Diagnosis Of Ovarian Cancer Research Articles

Identification of PDZD11 as a Potential Biomarker Associated with Immune Infiltration for Diagnosis and Prognosis in Epithelial Ovarian Cancer.

Evidence has indicated that PDZD11 is involved in regulating adherens junction. However, the distinct effect of its aberrant expression on epithelial ovarian cancer (EOC) awaits clarification. In this study, public databases (Gene Expression Omnibus, The Cancer Genome Atlas, and The Genotype-Tissue Expression), online analysis tools (Kaplan-Meier plotter and TIMER), and data analysis methods (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and the CIBERSORT algorithm) were fully utilized to analyze the differential expression, diagnostic efficiency, prognostic significance, potential function, and correlation with immune infiltration of PDZD11. The differential expression of PDZD11 was tested by immunohistochemistry in EOC tissues (78 cases) and control tissues (37 cases). Our results indicate that PDZD11 was remarkably overexpressed in EOC, which was associated with advanced cancer stages, no lymphatic metastasis status, and poor prognosis. Moreover, PDZD11 played a role in cell adhesion, cell proliferation, and immune responses. Also, PDZD11 was significantly related to the abundances of infiltrating immune cells in EOC, including neutrophils, macrophages, dendritic cells, CD8+ T cells, and CD4+ T cells, and its expression was positively co-expressed with well-known immune checkpoints, including TIGIT, TIM3, LAG3, CTLA4, and PD-1. These results suggest that PDZD11 could be a potential diagnostic and prognostic biomarker associated with immune infiltration in EOC, and our findings might help elucidate the function of PDZD11 in carcinogenesis.

Evaluating the effectiveness of pre-operative diagnosis of ovarian cancer using minimally invasive liquid biopsies by combining serum human epididymis protein 4 and cell-free DNA in patients with an ovarian mass

ObjectiveTo assess the feasibility of scalable, objective, and minimally invasive liquid biopsy-derived biomarkers such as cell-free DNA copy number profiles, human epididymis protein 4 (HE4), and cancer antigen 125 (CA125)...

Sensitivity and Specificity of Selected Biomarkers and Their Combinations in the Diagnosis of Ovarian Cancer.

One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers' efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage.

A Rare Case of Struma Ovarii in Afghanistan

Introduction: Struma ovarii is a rare ovarian tumor characterized by containing either entirely or predominantly thyroid tissue (> 50%). It accounts for less than 1% of all ovarian tumors and approximately 3% of ovarian teratomas. The condition was first described by Von Klden in 1895. Case Presentation: In this report, we described a 50-year-old Afghan menopausal woman who referred to our hospital with a 5-month history of weight loss, anorexia, hyperthyroidism, abdominal distension and sinus tachycardia. Abdominal radiological findings revealed left adnexal large multilocular cystic lesion and the patient’s CA-125 serum level was high. Preoperatively, the clinicians suggested a diagnosis of ovarian cancer based on the symptoms, radiological findings and high level of CA-125 serum. However, postoperatively, histopathological examination revealed the lesion to be a benign struma ovarii. Conclusions: Association of struma ovarii with CA-125 and hyperthyroidism is an extremely rare case and are usually misdiagnosed preoperatively due to nonspecific clinical manifestations, and indistinguishable imaging studies, and should be kept in the differential diagnosis of all ovarian cystic lesions. An exact diagnosis is only made by postoperative histopathological examination, and complete surgical resection of the lesion is considered the optimal treatment.

Identification of MAD2L1 and BUB1B as Potential Biomarkers Associated with Progression and Prognosis of Ovarian Cancer.

Ovarian cancer develops insidiously and is frequently diagnosed at advanced stages. Screening for ovarian cancer is an effective strategy for reducing mortality. This study aimed to investigate the molecular mechanisms underlying the development of ovarian cancer and identify novel tumor biomarkers for the diagnosis and prognosis of ovarian cancer. Three databases containing gene expression profiles specific to serous ovarian cancer (GSE18520, GSE12470, and GSE26712) were acquired. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were analyzed for the differentially expressed gene (DEGs). The protein-protein interaction (PPI) network was constructed using the STRING database. The pivotal genes in the PPI network were screened using the Cytoscape software. Survival curve analysis was performed using a Kaplan-Meier Plotter. The cancer genome atlas and Gene Expression Omnibus databases were used to find the relationship between Hub gene and serous ovarian cancer. PCR and immunohistochemistry were used to detect the expression of Hub gene in serous ovarian cancer tissues and cells. Downstream pathways of the candidate tumor marker genes were predicted using Gene Set Enrichment Analysis. In this study, 252 DEGs were screened for pathway enrichment. 20 Hub genes were identified. Survival analysis suggested that Aurka, Bub1b, Cenpf, Cks1b, Kif20a, Mad2l1, Racgap1, and Ube2c were associated with the survival of patients with serous ovarian cancer. MAD2L1 and BUB1B levels were significantly different in serous ovarian cancer at different stages. Finally, Mad2l1 was found to play a role in the cell cycle, oocyte meiosis, and ubiquitin-mediated proteolysis. Meanwhile, Bub1b may play a role in the cell cycle, ubiquitin-mediated proteolysis, and spliceosome processes. Mad2l1 and Bub1b could be used as markers to predict ovarian carcinogenesis and prognosis, providing candidate targets for the diagnosis and treatment of serous ovarian cancer.

Serum anti-CFL1, anti-EZR, and anti-CYPA autoantibody as diagnostic markers in ovarian cancer

The purpose of this study was to identify novel autoantibodies against tumor-associated antigens (TAAs) and explore a diagnostic panel for Ovarian cancer (OC). Enzyme-linked immunosorbent assay was used to detect the expression of five anti-TAA autoantibodies in the discovery (70 OC and 70 normal controls) and validation cohorts (128 OC and 128 normal controls). Machine learning methods were used to construct a diagnostic panel. Serum samples from 81 patients with benign ovarian disease were used to identify the specificity of anti-TAA autoantibodies for OC. In both the discovery and validation cohorts, the expression of anti-CFL1, anti-EZR, anti-CYPA, and anti-PFN1 was higher in patients with OC than that in normal controls. The area under the receiver operating characteristic curve, sensitivity, and specificity of the panel containing anti-CFL1, anti-EZR, and anti-CYPA were 0.762, 55.56%, and 81.31%. The panel identified 53.06%, 53.33%, and 51.11% of CA125 negative, HE4 negative and the Risk of Ovarian Malignancy Algorithm negative OC patients, respectively. The combination of the three anti-TAA autoantibodies can serve as a favorable diagnostic tool for OC and has the potential to be a complementary biomarker for CA125 and HE4 in the diagnosis of ovarian cancer.

Shared biological features between ovarian cancer and endometriosis based on co-expression networks analysis

Background: Endometriosis is a gynecologic disease common among younger females, impacts adversely the quality of life, and results in infertility. The evidence demonstrates that patients with endometriosis have a higher risk of developing ovarian cancer. Due to this similarity, we aimed to identify common molecular markers between ovarian cancer and endometriosis. Methods: We used publicly available microarray data (GSE9899) including ovarian tumor samples and alongside normal controls, common differentially expressed genes (DEGs) were identified in ovarian samples compared to controls. We constructed a co-expression network based on ovarian cancer and then extracted the hub genes. We validated the similarities between ovarian cancer and endometriosis by examining the expression of hub genes in endometriosis patients referred to Iranian Rasoul Akram Hospital. Results: In total 1000 DEGs were identified in grade 2 and grade 3 of ovarian cancer. We identified 34 co-expression modules in grade 3 ovarian cancer and 31 in grade 2. Modules were mostly enriched in inflammatory response and plasma membrane-bounded cell projection, mostly. To validate the comorbidity of ovarian cancer and endometriosis, we examined the expression of six significant hubs (resulting in an ovarian cancer co-expression network) in women with endometriosis. Conclusion: Our results suggest that patients with endometriosis have a higher risk of developing ovarian cancer. These findings will improve the diagnosis of ovarian cancer in endometriosis patients. More research is required to find the exact mechanism of hub genes affecting the development of ovarian cancer.

The roles of long non-coding RNAs in ovarian cancer: from functions to therapeutic implications.

Long non-coding RNAs (lncRNAs) are multifunctional and participate in a variety of biological processes and gene regulatory networks. The deregulation of lncRNAs has been extensively implicated in diverse human diseases, especially in cancers. Overwhelming evidence demonstrates that lncRNAs are essential to the pathophysiological processes of ovarian cancer (OC), acting as regulators involved in metastasis, cell death, chemoresistance, and tumor immunity. In this review, we illustrate the expanded functions of lncRNAs in the initiation and progression of OC and elaborate on the signaling pathways in which they pitch. Additionally, the potential clinical applications of lncRNAs as biomarkers in the diagnosis and treatment of OC were emphasized, cementing the bridge of communication between clinical practice and basic research.

Development of a labeled-free and labeled electrochemical aptasensor for the detection of cancer antigen 125 by using magnetic g-C3N4/MoS2 nanocomposite

Efficient and timely detection of cancer biomarkers is pivotal for enhancing treatment outcomes and mitigating patient mortality. This study addresses the pressing need for a swift, accurate, and non-invasive method to identify cancer antigen 125 (CA125), a vital biomarker in ovarian cancer. Leveraging the growing prominence of nano-biosensors for their high selectivity and sensitivity, we present the development and characterization of an innovative electrochemical nano-biosensor. The sensor, featuring aptamer strands immobilized on a glassy carbon electrode modified with graphitic carbon nitrides, molybdenum disulfide, and magnetic nanoparticles (g-C3N4/MoS2/Fe3O4), demonstrates superior sensitivity and accuracy in CA125 detection. Utilizing methylene blue for electrochemical detection of labeled CA125 and ferrocyanide for label-free detection, our aptasensor achieves a low limit of detection (LOD) at 0.202 U.mL−1 and 0.215 U.mL−1, respectively, with a broad detection range from 2 to 10 U.mL−1. The modified electrode exhibits a pronounced affinity for CA125, demonstrating enhanced stability compared to other biomolecules. Crucially, the evaluation of both patient and normal serum samples underscores the aptasensor's remarkable performance. These findings not only establish a robust foundation for future research in ovarian cancer diagnosis but also highlight the potential clinical impact of our electrochemical nano-biosensor in advancing early cancer detection methodologies.

Alteration of glucose metabolism and expression of glucose transporters in ovarian cancer.

Aerobic glycolysis also known as the Warburg effect, remains a hallmark of various cancers, including ovarian cancer. Cancer cells undergo metabolic changes to sustain their tumorigenic properties and adapt to environmental conditions, such as hypoxia and nutrient starvation. Altered metabolic pathways not only facilitate ovarian cancer cells' survival and proliferation but also endow them to metastasize, develop resistance to chemotherapy, maintain cancer stem cell phenotype, and escape anti-tumor immune responses. Glucose transporters (GLUTs), which play a pivotal role as the rate-limiting step in glycolysis, are frequently overexpressed in a variety of tumors, including ovarian cancer. Multiple oncoproteins can regulate GLUT proteins, promoting tumor proliferation, migration, and metastasis, either dependent or independent of glycolysis. This review examines the alteration of GLUT proteins, particularly GLUT1, in ovarian cancer and its impact on cancer initiation, progression, and resistance to treatment. Additionally, it highlights the role of these proteins as biomarkers for diagnosis and prognosis in ovarian cancer, and delves into novel therapeutic strategies currently under development that target GLUT isoforms.

Circular RNA circMAN1A2 promotes ovarian cancer progression through the microRNA-135a-3p/IL1RAP/TAK1 pathway.

Ovarian cancer (OC) is the most lethal malignancy in women owing to its diagnosis only at the advanced stage. Elucidation of its molecular pathogenesis may help identify new tumor markers and targets for therapy. Circular RNAs (circRNAs) are stable, conserved, and functional biomolecules that can be used as effective biomarkers for various cancers. In this study, a potential circRNA related to early diagnosis of OC, circMAN1A2, was analyzed. Overexpression/knockdown of circMAN1A2 in OC cells was used to decipher its effects on cell proliferation with a Cell Counting Kit-8, 5-ethynyl-2'-deoxyuridine (EdU), cell cycle, clone formation, and wound healing assay. RNA pull-down and Dual luciferase assay were used to explain the underlying mechanism by which circMAN1A2 regulates OC cell proliferation. In vivo, the effect of circMAN1A2 in OC was evaluated using nude mouse xenograft experiments. CircMAN1A2 was highly expressed in OC and promoted proliferation, clone formation, and tumorigenicity of OC cells. In addition, we found that circMAN1A2 acted as a sponge for microRNA (miR)-135a-3p; miR-135a-3p directly targeted the 3' untranslated region of interleukin 1 receptor accessory protein (IL1RAP) in OC cells, thereby regulating the phosphorylation of transforming growth factor-beta activated kinase 1 (TAK1), which resulted in promotion of OC cell growth. CircMAN1A2 promotes OC cell proliferation by inhibiting the miR-135a-3p/IL1RAP/TAK1 axis. In conclusion, circMAN1A2 may be a biomarker for early detection of OC and a target for subsequent therapy.

