUAD cell growth, migration, and invasion.

Conclusions:

  • GSH metabolism-related lncRNAs are significant prognostic indicators in LUAD.
  • The developed model enables effective risk stratification of LUAD patients.
  • High-risk patients exhibit increased tumor mutation burden and stemness, suggesting potential for personalized immunotherapy to enhance survival.