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

Cervical mucus can be used for metabolite screening in cervical cancer

Approximately 660,000 women are diagnosed with cervical cancer annually. Current screening options such as cytology or human papillomavirus testing have limitations, creating a need to identify more effective ancillary biomarkers for triage. Here, we evaluated whether metabolomic analysis of cervical mucus metabolism could be used to identify biomarkers of cervical intraepithelial neoplasia (CIN) and cervical cancer. The case-control group consisted of 181 CIN, 69 squamous cell carcinoma (SCC) patients, and 48 healthy controls in the primary cohort. We undertook metabolomic analyses using ultra-HPLC-tandem mass spectrometry. Univariate and multivariate analyses were carried out to profile metabolite characteristics, and receiver operating characteristic (ROC) analysis identified biomarker candidates. Five metabolites conferred the highest discriminatory power for SCC: oxidized glutathione (GSSG) (area under the ROC curve, 0.924; 95% confidence interval, 0.877-0.971), malic acid (0.914, 0.859-0.968), kynurenine (0.884, 0.823-0.945), GSSG/glutathione (GSH) (0.936, 0.892-0.979), and kynurenine/tryptophan (0.909, 0.856-0.961). Malic acid was the best marker for detection of CIN2 or worse (0.858, 0.793-0.922) and was a clinically useful metabolite. We confirmed the reproducibility of the results by validation cohort. Additionally, metabolomic analyses revealed eight pathways strongly associated with cervical neoplasia. Of these, only the tricarboxylic acid cycle was strongly associated with all CINs and cancer, indicating active energy production. Aberrant arginine metabolism by decreasing arginine and increasing citrulline might reduce tumor immunity. Changes in cysteine-methionine and GSH pathways might drive the initiation and progression of cervical cancer. These results suggest that metabolic analysis can identify ancillary biomarkers and could improve our understanding of the pathophysiological mechanisms underlying cervical neoplasia.

The effect of lipidomes on the risk of endometrioid endometrial cancer: a Mendelian randomization study

Objective

This study aimed to explore the potential effects between various human plasma lipidomes and endometrioid endometrial cancer (EEC) by using Mendelian randomization (MR) methods.

Methods

This study designated a total of 179 human plasma lipidomes from the genome-wide association study (GWAS) database as the exposure variable. An EEC-related dataset from the GWAS (GCST006465) served as the outcome variable. MR analyses used the inverse variance-weighted method (IVW), MR-Egger, weighted median, simple mode, and weighted mode methods for regression calculations, accounting for possible biases induced by linkage disequilibrium and weak instrument variables. Any lipidomes failing to pass heterogeneity and horizontal pleiotropy tests were deemed to lack significant causal impact on the outcome.

Results

The results of IVW analysis disclosed that a variety of human plasma lipidomes (n = 15) exhibited a significant causal effect on EEC (p < 0.05). A subset of these lipidomes (n = 13) passed heterogeneity and horizontal pleiotropy tests, which demonstrated consistent and viable causal effects (p < 0.05) including glycerophospholipids, glycerolipids, and sterols. Specifically, phosphatidylcholine (odds ratio [OR]: 1.065-1.129, p < 0.05) exhibited a significant positive causal effect on the occurrence of EEC. Conversely, sterol ester (OR = 0.936, p = 0.007), diacylglycerol (OR = 0.914, p = 0.036), phosphatidylcholine (OR: 0.903-0.927, p < 0.05), phosphatidylethanolamine (OR = 0.907, p = 0.046) and triacylglycerol (OR: 0.880-0.924, p < 0.05) showed a notable negative causal association with EEC, suggesting their inhibitory effects on the EEC occurrence.

Conclusions

The study revealed that human plasma lipidomes have complex impacts on EEC through Mendelian randomization. This indicated that the diversity of structural changes in lipidomes could show different effects on subtypes and then affect EEC occurrence. Although these lipids had the potential to be promising biomarkers, they needed to be further clinically validated nevertheless.

Oral nano-formulations for endocrine therapy of endometrioid adenocarcinomas

Endometrial cancer is one of the three major malignant tumors of the reproductive system that threaten women's lives and health. The incidence of this disease is on the rise globally. Most cases of endometrial cancer comprise endometrioid adenocarcinomas, whose treatment is challenged by factors such as their high recurrence rate and the need to preserve fertility among young patients. Thus, oral endocrine therapy has become the main treatment modality. The main drugs used in oral endocrine therapy are progestins, selective estrogen receptor antagonists, and aromatase inhibitors. However, their clinical use is hindered by their low solubility and low oral utilization. The rapid development of nanotechnology allows the combination of these drugs with oral nano-formulations to create a good carrier. Such nanocarriers, including nanospheres, nanocapsules, and micelles can protect the drug against clearance and increase the site specificity of drug delivery. This paper reviews the pathogenesis of endometrioid endometrial cancer (EEC) and oral nano-formulations for endocrine therapy.

Multi-omic analyses identified SFRP4 as a novel biomarker in abnormal uterine bleeding with ovulatory dysfunction

The goal of the study was to explore the mechanism underlying the progression from abnormal uterine bleeding with ovulatory dysfunction (AUB-O) to AUB with atypical hyperplasia/malignancy (AUB-M). AUB-O, AUB-M and control endometrial tissues were subjected to multi-omic analyses to identify biomarkers. Differentially expressed genes (DEGs) and differentially expressed proteins (DEPs), including SFRP4, between the AUB-O and AUB-M groups were identified. The expression of SFRP4 was upregulated in endometrial tissues from AUB-O groups compared to that from AUB-M groups. SFRP4 knockdown in human endometrial epithelial cells (hEECs) promoted cell migration, invasion, proliferation and colony formation but inhibited apoptosis. Furthermore, the levels of key Wnt pathway proteins were altered by SFRP4 knockdown: Wnt-5A was downregulated and Wnt-7A was upregulated. In conclusion, we identified SFRP4 as an AUB-O-related molecule. SFRP4 might play a key role in hEECs apoptosis, migration, invasion, proliferation and colony formation via the Wnt pathway. SFRP4 may serve as a repressive factor regarding the progression of AUB-O to AUB-M. However, further studies are warranted to elucidate the exact mechanism.

Revolutionizing Gastrointestinal Disorder Management: Cutting-Edge Advances and Future Prospects

In recent years, remarkable strides have been made in the management of gastrointestinal disorders, transforming the landscape of patient care and outcomes. This article explores the latest breakthroughs in the field, encompassing innovative diagnostic techniques, personalized treatment approaches, and novel therapeutic interventions. Additionally, this article emphasizes the use of precision medicine tailored to individual genetic and microbiome profiles, and the application of artificial intelligence in disease prediction and monitoring. This review highlights the dynamic progress in managing conditions such as inflammatory bowel disease, gastroesophageal reflux disease, irritable bowel syndrome, and gastrointestinal cancers. By delving into these advancements, we offer a glimpse into the promising future of gastroenterology, where multidisciplinary collaborations and cutting-edge technologies converge to provide more effective, patient-centric solutions for individuals grappling with gastrointestinal disorders.

Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer

BACKGROUND

Among gynaecological malignancies, endometrial cancer (EC) is the most prevalent type of uterine cancer affecting women. This study explored the proteomic profiles of plasma samples obtained from EC patients, those with hyperplasia (Hy), and a control group (CO). A combination of techniques, such as 2D-DIGE, mass spectrometry, and bioinformatics, including pathway analysis, was used to identify proteins with modified expression levels, biomarkers and their associated metabolic pathways in these groups.

METHODS

Thirty-four patients, categorized into three groups-10 with EC, 12 with Hy, and 12 CO-between the ages of 46 and 75 years old were included in the study. Untargeted proteomic analysis was carried out using two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

RESULTS

In all three groups, 114 proteins that were significantly (p ≤ 0.05 and fold change ≥ 1.5) altered were successfully identified using peptide mass fingerprints (PMFs). Compared with those in the control group (CO), the EC samples had 85 differentially expressed proteins (39 upregulated and 46 downregulated), and in the Hy group, 81 proteins were dysregulated (40 upregulated and 41 downregulated) compared to those in the CO group, while 33 proteins exhibited differential regulation (12 upregulated and 21 downregulated) in the EC plasma samples compared to those in the Hy group. Vitamin D binding protein and complement C3 distinguished Hy and EC from CO with the greatest changes in expression. Among the differentially expressed proteins identified, enzymes with catalytic activity represented the largest group (42.9%). In terms of biological processes, most of the proteins were involved in cellular processes (28.8%), followed by metabolic processes (16.7%). STRING analysis for protein interactions revealed that the significantly differentially abundant proteins in the three groups are involved in three main biological processes: signalling of complement and coagulation cascades, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), and plasma lipoprotein assembly, remodelling, and clearance.