Evaluation of serum CA125, HE4 and CA724 and the risk of ovarian malignancy algorithm score in the diagnosis of high-grade serous ovarian cancer

AimTo develop a new algorithm for the detection of high-grade serous ovarian cancer (HGSOC). MethodsPatients diagnosed with HGSOC, borderline ovarian tumours (BOTs) or benign ovarian masses (BOMs) were enrolled between February 2019 and December 2020. Patients with BOTs or BOMs were grouped as non-HGSOC. The cases were divided randomly into a training cohort (two-thirds of cases) and a validation cohort (one-third of cases). Logistic regression was used to find risk factors for HGSOC and to create a new algorithm in the training cohort. Receiver operating characteristic curves were used to compare the diagnostic value of tumour biomarkers. Sensitivity and specificity of tumour markers and the new algorithm were calculated in the training cohort and validation cohort. ResultsThis study found significant differences in age; BRCA1/2 mutation status; CA125, CA724 and HE4 levels; and Risk of Ovarian Malignancy Algorithm score between the two groups.Logistic regression analysis showed that CA125 and BRCA1/2 were risk factors for HGSOC. A new algorithm combining CA125 and BRCA1/2 increased the specificity of CA125 for diagnosis of HGSOC. The new algorithm had sensitivity of 81.08% and specificity of 93.10% in the training cohort. ConclusionThe new algorithm using CA125 and BRCA1/2 helped to distinguish between patients with HGSOC and patients with non-HGSOC.

Dual-Aptamer Recognition of DNA Logic Gate Sensor-Based Specific Exosomal Proteins for Ovarian Cancer Diagnosis.

Clinical diagnosis of ovarian cancer lacks high accuracy due to the weak selection of specific biomarkers along with the circumstance biomarkers localization. Clustering analysis of proteins transported on exosomes enables a more precise screening of effective biomarkers. Herein, through bioinformatics analysis of ovarian cancer and exosome proteomes, two coexpressed proteins, EpCAM and CD24, specifically enriched, were identified, together with the development of an as-derived dual-aptamer targeted exosome-based strategy for ovarian cancer screening. In brief, a DNA ternary polymer with aptamers targeting EpCAM and CD24 was designed to present a logic gate reaction upon recognizing ovarian cancer exosomes, triggering a rolling circle amplification chemiluminescent signal. A dynamic detection range of 6 orders of magnitude was achieved by quantifying exosomes. Moreover, for clinical samples, this strategy could accurately differentiate exosomes from healthy persons, other cancer patients, and ovarian cancer patients, enabling promising in situ detection. By accurately selecting biomarkers and constructing a dual-targeted exosomal protein detection strategy, the limitation of insufficient specificity of traditional protein markers was circumvented. This work contributed to the development of exosome-based prognosis monitoring in ovarian cancer through the identification of disease-specific exosome protein markers.

Comprehensive DNA methylation profiling by MeDIP-NGS identifies potential genes and pathways for epithelial ovarian cancer.

Ovarian cancer, among all gynecologic malignancies, exhibits the highest incidence and mortality rate, primarily because it is often presents with non-specific or no symptoms during its early stages. For the advancement of Ovarian Cancer Diagnosis, it is crucial to identify the potential molecular signatures that could significantly differentiate between healthy and ovarian cancerous tissues and can be used further as a diagnostic biomarker for detecting ovarian cancer. In this study, we investigated the genome-wide methylation patterns in ovarian cancer patients using Methylated DNA Immunoprecipitation (MeDIP-Seq) followed by NGS. Identified differentially methylated regions (DMRs) were further validated by targeted bisulfite sequencing for CpG site-specific methylation profiles. Furthermore, expression validation of six genes by Quantitative Reverse Transcriptase-PCR was also performed. Out of total 120 differentially methylated genes (DMGs), 68 genes were hypermethylated, and 52 were hypomethylated in their promoter region. After analysis, we identified the top 6 hub genes, namely POLR3B, PLXND1, GIGYF2, STK4, BMP2 and CRKL. Interestingly we observed Non-CpG site methylation in the case of POLR3B and CRKL which was statistically significant in discriminating ovarian cancer samples from normal controls. The most significant pathways identified were focal adhesion, the MAPK signaling pathway, and the Ras signaling pathway. Expression analysis of hypermethylated genes was correlated with the downregulation of the genes. POLR3B and GIGYF2 turned out to be the novel genes associated with the carcinogenesis of EOC. Our study demonstrated that methylation profiling through MeDIP-sequencing has effectively identified six potential hub genes and pathways that might exacerbate our understanding of underlying molecular mechanisms of ovarian carcinogenesis.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis.

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques

Globally, ovarian cancer affects women disproportionately, causing significant morbidity and mortality rates. The early diagnosis of ovarian cancer is necessary for enhancing patient health and survival rates. This research article explores the utilization of Machine Learning (ML) techniques alongside eXplainable Artificial Intelligence (XAI) methodologies to aid in the early detection of ovarian cancer. ML techniques have recently gained popularity in developing predictive models to detect early-stage ovarian cancer. These predictions are made using XAI in a transparent and understandable way for healthcare professionals and patients. The primary aim of this study is to evaluate the effectiveness of various ovarian cancer prediction methodologies. This includes assessing K Nearest Neighbors, Support Vector Machines, Decision trees, and ensemble learning techniques such as Max Voting, Boosting, Bagging, and Stacking. A dataset of 349 patients with known ovarian cancer status was collected from Kaggle. The dataset included a comprehensive range of clinical features such as age, family history, tumor markers, and imaging characteristics. Preprocessing techniques were applied to enhance input data, including feature scaling and dimensionality reduction. A Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select the features in the model. Our experimental results demonstrate that in Support Vector Machines, we found 85 % base model accuracy and 89 % accuracy after stacking several ensemble learning techniques. With the help of XAI, complex ML algorithms can be given more profound insights into their decision-making, improving their applicability. This paper aims to introduce the best practices for integrating ML and artificial intelligence in biomarker evaluation. Building and evaluating Shapley values-based classifiers and visualizing results were the focus of our investigation. The study contributes to the field of oncology and women's health by offering a promising approach to the early diagnosis of ovarian cancer.

The evaluation of miR-1181 and miR-4314 as serum microRNA biomarkers for epithelial ovarian cancer diagnosis and prognosis.

Epithelial ovarian cancer (EOC) is the most ominous tumor of gynecological cancers due to its poor early detection rate and unfavorable prognosis. To date, there is no reliable screening method for the diagnosis of ovarian cancer at an early stage. MiRNAs are small non-coding RNA molecules, and their main function is to regulate gene expression. The present study compared the serum miR-1181 and miR-4314 levels in patients with EOC and healthy controls to measure the diagnostic and prognostic value as candidate biomarkers. We collected serum samples from a total of 135 participants (69 patients with EOC and 66 healthy controls). Relative expressions of miR-1181 and miR-4314 were measured by quantitative real-time polymerase chain reaction assay (qPCR). The present study revealed that both serum miR-1181 and miR-4314 levels in patients with EOC were significantly increased compared to healthy controls for each marker. In addition, there was a significant relationship between miR-1181 and miR-4314 overexpressions and the stage and prognosis of the disease. Finally, patients with high expression levels of miR-1181 and miR-4314 had significantly shorter survival rates than those with low expression levels. The current study proposed that serum miR-1181 and miR-4314 could discriminate the EOC patients from healthy controls. In addition, both miR-1181 and miR-4314 may be predictive biomarkers for ovarian cancer prognosis. Further studies are needed to confirm the findings of the present study.

Construction and analysis of competitive endogenous RNA networks and prognostic models associated with ovarian cancer based on the exoRBase database.

