Multiple biomarker algorithms to predict epithelial ovarian cancer in women with a pelvic mass: Can additional makers improve performance?

Development of prediction model to estimate future risk of ovarian lesions: A multi-center retrospective study

Background

To develop the preoperative prediction of ovarian lesions using regression-based statistics analyses and machine learning methods based on multiple serological biomarkers in China.

Methods

1137 patients with ovarian lesions in Zhujiang Hospital and 518 patients in others hospital in China were randomly assigned to training, test and external validation cohorts. Five machine learning classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Classifier (SVC), K-nearest Neighbor (KN), Multi-Layer Perceptron (MLP) and the Lasso-Logistics prediction model (LLRM) were used to derive diagnostic information from 23 predictors.

Results

The RF model had a high diagnostic value (AUC = 0.968) in predicting benign and malignant ovarian disease. Age and MLR were also potential diagnostic indicators for predicting ovarian disease except tumor indicators. The RF model well distinguished borderline ovarian tumors (AUC = 0.742). The RFM had a high predictive power to identify ovarian serous adenocarcinoma (AUC = 0.943) and ovarian endometriosis cysts (AUC = 0.914).

Conclusions

The RF models can effectively predict adnexal lesions, promising to be adjuncts to the preoperative prediction of ovarian cancer.

Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics?

MicroRNAs (miRNAs) are small non-coding RNAs with the crucial regulatory functions of gene expression at post-transcriptional level, detectable in cell and tissue extracts, and body fluids. For their stability in body fluids and accessibility to sampling, circulating miRNAs and changes of their concentration may represent suitable disease biomarkers, with diagnostic and prognostic relevance. A solid literature now describes the profiling of circulating miRNA signatures for several tumor types. Among body fluids, saliva accurately reflects systemic pathophysiological conditions, representing a promising diagnostic resource for the future of low-cost screening procedures for systemic diseases, including cancer. Here, we provide a review of literature about miRNAs as potential disease biomarkers with regard to ovarian cancer (OC), with an excursus about liquid biopsies, and saliva in particular. We also report on salivary miRNAs as biomarkers in oncological conditions other than OC, as well as on OC biomarkers other than miRNAs. While the clinical need for an effective tool for OC screening remains unmet, it would be advisable to combine within a single diagnostic platform, the tools for detecting patterns of both protein and miRNA biomarkers to provide the screening robustness that single molecular species separately were not able to provide so far.

Proteomic analysis of radioiodine-refractory differentiated thyroid cancer identifies CHI3L1 upregulation in association with dysfunction of the sodium-iodine symporter

Radioiodine refractory differentiated thyroid cancer (RR-DTC) is the main factor adversely affecting the overall survival rate of patients with thyroid cancer. The aim of the present study was to investigate the underlying molecular mechanism of pathogenesis of RR-DTC and to explore novel therapeutic targets for clinical treatment. A proteomic analysis was performed using the tumor tissues of patients with RR-DTC. A total of 6 metastatic lymph nodes were collected during lymph node dissection, 3 from patients with RR-DTC and 3 from patients with papillary thyroid cancer. The expression of chitinase-3-like 1 (CHI3L1) and sodium-iodine symporter (NIS) in the tumor tissue was detected by immunohistochemistry (IHC). Western blotting was used to detect the expression of CHI3L1, phosphorylated (p)-MEK and p-ERK1/2 in PTC-K1 cells transfected with CHI3L1 overexpression vector. The proteomic analysis identified 665 differentially expressed proteins (DEPs), including 327 upregulated and 338 downregulated proteins in the RR-DTC group, which were enriched in 59 signaling pathways by Kyoto Encyclopedia of Genes and Genomes database analysis. In particular, CHI3L1 was demonstrated to be significantly upregulated in RR-DTC as evidenced by quantitative proteomic analysis and IHC. Western blotting suggested that the overexpression of CHI3L1 activated the MEK/ERK1/2 signaling pathway, which may lead to NIS dysfunction. In conclusion, the present study suggests that CHI3L1 is a potential molecular target for the radiotherapy of patients with RR-DTC.

Malignancy Assessment Using Gene Identification in Captured Cells Algorithm for the Prediction of Malignancy in Women With a Pelvic Mass

OBJECTIVE

To evaluate the detection of malignancy in women with a pelvic mass by using multiplexed gene expression analysis of cells captured from peripheral blood.

METHODS

This was an IRB-approved, prospective clinical study. Eligible patients had a pelvic mass and were scheduled for surgery or biopsy. Rare cells were captured from peripheral blood obtained preoperatively by using a microfluidic cell capture device. Isolated mRNA from the captured cells was analyzed for expression of 72 different gene transcripts. Serum levels for several commonly assayed biomarkers were measured. All patients had a tissue diagnosis. Univariate and multivariate logistic regression analyses for the prediction of malignancy using gene expression and serum biomarker levels were performed, and receiver operating characteristic curves were constructed and compared.

RESULTS

A total of 183 evaluable patients were enrolled (average age 56 years, range 19-91 years). There were 104 benign tumors, 17 low malignant potential tumors, and 62 malignant tumors. Comparison of the area under the receiver operating characteristic curve for individual genes and various combinations of genes with or without serum biomarkers to differentiate between benign conditions (excluding low malignant potential tumors) and malignant tumors showed that a multivariate model combining the expression levels of eight genes and four serum biomarkers achieved the highest area under the curve (AUC) (95.1%, 95% CI 92.0-98.2%). The MAGIC (Malignancy Assessment using Gene Identification in Captured Cells) algorithm significantly outperformed all individual genes (AUC 50.2-65.2%; all P <.001) and a multivariate model combining 14 different genes (AUC 88.0%, 95% CI 82.9-93.0%; P =.005). Further, the MAGIC algorithm achieved an AUC of 89.5% (95% CI 81.3-97.8%) for stage I-II and 98.9% (95% CI 96.7-100%) for stage III-IV patients with epithelial ovarian cancer.

CONCLUSION

Multiplexed gene expression evaluation of cells captured from blood, with or without serum biomarker levels, accurately detects malignancy in women with a pelvic mass.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT02781272.

FUNDING SOURCE

This study was funded by ANGLE Europe Limited (Surrey Research Park, Guildford, Surrey, United Kingdom).

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cases to an unfavourable prognosis. Current predictive algorithms used to estimate the risk of having Ovarian Cancer fail to provide sufficient sensitivity and specificity to be used widely in clinical practice. The use of additional biomarkers or parameters such as age or menopausal status to overcome these issues showed only weak improvements. It is necessary to identify novel molecular signatures and the development of new predictive algorithms able to support the diagnosis of HGSOC, and at the same time, deepen the understanding of this elusive disease, with the final goal of improving patient survival. Here, we apply a Machine Learning-based pipeline to an open-source HGSOC Proteomic dataset to develop a decision support system (DSS) that displayed high discerning ability on a dataset of HGSOC biopsies. The proposed DSS consists of a double-step feature selection and a decision tree, with the resulting output consisting of a combination of three highly discriminating proteins: TOP1, PDIA4, and OGN, that could be of interest for further clinical and experimental validation. Furthermore, we took advantage of the ranked list of proteins generated during the feature selection steps to perform a pathway analysis to provide a snapshot of the main deregulated pathways of HGSOC. The datasets used for this study are available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal (https://cptac-data-portal.georgetown.edu/).

