Human epididymis protein 4 antigen-autoantibody complexes complement cancer antigen 125 for detecting early-stage ovarian cancer

Role of exosomal non‑coding RNAs in ovarian cancer (Review)

Ovarian cancer (OC) is a common gynecological disease with a high mortality rate worldwide due to its insidious nature and undetectability at an early stage. The standard treatment, combining platinum‑based chemotherapy with cytoreductive surgery, has suboptimal results. Therefore, early diagnosis of OC is crucial. All cell types secrete extracellular vesicles, particularly exosomes. Exosomes, which contain lipids, proteins, DNA and non‑coding RNAs (ncRNAs), are novel methods of intercellular communication that participate in tumor development and progression. ncRNAs are categorized by size into long ncRNAs (lncRNAs) and small ncRNAs (sncRNAs). sncRNAs further include transfer RNAs, small nucleolar RNAs, PIWI‑interacting RNAs and microRNAs (miRNAs). miRNAs inhibit protein translation and promote messenger RNA (mRNA) cleavage to suppress gene expression. By sponging downstream miRNAs, lncRNAs and circular RNAs can regulate target gene expression, thereby weakening the interactions between miRNAs and mRNAs. Exosomes and exosomal ncRNAs, commonly present in human biological fluids, are promising biomarkers for OC. The present article aimed to review the potential role of exosomal ncRNAs in the diagnosis and prognosis of OC by summarizing the characteristics, processes, roles and isolation methods of exosomes and exosomal ncRNAs.

Adenocarcinoma of Mullerian Origin Found Through an Elective Inguinal Hernia Resection: A Case Report

We report an asymptomatic 59-year-old female undergoing an elective umbilical hernia excision who was found to have an ovarian adenocarcinoma within the excised hernia. Patients are rarely diagnosed with cancer after an umbilical hernia excision. An excised hernia is rarely the means for an initial diagnosis of cancer. We describe a case of an ovarian carcinoma incidentally found through an umbilical hernia excision with consequential treatment with neoadjuvant platinum-based chemotherapy followed by debulking surgery with a total hysterectomy with bilateral salpingo-oophorectomy with a transoperative pathology report of a high-grade serous carcinoma located in the left fimbrial frond surrounded by a background of serous tubal intraepithelial carcinomas. This case demonstrates the need to perform histological examinations of all excised hernias, even in asymptomatic patients, as malignancy can be found inside a hernia, and it emphasizes the importance of considering adenocarcinomas of Mullerian origin in the differential diagnosis of a malignancy found in a hernia in an asymptomatic female patient.

Autoantibodies, antigen-autoantibody complexes and antigens complement CA125 for early detection of ovarian cancer

BACKGROUND

Multiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer.

METHODS

Twenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone.

RESULTS

Eight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased sensitivity for distinguishing early-stage ovarian cancers from benign pelvic masses by 25% (p = 0.0004) at 95% specificity. From 21 autoantibody candidates, 3 AAb (anti-p53, anti-CTAG1 and annt-Il-8) detected 22% of early stage ovarian cancers, potentially lengthening lead time prior to diagnosis.

CONCLUSION

A four biomarker panel achieved greater sensitivity at the same specificity for early detection of ovarian cancer than CA125 alone.

Identification and Analysis of Gene Biomarkers for Ovarian Cancer

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.

Redefining serological diagnostics with immunoaffinity proteomics

Serological diagnostics is generally defined as the detection of specific human immunoglobulins developed against viral, bacterial, or parasitic diseases. Serological tests facilitate the detection of past infections, evaluate immune status, and provide prognostic information. Serological assays were traditionally implemented as indirect immunoassays, and their design has not changed for decades. The advantages of straightforward setup and manufacturing, analytical sensitivity and specificity, affordability, and high-throughput measurements were accompanied by limitations such as semi-quantitative measurements, lack of universal reference standards, potential cross-reactivity, and challenges with multiplexing the complete panel of human immunoglobulin isotypes and subclasses. Redesign of conventional serological tests to include multiplex quantification of immunoglobulin isotypes and subclasses, utilize universal reference standards, and minimize cross-reactivity and non-specific binding will facilitate the development of assays with higher diagnostic specificity. Improved serological assays with higher diagnostic specificity will enable screenings of asymptomatic populations and may provide earlier detection of infectious diseases, autoimmune disorders, and cancer. In this review, we present the major clinical needs for serological diagnostics, overview conventional immunoassay detection techniques, present the emerging immunoassay detection technologies, and discuss in detail the advantages and limitations of mass spectrometry and immunoaffinity proteomics for serological diagnostics. Finally, we explore the design of novel immunoaffinity-proteomic assays to evaluate cell-mediated immunity and advance the sequencing of clinically relevant immunoglobulins.

Nanotechnological approaches for diagnosis and treatment of ovarian cancer: a review of recent trends

Formulations from nanotechnology platform promote therapeutic drug delivery and offer various advantages such as biocompatibility, non-inflammatory effects, high therapeutic output, biodegradability, non-toxicity, and biocompatibility in comparison with free drug delivery. Due to inherent shortcomings of conventional drug delivery to cancerous tissues, alternative nanotechnological-based approaches have been developed for such ailments. Ovarian cancer is the leading gynecological cancer with higher mortality rates due to its reoccurrence and late diagnosis. In recent years, the field of medical nanotechnology has witnessed significant progress in addressing existing problems and improving the diagnosis and therapy of various diseases including cancer. Nevertheless, the literature and current reviews on nanotechnology are mainly focused on its applications in other cancers or diseases. In this review, we focused on the nanoscale drug delivery systems for ovarian cancer targeted therapy and diagnosis, and different nanocarriers systems including dendrimers, nanoparticles, liposomes, nanocapsules, and nanomicelles for ovarian cancer have been discussed. In comparison to non-functionalized counterparts of nanoformulations, the therapeutic potential and preferential targeting of ovarian cancer through ligand functionalized nanoformulations’ development has been reviewed. Furthermore, numerous biomarkers such as prostatic, mucin 1, CA-125, apoptosis repeat baculoviral inhibitor-5, human epididymis protein-4, and e-cadherin have been identified and elucidated in this review for the assessment of ovarian cancer. Nanomaterial biosensor-based tumor markers and their various types for ovarian cancer diagnosis are explained in this article. In association, different nanocarrier approaches for the ovarian cancer therapy have also been underpinned. To ensure ovarian cancer control and efficient detection, there is an urgent need for faster and less costly medical tools in the arena of oncology.

A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

PURPOSE

To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts.

EXPERIMENTAL DESIGN

Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed.

RESULTS

A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76-0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84-0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84-0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone.

CONCLUSIONS

A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma

Objective

To compare the performance of clinical factors, FS-T2WI, DWI, T1WI+C based radiomics and a combined clinic-radiomics model in predicting the type of serous ovarian carcinomas (SOCs).

Methods

In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (P < 0.05, and with the area under the curve (AUC) > 0.7) was retained to establish a clinical model. The radiomics model included FS-T2WI, DWI, and T1WI+C, and also, a multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical factors, combined with radiomics features, were developed in the training cohort. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the clinical model in identifying low- and high-grade SOC.

Results

The AUC of the clinical model and multisequence radiomics model in the training and validation cohorts was 0.90 and 0.89, 0.91 and 0.86, respectively. By incorporating clinical factors and multi-radiomics signature, the AUC of the radiomic-clinical nomogram in the training and validation cohorts was 0.98 and 0.95. The model comparison results show that the AUC of the combined model is higher than that of the uncombined models (P= 0.05, 0.002).

Conclusion

The nomogram models of clinical factors combined with MRI multisequence radiomics signatures can help identifying low- and high-grade SOCs and a provide a more comprehensive, effective method to evaluate preoperative risk stratification for SOCs.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma

Objective

To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC).

Methods

This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS).

Results

One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group.

Conclusion

By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.

Value of serum miR-21, HE4 and CA125 in surveillance for postoperative recurrent or metastatic ovarian cancer

Objectives

To study the value of serum miR21, human epididymal secretory protein 4 (HE4) and carbohydrate antigen 125 (CA125) in the surveillance for postoperative recurrent or metastatic ovarian cancer.