CONCLUSION

The identified plasma protein markers have the potential to serve as biomarkers for differentiating between EC and Hy, as well as for early diagnosis and monitoring of cancer progression.

Palmitic Acid Exerts Anti-Tumorigenic Activities by Modulating Cellular Stress and Lipid Droplet Formation in Endometrial Cancer

Epidemiological and clinical evidence have extensively documented the role of obesity in the development of endometrial cancer. However, the effect of fatty acids on cell growth in endometrial cancer has not been widely studied. Here, we reported that palmitic acid significantly inhibited cell proliferation of endometrial cancer cells and primary cultures of endometrial cancer and reduced tumor growth in a transgenic mouse model of endometrial cancer, in parallel with increased cellular stress and apoptosis and decreased cellular adhesion and invasion. Inhibition of cellular stress by N-acetyl-L-cysteine effectively reversed the effects of palmitic acid on cell proliferation, apoptosis, and invasive capacity in endometrial cancer cells. Palmitic acid increased the intracellular formation of lipid droplets in a time- and dose-dependent manner. Depletion of lipid droplets by blocking DGAT1 and DGAT2 effectively increased the ability of palmitic acid to inhibit cell proliferation and induce cleaved caspase 3 activity. Collectively, this study provides new insight into the effect of palmitic acid on cell proliferation and invasion and the formation of lipid droplets that may have potential clinical relevance in the treatment of obesity-driven endometrial cancer.

Endogenous estrogen metabolites as oxidative stress mediators and endometrial cancer biomarkers

BACKGROUND

Endometrial cancer is the most common gynecologic malignancy found in developed countries. Because therapy can be curative at first, early detection and diagnosis are crucial for successful treatment. Early diagnosis allows patients to avoid radical therapies and offers conservative management options. There are currently no proven biomarkers that predict the risk of disease occurrence, enable early identification or support prognostic evaluation. Consequently, there is increasing interest in discovering sensitive and specific biomarkers for the detection of endometrial cancer using noninvasive approaches.

CONTENT

Hormonal imbalance caused by unopposed estrogen affects the expression of genes involved in cell proliferation and apoptosis, which can lead to uncontrolled cell growth and carcinogenesis. In addition, due to their ability to cause oxidative stress, estradiol metabolites have both carcinogenic and anticarcinogenic properties. Catechol estrogens are converted to reactive quinones, resulting in oxidative DNA damage that can initiate the carcinogenic process. The molecular anticancer mechanisms are still not fully understood, but it has been established that some estradiol metabolites generate reactive oxygen species and reactive nitrogen species, resulting in nitro-oxidative stress that causes cancer cell cycle arrest or cell death. Therefore, identifying biomarkers that reflect this hormonal imbalance and the presence of endometrial cancer in minimally invasive or noninvasive samples such as blood or urine could significantly improve early detection and treatment outcomes.

Detection of endometrial cancer in cervico-vaginal fluid and blood plasma: leveraging proteomics and machine learning for biomarker discovery

BACKGROUND

The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from controls in cervico-vaginal fluid and blood plasma.

METHODS

Blood plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from symptomatic post-menopausal women with (n = 53) and without (n = 65) endometrial cancer. Digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins. The best diagnostic model was determined based on accuracy and model parsimony.

FINDINGS

A protein signature derived from cervico-vaginal fluid more accurately discriminated cancer from control samples than one derived from plasma. A 5-biomarker panel of cervico-vaginal fluid derived proteins (HPT, LG3BP, FGA, LY6D and IGHM) predicted endometrial cancer with an AUC of 0.95 (0.91-0.98), sensitivity of 91% (83%-98%), and specificity of 86% (78%-95%). By contrast, a 3-marker panel of plasma proteins (APOD, PSMA7 and HPT) predicted endometrial cancer with an AUC of 0.87 (0.81-0.93), sensitivity of 75% (64%-86%), and specificity of 84% (75%-93%). The parsimonious model AUC values for detection of stage I endometrial cancer in cervico-vaginal fluid and blood plasma were 0.92 (0.87-0.97) and 0.88 (0.82-0.95) respectively.

INTERPRETATION

Here, we leveraged the natural shed of endometrial tumours to potentially develop an innovative approach to endometrial cancer detection. We show proof of principle that endometrial cancers secrete unique protein signatures that can enable cancer detection via cervico-vaginal fluid assays. Confirmation in a larger independent cohort is warranted.

FUNDING

Cancer Research UK, Blood Cancer UK, National Institute for Health Research.

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.

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.

Identification of Potentially Novel Molecular Targets of Endometrial Cancer Using a Non-Biased Proteomic Approach

The present study was aimed at identifying novel proteins in endometrial cancer (EC), employing proteomic analysis of tissues obtained after surgery. A differential MS-based proteomic analysis was conducted from whole tissues dissected from biopsies from post-menopausal women, histologically confirmed as endometrial cancer (two endometrioid and two serous; n = 4) or normal atrophic endometrium (n = 4), providing 888 differentially expressed proteins with 246 of these previously documented elsewhere as expressed in EC and 372 proteins not previously demonstrated to be expressed in EC but associated with other types of cancer. Additionally, 33 proteins not recorded previously in PubMed as being expressed in any forms of cancer were also identified, with only 26 of these proteins having a publication associated with their expression patterns or putative functions. The putative functions of the 26 proteins (GRN, APP, HEXA, CST3, CAD, QARS, SIAE, WARS, MYH8, CLTB, GOLIM4, SCARB2, BOD1L1, C14orf142, C9orf142, CCDC13, CNPY4, FAM169A, HN1L, PIGT, PLCL1, PMFBP1, SARS2, SCPEP1, SLC25A24 and ZC3H4) in other tissues point towards and provide a basis for further investigation of these previously unrecognised novel EC proteins. The developmental biology, disease, extracellular matrix, homeostatic, immune, metabolic (both RNA and protein), programmed cell death, signal transduction, molecular transport, transcriptional networks and as yet uncharacterised pathways indicate that these proteins are potentially involved in endometrial carcinogenesis and thus may be important in EC diagnosis, prognostication and treatment and thus are worthy of further investigation.

Evaluating the use of machine learning in endometrial cancer: a systematic review

OBJECTIVE

To review the literature on machine learning in endometrial cancer, report the most commonly used algorithms, and compare performance with traditional prediction models.

METHODS

This is a systematic review of the literature from January 1985 to March 2021 on the use of machine learning in endometrial cancer. An extensive search of electronic databases was conducted. Four independent reviewers screened studies initially by title then full text. Quality was assessed using the MINORS (Methodological Index for Non-Randomized Studies) criteria. P values were derived using the Pearson's Χ2 test in JMP 15.0.

RESULTS

Among 4295 articles screened, 30 studies on machine learning in endometrial cancer were included. The most frequent applications were in patient datasets (33.3%, n=10), pre-operative diagnostics (30%, n=9), genomics (23.3%, n=7), and serum biomarkers (13.3%, n=4). The most commonly used models were neural networks (n=10, 33.3%) and support vector machine (n=6, 20%).The number of publications on machine learning in endometrial cancer increased from 1 in 2010 to 29 in 2021.Eight studies compared machine learning with traditional statistics. Among patient dataset studies, two machine learning models (20%) performed similarly to logistic regression (accuracy: 0.85 vs 0.82, p=0.16). Machine learning algorithms performed similarly to detect endometrial cancer based on MRI (accuracy: 0.87 vs 0.82, p=0.24) while outperforming traditional methods in predicting extra-uterine disease in one serum biomarker study (accuracy: 0.81 vs 0.61). For survival outcomes, one study compared machine learning with Kaplan-Meier and reported no difference in concordance index (83.8% vs 83.1%).

CONCLUSION

Although machine learning is an innovative and emerging technology, performance is similar to that of traditional regression models in endometrial cancer. More studies are needed to assess its role in endometrial cancer.

PROSPERO REGISTRATION NUMBER

CRD42021269565.

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

BACKGROUND

A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.

METHODS

This was a prospective case-control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.

RESULTS

The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96-0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86-0.9)).

CONCLUSION

A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.

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.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Altered Gut Microbial Profile Accompanied by Abnormal Fatty Acid Metabolism Activity Exacerbates Endometrial Cancer Progression

Endometrial cancer (EC) is the most prevalent gynecological malignancy, with a higher risk in obese woman, indicating the possibility of gut microbiota involvement in EC progression. However, no direct evidence of a relationship between EC and gut microbiota in humans has been discovered. Here, we performed 16S rRNA sequencing to explore the relationship between dysbiosis of gut microbiota and cancer development in different types of EC patients. The results clearly show the differential profiles of gut microbiota between EC patients and normal participants as well as the association between gut microbiota and EC progression. Targeted metabolomics of plasma revealed an increased level of C16:1 and C20:2, which was positively associated with the abundance of Ruminococcus sp. N15.MGS-57. The higher richness of Ruminococcus sp. N15.MGS-57 in EC subjects not only was positively associated with blood C16:1 and C20:2 but also was negatively correlated with betalain and indole alkaloid biosynthesis. Furthermore, the combined marker panel of gut bacteria, blood metabolites, and clinical indices could distinguish the EC patients under lean and overweight conditions from normal subjects with high accuracy in both discovery and validation sets. In addition, the alteration of tumor microenvironment metabolism of EC was characterized by imaging mass microscopy. Spatial visualization of fatty acids showed that C16:1 and C18:1 obviously accumulate in tumor tissue, and C16:1 may promote EC cell invasion and metastasis through mTOR signaling. The aberrant fecal microbiome, more specifically, Ruminococcus sp. N15.MGS-57 and spatially distributed C16:1 in EC tissues, can be used as a biomarker of clinical features and outcomes and provide a new therapeutic target for clinical treatment. IMPORTANCE A growing number of studies have shown the connection between gut microbiota, obesity, and cancer. However, to our knowledge, the association between gut microbiota and endometrial cancer progression in humans has not been studied. We recruited EC and control individuals as research participants and further subgrouped subjects by body mass index to examine the association between gut microbiota, metabolites, and clinical indices. The higher richness of Ruminococcus sp. N15.MGS-57 in EC subjects was not only positively associated with blood C16:1 but also negatively correlated with betalain and indole alkaloid biosynthesis. Spatial visualization of fatty acids by imaging mass microscopy showed that C16:1 obviously accumulates in tumor tissue, and C16:1 may promote the EC cell invasion and metastasis through mTOR signaling. The aberrant fecal microbiome, more specifically, Ruminococcus sp. N15.MGS-57 and spatially distributed C16:1, can be used as a biomarker of clinical features and outcomes and provide a new therapeutic target for clinical treatment.

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.

Screening for Potential Therapeutic Agents for Non-Small Cell Lung Cancer by Targeting Ferroptosis

Ferroptosis is a form of non-apoptotic and iron-dependent cell death originally identified in cancer cells. Recently, emerging evidence showed that ferroptosis-targeting therapy could be a novel promising anti-tumour treatment. However, systematic analyses of ferroptosis-related genes for the prognosis of non-small cell lung cancer (NSCLC) and the development of antitumor drugs exploiting the ferroptosis process remain rare. This study aimed to identify genes related to ferroptosis and NSCLC and to initially screen lead compounds that induce ferroptosis in tumor cells. We downloaded mRNA expression profiles and NSCLC clinical data from The Cancer Genome Atlas database to explore the prognostic role of ferroptosis-related genes. Four prognosis-associated ferroptosis-related genes were screened using univariate Cox regression analysis and the lasso Cox regression analysis, which could divide patients with NSCLC into high- and low-risk groups. Then, based on differentially expressed risk- and ferroptosis-related genes, the negatively correlated lead compound flufenamic acid (FFA) was screened through the Connective Map database. This project confirmed that FFA induced ferroptosis in A549 cells and inhibited growth and migration in a dose-dependent manner through CCK-8, scratch, and immunofluorescence assays. In conclusion, targeting ferroptosis might be a therapeutic alternative for NSCLC.