Comparison of HE4, CA125, ROMA and CPH-I for Preoperative Assessment of Adnexal Tumors

(1) OBJECTIVE: To assess the performance of CA125, HE4, ROMA index and CPH-I index to preoperatively identify epithelial ovarian cancer (EOC) or metastatic cancer in the ovary (MCO). (2) METHODS: single center retrospective study, including women with a diagnosis of adnexal mass. We obtained the AUC, sensitivity, specificity and predictive values were of HE4, CA125, ROMA and CPH-I for the diagnosis of EOC and MCO. Subgroup analysis for women harboring adnexal masses with inconclusive diagnosis of malignancy by ultrasound features and Stage I EOC was performed. (3) RESULTS: 1071 patients were included, 852 (79.6%) presented benign/borderline tumors and 219 (20.4%) presented EOC/MCO. AUC for HE4 was higher than for CA125 (0.91 vs. 0.87). No differences were seen between AUC of ROMA and CPH-I, but they were both higher than HE4 AUC. None of the tumor markers alone achieved a sensitivity of 90%; HE4 was highly specific (93.5%). ROMA showed a sensitivity and specificity of 91.1% and 84.6% respectively, while CPH-I showed a sensitivity of 91.1% with 79.2% specificity. For patients with inconclusive diagnosis of malignancy by ultrasound features and with Stage I EOC, ROMA showed the best diagnostic performance (4) CONCLUSIONS: ROMA and CPH-I perform better than tumor markers alone to identify patients harboring EOC or MCO. They can be helpful to assess the risk of malignancy of adnexal masses, especially in cases where ultrasonographic diagnosis is challenging (stage I EOC, inconclusive diagnosis of malignancy by ultrasound features).

OUP accepted manuscript

Currently, the only definitive method for diagnosing ovarian cancer involves histological examination of tissue obtained at time of surgery or by invasive biopsy. Blood has traditionally been the biofluid of choice in ovarian cancer biomarker discovery; however, there has been a growing interest in exploring urinary biomarkers, particularly as it is non-invasive. In this systematic review, we present the diagnostic accuracy of urinary biomarker candidates for the detection of ovarian cancer. A comprehensive literature search was performed using the MEDLINE/PubMed and EMBASE, up to 1 April 2021. All included studies reported the diagnostic accuracy using sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. Risk of bias and applicability of included studies were assessed using the QUADAS-2 tool. Twenty-seven studies were included in the narrative synthesis. Protein/peptide biomarkers were most commonly described (n = 18), with seven studies reporting composite scores of multiple protein-based targets. The most frequently described urinary protein biomarker was HE4 (n = 5), with three studies reporting a sensitivity and specificity > 80%. Epigenetic (n = 1) and metabolomic/organic compound biomarkers (n = 8) were less commonly described. Overall, six studies achieved a sensitivity and specificity of >90% and/or an AUC > 0.9. Evaluation of urinary biomarkers for the detection of ovarian cancer is a dynamic and growing field. Currently, the most promising biomarkers are those that interrogate metabolomic pathways and organic compounds, or quantify multiple proteins. Such biomarkers require external validation in large, prospective observational studies before they can be implemented into clinical practice.

Biomarkers in Ovarian Pathology: From Screening to Diagnosis. Review of the Literature

Background: Ovarian cancer has a low incidence, but high mortality due to a habitual diagnosis in advanced cancer stages. Currently, used biomarkers have good sensitivity, but low specificity. Aim: To determine the usefulness of the biomarkers and algorithms used up to now in the screening, diagnosis, response to treatments and identification of recurrence in patients with ovarian masses. Methodology: Systematic search of publications in English in the Medline-PubMed database with the terms: “biomarkers”, “tumour”, “tumour biomarkers”, “marker”, “tumour marker”, “ovarian cancer”, “ovarian”, “Neoplasms”, “cancer”, CA-125 Antigen; Human Epididymis-specific Protein E4; Risk of Malignancy Index (RMI); Risk of Ovarian Malignancy Algorithm (ROMA); Ovarian Neoplasms. Original articles, clinical trials, reviews, systematic reviews and meta-analyses, published between January 2000 and November 2020, were selected to determine the usefulness (among others) of CA 125 and HE4 antigen in ovarian cancer. Results: Finally, 39 transcendental publications were selected to write this article to determine the usefulness of tumour markers and algorithms in ovarian cancer. Conclusions: The usefulness of the tumour markers antigen CA125 and antigen HE4 individually or as a basis for decision-making algorithms has low specificity; however, there is little evidence that confirms their usefulness as markers in ovarian cancer screening.

