Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.

ABSTRACT: Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving changes in the interactions between thousands of genes. In addition to this staggering complexity, these interactions can vary among patients with the same diagnosis as well as within the same tumor. We hypothesized that reliable biomarkers that can be measured with routine methods could be identified by exploiting three facts: (i) the same tumor can have multiple grades of malignant transformation; (ii) these grades and their molecular changes can be characterized using spatial transcriptomics; and (iii) these changes can be integrated into models of malign

SUBMITTER: Smelik M 

PROVIDER: S-EPMC12214874 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature