Metabolomics in endometrial cancer diagnosis: A systematic review

Rapid screening of infertility-associated gynecological conditions via ambient glow discharge mass spectrometry utilizing urine metabolic fingerprints

Infertility presents a widespread challenge for many families worldwide, often arising from various gynecological diseases (GDs) that hinder successful pregnancies. Current diagnostic methods for GDs have disadvantages such as low efficiency, high cost, misdiagnose, invasive injury and etc. This paper introduces a rapid, non-invasive, efficient, and straightforward analytical method that utilizes desorption, separation, and ionization mass spectrometry (DSI-MS) platform in conjunction with machine learning (ML) to detect urine metabolite fingerprints in patients with different GDs. We analyzed 257 samples from patients diagnosed with polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI), diminished ovarian reserve (DOR), endometriosis (EMS), recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), and 87 samples from healthy control (HC) individuals. We identified metabolite differences and dysregulated pathways through dimensionality reduction methods, with the result of the discovery of 7 potential biomarkers for GDs diagnosis. The ML method effectively distinguished subtle differences in urine metabolite fingerprints. We anticipate that this innovative approach will offer a patient-friendly, rapid screening, and differentiation method for infertility-related GDs patients.

A novel immune-related gene signature for diagnosis and potential immunotherapy of microsatellite stable endometrial carcinoma

An immune-related gene signature (IRGS) was established to better understand the molecular and immunologic characteristics of microsatellite instable (MSI) and microsatellite stable (MSS) endometrial carcinoma (EC), and provide potential immunotherapy directions for MSS patients. Top 20 immune-related hub genes were screened by weight gene coexpression network analysis (WGCNA), and an IRGS was further established through Cox regression analysis. The molecular and immune characteristics were clarified in IRGS high and low risk groups. Expression and MS status validation of the IRGS were conducted through quantitative real-time Polymerase Chain Reaction (rt-qPCR) and immunohistochemistry (IHC) analysis. The IRGS includes 2 oncogenes (AGTR1 and HTR3C) and 2 tumor suppressor genes (CD3E and SERPIND1). Patients in IRGS high-risk group were more with MSS status, higher tumor grade, later FIGO stage, serous histology and elder ages compared with IRGS low-risk group (P < 0.05). Besides, patients in MSS group were more FIGO stages II-IV (42.7% vs. 26%), serous histology (35.7% vs. 5.3%) and with higher IRGS risk score (1.51 ± 3.11 vs. 1.02 ± 0.67) (P < 0.05) than patients in MSI group. Furthermore, patients in IRGS high-risk group had higher tumor purity, more Macrophages M1 and Macrophages M2 infiltrating, higher proportion of Macrophages M2 and Dendritic cells activated, lower proportion of T cells regulatory (Tregs), lower tumor mutation burden (TMB). Correspondingly, subjects in IRGS low-risk group had higher immunphenoscores than IRGS high-risk group. The relative mRNA level of AGTR1 and HTR3C were gradually increase, while CD3E and SERPIND1 were reversed in rt-qPCR. Through IHC experiments, AGTR1(69.2% vs 30%, P = 0.074) and HTR3C (76.9% vs 30%, P = 0.024) had higher positive staining rates in ECs than non-ECs. While SERPIND1 (84.6% vs 20%, P = 0.003) and CD3E (61.5% vs 40%, P = 0.000) had higher positive staining rates in non-ECs. IRGS is a potential diagnostic and prognostic biomarker for EC. IRGS low risk group might benefit from immune checkpoint inhibitors, while IRGS high risk group deserve other potential immunotherapy.

Metabolomic Profiling of Blood Plasma in Females with Hyperplasia and Endometrial Cancer

Uterine cancer is the most prevalent gynecologic malignancy in women worldwide. Endometrial cancer (EC) has an 81% five-year survival rate, depending on disease stage and time of diagnosis. While endometrial cancer is largely treatable when detected early, no established screening techniques are available in clinical practice. As a result, one of the most significant issues in the medical field is the development of novel ways for early cancer identification, which could boost treatment success rates. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based metabolomics was employed to explore the metabolomic markers and pathways unique to this cancer type and link them to the benign endometrial hyperplasia that may progress to cancer in 5% to 25% of patients. The study involved 59 postmenopausal participants, 20 with EC type 1, 20 with benign hyperplasia, and 19 healthy participants. Metabolite distribution changes were analyzed, and 338 of these features were dysregulated and significant. The first two main components, PC1 and PC2, were responsible for 11.5% and 12.2% of the total metabolites, respectively. Compared with the control group (CO), EC samples had 203 differentially expressed metabolites (180 upregulated and 23 downregulated); in hyperplasia (HP), 157 metabolites were dysregulated (127 upregulated and 30 downregulated) compared to the CO group while 21 metabolites exhibited differential regulation (16 upregulated and 5 downregulated) in EC plasma samples compared to the HP group. Hyperplasia samples exhibited similar metabolic changes to those reported in cancer, except for alterations in triglyceride levels, 7a,12 b-dihydroxy-5b-Cholan-24-oic acid, and Hept-2-enedioyl carnitine levels. The metabolites N-heptanoyl glycine and -(Methylthio)-2,3-isopentyl phosphate and formimino glutamic acid can be specific markers for hyperplasia conditions and dimethyl phosphatidyl ethanolamine and 8-isoprostaglandin E2 can be specific markers for EC conditions. Metabolic activities rely on mitochondrial oxidative phosphorylation for energy generation. The changes in metabolites identified in our study indicate that endometrial cancer cells adopt alternative strategies to increase energy production to meet the energy demand, thereby supporting proliferation.

Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry

OBJECTIVE

This study aimed to reveal the urinary and serum metabolic pattern of endometrial cancer (EC) and establish diagnostic models to identify EC from controls, high-risk from low-risk EC, and type II from type I EC.

METHOD

This study included 146 EC patients (comprising 79 low-risk and 67 high-risk patients, including 124 type I and 22 type II) and 59 controls. The serum and urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry. Analysis was used to elucidate the distinct metabolites and altered metabolic pathways. Receiver operating characteristic (ROC) analyses were employed to discover and validate the potential biomarker models.

RESULTS

Serum and urine metabolomes displayed significant differences between EC and controls, with metabolites related to amino acid and nicotinamide metabolisms. The serum and urine panels distinguished these two groups with Area Under the Curve (AUC) of 0.821 and 0.902, respectively. The panel consisting of serum and urine metabolites demonstrated the best predictive ability (AUC = 0.953 and 0.976 in discovering and validation group). In comparing high-risk and low risk EC, differential metabolites were enriched in purine and glutamine metabolism. The AUC values for serum and urine panels were 0.818, and 0.843, respectively. The combined panel exhibited better predictive accuracy (0.881 in discovering group and 0.936 in external validation). In the comparison between type I and type II group, altered folic acid metabolism was identified. The serum, urine and combined panels discriminated these two groups with the AUC of 0.829, 0.913 and 0.922, respectively.

