Diagnostic and Prognostic Biomarkers in ovarian cancer and the potential roles of cancer stem cells – An updated review

Targeting the SPC25/RIOK1/MYH9 Axis to Overcome Tumor Stemness and Platinum Resistance in Epithelial Ovarian Cancer

In epithelial ovarian cancer (EOC), platinum resistance, potentially mediated by cancer stem cells (CSCs), often leads to relapse and treatment failure. Here, the role of spindle pole body component 25 (SPC25) as a key determinant promoting stemness and platinum resistance in EOC cells, with its expression being correlated with adverse clinical outcomes is delineated. Mechanistically, SPC25 acts as a scaffolding platform, orchestrating the assembly of an SPC25/RIOK1/MYH9 trimeric complex, triggering RIOK1-mediated phosphorylation of MYH9 at Ser1943. This prompts MYH9 to disengage from the cytoskeleton, augmenting its nuclear accumulation, thus potentiating CTNNB1 transcription and subsequent activation of Wnt/β-catenin signaling. CBP1, a competitive inhibitory peptide, can disrupt the formation of the aforementioned trimeric complex, diminishing the activity of the SPC25/RIOK1/MYH9 axis-mediated Wnt/β-catenin signaling, and thus attenuate CSC phenotypes, thereby enhancing platinum efficacy in vitro, in vivo, and in patient-derived organoids. Therefore, targeting the SPC25/RIOK1/MYH9 axis, which mediates the maintenance of stemness and platinum resistance in EOC cells, may enhance platinum sensitivity and increase survival in patients with EOC.

Reactive oxygen species from non-thermal gas plasma (CAP): implication for targeting cancer stem cells

Cancer remains a major global health challenge, with the persistence of cancer stem cells (CSCs) contributing to treatment resistance and relapse. Despite advancements in cancer therapy, targeting CSCs presents a significant hurdle. Non-thermal gas plasma, also known as CAP, represents an innovative cancer treatment. It has recently gained attention for its often found to be selective, immunogenic, and potent anti-cancer properties. CAP is composed of a collection of transient, high-energy, and physically and chemically active entities, such as reactive oxygen species (ROS). It is acknowledged that the latter are responsible for a major portion of biomedical CAP effects. The dynamic interplay of CAP-derived ROS and other components contributes to the unique and versatile properties of CAP, enabling it to interact with biological systems and elicit various therapeutic effects, including its potential in cancer treatment. While CAP has shown promise in various cancer types, its application against CSCs is relatively unexplored. This review assesses the potential of CAP as a therapeutic strategy for targeting CSCs, focusing on its ability to regulate cellular states and achieve redox homeostasis. This is done by providing an overview of CSC characteristics and demonstrating recent findings on CAP's efficacy in targeting these cells. By contributing insights into the unique attributes of CSCs and the potential of CAP, this work contributes to an advanced understanding of innovative oncology strategies.

Predicting the Recurrence of Ovarian Cancer Based on Machine Learning

Background

Recurrence is the main factor for poor prognosis in ovarian cancer, but few prognostic biomarkers were reported. In this study, we used machine learning methods based on multiple biomarkers to develop a specific prediction model for the recurrence of ovarian cancer.

Methods

A total of 277 ovarian cancer patients were enrolled in this study and randomly classified into training and testing cohorts. The prediction information was obtained through 47 clinical parameters using six supervised clustering machine learning algorithms, including K-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost).

Results

In predicting the recurrence of ovarian cancer, machine learning algorithm was superior to conventional logistic regression analysis. In this study, XGBoost showed the best performance in predicting the recurrence of ovarian cancer, with an accuracy of 0.95. In addition, neoadjuvant chemotherapy, Monocyte ratio (MONO%), Hematocrit (HCT), Prealbumin (PAB), Aspartate aminotransferase (AST), and carbohydrate antigen 125 (CA125) are the most important biomarkers to predict the recurrence of ovarian cancer.

Conclusion

The machine learning techniques can achieve a more accurate assessment of the recurrence of ovarian cancer, which can help clinicians make decisions, and develop personalized treatment strategies.

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Role of Cancer Side Population Stem Cells in Ovarian Cancer Angiogenesis

Ovarian cancer is one of the most common gynecologic malignancies. Recurrence and metastasis often occur after treatment, and it has the highest mortality rate of all gynecological tumors. Cancer stem cells (CSCs) are a small population of cells with the ability of self-renewal, multidirectional differentiation, and infinite proliferation. They have been shown to play an important role in tumor growth, metastasis, drug resistance, and angiogenesis. Ovarian cancer side population (SP) cells, a type of CSC, have been shown to play roles in tumor formation, colony formation, xenograft tumor formation, ascites formation, and tumor metastasis. The rapid progression of tumor angiogenesis is necessary for tumor growth; however, many of the mechanisms driving this process are unclear as is the contribution of CSCs. The aim of this review was to document the current state of knowledge of the molecular mechanism of ovarian cancer stem cells (OCSCs) in regulating tumor angiogenesis.

Extracellular vesicles and cancer stem cells: a deadly duo in tumor progression

The global incidence of cancer is increasing, with estimates suggesting that there will be 26 million new cases and 17 million deaths per year by 2030. Cancer stem cells (CSCs) and extracellular vesicles (EVs) are key to the resistance and advancement of cancer. They play a crucial role in tumor dynamics and resistance to therapy. CSCs, initially discovered in acute myeloid leukemia, are well-known for their involvement in tumor initiation, progression, and relapse, mostly because of their distinct characteristics, such as resistance to drugs and the ability to self-renew. EVs, which include exosomes, microvesicles, and apoptotic bodies, play a vital role in facilitating communication between cells within the tumor microenvironment (TME). They have a significant impact on cellular behaviors and contribute to genetic and epigenetic changes. This paper analyzes the mutually beneficial association between CSCs and EVs, emphasizing their role in promoting tumor spread and developing resistance mechanisms. This review aims to investigate the interaction between these entities in order to discover new approaches for attacking the complex machinery of cancer cells. It highlights the significance of CSCs and EVs as crucial targets in the advancement of novel cancer treatments, which helps stimulate additional research, promote progress in ideas for cancer treatment, and provide renewed optimism in the effort to reduce the burden of cancer.

