Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics.

backgroundOvarian cancer (OC) is a formidable gynecological tumor marked with the highest mortality rate. The lack of effective biomarkers and treatment drugs places a substantial proportion of patients with OC at significant risk of mortality, primarily due to metastasis. Glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism are closely intertwined with the occurrence and progression of OC. Thus, it is of utmost significance to identify potent prognostic biomarkers and delve into the exploration of novel therapeutic drugs and targets, in pursuit of advancing the treatment of OC.

methodsSingle-cell RNA sequencing (scRNA-seq) data related to OC were analyzed using AUCell scores to identify subpopulations at the single-cell level. The "AddModuleScore" function of the "Seurat" package was adopted to score and select marker genes from four gene sets: glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism. A prognostic model for metabolism-related genes (MRGs) was constructed and validated using OC-related marker genes selected from bulk RNAseq data. The MRG-based prognostic model was further utilized for functional analysis of the model gene set, pan-cancer analysis of genomic variations, spatial transcriptomics analysis, as well as GO and KEGG enrichment analysis. CIBERSORT and ESTIMATE algorithms were utilized for assessing the immune microenvironment of TCGA-ovarian serous cystadenocarcinoma (OV) samples. Furthermore, the Tracking Tumor Immunophenotype (TIP) database was employed to examine the anti-cancer immune response in patients with OC. To gain a more in-depth understanding of the process, the frequency of somatic mutations and different types of mutated genes were visualized through the somatic mutation profile of the TCGA database. Moreover, the benefits of immune checkpoint inhibitor (ICI) therapy in individuals with OC were predicted in the TIDE database. In addition, the CMap database was used to predict small-molecule drugs for the treatment of OC. Furthermore, immunohistochemistry, RT-qPCR, CCK-8, Transwell assay, and in vivo tumor xenograft experiments were conducted to validate the prognostic ability of the MRG Triggering Receptor Expressed on Myeloid Cells-1 (TREM1) in OC.

resultsMonocytes were selected using AUCell scoring, and two subpopulations of monocytes, marked by the expression of C1QC

conclusionThe MRG-based prognostic model constructed through the combined analysis of glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism is potentially effective as a prognostic biomarker. Furthermore, candesartan and PD-123319 may be potential therapeutic drugs for OC, possibly acting on the target ATGR2.