CONCLUSION

The combined urine and serum metabolome effectively revealed the metabolic patterns in EC patients, offering valuable diagnostic models for EC diagnosis and classification.

Utilizing serum metabolomics for assessing postoperative efficacy and monitoring recurrence in gastric cancer patients

OBJECTIVE

(1) This study aims to identify distinct serum metabolites in gastric cancer patients compared to healthy individuals, providing valuable insights into postoperative efficacy evaluation and monitoring of gastric cancer recurrence; (2) Methods: Serum samples were collected from 15 healthy individuals, 16 gastric cancer patients before surgery, 3 months after surgery, 6 months after surgery, and 15 gastric cancer recurrence patients. T-test and analysis of variance (ANOVA) were performed to screen 489 differential metabolites between the preoperative group and the healthy control group. Based on the level of the above metabolites in the recurrence, preoperative, three-month postoperative, and six-month postoperative groups, we further selected 18 significant differential metabolites by ANOVA and partial least squares discriminant analysis (PLS-DA). The result of hierarchical clustering analysis about the above metabolites showed that the samples were regrouped into the tumor-bearing group (comprising the original recurrence and preoperative groups) and the tumor-free group (comprising the original three-month postoperative and six-month postoperative groups). Based on the results of PLS-DA, 7 differential metabolites (VIP > 1.0) were further selected to distinguish the tumor-bearing group and the tumor-free group. Finally, the results of hierarchical clustering analysis showed that these 7 metabolites could well identify gastric cancer recurrence; (3) Results: Lysophosphatidic acids, triglycerides, lysine, and sphingosine-1-phosphate were significantly elevated in the three-month postoperative, six-month postoperative, and healthy control groups, compared to the preoperative and recurrence groups. Conversely, phosphatidylcholine, oxidized ceramide, and phosphatidylglycerol were significantly reduced in the three-month postoperative, six-month postoperative, and healthy control groups compared to the preoperative and recurrence groups. However, these substances did not show significant differences between the preoperative and recurrence groups, nor between the three-month postoperative, six-month postoperative, and healthy control groups; (4) Conclusions: Our findings demonstrate the presence of distinct metabolites in the serum of gastric cancer patients compared to healthy individuals. Lysophosphatidic acid, triglycerides, lysine, sphingosine-1-phosphate, phosphatidylcholine, oxidized ceramide, and phosphatidylglycerol hold potential as biomarkers for evaluating postoperative efficacy and monitoring recurrence in gastric cancer patients. These metabolites exhibit varying concentrations across different sample categories.

Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review

Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.

Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry

OBJECTIVE

This study aimed to reveal the urine metabolic change of endometrial cancer (EC) patients during fertility-sparing treatment and establish non-invasive predictive models to identify patients with complete remission (CR).

METHOD

This study enrolled 20 EC patients prior to treatment (PT) and 22 patients with CR, aged 25-40 years. Eligibility criteria consisted of stage IA high-grade EC, lesions confined to endometrium, normal hepatic and renal function, normal urine test, no contraindication for fertility-sparing treatment and no prior therapy. Urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), a technique chosen for its high sensitivity and resolution, allows for rapid, accurate identification and quantification of metabolites, providing a comprehensive metabolic profile and facilitating the discovery of potential biomarkers. Analytical techniques were employed to determine distinct metabolites and altered metabolic pathways. The statistical analyses were performed using univariate and multivariate analyses, logistic regression and receiver operating characteristic (ROC) curves to discover and validate the potential biomarker models.

RESULTS

A total of 108 different urine metabolomes were identified between CR and PT groups. These metabolites were enriched in ascorbate and aldarate metabolism, one carbon pool by folate, and some amino acid metabolisms pathways. A panel consisting of Baicalin, 5beta-1,3,7 (11)-Eudesmatrien-8-one, Indolylacryloylglycine, Edulitine, and Physapubenolide were selected as biomarkers, which demonstrated the best predictive ability with the AUC values of 0.982/0.851 in training/10-fold-cross-validation group, achieving a sensitivity of 0.975 and specificity of 0.967, respectively.

CONCLUSION

The urine metabolic analysis revealed the metabolic changes in EC patients during the fertility-sparing treatment. The predictive biomarkers present great potential diagnostic value in fertility-sparing treatments for EC patients, offering a less invasive means of monitoring treatment efficacy. Further research should explore the mechanistic underpinnings of these metabolic changes and validate the biomarker panel in larger, diverse populations due to the small sample size and single-institution nature of our study.

A Multi-Omics Approach Revealed Common Dysregulated Pathways in Type One and Type Two Endometrial Cancers

Endometrial cancer (EC) is the most frequent gynecologic cancer in postmenopausal women. Pathogenetic mechanisms that are related to the onset and progression of the disease are largely still unknown. A multi-omics strategy can help identify altered pathways that could be targeted for improving therapeutical approaches. In this study we used a multi-omics approach on four EC cell lines for the identification of common dysregulated pathways in type 1 and 2 ECs. We analyzed proteomics and metabolomics of AN3CA, HEC1A, KLE and ISHIKAWA cell lines by mass spectrometry. The bioinformatic analysis identified 22 common pathways that are in common with both types of EC. In addition, we identified five proteins and 13 metabolites common to both types of EC. Western blotting analysis on 10 patients with type 1 and type 2 EC and 10 endometria samples confirmed the altered abundance of NPEPPS. Our multi-omics analysis identified dysregulated proteins and metabolites involved in EC tumor growth. Further studies are needed to understand the role of these molecules in EC. Our data can shed light on common pathways to better understand the mechanisms involved in the development and growth of EC, especially for the development of new therapies.

