Multiple biomarkers are more accurate than a combination of carbohydrate antigen 125 and human epididymis protein 4 for ovarian cancer screening

SERS biosensors for liquid biopsy towards cancer diagnosis by detection of various circulating biomarkers: current progress and perspectives

Liquid biopsy has emerged as a promising non-invasive strategy for cancer diagnosis, enabling the detection of various circulating biomarkers, including circulating tumor cells (CTCs), circulating tumor nucleic acids (ctNAs), circulating tumor-derived small extracellular vesicles (sEVs), and circulating proteins. Surface-enhanced Raman scattering (SERS) biosensors have revolutionized liquid biopsy by offering sensitive and specific detection methodologies for these biomarkers. This review comprehensively examines the application of SERS-based biosensors for identification and analysis of various circulating biomarkers including CTCs, ctNAs, sEVs and proteins in liquid biopsy for cancer diagnosis. The discussion encompasses a diverse range of SERS biosensor platforms, including label-free SERS assay, magnetic bead-based SERS assay, microfluidic device-based SERS system, and paper-based SERS assay, each demonstrating unique capabilities in enhancing the sensitivity and specificity for detection of liquid biopsy cancer biomarkers. This review critically assesses the strengths, limitations, and future directions of SERS biosensors in liquid biopsy for cancer diagnosis.

A personalized probabilistic approach to ovarian cancer diagnostics

OBJECTIVE

The identification/development of a machine learning-based classifier that utilizes metabolic profiles of serum samples to accurately identify individuals with ovarian cancer.

METHODS

Serum samples collected from 431 ovarian cancer patients and 133 normal women at four geographic locations were analyzed by mass spectrometry. Reliable metabolites were identified using recursive feature elimination coupled with repeated cross-validation and used to develop a consensus classifier able to distinguish cancer from non-cancer. The probabilities assigned to individuals by the model were used to create a clinical tool that assigns a likelihood that an individual patient sample is cancer or normal.

RESULTS

Our consensus classification model is able to distinguish cancer from control samples with 93% accuracy. The frequency distribution of individual patient scores was used to develop a clinical tool that assigns a likelihood that an individual patient does or does not have cancer.

CONCLUSIONS

An integrative approach using metabolomic profiles and machine learning-based classifiers has been employed to develop a clinical tool that assigns a probability that an individual patient does or does not have ovarian cancer. This personalized/probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests and represents a promising new direction in the early detection of ovarian cancer.

Solitary Pancreatic Metastasis from an Ovarian Carcinoma: A Diagnostic Perplexity

Ovarian cancer presenting as an isolated pancreatic metastasis after years of treatment is extremely rare. Most such patients are easily misdiagnosed as a case of primary pancreatic cancer. We herein describe a unique case of posttreatment high-grade serous papillary ovarian carcinoma metastasizing to the pancreas that mimicked primary pancreatic cancer and caused a diagnostic dilemma. The approach to such a case, pathogenesis, differential diagnosis, management, and a brief literature review is also presented.

Comparison of International Ovarian Tumor Analysis ADNEX model and Ovarian-Adnexal Reporting and Data System with final histological diagnosis in adnexal masses: a retrospective study

OBJECTIVE

The International ovarian tumor analysis (IOTA)-Assessment of Different NEoplasias in the adneXa (ADNEX) model and the ovarian-adnexal reporting and data system (O-RADS) were developed to improve the diagnostic accuracy of adnexal masses in the preoperative period. This study aimed to evaluate the predictive values of both models in patients who underwent surgery for an adnexal mass at our hospital, based on the final pathological results.

METHODS

This study included patients who underwent surgery for adnexal masses at our hospital between 2019 and 2021 and met the inclusion criteria. The IOTA ADNEX model and O-RADS scores were calculated preoperatively.

RESULTS

Of the 413 patients, 295 were diagnosed with benign tumors and 118 were diagnosed with malignant tumors. The mean cancer antigen 125 (CA-125) levels for patients diagnosed with benign and malignant were 15.2 unit/mL and 72.5 unit/mL, respectively. According to the receiver operator characteristic analysis for serum CA-125 in postmenopausal and premenopausal patients, the cutoff value of 34.8 unit/mL had a sensitivity of 70.8% and specificity of 83.8% and 180.5 unit/mL had a sensitivity of 32.1% and a specificity of 92.7%, respectively (P<0.001). The sensitivity and specificity values of the IOTA ADNEX model and O-RADS were found as 78.8-48.3% and 97.9-93.5% respectively (P<0.001). There was moderate agreement between the IOTA ADNEX model and O-RADS (Kappa=0.53).

CONCLUSION

The IOTA ADNEX model has a similar specificity to the O-RADS in malignancy risk assessment, but the sensitivity of the IOTA ADNEX model is higher than that of the O-RADS. The IOTA-ADNEX model can help avoid unnecessary surgeries.

Role of Selected Circulating Tumor Biomarkers in Patients with Skeletal Metastatic Pancreatic Neuroendocrine Neoplasms

We investigated the diagnostic capacity of selected circulating biomarkers (CBMs) for the early detection of bone metastasis (BMets) in patients with pancreatic neuroendocrine neoplasms (PanNENs). A total of 115 patients with PanNENs and 40 controls were enrolled. We measured the serum levels of ferritin, cytokeratin 18 (CY18), CA19-9, CA125, AFP, CEA, and beta-2 microglobulin (B2M). A total of eight PanNEN patients developed BMets, and one hundred seven remained BMets-free. We observed a significantly higher level of CA125 and CY18 in BMets patients vs. non-BMets patients (p = 0.01 and p = 0.04, respectively). CA125, CY18, and B2M area under receiver operator characteristic (AUROC) analyses differentiated both patients groups; CA125 area under the curve (AUC) 0.77, p < 0.01; CY18 AUC data were 0.72, p = 0.03, and B2M AUC 0.67, p = 0.02. On the basis of CBM metrics in both subgroups, we reached a sensitivity/specificity for CA125 of 75/76%; for CY18 of 75/69%, for B2M of 100/50%, for CA125, and the CY18 combination of 93/90%, respectively. According to current results, CA125 and CY18 seem to have the potential capacity as fair biomarkers for BMets detection, despite the small number of cases. Further studies are warranted in the larger PanNEN patient group.

Combining TNFR2-Expressing Tregs and IL-6 as Superior Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Masses

We hypothesised that the inclusion of immunosuppressive and inflammatory biomarkers in HGSOC patients would improve the sensitivity and specificity of the preoperative marker prediction of malignancy in patients with ovarian masses. We tested a panel of 29 soluble immune factors by multiplex bead immunoassay and 16 phenotypic T cell markers by flow cytometry in pre-treatment blood samples from 66 patients undergoing surgery for suspected ovarian cancer or ovarian cancer risk reduction. The potential diagnostic utility of all parameters was explored using Volcano plots, principal component analysis (PCA) and receiver operator characteristic (ROC) analysis. We also assessed the effect of culturing PBMCs from 20 healthy donors in the presence of malignant ascites fluid. The combination of TNFR2⁺ Tregs and IL-6 in the pre-treatment blood of patients with advanced HGSOC effectively discriminated patients with benign or malignant ovarian masses. In vitro culturing of the PBMCs of healthy donors in malignant ascites promoted an increase in TNFR2-expressing Tregs, which were decreased following blockade with IL-6 or STAT3 activity. Pre-treatment serum IL-6 and peripheral blood TNFR2⁺ Tregs may be potential clinical biomarkers that can discriminate patients with malignant compared to benign ovarian cancer masses, and the relationship between IL-6 and TNFR2⁺ Treg is likely to be mediated via the STAT3 signalling pathway.