Ovarian carcinoma is a gynecological cancer with poor long-term survival rates when detected at advanced disease stages. Early symptoms are non-specific, and currently, there are no adequate strategies to identify this disease at an early stage when much higher survival rates can be expected. Ovarian carcinoma is a heterogeneous disease, with various histotypes originating from different cells and tissues, and is characterized by distinct somatic mutations, progression profiles, and treatment responses. Our study presents a targeted metabolomics approach, characterizing seven different ovarian (cancer-) cell lines according to their extracellular, intracellular, and RNA-derived modified nucleoside profiles. Moreover, these data were correlated with transcriptomics data to elucidate the underlying mechanisms. Modified nucleosides are excreted in higher amounts in cancer cell lines due to their altered DNA/RNA metabolism. This study shows that seven different ovarian cancer cell lines, representing different molecular subtypes, can be discriminated according to their specific nucleoside pattern. We suggest modified nucleosides as strong biomarker candidates for ovarian cancer with the potential for subtype-specific discrimination. Extracellular modified nucleosides have the highest potential in the distinguishing of cell lines between control cell lines and themselves, and represent the closest to a desirable, non-invasive biomarker, since they accumulate in blood and urine.

Endometrial carcinoma (EC) is the most common malignancy of the female reproductive tract, with increasing incidence driven by aging populations and obesity. While molecular classification has improved diagnostic precision, the identification of clinically relevant metabolic biomarkers remains incomplete, and targeted therapies are not yet standardized. In this study, we investigated metabolic alterations in four EC cell lines (AN3-CA, EFE-184, HEC-1B and MFE-296) compared to non-malignant controls under normoxic and stress conditions (hypoxia and lactic acidosis) to identify metabolomic differences with potential clinical relevance. Untargeted gas chromatography–mass spectrometry (GC/MS) and targeted liquid chromatography–mass spectrometry (LC/MS) profiling revealed two distinct metabolic subtypes of EC. Cells of metabolic subtype 1 (AN3-CA and EFE-184) exhibited high biosynthetic and energy demands, enhanced cholesterol and hexosyl-ceramides synthesis and increased RNA stability, consistent with classical cancer-associated metabolic reprogramming. Cells of metabolic subtype 2 (HEC-1B and MFE-296) displayed a phospholipid-dominant metabolic profile and greater hypoxia tolerance, suggesting enhanced tumor aggressiveness and metastatic potential. Key metabolic findings were validated via real-time quantitative PCR. This study identifies and characterizes distinct metabolic subtypes of EC within the investigated cancer cell lines, thereby contributing to a better understanding of tumor heterogeneity. The results provide a basis for potential diagnostic differentiation based on specific metabolic profiles and may support the identification of novel therapeutic targets. Further validation in three-dimensional culture models and ultimately patient-derived samples is required to assess clinical relevance and integration with current molecular classifications.

Immunohistochemical (IHC) studies of formalin-fixed paraffin-embedded (FFPE) samples are a gold standard in oncology for tumor characterization, and the identification of prognostic and predictive markers. However, despite the abundance of archived FFPE samples, their research use is limited due to the labor-intensive nature of IHC on large cohorts. This study aimed to create a high-throughput workflow using modern technologies to facilitate IHC biomarker studies on large patient groups. Semiautomatic constructed tissue microarrays (TMAs) were created for two tumor patient cohorts and IHC stained for seven antibodies (ABs). AB expression in the tumor and surrounding stroma was quantified using the AI-supported image analysis software QuPath. The data were correlated with clinicopathological information using an R-script, all results were automatically compiled into formatted reports. By minimizing labor time to 7.7%—compared to whole-slide studies—the established workflow significantly reduced human and material resource consumption. It successfully correlated AB expression with overall patient survival and additional clinicopathological data, providing publication-ready figures and tables. The AI-assisted high-throughput TMA workflow, validated on two patient cohorts, streamlines modern histopathological research by offering cost and time efficiency compared to traditional whole-slide studies. It maintains research quality and preserves patient tissue while significantly reducing material and human resources, making it ideal for high-throughput research centers and collaborations.

Background/Objectives: HER2-directed antibody–drug conjugates have expanded the clinical relevance of low-level HER2 expression. However, the prevalence and clinical significance of HER2-ultralow expression in gynecologic malignancies remain insufficiently defined. This study characterized the full spectrum of HER2 expression in endometrial carcinoma (EC) and high-grade serous ovarian carcinoma (HGSOC), including matched metastatic lesions. Methods: HER2 expression was assessed by immunohistochemistry in tissue microarrays from 117 EC and 52 HGSOC patients. Available matched metastatic lesions were analyzed in 23 EC and 18 HGSOC cases. HER2 expression was categorized as zero, ultralow, 1+, 2+, or 3+. Associations with clinicopathological parameters and progression-free survival were evaluated. Results: HER2-ultralow expression was common in primary tumors, occurring in 49/117 EC cases (41.9%) and 11/52 HGSOC cases (21.2%), whereas HER2 3+ expression was rare (EC: 2/117, 1.7%; HGSOC: 1/52, 1.9%). HER2 expression in primary tumors was associated with progression-free survival in both entities, with HER2-zero tumors showing the longest estimated progression-free survival. HER2 score discordance between matched primary tumors and metastases occurred in 16/23 EC cases (69.6%) and 8/18 HGSOC cases (44.4%). Conclusions: HER2-ultralow expression represents a frequent and previously underrecognized category in EC and HGSOC. While its prognostic impact remains limited, the high prevalence of HER2-ultralow tumors suggests potential relevance for future HER2-targeted therapeutic strategies. Prospective studies are needed to determine whether this subgroup may benefit from a HER2-targeted therapy.