Prognostic Markers in Tyrosine Kinases Specific to Basal-like 2 Subtype of Triple-Negative Breast Cancer.
Praopim LimsakulPongsakorn ChoochuenThawirasm JungrungrueangKrit CharupanitPublished in: International journal of molecular sciences (2024)
Triple-negative breast cancer (TNBC), a heterogeneous and therapeutically challenging subtype, comprises over 50% of patients categorized into basal-like 1 (BL1) and basal-like 2 (BL2) intrinsic molecular subtypes. Despite their shared basal-like classification, BL2 is associated with a poor response to neoadjuvant chemotherapy and reduced relapse-free survival compared to BL1. Here, the study focused on identifying subtype-specific markers for BL2 through transcriptomic analysis of TNBC patients using RNA-seq and clinical integration. Six receptor tyrosine kinase (TK) genes, including EGFR , EPHA4 , EPHB2 , PDGFRA , PDGFRB , and ROR1 , were identified as potential differentiators for BL2. Correlations between TK mRNA expression and TNBC prognosis, particularly EGFR , PDGFRA , and PDGFRB , revealed potential synergistic interactions in pathways related to cell survival and proliferation. Our findings also suggest promising dual markers for predicting disease prognosis. Furthermore, RT-qPCR validation demonstrated that identified BL2-specific TKs were expressed at a higher level in BL2 than in BL1 cell lines, providing insights into unique characteristics. This study advances the understanding of TNBC heterogeneity within the basal-like subtypes, which could lead to novel clinical treatment approaches and the development of targeted therapies.
Keyphrases
- tyrosine kinase
- rna seq
- single cell
- end stage renal disease
- neoadjuvant chemotherapy
- epidermal growth factor receptor
- free survival
- small cell lung cancer
- newly diagnosed
- chronic kidney disease
- ejection fraction
- prognostic factors
- peritoneal dialysis
- machine learning
- squamous cell carcinoma
- genome wide
- locally advanced
- early stage
- transcription factor
- drug induced
- climate change
- genome wide analysis