Methods

A total of 169 patients diagnosed with ovarian conditions in Luanzhou Hospital of Traditional Chinese Medicine during January 2016 and March 2019 were divided into a benign lesion (BL) group and an ovarian cancer (OC) group by pathological findings and assigned to a good prognosis (GP) group and a poor prognosis (PP) group according to the follow-up results. A real-time fluorescence quantitative PCR (RT-fqPCR) system was utilized to detect the serum level of miR-21; an enzyme-linked immunosorbent assay (ELISA) was conducted to determine the serum level of HE4; electrochemiluminescence (ECL)-based imaging analysis was performed to measure serum CA125. A receiver operating characteristic (ROC) curve was depicted to analyze the predictive value of serum miR-21, HE4, and CA125 for poor postoperative prognosis in patients with ovarian cancer.

Results

Compared with the control group, the BL and OC groups had substantially elevated expression of miR-21, HE4, and CA125 in serum, and the serum levels of miR-21, HE4, and CA125 in the OC group were significantly higher than in the BL group. In the OC group, the serum levels of miR-21, HE4, and CA125 were independent of age and pathological patterns and associated with the clinical staging, degree of transformation and lymphatic metastasis of ovarian cancer; after laparoscopic ovarian tumorectomy, the serum levels of miR-21, HE4, and CA125 were markedly reduced in comparison with the preoperative levels. Compared with the GP group, the PP group experienced a dramatic increase in serum miR-21, HE4, and CA125 expression. The ROC curve showed that the detection of miR-21, HE4, and CA125 was a highly sensitive and specific method to predict the poor prognosis in ovarian cancer; a patient with ovarian cancer was at high risk of a poor prognosis when the serum levels of miR-21, HE4, and CA125 exceeded 1.536, 157.004 pmol/L and 175.243 kU/L, respectively, in which case early intervention should be made to prevent recurrent or metastatic ovarian cancer.

Conclusion

Elevated expression of miR-21, HE4, and CA125 in serum is closely associated with the disease status of ovarian cancer. Therefore, the simultaneous detection of these tumor markers has some diagnostic value for postoperative recurrence and metastasis of ovarian cancer.

New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers

The prevention and early diagnostics of precancerous stages are key aspects of contemporary oncology. In cervical cancer, well-organized screening and vaccination programs, especially in developed countries, are responsible for the dramatic decline of invasive cancer incidence and mortality. Cytological screening has a long and successful history, and the ongoing implementation of HPV triage with increased sensitivity can further decrease mortality. On the other hand, endometrial and ovarian cancers are characterized by a poor accessibility to specimen collection, which represents a major complication for early diagnostics. Therefore, despite relatively promising data from evaluating the combined effects of genetic variants, population screening does not exist, and the implementation of new biomarkers is, thus, necessary. The introduction of various circulating biomarkers is of potential interest due to the considerable heterogeneity of cancer, as highlighted in this review, which focuses exclusively on the most common tumors of the genital tract, namely, cervical, endometrial, and ovarian cancers. However, it is clearly shown that these malignancies represent different entities that evolve in different ways, and it is therefore necessary to use different methods for their diagnosis and treatment.

Fluorescence turn-on immunosensing of HE4 biomarker and ovarian cancer cells based on target-triggered metal-enhanced fluorescence of carbon dots

Rapid and sensitive detection of tumor biomarkers and cancer cells is of crucial importance for the early diagnosis and prognosis prediction of cancer. The present report describes a target-induced fluorescence enhancement immunosensor that utilizes the optical property of carbon dots (CDs) and the metal-enhanced fluorescence effect (MEF) property of silver nanoparticles (AgNPs) for the sensitive detection of the cancer biomarker human epididymis protein 4 (HE4) and ovarian cancer cells. Nitrogen and sulfur co-doped CDs with a quantum yield of 85.6% were prepared and served as the fluorophore in MEF. The HE4 antibody (Ab) specific to the HE4 antigen was linked covalently to the surface of the synthesized CDs as the capture. The HE4 Ab-conjugated AgNPs (AgNPs-Ab) were prepared and utilized as signal amplification elements. In the presence of the target HE4, composite sandwich structures were formed between the labeled CDs-Ab and AgNPs-Ab, which brought the CDs and AgNPs into proximity, resulting in the fluorescence of CDs enhancement owing to MEF. The intensity of fluorescence enhancement was positively correlated with the HE4 concentration in the clinically important range of 0.01-200 nM with a limit detection of 2.3 pM. Moreover, the immunosensor was also successfully applied to specific fluorescence labeling and quantitative determination of HE4-positive ovarian cancer cells. The proposed target-triggered MEF sensor platform demonstrated high sensitivity, excellent anti-interference ability, along with successful validation in complex biological matrices, providing a new approach for HE4 detection in early diagnosis and therapeutic monitoring.

