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Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer

  • 0College of Basic Medicine, Zhengzhou University, Henan, China.
Technology and Health Care : Official Journal of the European Society for Engineering and Medicine +

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September 27, 2024

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September 27, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new gene signature linked to Endoplasmic Reticulum (ER) stress can predict non-small cell lung cancer (NSCLC) patient outcomes and treatment response. This ER stress gene profile aids in risk assessment for improved NSCLC prognosis.

Area Of Science

  • Oncology
  • Molecular Biology
  • Genomics

Background

  • Lung cancer, particularly Non-Small Cell Lung Cancer (NSCLC), presents a growing global health challenge with increasing incidence.
  • Endoplasmic Reticulum (ER) stress is increasingly recognized for its role in tumor malignancy and treatment resistance in NSCLC, though its prognostic value is not fully established.

Purpose Of The Study

  • To develop a predictive gene profile associated with ER stress for non-small cell lung cancer (NSCLC).
  • To enable risk stratification and assessment for NSCLC patients based on ER stress markers.

Main Methods

  • Utilized The Cancer Genome Atlas (TCGA) database with clinical and RNA data from 1014 NSCLC patients.
  • Employed Kaplan-Meier analysis, Cox regression (including LASSO), and Pearson correlation to identify ER stress-associated prognostic genes.
  • Developed and validated a nomogram-based risk score model to classify patients into high- and low-risk groups.
  • Investigated the tumor immune microenvironment using CIBERSORT and ssGSEA.
  • Identified potential therapeutic targets using the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Main Results

  • A four-gene signature (FABP5, C5AR1, CTSL, LTA4H) was identified and formed the basis of the risk model.
  • Patients in the high-risk group exhibited significantly lower Overall Survival (OS) (P<0.05).
  • The developed risk model demonstrated superior predictive accuracy compared to traditional clinical factors.
  • Identified specific drug sensitivities for patients within the high-risk group.

Conclusions

  • A novel gene signature linked to ER stress has been established for Non-Small Cell Lung Cancer (NSCLC).
  • This signature can effectively predict patient prognosis and potential response to therapy.
  • The findings offer a new tool for personalized risk assessment and treatment strategies in NSCLC.

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