Generation of Rapid and High-Quality Serum by Recombinant Prothrombin Activator Ecarin (RAPClot™)

We recently reported the potential application of recombinant prothrombin activator ecarin (RAPClot™) in blood diagnostics. In a new study, we describe RAPClot™ as an additive to develop a novel blood collection prototype tube that produces the highest quality serum for accurate biochemical analyte determination. The drying process of the RAPClot™ tube generated minimal effect on the enzymatic activity of the prothrombin activator. According to the bioassays of thrombin activity and plasma clotting, γ-radiation (>25 kGy) resulted in a 30-40% loss of the enzymatic activity of the RAPClot™ tubes. However, a visual blood clotting assay revealed that the γ-radiation-sterilized RAPClot™ tubes showed a high capacity for clotting high-dose heparinized blood (8 U/mL) within 5 min. This was confirmed using Thrombelastography (TEG), indicating full clotting efficiency under anticoagulant conditions. The storage of the RAPClot™ tubes at room temperature (RT) for greater than 12 months resulted in the retention of efficient and effective clotting activity for heparinized blood in 342 s. Furthermore, the enzymatic activity of the RAPClot™ tubes sterilized with an electron-beam (EB) was significantly greater than that with γ-radiation. The EB-sterilized RAPClot™ tubes stored at RT for 251 days retained over 70% enzyme activity and clotted the heparinized blood in 340 s after 682 days. Preliminary clinical studies revealed in the two trials that 5 common analytes (K, Glu, lactate dehydrogenase (LD), Fe, and Phos) or 33 analytes determined in the second study in the γ-sterilized RAPClot™ tubes were similar to those in commercial tubes. In conclusion, the findings indicate that the novel RAPClot™ blood collection prototype tube has a significant advantage over current serum or lithium heparin plasma tubes for routine use in measuring biochemical analytes, confirming a promising application of RAPClot™ in clinical medicine.

Circulating tumor cells help differentiate benign ovarian lesions from cancer before surgery: A literature review and proof of concept study using flow cytometry with fluorescence imaging

Current tools are insufficient for distinguishing patients with ovarian cancer from those with benign ovarian lesions before extensive surgery. The present study utilized a readily accessible platform employing a negative selection strategy, followed by flow cytometry, to enumerate circulating tumor cells (CTCs) in patients with ovarian cancer. These counts were compared with those from patients with benign ovarian lesions. CTC counts at baseline, before and after anticancer therapy, and across various clinical scenarios involving ovarian lesions were assessed. A negative-selection protocol we proposed was applied to patients with suspected ovarian cancer and prospectively utilized in those subsequently confirmed to have malignancy. The protocol was implemented before anticancer therapy and at months 3, 6, 9 and 12 post-treatment. A cut-off value for CTC number at 4.75 cells/ml was established to distinguish ovarian malignancy from benign lesions, with an area under the curve of 0.900 (P<0.001). In patients with ovarian cancer, multivariate Cox regression analysis revealed that baseline CTC counts and the decline in CTCs within the first three months post-therapy were significant predictors of prolonged progression-free survival. Additionally, baseline CTC counts independently prognosticated overall survival. CTC counts obtained with the proposed platform, used in the present study, suggest that pre-operative CTC testing may be able to differentiate between malignant and benign tumors. Moreover, CTC counts may indicate oncologic outcomes in patients with ovarian cancer who have undergone cancer therapies.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Current Analytical Methods for the Sensitive Assay of New-Generation Ovarian Cancer Drugs in Pharmaceutical and Biological Samples

Ovarian cancer, which affects the female reproductive organs, is one of the most common types of cancer. Since this type of cancer has a high mortality rate from gynaecological cancers, the scientific community shows great interest in studies on its treatment. Chemotherapy, radiotherapy, and surgical treatment methods are used in its treatment. In the absence of targeted treatments in these treatment methods, side effects occur in patients, and patients show resistance to the drug. In addition, the underlying causes of ovarian cancer are still not fully known. The scientific world thinks that genetic factors, environmental conditions, and consumed foods may cause this cancer. The most important factor in the treatment of ovarian cancer is early diagnosis. Therefore, the drugs used in the treatment of ovarian cancer are platinum-based anticancer drugs. In addition to these drugs, the most preferred treatment method recently is targeted treatment approaches using poly(adenosine diphosphate ribose) polymerase (PARP) inhibitors. In this review, studies on the sensitive analysis of the treatment methods of these new-generation drugs used in the treatment of ovarian cancer have been comprehensively examined. In addition, the basic features, structural aspects, and biological data of analytical methods used in treatments with new-generation drugs are explained. Analytical studies carried out in the literature in recent years aim to show future developments in how these new-generation drugs are used today and to guide future studies by comprehensively examining and explaining the structure-activity relationship, mechanism of action, toxicity, and pharmacokinetic studies. Finally, in this study, the methods used in the analysis of drugs used in the treatment of ovarian cancer and the studies conducted between 2015 and 2023 were discussed in detail.

Multiple roles of baicalin and baicalein in the regulation of colorectal cancer

The prevalence of colorectal cancer is increasing worldwide, and despite advances in treatment, colorectal cancer (CRC) remains in the top three for mortality due to several issues, including drug resistance and low efficiency. There is increasing evidence that baicalin and baicalein, novel small molecule inhibitor extracts of the Chinese herb Scutellaria baicalensis, have better anti-colorectal cancer effects and are less likely to induce drug resistance in cancer cells. The present review article explains the anti-proliferative properties of baicalin and baicalein in the context of against CRC. Additionally, it explores the underlying mechanisms by which these compounds modulate diverse signaling pathways associated with apoptosis, cell proliferation, tumor angiogenesis, invasion, metastasis, and tumor microenvironment. Moreover, this review article highlights the inhibitory effect of colorectal inflammatory-cancer transformation and the near-term therapeutic strategy of using them as adjuvant agents in chemotherapy.

Deciphering the Epigenetic Symphony of Cancer: Insights and Epigenetic Therapies Implications

Epigenetic machinery is a cornerstone in normal cell development, orchestrating tissue-specific gene expression in mammalian cells. Aberrations in this intricate landscape drive substantial changes in gene function, emerging as a linchpin in cancer etiology and progression. While cancer was conventionally perceived as solely a genetic disorder, its contemporary definition encompasses genetic alterations intertwined with disruptive epigenetic abnormalities. This review explores the profound impact of DNA methylation, histone modifications, and noncoding RNAs on fundamental cellular processes. When these pivotal epigenetic mechanisms undergo disruption, they intricately guide the acquisition of the 6 hallmark characteristics of cancer within seemingly normal cells. Leveraging the latest advancements in decoding these epigenetic intricacies holds immense promise, heralding a new era in developing targeted and more efficacious treatment modalities against cancers driven by aberrant epigenetic modifications.

Top-Down Proteomics of Human Saliva, Analyzed with Logistic Regression and Machine Learning Methods, Reveal Molecular Signatures of Ovarian Cancer

Ovarian cancer (OC) is the most lethal of all gynecological cancers. Due to vague symptoms, OC is mostly detected at advanced stages, with a 5-year survival rate (SR) of only 30%; diagnosis at stage I increases the 5-year SR to 90%, suggesting that early diagnosis is essential to cure OC. Currently, the clinical need for an early, reliable diagnostic test for OC screening remains unmet; indeed, screening is not even recommended for healthy women with no familial history of OC for fear of post-screening adverse events. Salivary diagnostics is considered a major resource for diagnostics of the future. In this work, we searched for OC biomarkers (BMs) by comparing saliva samples of patients with various stages of OC, breast cancer (BC) patients, and healthy subjects using an unbiased, high-throughput proteomics approach. We analyzed the results using both logistic regression (LR) and machine learning (ML) for pattern analysis and variable selection to highlight molecular signatures for OC and BC diagnosis and possibly re-classification. Here, we show that saliva is an informative test fluid for an unbiased proteomic search of candidate BMs for identifying OC patients. Although we were not able to fully exploit the potential of ML methods due to the small sample size of our study, LR and ML provided patterns of candidate BMs that are now available for further validation analysis in the relevant population and for biochemical identification.