Next steps in the early detection of ovarian cancer

Bast et al. discuss the early detection of ovarian cancer in the context of the recent UKCTOCS screening trial. The authors suggest potential reasons why the trial failed to achieve a reduction in mortality and outline next steps in the development of biomarkers and imaging modalities to detect ovarian cancer.

High Expression of MYL9 Indicates Poor Clinical Prognosis of Epithelial Ovarian Cancer

BACKGROUND

The prognosis of Epithelial Ovarian Cancer (EOC) is poor, but the prognostic biomarkers are neither sensitive nor specific. Therefore, it is very important to search novel prognostic biomarkers for EOC.

OBJECTIVES

The present study aimed to investigate Myosin Light Chain 9(MYL9) expression in Epithelial Ovarian Cancer (EOC) tissues (including paraffin-embedded and fresh tissue samples) and its relationship with clinicopathological characteristics, as well as its potential prognostic value in patients with EOC.

METHODS

Between March 2009 and December 2018, all of 184 paraffin-embedded cancer tissues from patients with EOC and 41 paratumor tissues, pathologically confirmed at the Memorial Hospital of Sun Yat-sen University and Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, were collected for the present study and were assessed for MYL9 protein expression patterns using Immunohistochemistry (IHC). Furthermore, from August 2013 to November 2019, 16 fresh EOC tissues and their paired paratumor tissues, pathologically confirmed at the Integrated Hospital of Traditional Chinese Medicine, Southern Medical University were analyzed using Reverse-Transcription Quantitative PCR (RT-qPCR) to detect MYL9 mRNA expression levels.

RESULTS

The results showed that MYL9 expression was higher in cancer tissues compared with that in paratumor tissues, and MYL9 overexpression was associated with shorter Recurrence Free Survival (RFS) and Overall Survival (OS) of EOC patients. Furthermore, multivariate Cox model analysis indicated that MYL9 overexpression was an independent poor survival prediction in patients with EOC.

CONCLUSION

MYL9 is upregulated in EOC and may serve as a useful patent of prognostic biomarker in EOC, and it may demonstrate an important value for the clinical treatment and supervision of patients with EOC.

Serum Anti-PDLIM1 Autoantibody as Diagnostic Marker in Ovarian Cancer

Background

Serum autoantibodies (AAbs) against tumor-associated antigens (TAAs) could be useful biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody against PDLIM1 for improving the detection of ovarian cancer (OC).

Methods

Immunohistochemistry (IHC) test in tissue array containing 280 OC tissues, 20 adjacent tissues, and 8 normal ovarian tissues was performed to analyze the expression of PDLIM1 in tissues. Enzyme-linked immunosorbent assay (ELISA) was employed to measure the autoantibody to PDLIM1 in 545 sera samples from 182 patients with OC, 181 patients with ovarian benign diseases, and 182 healthy controls.

Results

The results of IHC indicated that 84.3% (236/280) OC tissues were positively stained with PDLIM1, while no positive staining was found in adjacent or normal ovarian tissues. The frequency of anti-PDLIM1 autoantibody was significantly higher in OC patients than that in healthy and ovarian benign controls in both training (n=122) and validation (n=423) sets. The area under the curves (AUCs) of anti-PDLIM1 autoantibody for discriminating OC from healthy controls were 0.765 in training set and 0.740 in validation set, and the AUC of anti-PDLIM1 autoantibody for discriminating OC from ovarian benign controls was 0.757 in validation set. Overall, it was able to distinguish 35.7% of OC, 40.6% of patients with early-stage, and 39.5% of patients with late-stage. When combined with CA125, the AUC increased to 0.846, and 79.2% of OC were detected, which is statistically higher than CA125 (61.7%) or anti-PDLIM1(35.7%) alone (p<0.001). Also, anti-PDLIM1 autoantibody could identify 15% (18/120) of patients that were negative with CA125 (CA125 <35 U/ml).

