Development and validation of a macrophage-related prognostic model for overall survival in ovarian cancer via integrated RNA sequencing analysis.

BACKGROUNDThe immunosuppressive tumor microenvironment (TME) poses challenges to effective immunotherapy in ovarian cancer (OC). As a key component of the TME, tumor-associated macrophages (TAMs) are strongly associated with prognosis, yet their precise molecular characteristics and impact on treatment response remain incompletely understood. This study aimed to develop and validate a prognostic model for OC based on TAM-related genes, using integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data, and to explore its implications for the immune landscape and therapeutic sensitivity.

METHODSRNA-seq data and prognostic information were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The R package "Seurat" was used to annotate the cell types and visualize the scRNA-seq data. The differentially expressed genes (DEGs) in the macrophages were identified using the "FindAllMarkers" function and further analyzed using the limma package. The resulting genes, combined with the survival data, underwent univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. Model accuracy was assessed through time-dependent receiver operating characteristic curve analysis, Kaplan-Meier survival analysis in the GEO cohort, independent prognostic evaluation via uni- and multivariate Cox regression, and predictive calibration using a nomogram with calibration curves.

RESULTSIntegrating scRNA-seq and bulk RNA-seq data, this study established a prognostic model comprising 19 macrophage-related genes. Validation confirmed the risk score as an independent prognostic factor for overall survival (OS) in patients with OC. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves for 1-, 3-, and 5-year survival were 0.71, 0.68, and 0.73, respectively. The subsequent immune analysis revealed distinct TMEs between the high- and low-risk groups. Compared to the low-risk group, the high-risk group exhibited higher immune infiltration, decreased M1 macrophage infiltration, elevated M2 macrophage infiltration, and decreased sensitivity to immunotherapy but increased sensitivity to anti-angiogenic drugs.

CONCLUSIONSThe study analyzed the DEGs related to macrophages in OC and constructed a prognostic model. Moreover, it identified the risk score as a prognostic factor in OC, which shows potential clinical relevance in predicting patient sensitivity to immunotherapy.