Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer

Potential tools for predicting response to chemotherapy in OC: Assessment of immune dysbiosis, participant's self-rated health and microbial dynamics

Epithelial ovarian cancer (OC) is the deadliest female reproductive cancer; an estimated 13,270 women will die from OC in 2023. Platinum-based chemotherapy resistance mechanisms contribute to poor OC 5-year survival rates. Peripheral inflammation is linked to various disease states and we previously identified unique peritoneal microbial features predictive of OC. We hypothesized that unique peripheral immune profiles and peritoneal microbial features may be predictive of disease-free interval (time to recurrence) and response to chemotherapy in participants with OC. We also investigated self-rated health (SRH) scores in the context of peripheral inflammation as a potential screening tool for OC. Blood and peritoneal fluid were collected from participants with OC or a benign adnexal mass (BPM). Lymphocyte populations were analyzed using Fluorescence Activated Cell Sorting, serum cytokine levels were analyzed using the Human Th17 Magnetic Bead Panel assay and peritoneal fluid microbial features were analyzed using Next Generation Sequencing (NGS). Participants completed a standardized questionnaire on self-rated physical and emotional health. Participants were classified into three chemotherapy response categories: platinum-refractory, platinum-resistant or platinum-sensitive. A significant positive correlation was found between elevated inflammatory status on the day of surgery and longer disease-free interval. SRH measures did not correlate with immune status in participants with OC or a BPM. We identified a correlation between peritoneal microbial features and chemotherapy response. We conclude that immune dysbiosis may be useful in predicting OC recurrence. The immune findings reported here set the framework for additional studies utilizing immune profiles to predict platinum-based chemotherapy responsiveness in OC.

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Association between ascites Gustave Roussy immune score and the intratumoural microbiome in malignant ascites secondary to hepatocellular carcinoma

BACKGROUNDS

The Gustave Roussy Immune (GRIm) score predicts survival outcomes in several cancers. However, the prognostic significance of the GRIm score in patients with malignant ascites has not yet been investigated.

METHODS

Clinical samples were collected from a cohort of patients with malignant ascites secondary to hepatocellular carcinoma (HCC). We calculated serum GRIm (sGRIm) and ascites GRIm (aGRIm) scores and divided the samples into low and high GRIm score groups. Survival analysis was used to compare the prognostic significance of the sGRIm and aGRIm scores. 16S rRNA sequencing was performed to determine the profiles of the intratumoral microbiota in the groups. A fluorescent multiplex immunohistochemistry (mIHC) assay was used to detect the expression of different immune cells by labeling seven markers of malignant ascites.

RESULTS

155 patients with HCC and malignant ascites were enrolled in this study. Survival analysis revealed that the aGRIm score showed a superior prognostic significance compared to the sGRIm score. Microbial analysis demonstrated that the bacterial richness and diversity were higher in the low aGRIm score group than in the high aGRIm score group. LefSe analysis revealed that certain bacteria were correlated with high aGRIm scores. Fluorescent mIHC displayed the tumor microenvironment of malignant ascites and found that the density of CD8 + T cells was significantly higher in the low aGRIm score group than in the high aGRIm score group.

CONCLUSIONS

Our present study identified a novel scoring system (aGRIm score) that can predict the survival outcome of patients with malignant ascites secondary to HCC.

High-Grade Serous Ovarian Cancer-A Risk Factor Puzzle and Screening Fugitive

High-grade serous ovarian cancer (HGSOC) is the most lethal tumor of the female genital tract. Despite extensive studies and the identification of some precursor lesions like serous tubal intraepithelial cancer (STIC) or the deviated mutational status of the patients (BRCA germinal mutation), the pathophysiology of HGSOC and the existence of particular risk factors is still a puzzle. Moreover, a lack of screening programs results in delayed diagnosis, which is accompanied by a secondary chemo-resistance of the tumor and usually results in a high recurrence rate after the primary therapy. Therefore, there is an urgent need to identify the substantial risk factors for both predisposed and low-risk populations of women, as well as to create an economically and clinically justified screening program. This paper reviews the classic and novel risk factors for HGSOC and methods of diagnosis and prediction, including serum biomarkers, the liquid biopsy of circulating tumor cells or circulating tumor DNA, epigenetic markers, exosomes, and genomic and proteomic biomarkers. The novel future complex approach to ovarian cancer diagnosis should be devised based on these findings, and the general outcome of such an approach is proposed and discussed in the paper.

