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

Cell type-specific regulation of the pentose phosphate pathway during development and metabolic stress-driven autoimmune diseases: Relevance for inflammatory liver, renal, endocrine, cardiovascular and neurobehavioral comorbidities, carcinogenesis, and aging

The pathogenesis of autoimmunity is incompletely understood which limits the development of effective therapies. New compelling evidence indicates that the pentose phosphate pathway (PPP) profoundly regulate lineage development in the immune system that are influenced by genetic and environmental factors during metabolic stress underlying the development of autoimmunity. The PPP provides two unique metabolites, ribose 5-phosphate for nucleotide biosynthesis in support of cell proliferation and NADPH for protection against oxidative stress. The PPP operates two separate branches, oxidative (OxPPP) and non-oxidative (NOxPPP). While the OxPPP functions in all organisms, the NOxPPP reflects adaptation to niche-specific metabolic requirements. The OxPPP primarily depends on glucose 6-phosphate dehydrogenase (G6PD), whereas transaldolase (TAL) controls the rate and directionality of metabolic flux though the NOxPPP. G6PD is essential for normal development but its partial deficiency protects from malaria. Although men and mice lacking TAL develop normally, they exhibit liver cirrhosis progressing to hepatocellular carcinoma. Mechanistic target of rapamycin-dependent loss of paraoxonase 1 drives autoimmunity and cirrhosis in TAL deficiency, while hepatocarcinogenesis hinges on polyol pathway activation via aldose reductase (AR). Accumulated polyols, such as erythritol, xylitol, and sorbitol, which are commonly used as non-caloric sweeteners, may act as pro-inflammatory oncometabolites under metabolic stress, such as TAL deficiency. The TAL/AR axis is identified as a checkpoint of pathogenesis and target for treatment of metabolic stress-driven systemic autoimmunity with relevance for inflammatory liver, renal and cardiovascular disorders, diabetes, carcinogenesis, and aging.

AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis

BACKGROUND

Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

OBJECTIVE

We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis.

METHODS

A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis.

RESULTS

A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis.

CONCLUSIONS

AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies.

TRIAL REGISTRATION

PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review

INTRODUCTION

Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology.

METHODS

We utilized PubMed to search for peer-reviewed research articles published between 2016 and 2021 with the query type "("deep learning" or "machine learning" or "neural network" or "artificial intelligence") and ("neoplas$" or "cancer$" or "tumor$" or "tumour$")." We included clinical trials and original research studies and excluded reviews and meta-analyses. Oncology-related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies.

RESULTS

Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%-93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. "White" vs. "non-White" or "White" vs. "Black").

CONCLUSIONS

We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non-White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology.

RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis

Ovarian cancer, a deadly female reproductive system disease, is a significant challenge in medical research due to its notorious lethality. Addressing ovarian cancer in the current medical landscape has become more complex than ever. This research explores the complex field of Ovarian Cancer Subtype Classification and the crucial task of Outlier Detection, driven by a progressive automated system, as the need to fight this unforgiving illness becomes critical. This study primarily uses a unique dataset painstakingly selected from 20 esteemed medical institutes. The dataset includes a wide range of images, such as tissue microarray (TMA) images at 40× magnification and whole-slide images (WSI) at 20× magnification. The research is fully committed to identifying abnormalities within this complex environment, going beyond the classification of subtypes of ovarian cancer. We proposed a new Attention Embedder, a state-of-the-art model with effective results in ovarian cancer subtype classification and outlier detection. Using images magnified WSI, the model demonstrated an astonishing 96.42% training accuracy and 95.10% validation accuracy. Similarly, with images magnified via a TMA, the model performed well, obtaining a validation accuracy of 94.90% and a training accuracy of 93.45%. Our fine-tuned hyperparameter testing resulted in exceptional performance on independent images. At 20× magnification, we achieved an accuracy of 93.56%. Even at 40× magnification, our testing accuracy remained high, at 91.37%. This study highlights how machine learning can revolutionize the medical field's ability to classify ovarian cancer subtypes and identify outliers, giving doctors a valuable tool to lessen the severe effects of the disease. Adopting this novel method is likely to improve the practice of medicine and give people living with ovarian cancer worldwide hope.

Aptamer-based carbohydrate antigen 125 sensor with molybdenum disulfide functional hybrid materials

Epithelial ovarian cancer is a malignant tumor of the female reproductive system with insidious symptoms, aggressiveness, risk of metastasis, and high mortality. Carbohydrate antigen 125 (CA125), a standard biomarker for screening epithelial ovarian cancer, can be applied to track cancer progression and treatment response. Here, we constructed an aptamer-based electrochemical biosensor to achieve sensitive detection of CA125. Molybdenum disulfide (MoS2) was used as the stable layered substrate, combined with the irregular branched structure of gold nanoflowers (AuNFs) to provide the sensing interface with a large specific surface area by one-step electrodeposition AuNFs@MoS2. The simplified electrode modification step increased the stability of the electrode while ensuring excellent electrochemical performance and providing many sulfhydryl binding sites. Then, AuNFs@MoS2/CA125 aptamer/MCH sensor was designed for CA125 detection. Based on AuNFs@MoS2 electrode, CA125 aptamer with sulfhydryl as the sensitive layer was fixed on the electrode by gold sulfur bonds. 6-Mercapto-1-hexanol (MCH) was used to block the electrode and reduce the non-specific adsorption. Finally, DPV analysis was applied for CA125 detection with the range of 0.0001 U/mL to 500 U/mL. Our designed aptamer sensor showed reasonable specificity, reproducibility, and stability. Clinical sample testing also proved the consistency of our sensor with the gold standard in negative/positive judgment. This work demonstrated a novel strategy for integrating nanostructures and biocompatibility to build advanced cancer biomarker sensors with promising applications.

Long noncoding RNAs: glycolysis regulators in gynaecologic cancers

The three most common gynaecologic cancers that seriously threaten female lives and health are ovarian cancer, cervical cancer, and endometrial cancer. Glycolysis plays a vital role in gynaecologic cancers. Several long noncoding RNAs (lncRNAs) are known to function as oncogenic molecules. LncRNAs impact downstream target genes by acting as ceRNAs, guides, scaffolds, decoys, or signalling molecules. However, the role of glycolysis-related lncRNAs in regulating gynaecologic cancers remains poorly understood. In this review, we emphasize the functional roles of many lncRNAs that have been found to promote glycolysis in gynaecologic cancers and discuss reasonable strategies for future research.

Bioinformatics analysis of the role of aldolase A in tumor prognosis and immunity

Aldolase A (ALDOA) is an enzyme that plays an important role in glycolysis and gluconeogenesis, which is closely related to tumor metabolism. In this study, the overall roles of ALDOA in pan-cancer have been investigated from several aspects using databases and online analysis tools. Using the ONCOMINE database, the expression of ALDOA in various cancers was analyzed. The prognostic role of ALDOA was explored by PrognoScan, GEPIA, and Kaplan–Meier Plotter. The immune-related role of ALDOA and its downstream substrates was decided by TIMER, cBioPortal and String. Our data indicate that ALDOA expression level in lung adenocarcinoma, liver hepatocellular carcinoma, head and neck squamous cell carcinoma is higher than that in normal tissues. Increased expression of ALDOA often indicates a poor prognosis for patients. The correlation between ALDOA and immune infiltration among different tumors is very different. We also investigate the relationship between ALDOA and its upstream/downstream proteins. Our results showed that ALDOA could be used as a biomarker for the tumor prognosis, and could be correlated with the infiltrating levels of macrophages, CD4+ T cells and CD8+ T cells.

Diagnostic potential of nanoparticle aided assays for MUC16 and MUC1 glycovariants in ovarian cancer

Our study reports the discovery and evaluation of nanoparticle aided sensitive assays for glycovariants of MUC16 and MUC1 in a unique collection of paired ovarian cyst fluids and serum samples obtained at or prior to surgery for ovarian carcinoma suspicion. Selected glycovariants and the immunoassays for CA125, CA15‐3 and HE4 were compared and validated in 347 cyst fluid and serum samples. Whereas CA125 and CA15‐3 performed poorly in cyst fluid to separate carcinoma and controls, four glycovariants including MUC16^MGL, MUC16^STn, MUC1^STn and MUC1^Tn provided highly improved separations. In serum, the two STn glycovariants outperformed conventional CA125, CA15‐3 and HE4 assays in all subcategories analyzed with main benefits obtained at high specificities and at postmenopausal and early‐stage disease. Serum MUC16^STn performed best at high specificity (90%‐99%), but sensitivity was also improved by the other glycovariants and CA15‐3. The highly improved specificity, excellent analytical sensitivity and robustness of the nanoparticle assisted glycovariant assays carry great promise for improved identification and early detection of ovarian carcinoma in routine differential diagnostics.

The Features of BRCA1 and BRCA2 Germline Mutations in Hakka Ovarian Cancer Patients: BRCA1 C.536 A>T Maybe a Founder Mutation in This Population

Objective

To investigate the frequencies of BRCA1 and BRCA2 mutations in Chinese Hakka patients with ovarian cancer.