Molecular Signature of Endometrial Cancer with Coexistent Adenomyosis: A Multicentric Exploratory Analysis

Adenomyosis has been associated with better survival outcomes in women with endometrial cancer. However, although the endometrial cancer patients' risk stratification has been revolutionized by molecular findings, the impact of the molecular signature on the favorable prognosis of endometrial cancer patients with coexistent adenomyosis is unknown. The aim of our study was to compare the prevalence of molecular groups at poor and intermediate prognosis between endometrial cancer patients with and without coexistent adenomyosis. A multicentric, observational, retrospective, cohort study was performed to assess the differences in the prevalence of p53-abnormal expression (p53-abn) and mismatch repair protein-deficient expression (MMR-d) signatures between endometrial cancer patients with and without coexistent adenomyosis. A total of 147 endometrial cancer patients were included in the study: 38 in the adenomyosis group and 109 in the no adenomyosis group. A total of 37 patients showed the MMR-d signature (12 in the adenomyosis group and 25 in the no adenomyosis group), while 12 showed the p53-abn signature (3 in the adenomyosis group and 9 in the no adenomyosis group). No significant difference was found in the prevalence of p53-abn (p = 1.000) and MMR-d (p = 0.2880) signatures between endometrial cancer patients with and without coexistent adenomyosis. In conclusion, the molecular signature does not appear to explain the better prognosis associated with coexistent adenomyosis in endometrial cancer patients. Further investigation of these findings is necessary through future larger studies.

Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling

BACKGROUND

Endometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC.

METHODS

In this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry.

RESULTS

The concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression.

CONCLUSIONS

Our data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients.

Detection rates and factors affecting thereof in endometrial hyperplasia, endometrial carcinoma, and cervical glandular lesions on cervical smear

INTRODUCTION

Endometrial lesions are morphologically diverse and uncommon on cervical smears, with its detection rate and associated diagnostic categories uncharacterized. In this study, cervical smears matched to histologically proven endometrial hyperplasias and carcinomas were reviewed and compared with cervical in-situ-carcinomas/carcinomas, aiming to detail the diagnostic performance of cervical smears for upper tract and glandular lesions.

METHODS

Pathology reports of cervical smears, hysterectomies, endometrial and cervical biopsies from 1995 to 2021 were retrieved. Diagnoses of cervical smears were matched to endometrial hyperplasias and carcinomas, or cervical carcinomas and reviewed.

RESULTS

Totally 832 cervical smears (272 cervical carcinomas, 312 endometrial carcinomas, and 248 hyperplasias) were included. Considering all cytologic glandular diagnosis as positive, the detection rate of cervical adenocarcinoma-in-situ was the highest (64.3%), followed by cervical adenocarcinoma (63.8%), endometrial carcinoma (31.7%), and hyperplasia (with atypia-8.5%; without atypia-2.3%) (p < 0.001). Endometrial hyperplasia was most often diagnosed as atypical squamous cells of undetermined significance (ASCUS) (5.0%) or atypical glandular cells, not otherwise specified (3.6%) without indication of endometrial origin. For endometrial carcinomas, higher FIGO grading and endocervical involvement were associated with higher detection rates across all diagnostic categories (p = 0.002-0.028). High FIGO grade was associated with suspicious/favor neoplastic (C4) (31.1%vs10.3%, p < 0.001) and carcinoma (C5) (17.8% vs. 5.6%, p = 0.005) categories, but not for all glandular diagnoses combined (33.3% vs. 31.0%, p = 0.761).

CONCLUSION

Detection rates for endometrial lesions are lower than cervical lesions but not insignificant. Endometrial hyperplasia should be recognized as a differential of human papilloma virus-negative ASCUS and prompt consideration of investigation of the upper genital tract.

Deep learning-based methods for classification of microsatellite instability in endometrial cancer from HE-stained pathological images

BACKGROUND

Microsatellite instability (MSI) is one of the essential tumor biomarkers for cancer treatment and prognosis. The presence of more significant PD-L1 expression on the surface of tumor cells in endometrial cancer with MSI suggests that MSI may be a promising biomarker for anti-PD-1/PD-L1 immunotherapy. However, the conventional testing methods are labor-intensive and expensive for patients.

METHODS

Inspired by classifiers for MSI based on fast and low-cost deep-learning methods in previous investigations, a new architecture for MSI classification based on an attention module is proposed to extract features from pathological images. Especially, slide-level microsatellite status will be obtained by the bag of words method to aggregate probabilities predicted by the proposed model. The H&E-stained whole slide images (WSIs) from The Cancer Genome Atlas endometrial cohort are collected as the dataset. The performances of the proposed model were primarily evaluated by the area under the receiver-operating characteristic curve, accuracy, sensitivity, and F1-Score.

RESULTS

On the randomly divided test dataset, the proposed model achieved an accuracy of 0.80, a sensitivity of 0.857, a F1-Score of 0.826, and an AUROC of 0.799. We then visualize the results of the microsatellite status classification to capture more specific morphological features, helping pathologists better understand how deep learning performs the classification.

CONCLUSIONS

This study implements the prediction of microsatellite status in endometrial cancer cases using deep-learning methods directly from H&E-stained WSIs. The proposed architecture can help the model capture more valuable features for classification. In contrast to current laboratory testing methods, the proposed model creates a more convenient screening tool for rapid automated testing for patients. This method can potentially be a clinical method for detecting the microsatellite status of endometrial cancer.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

The Role of Statins in the Prevention of Ovarian and Endometrial Cancers

Ovarian and endometrial cancers are the most common gynecologic malignancies and emerging evidence suggests that lipid metabolism and subsequent inflammation are important etiologic factors for both tumors. Statins (HMG-CoA reductase inhibitors) are the most widely prescribed lipid-lowering drugs in the United States and are used by 25% of adults aged 40+ years. In addition to their cardio-protective actions, statins have anti-inflammatory effects and have demonstrated antiproliferative and apoptotic properties in cancer cell lines, supporting a potential role in cancer prevention. To appropriately quantify potential public health impact of statin use for cancer prevention, there is a great need to understand the potential risk reduction among individuals at a higher risk of gynecologic cancers, the group that will likely need to be targeted to effectively balance risk/benefit of medications repurposed for cancer prevention. In this commentary, we focus on summarizing emerging evidence suggesting that the anti-inflammatory and lipid-lowering mechanisms of statins may provide important cancer-preventive benefits for gynecologic cancers as well as outline important unanswered questions and future research directions.

Minimally invasive approaches for the early detection of endometrial cancer

Endometrial cancer (EC) is one of the most common gynecologic cancers and its incidence is rising globally. Although advanced EC has a poor prognosis; diagnosing EC at an earlier stage could improve long-term patient outcomes. However, there is no consensus on the early detection strategies for EC and the current diagnostic practices such as transvaginal ultrasound, hysteroscopy and endometrial biopsy are invasive, costly and low in specificity. Thus, accurate and less invasive screening tests that detect EC in women with early stages of the disease are needed. Current research has revolutionized novel EC early detection methodologies in many aspects. This review aims to comprehensively characterizes minimally invasive screening techniques that can be applied to EC in the future, and fully demonstrate their potential in the early detection of EC.