Loss of B1 and marginal zone B cells during ovarian cancer

Recent advances in immunotherapy have not addressed the challenge presented by ovarian cancer. Although the peritoneum is an "accessible" locus for this disease there has been limited characterization of the immunobiology therein. We investigated the ID8-C57BL/6J ovarian cancer model and found marked depletion of B1 cells from the ascites of the peritoneal cavity. There was also selective loss of the B1 and marginal zone B cell subsets from the spleen. Immunity to antigens that activate these subsets validated their loss rather than relocation. A marked influx of myeloid-derived suppressor cells correlated with B cell subset depletion. These observations are discussed in the context of the housekeeping burden placed on innate B cells during ovarian cancer and to foster consideration of B cell biology in therapeutic strategies to address this challenge.

The Role of the Human Microbiome in Epithelial Ovarian Cancer

Ovarian cancer is the fifth-leading cause of cancer deaths among women due to the absence of available screening methods to identify early disease. Thus, prevention and early disease detection investigations are of high priority, surrounding a critical window of opportunity to better understand important pathogenic mechanisms of disease progression. Microorganisms modulate molecular interactions in humans that can influence states of health and disease, including ovarian cancer. While the mechanisms of infectious microbial invasion that trigger the immune-inflammatory axis are well studied in cancer research, the complex interactions that promote the transition of noninfectious healthy microbes to pathobiont expansion are less understood. As traditional research has focused on the influences of infectious pathogens on ovarian cancer development and progression, the impact of noninfectious microbes has gained scientific attention. The objective of this chapter is to summarize current evidence on the role of microbiota in epithelial ovarian cancer throughout disease.

Microbiota as the unifying factor behind the hallmarks of cancer

The human microbiota is a complex ecosystem that colonizes body surfaces and interacts with host organ systems, especially the immune system. Since the composition of this ecosystem depends on a variety of internal and external factors, each individual harbors a unique set of microbes. These differences in microbiota composition make individuals either more or less susceptible to various diseases, including cancer. Specific microbes are associated with cancer etiology and pathogenesis and several mechanisms of how they drive the typical hallmarks of cancer were recently identified. Although most microbes reside in the distal gut, they can influence cancer initiation and progression in distant tissues, as well as modulate the outcomes of established cancer therapies. Here, we describe the mechanisms by which microbes influence carcinogenesis and discuss their current and potential future applications in cancer diagnostics and management.

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

Utility of human epididymis protein 4 in the differential diagnosis of ascites

BACKGROUND AND AIMS

Human epididymal protein 4 (HE4) may be a useful tool in the differential diagnosis of malignant ascites. The aim of this study was to evaluate the diagnostic utility of HE4 for detecting malignant ascites, taking into account the possible false positives identified with adenosine deaminase (ADA), C-reactive protein (CRP), % polynuclear cells (%PMN) and glomerular filtration rate (eGFR).

METHODS

Concentrations of HE4, ADA, %PMN and CRP were determined in 114 samples of peritoneal fluid and creatinine in serum in order to calculate eGFR.

RESULTS

Concentrations of HE4 presented significant differences (P = 0.028) in benign [median (interquartile range)] [582(372)] pmol/L) and malignant ascites ([8241(367)] pmol/L. Sensitivity was 21.2% and specificity 100%. Significant differences were also observed for HE4 between tumors of gynecological origin ([3165(8769)] pmol/L) and others ([665(663)] pmol/L), with a sensitivity of 67% and a specificity of 100%. Classifying according to possible false positives (ADA > 45U/L, CRP > 50 mg/L, %PMN > 90 and eGFR < 30 mL/min/1.73 m2) at maximum specificity, a sensitivity of 33.3% was obtained for HE4, with a cut-off point of 2660 pmol/L. Without possible false positives (ADA < 45U/L, CRP < 50 mg/L, %PMN < 90 and eGFR ≥ 30 mL/min/1.73 m2), a sensitivity of 37.7% was obtained at 100% specificity for a cut-off point of 1041 pmol/L. Applying these criteria to the entire group, a sensitivity of 36.4% was obtained at maximum specificity.

CONCLUSIONS

HE4 allows the identification of malignant ascites with moderate sensitivity at maximum specificity. HE4 levels can differentiate between tumors of gynecological origin and others. Classification according to possible false positives increases sensitivity without losing specificity.

Evaluation of sterol‑o‑acyl transferase 1 and cholesterol ester levels in plasma, peritoneal fluid and tumor tissue of patients with endometrial cancer: A pilot study

Endometrial cancer (EC) is the most prevalent gynecological malignancy. Abnormal accumulation of sterol-O-acyl transferase 1 (SOAT1) and SOAT1-mediated cholesterol ester (CE) contributes to cancer progression in various malignancies, including ovarian cancer. Therefore, it was hypothesized that similar molecular changes may occur in EC. The present study aimed to evaluate the diagnostic and/or prognostic potential of SOAT1 and CE in EC by: i) Determining SOAT1 and CE levels in plasma, peritoneal fluid and endometrial tissue from patients with EC and control subjects; ii) performing receiver operating characteristic curve analysis to determine diagnostic performance; iii) comparing SOAT1 and CE expression to that of the tumor proliferation marker Ki67; and iv) assessing the association between SOAT1 expression and survival. Enzyme-linked immunosorbent assay was used to determine the levels of SOAT1 protein in tissue, plasma and peritoneal fluid. The mRNA and protein expression levels of SOAT1 and Ki67 in tissues were detected by reverse transcription-quantitative polymerase chain reaction and immunohistochemistry, respectively. CE levels were determined colorimetrically in plasma and peritoneal fluid. SOAT1-associated survival data from the cBioPortal cancer genomics database were used to assess prognostic relevance. The results revealed that SOAT1 and CE levels were significantly elevated in tumor tissue and peritoneal fluid samples collected from the EC group. By contrast, the plasma levels of SOAT1 and CE in the EC and control groups were similar. Significant positive associations between CE and SOAT1, SOAT1/CE and Ki67, and SOAT1/CE and poor overall survival in patients with EC suggested that SOAT1/CE may be associated with malignancy, aggressiveness and poor prognosis. In conclusion, SOAT1 and CE may serve as potential biomarkers for prognosis and target-specific treatment of EC.

The Influence of the Microbiome on Metastatic Colorectal Cancer

The microbiome (bacteria, viruses, and fungi) that exist within a patient's gastrointestinal tract and throughout their body have been increasingly understood to play a critical role in a variety of disease, including a number of cancer histologies. These microbial colonies are reflective of a patient's overall health state, their exposome, and germline genetics. In the case of colorectal adenocarcinoma, significant progress has been made in understanding the mechanism the microbiome plays beyond mere associations in both disease initiation and progression. Importantly, this improved understanding holds the potential to further identify the role these microbes play in colorectal cancer. We hope this improved understanding will be able to be leveraged in the future through either biomarkers or next-generation therapeutics to augment contemporary treatment algorithms through the manipulation of a patient's microbiome-whether through diet, antibiotics, prebiotics, or novel therapeutics. Here we review the role of the microbiome in the setting of patients with stage IV colorectal adenocarcinoma in both the development and progression or disease as well as response to therapeutics.

Tumor microbiome - an integral part of the tumor microenvironment

The tumor microenvironment (TME) plays a significant role in tumor progression and cancer cell survival. Besides malignant cells and non-malignant components, including immune cells, elements of the extracellular matrix, stromal cells, and endothelial cells, the tumor microbiome is considered to be an integral part of the TME. Mounting evidence from preclinical and clinical studies evaluated the presence of tumor type-specific intratumoral bacteria. Differences in microbiome composition between cancerous tissues and benign controls suggest the importance of the microbiome-based approach. Complex host-microbiota crosstalk within the TME affects tumor cell biology via the regulation of oncogenic pathways, immune response modulation, and interaction with microbiota-derived metabolites. Significantly, the involvement of tumor-associated microbiota in cancer drug metabolism highlights the therapeutic implications. This review aims to summarize current knowledge about the emerging role of tumor microbiome in various types of solid malignancies. The clinical utility of tumor microbiome in cancer progression and treatment is also discussed. Moreover, we provide an overview of clinical trials evaluating the role of tumor microbiome in cancer patients. The research focusing on the communication between the gut and tumor microbiomes may bring new opportunities for targeting the microbiome to increase the efficacy of cancer treatment and improve patient outcomes.

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.

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Objectives

This study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.

Methods

A total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.

Results

The MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p&lt;0.001).

Conclusions

Machine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

Influences of Gastrointestinal Microbiota Dysbiosis on Serum Proinflammatory Markers in Epithelial Ovarian Cancer Development and Progression

GI microbiota has been implicated in producing the inflammatory tumor microenvironment of several cancers. Women with ovarian cancer often report GI-related symptoms at diagnosis although minimal is known about the possible GI bacteria that may trigger pro-tumorigenic immune responses in early EOC. The purpose of this study was to investigate the influences of GI microbiota dysbiosis on serum inflammatory markers during EOC utilizing a rodent model. This experimental design consisted of C57BL/6 mice randomly assigned to either the microbiota dysbiosis group (n = 6) or control group (n = 5). The CD7BL/6 mice assigned to the microbiota dysbiosis group were administered a mixture of broad-spectrum antibiotics (bacitracin and neomycin) for 2 weeks. Both groups were injected intraperitoneally with mouse ovarian epithelial cells that induce ovarian tumorigenesis. Levels of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were assessed in the serum, and the composition of the GI microbiota in fecal samples was measured using 16S rRNA gene sequencing. Overall CRP serum levels were significantly lower and TNFα levels were significantly higher in the microbiota dysbiosis group compared to the control group. The abundances of microbiota that correlated with CRP serum levels in the combined groups were genus Parabacteroides, Roseburia, and Emergencia and species Ruminococcus faecis, Parabacteroides distasonis, Roseburia Faecis, and Emergencia timonensis. This study provides evidence to support for further investigation of the GI microbial profiles in patients at risk of EOC.

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

Assessment of the diagnostic and prognostic relevance of ACAT1 and CE levels in plasma, peritoneal fluid and tumor tissue of epithelial ovarian cancer patients - a pilot study

Background

Abnormal accumulation of acyl-CoA cholesterol acyltransferase-1 (ACAT1) and ACAT1-mediated cholesterol esterified with fatty acids (CE) contribute to cancer progression in various cancers. Our findings of increased CE and ACAT1 levels in epithelial ovarian cancer (EOC) cell lines prompted us to investigate whether such an increase occurs in primary clinical samples obtained from human subjects diagnosed with EOC. We evaluated the diagnostic/prognostic potential of ACAT1 and CE in EOC by: 1) assessing ACAT1 and CE levels in plasma, peritoneal fluid, and ovarian/tumor tissues; 2) assessing diagnostic performance by Receiver Operating Characteristic (ROC) analysis; and 3) comparing expression of ACAT1 and CE with that of tumor proliferation marker, Ki67.

Methods

ACAT1 protein levels in plasma, peritoneal fluid and tissue were measured via enzyme-linked immunosorbent assay. Tissue expression of ACAT1 and Ki67 proteins were confirmed by immunohistochemistry and mRNA transcript levels were evaluated using quantitative real-time polymerase chain reaction (qRT-PCR). CE levels were assessed in plasma, peritoneal fluid (colorimetric assay) and in tissue (thin layer chromatography).

Results

Preoperative levels of ACAT1 and CE on the day of surgery were significantly higher in tissue and peritoneal fluid from EOC patients vs. the non-malignant group, which included subjects with benign tumors and normal ovaries; however, no significant differences were observed in plasma. In tissue and peritoneal fluid, positive correlations were observed between CE and ACAT1 levels, as well as between ACAT1/CE and Ki67.

Conclusions

ACAT1 and CE accumulation may be linked to the aggressive potential of EOC; therefore, these mediators may be useful biomarkers for EOC prognosis and target-specific treatments.

Human microbial dysbiosis as driver of gynecological malignancies

Gynecological cancers that affect female reproductive tract, remain at the top of the global cancer burden list with high relapse rate and mortality. Notwithstanding development of several novel therapeutic interventions including poly-ADP-ribose polymerase inhibitors, this family of malignancies remain deadly. The human microbiome project demonstrated that dysbiosis of health resident microflora is associated with several pathologies including malignancies of the female reproductive tract and detailed characterization of species variation and host-microbe interaction could provide clues for identification of early diagnostic biomarker, preventive and therapeutic interventions. Emerging evidence suggests that several microbial signatures are significantly associated with gynecological cancers. An increased population of Proteobacteria and Firmicutes followed by significantly reduced Lactobacilli are associated with lethal epithelial ovarian cancer. Similarly, a constant association of elevated level of Atopobium vaginae, Porphyromonas somerae, Micrococci and Gardnerella vaginalis are observed in endometrial and cervical cancers. Moreover, human papilloma virus infection significantly augments colonization of pathogenic microbes including Sneathia sanguinegens, Anaerococcus tetradius, and Peptostreptococcus anaerobius and drives carcinoma of the cervix. Interestingly, microbial dysbiosis in female reproductive tract modulates expression of several microbial and immune-responsive genes such as TLR-4, TLR-5, TLR-6 and NOD-1. Therefore, stringent investigation into the microbial dysbiosis and its underlying mechanism could provide valuable cues for identification of early diagnostic biomarker, preventive and therapeutic interventions against rogue gynecological malignancies.

Human Gut Microbiota in Health and Selected Cancers

The majority of the epithelial surfaces of our body, and the digestive tract, respiratory and urogenital systems, are colonized by a vast number of bacteria, archaea, fungi, protozoans, and viruses. These microbiota, particularly those of the intestines, play an important, beneficial role in digestion, metabolism, and the synthesis of vitamins. Their metabolites stimulate cytokine production by the human host, which are used against potential pathogens. The composition of the microbiota is influenced by several internal and external factors, including diet, age, disease, and lifestyle. Such changes, called dysbiosis, may be involved in the development of various conditions, such as metabolic diseases, including metabolic syndrome, type 2 diabetes mellitus, Hashimoto's thyroidis and Graves' disease; they can also play a role in nervous system disturbances, such as multiple sclerosis, Alzheimer's disease, Parkinson's disease, and depression. An association has also been found between gut microbiota dysbiosis and cancer. Our health is closely associated with the state of our microbiota, and their homeostasis. The aim of this review is to describe the associations between human gut microbiota and cancer, and examine the potential role of gut microbiota in anticancer therapy.

Estrogens and the Schrödinger's Cat in the Ovarian Tumor Microenvironment

Simple Summary

Ovarian cancer is a complex pathology for which we require effective screening and therapeutical strategies. Apart from the cancer cell portion, there exist plastic immune and non-immune cell populations, jointly constituting the context-adaptive tumor microenvironment, which is pivotal in tumorigenesis. Estrogens might be synthesized in the ovarian tumor tissue and actively contribute to the shaping of an immunosuppressive microenvironment. Current immune therapies have limited effectiveness as a multitude of factors influence the outcome. A thorough understanding of the ovarian cancer biology is crucial in the efforts to reestablish homeostasis.

Abstract

Ovarian cancer is a heterogeneous disease affecting the aging ovary, in concert with a complex network of cells and signals, together representing the ovarian tumor microenvironment. As in the “Schrödinger’s cat” thought experiment, the context-dependent constituents of the—by the time of diagnosis—well-established tumor microenvironment may display a tumor-protective and -destructive role. Systemic and locally synthesized estrogens contribute to the formation of a pro-tumoral microenvironment that enables the sustained tumor growth, invasion and metastasis. Here we focus on the estrogen biosynthetic and metabolic pathways in ovarian cancer and elaborate their actions on phenotypically plastic, estrogen-responsive, aging immune cells of the tumor microenvironment, altogether highlighting the multicomponent-connectedness and complexity of cancer, and contributing to a broader understanding of the ovarian cancer biology.

Dr Effects of Adiponectin, Plasma D-Dimer, Inflammation and Tumor Markers on Clinical Characteristics and Prognosis of Patients with Ovarian Cancer

 [Abstract] Objective: To investigate the effects of adiponectin (ADPN), plasma D-dimer (D-D), inflammation and tumor markers on clinical characteristics and prognosis of patients with ovarian cancer. Methods: A total of 80 patients with ovarian cancer treated in our hospital from April 2017 to November 2019 were enrolled as study subjects and evenly divided into observation group (patients with ovarian cancer) and control group (patients with benign ovarian tumor) based on the results of postoperative pathological biopsy. The levels of ADPN, plasma D-D, inflammatory factors and serum tumor markers [carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4) and risk of ovarian malignancy algorithm (ROMA)] were compared between the two groups. The diagnostic value of serum tumor markers CA125, HE4 and ROMA in ovarian cancer was explored. The correlations of the changes of ROMA with the changes in the levels of ADPN, plasma D-D, high-sensitivity C-reactive protein (hs-CRP), CA125 and HE4 were analyzed. Additionally, the related risk factors affecting the development of ovarian cancer were subjected to univariate and multivariate logistic regression analyses. Results: In comparison with control group, observation group exhibited a lowered ADPN level (p<0.05), notably raised levels of plasma D-D, inflammatory factors hs-CRP and interleukin-6 (IL-6) and serum tumor markers CA125 and HE4 and an evidently increased ROMA (p<0.05). Besides, the detection of serum ROMA showed the highest specificity and sensitivity and low false positive rate and false negative rate. The changes of ROMA were positively correlated with the changes in the levels of plasma D-D, hs-CRP, CA125 and HE4 (p<0.05), and negatively associated with the changes in ADPN level (p<0.05). The results of univariate analysis showed that abnormal ADPN, D-D, hs-CRP, IL-6, CA125 and HE4 levels were related risk factors affecting the development of ovarian cancer. It was found through multivariate logistic regression analysis that decreased ADPN level and increased D-D, hs-CRP, IL-6, CA125 and HE4 levels were independent risk factors affecting the development of ovarian cancer. Conclusion: In the case of ovarian cancer, the ADPN level declines, while the levels of plasma D-D, inflammatory factors, and serum tumor markers CA125, HE4 and ROMA rise obviously. Besides, the ROMA level displays a positive relation to the content of CA125, HE4, plasma D-D and inflammatory factors and a negative association with ADPN level.

