• info@icbcongress.com
• English
• Persian

• Login

Create Free Account

INTERNATIONAL CONGRESS OF

CancerGenetics

• Utilizing WGCNA to uncover E2F8 and SPC24 as promising therapeutic targets in breast cancer

• Amir Hossein Kiani Darabi,¹ Seyed Hossein Khoshraftar ,² Saba Hadi ,³ Maryam Rezazadeh,^4,*
  1. Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
  2. Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
  3. Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
  4. Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
  
  
  
• Introduction: Breast cancer is one of the leading causes of cancer-related mortality among women worldwide, affecting nearly one in eight women in industrialized countries. In recent years, RNA sequencing (RNA-seq) has become a powerful and versatile tool for studying gene expression profiles , enabling the identification of key molecular mechanisms underlying cancer progression.
• Methods: In this study, the RNA-seq dataset identified as BioProject: PRJNA855324 was retrieved from the SRA database and analyzed using Linux Ubuntu v22.04.4 and RStudio v4.3. The “EdgeR” package was employed to process the data and identify differentially expressed genes (DEGs) between breast cancer samples. A total of 802 genes were selected for constructing the co-expression network, based on the criteria of an absolute log fold change (logFC) greater than 1 and an adjusted p-value below 0.05. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to construct the gene co-expression network. The soft-thresholding power was set to β = 16. The adjacency matrix was then transformed into a topological overlap matrix (TOM). Using average linkage hierarchical clustering and a minimum module size of 15, genes with high correlation were grouped into distinct modules based on TOM-based dissimilarity. The relationships between module eigengenes and clinical traits were then assessed. Finally, Gene Ontology (GO) and pathway enrichment analyses for clinically significant modules were carried out using R packages including “clusterProfiler”, “AnnotationDbi”, and “enrichplot”.
• Results: Using average linkage hierarchical clustering, two gene modules were identified that exhibited a significant association with the Luminal B breast cancer subtype (correlation coefficient = 0.41, p-value = 5.6e−13). These modules were prioritized for subsequent in-depth analyses. Among the identified genes, E2F8 and SPC24 were recognized as central (hub) genes within the blue module, exhibiting high values of degree distribution and bottleneck scores, respectively. Functional enrichment analyses based on Gene Ontology and pathway data indicated that these genes play key regulatory roles in essential biological processes such as the cell cycle and mitotic division.
• Conclusion: Targeting the differentially expressed genes (DEGs) identified in this study may offer a promising therapeutic approach for improving the treatment outcomes of the Luminal B subtype of breast cancer. Further experimental validation is warranted to explore their potential in improving treatment strategies.
• Keywords: Breast cancer, WGCNA , RNA-seq , therapeutic target