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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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Machine learning-based identification of telomere-related gene signatures for prognosis and immunotherapy response in

Zhengmei Lu1, Xiaowei Chai2, Shibo Li3

  • 1Department of Infectious Diseases, Wenzhou Medical University Affiliated, Zhoushan Hospital, Zhoushan, 316000, China.

Molecular Cytogenetics
|March 19, 2025
PubMed
Summary

This study identifies six telomere-related genes (TRGs) that predict hepatocellular carcinoma (HCC) patient prognosis and response to immunotherapy. The developed risk score model offers new strategies for cancer therapy.

Keywords:
Hepatocellular carcinomaImmunotherapyPrognosisTelomere-related genes

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Area of Science:

  • Oncology
  • Genetics
  • Immunotherapy

Background:

  • Telomeres are crucial for chromosomal stability and cancer cell proliferation.
  • Identifying telomere-related genes (TRGs) in hepatocellular carcinoma (HCC) is key for developing predictive biomarkers.

Purpose of the Study:

  • To detect TRGs in HCC and develop a novel predictive marker for prognosis and immunotherapy response.
  • To establish a risk score (RS) model based on TRGs for HCC patients.

Main Methods:

  • Utilized TCGA and GEO databases for HCC clinical and gene expression data.
  • Identified TRGs using the TelNet database and intersected with differentially expressed genes.
  • Developed and validated a prognostic risk model using LASSO regression and Cox analysis.

Main Results:

  • Identified six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, DNAJC6) forming the RS model.
  • The high-risk group showed poorer survival, increased M0 Macrophages and Tregs infiltration.
  • The RS model correlated with immune checkpoint genes and predicted immunotherapy outcomes.

Conclusions:

  • The developed RS model accurately predicts HCC prognosis and immune response.
  • This TRG-based model offers innovative strategies for HCC treatment and immunotherapy.