Ovarian cancer (OC), a leading cause of gynecological cancer mortality, is frequently detected at advanced stages due to asymptomatic early progression. This study investigates plasma-based untargeted metabolomics for identifying biomarkers to screen and differentiate ovarian tumors (OT). Plasma samples from OC, benign ovarian tumors (BOT), and healthy controls (HC) were analyzed. Samples were randomized into train and test sets, with differential metabolites screened via two-tailed Student's t-test and partial least squares discriminant analysis. ROC models evaluated discriminatory capacity. Key metabolites demonstrated high predictive value: TMAO and hippuric acid distinguished OT from HC (AUC > 0.95), while linoleic acid, alpha-linolenic acid, and arachidonic acid (AUC > 0.9) further supported OT screening. Kynurenine differentiated OC from BOT (AUC = 0.808). Reduced levels of specific lysophosphatidylcholines (LPC (17:0/0:0), LPC (15:0/0:0)) also distinguished OT from HC (AUC = 0.771-0.89). These findings suggest plasma metabolomics holds promise for noninvasive biomarker discovery in OT screening and distinguishing between malignant and benign cases, though further validation of metabolite quantification is warranted prior to clinical application.

To create a radiomics model based on computed tomography (CT) to predict overall survival in ovarian cancer patients. To combine Rad-score with genomic data to explore the association between gene expression and Rad-score.

Imaging and clinical data from 455 patients with ovarian cancer were retrospectively analyzed. Patients were categorized into training cohort, validation cohort and test cohort. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) methods were utilized to identify characteristics and develop the Rad-score. Radiomics models were developed and evaluated for predictive efficacy and clinical incremental value. Application of genomic data from the cancer genome atlas (TCGA) to reveal differential genes in different Rad-score groups. Screening hub genes and exploring the functions of hub genes through bioinformatics analysis and machine learning.

Prognostic models based on FIGO, tumor residual disease and Rad-score were developed. The receiver operating characteristic (ROC) curves showed that the 1, 3, and 5 year area under curves (AUCs) of the model were in the training group (0.816, 0.865 and 0.862, respectively), validation group (0.845, 0.877, 0.869, respectively) and test group (0.899, 0.906 and 0.869, respectively) had good predictive accuracy. Calibration curves showed good agreement between observations and predictions. Decision curve analysis revealed a high net benefit of the clinical-radiomics model. The clinical impact curve (CIC) showed good clinical applicability of the clinical-radiomics model. Analysis of sequencing data from the TCGA database revealed EMP1 as a hub gene for radiomics modeling. It revealed that its biological function may be associated with extracellular matrix organization and focal adhesion.

Prognostic models based on FIGO, Tumor residual disease, and Rad-score can effectively predict the overall survival (OS) of ovarian cancer patients. Rad-score may enable prognostic prediction of ovarian cancer patients by revealing the expression level of EMP1 and its biological function.

Antibodies have long served as fundamental tools in disease diagnosis and surveillance. Their utility as biomarkers has expanded beyond infectious diseases to encompass a wide range of health conditions.

This review aims to explore recent advancements in antibody biomarker discovery and their applications in diagnosing and monitoring diverse health conditions. It also examines the role of antibody surveillance in public health and epidemiological studies.

A comprehensive analysis of recent literature was conducted, focusing on studies that identify and characterize disease-specific antibodies. Particular attention was given to their relevance in autoimmune diseases, infections, cancers, and neurological disorders.

The review highlights disease-specific antibody biomarkers and their clinical significance. It also discusses the utility and challenges of antibody-based surveillance in assessing disease prevalence, tracking immunity trends, and supporting One Health strategies.

Recent advancements in antibody biomarker discovery demonstrate significant potential in improving early diagnosis, personalized treatment, and population-level health management. Antibody surveillance continues to play a pivotal role in guiding public health responses and understanding disease dynamics.

Long noncoding RNAs (lncRNAs) are RNA molecules exceeding 200 nucleotides that do not encode proteins yet play critical roles in regulating gene expression at multiple levels, such as chromatin modification and transcription. These molecules are significantly engaged in cancer progression, development, metastasis, and chemoresistance. However, the function of lncRNAs in epithelial ovarian cancer (EOC) has not yet been thoroughly studied. EOC remains challenging due to its complex molecular pathogenesis, characterized by genetic and epigenetic alterations. Emerging evidence suggests that lncRNAs, such as XIST, H19, NEAT1, and MALAT1, are involved in EOC by modulating gene expression and signaling pathways, influencing processes like cell proliferation, invasion, migration, and chemoresistance. Despite extensive research, the precise mechanism of acting of lncRNAs in EOC pathogenesis and treatment resistance still needs to be fully understood, highlighting the need for further studies. This review aims to provide an updated overview of the current understanding of lncRNAs in EOC, emphasizing their potential as biomarkers and therapeutic targets. We point out the gaps in the knowledge regarding lncRNAs' influence on epithelial ovarian cancer (EOC), deliberating on new possible research areas.

The clinical prognostic factors for ovarian clear cell carcinoma (OCCC) are limited, and we aim to construct a model to predict the survival of OCCC patients.

Data were extracted from the SEER database for patients diagnosed with OCCC. Cox regression analyses were used to identify independent risk factors for OCCC. Two nomograms were developed, and the results were evaluated comprehensively by C-index, ROC curve, calibration curve, and DCA curve. Patients diagnosed with OCCC were used as the validation set to verify the model.

A total of 1855 OCCC patients from the SEER database were used as the training set, and 101 patients from our hospital were used as the validation set. Cox regression analysis of the independent risk factors affecting the prognosis of OCCC was used to construct nomograms. The C-index of the training set OS was 0.76, and the validation set OS was 0.75. The AUC of the training set OS is 0.803, 0.794, and 0.802 for 1, 3, and 5 years, and 0.774, 0.800, and 0.923 for the validation set. The calibration curve and DCA curve also showed that OS and CSS have good predictive power.

A nomogram based on 8 prognostic factors analyzed by Cox regression can predict the prognosis of OCCC patients effectively.

First-line therapy for ovarian cancer involves cytoreductive surgery and platinum-based chemotherapy, with or without bevacizumab. Bevacizumab can be administered at low (7.5 mg/kg every three weeks [Q3W]) or high dose (15 mg/kg Q3W). This study compared the efficacy and safety of these dosing strategies.

Systematic literature review of Embase, MEDLINE®, and CENTRAL (18/09/2023) identified randomized controlled trials (RCTs) evaluating bevacizumab versus any therapy or control in ovarian, fallopian tube, or primary peritoneal cancer. Indirect treatment comparisons (ITC) of response, survival, and safety outcomes were performed, including sensitivity/subgroup analyses adjusting for heterogeneity.

