Diagnostic accuracy of haptoglobin within ovarian cyst fluid as a potential point-of-care test for epithelial ovarian cancer: an observational study

Amide proton transfer weighted MRI in differential diagnosis of ovarian masses with cystic components: A preliminary study

RATIONALE AND OBJECTIVES

To evaluate the performance of three-dimensional (3D) amide proton transfer-weighted (APTw) MRI in the differentiation between benign and malignant ovarian masses based on single-slice and all-slice analysis of cystic regions.

MATERIALS AND METHODS

Patients were consecutively recruited and underwent conventional pelvic MRI and APTw MRI. Two radiologists independently assessed ovarian masses blinded to the histopathological results. Three APTw SI values were generated from the cystic regions of the masses: (1) APTw SI of a single representative slice (RS); (2) average (AVE) of APTw SIs of all slices of the mass; (3) area-weighted (AW) average of APTw SIs of all slices of the mass. O-RADS MRI score of each mass was reported. Independent sample t-test and receiver operating characteristic (ROC) curve analysis were performed for comparison. Inter- and intra-observer reliability were assessed by the intraclass correlation coefficient (ICC) and quadratic kappa coefficient.

RESULTS

46 ovarian masses were included for final analysis. The three APTw SI values were higher in cystic regions of malignant ovarian masses compared with benign lesions (p<0.0001). ROC curve analysis showed no significant difference in diagnostic performance among three APTw SI values and the O-RADS MRI score (AUC: RS-APTw SI, 0.930; AVE-APTw SI, 0.927; AW-APTw SI, 0.935; O-RADS score, 0.937).

CONCLUSIONS

APTw MRI may be used as a noninvasive tool for the differentiation of benign and malignant ovarian masses based on the analysis of the cystic regions.

Rapid and sensitive detection of ovarian cancer biomarker using a portable single peak Raman detection method

Raman spectroscopy (RS) is a widely used non-destructive technique for biosensing applications because of its ability to detect unique ‘fingerprint’ spectra of biomolecules from the vibrational bands. To detect these weak fingerprint spectra, a complex detection system consisting of expensive detectors and optical components are needed. As a result, surface enhanced Raman spectroscopy (SERS) method were used to increase the Raman signal multifold beyond 10¹² times. However, complexity of the entire Raman detection system can be greatly reduced if a short wavelength region/unique single spectral band can distinctly identify the investigating analyte, thereby reducing the need of multiple optical components to capture the entire frequency range of Raman spectra. Here we propose the development of a rapid, single peak Raman technique for the detection of epithelial ovarian cancers (EOC)s through haptoglobin (Hp), a prognostic biomarker. Hp concentration in ovarian cyst fluid (OCF) can be detected and quantified using Raman spectroscopy-based in vitro diagnostic assay. The uniqueness of the Raman assay is that, only in the presence of the analyte Hp, the assay reagent undergoes a biochemical reaction that results in product formation. The unique Raman signature of the assay output falls within the wavenumber region 1500–1700 cm⁻¹ and can be detected using our single peak Raman system. The diagnostic performance of our Raman system had 100.0% sensitivity, 85.0% specificity, 100.0% negative predictive value and 84.2% positive predictive value when compared to gold standard paraffin histology in a proof-of-concept study on 36 clinical OCF samples. When compared to blood-based serum cancer antigen 125 (CA125) levels, the Raman system-based assay had higher diagnostic accuracy when compared to CA125, especially in early-stage EOCs.

Screening and identification of haptoglobin showing its important role in pathophysiological process of gallbladder carcinoma

Gallbladder cancer (GBC) with poor prognosis has been a major cause of cancer-related deaths worldwide. In this study, we aimed to screen and identify crucial genes in GBC through integrative analysis of multiple datasets and further experimental validation. A candidate crucial gene, up-regulated haptoglobin (HP), was firstly screened, and then further analysis and validation mainly focused on whether higher enrichment level of HP was responsible for pathophysiological process of GBC. HP was found with diverse expression patterns in various cancer types, and the dynamic expression patterns indicated its spatiotemporal characteristics in different tissues and disease stages, implicating its role in multiple biological processes. Further experimental validation showed that HP could promote the GBC-SD cell proliferation, migration and invasion, implying its role in pathophysiological process of GBC. HP may have a crucial role in occurrence and development of GBC, and it provides possibility as a potential biomarker or target in cancer prognosis and treatment.

