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Construction and validation of a prognostic model based on oxidative stress-related genes in non-small cell lung cancer (NSCLC): predicting patient outcomes and therapy responses

  • 0Department of Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital Affiliated to Shandong First Medical University, Shandong Academy of Medical Science, Jinan, China.
Translational Lung Cancer Research +

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December 13, 2024

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December 13, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a prognostic model for non-small cell lung cancer (NSCLC) using oxidative stress (OS)-related genes. The model accurately predicts patient outcomes and guides personalized immunotherapy and chemotherapy decisions.

Area Of Science

  • Oncology
  • Genomics
  • Biomarker Discovery

Background

  • Non-small cell lung cancer (NSCLC) poses a significant health challenge.
  • The prognostic implications of oxidative stress (OS)-related genes in NSCLC are not well-understood.
  • This research investigates the prognostic value of OS-genes in NSCLC using TCGA and GEO datasets.

Purpose Of The Study

  • To explore the prognostic significance of OS-genes in NSCLC.
  • To develop and validate a predictive risk-score model for NSCLC patient prognosis.
  • To assess the model's utility in guiding treatment decisions for immunotherapy and chemotherapy.

Main Methods

  • Utilized NSCLC patient expression data and clinical information from TCGA and GEO.
  • Identified 74 differentially expressed OS-related genes (DEGs).
  • Employed univariate Cox and LASSO regression for biomarker identification and constructed/validated a risk-score model using ROC curves and Cox regression.

Main Results

  • Identified LDHA, PTPRN, and TRPA1 as significant prognostic biomarkers for NSCLC.
  • The risk-score model demonstrated good predictive accuracy across TCGA and GSE72094 datasets (AUCs ranging from 0.634 to 0.662).
  • Developed a nomogram, found the signature predicted immunotherapy response, and linked high-risk groups to an immunosuppressive microenvironment, increased TMB, and specific gene mutations; low-risk patients responded better to chemotherapy.

Conclusions

  • Successfully developed a robust prognostic model for NSCLC based on OS-genes.
  • The findings underscore the critical prognostic value of OS-genes, offering potential to refine NSCLC treatment strategies.
  • The model facilitates personalized therapy by enabling tailored decisions for immunotherapy and chemotherapy, aiming to optimize patient management and outcomes.

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