The -216G/T polymorphism in the EGFR gene: A review focusing on Non-Small lung cancer

Jasmina Obradovic1, Vladimir Jurisic2

  • 1Department of Sciences, Institute for Information Technologies Kragujevac, University of Kragujevac, Kragujevac, Serbia. jasmina.obradovic@uni.kg.ac.rs.

Molecular Biology Reports
|December 10, 2025
PubMed

Insights

The EGFR -216G/T polymorphism may predict non-small-cell lung cancer (NSCLC) risk and EGFR mutations. Further research is needed to validate its role in precision oncology and diverse health conditions.

Area of Science:

  • Genetics and Genomics
  • Oncology
  • Molecular Biology

Background:

  • Epidermal growth factor receptor (EGFR) is crucial for cell growth and a target in non-small-cell lung cancer (NSCLC).
  • Germline polymorphisms in EGFR, particularly the -216G/T SNP (rs712829) in the promoter region, are gaining attention as predictive biomarkers.
  • Understanding these germline variants is vital for personalized cancer treatment strategies.

Purpose of the Study:

  • To provide a chronological overview of the EGFR -216G/T SNP, including its discovery, functional effects, and clinical relevance.
  • To explore the associations of this SNP with NSCLC risk, progression, and treatment outcomes.
  • To review the SNP's role in other cancers and non-oncological conditions.

Main Methods:

  • A narrative review of studies published between 2005 and 2025.
  • Literature searches across scientific databases and bibliographies of key publications.
  • Analysis of functional, clinical, and epidemiological data related to the EGFR -216G/T SNP.

Main Results:

  • The EGFR -216G/T SNP is linked to increased EGFR promoter activity, pleural metastasis, and susceptibility to EGFR mutations.
  • This polymorphism is associated with NSCLC risk and shows varying allele frequencies across different populations.
  • Conflicting data exist regarding its impact on survival and toxicity, necessitating further investigation.

Conclusions:

  • The EGFR -216G/T polymorphism shows promise as a biomarker for NSCLC susceptibility.
  • Its relevance extends to other cancers (colorectal, glioma, breast) and conditions like cardiovascular disease and COVID-19.
  • Population-specific studies integrating multi-omics and machine learning are crucial for its application in precision oncology.