Decrease in Neutrophil-to-Lymphocyte Ratio during Neoadjuvant Chemotherapy as a Predictive and Prognostic Marker in Advanced Ovarian Cancer

Since chronic inflammation is associated with ovarian cancer growth and progression, some clinical studies have assessed the association between the pre-treatment neutrophil-to-lymphocyte ratio (NLR) and the prognosis of ovarian cancer. The purpose of this study was to assess the dynamic behavior of the NLR during the course of neoadjuvant chemotherapy (NACT) in patients with high grade serous (HGS) advanced epithelial ovarian cancer and assess its correlation with clinical response, progression free survival (PFS) and changes in other inflammatory indexes. We performed a prospective observational study on 161 patients who underwent NACT at the Department of Gynecologic Oncology, ARNAS G. Brotzu, Cagliari, between 2009 and 2019. NLR was evaluated before starting and after three cycles of NACT. Based on response after three cycles of NACT, patients were divided into two groups: responsive and non-responsive. The primary endpoint was to assess the predictive role of NLR by comparing the responsive and non-responsive patients at baseline and after three cycles of NACT. Secondary endpoints were (a) to correlate NLR with other inflammation markers (CRP, fibrinogen, ferritin, IL-6), albumin, and modified Glasgow Prognostic Score (mGPS) with NLR at baseline and after NACT; (b) to assess the association between NLR and PFS. We found that the NLR value at baseline was not associated with response to NACT, while a decrease in NLR after three cycles was correlated with a better response to NACT. Also, values of CRP, IL-6, ferritin, and mGPS after three cycles of NACT (but not at baseline) were significantly associated with clinical response. Moreover, we found that patients with a low NLR value after 3 cycles of NACT, but not at baseline, had a significantly higher PFS than patients with high NLR after 3 cycles of NACT. In conclusion, NLR change during treatment could serve as a predictive marker of response to NACT in patients with HGS advanced ovarian cancer. This allows for the early identification of non-responsive patients who will need treatment remodeling.

Review of biomarker systems as an alternative for early diagnosis of ovarian carcinoma

Early diagnosis of ovarian carcinoma is bound to boost the long-term endurance rate of the patients. Most ovarian tumors happen post menopause when the ovaries have no vital operation and therefore irregular ovarian role causes no signs. According to Muinao T. et al. (Heliyon. 5(12):e02826, 2019), if we consider the frequency of ovarian carcinoma to be moderate, a screening technique must accomplish a base specificity of 99.6% and sensitivity of over 75%. The classification and approval of early diagnostic biomarkers explicit to ovarian carcinoma are essentially required. Prevailing methods for early diagnosis of ovarian carcinoma incorporate TVS, biological marker examination, or a blend of the two or other. In recent years, it has been revealed that a combination of at least two biomarkers has beaten single biomarkers in measures for early diagnosis of the illness. In the present document, we survey the ongoing exploration of innovative characteristic methodologies and possible panels of carcinoma biological markers for the early diagnosis of ovarian carcinoma and discuss biomarkers as the plausible apparatus for model improvement and other progressed approaches as an effective alternative to the prevailing methods for early diagnosis of this dreadful disease to evade bogus analysis and inordinate expense.

The value of human epididymis 4, D-dimer, and fibrinogen compared with CA 125 alone in triaging women presenting with pelvic masses: a retrospective cohort study

INTRODUCTION

CA 125, the biomarker in common clinical use for ovarian cancer, is limited by low sensitivity for early disease and high false positives. The aim of this study was to evaluate several candidate biomarkers, alone or in combination, compared with CA 125 in the prediction of malignant/borderline vs benign tumor status in premenopausal and postmenopausal women with pelvic masses.

