Comprehensive Analysis of DGATs and PLINs in Ovarian Cancer: Implications for Diagnosis and Prognosis.

Background: Lipid droplet (LD) dynamics drive cancer cell proliferation, resistance, and aggressiveness. Diacylglycerol O-acyltransferases (DGATs) and perilipins (PLINs) are key LD-associated genes implicated in cancer pathophysiology. Objective: This study aimed to comprehensively analyze the expression and clinical significance of DGATs and PLINs in ovarian cancer (OC), focusing on their correlation with LDs and triglyceride (TG) levels, and to explore their diagnostic and prognostic implications. Methods: LD and TG levels in ovarian cell lines and clinical samples were assessed using BODIPY staining, fluorometric, colorimetric assays, and thin-layer chromatography (TLC). Gene expression profiling of DGATs and PLINs in cell lines and tissue was conducted via RT-qPCR, ELISA, and bioinformatics analysis. Correlation analyses between gene expression, Ki67, and survival data were performed. ROC curve analysis evaluated diagnostic potential. Results: LD accumulation was significantly higher in OC cell lines and tissues compared with normal controls. Diacylglycerol O-acyltransferase 1 (DGAT1) and diacylglycerol O-acyltransferase 2 (DGAT2) were overexpressed in OC cell lines and tissues, particularly in advanced stages (III and IV). Elevated TG levels were observed in OC cell lines and clinical samples, correlating with LD abundance and the expression of DGAT1 and DGAT2. PLIN2 and PLIN3 were significantly upregulated in OC tissues. Bioinformatics analysis identified dysregulation of DGATs and PLINs in OC. Survival analysis indicated DGAT2 is a predictor of poor prognosis. Diagnostic assessments revealed DGAT2 as a potential biomarker for OC detection. Conclusion: DGATs and PLINs are pivotal in LD metabolism and tumor progression in OC, with DGAT2 being a good candidate as prognostic and diagnostic marker. They present promising avenues for therapeutic targeting and diagnostic biomarkers, holding the potential to improve patient outcomes. Further exploration of their mechanistic roles and clinical implications is essential for advancing personalized cancer care.