Update on immune-based therapy strategies targeting cancer stem cells

Accumulating data reveals that tumors possess a specialized subset of cancer cells named cancer stem cells (CSCs), responsible for metastasis and recurrence of malignancies, with various properties such as self-renewal, heterogenicity, and capacity for drug resistance. Some signaling pathways or processes like Notch, epithelial to mesenchymal transition (EMT), Hedgehog (Hh), and Wnt, as well as CSCs' surface markers such as CD44, CD123, CD133, and epithelial cell adhesion molecule (EpCAM) have pivotal roles in acquiring CSCs properties. Therefore, targeting CSC-related signaling pathways and surface markers might effectively eradicate tumors and pave the way for cancer survival. Since current treatments such as chemotherapy and radiation therapy cannot eradicate all of the CSCs and tumor relapse may happen following temporary recovery, improving novel and more efficient therapeutic options to combine with current treatments is required. Immunotherapy strategies are the new therapeutic modalities with promising results in targeting CSCs. Here, we review the targeting of CSCs by immunotherapy strategies such as dendritic cell (DC) vaccines, chimeric antigen receptors (CAR)-engineered immune cells, natural killer-cell (NK-cell) therapy, monoclonal antibodies (mAbs), checkpoint inhibitors, and the use of oncolytic viruses (OVs) in pre-clinical and clinical studies. This review will mainly focus on blood malignancies but also describe solid cancers.

Advances of nanotechnology applied to cancer stem cells

Cancer stem cells (CSCs) are a small proportion of the cells that exist in cancer tissues. They are considered to be the culprit of tumor genesis, development, drug resistance, metastasis and recurrence because of their self-renewal, proliferation, and differentiation potential. The elimination of CSCs is thus the key to cure cancer, and targeting CSCs provides a new method for tumor treatment. Due to the advantages of controlled sustained release, targeting and high biocompatibility, a variety of nanomaterials are used in the diagnosis and treatments targeting CSCs and promote the recognition and removal of tumor cells and CSCs. This article mainly reviews the research progress of nanotechnology in sorting CSCs and nanodrug delivery systems targeting CSCs. Furthermore, we identify the problems and future research directions of nanotechnology in CSC therapy. We hope that this review will provide guidance for the design of nanotechnology as a drug carrier so that it can be used in clinic for cancer therapy as soon as possible.

VCAM-1 complements CA-125 in detecting recurrent ovarian cancer

BACKGROUND

Close to three-quarters of ovarian cancer cases are frequently diagnosed at an advanced stage, with more than 70% of them failing to respond to primary therapy and relapsing within 5 years. There is an urgent need to identify strategies for early detection of ovarian cancer recurrence, which may lead to earlier intervention and better outcomes.

METHODS

A customized magnetic bead-based 8-plex immunoassay was evaluated using a Bio-Plex 200 Suspension Array System. Target protein levels were analyzed in sera from 58 patients diagnosed with advanced ovarian cancer (including 34 primary and 24 recurrent tumors) and 46 healthy controls. The clinical performance of these biomarkers was evaluated individually and in combination for their ability to detect recurrent ovarian cancer.

RESULTS

An 8-plex immunoassay was evaluated with high analytical performance suitable for biomarker validation studies. Logistic regression modeling selected a two-marker panel of CA-125 and VCAM-1 that improved the performance of CA-125 alone in detecting recurrent ovarian cancer (AUC: 0.813 versus 0.700). At a fixed specificity of 83%, the two-marker panel significantly improved sensitivity in separating primary from recurrent tumors (70.8% versus 37.5%, P = 0.004), demonstrating that VCAM-1 was significantly complementary to CA-125 in detecting recurrent ovarian cancer.

CONCLUSIONS

A two-marker panel of CA-125 and VCAM-1 showed strong diagnostic performance and improvement over the use of CA-125 alone in detecting recurrent ovarian cancer. The experimental results warrant further clinical validation to determine their role in the early detection of recurrent ovarian cancer.

Epigallocatechin-3-Gallate Prevents the Acquisition of a Cancer Stem Cell Phenotype in Ovarian Cancer Tumorspheres through the Inhibition of Src/JAK/STAT3 Signaling

Three-dimensional tumorsphere cultures recapitulate the expression of several cancer stem cell (CSC) biomarkers and represent an effective in vitro platform to screen the anti-CSC properties of drugs. Whereas ovarian carcinoma is among the leading causes of death for women, ovarian CSC (OvCSC), a highly malignant subpopulation of ovarian cancer cells, is thought to be responsible for therapy resistance, metastasis, and tumor relapse. Epigallocatechin-3-gallate (EGCG), a diet-derived active polyphenol found in green tea leaves, can suppress ovarian cancer cell proliferation and induce apoptosis. However, its capacity to prevent the acquisition of cancer stemness traits in ovarian malignancies remains unclear. Here, we exploited the in vitro three-dimensional tumorsphere culture model to explore the capacity of EGCG to alter CSC biomarkers expression, signal transducing events and cell chemotaxis. Total RNA and protein lysates were isolated from human ES-2 ovarian cancer cell tumorspheres for gene assessment by RT-qPCR and protein expression by immunoblot. Real-time cell chemotaxis was assessed with xCELLigence. Compared with their parental adherent cells, tumorspheres expressed increased levels of the CSC markers NANOG, SOX2, PROM1, and Fibronectin. EGCG treatment reduced dose-dependently tumorspheres size and inhibited the transcriptional regulation of those genes. Src and JAK/STAT3 signaling pathways appeared to be relevant for CSC phenotype and chemotactic response. In conclusion, these data highlight and support the chemopreventive benefits of the diet-derived EGCG and its capacity to target intracellular transducing events that regulate the acquisition of an invasive CSC phenotype.

Through the Looking Glass: Updated Insights on Ovarian Cancer Diagnostics

Epithelial ovarian cancer (EOC) is the deadliest gynaecological malignancy and the eighth most prevalent cancer in women, with an abysmal mortality rate of two million worldwide. The existence of multiple overlapping symptoms with other gastrointestinal, genitourinary, and gynaecological maladies often leads to late-stage diagnosis and extensive extra-ovarian metastasis. Due to the absence of any clear early-stage symptoms, current tools only aid in the diagnosis of advanced-stage patients, wherein the 5-year survival plummets further to less than 30%. Therefore, there is a dire need for the identification of novel approaches that not only allow early diagnosis of the disease but also have a greater prognostic value. Toward this, biomarkers provide a gamut of powerful and dynamic tools to allow the identification of a spectrum of different malignancies. Both serum cancer antigen 125 (CA-125) and human epididymis 4 (HE4) are currently being used in clinics not only for EOC but also peritoneal and GI tract cancers. Screening of multiple biomarkers is gradually emerging as a beneficial strategy for early-stage diagnosis, proving instrumental in administration of first-line chemotherapy. These novel biomarkers seem to exhibit an enhanced potential as a diagnostic tool. This review summarizes existing knowledge of the ever-growing field of biomarker identification along with potential future ones, especially for ovarian cancer.

The role of urine and serum biomarkers in the early detection of ovarian epithelial tumours

Ovarian cancer (OC) is one of the leading causes of gynaecological cancer mortality in women worldwide. If detected at an early stage (I, II), OC has a 90% 5-year survival rate; nevertheless, symptoms are often hidden, leading to late-stage (III, IV) diagnosis and a poor prognosis. The current diagnostic procedures, such as a pelvic exam, transvaginal ultrasound, CA-125 blood tests, serum HE4 tests and multivariate index assays (MIA), are insufficient. Sadly, surgery is frequently required to confirm a positive diagnosis. Therefore, there has been an increased interest in different biomarkers using a non-invasive test as a tool for the earlier diagnosis of OC to resolve the need for precise and non-invasive diagnostic methods. This review article aims to investigate how biomarkers influence early OC detection and to emphasise the role of using a combination of serum biomarkers panel rather than a single biomarker. In addition, this review provides insights into the current serum biomarkers, urine biomarkers and other emerging biomarkers in the early detection of OC for better specificity and sensitivity and to improve the overall survival (OS) rate.

Hypoxia-Related lncRNA Prognostic Model of Ovarian Cancer Based on Big Data Analysis

Background

Hypoxia is regarded as a key factor in promoting the occurrence and development of ovarian cancer. In ovarian cancer, hypoxia promotes cell proliferation, epithelial to mesenchymal transformation, invasion, and metastasis. Long non-coding RNAs (lncRNAs) are extensively involved in the regulation of many cellular mechanisms, i.e., gene expression, cell growth, and cell cycle.

Materials and Methods

In our study, a hypoxia-related lncRNA prediction model was established by applying LASSO-penalized Cox regression analysis in public databases. Patients with ovarian cancer were divided into two groups based on the median risk score. The survival rate was analyzed in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets, and the mechanisms were investigated.

Results

Through the prognostic analysis of DElncRNAs (differentially expressed long non-coding RNAs), a total of 5 lncRNAs were found to be closely associated with OS (overall survival) in ovarian cancer patients. It was evaluated through Kaplan-Meier analysis that low-risk patients can live longer than high-risk patients (TCGA: p = 1.302e - 04; ICGC: 1.501e - 03). The distribution of risk scores and OS status revealed that higher risk score will lead to lower OS. It was evaluated that low-risk group had higher immune score (p = 0.0064) and lower stromal score (p = 0.00023).

Conclusion

It was concluded that a hypoxia-related lncRNA model can be used to predict the prognosis of ovarian cancer. Our designed model is more accurate in terms of age, grade, and stage when predicting the overall survival of the patients of ovarian cancer.

The prognostic role of systemic inflammatory markers in apparent early-stage ovarian cancer

BACKGROUND

Few studies analyzed the prognostic role of systemic inflammatory markers in early-stage ovarian cancer. The primary endpoint of the present study was to assess the prognostic impact of baseline inflammatory markers in early-stage ovarian cancer. The secondary endpoints were to compare the disease-free survival (DFS) of inflammatory markers with standard risk factors and to correlate these with BRCA mutational status.

METHODS

Retrospective, single-center, observational study. Patients with FIGO-stage I-II and IIIA1 epithelial ovarian cancer undergoing primary surgery between 10/2012 and 12/2019 were included. Inflammatory markers were evaluated on the results of the complete blood count and coagulation tests, performed before ovarian cancer surgery. The Receiver Operating Characteristic curve was used to determine the optimal cut-off value of different baseline inflammatory biomarkers for the DFS analysis.

RESULTS

Three hundred fifty-nine patients were included in the study period. Baseline neutrophil-lymphocyte ratio (NLR) ≥ 3 and systemic immune inflammation index (SII, defined as platelet x neutrophil-lymphocyte ratio) ≥ 1000 were associated with worse 3 year DFS and baseline SII ≥ 1000 was associated with worse 3 year OS. BRCA-mutated patients with SII ≥ 1000 and with NLR ≥ 3 had significantly worse DFS compared to SII < 1000 and with NLR < 3. FIGO stage > I was the only independent risk factor for higher risk of recurrence.

CONCLUSION

SII ≥ 1000 and NLR ≥ 3 were associated with worse 3 year DFS and SII ≥ 1000 was associated with worse 3 year OS. The subgroups of BRCA-mutated patients with higher inflammation markers (SII ≥ 1000 and NLR ≥ 3) were associated with worse DFS. These findings might be helpful to design personalized treatment and more intensive surveillance.

Performance of the IOTA ADNEX model combined with HE4 for identifying early-stage ovarian cancer

Objective

This work was designed to investigate the performance of the International Ovarian Tumor Analysis (IOTA) ADNEX (Assessment of Different NEoplasias in the adneXa) model combined with human epithelial protein 4 (HE4) for early ovarian cancer (OC) detection.

Methods

A total of 376 women who were hospitalized and operated on in Women and Children’s Hospital of Chongqing Medical University were selected. Ultrasonographic images, cancer antigen-125 (CA 125) levels, and HE4 levels were obtained. All cases were analyzed and the histopathological diagnosis serves as the reference standard. Based on the IOTA ADNEX model post-processing software, the risk prediction value was calculated. We analyzed receiver operating characteristic curves to determine whether the IOTA ADNEX model alone or combined with HE4 provided better diagnostic accuracy.

Results

The area under the curve (AUC) of the ADNEX model alone or combined with HE4 in predicting benign and malignant ovarian tumors was 0.914 (95% CI, 0.881–0.941) and 0.916 (95% CI, 0.883–0.942), respectively. With the cutoff risk of 10%, the ADNEX model had a sensitivity of 0.93 (95% CI, 0.87–0.97) and a specificity of 0.73 (95% CI, 0.67–0.78), while combined with HE4, it had a sensitivity of 0.90 (95% CI, 0.84–0.95) and a specificity of 0.81 (95% CI, 0.76–0.86). The IOTA ADNEX model combined with HE4 was better at improving the accuracy of the differential diagnosis between different OCs than the IOTA ADNEX model alone. A significant difference was found in separating borderline masses from Stage II–IV OC (p = 0.0257).

Conclusions

A combination of the IOTA ADNEX model and HE4 can improve the specificity of diagnosis of ovarian benign and malignant tumors and increase the sensitivity and effectiveness of the differential diagnosis of Stage II–IV OC and borderline tumors.

