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INTERNATIONAL CONGRESS OF

CancerGenetics

• Identification of Hub Genes Associated with Tamoxifen Treatment in Recurrent Breast Cancer: An Integrated Bioinformatics Analysis

• Sara Rafiee Komachali,¹ Alireza Raghibi,² Seyede Mahsa Mousavikia,³ Amirhossein Mohajeri Khorasani,^4,*
  1. 1 Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 2 Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 3 Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
  2. 1 Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 2 Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 3 Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
  3. 1 Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 2 Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 3 Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
  4. 1 Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 2 Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. 3 Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
  
  
  
• Introduction: Breast cancer is a highly heterogeneous malignancy and a leading cause of cancer-related morbidity and mortality worldwide. Estrogen receptor-positive (ER-positive) breast cancer represents a major molecular subtype for which tamoxifen remains an established endocrine therapy. However, disease recurrence and variable treatment responses remain major clinical challenges. Characterizing molecular alterations associated with tamoxifen treatment may provide insights into the biology of recurrent breast cancer and facilitate the identification of potential prognostic biomarkers. Therefore, this study aimed to identify hub genes associated with tamoxifen-related molecular alterations and evaluate their prognostic significance in recurrent breast cancer using an integrated bioinformatics approach.
• Methods: The gene expression dataset GSE263089 was retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between tamoxifen-treated and untreated groups were identified using GEO2R, applying thresholds of |log₂FC|≥2 and Benjamini–Hochberg-adjusted P<0.05. The identified DEGs were used to construct a protein–protein interaction (PPI) network using the STRING database, and the 15 hub genes with the highest node degree were selected. Their prognostic significance was subsequently assessed using Kaplan–Meier survival analysis.
• Results: Integrated bioinformatics analysis identified WNT11 and GNG4 as the only two candidate prognostic genes among the 15 PPI hub genes associated with differential expression between tamoxifen-treated and untreated recurrent breast cancer samples. WNT11 was significantly upregulated following tamoxifen treatment (log₂FC=2.28, adjustedP=2.66×10⁻³), whereas GNG4 was significantly downregulated (log₂FC=−2.49, adjustedP=1.45×10⁻²). In the PPI network, WNT11 and GNG4 exhibited node-degree scores of 5 and 7, respectively. Survival analysis demonstrated that WNT11 expression was significantly associated with overall survival (log-rankP=0.023), while GNG4 expression was significantly associated with disease-free survival (log-rank =0.046).
• Conclusion: WNT11 and GNG4 were identified as prognostically relevant hub genes associated with tamoxifen-related gene expression alterations in recurrent breast cancer. Their significant associations with OS and DFS, respectively, suggest that these genes may have clinical relevance in the context of endocrine therapy response and disease outcome.
• Keywords: Breast cancer; Tamoxifen resistance; WNT11; GNG4; Bioinformatics