LIM1 contributes to the malignant potential of endometrial cancer

Introduction

The incidence of endometrial cancer (EC) has been increasing worldwide. However, because there are limited chemotherapeutic options for the treatment of EC, the prognosis of advanced-stage EC is poor.

Methods

Gene expression profile datasets for EC cases registered in The Cancer Genome Atlas (TCGA) was reanalyzed. Highly expressed genes in advanced-stage EC (110 cases) compared with early-stage EC (255 cases) were extracted and Gene Ontology (GO) enrichment analysis was performed. Among the enriched genes, Kaplan-Meier (KM) plotter analysis was performed. Candidate genes expression was analyzed in HEC50B cells and Ishikawa cells by RT-qPCR. In HEC50B cells, LIM homeobox1 (LIM1) was knocked down (KD) and cell proliferation, migration, and invasion ability of the cells were evaluated. Xenografts were generated using LIM1-KD cells and tumor growth was evaluated. Ingenuity Pathway Analysis (IPA) of RNA-seq data using LIM-KD cells was performed. Expression of phospho-CREB and CREB-related proteins were evaluated in LIM1-KD cells by western blotting and in xenograft tissue by immunofluorescent staining. Two different CREB inhibitors were treated in HEC50B and cell proliferation was evaluated by MTT assay.

Results

Reanalysis of TCGA followed by GO enrichment analysis revealed that homeobox genes were highly expressed in advanced-stage EC. Among the identified genes, KM plotter analysis showed that high LIM1 expression was associated with a significantly poorer prognosis in EC. Additionally, LIM1 expression was significantly higher in high-grade EC cell lines, HEC50B cells than Ishikawa cells. Knockdown of LIM1 showed reduced cell proliferation, migration and invasion in HEC50B cells. Xenograft experiments revealed that tumor growth was significantly suppressed in LIM1-KD cells. IPA of RNA-seq data using LIM-KD cells predicted that the mRNA expression of CREB signaling-related genes was suppressed. Indeed, phosphorylation of CREB was decreased in LIM1-KD cells and LIM1-KD cells derived tumors. HEC50B cells treated by CREB inhibitors showed suppression of cell proliferation.

Conclusion and discussion

Collectively, these results suggested that high LIM1 expression contributed to tumor growth via CREB signaling in EC. Inhibition of LIM1 or its downstream molecules would be new therapeutic strategies for EC.

Endometrial Cancer Arising in Adenomyosis (EC-AIA): A Systematic Review

Endometrial cancer arising in adenomyosis (EC-AIA) is a rare uterine disease characterized by the malignant transformation of the ectopic endometrium within the adenomyotic foci. Clinicopathological and survival data are mostly limited to case reports and a few cohort studies. We aimed to assess the clinicopathological features and survival outcomes of women with EC-AIA through a systematic review of the literature. Six electronic databases were searched, from 2002 to 2022, for all peer-reviewed studies that reported EC-AIA cases. Thirty-seven EC-AIA patients from 27 case reports and four case series were included in our study. In our analysis, EC-AIA appeared as a rare disease that mainly occurs in menopausal women, shares symptoms with endometrial cancer, and is challenging to diagnose preoperatively. Differently from EC, it shows a higher prevalence of the non-endometrioid histotype, advanced FIGO stages, and p53-signature, which might be responsible for its worse prognosis. Future studies are necessary, to confirm our findings and further investigate this rare condition.

Fast and reliable Sanger POLE sequencing protocol in FFPE tissues of endometrial cancer

BACKGROUND

The new classification of endometrial carcinoma (EC) requires molecular interpretation of somatic polymerase epsilon (POLE) exonuclease domain mutations. The identification of pathogenic mutations within the POLE gene defines the important subtype of ultramutated tumours ("POLE-ultramutated") with specified prognostic and predictive utility. POLE somatic mutations are present in 7-12% of ECs, usually high-grade tumours with aggressive appearance. Molecular analysis of the POLE gene can be performed using a qPCR test, the Sanger sequencing method, a next generation sequencing (NGS) panel test and also in situ hybridisation (IHC) assay. We describe our current approach of identification of POLE mutations using Sanger sequencing technology, which is still the most robust, accurate and fast technique to sequence DNA.

MATERIALS AND METHODS

We present a reliable protocol for Sanger sequencing of the entire sequence coding exonuclease domain of POLE - exons 9, 10, 11, 12, 13 and 14 (codons 268-491) with 5-10 nucleotides in exon/intron boundaries (reference sequences: NM_006231.4, NP_006222.2).

RESULT

The protocol has been optimized for formalin-fixed, paraffin-embedded (FFPE) EC tissues.

CONCLUSION

The method developed in our laboratory allows better diagnosis of patients with EC according to current standards.

Cervical Fluids Are a Source of Protein Biomarkers for Early, Non-Invasive Endometrial Cancer Diagnosis

BACKGROUND

Abnormal uterine bleeding is the main symptom of endometrial cancer (EC), but it is highly nonspecific. This represents a huge burden for women's health since all women presenting with bleeding will undergo sequential invasive tests, which are avoidable for 90-95% of those women who do not have EC.

METHODS

This study aimed to evaluate the potential of cervical samples collected with five different devices as a source of protein biomarkers to diagnose EC. We evaluated the protein quantity and the proteome composition of five cervical sampling methods.

RESULTS

Samples collected with a Rovers Cervex Brush^® and the HC2 DNA collection device, Digene, were the most suitable samples for EC proteomic studies. Most proteins found in uterine fluids were also detected in both cervical samples. We then conducted a clinical retrospective study to assess the expression of 52 EC-related proteins in 41 patients (22 EC; 19 non-EC), using targeted proteomics. We identified SERPINH1, VIM, TAGLN, PPIA, CSE1L, and CTNNB1 as potential protein biomarkers to discriminate between EC and symptomatic non-EC women with abnormal uterine bleeding in cervical fluids (AUC > 0.8).

CONCLUSIONS

This study opens an avenue for developing non-invasive protein-based EC diagnostic tests, which will improve the standard of care for gynecological patients.

Accuracy of cytological examination of Tao brush endometrial sampling in diagnosing endometrial premalignancy and malignancy

Although Tao brush has become one of the most studied and used endometrial cytological samplers, concerns remain about the adequacy of the cytological sample compared with definitive histology. We aimed to assess accuracy of cytological examination from Tao brush sampling in diagnosing endometrial premalignancy and malignancy through a systematic review and meta-analysis. Seven electronic databases were searched from January 2000 to July 2021 for all studies which allowed assessment of accuracy of Tao brush in diagnosing endometrial premalignancy and malignancy. We calculated sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratio (DOR) and area under the curve (AUC) on summary receiver operating characteristic (SROC) curve. Five studies with 774 patients were included. In diagnosing endometrial premalignancy and malignancy, cytological examination from Tao brush endometrial sampling showed pooled sensitivity of 0.95 (95% CI, 0.90-0.98), specificity of 0.92 (95% CI, 0.90-0.94), LR+ of 12.73 (95% CI, 3.94-41.18), LR- of 0.09 (95% CI, 0.05-0.18), DOR of 184.84 (95% CI, 24.37-1401.79), AUC of 0.9757 (standard error: 0.013). In conclusion, cytological examination from Tao brush seems to have a high diagnostic accuracy and might be proposed as both screening and diagnostic tool. However, further studies are necessary to confirm these findings.

tRF-20-S998LO9D inhibits endometrial carcinoma by upregulating SESN2

Aim: To explore the roles of transfer RNA-derived small RNAs (tsRNAs) in endometrial carcinoma (EC). Materials & methods: tsRNA profiles for EC from TCGA were analyzed. The functions and mechanisms of tsRNA were explored using in vitro experiments. Results: 173 dysregulated tsRNAs were identified. After validating in EC tissues and serumal exosome samples from EC patients, a downregulated tsRNA in both EC tissues and serumal exosomes (i.e., tRF-20-S998LO9D) was observed. Exosomal tRF-20-S998LO9D had an area under the curve of 0.768. tRF-20-S998LO9D overexpression inhibited proliferation, migration and invasion and promoted apoptosis of EC cells and tRF-20-S998LO9D knockdown further confirmed its effects. Further analyses showed that tRF-20-S998LO9D upregulated SESN2 in protein levels. Conclusion: tRF-20-S998LO9D inhibits EC cells by upregulating SESN2.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery

Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models’ classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.

Improving the Management of Endometrial Cancer Patients through the Use of Liquid Biopsy Analyses: A Case Report

Endometrial cancer (EC) is the 4th most common neoplasm of the female genital tract, with 15–20% of patients being of high risk of recurrence which leads to a significant decrease in patient survival. Current therapeutic options for patients with EC are poor, being the combined therapy of carboplatin and paclitaxel the standard of care, with limited efficacy. Therefore, new therapeutic options and better monitoring tools are needed to improve the management of the disease. In the current case report, we showcase the value of liquid biopsy analyses in a microsatellite instability EC patient with initially good prognosis that however underwent rapid progression disease within 6 months post-surgery; through the study of plasma cfDNA/ctDNA dynamics to assess the tumour evolution during treatment, as well as the study of the uterine aspirate as a valuable sample that captures the intra-tumour heterogeneity that allows a comprehensive genomic profiling of the disease to identify potential therapeutic options. Furthermore, preclinical models were generated at the time of tumour progression to assess the efficacy of the identified targeted therapies.

ERRγ, a Novel Biomarker, Associates with Pathoglycemia of Endometrial Cancer to Predict Myometrial Invasion

We aim to investigate the correlation between the expression of estrogen-related receptor γ (ERRγ) and endometrial cancer (EC) progression and to evaluate the potential of ERRγ as a new biomarker for EC diagnosis. We analyzed the ERRγ expression profile and the correlation with the corresponding clinical characteristics of EC samples from The Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, and the International Cancer Genome Consortium (ICGC) databases. Immunohistochemical (IHC) analysis was conducted on tissue samples, and enzyme linked immunosorbent assay (ELISA) was used in serum samples to detect the levels of ERRγ. The diagnostic performance of ERRγ proteins was assessed using the receiver operating characteristic (ROC). ERRγ showed notably higher expression in EC tissues than in normal endometrium tissues (P < 0.001), which was consistent with the result of TCGA. Overexpression of ERRγ was significantly associated with deep myometrial invasion of EC (P=0.004), and fasting blood glucose (FBG) was higher in EC patients with deep myometrial invasion than in those with superficial myometrial invasion (P=0.040). Further analysis using ELISA showed that the serum ERRγ level was positively correlated with FBG (R = 0.355, P < 0.001). ERRγ is overexpressed in EC and may be involved in regulating glucose metabolism and promoting myometrial invasion of EC. In addition, the area under the ROC curve (AUC) for ERRγ was 0.834, in distinguishing EC patients from healthy individuals, presented 84.0% and 80.0% sensitivity and specificity, respectively, and serum ERRγ has a good diagnostic performance in distinguishing EC patients from healthy people and may be a promising noninvasive biomarker in EC.

Metabolomic Analysis Points to Bioactive Lipid Species and Acireductone Dioxygenase 1 (ADI1) as Potential Therapeutic Targets in Poor Prognosis Endometrial Cancer

Simple Summary

Uterine serous carcinoma is considered a rare and aggressive variant of endometrial cancer that accounts for 10% of all endometrial cancers diagnosed but is responsible for 40% of endometrial cancer-related deaths. Unfortunately, current treatments for serous endometrial carcinoma are ineffective. Therefore, there is a need to find new therapeutic targets. The aim of this study was to analyse the metabolic profile of serous cancer in order to identify new molecules and thereby define potential therapeutic targets. We observed that most of the differential metabolites are lipid species (suggesting the important role of the lipid metabolism). In addition, we found an increase in 2-Oxo-4-methylthiobutanoic acid (synthesised by the ADI1 enzyme) in serous carcinomas. Using public database analysis and immunohistochemistry, we established a correlation between elevated ADI1 levels and serous carcinoma. Furthermore, the ectopic modification of ADI1 expression in vitro revealed the ability of ADI1 to induce pathological cell migration and invasion capabilities.

Abstract

Metabolomic profiling analysis has the potential to highlight new molecules and cellular pathways that may serve as potential therapeutic targets for disease treatment. In this study, we used an LC-MS/MS platform to define, for the first time, the specific metabolomic signature of uterine serous carcinoma (SC), a relatively rare and aggressive variant of endometrial cancer (EC) responsible for 40% of all endometrial cancer-related deaths. A metabolomic analysis of 31 ECs (20 endometrial endometrioid carcinomas (EECs) and 11 SCs) was performed. Following multivariate statistical analysis, we identified 232 statistically different metabolites among the SC and EEC patient samples. Notably, most of the metabolites identified (89.2%) were lipid species and showed lower levels in SCs when compared to EECs. In addition to lipids, we also documented metabolites belonging to amino acids and purine nucleotides (such as 2-Oxo-4-methylthiobutanoic acid, synthesised by acireductone dioxygenase 1 (ADI1) enzyme), which showed higher levels in SCs. To further investigate the role of ADI1 in SC, we analysed the expression protein levels of ADI1 in 96 ECs (67 EECs and 29 SCs), proving that the levels of ADI1 were higher in SCs compared to EECs. We also found that ADI1 mRNA levels were higher in p53 abnormal ECs compared to p53 wild type tumours. Furthermore, elevated ADI1 mRNA levels showed a statistically significant negative correlation with overall survival and progression-free survival among EEC patients. Finally, we tested the ability of ADI1 to induce migration and invasion capabilities in EC cell lines. Altogether, these results suggest that ADI1 could be a potential therapeutic target in poor-prognosis SCs and other Ecs with abnormal p53 expression.

