Prognostic Modeling of Ovarian Cancer Based on Perioperative Anesthesia-Related Drug Target Genes: A Bioinformatics Approach.

The heterogeneity of ovarian cancer (OV) poses significant challenges to disease subtype classification, risk stratification, and precision clinical management. Therefore, this study developed a prognostic model based on perioperative anesthesia-related drug target genes (PARDTGs) to find the clinical significance of PARDTGs in OV patients. This study comprehensively analyzed PARDTGs in OV by integrating multi-omics data, including bulk transcriptomic data, single-cell RNA-sequencing (scRNA-seq) data, and spatial transcriptomic data. Based on the expression characteristics of PARDTGs, we developed a prognostic feature using a stepAIC Cox proportional hazards model. This model was built on the TCGA-OV dataset and validated using the GSE26193, GSE30161 and GSE63885 datasets. Furthermore, we constructed nomograms combining PARDTG features and clinical factors. We analyzed the correlation between risk scores and functional enrichment, signaling pathways, and the tumor immune microenvironment. We identified 17 PARDTGs that are strongly associated with OV prognosis. The prognostic signature, validated across the TCGA-OV, GSE26193, GSE30161 and GSE63885 cohorts, demonstrated robust predictive accuracy for OS. Compared with the gene signature alone, the nomogram integrating the prognostic model and clinical parameters exhibited improved prognostic performance. Additionally, tumor microenvironment analysis revealed significant enrichment of immune-related pathways and lower TIDE scores in low-risk patients, which reveals that these patients may be more likely to benefit from immunotherapy. The study demonstrates both the prognostic relevance and the clinical utility of PARDTGs in ovarian cancer. Integrating genetic characteristics into clinical testing holds promise for improving clinical treatment and prognosis.