Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer.

ABSTRACT: The tumor heterogeneity is an important cause of clinical therapy failure and yields distinct prognosis in ovarian cancer (OV). Using the advantages of integrated single cell RNA sequencing (scRNA-seq) and bulk data to decode tumor heterogeneity remains largely unexplored. Four public datasets were enrolled in this study, including E-MTAB-8107, TCGA-OV, GSE63885, and GSE26193 cohorts. Random forest algorithm was employed to construct a multi-gene prognostic panel and further evaluated by receiver operator characteristic (ROC), calibration curve, and Cox regression. Subsequently, molecular characteristics were deciphered, and treatments strategies were explored to deliver precise therapy. The landscape of cell subpopulations and functional characteristics, as well as the dynamic of macropha

SUBMITTER: Ding H 

PROVIDER: S-EPMC10861092 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature