Clinica Chimica Acta

Tumor microenvironment and biomarker innovation in ovarian cancer: mechanistic insights into immune evasion, angiogenesis, and therapeutic resistance

Ovarian cancer remains one of the most lethal gynecologic malignancies, largely because of late-stage diagnosis, extensive intratumoral heterogeneity, and the dynamic complexity of the tumor microenvironment (TME). Emerging evidence highlights the TME as a central orchestrator of immune evasion, angiogenic remodeling, and therapeutic resistance, which are three mechanistic pillars that critically shape disease progression and treatment outcomes. This narrative review synthesizes current mechanistic insights into how stromal, immune, and vascular components interact to promote tumor survival and metastasis. We examine the roles of immunosuppressive cell populations, cytokine networks, and checkpoint pathways in facilitating immune escape; delineate angiogenic drivers and endothelial-tumor crosstalk that sustain aberrant vascularization; and explore TME-mediated mechanisms that underlie chemoresistance, targeted therapy failure, and limited immunotherapy responsiveness. Furthermore, we evaluate recent advances in biomarker discovery, including the identification of circulating ncRNAs, exosomal signatures, spatial immune profiles, and TME-derived molecular indicators, which hold promise for improving early detection, prognostication, and therapeutic stratification. By integrating mechanistic biology with translational biomarker innovation, this review outlines a forward-looking framework for leveraging TME-informed diagnostics and therapeutics to enhance precision oncology in ovarian cancer.

Cervical cancer diagnostics: non-coding RNAs and biosensors to AI-derived methods

Cervical cancer ranks fourth in terms of cancer mortality among women. The most important risk factor for cervical cancer is infection with HPV 16 and HPV 18. The prevalence and mortality rates of this cancer are much higher in countries with low and medium development indices than in developed countries. Improving health, access to vaccination, and screening tests are highly helpful in preventing this type of cancer. Recent advances have revealed novel biomarkers, particularly noncoding RNAs, including microRNAs, long noncoding RNAs, and circular RNAs, which are promising biomarkers for early detection and disease monitoring. Concurrently, artificial intelligence (AI)-derived methods, which leverage machine learning and deep learning algorithms, have revolutionized diagnostic accuracy by enhancing image analysis and pattern recognition in cytology and histopathology. This review focused on the latest developments in cervical cancer diagnostic technologies, with a focus on the role of noncoding RNAs, biosensors, and AI-derived methods (machine learning and deep learning approaches) in clinical diagnosis. By evaluating the strengths, challenges, and future potential of these innovations, we aim to provide a deeper understanding of noncoding RNAs and AI-derived methods as a future for the laboratory diagnosis of cervical cancer.

Rapid prediction of cervical cancer and high-grade precursor lesions: An integrated approach using low-field 1H NMR and chemometric analysis

Cervical cancer (CC) is a significant cause of morbidity and mortality in women, often preceded by high-grade cervical intraepithelial lesions (HSIL). Although conventional cytology (Pap smear) is widely used for screening, its sensitivity limitations and high false-positive rate reinforce the need for complementary methods. This study investigated the feasibility of low-field

An adhesion-based method for rapid and low-cost isolation of circulating tumor cells

Noninvasive monitoring of cancer through circulating tumor cells (CTCs) is hampered long by unsatisfactory CTCs testing techniques. Efficient isolation of CTCs in a rapid and price-favorable way from billions of leukocytes is crucial for testing. We developed a new method based on the stronger adhesive power of CTCs versus leukocytes to sensitively isolate CTCs. Using a BSA-coated microplate and low-speed centrifuge, this method could easily separate cancer cells within 20 min at a very low cost. The capture ratio can reach 70.7-86.6% in various cancer cell lines (breast/lung/liver/cervical/colorectal cancer) covering different epithelial-mesenchymal transformation (EMT) phenotypes and cell sizes, demonstrating the potential for efficient pan-cancer CTCs detection. Moreover, the label-free process can well preserve cell viability (∼99%) to fit downstream DNA/RNA sequencing. A novel technique for non-destructive and rapid enrichment of CTCs has been devised. It has enabled the successful isolation of rare tumor cells in the patient blood sample and pleural effusion, highlighting a promising future of this method in clinical translation.

BRCA1 and BRCA2 truncating mutations and variants of unknown significance in Egyptian female breast cancer patients

Breast cancer is the most common malignancy among women worldwide and the leading cause of cancer death in economically developing countries. We sought to study the contribution of BRCA1/2 mutations to the burden of breast cancer in Egypt. 103 Egyptian female breast cancer patients, unselected for age of onset or family history, were included in the study. Mutational screening of some exons of BRCA1/2 genes was performed using High Resolution Melting analysis followed by direct sequencing of detected variants. Twenty sequence variants were identified. According to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines, 8 variants were classified as pathogenic (Class 5), 1 as likely pathogenic and 11 as variants of unknown significance (Class 3). The pathogenic variants comprised 5 novel frameshift mutations; BRCA1 c.5205delA and BRCA2 (c.3641delT, c.3291dupT, c.3292delA, and c.787dupA) mutations; 1 novel nonsense mutation (BRCA2 c.3280A>T) and 2 previously described missense mutations (BRCA1 c.117T>G, c.110C>A). This study provides the results of our attempt to delineate the genetic aspect of breast cancer among the Egyptian population and emphasizes the necessity of implementing screening strategies for early diagnosis and counseling for breast cancer in Egypt.

Prognostic significance of preoperative circulating FAR and FCI scores in patients with ovarian cancer

Ovarian malignancy is among the most common lethal cancers in gynaecology. It has been considered that nutrition status and inflammation are two essential factors associated with the poor survival rates of patients suffering from multiple tumours. This research included 174 subjects who suffered from FIGO stage I-IV OC and underwent surgeries between May 2008 and March 2013. The results showed that FAR, T3-T4 depth, high CA125, stage III-IV and metastasis were highly associated with low overall survival rates. Moreover, higher FCI was markedly correlated with decreased survival in patients with OC. The combination of FAR and CA125 could be a new non-invasive marker in blood and probably assist physicians in evaluating the prognosis of patients.

Immunosensors for ovarian cancer detection

Ovarian cancer remains one of the most lethal gynecologic malignancies due to its asymptomatic onset and the lack of reliable early stage diagnostic tools. Immunosensor technologies have emerged as powerful platforms capable of detecting ovarian cancer-associated biomarkers with high sensitivity, specificity, and rapid turnaround times. Recent advances in nanomaterials, microfluidic integration, and signal amplification strategies have significantly enhanced the analytical performance of electrochemical, optical, and piezoelectric immunosensors. These innovations enable ultrasensitive quantification of key biomarkers such as CA 125, HE4, mesothelin, and emerging multi marker panels, supporting earlier detection and improved disease monitoring. Furthermore, the development of portable and point of care immunosensing devices offers promising avenues for decentralized screening and personalized clinical decision making. This review highlights the latest technological breakthroughs, design principles, and translational challenges in immunosensor based ovarian cancer diagnostics, emphasizing their potential to transform early detection and patient outcomes.

Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics

Epithelial ovarian cancer (EOC) is a common gynecological cancer with high mortality rates. The main objective of this study was to investigate the serum amino acid and organic acid profiles to distinguish key metabolites for screening EOC patients. In total, 39 patients with EOC and 31 healthy controls were selected as the training set. Serum amino acid and organic acid profiles were determined using the targeted metabolomics approach. Metabolite profiles were processed via multivariate analysis to identify potential metabolites and construct a metabolic network. Finally, a test dataset derived from 29 patients and 28 healthy controls was constructed to validate the potential metabolites. Distinct amino acid and organic acid profiles were obtained between EOC and healthy control groups. Methionine, glutamine, asparagine, glutamic acid and glycolic acid were identified as potential metabolites to distinguish EOC from control samples. The areas under the curve for methionine, glutamine, asparagine, glutamic acid and glycolic acid were 0.775, 0 778, 0.955, 0.874 and 0.897, respectively, in the validation study. Metabolic network analysis of the training set indicated key roles of alanine, aspartate and glutamate metabolism as well as D-glutamine and D-glutamate metabolism in the pathogenesis of EOC. Amino acid and organic acid profiles may serve as potential screening tools for EOC. Data from this study provide useful information to bridge gaps in the understanding of the amino acid and organic acid alterations associated with epithelial ovarian cancer.

Genistein upregulates cyclin D1 and CDK4 expression and promotes the proliferation of ovarian cancer OVCAR-5 cells

Ovarian epithelial cancer is the leading cause of deaths associated with gynecologic malignancies. Genistein represents a major type of phytoestrogens widely found in foods and herbal medicines. Although multiple epidemiological studies indicated that the consumption of genistein or other isoflavones is associated with a decreased ovarian cancer risk, the cellular effects and underlying mechanisms are not fully understood. This study focuses on the effect of genistein on the proliferation and cell cycle regulation of ovarian cancer cells. Ovarian cancer OVCAR-5 cells were treated with genistein in an estrogen-free condition. Cell counting and MTS assays were performed to determine the cell proliferation alterations. Real-time PCR and Western blotting were conducted to examine the expression changes in key cell cycle regulators. Genistein significantly promoted the proliferation and the viability of OVCAR-5 cells. Upon genistein treatment, cellular mRNA and protein expression levels of PCNA, Cyclin D1 and CDK4 were increased, but those of p21 and p27 were decreased. In contrary to results of many previous studies, we observed that genistein was able to upregulate the proliferation and G1-S transition of ovarian cancer OVCAR-5 cells. The discrepancy could be caused by diverged experimental conditions and/or different ER expression patterns of cell lines. The findings may provide basic information for in-depth analysis on the role(s) and mechanisms by which genistein confers its effect on ovarian cancer progression.

Co-occurrence of congenital isolated FSH deficiency and androgen-secreting steroid cell tumour in a Chinese female – Intermittent menses in a patient with primary amenorrhoea

Congenital isolated FSH deficiency is a rare autosomal recessive disorder characterized by primary amenorrhoea, absent or partial breast development, infertility, undetectable serum FSH, and pathogenic variant detected in FSHB gene. Ovarian steroid cell tumour is another rare disease entity that can present in the young, with features of androgenic, estrogenic, or cortisol excess. To date, there have been no reports of the two disease entities occurring in a single patient. A Chinese female presented with primary amenorrhoea and undetectable serum FSH at the age of 16. She developed spontaneous menses intriguingly at the age of 19, with elevated serum testosterone, leading to subsequent diagnosis of right ovarian steroid cell tumour, not otherwise specified (NOS). After surgical resection, the patient redeveloped amenorrhoea, along with normalized testosterone and undetectable estradiol. Sequencing of FSHB gene revealed homozygosity of a novel variant c.366C > A p.(Cys122*), which is predicted to disrupt FSH heterodimer formation. Literature and case reports on congenital isolated FSH deficiency and steroid cell tumours published in English language were reviewed. The common involvement of gonadotropins and sex steroids by the two pathologies raises the suspicion of possible disease linkage. We herein report the first case of steroid cell tumour identified in a Chinese female with isolated FSH deficiency. The unique presentation of primary amenorrhoea, spontaneous menses, and secondary amenorrhoea post-surgery highlights the role of peripheral aromatization in FSH deficiency. Co-occurrence of the two rare disease entities may help uncover the role of FSH, inhibin, and LH in ovarian tumorigenesis.

Prolactin as a novel biomarker in malignancy

Ovarian cancer extracellular vesicle biomarkers

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.