Development of highly reliable SERS-active photonic crystal fiber probe and its application in the detection of ovarian cancer biomarker in cyst fluid

Conventionally Surface-enhanced Raman spectroscopy (SERS) is realized by adsorbing analytes onto nano-roughened planar substrate coated with noble metals (silver or gold) or their colloidal nanoparticles (NPs). Nanoscale irregularities in such substrates/NPs could lead to SERS sensors with poor reproducibility and repeatability. Herein, we demonstrate a suspended core photonic crystal fiber (PCF) based SERS sensor with extremely high reproducibility and repeatability in measurement with a relative SD of only 1.5% and 4.6%, respectively, which makes it more reliable than any existing SERS sensor platforms. In addition, our platform could improve the detection sensitivity owing to the increased interaction area between the guided light and the analyte, which is incorporated into the holes that runs along the length of the PCF. Numerical calculation established the significance of the interplay between light coupling efficiency and evanescent field distribution, which could eventually determine the sensitivity and reliability of the developed SERS active-PCF sensor. As a proof of concept, using this sensor, we demonstrated the detection of haptoglobin, a biomarker for ovarian cancer, contained within the ovarian cyst fluid, which facilitated in differentiating the stages of cancer. We envision that with necessary refinements, this platform could potentially be translated as a next-generation highly sensitive SERS-active opto-fluidic biopsy needle for the detection of biomarkers in body fluids.

Proteome profiling of low grade serous ovarian cancer

Background

Serous carcinoma, the subtype of ovarian cancer has the highest occurrence and mortality in women. Proteomic profiling using mass spectrometry (MS) has been used to detect biomarkers in tissue s obtained from patients with ovarian cancer.

Thus, this study aimed at analyzing the interactome (protein-protein interaction (PPI)) and (MS) data to inspect PPI networks in patients with Low grade serous ovarian cancer.

Methods

For proteome profiling in Low grade serous ovarian cancer, 2DE and mass spectrometry were used. Differentially expressed proteins which had been determined in Low grade serous ovarian cancer and experimental group separately were integrated with PPI data to construct the (QQPPI) networks.

Results

Six Hub-bottlenecks proteins with significant centrality values, based on centrality parameters of the network (Degree and between), were found including Transgelin (TAGLN), Keratin (KRT14), Single peptide match to actin, cytoplasmic 1(ACTB), apolipoprotein A-I (APOA1), Peroxiredoxin-2 (PRDX2), and Haptoglobin (HP).

Discussion

This study showed these six proteins were introduced as hub-bottleneck protein. It can be concluded that regulation of gene expression can have a critical role in the pathology of Low-grade serous ovarian cancer.

SERS-based detection of haptoglobin in ovarian cyst fluid as a point-of-care diagnostic assay for epithelial ovarian cancer

Purpose

To evaluate haptoglobin (Hp) in ovarian cyst fluid as a diagnostic biomarker for epithelial ovarian cancers (EOCs) using surface-enhanced Raman spectroscopy (SERS)-based in vitro diagnostic assay for use in an intraoperative setting.

Methods

SERS-based method was used to detect and quantify Hp in archived ovarian cyst fluids collected from suspicious ovarian cysts and differentiate benign tumors from EOCs. The diagnostic performance of SERS-based assay was verified against the histopathology conclusions and compared with the results of CA125 test and frozen sections.

Results

Hp concentration present in the clinical cyst fluid measured by SERS was normalized to 3.3 mg/mL of standard Hp. Normalized mean values for patients with benign cysts were 0.65 (n=57) and malignant cysts were 1.85 (n=54), demonstrating a significantly (P<0.01) higher Hp in malignant samples. Verified against histology, Hp measurements using SERS had a sensitivity of 94% and specificity of 91%. Receiver operating characteristic curve analysis of SERS-based Hp measurements resulted in area under the curve of 0.966±0.03, establishing the robustness of the method. CA125 test on the same set of patients had a sensitivity of 85% and specificity of 90%, while frozen section analysis on 65 samples had 100% sensitivity and specificity.

Conclusion

With a total execution time of <10 minutes and consistent performance across different stages of cancer, the SERS-based Hp detection assay can serve as a promising intra-operative EOC diagnostic test.

Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 2

Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT-LC-MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p < .05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. SIGNIFICANCE: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics.