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Proteomics-based Model for Predicting the Risk of Brain Metastasis in Patients with Resected Lung Adenocarcinoma carrying the EGFR Mutation

  • 0Department of Clinical Laboratory, the First Affiliated Hospital of Guangzhou Medical University, National Center for Respiratory Medicine, Guangzhou 510120, China.
International Journal of Medical Sciences +

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March 11, 2024

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March 11, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed a protein-based model to predict brain metastases in lung adenocarcinoma patients with EGFR mutations. This discovery aids in early risk identification for improved patient outcomes.

Area Of Science

  • Oncology
  • Proteomics
  • Genetics

Background

  • Epidermal growth factor receptor (EGFR) mutations are prevalent in Chinese lung adenocarcinoma (LUAD) patients.
  • Brain metastases (BMs) are a frequent and serious complication in LUAD, leading to poor prognosis.
  • Predicting BMs in EGFR-mutant LUAD is crucial for timely intervention.

Purpose Of The Study

  • To develop a proteinomics-level predictive model for brain metastases in EGFR-mutant LUAD.
  • To identify potential protein biomarkers associated with the risk of developing BMs.
  • To analyze pathways involved in the development of BMs.

Main Methods

  • Retrospective study of LUAD patients with EGFR mutations and BMs.
  • Tissue proteomic analysis using liquid chromatography-mass spectrometry (LC-MS/MS).
  • Development of a random forest algorithm model using identified protein markers.

Main Results

  • A three-protein combination showed discrimination between distal metastasis and local recurrence.
  • Gene Ontology analysis revealed lipid metabolism and cell cycle pathways involved in BMs.
  • A random forest model with eight proteins achieved an AUC of 0.9401 for predicting BMs.

Conclusions

  • A novel, accurate protein-based predictive model for postoperative BMs in EGFR-mutant LUAD was developed.
  • The model demonstrates significant potential for clinical application in risk stratification.
  • Further research can refine the model and explore therapeutic strategies targeting identified pathways.

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