Integration of scRNA-Seq and bulk RNA-Seq to Establish a Macrophage-related Prognostic Model in Ovarian Cancer

Background The immunosuppressive tumor microenvironment (TME) poses challenges to effective immunotherapy in ovarian cancer (OC). Tumor-associated macrophages play a crucial role in the TME and are closely linked to OC prognosis. While many studies have used bulk RNA-seq for prognostic biomarker exploration, its limitation is in discerning gene expression differences between individual cells. Methods This study integrates single-cell RNA sequencing (scRNA-seq) with bulk RNA-seq data to accurately investigate the relationship between macrophage molecular characteristics and prognosis. RNA-seq data and prognostic information were obtained from the GEO and TCGA datasets. Using the R package "Seurat," this study annotates cell types and visualizes single-cell data. Differentially expressed genes in macrophages are identified using the FindAllMarkers function and further analyzed with the limma package. The resulting genes, combined with survival data, undergo single-factor COX regression and LASSO regression to construct a prognosis model. Results Integrating scRNA-seq and bulk RNA-seq data, this study established a prognosis model comprising 19 macrophage-related genes. Validation confirms the risk score as an independent prognostic factor for overall survival (OS) in patients with OC. The area under the ROC curve (AUC) for 1-year, 3-year, and 5-year survival periods were 0.71, 0.68, and 0.73, respectively. Subsequent immune analysis revealed distinct TMEs between high- and low-risk groups. The high-risk group shows distinct TMEs. The high-risk group exhibited higher immune infiltration, increased M1 macrophage infiltration, elevated M2 macrophage infiltration, and reduced sensitivity to immunotherapy but enhanced sensitivity to anti-angiogenic drugs compared to that in the low-risk group. Conclusion The study analyzed differentially expressed genes related to macrophages in OC and constructed a prognosis model. Moreover, it revealed the risk score as a prognostic factor in OC, with implications for patient sensitivity to immunotherapy.