Recent advances in the early detection of ovarian cancer

Ovarian cancer (OC), predominantly epithelial OC, remains the most lethal gynecological malignancy. Owing to its often asymptomatic or non-specific clinical presentation, approximately 70 % of patients are diagnosed at advanced stages (FIGO III-IV), typically characterized by extensive peritoneal dissemination. Although early detection is critical for improving survival outcomes, current standard diagnostic modalities, including serum CA125 and transvaginal ultrasound, lack sufficient sensitivity and specificity for population-level screening of early-stage disease. This review comprehensively evaluates emerging biomarkers and advanced diagnostic technologies, with a particular focus on liquid biopsy analytes, including circulating tumor DNA, microRNAs, and uterine liquid biopsies. We further discuss the clinical utility of multi-biomarker panels and artificial intelligence (AI)-driven models that integrate genomic, proteomic, and radiomic data, while highlighting their current performance limitations and stage-dependent diagnostic accuracy. Despite the considerable potential of liquid biopsies and AI-based approaches, challenges related to assay standardization and the need for large-scale prospective validation remain major barriers to widespread clinical implementation. Overall, this review underscores the need for robust, multimodal diagnostic strategies that may enable earlier detection and ultimately reduce OC-associated mortality.

Ovarian cancer detection and prognosis: Unveiling circRNAs potential

Ovarian cancer remains one of the most aggressive and fatal gynecologic malignancies, primarily due to its asymptomatic onset, early metastatic potential, frequent resistance to conventional therapies, and the absence of effective tools for early detection and prognostic assessment. As a result, sensitive, specific and minimally invasive biomarkers are urgently needed to enable earlier detection, more accurate prediction of clinical outcomes and ongoing monitoring of therapeutic response. Circular RNAs (circRNAs) are endogenous RNA molecules formed by covalent backsplice junctions that render them highly resistant to exonuclease degradation. Many circRNAs serve as competitive endogenous RNAs that sequester microRNAs. This sequestration modulates key oncogenic signaling cascades, ultimately regulating cellular proliferation, migration, and apoptosis. Their exceptional stability, measurable abundance in body fluids and distinctive dysregulation in pathological states highlight their potential as both diagnostic and prognostic indicators. In fact, biomarker panels based on circRNA expression have achieved an area under the receiver operating characteristic curve of 0.923 for ovarian cancer detection. Individually, specific circRNAs have been shown to correlate with overall survival, histologic grade, tumor burden and other clinicopathologic features. In this review, by surveying all relevant data in major databases without language restrictions, we comprehensively update and analyze the latest molecular and clinical findings on the diagnostic and prognostic value of circRNAs in patients with ovarian cancer.

Exosomal microRNA biomarkers in ovarian cancer detection: systematic review and meta-analysis

Ovarian cancer (OC) is the deadliest gynecologic malignancy, mainly due to late-stage diagnosis and the limited accuracy of biomarkers such as Carbohydrate Antigen 125 and Human Epididymis Protein 4. Exosomal microRNAs are promising non-invasive biomarkers for early OC detection. This study assessed the diagnostic accuracy of these methods through a systematic review and meta-analysis. PubMed, EMBASE, and Web of Science were searched for studies indexed to 29 June 2025 using predefined MeSH and free-text terms. Studies were screened in Rayyan under a PICO framework. Reviewers extracted data on study design, exosomal miRNAs, sample type, detection method, and diagnostic performance. Quality was assessed using a modified QUADAS-2 tool. Meta-analysis in STATA ("MIDAS" package) calculated pooled sensitivity, specificity, and area under the summary ROC (SROC) curve. Heterogeneity was evaluated using the I Twenty-seven studies met inclusion criteria; diagnostic data from 16 cohorts were pooled. Sensitivity and specificity were 0.82 (95 % CI: 0.76-0.86) and 0.81 (95 % CI: 0.75-0.86), with an SROC area of 0.88. No publication bias was detected. Exosomal miRNAs show strong diagnostic accuracy for non-invasive OC detection. Standardized methods and prospective validation are required for clinical application.

Ovarian cancer biosensors: established glycoproteins to emerging molecular biomarkers

Ovarian cancer is one of the leading causes of death in women primarily due to late diagnosis. Consequently, there is a critical need for more accurate diagnostic technologies beyond the limited sensitivity of current methods is of great importance. This review comprehensively explores the integration of specific biomarkers with advanced biosensor platforms to revolutionize disease management. It reviews the landscape of ovarian cancer biomarkers, from established glycoproteins (CA-125, HE4) to emerging molecular markers (miRNAs, ctDNA), and reviews recent advances in optical (e.g., fluorescence, SPR), electrochemical, and point-of-care biosensors. The findings highlight the remarkable capabilities of these technologies and confirm the high sensitivity of optical biosensors with detection limits down to the fg/mL range for targets such as MUC1. On the other hand, electrochemical platforms have been shown to offer robust and portable alternatives with detection limits in the μU/mL to fg/mL range. The advent of point-of-care and microfluidic systems, the potential for rapid and multiplexed analysis, has led to synergies between an expanding biomarker panel and advanced biosensor technology, paving the way for a new diagnostic era. However, this review also critically examines the key challenges in translating these biosensors from research laboratories to clinical practice. Therefore, future efforts should focus on clinical validation and increased survival and personalized treatment through the development of integrated lab-on-a-chip devices and the translation of these innovations into robust and cost-effective tools for enabling early diagnosis.

Multi-omic biomarker detection in ovarian cancer

Ovarian cancer remains one of the most lethal gynecologic malignancies, largely because of late-stage diagnosis and the absence of reliable biomarkers for early detection and therapeutic stratification. Recent advances in high-throughput technologies have enabled multi-omics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics to elucidate the comprehensive molecular landscape of ovarian cancer. This narrative review synthesizes current progress in applying multi-omics strategies to biomarker discovery, highlighting how integrative analyses uncover novel diagnostic, prognostic, and predictive candidates beyond the limitations of single-omics studies. We discuss methodological frameworks, computational pipelines and translational challenges in harmonizing heterogeneous datasets, as well as the potential of systems biology and machine learning to improve biomarker validation. Particular emphasis is placed on the identification of noncoding RNAs, protein signatures, and metabolic alterations as promising biomarker classes. Finally, we outline future directions for clinical implementation, including the development of multiparameter biomarker panels and precision medicine applications. By bridging molecular complexity with translational utility, multi-omics approaches hold transformative potential to advance biomarker identification and improve patient outcomes in ovarian cancer.

Plasma-based untargeted metabolomics reveals potential biomarkers for screening and distinguishing of ovarian tumors

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.

Clinical usefulness of E4-phytohemagglutinin reactive C4-binding protein relative front value in serum for distinguishing between ovarian clear cell adenocarcinoma and endometrioma

We identified a fully sialylated glycopeptide of the α-chain of Complement 4 binding protein (C4BP) in blood using Liquid chromatography/Mass spectrometry, which helps differentiate ovarian clear cell carcinoma (OCCC) from endometrioma (EM). We aimed to develop a blood measurement system to immunologically assess fully sialylated α-chain of C4BP, specifically E4-phytohemagglutinin-reactive C4BP (P-C4BP-Rf). Ninety-seven serum samples were collected from healthy (HE) (n = 11), uterine fibroid (UF) patients (n = 15), EM patients (n = 32), and OCCC patients (n = 39). P-C4BP-Rf was measured using lectin affinity electrophoresis and antibody affinity blotting. CA125 and human epididymal protein 4 (HE4) levels were also measured and compared. We selected 22 EM and 24 OCCC with no significant differences in CA125 levels. Tissue factor pathway inhibitor 2 (TFPI2) and FS-C4BP were added to the analysis to distinguish OCCC from EM using five biomarkers. P-C4BP-Rf was significantly higher in OCCC than in HE, UF, and EM (P < 0.0001). AUC values were: CA125, 0.547; HE4, 0.850; TFPI2, 0.847; FS-C4BP, 0.715; and P-C4BP-Rf, 0.887. The combination of P-C4BP-Rf and HE4 yielded the highest AUC (0.960). P-C4BP-Rf is useful for distinguishing OCCC from EM and may be more accurate when combined with HE4.

Genomic instability in ovarian cancer: Through the lens of single nucleotide polymorphisms

Ovarian cancer (OC) is the deadliest gynecological malignancy among all female reproductive cancers. It is characterized by high mortality rate and poor prognosis. Genomic instability caused by mutations, single nucleotide polymorphisms (SNPs), copy number variations (CNVs), microsatellite instability (MSI), and chromosomal instability (CIN) are associated with OC predisposition. SNPs, which are highly prevalent in the general population, show a greater relative risk contribution, particularly in sporadic cancers. Understanding OC etiology in terms of genetic basis can increase the use of molecular diagnostics and provide promising approaches for designing novel treatment modalities. This will help deliver personalized medicine to OC patients, which may soon be within reach. Given the pivotal impact of SNPs in cancers, the primary emphasis of this review is to shed light on their prevalence in key caretaker genes that closely monitor genomic integrity, viz., DNA damage response, repair, cell cycle checkpoints, telomerase maintenance, and apoptosis and their clinical implications in OC. We highlight the current challenges faced in different SNP-based studies. Various computational methods and bioinformatic tools employed to predict the functional impact of SNPs have also been comprehensively reviewed concerning OC research. Overall, this review identifies that variants in the DDR and HRR pathways are the most studied, implying their critical role in the disease. Conversely, variants in other pathways, such as NHEJ, MMR, cell cycle, apoptosis, telomere maintenance, and PARP genes, have been explored the least.

miRNA-based electrochemical biosensors for ovarian cancer

Ovarian cancer, a prevalent and deadly cancer among women, presents a significant challenge for early detection due to its heterogeneous nature. MicroRNAs, short non-coding regulatory RNA fragments, play a role in various cellular processes. Aberrant expression of these microRNAs has been observed in the carcinogenesis-related processes of many cancer types. Numerous studies highlight the critical role of microRNAs in the initiation and progression of ovarian cancer. Given their clinical importance and predictive value, there has been considerable interest in developing simple, prompt, and sensitive miRNA biosensor strategies. Among these, electrochemical sensors have demonstrated advantageous characteristics such as simplicity, sensitivity, low cost, and scalability. These microRNA-based electrochemical biosensors are valuable tools for early detection and point-of-care applications. This article discusses the potential role of microRNAs in ovarian cancer and recent advances in the development of electrochemical biosensors for miRNA detection in ovarian cancer samples.

Metabolomic biomarkers for benign conditions and malignant ovarian cancer: Advancing early diagnosis

Ovarian cancer (OC) is a major global cause of death among gynecological cancers, with a high mortality rate. Early diagnosis, distinguishing between benign conditions and early malignant OC forms, is vital for successful treatment. This research investigates serum metabolites to find diagnostic biomarkers for early OC identification. Metabolomic profiles derived from the serum of 60 patients with benign conditions and 60 patients with malignant OC were examined using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Comparative analysis revealed differential metabolites linked to OC, aiding biomarker identification for early-diagnosis of OC via machine learning features. The predictive ability of these biomarkers was evaluated against the traditional biomarker, cancer antigen 125 (CA125). 84 differential metabolites were identified, including 2-Thiothiazolidine-4-carboxylic acid (TTCA), Methionyl-Cysteine, and Citrulline that could serve as potential biomarkers to identify benign conditions and malignant OC. In the diagnosis of early-stage OC, the area under the curve (AUC) for Citrulline was 0.847 (95 % Confidence Interval (CI): 0.719-0.974), compared to 0.770 (95 % CI: 0.596-0.944) for TTCA, and 0.754 for Methionine-Cysteine (95 % CI: 0.589-0.919). These metabolites demonstrate a superior diagnostic capability relative to CA125, which has an AUC of 0.689 (95 % CI: 0.448-0.931). Among these biomarkers, Citrulline stands out as the most promising. Additionally, in the diagnosis of benign conditions and malignant OC, using logistic regression to combine potential biomarkers with CA125 has an AUC of 0.987 (95 % CI: 0.9708-1) has been proven to be more effective than relying solely on the traditional biomarker CA125 with an AUC of 0.933 (95 % CI: 0.870-0.996). Furthermore, among all the differential metabolites, lipid metabolites dominate, significantly impacting glycerophospholipid metabolism pathway. The discovered serum metabolite biomarkers demonstrate excellent diagnostic performance for distinguishing between benign conditions and malignant OC and for early diagnosis of malignant OC.

Diagnostic performance of HE4 and ROMA among Chinese women

Human epididymis protein 4 (HE4) has been suggested as a new biomarker for the detection of ovarian cancer. Because there are few reports on HE4 in terms of ovarian cancer diagnosis in the Chinese population, we evaluated the diagnostic performances of HE4 and the ROMA in Chinese women with pelvic masses. The serum concentrations of CA 125 and HE4 in 318 Chinese women with pelvic masses (39 of which were ovarian cancers) were determined. For the discrimination of benign gynecological diseases from ovarian cancer, the sensitivity and specificity values were 87.2% and 75.8% for the ROMA and 51.3% and 97.3% for HE4. The ROMA also showed higher sensitivity than HE4 in both the early and advanced stages. In the ROC curve analysis, the AUC values for ROMA, HE4 and CA 125 were 0.927, 0.907 and 0.785, respectively. As a new tumor marker, HE4 shows high specificity and efficacy in the Chinese population, while the ROMA that combines HE4 and CA 125 shows high sensitivity and a high Youden's index. These markers should be extended to China since they have good diagnostic performances.

Anti-Ro52 antibody is highly prevalent and a marker of better prognosis in patients with ovarian cancer

Anti-Ro52 antibody (Ab) reactivity is highly prevalent in autoimmune rheumatic diseases (ARDs), mainly Sjögren's syndrome (SjS) and systemic lupus erythematosus (SLE), but also in other inflammatory disorders. Thorough assessment of the prevalence, clinical significance and epitope specificity of Ro52-autoAbs in cancerous diseases is still lacking. Anti-Ro52 Ab reactivity was tested in a large cohort of 490 patients with various malignant diseases. Ro52-autoAb epitope mapping by an in house line immunoassay was carried out using 5 recombinant Ro52 polypeptides spanning Ro52. Anti-Ro52 abs were significantly more prevalent in patients with ovarian cancer (30%) compared to patients with 6 other malignant diseases (median 8.1%, range 5.9-15.8%). The presence of anti-Ro52 abs in patients with ovarian cancer was strongly associated with better overall survival. Ro52 epitope mapping of patients with ovarian cancer was dissimilar to that of SLE and SjS ARDs, less frequently recognizing Ro52-1 and Ro52-4 fragments compared to patients with SLE and SjS. We demonstrate for first time an unexpectedly high frequency of anti-Ro52 abs in patients with ovarian cancer, their presence indicating better overall survival. Their distinguishing epitope profile may suggest a non-SLE or SjS-related stimulus for autoAb production.

Prognostic value of changes in neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and lymphocyte-to-monocyte ratio (LMR) for patients with cervical cancer undergoing definitive chemoradiotherapy (dCRT)

Peripheral NLR, PLR, and LMR have prognostic value in various malignancies as they are surrogates for inflammation. Recent studies have identified NLR, PLR, and LMR correlate with patient outcomes in cervical cancer patients however there remains uncertainty regarding the optimal time point for assessing these markers. We retrospectively reviewed cervical cancer patients underoing definitive chemoradiation therapy (dCRT). NLR, PLR, and LMR values were identified before, during, and after dCRT and both relative and absolute changes in these values were calculated and compared with patient outcmoes. Ninety-nine patients who met the includsion criteria were identified. NLR values before, during, and after dCRT correlated with progression free survival (PFS) and overall survival (OS). In addition, increasing NLR after treatment was associated with worse PFS and OS. LMR before and after treatment had a positive correlation with PFS however increasing LMR during dCRT was found to have a negative correlation with PFS and OS. NLR serves as a prognostic indicator irrespective of timing with response to dCRT. While higher LMR before treatment was a positive prognostic indicator, increasing LMR was found to negatively correlate with PFS and OS.

Potential of nano-phytochemicals in cervical cancer therapy

Cervical cancer is common among women with a recurrence rate of 35% despite surgery, radiation, and chemotherapy. Patients receiving chemotherapy or radiotherapy routinely experience several side effects including toxicity, non-targeted damage of tissues, hair loss, neurotoxicity, multidrug resistance (MDR), nausea, anemia and neutropenia. Phytochemicals can interfere with almost every stage of carcinogenesis to prevent cancer development. Many natural compounds are known to activate/deactivate multiple redox-sensitive transcription factors that modulate tumor signaling pathways. Polyphenols have been found to be promising agents against cervical cancer. However, applications of phytochemicals as a therapeutic drug are limited due to low oral bioavailability, poor aqueous solubility and requirement of high doses. Nano-sized phytochemicals (NPCs) are promising anti-cancer agents as they are required in minute quantities which lowers overall treatment costs. Several phytochemicals, including quercetin, lycopene, leutin, curcumin, green tea polyphenols and others have been packaged as nanoparticles and proven to be useful in nano-chemoprevention and nano-chemotherapy. Nanoparticles have high biocompatibility, biodegradability and stability in biological environment. Nano-scale drug delivery systems are excellent source for enhanced drug specificity, improved absorption rates, reduced drug degradation and systemic toxicity. The present review discusses current knowledge in the involvement of phytochemical nanoparticles in cervical cancer therapy over conventional chemotherapy.

Circular RNAs as biomarkers and targets in ovarian cancer

Current laboratory diagnostic tools for epithelial ovarian cancer (EOC) primarily rely on cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). However, their effectiveness is suboptimal for early disease diagnosis and triage of adnexal masses. The evidence reviewed on the topic of tissue and circulating circular RNAs (circRNAs) as diagnostic, prognostic, and treatment-response biomarkers in EOC and what is needed to implement circRNA assays in clinical laboratory practice are summarized herein. Several circRNAs identified in plasma/serum (such as circBNC2, hsacirc0003972, hsacirc0007288, and circN4BP2L2) have diagnostic potential (area under the receiver operating characteristic curve [AUC] between the typical range of 0.79-0.95) and can be used to enhance multimarker algorithms with CA125 and HE4. In addition to biomarker performance, we are interested in laboratory medicine challenges that can define clinical usability, such as Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE)-compatible assay design to detect back-splice junction, specimen-specific pre-analytical variables (e.g., hemolysis, clotting time, and exosome isolation workflows), normalization strategies of liquid biopsy, analytical validation, reference intervals/decision limits, and external quality assessment. Although published accuracies are encouraging, the majority of studies have small cohorts and lack external validation, and preanalytical handling and reporting heterogeneity are significant. Thus, strong standardization and regulatory level validation are necessary for circRNA testing to be established as a routine EOC diagnosis, risk stratification, or treatment monitoring tool.

Targeting tumour markers in ovarian cancer treatment

Ovarian cancers (OC) are the most common, lethal, and stage-dependent cancers at the global level, specifically in female patients. Targeted therapies involve the administration of drugs that specifically target the alterations in tumour cells responsible for their growth, proliferation, and metastasis, with the aim of treating particular patients. Presently, within the realm of gynaecological malignancies, specifically in breast and OCs, there exist various prospective therapeutic targets encompassing tumour-intrinsic signalling pathways, angiogenesis, homologous-recombination deficit, hormone receptors, and immunologic components. Breast cancers are often detected in advanced stages, primarily due to the lack of a reliable screening method. However, various tumour markers have been extensively researched and employed to evaluate the condition, progression, and effectiveness of medication treatments for this ailment. The emergence of recent technological advancements in the domains of bioinformatics, genomics, proteomics, and metabolomics has facilitated the exploration and identification of hitherto unknown biomarkers. The primary objective of this comprehensive review is to meticulously investigate and analyze both established and emerging methodologies employed in the identification of tumour markers associated with OC.

Profiling of the genetic features of patients with breast, ovarian, colorectal and extracolonic cancers: Association to CHEK2 and PALB2 germline mutations

Cancer predisposition goes beyond BRCA and DNA Mismatch Repair (MMR) genes since multi-gene panel testing has become the routine diagnostic tool for hereditary cancer suspicion (HCS) cases. CHEK2 and PALB2 are some of the foremost-mutated non-BRCA/MMR actionable genes in families with a significant familial aggregation. Therefore, the purpose of this work is to unravel which tumours other than breast, ovary or colorectal display the patients. We have analysed 528 probands that meet the inclusion criteria for Hereditary Breast and Ovarian Cancer and Lynch Syndrome established by our Hereditary Cancer Regional Program with a customized 35 genes-panel by using Ion Torrent™ Technology. We have identified pathogenic variants (PVs) in 61 families (1.55%), of which more than half (31 probands) harboured PVs in CHEK2 and PALB2 genes. Ours results reveal that not only were PVs CHEK2 and PALB2 carriers more likely to have family history of cancer not limited to breast, ovarian or colorectal cancers, but also they are prone to other extracolonic cancers, noteworthy endometrial and gastric cancers. Multigene panel testing improves the chance of finding PVs in actionable genes in families with HCS. In addition, the coexistence of variants should be recorded to implement a polygenic risk algorithm that might explain the missing heritability in the aforementioned families.

Endometrial cancer: A systematic review of HE4, REM and REM-B

Endometrial cancer, one of the most frequent pelvic gynecologic cancer worldwide, currently has no biomarker used to assess it in daily practice. Nonetheless, human epididymis 4 (HE4) appears to offer the best prospects, alone or combined with CA125. This study sought to systematically review the work on HE4 from the first publications in 2008 until now. Two independent reviewers searched the PubMed database with the terms "HE4″, "endometrial cancer", "endometrial carcinoma", and HE4 or human epididymis protein 4. Only original clinical research articles and meta-analyses, published in English, were included, with literature reviews and case reports excluded. Studies were organized into 3 categories: diagnosis, prognosis, and recurrence/survival. Overall we identified 117 articles dealing with HE4 and endometrial cancer and selected 52 relevant texts: 46 articles, 6 meta-analyses. The sensitivity of HE4 for the diagnosis of endometrial cancer varied from 44.2% to 91% and its specificity from 65.5 to 100%, versus 24.1 to 71.5% and from 65.6 to 100% for CA125. Two meta-analyses of their combination produced areas under the curve (AUC): 0.83 and 0.86. Two available algorithms - the REM (risk of endometrial malignancy) and REM-B (risk of endometrial malignancy associated with BMI) scores - require more study. HE4 is also strongly associated with prognostic factors such as myometrial invasion, tumor grade, FIGO stage, and lymph node involvement. It also predicts recurrence and can serve as a monitoring tool, as reported by a 2018 meta-analysis with a hazard ratio of 2.15 (P < 0.001). HE4, alone or associated with CA125, appears to be an important tool in the management of endometrial cancer, initially for diagnosis, but for assessing prognosis and survival. Other prospective and multicenter studies are necessary to confirm these hopes and be able to recommend the use of HE4 in regular practice.

Aberrant glycosylation of α3 integrins as diagnostic markers in epithelial ovarian cancer

Glycans are strongly involved in stability and function of integrins (ITG) and tetraspanin protein CD63 and their respective interaction partners as they are dysregulated in the tumorigenic processes. Glycosylation changes is a universal phenomenon of cancer cells. In this study, glycosylation changes in epithelial ovarian cancer (EOC) are explored using tetraspanin and integrin molecules. ITG and CD63 were immobilized from 10 EOC and 5 benign ovarian cyst fluid on microtiter wells and traced with 3 glycan binding proteins (STn, WGA, UEA) conjugated on europium nanoparticles. Total protein measurements (ITG & CD63 immunoassays) were also performed. The most promising glycovariant candidates identified were then clinically evaluated on the whole cohort of 77 ovarian cyst fluids. Additional testing was performed in ascites fluid samples of liver cirrhosis (n = 2) and EOC (n = 4). Sialylated Tn antibody based glycovariants of ITGα3 (ITGα3 Our findings indicate the potential diagnostic application of ITGα3

Electrochemical detection of cervical cancer biomarkers

Cervical cancer (CC) is the fourth most common cancer among women worldwide, following breast, colorectal, and lung cancers. Each year, it accounts for approximately 600,000 new cases and 340,000 deaths. Early-stage cervical cancer is treatable with surgery and chemoradiotherapy (CCRT). However, treatment for metastatic cervical cancer is limited, with bevacizumab combined with chemotherapy being one of the few options, though survival rates remain low. Currently, the diagnosis of cervical cancer primarily relies on Pap smears and colposcopy. Although these methods are essential for detection, they are costly, labor-intensive, and require significant resources. Therefore, there is an urgent need to identify effective biomarkers that can detect cervical cancer at an early stage, improving both the accuracy of diagnosis and the efficacy of treatment. Although numerous cervical cancer biomarkers have been identified for the cervical cancer thanks to advances in technology. In recent times, electrochemical methods have proven to be particularly effective in cervical cancer detection. In this paper, we reviewed the important cervical cancer biomarkers and their detection through electrochemical biosensors, which offer advantages such as higher sensitivity, affordability, and ease of analysis. Furthermore, we discussed the limitations and future prospects of electrochemical biosensors in this field.

Nanoparticle-based colorimetric assays for early and rapid screening of the oncogenic HPV variants 16 and 18

Cervical cancer is predominantly caused by human papillomavirus (HPV), with oncogenic strains HPV 16 and 18 accounting for most cases worldwide. Prompt and precise identification of these high-risk HPV types is essential for enhancing patient outcomes as it enables timely intervention and management. However, the existing HPV detection techniques are time-consuming, expensive, and require highly skilled personnel. This study presents the development and evaluation of a colorimetric nanosensor for the rapid detection of high-risk human papillomavirus (HPV) variants 16 and 18. Gold nanoparticles (AuNPs) were synthesized using an optimized method based on response surface methodology and then functionalized with monoclonal antibodies specific to HPV16-L1 and HPV18-L1 proteins. The nanosensor exhibited a visible color shift from red to violet upon the detection of the target proteins. The analytical validation demonstrated good linearity, sensitivity, precision, accuracy, robustness, and selectivity for detecting recombinant HPV16-L1 and HPV18-L1 proteins. The nanosensor remained stable for at least 90 days when stored at 4 °C. Clinical evaluation of 173 patients, obtained from cervical samples, showed high specificity (77.8 % for HPV16 and 87.3 % for HPV18) and excellent negative predictive value (>96 % for both). Several false-positive results have been associated with other HPV variants or cervical abnormalities. While the sensitivity was limited by the low prevalence of positive samples, the simple, rapid, and equipment-free nature of this colorimetric nanosensor makes it a promising tool for HPV screening, especially in resource-limited settings.

Analysis of HOXA9 methylated ctDNA in ovarian cancer using sense-antisense measurement

DNA promoter methylation is an early event in tumorigenesis and holds promise as a valuable marker in ovarian cancer (OC). It can be measured using circulating tumor specific DNA (ctDNA) isolated from the bloodstream. Sensitivity, however, is a limiting factor of its diagnostic feasibility in OC. DNA methylation analyses are based on bisulfite conversion, resulting in two DNA strands that are no longer complementary. The current standard strategy would then target only one of the double stranded DNA strands, but the potential to increase the sensitivity by targeting both DNA strands is available. In this study, we aimed at evaluating the diagnostic potential of methylated HOXA9 ctDNA in OC by targeting both the DNA sense and antisense strand. Methylated HOXA9 was detected in the plasma of 47/79 (59.5%) patients with newly diagnosed OC using sense-antisense droplet digital PCR. Simultaneous sense-antisense measurement increased the sensitivity by 14.6% (51.9% to 59.5%) as compared to antisense only. In patients with FIGO stage I-II disease the sensitivity was increased by 25%. In conclusion, simultaneous measurement targeting both DNA strands can increase the sensitivity and the analytical approach appears valuable in the diagnostic setting of OC.

A combined strategy of TK1, HE4 and CA125 shows better diagnostic performance than risk of ovarian malignancy algorithm (ROMA) in ovarian carcinoma

The dual marker algorithm Risk of Ovarian Malignancy Algorithm (ROMA) has been widely used in the clinic for the identification of equivocal pelvic masses in ovarian carcinoma. To obtain higher diagnostic efficiency, we created a new diagnostic index, Risk of Ovarian Malignancy Index (ROMI), by combing thymidine kinase 1 (TK1), HE4 and CA125. 335 patients with pelvic masses on imaging and 46 healthy controls were enrolled. Serum TK1 was analyzed before further study. ROMI and ROMA were evaluated for diagnostic efficiency. The level of TK1 was elevated in malignant ovarian tumors compared to benign masses (p < 0.001) and healthy controls (p < 0.001). TK1 expression was positively correlated with stage, intrapelvic metastasis, lymphatic metastasis and distant metastasis (all p values < 0.001). The area under the receiver operating characteristic curve (AUC) of ROMI was higher than that of ROMA for both pre- and postmenopausal women. ROMI had better sensitivity, specificity, accuracy, and positive and negative predictive values than ROMA in diagnosis of all-stage or stage I + II ovarian carcinoma for both pre- and postmenopausal women. TK1 is a potential biomarker in detection of ovarian carcinoma. ROMI shows better diagnostic performance than ROMA in distinguishing malignant ovarian tumors from benign masses.

