Paula Diaz‐Sylvester

Bioinformatics Analysis Identifies Lipid Droplet‐Associated Gene Signatures as Promising Prognostic and Diagnostic Models for Endometrial Cancer

ABSTRACTBackgroundEffective diagnostic and prognostic tools are critical for early detection and improved outcomes in endometrial cancer (EC). Although metabolic dysregulation plays a key role in EC pathogenesis, the clinical relevance of lipid droplet–associated genes (LDAGs) remains largely unexplored. This study aims to establish LDAG‐based gene signatures with strong diagnostic and prognostic potential in EC.AimsTo identify LDAG signatures with prognostic and diagnostic utility in EC.Methods and ResultsA curated set of LDAGs was systematically analyzed across publicly available EC datasets to identify differentially expressed LDAGs (DE‐LDAGs). Survival‐associated DE‐LDAGs were then identified using univariate Cox regression. A four‐gene prognostic model was developed through LASSO‐based feature selection followed by multivariate Cox regression and validated using Kaplan–Meier survival and time‐dependent receiver operating characteristic (ROC) analyses. From the same pool of survival‐associated DE‐LDAGs, a six‐gene diagnostic model was constructed using LASSO, ROC analysis, and logistic regression. Model performance was evaluated using ROC curves and support vector machine (SVM) classification. Functional enrichment and protein–protein interaction (PPI) network analyses were conducted to assess the biological relevance of the identified genes.Our results demonstrate that the four‐gene prognostic model (LMLN, LMO3, PRKAA2, and RAB10) stratified EC patients into high‐ and low‐risk groups with significantly different survival outcomes (p < 0.05; time‐dependent AUC > 0.70). The six‐gene diagnostic model (AIFM2, ABCG1, LIPG, DGAT2, LPCAT1, and VCP) demonstrated near‐perfect classification of tumor versus normal tissues (AUC ≈0.99 in ROC analysis; 99.8% accuracy in SVM analysis). Functional enrichment linked DE‐LDAGs to lipid metabolism, ER stress response, cholesterol homeostasis, and autophagy, underscoring their biological relevance in EC pathobiology.ConclusionThis study provides the first comprehensive analysis of LDAGs in EC, establishing robust prognostic and diagnostic gene signatures with strong biological relevance. These signatures support a metabolism‐driven framework for EC classification and may offer potential clinical utility in early detection, risk stratification, and personalized treatment.

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.

Assessment of acyl-CoA cholesterol acyltransferase (ACAT-1) role in ovarian cancer progression—An in vitro study

Abnormal accumulation of acyl-CoA cholesterol acyltransferase-1 (ACAT-1) mediated cholesterol ester has been shown to contribute to cancer progression in various cancers including leukemia, glioma, breast, pancreatic and prostate cancers. However, the significance of ACAT-1 and cholesterol esters (CE) is relatively understudied in ovarian cancer. In this in vitro study, we assessed the expression and contribution of ACAT-1 in ovarian cancer progression. We observed a significant increase in the expression of ACAT-1 and CE levels in a panel of ovarian cancer cell lines (OC-314, SKOV-3 and IGROV-1) compared to primary ovarian epithelial cells (normal controls). To confirm the tumor promoting capacity of ACAT-1, we inhibited ACAT-1 expression and activity by treating our cell lines with an ACAT inhibitor, avasimibe, or by stable transfection with ACAT-1 specific short hairpin RNA (shRNA). We observed significant suppression of cell proliferation, migration and invasion in ACAT-1 knockdown ovarian cancer cell lines compared to their respective controls (cell lines transfected with scrambled shRNA). ACAT-1 inhibition enhanced apoptosis with a concurrent increase in caspases 3/7 activity and decreased mitochondrial membrane potential. Increased generation of reactive oxygen species (ROS) coupled with increased expression of p53 may be the mechanism(s) underlying pro-apoptotic action of ACAT-1 inhibition. Additionally, ACAT-1 inhibited ovarian cancer cell lines displayed enhanced chemosensitivity to cisplatin treatment. These results suggest ACAT-1 may be a potential new target for the treatment of ovarian cancer.

Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer

Epithelial ovarian cancer (OC) is the most deadly cancer of the female reproductive system. To date, there is no effective screening method for early detection of OC and current diagnostic armamentarium may include sonographic grading of the tumor and analyzing serum levels of tumor markers, Cancer Antigen 125 (CA-125) and Human epididymis protein 4 (HE4). Microorganisms (bacterial, archaeal, and fungal cells) residing in mucosal tissues including the gastrointestinal and urogenital tracts can be altered by different disease states, and these shifts in microbial dynamics may help to diagnose disease states. We hypothesized that the peritoneal microbial environment was altered in patients with OC and that inclusion of selected peritoneal microbial features with current clinical features into prediction analyses will improve detection accuracy of patients with OC. Blood and peritoneal fluid were collected from consented patients that had sonography confirmed adnexal masses and were being seen at SIU School of Medicine Simmons Cancer Institute. Blood was processed and serum HE4 and CA-125 were measured. Peritoneal fluid was collected at the time of surgery and processed for Next Generation Sequencing (NGS) using 16S V4 exon bacterial primers and bioinformatics analyses. We found that patients with OC had a unique peritoneal microbial profile compared to patients with a benign mass. Using ensemble modeling and machine learning pathways, we identified 18 microbial features that were highly specific to OC pathology. Prediction analyses confirmed that inclusion of microbial features with serum tumor marker levels and control features (patient age and BMI) improved diagnostic accuracy compared to currently used models. We conclude that OC pathogenesis alters the peritoneal microbial environment and that these unique microbial features are important for accurate diagnosis of OC. Our study warrants further analyses of the importance of microbial features in regards to oncological diagnostics and possible prognostic and interventional medicine.