Metabolomic analysis of endometrial cancer by high-resolution magic angle spinning NMR spectroscopy

PURPOSE

To analyze endometrial metabolite profiles between patients with endometrial cancer and controls.

METHODS

Seventeen (17) women with endometrium cancer and 18 controls were enrolled in this study. ¹H HR-MAS (High Resolution-Magic Angle Spinning) NMR (Nuclear Magnetic Resonance) spectroscopy data obtained from endometrial tissue samples of patients with endometrial cancer and control group were analyzed with bioinformatics methods.

RESULTS

Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of pre-processed spectral data shows a separation between the samples from patients with endometrial cancer and controls. Analysis results suggest that the levels of lactate, glucose, o-phosphoethanolamine, choline, glycerophosphocholine, phosphocholine, leucine, isoleucine, valine, glutamate, glutamine, n-acetyltyrosine, methionine, taurine, alanine, aspartate and phenylalanine are increased in patients with endometrial cancer compared to the controls.

CONCLUSION

The metabolomics signature of patients with endometrial cancer is different from that of benign endometrial tissue.

Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer

Background

Endometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics.

Methods

Forty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples.

Results

By integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples.

Conclusions

With multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.

Current and Emerging Prognostic Biomarkers in Endometrial Cancer

Endometrial cancer is the most common gynaecological malignancy in high income countries and its incidence is rising. Whilst most women with endometrial cancer are diagnosed with highly curable disease and have good outcomes, a significant minority present with adverse clinico-pathological characteristics that herald a poor prognosis. Prognostic biomarkers that reliably select those at greatest risk of disease recurrence and death can guide management strategies to ensure that patients receive appropriate evidence-based and personalised care. The Cancer Genome Atlas substantially advanced our understanding of the molecular diversity of endometrial cancer and informed the development of simplified, pragmatic and cost-effective classifiers with prognostic implications and potential for clinical translation. Several blood-based biomarkers including proteins, metabolites, circulating tumour cells, circulating tumour DNA and inflammatory parameters have also shown promise for endometrial cancer risk assessment. This review provides an update on the established and emerging prognostic biomarkers in endometrial cancer.

Potential of blood-based biomarker approaches in endometrium and breast cancer: a case-control comparison study

Purpose

Endometrial carcinoma is the second most common gynecological malignancy. Until today lacking a screening tool. A blood-based biomarker could help address this need.

Methods

The expression levels of 30 acylcarnitines, 18 amino acids, 6 miRNAs, and 7 DNA methylation sites were measured in blood samples from 331 women (20 EC, 14 benign uterine lesions (benign), 140 breast cancers (BC), 157 controls). Areas under the ROC curves (AUC), sensitivity (sens.) and specificity (spec.) were computed to identify the variables best distinguishing.

Results

The best top ten markers for the four comparisons (cancer vs. cancer-free; EC vs. BC, EC vs. controls; EC vs. benign), were identified via AUC. Malonylcarnitine distinguished best patients with EC from controls (AUC: 0.827, sens. 80%, spec. 73.1%) or BC (AUC: 0.819, sens. 84.3%, spec. 80%) being most notable. Tryptophan best differentiated benign from EC (AUC: 0.846, sens. 70%, spec. 92.9%).

Conclusions

The levels of the analyzed blood markers yielded promising results in the detection of EC and warrant further evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00404-022-06482-8.

The Diagnostic value of Serum YKL-40 in Endometrial Cancer: a meta-analysis

BACKGROUND

Serum YKL-40 is a promising noninvasive biomarker for the early diagnosis of endometrial cancer (EC), but its value is disputed.

OBJECTIVE

To investigate the serum YKL-40 in the early diagnostic value of EC.

METHODS

Databases were systematically searched again before April 2021 and 14 studies were finally included in this meta-analysis. Pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve analyses were assessed. This meta-analysis investigated the source of heterogeneity using sensitivity analysis, subgroup analysis, and meta-regression.

RESULTS

Databases were systematically searched again before April 2021 and 14 studies were finally included in this meta-analysis. First, the SROC curve presented an area under the curve (AUC) of 0.853 (SE =0.0213) for YKL-40 alone and an AUC of 0.946 (SE =0.0268) for YKL-40 combined with other biomarkers. Second, diagnostic types might be related to the diagnostic accuracy and is a significant source of heterogeneity (P = 0.035).

CONCLUSION

Serum YKL-40 helped diagnose EC, and its combination with other biomarkers was better than itself alone.

New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers

The prevention and early diagnostics of precancerous stages are key aspects of contemporary oncology. In cervical cancer, well-organized screening and vaccination programs, especially in developed countries, are responsible for the dramatic decline of invasive cancer incidence and mortality. Cytological screening has a long and successful history, and the ongoing implementation of HPV triage with increased sensitivity can further decrease mortality. On the other hand, endometrial and ovarian cancers are characterized by a poor accessibility to specimen collection, which represents a major complication for early diagnostics. Therefore, despite relatively promising data from evaluating the combined effects of genetic variants, population screening does not exist, and the implementation of new biomarkers is, thus, necessary. The introduction of various circulating biomarkers is of potential interest due to the considerable heterogeneity of cancer, as highlighted in this review, which focuses exclusively on the most common tumors of the genital tract, namely, cervical, endometrial, and ovarian cancers. However, it is clearly shown that these malignancies represent different entities that evolve in different ways, and it is therefore necessary to use different methods for their diagnosis and treatment.

Clinical Characteristics of Patients with Endometrial Cancer and Adenomyosis

Simple Summary

Endometrial cancer (EC) reportedly have a better prognosis in patients with coexistent adenomyosis. It is possible to hypothesize that this difference may be attributable to different clinical profiles. On this account, we aimed to define the clinical profile of endometrial cancer (EC) patients with and without adenomyosis through a systematic review and meta-analysis. We included 8 studies with 5681 patients and found that EC women with adenomyosis were less likely to be nulliparous than EC women without adenomyosis, while no significant differences were found with regard to age, BMI, and premenopausal status.

Abstract

A better endometrial cancer (EC) prognosis in patients with coexistent adenomyosis has been reported. Unfortunately, it is still unclear if this better prognosis is related to a more favorable clinical profile of adenomyosis patients. We aimed to evaluate differences in the clinical profiles of EC patients with and without adenomyosis. A systematic review and meta-analysis was performed by searching seven electronics databases for all studies that allowed extraction of data about clinical characteristics in EC patients with and without adenomyosis. Clinical characteristics assessed were: age, Body Mass Index (BMI), premenopausal status, and nulliparity. Mean difference in mean ± standard deviation (SD) or odds ratio (OR) for clinical characteristics between EC patients with and without adenomyosis were calculated for each included study and as a pooled estimate, and graphically reported on forest plots with a 95% confidence interval (CI). The Z test was used for assessing the overall effect by considering a p value < 0.05 as significant. Overall, eight studies with 5681 patients were included in the qualitative analysis, and seven studies with 4366 patients in the quantitative analysis. Pooled mean difference in mean ± SD between EC women with and without adenomyosis was −1.19 (95% CI: −3.18 to 0.80; p = 0.24) for age, and 0.23 (95% CI: −0.62 to 1.07; p = 0.60) for BMI. When compared to EC women without adenomyosis, EC women with adenomyosis showed a pooled OR of 1.53 (95% CI: 0.92 to 2.54; p = 0.10) for premenopausal status, and of 0.60 (95% CI: 0.41 to 0.87; p = 0.007) for nulliparity. In conclusion, there are not significant differences in clinical characteristics between EC patients with and without adenomyosis, with the exception for nulliparity. Clinical features seem to not underlie the better EC prognosis of patients with adenomyosis compared to patients without adenomyosis.

Impact of adenomyosis on the prognosis of patients with endometrial cancer

Background

Despite the high prevalence of adenomyosis in hysterectomy specimens of endometrial carcinoma (EC) patients, the relationship between adenomyosis and EC prognosis appears unclear.

Objective

To assess the prognostic value of coexistent adenomyosis in patients with EC.

Methods

A systematic review and meta‐analysis was performed by searching six electronic databases for studies reporting data on prognosis of EC patients with and without coexistent adenomyosis. Studies with patient selection based on prognostic factors were excluded. Pooled univariate hazard ratio (HR) analyses for overall survival (OS) and disease‐free survival (DRF) were performed, using EC patients without adenomyosis as a control group. For DFS, pooled multivariate HR analysis was also evaluable.

Results

Three studies of 2505 EC patients (553 with and 1952 without adenomyosis) were included. Compared with EC patients without adenomyosis, EC patients with coexistent adenomyosis showed a pooled HR of 0.533 (CI 95%, 0.329–0.864) for OS at univariate analysis; 0.536 (CI 95%, 0.334–0.859) for DFS at univariate analysis; and 0.875 (CI 95%, 0.331–2.315) for DFS at multivariate analysis.

Conclusion

In EC patients with coexistent adenomyosis, the risk of death is halved compared with EC patients without adenomyosis. However, the independence of this association needs to be verified in future studies.

The risk of death is halved in EC patients with coexistent adenomyosis compared with EC patients without adenomyosis, whereas the risk of EC recurrence does not appear to be affected.

The Clinical Significance of DJ1 and L1CAM Serum Level Monitoring in Patients with Endometrial Cancer

Circulating tumor markers are not routinely used in patients with endometrial cancer (EC). This pilot study evaluated the role of monitoring new biomarkers DJ1 and L1CAM, in correlation with CA125 and HE4, for the effects of anticancer treatment and preoperative management in EC patients. Serial serum levels of DJ1, L1CAM, CA125 and HE4 were collected in 65 enrolled patients. Serum DJ1, L1CAM, CA125 and HE4 levels were significantly higher at the time of diagnosis compared to those measured during follow-up (FU). In patients with recurrent disease, serum DJ1, CA125 and HE4 levels were significantly higher at the time of recurrence compared to levels in disease-free patients. Serum L1CAM levels were also higher in patients with recurrence but without reaching statistical significance. While DJ1 levels were not affected by any of the observed patient-related characteristics, L1CAM levels were significantly higher in patients with age ≥60 years who were overweight. At the time of EC diagnosis, DJ1 and L1CAM serum levels did not correlate with stage, histological type or risk of recurrence. This is a preliminary description of the potential of serial DJ1 and L1CAM serum level measurement for monitoring the effects of treatment in EC patients.

Laparotomic versus robotic surgery in elderly patients with endometrial cancer: A systematic review and meta-analysis

Background

Although robotics has been shown to improve outcomes in some high‐difficulty surgical category patients, it is unclear if such an approach may improve outcomes in elderly patients with endometrial carcinoma (EC).

Objective

To compare robotic and laparotomic surgery in the treatment and staging of elderly EC patients.

Materials and methods

A systematic review and meta‐analysis was performed assessing the risk of overall, intra‐operative, and peri‐operative complications associated with the surgical approach (laparotomic vs robotic) for elderly patients with EC by relative risk (RR). Pooled means ± standard deviation of length of stay were compared with the unpaired t test. Subgroup analyses for overall complications were performed based on different age cut‐offs (>70, >65, and >75 years) and severity of complications (minor and major). A value of P less than 0.05 was considered significant.

Results

Five studies with 7629 EC patients were included. Pooled RR for robotic compared with laparotomic surgery was 0.40 (P < 0.001) for overall, 0.46 (P = 0.18) for intra‐operative, and 0.43 (P < 0.001) for peri‐operative complications. Pooled difference between means ± standard deviation of length of stay for robotic versus laparotomic surgery was −3.34 (P < 0.001). At subgroup analyses, pooled RR of overall complications for robotic surgery versus laparotomic surgery was 0.34 (P < 0.001) in the >70 years, 0.51 (P < 0.01) in the >65 years, 0.20 (P = 0.12) in the >75 years groups. Pooled RR was 0.50 (P = 0.1) in the minor complications subgroup, and 0.42 (P = 0.002) in the major complications subgroup.

Conclusion

Robotics might be a viable alternative to the laparotomic approach for EC in elderly patients because it significantly decreases the risk of overall and peri‐operative complications (mainly major complications), and the length of stay when compared with laparotomy. The decrease in risk of overall complications is greater with increasing patient age.

Metabolic signatures of greater body size and their associations with risk of colorectal and endometrial cancers in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

The mechanisms underlying the obesity-cancer relationship are incompletely understood. This study aimed to characterise metabolic signatures of greater body size and to investigate their association with two obesity-related malignancies, endometrial and colorectal cancers, and with weight loss within the context of an intervention study.

