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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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Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
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Construction and validation of a prognostic model of lncRNAs associated with RNA methylation in lung adenocarcinoma.

Liren Zhang1, Lei Yang2, Xiaobo Chen3

  • 1Department of Thoracic Surgery, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.

Translational Cancer Research
|March 19, 2025
PubMed
Summary

This study developed a prognostic model using six long non-coding RNAs (lncRNAs) to predict outcomes in lung adenocarcinoma (LUAD). The model shows potential for evaluating LUAD prognosis and guiding treatment strategies.

Keywords:
Long non-coding RNAs (lncRNAs)RNA methylation regulatorsimmunotherapy responseprognostic model

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Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Lung adenocarcinoma (LUAD) is a major global cancer burden.
  • Long non-coding RNAs (lncRNAs) are implicated in tumor development.
  • RNA methylation modifications play a role in cancer progression.

Purpose of the Study:

  • To investigate lncRNAs involved in RNA methylation modification in LUAD.
  • To assess the prognostic value of these RNA methylation-associated lncRNAs (RMlncRNAs) in LUAD patients.

Main Methods:

  • Utilized The Cancer Genome Atlas (TCGA) dataset for RNA sequencing and clinical data.
  • Identified RMlncRNAs through correlation analysis with RNA methylation regulators.
  • Constructed a prognostic model using Cox regression and LASSO analysis, validated with ROC curves.
  • Analyzed tumor mutational burden (TMB), microsatellite instability, and drug sensitivity (IC50).

Main Results:

  • Identified 18 RMlncRNAs associated with LUAD prognosis.
  • Developed a six-lncRNA prognostic model (NFYC-AS1, OGFRP1, MIR4435-2HG, TDRKH-AS1, DANCR, TMPO-AS1) with high predictive accuracy.
  • The model correlated with TMB and suggested immune resistance in high-risk groups.
  • Found differential drug sensitivity (IC50) between risk groups for 11 drugs.

Conclusions:

  • A novel prognostic model based on six RMlncRNAs was successfully constructed for LUAD.
  • This bioinformatics-derived model offers potential for improved LUAD evaluation and treatment.
  • The findings highlight the role of RMlncRNAs in LUAD pathogenesis and therapeutic response.