Six RCTs (sample size: 24-1528 patients) were included for ITC. Five evaluated high-dose bevacizumab with chemotherapy. The common comparator was carboplatin + paclitaxel. Trials mainly included stage III (n = 4) or stage II-III (n = 1) ovarian cancer patients; one did not report cancer stage. Primary analyses showed no significant differences between low- versus high-dose bevacizumab for partial response (risk ratio [95 % confidence interval]: 0.66 [0.42, 1.02]), complete response (1.76 [0.76, 4.11]), objective response rate (1.01 [0.63, 1.61]), progressive disease (1.08 [0.38, 3.10]), clinical benefit (0.89 [0.76, 1.03]), any grade ≥ 3 adverse event (1.53 [0.96, 2.44]), specific grade ≥ 3 adverse events, overall survival (hazard ratio: 0.93 [0.77, 1.13]), or progression-free survival (1.02 [0.86, 1.22]). Sensitivity and subgroup analyses confirmed findings.

This ITC found no significant difference in clinical outcomes between low- and high-dose bevacizumab combination therapy. Despite limitations of small sample size and heterogeneities, findings suggest that bevacizumab dose may not significantly impact ovarian cancer outcomes.

Metabolic reprogramming is a hallmark of ovarian cancer, enabling tumor progression, immune evasion and drug resistance. The tumor microenvironment (TME) further shapes metabolic adaptations, enabling cancer cells to withstand hypoxia and nutrient deprivation. While organoid models provide a physiologically relevant platform for studying these processes, they still lack immune and vascular components, limiting their ability to fully recapitulate tumor metabolism and drug responses. In this study, we investigated the key metabolic mechanisms involved in ovarian cancer progression, focusing on glycolysis, lipid metabolism and amino acid metabolism. We integrated metabolomic analyses and drug sensitivity assays to explore metabolic-TME interactions using patient-derived, adult stem cell-derived and iPSC-derived organ tissues. Among these, we found that glycolysis, lipid metabolism and amino acid metabolism play a central role in tumor progression and chemotherapy resistance. We identified methylglyoxal (MGO)-mediated BRCA2 dysfunction as a driver of immune escape, a role for sphingolipid signaling in tumor proliferation and a role for kynurenine metabolism in CD8+ T cell suppression. In addition, PI3K/AKT/mTOR and Wnt/β-catenin pathways promote chemoresistance through metabolic adaptation. By elucidating the link between metabolic reprogramming and immune evasion, this study identifies key metabolic vulnerabilities and potential drug targets in ovarian cancer. Our findings support the development of metabolically targeted therapies and increase the utility of organoid-based precision medicine models.

Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers intricately linked to OC's molecular pathogenesis represents a critical avenue for enhancing patient outcomes and survival prospects. In this comprehensive study, we embarked on a bioinformatics journey, leveraging the vast repository of single nucleotide polymorphism (SNP) data from OC patients available within the TCGA database. Our overarching goal was to unearth the genetic underpinnings of OC, shedding light on potential prognostic markers that could significantly impact clinical decision-making and patient care. Our meticulous analysis led to the discovery of five mutated genes-APOB, BRCA1, COL6A3, LRP1, and LRP1B-engaged in the intricate world of lipid metabolism. These genes, previously unexplored in the context of OC, emerged as prominent figures in our investigation, showcasing their potential roles in OC progression. The intricate interplay between lipid metabolism and cancer development has garnered considerable attention in recent years, and our findings underscore the relevance of these genes in the context of OC. To fortify our discoveries, we delved into the realm of survival analysis, a pivotal component of our investigation. The results yielded compelling evidence of significant correlations between patient survival and the expression levels of the aforementioned genes. This critical insight underscores the potential utility of these genes as prognostic markers, illuminating a path toward more personalized and effective approaches to patient care. Our study represents a multifaceted approach to unraveling the complex molecular pathogenesis of OC. By harnessing the power of high-throughput data mining, we uncovered genetic insights that may reshape our understanding of this formidable disease. We complemented these findings with advanced techniques such as RT-qPCR and Western blot, further dissecting the intricacies of OC's molecular landscape. This holistic approach not only deepens our understanding but also provides essential bioinformatics information that holds promise in assessing patient prognosis. In summary, our study represents a significant stride in the quest to decode the molecular intricacies of ovarian cancer. Our findings spotlight the potential prognostic significance of APOB, BRCA1, COL6A3, LRP1, and LRP1B, inviting further exploration into their roles in OC progression. Ultimately, our research carries the potential to shape the future of OC management, offering a glimpse into a more personalized and effective approach to patient care.

Background: Identification of targetable biomarkers to improve early disease detection and overall patient outcomes is becoming an urgent need in clinical oncology. Ovarian cancer (OC) has one of the highest mortality rates among gynecological cancers. It is asymptomatic and almost always diagnosed at an advanced stage (III or IV), leading to a 5-year survival rate of approximately 35%. Methods: Current therapeutic approaches for OC are very limited and mainly consist of cytoreductive surgery and cisplatin plus taxane-based chemotherapy. No gender and tumor specific biomarkers are known. Exosomes, lipid bilayer vesicles of endocytic origin secreted by most cell types, represent sources of information for their involvement in the onset and progression of many diseases. Hence, research on exosome contents as tools and targets in precise oncology therapy provides knowledge essential to improving diagnosis and prognosis of the disease. Results: This review attempts to give an overview of how exosomes are implicated in ovarian carcinoma pathogenesis to trigger further cancer exosome-based investigations aimed at developing ovarian cancer fine-tuning diagnostic methodologies. Conclusions: It is essential to investigate exosome-based cancer drugs to advance understanding, improve treatment plans, create personalized strategies, ensure safety, and speed up clinical translation to increase patients' overall survival and quality of life. Papers published in PubMed and Web of Science databases in the last five years (2020-2024) were used as a bibliographic source.

Tumor-associated macrophages (TAM) is related to Ovarian cancer (OC) pathogenesis, but the exact mechanism remains unclear. This study investigated the expression of Kelch Domain Containing 8 A (KLHDC8A) in OC and the mechanism associated with TAM.

Bioinformatics analysis of differential expression genes between normal and OC tissues were analyzed based on the Tumor Genome Atlas (TCGA) databases. KLHDC8A mRNA expression was knocked down in normal epithelial cells (IOSE80), and then the effects of siKLHDC8A on the proliferation, invasion, migration and C5a secretion of IOSE80 cells were explored. THP1-derived macrophages were cultured with medium of NC-IOSE80 cells, siKLHDC8A-IOSE80 cells with or without C5aR antagonists.

KLHDC8A was lowly expressed in OC and negatively correlated with the infiltration of tumor-promoting macrophages, contributing to the survival of OC patients. Furthermore, siKLHDC8A promotes the proliferation, invasion and migration of IOSE80 cells and leads to polarization of pro-tumoral macrophages, which can be rescued by C5aR antagonists.

Our results indicated that KLHDC8A knockdown could modulate the development of OC by affecting macrophage polarization to pro-tumoral type via the C5a/C5aR/p65 NFκB signaling pathway. It may play an essential role as the tumor suppressor genes in diagnosis and treatment of OC.