The Female Reproductive Tract Microbiome-Implications for Gynecologic Cancers and Personalized Medicine

The microbial colonization of the lower female reproductive tract has been extensively studied over the past few decades. In contrast, the upper female reproductive tract including the uterine cavity and peritoneum where the ovaries and fallopian tubes reside were traditionally assumed to be sterile under non-pathologic conditions. However, recent studies applying next-generation sequencing of the bacterial 16S ribosomal RNA gene have provided convincing evidence for the existence of an upper female reproductive tract microbiome. While the vaginal microbiome and its importance for reproductive health outcomes has been extensively studied, the microbiome of the upper female reproductive tract and its relevance for gynecologic cancers has been less studied and will be the focus of this article. This targeted review summarizes the pertinent literature on the female reproductive tract microbiome in gynecologic malignancies and its anticipated role in future research and clinical applications in personalized medicine.

The role of the microbiome in ovarian cancer: mechanistic insights into oncobiosis and to bacterial metabolite signaling

Ovarian cancer is characterized by dysbiosis, referred to as oncobiosis in neoplastic diseases. In ovarian cancer, oncobiosis was identified in numerous compartments, including the tumor tissue itself, the upper and lower female genital tract, serum, peritoneum, and the intestines. Colonization was linked to Gram-negative bacteria with high inflammatory potential. Local inflammation probably participates in the initiation and continuation of carcinogenesis. Furthermore, local bacterial colonies in the peritoneum may facilitate metastasis formation in ovarian cancer. Vaginal infections (e.g. Neisseria gonorrhoeae or Chlamydia trachomatis) increase the risk of developing ovarian cancer. Bacterial metabolites, produced by the healthy eubiome or the oncobiome, may exert autocrine, paracrine, and hormone-like effects, as was evidenced in breast cancer or pancreas adenocarcinoma. We discuss the possible involvement of lipopolysaccharides, lysophosphatides and tryptophan metabolites, as well as, short-chain fatty acids, secondary bile acids and polyamines in the carcinogenesis of ovarian cancer. We discuss the applicability of nutrients, antibiotics, and probiotics to harness the microbiome and support ovarian cancer therapy. The oncobiome and the most likely bacterial metabolites play vital roles in mediating the effectiveness of chemotherapy. Finally, we discuss the potential of oncobiotic changes as biomarkers for the diagnosis of ovarian cancer and microbial metabolites as possible adjuvant agents in therapy.

Using machine learning to predict ovarian cancer

OBJECTIVE

Ovarian cancer (OC) is one of the most common types of cancer in women. Accurately prediction of benign ovarian tumors (BOT) and OC has important practical value.

METHODS

Our dataset consists of 349 Chinese patients with 49 variables including demographics, blood routine test, general chemistry, and tumor markers. Machine learning Minimum Redundancy - Maximum Relevance (MRMR) feature selection method was applied on the 235 patients' data (89 BOT and 146 OC) to select the most relevant features, with which a simple decision tree model was constructed. The model was tested on the rest of 114 patients (89 BOT and 25 OC). The results were compared with the predictions produced by using the risk of ovarian malignancy algorithm (ROMA) and logistic regression model.

RESULTS

Eight notable features were selected by MRMR, among which two were identified as the top features by the decision tree model: human epididymis protein 4 (HE4) and carcinoembryonic antigen (CEA). Particularly, CEA is a valuable marker for OC prediction in patients with low HE4. The model also yields better prediction result than ROMA.

CONCLUSION

Machine learning approaches were able to accurately classify BOT and OC. Our goal is to derive a simple predictive model which also carries a good performance. Using our approach, we obtained a model that consists of just two biomarkers, HE4 and CEA. The model is simple to interpret and outperforms the existing OC prediction methods. It demonstrates that the machine learning approach has good potential in predictive modeling for the complex diseases.