Abstract:
Breast Cancer, particularly Triple-Negative Breast Cancer (TNBC), remains a formidable
challenge due to its aggressive nature, lack of targeted therapies, and poor prognosis. This
research addresses the critical need for more accurate prognostic markers by integrating spatial
transcriptomics with artificial intelligence (AI) to explore the spatial heterogeneity of gene
expression within TNBC tumors. Spatial transcriptomics offers a high-resolution view of the
tumor microenvironment, preserving the spatial context of gene expression, while single-cell
RNA sequencing (scRNA-seq) provides detailed insights into the cellular composition of the
tumors. By identifying and analyzing differentially expressed genes (DEGs) across spatial and
single-cell datasets, this study aims to uncover key biomarkers that could serve as therapeutic
targets and improve patient outcomes. Machine learning models, including XGBoost and
Support Vector Machines (SVM), were employed to develop predictive models for cancer
classification, disease staging, and prognosis. These models demonstrated high accuracy,
enhancing the understanding of TNBC's complex molecular landscape and supporting the
development of personalized treatment strategies. The findings highlight the potential of
integrating spatial transcriptomics with AI to revolutionize cancer research, offering new
avenues for precision medicine in TNBC.