Endometrial carcinoma (EC) is the most common gynecological cancer among women, and in more than 90% of cases, the initial manifestation of the disease is postmenopausal bleeding. Unfortunately, despite early diagnosis and treatment, EC often recurs. Among the many serum tumor markers studied, human epididymis protein 4 (HE4) and cancer antigen 125 (CA125) show the most promise as tools for EC diagnosis, prognosis, and monitoring.

Exosomes, extracellular vesicles with an average diameter of 30 ~ 150 nm, are pivotal in mediating the cellular microenvironment (CM) through their cargo-carrying capability. Despite extensive studies, the dynamic and regulatory mechanisms of exosomal cargoes, including lipids, proteins, nucleic acids, and metabolites, remain poorly understood.

In this study, we collected culture medium of ovarian cancer cells at four different time points (12, 24, 36, 48 h). Exosomes were isolated using ultracentrifugation, and miRNA sequencing was performed for exosomes from each group (T12, T24, T36, and T48).

A total of 131 miRNAs were identified in all groups. Specifically, 41, 115, 63, and 24 miRNAs were detected in the T12, T24, T36, and T48 groups, respectively. Among these, 15 miRNAs were common to the all groups, while 3, 57, 10, and 3 miRNAs were unique to the T12, T24, T36, and T48 groups, respectively. Functional analyses of the target genes for both common and specific miRNAs indicated that numerous target genes were involved in signaling pathways and cancer-related processes.

It suggested that exosomal miRNAs might be critical in intercellular communication and in dynamically remodeling the tumor microenvironment. These insights could enhance our understanding of the role of exosomal miRNAs in cancer biology and inform the development of novel therapeutic strategies.

Ovarian cancer (OC), the seventh most common cancer in women and the most lethal gynecological malignancy, is a significant global health challenge, with >324,000 new cases and >200,000 deaths being reported annually. OC is characterized by late-stage diagnosis, a poor prognosis, and 5-year survival rates ranging from 93% (early stage) to 20% (advanced stage). Despite advances in genomics and proteomics, effective early-stage diagnostic tools and population-wide screening strategies remain elusive, contributing to high mortality rates. The complex pathogenesis of OC involves diverse histological subtypes and genetic predispositions, including BRCA1/2 mutations; notably, a considerable proportion of OC cases have a hereditary component. Current diagnostic modalities, including imaging techniques (transvaginal ultrasound, computed/positron emission tomography, and magnetic resonance imaging) and biomarkers (CA-125 and human epididymis protein 4), with varying degrees of sensitivity and specificity, have limited efficacy in detecting early-stage OC. Emerging technologies, such as liquid biopsy, multiomics, and artificial intelligence (AI)-assisted diagnostics, may enhance early detection. Liquid biopsies using circulating tumor DNA and microRNAs are popular minimally invasive diagnostic tools. Integrated multiomics has advanced biomarker discovery. AI algorithms have improved imaging interpretation and risk prediction. Novel screening methods including organoids and multiplex panels are being explored to overcome current diagnostic limitations. This review highlights the critical need for continued research and innovation to enhance early diagnosis, reduce mortality, and improve patient outcomes in OC and posits personalized medicine, integrated emerging technologies, and targeted global initiatives and collaborative efforts, which address care access disparities and promote cost-effective, scalable screening strategies, as potential tools to combat OC.

Ovarian cancer is a deadly disease, often diagnosed at advanced stages due to a lack of reliable biomarkers. Exosomes, which carry a variety of molecules such as proteins, lipids, DNA, and non-coding RNAs, have recently emerged as promising tools for early cancer detection. While exosomes have been studied in various cancer types, comprehensive network analyses of exosome proteins in ovarian cancer remain limited. In this study, we used a protein-protein interaction (PPI) network. Using the Clustermaker2 app and the MCODE algorithm, we identified six significant clusters within the network, highlighting regions involved in functional pathways. A four-fold algorithmic approach, including MCC, DMNC, Degree, and EPC, identified 12 common hub genes. STRING analysis and visualization techniques provided a detailed understanding of the biological processes associated with these hub genes. Notably, 91.7% of the identified hub genes were involved in translational processes, showing an important role in protein synthesis regulation in ovarian cancer. In addition, we identified the miRNAs and LncRNAs carried by ovarian cancer exosomes. These findings highlight potential biomarkers for early detection and therapeutic targets.

Ovarian cancer is one of the top seven causes of cancer deaths. Incidence of ovarian cancer varies by ethnicity, where Asian women demonstrate lower incidence rates than non-Hispanic Blacks and Whites. Survival prediction models for ovarian cancer have been developed for Caucasians and Black populations using national databases; however, whether these models work for Asians is unclear. Therefore, a retrospective cohort study was conducted to develop survival prediction models for patients with epithelial ovarian cancer from a Taiwan Cancer Registry (TCR) who underwent de-bulking and chemotherapy, with the aim to identify variables that can predict prognosis accurately. Patients diagnosed with OC from TCR were included.

Two prognostic models (M1 and M2) were developed: M1 utilized clinical variables only, M2 additionally included cancer-specific variables with the aim to improve the accuracy. All methods were repeated independently for patients with only serous ovarian cancer. All findings for model M1 were validated among Black, White, and Asian populations from Surveillance, Epidemiology, and End Results (SEER) database and 10-fold internal cross-validations. Due to absence of cancer-specific site variables in SEER, model M2 was only internally validated. Cox-proportional hazards regression analysis was performed and a stepwise strategy with Akaike-information criterion was used to select appropriate variables as predictors to develop both M1 and M2.

The c-index values of both models were >0.7 in both TCR and SEER populations for epithelial ovarian cancer. Calibration analysis demonstrated good prediction performance with the proportional difference between predicted and observed survival to be <5%. The performance was similar for the subset of patients with serous epithelial ovarian cancer. Notably, no significant racial differences were observed.

The prognostic models proposed in this study can potentially be used for identifying patients, especially from Taiwan, at higher risk of ovarian cancer mortality early on, leading to improved prognosis, through shared decision-making between physicians and patients.

Oxidative stress is a state of imbalance between the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) and the antioxidant defence system in the body. Oxidative stress may be associated with a variety of diseases, such as ovarian cancer, diabetes mellitus, and neurodegeneration. The generation of oxidative stress in ovarian cancer, one of the common and refractory malignancies among gynaecological tumours, may be associated with several factors. On the one hand, the increased metabolism of ovarian cancer cells can lead to the increased production of ROS, and on the other hand, the impaired antioxidant defence system of ovarian cancer cells is not able to effectively scavenge the excessive ROS. In addition, chemotherapy and radiotherapy may elevate the oxidative stress in ovarian cancer cells. Oxidative stress can cause oxidative damage, promote the development of ovarian cancer, and even result in drug resistance. Therefore, studying oxidative stress in ovarian cancer is important for the prevention and treatment of ovarian cancer. Antioxidants, important markers of oxidative stress, might serve as one of the strategies for preventing and treating ovarian cancer. In this review, we will discuss the complex relationship between oxidative stress and ovarian cancer, as well as the role and therapeutic potential of antioxidants in ovarian cancer, thus guiding future research and clinical interventions.

Ovarian cancer (OC) remains a significant women's health concern due to its high mortality rate and the challenges posed by late detection. Exploring novel biomarkers could lead to earlier, more specific diagnoses and improved survival rates for OC patients. This review focuses on biomarkers associated with extracellular vesicles (EVs) found in various proximal fluids, including urine, ascites, utero-tubal lavage fluid of OC patients. We highlight these proximal fluids as rich sources of potential biomarkers. The review explains the roles of EV biomarkers in ovarian cancer progression and discusses EV-related proteins and miRNAs as potential diagnostic or prognostic indicators and therapeutic targets. Finally, we highlighted the limitations of examining proximal fluids as sources of biomarkers and encourage researchers to proactively pursue innovative solutions to overcome these challenges.

Ovarian cancer (OC) is a prevalent gynecological malignancy with a relatively dismal prognosis. The SGT1 homolog (SUGT1) protein, which interacts with heat shock protein 90 and is essential for the G1/S and G2/M transitions, was formerly thought to be a cancer promoter, but its precise role in OC remains unknown.

We conducted a comprehensive bioinformatics analysis of SUGT1 expression in patients with OC compared with their normal controls, including the data from the cancer genome atlas (TCGA), genotype-tissue expression (GTEx) databases, gene ontology (GO) analysis, Kyoto Encylopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), single sample gene set enrichment analysis (ssGSEA). In addition, Kaplan-Meier (KM) analysis, univariate and multivariate Cox analyses were applied to investigate the prognostic role of SUGT1 in ovarian cancer. Furthermore, we validated the expression of SUGT1 in OC and normal tissues through immunohistochemistry.

SUGT1 was significantly overexpressed in OC than in normal tissues. In addition, the GO, KEGG and GSEA analysis revealed that SUGT1 was associated with the functions related to immunoglobulin complex, antigen binding, immunoglobulin receptor binding, among others. Besides, ssGSEA demonstrated a positive correlation between SUGT1 expression and the abundance of T central memory cells, natural killer cells, and T gamma delta cells, although it showed a negative association with activated dendritic cells, cytotoxic cells, T cells, and T helper 1 cells. Subsequently, KM survival analysis revealed that high SUGT1 expression indicated a shorter overall survival, disease specific survival and progression free interval in OC patients. Univariate and multivariate Cox regression revealed that SUGT1 could serve as an independent risk factor for prognosis of patients with OC.

All these results of this study show that SUGT1 is an important molecular component in immune infiltration in OC and may have a new significant prognostic role in OC.

This article focusing on examining the function and further, molecular function of SHP2 in ovarian cancer (OC). For the molecular mechanism, bioinformatics was applied to study the specifically expressed genes in ovarian cancer ; the western blotting was applied to identify the EGF, p-SHP2, ZEB1, and E-Cadherin expressions in ovarian cancer tissue and pair adjacent tissue; then SKOV3 cells were treated with EGF and infected with E-Cadherin overexpression lentivirus, and then cells were treated with benzyl butyl phthalate and IRS-1 respectively. Detection of expression of p-SHP2, ZEB1, E-Cadherin, α3-integrin, p-Src, p-SMAD2, Snail, Slug and SKOV3 cells of migration and invasion abilities were detected using Western blot method and cell scratch assay and Transwell assay; Progression of ovarian cancer was detected using subcutaneous tumor transplantation assay in nude mice and HE staining method and immunocyto. The bioinformatics analysis results suggested that SHP2 is highly specifically expressed and E-Cadherin, which is low specifically expressed in ovarian cancer tissues, while EGF, p-SHP2 or ZEB1 are highly expressed and E-Cadherin is low expressed in ovarian cancer tissues; Overexpression of E-Cadherin could reduce the expressions of p-SHP2, ZEB1, α3-integrin, p-Src, p-SMAD2, Snail and Slug might has roles in alleviating the ovarian cancer development and decreasing the levels of p-SHP2 and ZEB1 in tumor samples. And E-Cadherin overexpression reduced the migration and invasion ability of SKOV3 cells. SHP2 tyrosine phosphatase enhances the ovarian cancer cells' motility and invasiveness by upregulation of the integrin/E-Cadherin switch through ZEB1 signal.

Schizophrenia is characterized by symptoms such as delusions, hallucinations, and avolition. The diagnosis is clinical, based on interviews and the main treatment involves antipsychotics. Currently, given the lack of clinically applicable biomarkers for schizophrenia, there is no molecular test based on its biological mechanisms to assist psychiatrists either in the prediction or diagnosis of the disorder, nor to measure medication efficacy. This scoping review assessed original articles in English about protein biomarkers for schizophrenia with samples that could be used in a clinical context, classifying them into diagnosis, prognosis, therapeutics, risk for psychosis, and side-effects. The search was conducted on PubMed and key findings were inserted on a summary table. We discussed the methodologies used in these papers, suggested protein panels for validation in longitudinal research, and proposed a hypothesis to explain the observed variability in results. This heterogeneity is explored in light of the debated validity of this construct, applying recent discussions and the disorder's history. Our data suggest that there is insufficient evidence to integrate protein biomarkers into clinical psychiatry for schizophrenia, not due to study quality, but possibly due to flaws in the current diagnostic system. We propose exploring alternative categorization systems.

Our study was aimed to construct a predictive model to advance ovarian cancer diagnosis by machine learning.

A retrospective analysis of patients with pelvic/adnexal/ovarian mass was performed. Potential features related to ovarian cancer were obtained as many as possible. The optimal machine learning algorithm was selected among six candidates through 5-fold cross validation. Top 20 features having the most powerful predictive significance were ranked by Shapley Additive Interpretation (Shap) method. Clinical validation was further performed to confirm whether our model could advance diagnosis of ovarian cancer.

A total of 9,799 patients were collected. The inclusion criteria included age >18 years old, the first diagnosis being pelvic/adnexal/ovarian mass of undetermined significance, and pathological report indispensable. Four hundred and thirty-eight dimensional features were obtained after filtration. LightGBM showed the best performance with accuracy 88%. Among the top 20 features, 55% belonged to laboratory test report, 35% came from imaging examination report, and 10% were attributed to basic demographics and main symptom. Age, CA125, and risk of ovarian malignancy algorithm were the top three. Our predictive model performed stably in testing and clinical validation datasets, and was found to advance the diagnosis of ovarian cancer about 17 days before clinical pathological examination.

LightGBM was the optimal algorithm for our predictive model with accuracy of 88%. Laboratory test and imaging examination played essential roles in diagnosing ovarian cancer. Our model could advance the diagnosis of ovarian cancer before clinical pathological examination.

Salidroside (SAL) is a bioactive substance extracted from the traditional Chinese medicine Rhodiola rosea, which exhibits multiple pharmacological effects, such as anti-inflammatory, antioxidant, and anti-tumor properties. Currently, the effects of SAL on the malignant progression of ovarian cancer (OC) and its specific mechanism of action are not clear. Cell Counting Kit 8 (CCK-8), clone formation, Hoechst 33258 staining, flow cytometry, transwell, western blotting and immunofluorescence assays were performed to determine the impacts of SAL on the biological properties of OC cells (CAOV3 and SKOV3) and human normal ovarian epithelial cells (IOSE80). The binding activity of SAL and proteins was evaluated. Glucose consumption, lactate and ATP production, extracellular acidification rate (ECAR) and related proteins were measured to assess glycolysis. Animal models were established to evaluate the impact of SAL treatment in vivo and the expression levels of STAT3/c-Myc pathway-related proteins were determined to explore the relationship between SAL and OC. The results showed that SAL reduced the viability, clone formation, migration and invasion ability of CAOV3 and SKOV3 cells, and induced apoptosis. SAL inhibited epithelial-mesenchymal transition (EMT) and decreased glucose consumption, lactate and ATP production and ECAR. SAL exhibited good binding activity with STAT3 and c-Myc and reduced the expression levels of STAT3/c-Myc pathway and glycolysis-related proteins in vitro and in vivo. In conclusion, SAL exerted anti-tumor effects by interfering with the malignant biological progression of OC cells by inhibiting STAT3/c-Myc pathway-mediated glycolysis.

Ovarian cancer (OC) must be detected in its early stages when the mortality rate is the lowest to provide patients with the best chance of survival. Lysophosphatidic acid (LPA) is a critical OC biomarker since its levels are elevated across all stages and increase with disease progression. This paper presents an LPA assay based on a thickness shear mode acoustic sensor with dissipation monitoring that involves a new thiol molecule 3-(2-mercaptoethanoxy)propanoic acid (HS-MEG-COOH). HS-MEG-COOH is an antifouling linker that provides (a) antifouling properties for gold substrates and (b) linking ability via its terminal carboxylic acid functional group. The antifouling ability of HS-MEG-COOH was tested in whole human serum. The new molecule was applied to the LPA assay in conjunction with a spacer molecule, 2-(2-mercaptoethoxy)ethan-1-ol (HS-MEG-OH), in a 1:1 v/v ratio. HS-MEG-COOH was covalently linked to gelsolin-actin, a protein complex probe that dissociates due to LPA-binding. LPA was detected in phosphate-buffered saline and undiluted human serum and achieved a low limit of detection (1.0 and 0.7 μM, respectively) which was below the concentration of LPA in healthy individuals. The antifouling properties of HS-MEG-COOH and the detection of LPA demonstrate the ability of the sensor to successfully identify the early-stage OC biomarker in undiluted human serum.

This study aimed to analyse the diagnostic performance of miR200b in epithelial ovarian cancer (EOC) in a group of Egyptian patients and to evaluate the combined use of miR200b with other biomarkers as a reliable diagnostic and prognostic indicator of EOC.

We tested the expression of cell-free miR200b in 30 EOC patients before undergoing optimum cytoreductive surgery, 19 females with benign ovarian disease and 14 normal healthy females using quantitative real time PCR. All cases were tested for CA125, HE4 and CRP. The results were compared between the three groups. The double combination of miR200b with each biomarker was tested for a possible improvement of the diagnostic power of the test.

MiR200b was significantly overexpressed in EOC patients compared to the other groups, we determined 1.88 folds as the best cutoff for miR200b expression to discriminate between EOC and non-malignant cases with a sensitivity of 63.3% and a specificity of 71%. CA125, HE4 and CRP were evaluated and showed a sensitivity of 96.7%, 45.5%, 81.8% respectively and a specificity of 75.9%, 66.7% and 90% respectively. We evaluated the combined use of miR200b with each biomarker, the best results were seen with the combined use of miR200b and CA125.

We concluded that the combined use of miR200b and CA125 could serve as a reliable tool in the initial diagnosis of EOC, and a predictor of event free survival of the disease.

Among all malignant gynecological tumors, ovarian cancer (OC) has the highest mortality rate. OC is often diagnosed at advanced and incurable stages; however, early diagnosis can enable the use of optimized and personalized treatments. Intensive research into the synthesis and characterization of gold nanoparticles (AuNPs) has been performed with the aim of developing innovative materials for use in biological and photothermal therapies for OC. AuNPs can be chemically modified and functionalized by binding to a variety of organic compounds and biomolecules, such as peptides, antibodies and therapeutic agents, via simple synthetic processes. They are particularly suitable for use as carriers for drug delivery. In the present review, the synthesis and characteristics of AuNPs are summarized, and their potential in OC therapy are discussed.

A granulosa cell tumor (GCT) is a type of ovarian cancer that constitutes approximately 10% of all ovarian malignancies. In this case, a female patient with hypothyroidism and a history of irregular periods was diagnosed with polycystic ovarian disease (PCOD) after a radiological examination, which subsequently recommended surgery. Frozen section analysis and imprint cytology assisted in the intraoperative diagnosis of the tumor. These diagnostic tools helped adopt a minimally invasive surgery where only the affected ovary was removed, thus avoiding an extra surgical procedure. It is usually described as a low-grade cancer, but it can recur many years later. Therefore, repeated imaging and tumor markers should be used to monitor the patient after the treatment and ensure early detection of the relapse, thus improving the patient's prognosis.

Ovarian cancer is a highly heterogeneous tumor with a poor prognosis. The lack of reliable and efficient research models that can accurately mimic heterogeneity has impeded in-depth investigations and hindered the clinical translation of research findings in ovarian cancer. Organoid models have emerged as a promising in vitro approach, demonstrating remarkable fidelity to the histological, molecular, genomic, and transcriptomic features of their tissues of origin. In recent years, organoids have contributed to advancing our understanding of ovarian cancer initiation, metastasis, and drug resistance mechanisms, as well as facilitating clinical screening of effective therapeutic agents. The establishment of high-throughput organoid culture systems, coupled with cutting-edge technologies such as organ-on-a-chip, genetic engineering, and 3D printing, has tremendous potential for accelerating ovarian cancer research translation. In this review, we present a comprehensive overview of the latest exploration of organoids in basic ovarian cancer research and clinical translation. Furthermore, we discuss the prospects and challenges associated with the use of organoids and related novel technologies in the context of ovarian cancer. This review provides insights into the application of organoids in ovarian cancer.

Ovarian carcinoma (OC) has an unfavorable prognosis due to lack of screening and an asymptomatic course. New diagnostic methods are being sought to enable earlier diagnosis of this condition. The purpose of this study was to determine the diagnostic utility of collagenases (MMP-1, MMP-8 and MMP-13) in the diagnosis of OC compared to HE4 and CA125 and the ROMA.

The study group consisted of 120 patients with OC, the control group: 70 patients with benign ovarian lesions (BLs) and 50 healthy women (HS). MMP-1, MMP-8 and MMP-13 were determined by ELISA and HE4 and CA125 by CMIA.