METHODS

Targeted mass spectrometry metabolomics data from 4326 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 17 individuals from a single-arm pilot weight loss intervention (Intercept) were used in this analysis. Metabolic signatures of body size were first determined in discovery (N = 3029) and replication (N = 1297) sets among EPIC participants by testing the associations between 129 metabolites and body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) using linear regression models followed by partial least squares analyses. Conditional logistic regression models assessed the associations between the metabolic signatures with endometrial (N = 635 cases and 648 controls) and colorectal (N = 423 cases and 423 controls) cancer risk using nested case-control studies in EPIC. Pearson correlation between changes in the metabolic signatures and weight loss was tested among Intercept participants.

RESULTS

After adjustment for multiple comparisons, greater BMI, WC, and WHR were associated with higher levels of valine, isoleucine, glutamate, PC aa C38:3, and PC aa C38:4 and with lower levels of asparagine, glutamine, glycine, serine, lysoPC C17:0, lysoPC C18:1, lysoPC C18:2, PC aa C42:0, PC ae C34:3, PC ae C40:5, and PC ae C42:5. The metabolic signature of BMI (OR1-sd 1.50, 95% CI 1.30-1.74), WC (OR1-sd 1.46, 95% CI 1.27-1.69), and WHR (OR1-sd 1.54, 95% CI 1.33-1.79) were each associated with endometrial cancer risk. Risk of colorectal cancer was positively associated with the metabolic signature of WHR (OR1-sd: 1.26, 95% CI 1.07-1.49). In the Intercept study, a positive correlation was observed between weight loss and changes in the metabolic signatures of BMI (r = 0.5, 95% CI 0.06-0.94, p = 0.03), WC (r = 0.5, 95% CI 0.05-0.94, p = 0.03), and WHR (r = 0.6, 95% CI 0.32-0.87, p = 0.01).

CONCLUSIONS

Obesity is associated with a distinct metabolic signature comprising changes in levels of specific amino acids and lipids which is positively associated with both colorectal and endometrial cancer and is potentially reversible following weight loss.

Identification of novel diagnostic biomarkers in endometrial cancer using targeted metabolomic profiling

PURPOSE

Endometrial cancer (EC) is the most common gynecological malignancy with high disease burden especially in advanced stages of the disease. Our study investigated the metabolomic profile of EC patient's serum with the aim of identifying novel diagnostic biomarkers that could be used especially in early disease detection.

MATERIAL AND METHODS

Using targeted metabolomic serum profiling based on HPLC-TQ/MS, women with EC (n ​= ​15) and controls (n ​= ​21) were examined for 232 endogenous metabolites.

RESULTS

Top performing biomarkers included ceramides, acylcarnitines and 1-methyl adenosine. Top 4 biomarkers combined achieved 94% sensitivity with 75% specificity with AUC 92.5% (CI 90.5-94.5%). Individual markers also provided significant predictive values: C16-ceramide achieved sensitivity 73%, specificity 81%, AUC 0.83, C22-ceramide sensitivity 67%, specificity 81%, AUC 0.77, hydroxyhexadecenoylcarnitine sensitivity 60%, specificity 96%, AUC 0.76 and 1-methyladenosine sensitivity 67%, specificity 81%, AUC 0.75. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.

CONCLUSIONS

Using mass spectrometry targeted metabolomic profiling, ceramides, acylcarnitines and 1-methyladenosine were identified as potential diagnostic biomarkers for EC. Additionally, these identified metabolites may provide additional insight into cancer cell metabolism.

Metabolomic Biomarkers for the Detection of Obesity-Driven Endometrial Cancer

Simple Summary

Endometrial cancer is the commonest cancer of the female genital tract and obesity is its main modifiable risk factor. Over 80% of endometrial cancers develop in the context of obesity-induced metabolic changes. This study focuses on the potential of plasma-based metabolites to enable the early detection of endometrial cancer in a cohort of women with body mass index (BMI) ≥ 30 kg/m². Specific lipid metabolites including phospholipids and sphingolipids (sphingomyelins) demonstrated good accuracy for the detection of endometrial cancer, especially when combined in a diagnostic model. This study advances our knowledge of the role of metabolomics in endometrial cancer and provides a basis for the minimally invasive screening of women with elevated BMI.

Abstract

Endometrial cancer is the most common malignancy of the female genital tract and a major cause of morbidity and mortality in women. Early detection is key to ensuring good outcomes but a lack of minimally invasive screening tools is a significant barrier. Most endometrial cancers are obesity-driven and develop in the context of severe metabolomic dysfunction. Blood-derived metabolites may therefore provide clinically relevant biomarkers for endometrial cancer detection. In this study, we analysed plasma samples of women with body mass index (BMI) ≥ 30 kg/m² and endometrioid endometrial cancer (cases, n = 67) or histologically normal endometrium (controls, n = 69), using a mass spectrometry-based metabolomics approach. Eighty percent of the samples were randomly selected to serve as a training set and the remaining 20% were used to qualify test performance. Robust predictive models (AUC > 0.9) for endometrial cancer detection based on artificial intelligence algorithms were developed and validated. Phospholipids were of significance as biomarkers of endometrial cancer, with sphingolipids (sphingomyelins) discriminatory in post-menopausal women. An algorithm combining the top ten performing metabolites showed 92.6% prediction accuracy (AUC of 0.95) for endometrial cancer detection. These results suggest that a simple blood test could enable the early detection of endometrial cancer and provide the basis for a minimally invasive screening tool for women with a BMI ≥ 30 kg/m².

Metabolomics in Radiation Biodosimetry: Current Approaches and Advances

Triage and medical intervention strategies for unanticipated exposure during a radiation incident benefit from the early, rapid and accurate assessment of dose level. Radiation exposure results in complex and persistent molecular and cellular responses that ultimately alter the levels of many biological markers, including the metabolomic phenotype. Metabolomics is an emerging field that promises the determination of radiation exposure by the qualitative and quantitative measurements of small molecules in a biological sample. This review highlights the current role of metabolomics in assessing radiation injury, as well as considerations for the diverse range of bioanalytical and sampling technologies that are being used to detect these changes. The authors also address the influence of the physiological status of an individual, the animal models studied, the technology and analysis employed in interrogating response to the radiation insult, and variables that factor into discovery and development of robust biomarker signatures. Furthermore, available databases for these studies have been reviewed, and existing regulatory guidance for metabolomics are discussed, with the ultimate goal of providing both context for this area of radiation research and the consideration of pathways for continued development.

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.