MATERIAL AND METHODS

This was a retrospective observational cohort study set in St James's Hospital, a tertiary referral center for gynecological malignancy in Dublin, Ireland. Women undergoing surgery for pelvic masses between 2012 and 2018 were included. Preoperative human epididymis protein 4 (HE4), the Risk of Ovarian Malignancy Algorithm, the Risk of Malignancy Index I and II, D-dimer, and fibrinogen were assessed. Logistic regression models were fitted for each biomarker alone and in combination. Receiver operating characteristics-area under the curve (ROC-AUC) and partial AUCs in the 90%-100% specificity range were determined.

RESULTS

In all, 89 premenopausal and 185 postmenopausal women were included. In premenopausal women, no biomarker(s) outperformed CA 125 (AUC 0.73; 95% CI 0.63-0.84). In postmenopausal women, HE4 had a partial AUC (pAUC) of 0.71 (95% CI 0.64-0.79) compared with 0.57 (95% CI 0.51-0.69) for CA 125 (p = 0.009). HE4 + D-dimer had an improved pAUC of 0.74 (95% CI 0.68-0.81, p < 0.001) and HE4 + D-dimer + fibrinogen had a pAUC of 0.75 (95% CI 0.68-0.82).

CONCLUSIONS

A novel biomarker panel of HE4 ± D-dimer ± fibrinogen outperformed CA 125 alone as a high-specificity biomarker in postmenopausal women and could aid in the preoperative triaging of pelvic masses. No biomarker(s) outperformed CA 125 in premenopausal women.

Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer

After it was discovered approximately 40 years ago, carbohydrate antigen 125 (CA125) became the most widely used and concerning biomarker in ovarian cancer screening. However, there is still controversy about its role in clinical practice. CA125 is not sufficiently reliable in diagnosis to screen for early-stage ovarian cancer. On the other hand, CA125 has been a valuable indicator for evaluating chemotherapeutic efficacy and prognosis. We still do not know much about its biological role, and several studies have indicated that this marker participates in the occurrence and development of ovarian cancer. Currently, an increasing number of scholars have begun to pay attention to CA125-targeted treatment strategies. In the interest of better design and development of anticancer therapies, a renewed and systematic understanding of the roles of CA125 in diagnosis, prediction, and tumorigenesis is warranted.

MicroRNA as Biomarker in Ovarian Cancer Management: Advantages and Challenges

Ovarian cancer is the most prevalent gynecological malignancy affecting women throughout the globe. Ovarian cancer has several subtypes, including epithelial ovarian cancer (EOC) with a whopping incidence rate of 239,000 per year, making it the sixth most common gynecological malignancy worldwide. Despite advancement of detection and therapeutics, death rate accounts for 152,000 per annum. Several protein-based biomarkers such as CA125 and HE4 are currently being used for diagnosis, but their sensitivity and specificity for early detection of ovarian cancer are under question. MicroRNA (a small noncoding RNA molecule that participates in post-transcription regulation of gene expression) and its functional deregulation in most cancers have been discovered in the previous two decades. Studies support that miRNA deregulation has an epigenetic component as well. Aberrant miRNA expression is often correlated with the form of EOC tumor, histological grade, prognosis, and FIGO stage. In this review, we addressed epigenetic regulation of miRNAs, the latest research on miRs as a biomarker in the detection of EOC, and tailored assays to use miRNAs as a biomarker in ovarian cancer diagnosis.

Scoring systems for the evaluation of adnexal masses nature: current knowledge and clinical applications

Adnexal masses are a common finding in women, with 20% of them developing at least one pelvic mass during their lifetime. There are more than 30 different subtypes of adnexal tumours, with multiple different subcategories, and the correct characterisation of the pelvic masses is of paramount importance to guide the correct management. On that basis, different algorithms and scoring systems have been developed to guide the clinical assessment. The first scoring system implemented into the clinical practice was the Risk of Malignancy Index, which combines ultrasound evaluation, menopausal status, and serum CA-125 levels. Today, current guidelines regarding female patients with adnexal masses include the application of International Ovarian Tumours Analysis simple rules, logistic regression model 1 (LR1) and LR2, OVERA, cancer ovarii non-invasive assessment of treating strategy, and assessment of Different Neoplasias in the adnexa. In this scenario, the choice of the scoring system for the discrimination between benign and malignant ovarian tumours can be complex when approaching patients with adnexal masses. This review aims to summarise the available evidence regarding the different scoring systems to provide a complete overview of the topic.

Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma

Initial identification of biomarkers predicting the exact prognosis of high-grade serous ovarian carcinoma (HGSOC) is important in precision cancer medicine. This study aimed to investigate prognostic biomarkers of HGSOC through proteomic analysis. We conducted label-free liquid chromatography-mass spectrometry using chemotherapy-naïve, fresh-frozen primary HGSOC specimens, and compared the results between a favorable prognosis group (progression-free survival (PFS) ≥ 18 months, n = 6) and a poor prognosis group (PFS < 18 months, n = 6). Among 658 differentially expressed proteins, 288 proteins were upregulated in the favorable prognosis group and 370 proteins were upregulated in the poor prognosis group. Using hierarchical clustering, we selected α1-antitrypsin (AAT), nuclear factor-κB (NFKB), phosphomevalonate kinase (PMVK), vascular adhesion protein 1 (VAP1), fatty acid-binding protein 4 (FABP4), platelet factor 4 (PF4), apolipoprotein A1 (APOA1), and α1-acid glycoprotein (AGP) for further validation via immunohistochemical (IHC) staining in an independent set of chemotherapy-naïve primary HGSOC samples (n = 107). Survival analyses revealed that high expression of AAT, NFKB, and PMVK were independent biomarkers for favorable PFS. Conversely, high expression of VAP1, FABP4, and PF4 were identified as independent biomarkers for poor PFS. Furthermore, we constructed models predicting the 18-month PFS by combining clinical variables and IHC results. Through leave-one-out cross-validation, the optimal model was based on initial serum CA-125, germline BRCA1/2 mutations, residual tumors after surgery, International Federation of Gynecology and Obstetrics (FIGO) stage, and expression levels of the six proteins. The present results elucidate the proteomic landscape of HGSOC and six protein biomarkers to predict the prognosis of HGSOC.

Preoperative serum levels of HE4 and CA125 predict primary optimal cytoreduction in advanced epithelial ovarian cancer: a preliminary model study

Objective

The aim of this study is to establish a noninvasive preoperative model for predicting primary optimal cytoreduction in advanced epithelial ovarian cancer by HE4 and CA125 combined with clinicopathological parameters.

Methods

Clinical data including preoperative serum HE4 and CA125 level of 83 patients with advanced epithelial ovarian cancer were collected. The sensitivity, specificity, positive predictive value, negative predictive value and overall accuracy of each clinical parameter were calculated. The Predictive Index score model and the logistic model were constructed to predict the primary optimal cytoreduction.

Results

Optimal surgical cytoreduction was achieved in 62.65% (52/83) patients. Cutoff values of preoperative serum HE4 and CA125 were 777.10 pmol/L and 313.60 U/ml. (1) Patients with PIV ≥ 6 may not be able to achieve optimal surgical cytoreduction. The diagnostic accuracy, NPV, PPV and specificity for diagnosing suboptimal cytoreduction were 71, 100, 68, and 100%, respectively. (2) The logistic model was: logit p = 0.12 age − 2.38 preoperative serum CA125 level − 1.86 preoperative serum HE4 level-2.74 histological type-3.37. AUC of the logistic model in the validation group was 0.71(95%CI 0.54–0.88, P = 0.025). Sensitivity and specificity were 1.00 and 0.44, respectively.

Conclusion

Age, preoperative serum CA125 level and preoperative serum HE4 level are important non-invasive predictors of primary optimal surgical cytoreduction in advanced epithelial ovarian cancer. Our PIV and logistic model can be used for assessment before expensive and complex predictive methods including laparoscopy and diagnostic imaging. Further future clinical validation is needed.

Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers

Ovarian cancer is the most lethal gynecologic cancer. Pre-diagnostic testing lacks sensitivity and specificity, and surgery is often the only way to secure the diagnosis. Exploring new biomarkers is of great importance, but the rationale of combining validated well-established biomarkers and algorithms could be a more effective way forward. We hypothesized that we can improve differential diagnostics and reduce false positives by combining (a) risk of malignancy index (RMI) with serum HE4, (b) risk of ovarian malignancy algorithm (ROMA) with a transvaginal ultrasound score or (c) adding HE4 to CA125 in a simple algorithm. With logistic regression modeling, new algorithms were explored and validated using leave-one-out cross validation. The analyses were performed in an existing cohort prospectively collected prior to surgery, 2013-2016. A total of 445 benign tumors and 135 ovarian cancers were included. All presented models improved specificity at cut-off compared to the original algorithm, and goodness of fit was significant (p < 0.001). Our findings confirm that HE4 is a marker that improves specificity without hampering sensitivity or diagnostic accuracy in adnexal tumors. We provide in this study "easy-to-use" algorithms that could aid in the triage of women to the most appropriate level of care when presenting with an unknown ovarian cyst or suspicious ovarian cancer.

Multi-biomarker panel signature as the key to diagnosis of ovarian cancer

Early detection of ovarian cancer has been a challenge to manage the high mortality rate caused by this deadly disease. The trends in mortality have been reduced by the scientific contributions from the corners across the globe, however accounting for the fifth leading cause of gynecological mortality. The complexities in the clinical presentation, origin of tumor, and gene expression profiles had added to much difficulty in understanding and diagnosis of the disease. Stage 1 diagnosis of ovarian cancer improves the 5-year survival rate to around 92%. Cancer antigen-125 (CA-125) is the gold standard tumor marker found at abnormally high levels in the blood of many women in ovarian cancer. However, many non-cancerous conditions exhibit high levels of CA-125 and several women have normal CA-125 level in the early stage of ovarian cancer, suggesting CA-125 biomarker is not specific enough for the screening of early stage ovarian cancer. In addition, several other biomarkers, including HE4 have been added in the diagnostic field for higher sensitivity and specificity in the diagnosis and progression of ovarian cancer. HE4 is a prospective single serum biomarker which has been approved by the FDA to monitor the disease progression in epithelial ovarian cancer. However, owing to low sensitivity and specificity, combination of a panel of biomarkers has been proposed in the diagnosis of the disease. Based on extensive biomarkers research findings, here we discuss current trends in diagnostic approaches and updated potential several panels of cancer biomarkers for early detection of ovarian cancer. It has been recently reported that CA125 in combinations with two or more biomarkers have outperformed single biomarker assays for early detection of the disease. Moreover, CA-125 with CA 19–9, EGFR, G-CSF, Eotaxin, IL-2R, cVCAM, MIF improved the sensitivity with 98.2 % and specificity of 98.7% in early stage detection of ovarian cancer. Overall, this review demonstrates a panel of biomarkers signature as the potential tool for prototype development in future and other advanced approaches for early diagnosis of ovarian cancer to avoid false-diagnosis and excessive cost.

Targeting Lysophosphatidic Acid in Cancer: The Issues in Moving from Bench to Bedside

Since the clear demonstration of lysophosphatidic acid (LPA)'s pathological roles in cancer in the mid-1990s, more than 1000 papers relating LPA to various types of cancer were published. Through these studies, LPA was established as a target for cancer. Although LPA-related inhibitors entered clinical trials for fibrosis, the concept of targeting LPA is yet to be moved to clinical cancer treatment. The major challenges that we are facing in moving LPA application from bench to bedside include the intrinsic and complicated metabolic, functional, and signaling properties of LPA, as well as technical issues, which are discussed in this review. Potential strategies and perspectives to improve the translational progress are suggested. Despite these challenges, we are optimistic that LPA blockage, particularly in combination with other agents, is on the horizon to be incorporated into clinical applications.