To construct a competitive endogenous RNA (ceRNA) regulatory network in blood exosomes of patients with ovarian cancer (OC) using bioinformatics and explore its pathogenesis. The exoRbase2.0 database was used to download blood exosome gene sequencing data from patients OC and normal controls and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were detected independently using R language for differential expression analysis. TargetScan and miRanda databases were combined for the prediction and differential expression of mRNA-binding microRNAs (miRNA). The miRcode and starBase databases were used to predict miRNAs that bind to differentially expressed lncRNAs and circRNAs repectively. The relevant mRNA, circRNA, lncRNA and their corresponding miRNA prediction data were imported into Cytoscape software for visualization of the ceRNA network. The R language and KEGG Orthology-based Annotation System (KOBAS) were used to execute and illustrate the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using The CytoHubba plugin. Thirty-one differentially expressed mRNAs, 17 differentially expressed lncRNAs, and 24 differentially expressed circRNAs were screened. Cytoscape software was used to construct the ceRNA network with nine mRNA nodes, two lncRNA nodes, eight circRNA nodes, and 51 miRNA nodes. Both GO and KEGG were focused on the Spliceosome pathway, indicating that spliceosomes are closely linked with the development of OC, while heterogenous nuclear ribonucleoprotein K and RNA binding motif protein X-linked genes were the top 10 score Hub genes screened by Cytoscape software, including two lncRNAs, four mRNAs, and four circRNAs. In patients with OC, the expression of eukaryotic translation initiation factor 4 gamma 2 (EIF4G2), SERPINE 1 mRNA binding protein 1 (SERBP1), ribosomal protein L15 (RPL15) and human leukocyte antigen complex P5 (HCP5) was significantly higher whereas that of testis expressed transcript, Y-linked 15 and DEAD-box helicase 3 Y-linked genes was lower compared to normal controls Immunocorrelation scores revealed that SERBP1 was significantly and negatively correlated with endothelial cells and CD4+ T cells and positively correlated with natural killer (NK) cells and macrophages, respectively; RPL15 was significantly positively correlated with macrophages and endothelial cells and negatively correlated with CD8+ T cells and uncharacterized cells, respectively. EIF4G2 was significantly and negatively correlated with endothelial cells and CD4+ T cells, and positively correlated with uncharacterized cells, respectively. Based on the survival data and the significant correlation characteristics derived from the multifactorial Cox analysis (P < 0.05), the survival prediction curves demonstrated that the prognostic factors associated with 3-year survival in patients with OC were The prognostic factors associated with survival were Macrophage, RPL15. This study successfully constructs a ceRNA regulatory network in blood exosomes of OV patients, which provides the specific targets for diagnosis and treatment of OC.

Epithelial Ovarian Cancer: The Importance of Early Diagnosis and Individualized Treatment with Combination Chemotherapy and Targeted Therapy

Epithelial ovarian cancer is a malignant neoplasm that represents the majority of ovarian cancer cases. It is characterized by aggressiveness, often late diagnosis and therapeutic challenges. Early diagnosis is crucial to improving patients' survival rates and quality of life. Individualized treatment, combining chemotherapy and targeted therapy, has been a promising approach to optimize therapeutic results and minimize adverse effects. Objective: to analyze and synthesize the available evidence on the importance of early diagnosis and individualized treatment with combined chemotherapy and targeted therapy in epithelial ovarian cancer. Methodology: Using the PRISMA checklist, this review was conducted based on articles published in the last 10 years. The databases used were: PubMed, Scielo, Web of Science. The descriptors: "epithelial ovarian cancer", "early diagnosis", "individualized treatment", "combined chemotherapy", "targeted therapy". Inclusion criteria: original studies in humans, published in English or Portuguese, addressing the early diagnosis and/or individualized treatment of epithelial ovarian cancer. Exclusion criteria: animal studies, systematic reviews, studies with non-representative samples. Results: The results of this review highlight the importance of early diagnosis through imaging techniques and tumor markers. Furthermore, they highlight the effectiveness of combination chemotherapy, especially regimens containing platinum and taxanes, together with targeted therapy, such as PARP inhibitors, in improving response rates and survival in patients with epithelial ovarian cancer. Conclusion: Early diagnosis and individualized treatment with combination chemotherapy and targeted therapy play a key role in the management of epithelial ovarian cancer. This multifaceted approach can significantly improve patients' clinical outcomes and quality of life, highlighting the importance of personalized therapeutic strategies in this challenging clinical context.

RETRACTED: Nobel et al. Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis. Tomography 2024, 10, 105-132.

The Tomography Editorial Office retracts the article "Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis" [...].