Construction of an endoplasmic reticulum stress and cuproptosis -related lncRNAs signature in chemosensitivity in hepatocellular carcinoma by comprehensive bioinformatics analysis

  • 0Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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Summary

This summary is machine-generated.

This study identifies a four-lncRNA signature linked to endoplasmic reticulum stress and cuproptosis in hepatocellular carcinoma (HCC). This signature accurately predicts patient prognosis and clinical outcomes in HCC.

Area Of Science

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background

  • Hepatocellular carcinoma (HCC) prognosis is significantly impacted by endoplasmic reticulum stress (ERS) and cuproptosis.
  • Identifying reliable prognostic markers is crucial for improving HCC patient outcomes.

Purpose Of The Study

  • To develop a prognostic signature for HCC based on ERS and cuproptosis-related long non-coding RNAs (lncRNAs).
  • To evaluate the predictive value of this signature for patient survival and clinical classification.

Main Methods

  • Utilized TCGA database for clinical and transcriptomic data of HCC patients.
  • Applied correlation tests, Cox regression, and LASSO for signature development.
  • Validated prognostic value using Kaplan-Meier, ROC curves, and nomograms.
  • Assessed immune and mutational status (TMB, ESTIMATE) and performed in vitro experiments (qRT-PCR, CCK-8).

Main Results

  • A four-lncRNA signature (NRAV, SNHG3, LINC00839, AC004687.1) was identified.
  • High-risk groups showed poorer prognosis, with the signature acting as an independent prognostic indicator.
  • The signature correlated with clinicopathological features and predicted immune status and TMB.
  • Enrichment analysis indicated involvement of biological membrane pathways.

Conclusions

  • The developed ERS and cuproptosis-related lncRNA signature is a robust predictor of HCC survival.
  • This signature can aid in clinical classification and predicting treatment efficacy for HCC patients.

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