Gene Expression Profiling of Fibroepithelial Lesions of the Breast.
Xiaomo LiEric VailHoracio MalufManita ChaumMatthew LeongJoseph LownikMingtian CheArmando GiulianoDuoyao CaoFarnaz DadmaneshPublished in: International journal of molecular sciences (2023)
Fibroepithelial lesions of the breast (FELs) are a heterogeneous group of neoplasms exhibiting a histologic spectrum ranging from fibroadenomas (FAs) to malignant phyllodes tumors (PTs). Despite published histologic criteria for their classification, it is common for such lesions to exhibit overlapping features, leading to subjective interpretation and interobserver disagreements in histologic diagnosis. Therefore, there is a need for a more objective diagnostic modality to aid in the accurate classification of these lesions and to guide appropriate clinical management. In this study, the expression of 750 tumor-related genes was measured in a cohort of 34 FELs (5 FAs, 9 cellular FAs, 9 benign PTs, 7 borderline PTs, and 4 malignant PTs). Differentially expressed gene analysis, gene set analysis, pathway analysis, and cell type analysis were performed. Genes involved in matrix remodeling and metastasis (e.g., MMP9 , SPP1 , COL11A1 ), angiogenesis ( VEGFA , ITGAV , NFIL3 , FDFR1 , CCND2 ), hypoxia ( ENO1 , HK1 , CYBB , HK2 ), metabolic stress (e.g., UBE2C , CDKN2A , FBP1 ), cell proliferation (e.g., CENPF , CCNB1 ), and the PI3K-Akt pathway (e.g., ITGB3 , NRAS ) were highly expressed in malignant PTs and less expressed in borderline PTs, benign PTs, cellular FAs, and FAs. The overall gene expression profiles of benign PTs, cellular FAs, and FAs were very similar. Although a slight difference was observed between borderline and benign PTs, a higher degree of difference was observed between borderline and malignant PTs. Additionally, the macrophage cell abundance scores and CCL5 were significantly higher in malignant PTs compared with all other groups. Our results suggest that the gene-expression-profiling-based approach could lead to further stratification of FELs and may provide clinically useful biological and pathophysiological information to improve the existing histologic diagnostic algorithm.
Keyphrases
- gene expression
- cell proliferation
- machine learning
- genome wide
- deep learning
- single cell
- dna methylation
- healthcare
- endothelial cells
- systematic review
- stem cells
- physical activity
- signaling pathway
- wastewater treatment
- social media
- drug induced
- liver injury
- cell cycle
- stress induced
- cell therapy
- pi k akt
- antibiotic resistance genes