Methods

The protein coding regions and exon intron boundary regions of the BRCA gene were sequenced using genomic DNA isolated from the lymphocytes of patients with next-generation sequencing. The patients’ family history and clinical records were collected.

Results

A total of 195 patients with ovarian cancer were included in the study, and 52 distinct variants of the BRCA gene were identified. It was found that 64 patients (64/195, 32.8%) had BRCA gene mutations, including 32 patients (50.0%) with BRCA1 mutation, 27 patients (42.2%) with BRCA2 mutation, and 5 patients (7.8%) with both mutations. Furthermore, 22 pathogenic mutations were detected in 26 patients, 2 likely pathogenic variants in 2 patients, 12 variants of uncertain significance in 20 patients, and 16 likely benign variants in 24 patients. The mutations were mainly found to occur in exons 8, 14, and 17 of BRCA1 and exons 10, 11, 14, and 15 of BRCA2. The results showed that the BRCA genes possess different mutation hotspots in different ethnic groups. In addition, recurrent mutations were noted in many patients. BRCA1 c.536 A>T, considered a founder mutation, was identified in 10 patients (15.63%, 10/64), followed by BRCA1 c.2635 G>T (6.25%, 4/64) and BRCA2 c.2566 T>C (6.25%, 4/64).

Conclusion

The BRCA1 c.536 A>T could be considered to be a founder mutation in this ovarian cancer population. This recurrent BRCA1 mutation has rarely been observed in other ethnic groups. Our findings are expected to provide valuable data for clinical consultation and for designing individualized treatment for ovarian cancer.

Prognostic Implications and Immune Infiltration Analysis of ALDOA in Lung Adenocarcinoma

Background: aldolase A (ALDOA) has been reported to be involved in kinds of cancers. However, the role of ALDOA in lung adenocarcinoma has not been fully elucidated. In this study, we explored the prognostic value and correlation with immune infiltration of ALDOA in lung adenocarcinoma. Methods: The expression of ALDOA was analyzed with the Oncomine database, the Cancer Genome Atlas (TCGA), and the Human Protein Atlas (HPA). Mann-Whitney U test was performed to examine the relationship between clinicopathological characteristics and ALDOA expression. The receiver operating characteristic (ROC) curve and Kaplan-Meier method were conducted to describe the diagnostic and prognostic importance of ALDOA. The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct PPI networks and identify hub genes. Functional annotations and immune infiltration were conducted. Results: The mRNA and protein expression of ALDOA were higher in lung adenocarcinoma than those in normal tissues. The overexpression of ALDOA was significantly correlated with the high T stage, N stage, M stage, and TNM stage. Kaplan-Meier showed that high expression of ALDOA was correlated with short overall survival (38.9 vs 72.5 months, p < 0.001). Multivariate analysis revealed that ALDOA (HR 1.435, 95%CI, 1.013-2.032, p = 0.042) was an independent poor prognostic factor for overall survival. Functional enrichment analysis showed that positively co-expressed genes of ALDOA were involved in the biological progress of mitochondrial translation, mitochondrial translational elongation, and negative regulation of cell cycle progression. KEGG pathway analysis showed enrichment function in carbon metabolism, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis. The "SCNA" module analysis indicated that the copy number alterations of ALDOA were correlated with three immune cell infiltration levels, including B cells, CD8⁺ T cells, and CD4⁺ T cells. The "Gene" module analysis indicated that ALDOA gene expression was negatively correlated with infiltrating levels of B cells, CD8⁺ T cells, CD4⁺ T cells, and macrophages. Conclusion: Our study suggested that upregulated ALDOA was significantly correlated with tumor progression, poor survival, and immune infiltrations in lung adenocarcinoma. These results suggest that ALDOA is a potential prognostic biomarker and therapeutic target in lung adenocarcinoma.

Targeted Selected Reaction Monitoring Verifies Histology Specific Peptide Signatures in Epithelial Ovarian Cancer

Simple Summary

Ovarian cancer is a lethal disease due to its late phase discovery. Any steps towards improving early diagnostics will dramatically increase survival rates. To identify new ovarian cancer biomarker panels, we need to focus on early-stage disease and all histologic subtypes. In this study we have, based on prior discoveries, constructed a multiplexed targeted selected-reaction-monitoring assay to detect peptides from 177 proteins in only 20 µL of plasma. The assay was evaluated in patients with a focus on early-stages and all ovarian cancer histologies in separate groups. With multivariate analysis, we found the highest predictive value in the benign vs. low-grade serous (Q2 = 0.615) and mucinous (Q2 = 0.611) early stage compared to all malignant (Q2 = 0.226) or late stage (Q2 = 0.43) ovarian cancers. The results show that each ovarian cancer histology subgroup can be identified by a unique panel of proteins.