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry

High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor β signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Background

Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC).

Methods

The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups.

Results

We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05).

Conclusion

A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02011-0.

Potential roles of claudin-3 and claudin-4 in ovarian cancer management

BACKGROUND

Ovarian cancer has the highest mortality amongst all gynaecological malignancies, with around two-thirds of patients diagnosed with advanced disease due to late presentation. Furthermore, around 90% of patients develop recurrence and eventually become chemoresistant. Therefore, there is a high demand to identify biomarkers specific to this disease for screening for early detection, as well as new therapeutic targets. Tight junctions (TJs) regulate paracellular permeability and are vital in establishing epithelial cell polarity. One hallmark of tumorigenesis is the loss of TJs, with loss of cell-to-cell adhesion. Claudins are integral TJ membrane proteins, which have been found to play a critical role in maintaining the TJ's barrier function. Furthermore, claudin-3 (CLDN3) and claudin-4 (CLDN4) are overexpressed in ovarian cancer. This article aims to explore the biological role of CLDN3 and CLDN4 and their potential in different aspects of the management of ovarian cancer.

MAIN BODY

CLDN3 and CLDN4 have been shown to be effective markers for the early detection of ovarian cancer. Whilst there is difficulty in screening for both claudins in serum, their assessment by gene expression analysis and immunohistochemical methods shows promising potential as diagnostic and prognostic biomarkers for ovarian cancer. The localisation and overexpression of claudins, such as CLDN3, have been shown to correlate with poorer survival outcomes. The added value of combining claudins with other markers such as CA125 for diagnosis has also been highlighted. Therapeutically, CLDN3 and more so CLDN4 have been shown to be effective targets of Clostridium perfringens enterotoxin (CPE). Interestingly, CPE has also been shown to resensitise chemoresistant tumours to therapy.

CONCLUSIONS

This review presents the diagnostic and prognostic potential of CLDN3 and CLDN4 and their emerging role as therapeutic targets in ovarian cancer. Clinical trials are required to validate the promising results of the in vitro and in vivo studies for CLDN3 and CLDN4, possibly adding onto current ovarian cancer management.

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.

Identification of epigenetic genes for predicting prognosis and immunotherapy response of ovarian cancer

BACKGROUND

Epigenetic factors play a critical role in tumour development and progression. The aim of this study was to construct and validate a robust epigenetic gene set-based signature for predicting prognosis of ovarian cancer.

METHODS

By using LASSO Cox regression model, we screened out the most useful prognostic epigenetic factors and a prognostic signature was developed based on them. Survival receiver operating characteristic was used to test the prognostic accuracy of signature in training and validation sets. The associations between the risk scores and immune cell infiltration, tumour purity, immune checkpoint inhibitor genes expression were also assessed in ovarian cancer .

RESULTS

A total of 26 epigenetic factors were identified to develop the prognostic signature. In the training set, the prognosis of high-risk patients was strikingly poorer than that of low-risk patients (hazard ratio: 2.11, 95% confidence interval: 1.65-2.72, P < 0.001). Similar results were further observed in the internal validation set (hazard ratio: 1.69, 95% confidence interval: 1.07-2.63, P = 0.020) and external validation set (hazard ratio:1.95, 95% confidence interval: 1.41-2.69; P < 0.001). Survival receiver operating characteristic at 5 year showed the epigenetic signature (area under the curve = 0.700) performed better than other clinical features in predicting prognosis. Distinct difference in immune activation related pathways, immune cells infiltration, tumour purity reflected by immune and stromal score and immune checkpoint inhibitor genes gene expression was observed between high- and low-risk samples.

CONCLUSIONS

This study constructed an epigenetic signature that was capable of predicting postoperative outcomes and may also serve as potential biomarker for immunotherapy responses for ovarian cancer.

Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer

BACKGROUND

Ovarian cancer (OC) is an invasive gynaecologic cancer with a high cancer-related death rate. The purpose of this study was to establish an invasion-related multigene signature to predict the prognostic risk of OC.

METHODS

We extracted 97 invasion-related genes from The Cancer Genome Atlas (TCGA) database. Then, the ConsensusClusterPlus and limma packages were used to calculate differentially expressed genes (DEGs). To calculate the immune scores of the molecular subtypes, we used ESTIMATE to evaluate the stromal score, immune score and ESTIMATE score. MCP-counter and the GSVA package ssgsea were used to evaluate the types of infiltrating immune cells. Survival and nomogram analyses were performed to explore the prognostic value of the signature. Finally, qPCR, immunohistochemistry staining and functional assays were used to evaluate the expression and biological abilities of the signature genes in OC.

RESULTS

Based on the consistent clustering of invasion-related genes, cases in the OC datasets were divided into two subtypes. A significant difference was observed in prognosis between the two subtypes. Most genes were highly expressed in the C1 group. Based on the C1 group genes, we constructed an invasion-related 6-gene prognostic risk model. Furthermore, to verify the signature, we used the TCGA-test and GSE32062 and GSE17260 chip datasets for testing and finally obtained a good risk prediction effect in those datasets. Moreover, the results of the qPCR and immunohistochemistry staining assays revealed that KIF26B, VSIG4 and COL6A6 were upregulated and that FOXJ1, MXRA5 and CXCL9 were downregulated in OC tissues. The functional study showed that the expression of KIF26B, VSIG4, COL6A6, FOXJ1, MXRA5 and CXCL9 can regulate the migration and invasion abilities of OC cells.

CONCLUSION

We developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features. These results suggest that the signature could potentially be used to evaluate the prognostic risk of OC patients.

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/).

A combined biomarker panel shows improved sensitivity and specificity for detection of ovarian cancer

Background

Combined biomarkers can improve the sensitivity and specificity of ovarian cancer (OC) diagnosis and effectively predict patient prognosis. This study explored the diagnostic and prognostic values of serum CCL18 and CXCL1 antigens combined with C1D, FXR1, ZNF573, and TM4SF1 autoantibodies in OC.

Methods

CCL18 and CXCL1 monoclonal antibodies and C1D, FXR1, ZNF573, and TM4SF1 antigens were coated with microspheres. Logistic regression was used to construct a serum antigen‐antibody combined detection model; receiver‐operating characteristic curve (ROC) was used to evaluate the diagnostic efficacy of the model; and the Kaplan‐Meier method and Cox regression models were used for survival analysis to evaluate the prognosis of OC. Data from The Cancer Genome Atlas (TCGA) and Genotype‐Tissue Expression (GTEx) projects and online survival analysis tools were used to evaluate prognostic genes for OC. The CIBERSORT immune score was used to explore the factors influencing prognosis and their relationship with tumor‐infiltrating immune cells.

Results

The levels of each index in the blood samples of patients with OC were higher than those of the other groups. The combined detection model has higher specificity and sensitivity in the diagnosis of OC, and its diagnostic efficiency is better than that of CA125 alone and diagnosing other malignant tumors. CCL18 and TM4SF1 may be factors affecting the prognosis of OC, and CCL18 may be related to immune‐infiltrating cells.