OC patients had higher levels of MMP-1 and MMP-13 compared to the BL and HS groups. MMP-1 (SE: 81.66%; SP: 94%; PPV: 97.02%; NPV: 68.11%; AUC: 0.9625) and MMP-13 (SE: 77.50%; SP: 94%; PPV: 96.875%; NPV: 63.51%; AUC: 0.917) showed similar or higher diagnostic values to routine markers (HE4: SE:85%; SP: 92%; PPV: 96.22%; NPV: 71.875%; AUC: 0.943; CA125: SE: 80%; SP: 98%; PPV: 98.96%; NPV: 67.12%; AUC: 0.909) and the ROMA (SE: 90.83%; SP: 94%; PPV: 97.32%; NPV: 81.03%; AUC: 0.955). Performing combined analyses of individual MMPs and MMPs with ROMA was associated with further increases in diagnostic parameters.

MMP-1 and MMP-13 have shown preliminary potential as diagnostic markers and auxiliary markers to ROMA in biochemical diagnosis of OC.

Modern oncology increasingly relies on the role of proteins as key components in cancer diagnosis, prognosis, and targeted therapy. This review examines advancements in protein biomarkers across several cancer types, including breast cancer, lung cancer, ovarian cancer, and hepatocellular carcinoma. These biomarkers have proven critical for early detection, treatment response monitoring, and tailoring personalized therapeutic strategies. The article highlights the utility of targeted therapies, such as tyrosine kinase inhibitors and monoclonal antibodies, in improving treatment efficacy while minimizing systemic toxicity. Despite these advancements, challenges like tumor resistance, variability in protein expression, and diagnostic heterogeneity persist, complicating universal application. The review underscores future directions, including the integration of artificial intelligence, advanced protein analysis technologies, and the development of combination therapies to overcome these barriers and refine personalized cancer treatment.

Pyruvate dehydrogenase kinase 1 (PDK1) is a new therapeutic target that is dysregulated in multiple tumors. This study aims to explore the potential role and regulatory mechanism of PDK1 in epithelial ovarian cancer (EOC). We detect PDK1 expression in EOC tissues and cells using qRT-PCR and western blot analysis, and the effects of PDK1 on EOC cell malignant behaviors are explored. RNA sequencing analyses are performed to explore the differentially expressed genes in PDK1-silenced EOC cells. Furthermore, tumor-bearing mouse models are established to assess the impacts of PDK1 and BGN on EOC tumor growth and metastasis in vivo. The results show that PDK1 is upregulated in EOC tissues and cell lines. Biglycan (BGN) is downregulated in PDK1-silenced EOC cells, and its expression is positively correlated with PDK1 levels in EOC tissues. PDK1 depletion inhibits EOC cell proliferation, migration and invasion. Mechanistically, PDK1 and BGN are colocalized in the cytoplasm of EOC cells and interact with each other. PDK1 positively regulates BGN expression by enhancing BGN mRNA stability. BGN overexpression partially reverses the anti-tumor effects of PDK1 depletion on EOC cell malignant behaviors. PDK1 has also been revealed to upregulate BGN to activate the NF-κB oncogenic pathway in EOC cells. Additionally, PDK1 accelerates tumor growth and metastasis by modulating BGN expression. In conclusion, PDK1 functions as an oncogene, facilitating EOC progression by upregulating BGN and activating the NF-κB pathway. These findings may provide valuable biomarkers for the diagnosis and treatment of EOC.

Ovarian cancer is one of the leading causes of mortality among women worldwide. However, early detection can significantly reduce mortality rates and mitigate subsequent complications related to both economic burden and mental well-being. Despite the development in the field of medical diagnosis, the death rates due to ovarian cancer have sharply increased. Among the recent technologies suggested as suitable diagnostic techniques for the early detection of ovarian cancer, biosensor technology has emerged as a cutting-edge technology, with electrochemical biosensors providing one of the most efficient types of biosensors. Therefore, this review discusses the application of electrochemical biosensors as a viable alternative to conventional diagnostic techniques for the timely identification of ovarian cancer, its advantages over other types of biosensors and conventional diagnostic techniques, and the types of electrochemical biosensors.

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy worldwide. Due to its nonspecific symptoms and unreliable screening tools, EOC is not diagnosed at an early stage in most cases. Unfortunately, despite achieving initial remission after debulking surgery and platinum-based chemotherapy, most patients experience the recurrence of the disease. The limited therapy approaches have encouraged scientists to search for new detection and therapeutic strategies. In this review, we discuss the role of folate receptor alpha (FRα) in EOC development and its potential application as a biomarker and molecular target in designing new EOC screening and treatment methods. We summarize the mechanisms of the action of various therapeutic strategies based on FRα, including MABs (monoclonal antibodies), ADCs (antibody-drug conjugates), FDCs (folate-drug conjugates), SMDCs (small molecule-drug conjugates), vaccines, and CAR-T (chimeric antigen receptor T) cells, and present the most significant clinical trials of some FRα-based drugs. Furthermore, we discuss the pros and cons of different FR-based therapies, highlighting mirvetuximab soravtansine (MIRV) as the currently most promising EOC-targeting drug.

In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.

Transposable elements (TEs) are often expressed at higher levels in tumor cells than normal cells, implicating these genomic regions as an untapped pool of tumor-associated antigens. In ovarian cancer (OC), protein from the TE ERV-K is frequently expressed by tumor cells. Here we determined whether the targeting of previously identified epitope in the envelope gene (env) of ERV-K resulted in target antigen specificity against cancer cells. We found that transducing healthy donor T cells with an ERV-K-Env-specific T cell receptor construct resulted in antigen specificity only when co-cultured with HLA-A*03:01 B lymphoblastoid cells. Furthermore, in vitro priming of several healthy donors with this epitope of ERV-K-Env did not result in target antigen specificity. These data suggest that the T cell receptor is a poor candidate for targeting this specific ERV-K-Env epitope and has limited potential as a T cell therapy for OC.

Ferroptosis, a non-apoptotic form of regulated cell death, plays a critical role in the suppression of various tumor types, including ovarian cancer. Artesunate (ART), a derivative of artemisinin, exhibits extensive antitumor effects and is associated with ferroptosis. This study aimed to investigate the mechanisms through which ART induces ferroptosis to inhibit ovarian cancer.

RNA sequencing was conducted to identify differentially expressed genes associated with ART-induced ferroptosis. Dual-luciferase reporter assays and electrophoretic mobility shift assays were performed to confirm the interaction between Homeobox C11 (HOXC11) and the Prominin 2 (PROM2) promoter. Cell Counting Kit-8 (CCK-8) assays, flow cytometry, and wound healing assays were used to analyze the antitumor effects of ART. Western blot, biochemical assays and transmission electron microscope were utilized to further characterize ART-induced ferroptosis. In vivo, the effects of ART on ferroptosis were examined using a xenograft mouse model.

RNA sequencing analysis revealed that the HOXC11, PROM2 and Phosphatidylinositol 3-Kinase/ Protein Kinase B (PI3K/AKT) pathways were downregulated by ART. HOXC11 was found to regulate PROM2 expression by binding to its promoter directly. HOXC11 overexpression reversed ART-induced effects on ovarian cancer cell proliferation, migration, apoptosis and ferroptosis by activating the PROM2/PI3K/AKT signaling axis. Conversely, silencing PROM2 in HOXC11-overexpressing cells restored ART-induced ferroptosis and its associated antitumor effects by inhibiting the PI3K/AKT pathway. Consistently, in vivo studies using a xenograft mouse model confirmed that ART-induced tumor inhibition was mediated by ferroptosis through the suppression of the HOXC11/PROM2/PI3K/AKT pathway.

This study identifies the HOXC11/PROM2/PI3K/AKT axis as a novel regulatory mechanism underlying ART-induced ferroptosis in ovarian cancer. Targeting the HOXC11/PROM2 axis may represent a promising therapeutic strategy for enhancing ferroptosis, offering new insights for the treatment of ovarian cancer.

Women diagnosed with late-stage ovarian cancer suffer a very high rate of mortality. Accordingly, it is imperative to detect and diagnose the disease as early as possible in its development. Achievement of this aim implies relatively large-scale screening of women at an age of clinical significance through assay of biomarkers for disease present in blood or serum. Biosensor detection offers an attractive technology for the automated detection of such species. Among several biomarkers that have been identified that are present in patients with ovarian cancer, the only one that is commonly tested for in clinical use is cancer antigen 125, which is considered to be a poor biomarker for the disease. Here, we describe several biosensors that developed in the past decade for the detection of ovarian cancer biomarkers such as CA125, human epididymis protein 4 (HE4) and apolipoprotein A1. The challenges presented by the fabrication of biosensor devices for detecting ovarian cancer and the limited number of biosensors developed for this purpose are discussed.

LINC01094 is a long non-coding RNA that plays a crucial role in cancer progression by modulating key signaling pathways, such as PI3K/AKT, Wnt/β-catenin and TGF-β Signaling Pathway Feedback Loop. In this review we summarize the recent research on the functional mechanisms of LINC01094 in various cancers, including its impact on tumor growth, metastasis, and resistance to therapy. We also discuss the therapeutic potential of targeting LINC01094 and highlight the current strategies and challenges in this area. Perspectives on future development of LINC01094-based therapies are also provided.

This study aimed to investigate the changes in the incidence and mortality trends of ovarian cancer (OC), cervical cancer (CC) and uterine cancer (UC) in the Fujian Province, southeastern China.

Provincial, population-based, retrospective observational study.

Fujian province, southeastern China between 2011-2020.

From 2011 to 2020, 6178 new cases and 2037 deaths caused by 3 gynaecological cancers were eligible for analysis.

The primary outcome measures were the incidence and mortality rates, including the age-standardised incidence rate (ASIR) and age-standardised mortality rate (ASMR), of three gynaecological cancers. The secondary outcome measure was the prevalence (average annual per cent changes (AAPC)).

The incidence of all three gynaecological cancers increased from 2011 to 2020. CC had the slowest upward trend, with an AAPC of 2.54% over the period. However, it had the highest ASIR among the 3 cancers in 2020 (10.41/100 000). UC showed a rapid increase, with an AAPC of 15.15% from 2016 to 2020. While the mortality rate of UC remained stable, both CC and OC also exhibited rising trends, with the CC having the highest ASMR (3.05/100 000) in 2020. The ASMR for CC increased rapidly, with the highest AAPC of 5.51%. Conversely, changes in the ASMR for UC were not statistically significant (p=0.601). Moreover, high incidence rates were more common among perimenopausal women and older participants in the respective cancer groups where the increased mortality was observed.

Gynaecological cancer burden remains a public health issue in Fujian Province, with an increasing incidence. Improving the healthcare system and promoting a healthy lifestyle should be highlighted to reduce the cancer burden.

Background/Objectives: In 2020, ovarian cancer ranked fourth in global incidence among gynecological cancers and remains the deadliest cancer affecting women's health. Survival rates are significantly higher when the disease is detected at early stages; however, the lack of effective early detection methods underscores the importance of identifying risk factors in order to implement preventive strategies. The objective of this work is to provide an overview of the risk factors of ovarian cancer in South America, emphasizing those linked to social determinants, genetic components, and comorbidities. Methods: A literature search was performed using PubMed and Google Scholar. MeSH descriptors and keywords, such as "BRCA1 genes," "BRCA2 genes", "Latin America", and "ovarian neoplasms" were used, along with terms related to socioeconomic and health factors. Inclusion criteria focused on original studies published in the last five years involving South American women. Results: Studies were identified from Argentina, Brazil, Chile, Colombia, Ecuador, and Peru. These studies addressed genetic factors, health status at diagnosis, and sociodemographic factors, revealing important data gaps, particularly on contraception and hormone replacement therapy. The prevalence of BRCA1 and BRCA2 mutations in South America is estimated to be 15-20% among women with inherited risk factors. Social, demographic and economic factors vary by country, although commonalities include a higher prevalence among women over 50 years of age, those with limited education, and those who face barriers to accessing health care. Conclusions: Although the literature does not conclusively establish a direct link between obesity and/or diabetes and the development of ovarian cancer, the indirect association highlights the need for further clinical studies. A general research gap related to risk factors of ovarian cancer could be observed in the South American region.

We aimed to investigate human epididymis protein 4 (HE4) as a potential biomarker in patients with pediatric-onset systemic lupus erythematosus (pSLE), particularly on the association of serum HE4 levels with disease activity and other laboratory tests.

We included 137 patients with pSLE and 75 age- and sex-matched healthy controls (HCs). Serum HE4 level was measured by a chemiluminescent microparticle on an Abbott ARCHITECT i2000SR Immunoassay Analyzer. Comparisons between groups were performed using the independent Student t-test, Mann-Whitney U test, Chi-square test, or Fisher's exact test, as appropriate. We also determined the relationships between HE4 and clinical parameters and evaluated disease activity using SLE Disease Activity Index (SLEDAI) and renal SLEDAI (rSLEDAI).

Serum HE4 levels in patients with pSLE (44.6 pmol/L; IQR, 32.5-73.5) were significantly higher than those in HCs (38.9 pmol/L; IQR, 34-46.1). HE4 levels were significantly higher in moderate to severe disease activities (57.4 pmol/L, IQR 37.7-164.5) than in mild disease activities (38.8 pmol/L, IQR 30.1-48.5) or HCs (38.9 pmol/L, IQR 34.0-46.1), as well as in active renal disease activities (77.2 pmol/L, IQR 47.4-224.1) than in inactive renal disease activities (36.1 pmol/L, IQR 27.8-46.7). The ROC curve analysis showed that HE4 could discriminate pSLE with renal (AUC, 0.717; 95% CI, 0.632-0.801), hematological (AUC, 0.740; 95% CI, 0.648-0.831), and cardiovascular involvement (AUC:0.775, 95% CI 0.669-0.880). Serum HE4 levels significantly correlated with several indicators related to renal morbidity, such as creatinine, blood urea nitrogen, uric acid, cystatin C, urine protein/24 h, etc.

Serum HE4 levels in pSLE were elevated and highly associated with disease activity and systemic involvement, indicating HE4 as a potential biomarker for pSLE.

This study retrospectively analyzed the medical records of 200 patients with endometrial hyperplasia to predict the risk of concurrent endometrial cancer.

Patients were categorized into either the endometrial cancer group or the endometrial hyperplasia group based on post-hysterectomy pathology. The investigation compared general information, tumor indices, fertility history, preoperative endometrial sampling methods, comorbidities, and clinical symptoms between the groups to identify risk factors for endometrial hyperplasia complicating endometrial cancer.

(1) Of the 200 patients, 68 (34.0%) were diagnosed with concurrent endometrial cancer post-hysterectomy. Among these, 60 (88.24%) had endometrioid adenocarcinoma, while 8 (11.76%) had other types. Stage I was identified in 58 patients (85.29%) and Stage II in 10 patients (14.71%). High differentiation was observed in 57 cases (83.82%), moderate differentiation in 7 cases (10.29%), and poor differentiation in 4 cases (5.89%), indicating that most endometrial cancers complicated by hyperplasia were early-stage, well-differentiated endometrioid carcinomas; (2) Univariate analysis revealed statistically significant differences in age, menopausal status, length of menopause, and preoperative endometrial pathology of severe atypical hyperplasia between the groups; (3) Multivariate analysis indicated significant differences for age ≥ 53.5 years (OR: 4.307, 95% CI: 2.018-9.192, p < 0.05), menopausal status (OR: 5.250, 95% CI: 2.449-11.252, p < 0.05), and severe atypical endometrial hyperplasia (OR: 4.817, 95% CI: 1.260-18.419, p < 0.05); (4) Significant differences were observed among patients with endometrial hyperplasia when stratified by the presence of zero, one, two, or three high-risk factors.

In conclusion, patients aged ≥ 53.5 years, those who are menopausal, and those with severe atypical endometrial hyperplasia preoperatively are at higher risk for endometrial cancer. The risk increases with the number of high-risk factors present in patients with atypical endometrial hyperplasia.

Meningiomas, characterized primarily as benign intracranial or spinal tumors, present distinctive challenges due to their variable clinical behavior, with certain cases exhibiting aggressive features linked to elevated morbidity and mortality. Despite their prevalence, the underlying molecular mechanisms governing the initiation and progression of meningiomas remain insufficiently understood. MicroRNAs (miRNAs), small endogenous non-coding RNAs orchestrating post-transcriptional gene expression, have garnered substantial attention in this context. They emerge as pivotal biomarkers and potential therapeutic targets, offering innovative avenues for managing meningiomas. Recent research delves into the intricate mechanisms by which miRNAs contribute to meningioma pathogenesis, unraveling the molecular complexities of this enigmatic tumor. Meningiomas, originating from arachnoid meningothelial cells and known for their gradual growth, constitute a significant portion of intracranial tumors. The clinical challenge lies in comprehending their progression, particularly factors associated with brain invasion and heightened recurrence rates, which remain elusive. This comprehensive review underscores the pivotal role of miRNAs, accentuating their potential to advance our comprehension of meningioma biology. Furthermore, it suggests promising directions for developing diagnostic biomarkers and therapeutic interventions, holding the promise of markedly improved patient outcomes in the face of this intricate and variable disease.

We aim to explore the possible association between ovarian cancer and the subsequent development of open-angle glaucoma (OAG) using the Taiwan Longitudinal Health Insurance Database (LHID) 2000. A retrospective cohort study was executed, and individuals with ovarian cancer were enrolled and age-matched (with a 1:4 ratio) to non-ovarian cancer individuals. A total of 4990 and 19,960 patients were put into the ovarian cancer and control groups. The main outcome was the presence of OAG according to the LHID 2000 codes. The Cox proportional hazard regression was adopted to demonstrate the adjusted hazard ratio (aHR) and 95% confidence interval (CI) of OAG between the ovarian cancer and control groups. There were a total of 241 and 1029 OAG cases observed in the ovarian cancer group and the control group, respectively. The incidence of OAG was significantly higher in the ovarian cancer group than in the control group according to multivariable analysis (aHR: 1.18, 95% CI: 1.02-1.37, p = 0.022). The ovarian cancer patients older than 60 years showed a significantly higher risk of OAG compared to the non-ovarian cancer individuals of the same age (aHR: 1.39, 95% CI: 1.16-1.63, p = 0.001). Additionally, ovarian cancer individuals with a disease interval of more than two years presented a significantly higher incidence of OAG than the non-ovarian cancer group (p < 0.05). In conclusion, ovarian cancer positively correlates with a high rate of subsequent OAG, especially in elderly persons with a long disease interval.

To investigate the role of prognostic genes related to cisplatin resistance in ovarian cancer during disease progression.

The gene expression profile of the NCI-60 cell line was acquired through comprehensive analysis of the GEO database accession GSE116439. We performed a thorough analysis of gene expression differences in samples from seven individuals exposed to cisplatin concentrations of 0 nM compared to seven samples exposed to 15000 nM over a 24-h period. Key genes were initially identified through LASSO regression, followed by their enrichment through differential gene function analysis (GO) and pathway enrichment analysis (KEGG). Subsequently, a prognostic risk model was established for these key genes. The prognostic model's performance was assessed through K-M survival curves and ROC curves. To examine the variance in immune cell infiltration between the high and low-risk groups, CIBERSORTx analysis was employed. Finally, validation of prognostic gene expression in cisplatin-resistant ovarian cancer was carried out using clinical samples, employing RT-qPCR and Western Blot techniques.

A total of 132 differential genes were found between cisplatin resistance and control group, and 8 key prognostic genes were selected by analysis, namely VPS13B, PLGRKT, CDKAL1, TBC1D22A, TAP1, PPP3CA, CUX1 and PPP1R15A. The efficacy of the risk assessment model derived from prognostic biomarkers, as indicated by favorable performance on both Kaplan-Meier survival curves and ROC curves. Significant variations in the abundance of Macrophages M1, T cells CD4 memory resting, T cells follicular helper, and T cells gamma delta were observed between the high and low-risk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming differential expression of the identified eight key genes between the two groups.

VPS13B, TBC1D22A, PPP3CA, CUX1 and PPP1R15A were identified as poor prognostic genes of cisplatin resistance in ovarian cancer, while PLGRKT, CDKAL1 and TAP1 were identified as good prognostic genes. This offers a novel perspective for future advancements in ovarian cancer treatment, suggesting potential avenues for the development of new therapeutic targets.