Abstract

Epithelial ovarian cancer (OC) is a disease with high mortality due to vague early clinical symptoms. Benign ovarian cysts are common and accurate diagnosis remains a challenge because of the molecular heterogeneity of OC. We set out to investigate whether the disease diversity seen in ovarian cyst fluids and tumor tissue could be detected in plasma. Using existing mass spectrometry (MS)-based proteomics data, we constructed a selected reaction monitoring (SRM) assay targeting peptides from 177 cancer-related and classical proteins associated with OC. Plasma from benign, borderline, and malignant ovarian tumors were used to verify expression (n = 74). Unsupervised and supervised multivariate analyses were used for comparisons. The peptide signatures revealed by the supervised multivariate analysis contained 55 to 77 peptides each. The predictive (Q2) values were higher for benign vs. low-grade serous Q2 = 0.615, mucinous Q2 = 0.611, endometrioid Q2 = 0.428 and high-grade serous Q2 = 0.375 (stage I–II Q2 = 0.515; stage III Q2 = 0.43) OC compared to benign vs. all malignant Q2 = 0.226. With targeted SRM MS we constructed a multiplexed assay for simultaneous detection and relative quantification of 185 peptides from 177 proteins in only 20 µL of plasma. With the approach of histology-specific peptide patterns, derived from pre-selected proteins, we may be able to detect not only high-grade serous OC but also the less common OC subtypes.

Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers

Despite recent technological advancements allowing the characterization of cancers at a molecular level along with biomarkers for cancer diagnosis, the management of ovarian cancers (OC) remains challenging. Proteins assume functions encoded by the genome and the complete set of proteins, termed the proteome, reflects the health state. Comprehending the circulatory proteomic profiles for OC subtypes, therefore, has the potential to reveal biomarkers with clinical utility concerning early diagnosis or to predict response to specific therapies. Furthermore, characterization of the proteomic landscape of tumor-derived tissue, cell lines, and PDX models has led to the molecular stratification of patient groups, with implications for personalized therapy and management of drug resistance. Here, we review single and multiple marker panels that have been identified through proteomic investigations of patient sera, effusions, and other biospecimens. We discuss their clinical utility and implementation into clinical practice.

Ovarian cancer circulating extracelluar vesicles promote coagulation and have a potential in diagnosis: an iTRAQ based proteomic analysis

Background

Circulating extracelluar vesicles (EVs) in epithelial ovarian cancer (EOC) patients emanate from multiple cells. These EVs are emerging as a new type of biomarker as they can be obtained by non-invasive approaches. The aim of this study was to investigate circulating EVs from EOC patients and healthy women to evaluate their biological function and potential as diagnostic biomarkers.

Methods

A quantitative proteomic analysis (iTRAQ) was applied and performed on 10 EOC patients with advanced stage (stage III–IV) and 10 controls. Twenty EOC patients and 20 controls were applied for validation. The candidate proteins were further validated in another 40-paired cohort to investigate their biomarker potential. Coagulation cascades activation was accessed by determining Factor X activity.

Results

Compared with controls, 200 proteins were upregulated and 208 proteins were downregulated in the EOC group. The most significantly involved pathway is complement and coagulation cascades. ApoE multiplexed with EpCAM, plg, serpinC1 and C1q provide optimal diagnostic information for EOC with AUC = 0.913 (95% confidence interval (CI) =0.848–0.957, p < 0.0001). Level of activated Factor X was significantly higher in EOC group than control (5.35 ± 0.14 vs. 3.69 ± 0.29, p < 0.0001).

Conclusions

Our study supports the concept of circulating EVs as a tool for non-invasive diagnosis of ovarian cancer. EVs also play pivotal roles in coagulation process, implying the inherent mechanism of generation of thrombus which often occurred in ovarian cancer patients at late stages.

High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer

Ovarian cancer is usually detected at a late stage and the overall 5-year survival is only 30-40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model consisting of 11 biomarkers plus age was developed into a multiplex PEA test reporting in absolute concentrations. The final model was evaluated in a fourth independent cohort and has an AUC = 0.94, PPV = 0.92, sensitivity = 0.85 and specificity = 0.93 for detection of ovarian cancer stages I-IV. The novel plasma protein signature could be used to improve the diagnosis of women with adnexal ovarian mass or in screening to identify women that should be referred to specialized examination.