Conclusions

The serum antigen‐antibody combined detection model established in this study has high sensitivity and specificity for the diagnosis of OC.

Alternative Splicing: A New Therapeutic Target for Ovarian Cancer

Background: Increasing evidences have shown that abnormal alternative splicing (AS) events are closely related to the prognosis of various tumors. However, the role of AS in ovarian cancer (OV) is poorly understood. This study aims to explore the correlation between AS and the prognosis of OV and establish a prognostic model for OV. Methods: We downloaded the RNA-seq data of OV from The Cancer Genome Atlas databases and assessed cancer-specific AS through the SpliceSeq software. Then systemically investigated the overall survival (OS)-related AS and splicing factors (SFs) by bioinformatics analysis. The nomogram was established based on the clinical information, and the clinical practicability of the nomogram was verified through the calibration curve. Finally, a splicing correlation network was constructed to reveal the relationship between OS-related AS and SFs. Results: A total of 48,049 AS events were detected from 10,582 genes, of which 1523 were significantly associated with OS. The area under the curve of the final prediction model was 0.785, 0.681, and 0.781 in 1, 3, and 5 years, respectively. Moreover, the nomogram showed high calibration and discrimination in OV patients. Spearman correlation analysis was used to determine 8 SFs significantly related to survival, including major facilitator superfamily domain containing 11, synaptotagmin binding cytoplasmic RNA interacting protein, DEAH-box helicase 35, CWC15, integrator complex subunit 1, LUC7 like 2, cell cycle and apoptosis regulator 1, and heterogeneous nuclear ribonucleoprotein A2/B1. Conclusion: This study provides a prognostic model related to AS in OV, and constructs an AS-clinicopathological nomogram, which provides the possibility to predict the long-term prognosis of OV patients. We have explored the wealth of RNA splicing networks and regulation patterns related to the prognosis of OV, which provides a large number of biomarkers and potential targets for the treatment of OV. Put forward the potential possibility of interfering with the AS of OV in the comprehensive treatment of OV.

Carbohydrate Antigen 125: A Biomarker at the Crossroads of Congestion and Inflammation in Heart Failure

Because heart failure (HF) is more lethal than some of the common malignancies in the general population, such as prostate cancer in men and breast cancer in women, there is a need for a cost-effective prognostic biomarker in HF beyond natriuretic peptides, especially concerning congestion, the most common reason for the hospitalisation of patients with worsening of HF. Furthermore, despite diuretics being the mainstay of treatment for volume overload in HF patients, no randomised trials have shown the mortality benefits of diuretics in HF patients, and appropriate diuretic titration strategies in this population are unclear. Recently, carbohydrate antigen (CA) 125, a well-established marker of ovarian cancer, emerged as both a prognostic indicator and a guide in tailoring decongestion therapy for patients with HF. Hence, in this review the authors present the molecular background regarding the role of CA125 in HF and address valuable clinical aspects regarding the relationship of CA125 with both prognosis and therapeutic management in HF.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Low Expression of SLC7A11 Confers Drug Resistance and Worse Survival in Ovarian Cancer via Inhibition of Cell Autophagy as a Competing Endogenous RNA

Drug resistance is the main cause of chemotherapy failure in ovarian cancer (OC), and identifying potential druggable targets of autophagy is a novel and promising approach to overcoming drug resistance. In this study, 131 genes associated with autophagy were identified from three autophagy-related databases, and of these, 14 were differentially expressed in 90 drug-resistant OC tissues versus 197 sensitive tissues according to the Cancer Genome Atlas ovarian cancer cohort. Among these 14 genes, SLC7A11 was significantly decreased in two paclitaxel-resistant OC cells (HeyA8-R and SKOV3-R) and in 90 drug-resistant tissues compared with their controls. In vitro overexpression of SLC7A11 significantly increased the sensitivity of HeyA8-R cells to paclitaxel, inhibited colony formation, induced apoptosis, and arrested cell cycle. Further, low SLC7A11 expression was correlated with poor overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS) in 1815 OC patients. Mechanistically, SLC7A11 strongly regulated cell autophagy as a competing endogenous RNA (ceRNA) based on pan-cancer analyses of 32 tumor types. Specifically, as a ceRNA for autophagy genes STX17, RAB33B, and UVRAG, SLC7A11 was strongly and positively co-expressed with these three genes in 20, 12, and 12 different tumors, respectively, in 379 OC tissues and in 90 drug-resistant OC tissues, and the former two were significantly upregulated in SLC7A11-overexpressed HeyA8-R cells. Further, SLC7A11 induced the protein expression of other autophagy genes, such as LC3, Atg16L1, and Atg7, and the expression of the respective proteins was further increased when the cells were treated with paclitaxel. The results strongly suggest that SLC7A11 regulates autophagy via ceRNA interactions with the three abovementioned genes in pan-cancer and in drug-resistant OC. Moreover, low expression of STX17 and UVRAG also significantly predicted low OS, PFS, and PPS. The combination of SLC7A11 with STX17 was more predictive of OS and PFS than either individually, and the combination of SLC7A11 with UVRAG was highly predictive of OS and PPS. The above results indicated that decreased SLC7A11 resulted in drug resistance and effected low rates of survival in OC patients, probably via ceRNA interactions with autophagy genes, and thus the gene could serve as a therapeutic target and potential biomarker in OC.

Model constructions of chemosensitivity and prognosis of high grade serous ovarian cancer based on evaluation of immune microenvironment and immune response

BACKGROUND

The prognosis of high grade serous ovarian cancer (HGSOC) patients is closely related to the immune microenvironment and immune response. Based on this, the purpose of this study was to construct a model to predict chemosensitivity and prognosis, and provide novel biomarkers for immunotherapy and prognosis evaluation of HGSOC.

METHODS

GSE40595 (38 samples), GSE18520 (63 samples), GSE26712 (195 samples), TCGA (321 samples) and GTEx (88 samples) were integrated to screen differential expressed genes (DEGs) of HGSOC. The prognosis related DEGs (DEPGs) were screened through overall survival analysis. The DEGs-encoded protein-protein interaction network was constructed and hub genes of DEPGs (DEPHGs) were generated by STRING. Immune characteristics of the samples were judged by ssGSEA, ESTIMATE and CYBERSORT. TIMER was used to analyze the relationship between DEPHGs and tumor-infiltrating immunocytes, as well as the immune checkpoint genes, finally immune-related DEPHGs (IDEPHGs) were determined, and whose expression in 12 pairs of HGSOC tissues and tumor-adjacent tissues were analyzed by histological verification. Furthermore, the chemosensitivity genes in IDEPHGs were screened according to GSE15622 (n = 65). Finally, two prediction models of paclitaxel sensitivity score (PTX score) and carboplatin sensitivity score (CBP score) were constructed by lasso algorithm. The area under curve was calculated to estimate the accuracy of candidate gene models in evaluating chemotherapy sensitivity.

RESULTS

491 DEGs were screened and 37 DEGs were identified as DEPGs, and 11 DEPHGs were further identified. Among them, CXCL13, IDO1, PI3, SPP1 and TRIM22 were screened as IDEPHGs and verified in the human tissues. Further analysis showed that IDO1, PI3 and TRIM22 could independently affect the chemotherapy sensitivity of HGSOC patients. The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity. What's more, both of the HGSOC patients with high PTX score or high CBP score had longer survival time.

CONCLUSIONS

Five IDEPHGs identified through comprehensive bioinformatics analysis were closely related with the prognosis, immune microenvironment and chemotherapy sensitivity of HGSOC. Two prediction models based on IDEPHGs might have potential application of chemotherapy sensitivity and prognosis for patients with HGSOC.

HIF-Dependent Mechanisms of Relationship between Hypoxia Tolerance and Tumor Development

Oxygen deficiency is one of the key pathogenetic factors determining development and severity of many diseases, including inflammatory, infectious diseases, and cancer. Lack of oxygen activates the signaling pathway of the hypoxia-inducible transcription factor HIF in cells that has three isoforms, HIF-1, HIF-2, HIF-3, regulating expression of several thousand genes. Throughout tumor progression, HIF activation stimulates angiogenesis, promotes changes in cell metabolism, adhesion, invasiveness, and ability to metastasize. HIF isoforms can play opposite roles in the development of inflammatory and neoplastic processes. Humans and laboratory animals differ both in tolerance to hypoxia and in the levels of expression of HIF and HIF-dependent genes, which may lead to predisposition to the development of certain oncological disorders. In particular, the ratio of different histogenetic types of tumors may vary among people living in the mountains and at the sea level. However, despite the key role of hypoxia at almost all stages of tumor development, basal tolerance to oxygen deficiency is not considered as a factor of predisposition to the tumor growth initiation. In literature, there are many works characterizing the level of local hypoxia in various tumors, and suggesting fundamental approaches to its mitigation by HIF inhibition. HIF inhibitors, as a rule, have a systemic effect on the organism, however, basal tolerance of an organism to hypoxia as well as the level of HIF expression are not taken into account in the process of their use. The review summarizes the literature data on different HIF isoforms and their role in tumor progression, with extrapolation to organisms with high and low tolerance to hypoxia, as well as on the prevalence of various types of tumors in the populations living at high altitudes.

Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer

Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein-protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the "Human Protein Atlas" database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.

Transcriptome-based stemness indices analysis reveals platinum-based chemo-theraputic response indicators in advanced-stage serous ovarian cancer

Serous ovarian cancer (SOC) is a main histological subtype of ovarian cancer, in which cancer stem cells (CSC) are responsible for its chemoresistance. However, the underlying modulation mechanisms of chemoresistance led by cancer stemness are still undefined. We aimed to investigate potential drug-response indicators among stemness-associated biomarkers in advanced SOC samples. The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Weighted gene co-expression network analysis (WGCNA) was utilized to explore the gene modules and key genes involved in stemness characteristics. We found that mRNAsi and corrected mRNAsi scores were both greater in tumors of Grade 3 and 4 than that of Grade 1 and 2. Forty-two key genes were obtained from the most significant mRNAsi-related gene module. Functional annotation revealed that these key genes were mainly involved in the mitotic division. Thirteen potential platinum-response indicators were selected from the genes enriched to platinum-response associated pathways. Among them, we identified 11 genes with prognostic value of progression-free survival (PFS) in advanced SOC patients treated with platinum and 7 prognostic genes in patients treated with a combination of platinum and taxol. The expressions of the 13 key genes were also validated between platinum-resistant and -sensitive SOC samples of advanced stages in two Gene Expression Omnibus (GEO) datasets. The results revealed that CDC20 was a potential platinum-sensitivity indicator in advanced SOC. These findings may provide a new insight for chemotherapies in advanced SOC patients clinically.

The Assessment of IL-21 and IL-22 at the mRNA Level in Tumor Tissue and Protein Concentration in Serum and Peritoneal Fluid in Patients with Ovarian Cancer

The aim of the analysis was for the first time to assess the expression of genes encoding IL-21 and IL-22 at the mRNA level in ovarian tumor specimens and the concentration of these parameters in serum and peritoneal fluid in patients with ovarian serous cancer. The levels of IL-21 and IL-22 transcripts were evaluated with the use of the real-time RT-qPCR. Enzyme-linked immunosorbent assay (ELISA) was used to determine the concentration of proteins. Quantitative analysis of IL-21 gene mRNA in the tumor tissue showed the highest activity in the G1 degree of histopathological differentiation and was higher in G1 compared to the control group. The concentration of IL-21 and IL-22 in the serum and in the peritoneal fluid of women with ovarian cancer varied depending on the degree of histopathological differentiation of the cancer and showed statistical variability compared to controls. The conducted studies have shown that the local and systemic changes in the immune system involving IL-21 and IL-22 indicate the participation of these parameters in the pathogenesis of ovarian cancer, and modulation in the IL-21/IL-22 system may prove useful in the development of new diagnostic and therapeutic strategies used in patients, which require further research.

BMI-1 Expression Heterogeneity in Endometriosis-Related and Non-Endometriotic Ovarian Carcinoma

BMI-1 is a key component of stem cells, which are essential for normal organ development and cell phenotype maintenance. BMI-1 expression is deregulated in cancer, resulting in the alteration of chromatin and gene transcription repression. The cellular signaling pathway that governs BMI-1 action in the ovarian carcinogenesis sequences is incompletely deciphered. In this study, we set out to analyze the immunohistochemical (IHC) BMI-1 expression in two different groups: endometriosis-related ovarian carcinoma (EOC) and non-endometriotic ovarian carcinoma (NEOC), aiming to identify the differences in its tissue profile. Methods: BMI-1 IHC expression has been individually quantified in epithelial and in stromal components by using adapted scores systems. Statistical analysis was performed to analyze the relationship between BMI-1 epithelial and stromal profile in each group and between groups and its correlation with classical clinicopathological characteristics. Results: BMI-1 expression in epithelial tumor cells was mostly low or negative in the EOC group, and predominantly positive in the NEOC group. Moreover, the stromal BMI-1 expression was variable in the EOC group, whereas in the NEOC group, stromal BMI-1 expression was mainly strong. We noted statistically significant differences between the epithelial and stromal BMI-1 profiles in each group and between the two ovarian carcinoma (OC) groups. Conclusions: Our study provides solid evidence for a different BMI-1 expression in EOC and NEOC, corresponding to the differences in their etiopathogeny. The reported differences in the BMI-1 expression of EOC and NEOC need to be further validated in a larger and homogenous cohort of study.

Diagnostic value of serum human epididymis protein 4 and cancer antigen 125 in the patients with ovarian carcinoma: A protocol for systematic review and meta-analysis

BACKGROUND

Ovarian carcinoma (OC) is considered among the most prevalent triggers of cancer-related deaths in women. Many studies have demonstrated that human epididymis protein 4 (HE-4) as well as cancer antigen 125 (CA-125) are over-expressed in various malignant tumors, such as lung, liver, endometrial, gastric, breast, as well as ovarian cancers. Nonetheless, the overall diagnostic value of serum HE-4, in addition to CA-125 n patients experiencing OC, is still largely undetermined. Therefore, the current study intends to investigate the general diagnostic significance of HE-4 along with CA-125 in patients with OC.

METHODS

We aim to systematically search retrospective or prospective study for potential eligible studies from electronic databases, such as MEDLINE, EMBASE, Cochrane Library, Web of Science, as well as Chinese National Knowledge Infrastructure. We will relevant articles evaluating the general diagnostic significance of HE-4 and CA-125 in patients with OC from these databases. We will define our search in English and Chinese. Likewise, we will use 2 independent authors to extract the required data, using the Quality Assessment of Diagnostic Accuracy Studies-2 tool to evaluate he procedural quality of all included literature. We will use the appropriate statistical method to complete data analyses.

RESULTS

The present study aims to investigate the general diagnostic significance of HE-4 and CA-125 in patients suffering from OC.

CONCLUSION

The present study will systematically summarise current evidence of HE-4 in combination with CA-125 in relation to diagnosing OC.

ETHICS AND DISSEMINATION

Ethical approval will not be required.

PROTOCOL REGISTRATION NUMBER

DOI 10.17605/OSF.IO/YQPC7 (https://osf.io/yqpc7/).

Glycomic-Based Biomarkers for Ovarian Cancer: Advances and Challenges

Ovarian cancer remains one of the most common causes of death among gynecological malignancies afflicting women worldwide. Among the gynecological cancers, cervical and endometrial cancers confer the greatest burden to the developing and the developed world, respectively; however, the overall survival rates for patients with ovarian cancer are worse than the two aforementioned. The majority of patients with ovarian cancer are diagnosed at an advanced stage when cancer has metastasized to different body sites and the cure rates, including the five-year survival, are significantly diminished. The delay in diagnosis is due to the absence of or unspecific symptoms at the initial stages of cancer as well as a lack of effective screening and diagnostic biomarkers that can detect cancer at the early stages. This, therefore, provides an imperative to prospect for new biomarkers that will provide early diagnostic strategies allowing timely mitigative interventions. Glycosylation is a protein post-translational modification that is modified in cancer patients. In the current review, we document the state-of-the-art of blood-based glycomic biomarkers for early diagnosis of ovarian cancer and the technologies currently used in this endeavor.

Hypoxia-Driven Effects in Cancer: Characterization, Mechanisms, and Therapeutic Implications

Hypoxia, a common feature of solid tumors, greatly hinders the efficacy of conventional cancer treatments such as chemo-, radio-, and immunotherapy. The depletion of oxygen in proliferating and advanced tumors causes an array of genetic, transcriptional, and metabolic adaptations that promote survival, metastasis, and a clinically malignant phenotype. At the nexus of these interconnected pathways are hypoxia-inducible factors (HIFs) which orchestrate transcriptional responses under hypoxia. The following review summarizes current literature regarding effects of hypoxia on DNA repair, metastasis, epithelial-to-mesenchymal transition, the cancer stem cell phenotype, and therapy resistance. We also discuss mechanisms and pathways, such as HIF signaling, mitochondrial dynamics, exosomes, and the unfolded protein response, that contribute to hypoxia-induced phenotypic changes. Finally, novel therapeutics that target the hypoxic tumor microenvironment or interfere with hypoxia-induced pathways are reviewed.

AKR1C3 Is Associated with Better Survival of Patients with Endometrial Carcinomas

The aldo-keto reductase (AKR) superfamily is gaining attention in cancer research. AKRs are involved in important biochemical processes and have crucial roles in carcinogenesis and chemoresistance. The enzyme AKR1C3 has many functions, which include production of prostaglandins, androgens and estrogens, and metabolism of different chemotherapeutics; AKR1C3 is thus implicated in the pathophysiology of different cancers. Endometrial and ovarian cancers represent the majority of gynecological malignancies in developed countries. Personalized treatments for these cancers depend on identification of prognostic and predictive biomarkers that allow stratification of patients. In this study, we evaluated the immunohistochemical (IHC) staining of AKR1C3 in 123 paraffin-embedded samples of endometrial cancer and 99 samples of ovarian cancer, and examined possible correlations between expression of AKR1C3 and other clinicopathological data. The IHC expression of AKR1C3 was higher in endometrial cancer compared to ovarian cancer. In endometrioid endometrial carcinoma, high AKR1C3 IHC expression correlated with better overall survival (hazard ratio, 0.19; 95% confidence interval, 0.06−0.65, p = 0.008) and with disease-free survival (hazard ratio, 0.328; 95% confidence interval, 0.12–0.88, p = 0.027). In patients with ovarian cancer, there was no correlation between AKR1C3 IHC expression and overall and disease-free survival or response to chemotherapy. These results demonstrate that AKR1C3 is a potential prognostic biomarker for endometrioid endometrial cancer.

WGCNA reveals key gene modules regulated by the combined treatment of colon cancer with PHY906 and CPT11

Irinotecan (CPT11) is one of the most effective drugs for treating colon cancer, but its severe side effects limit its application. Recently, a traditional Chinese herbal preparation, named PHY906, has been proved to be effective for improving therapeutic effect and reducing side effects of CPT11. The aim of the present study was to provide novel insight to understand the molecular mechanism underlying PHY906-CPT11 intervention of colon cancer. Based on the GSE25192 dataset, for different three treatments (PHY906, CPT11, and PHY906-CPT11), we screened out differentially expressed genes (DEGs) and constructed a co-expression network by weighted gene co-expression network analysis (WGCNA) to identify hub genes. The key genes of the three treatments were obtained by merging the DEGs and hub genes. For the PHY906-CPT11 treatment, a total of 18 key genes including Eif4e, Prr15, Anxa2, Ddx5, Tardbp, Skint5, Prss12 and Hnrnpa3, were identified. The results of functional enrichment analysis indicated that the key genes associated with PHY906-CPT11 treatment were mainly enriched in ‘superoxide anion generation’ and ‘complement and coagulation cascades’. Finally, we validated the key genes by Gene Expression Profiling Interactive Analysis (GEPIA) and RT-PCR analysis, the results indicated that EIF4E, PRR15, ANXA2, HNRNPA3, NCF1, C3AR1, PFDN2, RGS10, GNG11, and TMSB4X might play an important role in the treatment of colon cancer with PHY906-CPT11. In conclusion, a total of 18 key genes were identified in the present study. These genes showed strong correlation with PHY906-CPT11 treatment in colon cancer, which may help elucidate the underlying molecular mechanism of PHY906-CPT11 treatment in colon cancer.