RESUMO
The tyrosine-kinase receptor that is specified by the KIT locus is demarcated by KITLG. This multifaceted factor is instrumental during in-utero germ and neural cell maturation and hematopoiesis, ostensibly reflecting its role in facilitating cell migration. Concurrently, KITLG is prone to a mutation in germ cell tumors, entailing a presumed connection to tumorigenesis. Despite this, the intricacies of its function in breast cancer and the relevant mechanisms remain elusive. Multiple independent databases depict a consistently low expression of KITLG within tissues affected by triple-negative breast cancers (TNBC), a trend strongly coupled with reduced survival rates. Interestingly, non-triple-negative breast cancers exhibit a markedly high expression of KITLG compared to the norm. An initial analysis of the GEO database speculates that KITLG may serve as an oncogene suppressor in TNBC, hinting at varied roles for KITLG isoforms within this disease context. In conclusion, our preliminary analysis offers valuable insights into the role and expression pattern of KITLG in TNBC. We provide evidence supporting its consideration as a promising new prognostic marker, thereby potentially enriching therapeutic strategies for TNBC. Indeed, given the limited advances in molecularly targeted therapy for TNBC, a significant need exists for a more precise therapeutic approach and a comprehensive understanding of its inherent mechanisms of action.
RESUMO
Breast cancer (BRCA) is a common malignancy worldwide that is associated with a high mortality rate. Despite recent improvements in diagnosis and treatment, there is an urgent need to investigate the processes underlying cancer progression and identify novel prognostic indicators. Anoikis, which plays a role in the development of human malignant tumors, has been gaining increasing interest from researchers. However, the potential role of anoikis-related genes (ANRGs) in the advancement of BRCA remains unknown. In this study, we aimed to assess the predictive value of ANRGs in BRCA, construct a prognostic model based on ANRGs, and explore the tumor microenvironment in different prognostic score groups. This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect clinical information and RNA sequencing data from patients with BRCA. Information on ANRGs was gathered from GeneCards and Harmonizome portals. A risk score model based on ANRGs was created using least absolute shrinkage and selection operator Cox (LASSO) regression analysis. Additionally, the study explored the tumor microenvironment and enriched pathways in different risk groups. Finally, a novel ANRG-based nomogram is developed. A total of 142 differentially expressed genes associated with survival were identified, of which 5 genes were selected to create the ANRG signature. The risk score based on this signature proved to be an independent prognostic factor. Further analysis revealed that different risk subgroups exhibited variations in the tumor microenvironment and drug sensitivities. Subsequently, a nomogram was developed using risk scores and clinicopathological factors. The decision curve analysis results suggest that patients with BRCA might derive clinical treatment benefits from utilizing this prognostic model. Based on the results of this study, the ANRG signature and nomograph established can be used for clinical decision-making in patients with BRCA.