Conclusions

The anti-PDLIM1 autoantibody response in OC patients was positively correlated with PDLIM1 high expression in OC tissues, suggesting that the autoantibody against PDLIM1 might have the potential to be a novel serological biomarker of OC, serving as a complementary measure of CA125, which could improve the power of OC detection.

Nanotechnology in ovarian cancer: Diagnosis and treatment

To overcome the drawbacks of conventional delivery, this review spotlights a number of nanoscale drug delivery systems, including nanoparticles, liposomes, nano micelles, branched dendrimers, nanocapsules, and nanostructured lipid formulations for the targeted therapy of ovarian cancer. These nanoformulations offer numerous advantages to promote therapeutic drug delivery such as nontoxicity, biocompatibility, good biodegradability, increased therapeutic impact than free drugs, and non-inflammatory effects. Importantly, the development of specific ligands functionalized nanoformulations enable preferential targeting of ovarian tumors and eventually amplify the therapeutic potential compared to nonfunctionalized counterparts. Ovarian cancer is typically identified by biomarker assessment such as CA125, HE4, Mucin 1, and prostatic. There is, nevertheless, a tremendous demand for less costly, faster, and compact medical tools, both for timely detection and ovarian cancer control. This paper explored multiple types of tumor marker-based on nanomaterial biosensors. Initially, we mention different forms of ovarian cancer biomarkers involving CA125, human epididymis protein 4 (HE4), mucin 1 (MUC1), and prostate. It is accompanied by a brief description of new nanotechnology methods for diagnosis. Nanobiosensors for evaluating ovarian cancer biomarkers can be categorized based on electrochemical, optical, paper-based, giant magnetoresistive, and lab-on-a-chip devices.

Biomarkers and Strategies for Early Detection of Ovarian Cancer

Early detection of ovarian cancer remains an important unmet medical need. Effective screening could reduce mortality by 10%-30%. Used individually, neither serum CA125 nor transvaginal sonography (TVS) is sufficiently sensitive or specific. Two-stage strategies have proven more effective, where a significant rise above a woman's baseline CA125 prompts TVS and an abnormal sonogram prompts surgery. Two major screening trials have documented that this strategy has adequate specificity, but sensitivity for early-stage (I-II) disease must improve to have a greater impact on mortality. To improve the first stage, different panels of protein biomarkers have detected cases missed by CA125. Autoantibodies against TP53 have detected 20% of early-stage ovarian cancers 8 months before elevation of CA125 and 22 months before clinical diagnosis. Panels of autoantibodies and antigen-autoantibody complexes are being evaluated with the goal of detecting >90% of early-stage ovarian cancers, alone or in combination with CA125, while maintaining 98% specificity in control subjects. Other biomarkers, including micro-RNAs, ctDNA, methylated DNA, and combinations of ctDNA alterations, are being tested to provide an optimal first-stage test. New technologies are also being developed with greater sensitivity than TVS to image small volumes of tumor.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Proteome Profiling Uncovers an Autoimmune Response Signature That Reflects Ovarian Cancer Pathogenesis

Harnessing the immune response to tumor antigens in the form of autoantibodies, which occurs early during tumor development, has relevance to the detection of cancer at early stages. We conducted an initial screen of antigens associated with an autoantibody response in serous ovarian cancer using recombinant protein arrays. The top 25 recombinants that exhibited increased reactivity with cases compared to controls revealed TP53 and MYC, which are ovarian cancer driver genes, as major network nodes. A mass spectrometry based independent analysis of circulating immunoglobulin (Ig)-bound proteins in ovarian cancer and of ovarian cancer cell surface MHC-II bound peptides also revealed a TP53–MYC related network of antigens. Our findings support the occurrence of a humoral immune response to antigens linked to ovarian cancer driver genes that may have utility for early detection applications.