Butyrate, a postbiotic of intestinal bacteria, affects pancreatic cancer and gemcitabine response in in vitro and in vivo models

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer. The characteristic excessive stromatogenesis accompanying the growth of this tumor is believed to contribute to chemoresistance which, together with drug toxicity, results in poor clinical outcome. An increasing number of studies are showing that gut microbiota and their metabolites are implicated in cancer pathogenesis, progression and response to therapies. In this study we tested butyrate, a product of dietary fibers' bacterial fermentation, whose anticancer and anti-inflammatory functions are known. We provided in vitro evidence that, beside slowing proliferation, butyrate enhanced gemcitabine effectiveness against two human pancreatic cancer cell lines, mainly inducing apoptosis. In addition, we observed that, when administered to a PDAC mouse model, alone or combined with gemcitabine treatment, butyrate markedly reduced the cancer-associated stromatogenesis, preserved intestinal mucosa integrity and affected fecal microbiota composition by increasing short chain fatty acids producing bacteria and decreasing some pro-inflammatory microorganisms. Furthermore, a biochemical serum analysis showed butyrate to ameliorate some markers of kidney and liver damage, whereas a metabolomics approach revealed a deep modification of lipid metabolism, which may affect tumor progression or response to therapy. Such results support that butyrate supplementation, in addition to conventional therapies, can interfere with pancreatic cancer biology and response to treatment and can alleviate some damages associated to cancer itself or to chemotherapy.

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.

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.

Contemporary Fertility-Sparing Management Options of Early Stage Endometrioid Endometrial Cancer in Young Nulliparous Patients

Incidence of endometrial cancer (EC) has been increasing in recent years, especially in high-income countries. The disease commonly affects peri- and postmenopausal women; however, about 5% of women are diagnosed with EC in their reproductive age. Due to both the increasing incidence of EC among reproductive age women and trends to delayed childbearing, fertility-sparing treatment for young patients with EC has become extremely important for researchers and practitioners. Because the classic treatment with total hysterectomy and bilateral saplingo-oophorectomy is not an appropriate approach for young women demanding fertility preservation, several fertility-sparing options have been developed and summarized in this review. Utilization of different medications and their combination (progestagens, gonadotropin releasing hormones analogues, and metformin in different formulations) are tested and found as efficient for fertility-sparing treatment. New minimally invasive surgical techniques, combined with progestagens, are also confirmed as valuable. There are many novel conservative and surgical treatment approaches under investigation. Assuming that molecular biomarkers can be both diagnostic and prognostic to assist in prediction of response to a certain therapy, prognostic risk groups' stratification along with specific biomarkers' identification will ensure low recurrence and decrease mortality rates in young women with EC.

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.

Pre-treatment inflammatory parameters predict survival from endometrial cancer: A prospective database analysis

PURPOSE

Inflammation predisposes to tumorigenesis by damaging DNA, stimulating angiogenesis and potentiating pro-proliferative and anti-apoptotic processes. The aim of this study was to investigate whether pre-treatment biomarkers of systemic inflammation are associated with survival outcomes in endometrial cancer.

PATIENTS AND METHODS

Women with endometrial cancer were recruited to a prospective database study. Pre-treatment systemic markers of inflammation, including C-reactive protein (CRP), Glasgow Prognostic Score and lymphocyte-based ratios [neutrophil-lymphocyte ratio (NMR), monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII)], were analysed in relation to overall, endometrial cancer-specific and recurrence-free survival using Kaplan-Meier estimation and multivariable Cox regression.

RESULTS

In total, 522 women of mostly White British ethnicity, with a median age of 66 years (interquartile range (IQR), 56, 73) and BMI of 32 kg/m² (IQR 26, 39) were included in the analysis. Most had low-grade (67.2%), early-stage (85.4% stage I/II), endometrioid (74.5%) tumors. Women with pre-treatment CRP ≥5.5 mg/L had a 68% increase in overall (adjusted HR = 1.68, 95% CI 1.00-2.81, p = 0.049) and a two-fold higher cancer-specific mortality risk than those with CRP <5.5 mg/L (adjusted HR = 2.04, 95%CI 1.03-4.02, p = 0.04). Absolute lymphocyte count, NLR, MLR and SII were associated with adverse clinico-pathologic factors, but not overall, cancer-specific or recurrence-free survival in the multivariable analysis.

CONCLUSION

If confirmed in an independent cohort, CRP may offer a simple, low-cost test to refine pre-treatment risk assessment and guide personalised care in endometrial cancer. Our participants were mostly of White British ethnicity and further studies are needed to confirm the utility of CRP as a prognostic biomarker in other populations.

Grading of endometrial cancer using 1H HR-MAS NMR-based metabolomics

The tissue metabolomic characteristics associated with endometrial cancer (EC) at different grades were studied using high resolution (400 MHz) magic angle spinning (HR-MAS) proton spectroscopy. The metabolic profiles were obtained from 64 patients (14 with grade 1 (G1), 33 with grade 2 (G2) and 17 with grade 3 (G3) tumors) and compared with the profile acquired from 10 patients with the benign disorders. OPLS-DA revealed increased valine, isoleucine, leucine, hypotaurine, serine, lysine, ethanolamine, choline and decreased creatine, creatinine, glutathione, ascorbate, glutamate, phosphoethanolamine and scyllo-inositol in all EC grades in reference to the non-transformed tissue. The increased levels of taurine was additionally detected in the G1 and G2 tumors in comparison to the control tissue, while the elevated glycine, N-acetyl compound and lactate—in the G1 and G3 tumors. The metabolic features typical for the G1 tumors are the increased dimethyl sulfone, phosphocholine, and decreased glycerophosphocholine and glutamine levels, while the decreased myo-inositol level is characteristic for the G2 and G3 tumors. The elevated 3-hydroxybutyrate, alanine and betaine levels were observed in the G3 tumors. The differences between the grade G1 and G3 malignances were mainly related to the perturbations of phosphoethanolamine and phosphocholine biosynthesis, inositol, betaine, serine and glycine metabolism. The statistical significance of the OPLS-DA modeling was also verified by an univariate analysis. HR-MAS NMR based metabolomics provides an useful insight into the metabolic reprogramming in endometrial cancer.

Comprehensive Library Generation for Identification and Quantification of Endometrial Cancer Protein Biomarkers in Cervico-Vaginal Fluid

Simple Summary

Endometrial cancer is the most common cancer of the female reproductive tract, and its incidence is rising. Early diagnosis has the potential to improve survival as women can receive care at the earliest possible stage when curative treatment is likely. Current tests for endometrial cancer diagnosis are sequentially invasive with low patient acceptability. A detection tool based on minimally invasive samples such as cervico-vaginal fluid would be a major advance in the field. This study focuses on the potential of detecting endometrial cancer based on the proteins and peptides expressed in cervico-vaginal fluid. Using Sequential window acquisition of all theoretical mass spectra (SWATH-MS), we present a spectral library of thousands of proteins in the cervico-vaginal fluid of women with or at risk of endometrial cancer. This important resource will enable the identification of endometrial cancer biomarkers in cervico-vaginal fluid and advances our knowledge of the role of proteomics in endometrial cancer detection.

Abstract

Endometrial cancer is the most common gynaecological malignancy in high-income countries and its incidence is rising. Early detection, aided by highly sensitive and specific biomarkers, has the potential to improve outcomes as treatment can be provided when it is most likely to effect a cure. Sequential window acquisition of all theoretical mass spectra (SWATH-MS), an accurate and reproducible platform for analysing biological samples, offers a technological advance for biomarker discovery due to its reproducibility, sensitivity and potential for data re-interrogation. SWATH-MS requires a spectral library in order to identify and quantify peptides from multiplexed mass spectrometry data. Here we present a bespoke spectral library of 154,206 transitions identifying 19,394 peptides and 2425 proteins in the cervico-vaginal fluid of postmenopausal women with, or at risk of, endometrial cancer. We have combined these data with a library of over 6000 proteins generated based on mass spectrometric analysis of two endometrial cancer cell lines. This unique resource enables the study of protein biomarkers for endometrial cancer detection in cervico-vaginal fluid. Data are available via ProteomeXchange with unique identifier PXD025925.

Metabolic Profiling Revealed Prediction Biomarkers for Infantile Hemangioma in Umbilical Cord Blood Sera: A Prospective Study

Infantile hemangioma (IH), the most common benign tumor in infancy, mostly arises and has rapid growth before 3 months of age. Because irreversible skin changes occur in the early proliferative stage, early medical treatment is essential to reduce the permanent sequelae caused by IH. Yet there are still no early screening biomarkers for IH before its visible emergence. This study aimed to explore prediction biomarkers using noninvasive umbilical cord blood (UCB). A prospective study of the metabolic profiling approach was performed on UCB sera from 28 infants with IH and 132 matched healthy controls from a UCB population comprising over 1500 infants (PeptideAtlas: PASS01675) using liquid chromatography-mass spectrometry. The metabolic profiling results exhibited the characteristic metabolic aberrance of IH. Machine learning suggested a panel of biomarkers to predict the occurrence of IH, with the area under curve (AUC) values in the receiver operating characteristic analysis all >0.943. Phenylacetic acid had potential to predict infants with large IH (diameter >2 cm) from those with small IH (diameter <2 cm), with an AUC of 0.756. The novel biomarkers in noninvasive UCB sera for predicting IH before its emergence might lead to a revolutionary clinical utility.