The performance of multi-gene panels for breast/ovarian cancer predisposition

BRCA1 and BRCA2 are the most mutated genes in breast cancer. We analyzed 48 breast cancer subjects using two methods that differ in terms of number of genes investigated and strategy used (primers: Panel A - 12 genes - vs probes: Panel B - 48 genes). Both the panels and procedures identified "pathogenic" or "likely pathogenic" variants in TP53, ATM, CHEK2 and BARD1 besides BRCA1 and BRCA2. Panel B identified two other putatively pathogenic variants in RNASEL and in RAD50. Identification of variants other than the BRCA genes can be useful in patient management. A total of 121 variants were distributed within the 12 genes and were correctly detected by both panels. However, the number of calls without divergence, namely ± 0.10 difference of allelic frequency, was 78.3%, while calls with a divergence below 0.10 was 16.7%, thus indicating that only 5% (n = 275) of 5,412 calls had a divergence above 0.10. Although these panels differ from each other, both are useful in different situations, particularly when patients should be tested for genes other than BRCA1/2 (as occurs in patients affected by a so called hereditary syndrome) or for therapeutic purposes.

Integration of coagulation parameters Enhances deep Learning-Based survival prediction in High-Grade serous ovarian Cancer: A comprehensive prognostic model

High-grade serous ovarian cancer (HGSOC) remains a leading cause of gynecologic cancer mortality, with heterogeneous clinical outcomes necessitating improved prognostic models. This study aimed to develop and validate a comprehensive survival prediction model integrating traditional clinicopathological factors with novel molecular and coagulation parameters. We retrospectively analyzed 216 HGSOC patients treated between 2012 and 2017. A comprehensive machine learning framework incorporating 88 algorithms was developed to predict survival outcomes, integrating conventional prognostic factors with coagulation parameters, particularly D-dimer levels. Model performance was evaluated using time-dependent AUC, concordance index, and calibration curves. External validation was performed using an independent cohort of 108 patients from three institutions. The machine learning model demonstrated excellent discriminative capability (AUC 0.771, 95% CI 0.709-0.832), with improving predictive accuracy from 1-year to 5-year follow-up. Multivariate analysis identified five independent prognostic factors: p53 expression, lymphadenectomy, TNM stage, hypercoagulability, and Ki67 expression. The model showed robust performance in external validation. Our novel machine learning-based survival prediction model demonstrates superior prognostic accuracy and temporal stability compared to conventional approaches. The integration of coagulation parameters provides new insights into disease progression. This model could facilitate personalized treatment decisions in clinical practice, though further prospective validation is warranted.

Droplet digital PCR for large genomic rearrangements detection: A promising strategy in tissue BRCA1 testing

With the introduction of Olaparib as target therapy for High Grade Serous Ovarian Cancer (HGSOC) patients with germline and somatic BRCA1/2 mutations, the genetic test performed on tumor tissue has become important like the germline test. In somatic testing the evaluation of Large Genomic Rearrangements (LGRs) represents the main challenge. We describe a droplet digital PCR (ddPCR) assay for the evaluation of target BRCA1 LGRs on blood and formalin-fixed paraffin-embedded (FFPE)/Fresh Frozen Tissue (FFT) samples. We analyzed blood, FFPE and FFT samples in a validation setting of n = 78 HGSOC patients. We applied the ddPCR to BRCA1 exons 2, 20 and 21 as some of the most common BRCA1 exons involved in LGRs in our cohort of patients. The ddPCR custom assays allowed the identification of LGRs in all sample types, including FFPE specimens. Moreover, we were able to clearly detect LGRs accounted as somatic event. The introduction of ddPCR in a comprehensive workflow, encompassing both germline and somatic tests, represents an improvement in BRCA1/2 testing. ddPCR can overcome challenges related to BRCA testing, especially on FFPE analysis. Finally, ddPCR represents a promising alternative strategy to the established standard methods currently used in clinical setting.

Liquid biopsy in ovarian cancer

Ovarian cancer is typically diagnosed at an advanced stage and poses a significant challenge to treatment and recovery. Relapsed ovarian cancer and chemoresistance of ovarian tumor cells are other clinical challenges. Liquid biopsy is an essential non-invasive diagnostic test that evaluates circulating tumor cells and tumor DNA, as well as other blood markers that may be useful in guiding precision medicine. Although liquid biopsy is not a routinely used diagnostic test, the potential applications in the diagnosis and prognosis in ovarian cancer are rapidly growing. This review explores recent studies examining the clinical potential of circulating tumor cells, cell-free microRNA, exosomes, tumor DNA, and other analytes as a source of liquid biopsy biomarkers in ovarian cancer diagnosis, prognosis and response to treatment.

Label-free gold nanostar-based SERS with machine learning: A platform for detecting endometrial cancer-associated polyamine metabolites

The occurrence and progression of endometrial cancer are closely associated with metabolic reprogramming, in which polyamine metabolites play a critical role in tumor cell proliferation and invasion. In this study, we developed a label free surface enhanced Raman scattering (SERS) detection platform based on gold nanostars (AuNS), integrated with machine learning algorithms, to achieve highly sensitive detection and precise identification of polyamine metabolites related to endometrial cancer. In complex biological matrices such as serum, the platform yielded stable and reproducible spectral fingerprints, with a detection limit at the nanogram level. Furthermore, by constructing a polyamine metabolite spectral database and introducing machine learning models, both the classification accuracy and AUC values exceeded 95 %, enabling effective discrimination of different metabolic states and mixed systems. Taken together, the AuNS SERS strategy combined with machine learning provides a rapid, non-invasive, and intelligent detection tool for the early diagnosis and metabolic subtyping of endometrial cancer, with significant clinical application potential.