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

• DOI: 10.1371/journal.pone.0298125
• Publication Date: 2024-02-12T18:34:25Z
• Authors: Huanfei Ding; Bowen Hu; Ruixia Guo
• Keywords: Nomogram

The tumor heterogeneity is an important cause of clinical therapy failure and yields distinct prognosis in ovarian cancer (OV). Using the advantages integrated single cell RNA sequencing (scRNA-seq) bulk data to decode remains largely unexplored. Four public datasets were enrolled this study, including E-MTAB-8107, TCGA-OV, GSE63885, GSE26193 cohorts. Random forest algorithm was employed construct a multi-gene prognostic panel further evaluated by receiver operator characteristic (ROC), calibration curve, Cox regression. Subsequently, molecular characteristics deciphered, treatments strategies explored deliver precise therapy. landscape subpopulations functional characteristics, as well dynamic macrophage cells detailly depicted at level, then screened candidate genes. Based on expression genes, stable robust characterized gene associated signature (CCIS) developed, which harbored excellent performance assessment patient stratification. ROC curves, regression analysis elucidated CCIS could serve independent factor for predicting prognosis. Moreover, promising tool nomogram also constructed according stage CCIS. Through comprehensive investigations, patients low-risk group charactered favorable prognosis, elevated genomic variations, higher immune infiltrations, superior antigen presentation. For individualized treatment, inclined better immunotherapy responses. This study dissected afforded signature, conducive facilitating outcomes with OV.