Innovative Follow-up Strategies for Endometrial Cancer

Increasing recognition of the heterogeneous nature of endometrial cancer, the excellent prognosis of low-risk cases and improvements in risk stratification offer opportunities for innovative, personalised follow-up strategies. This review article outlines the evidence base for alternative follow-up strategies in the different risk categories of endometrial cancer, cancer survivorship programmes and considers future directions in endometrial cancer follow-up, including emerging new techniques, such as the liquid biopsy, and opportunities for combining molecular and clinicopathological features to personalise endometrial cancer follow-up.

Impact of socio-economic deprivation on endometrial cancer survival in the North West of England: a prospective database analysis

OBJECTIVE

To assess the impact of socio-economic deprivation on endometrial cancer survival.

DESIGN

Single-centre prospective database study.

SETTING

North West England.

POPULATION

Women with endometrial cancer treated between 2010 and 2015.

METHODS

Areal-level socio-economic status, using the English indices of multiple deprivation from residential postcodes, was analysed in relation to survival using Kaplan-Meier estimation and multivariable Cox regression.

MAIN OUTCOME MEASURES

Overall survival, cancer-specific survival and patterns and rates of recurrence.

RESULTS

A total of 539 women, with a median age of 66 years (interquartile range, IQR 56-73 years) and a body mass index (BMI) of 32 kg/m2 (IQR 26-39 kg/m2 ), were included in the analysis. Women in the most deprived social group were younger (median 64 years, IQR 55-72 years) and more obese (median 34 kg/m2 , IQR 28-42 kg/m2 ) than women in the least deprived group (median age 68 years, IQR 60-74 years; BMI 29 kg/m2 , IQR 25-36 kg/m2 ; P = 0.002 and <0.001, respectively). There were no differences in endometrial cancer type, stage or grade between social groups. There was no difference in recurrence rates, however, women in the middle and most deprived social groups were more likely to present with distant/metastatic recurrence (80.6 and 79.2%, respectively) than women in the least deprived group (43.5%, P < 0.001). Women in the middle and most deprived groups had a two-fold (adjusted hazard ratio, HR = 2.00, 95% CI 1.07-3.73, P = 0.030) and 53% (adjusted HR = 1.53, 95% CI 0.77-3.04, P = 0.221) increase in cancer-specific mortality compared with women in the least deprived group. There were no differences in overall survival.

CONCLUSIONS

We found that socio-economically deprived women with endometrial cancer were more likely to develop fatal recurrence. Larger studies are needed to confirm these findings and to identify modifiable contributing factors.

TWEETABLE ABSTRACT

Socio-economic deprivation is linked to an increased risk of death from endometrial cancer in the North West of England.

Topic Evolution Analysis for Omics Data Integration in Cancers

One of the vital challenges for cancer diseases is efficient biomarkers monitoring formation and development are limited. Omics data integration plays a crucial role in the mining of biomarkers in the human condition. As the link between omics study on biomarkers discovery and cancer diseases is deepened, defining the principal technologies applied in the field is a must not only for the current period but also for the future. We utilize topic modeling to extract topics (or themes) as a probabilistic distribution of latent topics from the dataset. To predict the future trend of related cases, we utilize the Prophet neural network to perform a prediction correction model for existing topics. A total of 2,318 pieces of literature (from 2006 to 2020) were retrieved from MEDLINE with the query on “omics” and “cancer.” Our study found 20 topics covering current research types. The topic extraction results indicate that, with the rapid development of omics data integration research, multi-omics analysis (Topic 11) and genomics of colorectal cancer (Topic 10) have more studies reported last 15 years. From the topic prediction view, research findings in multi-omics data processing and novel biomarker discovery for cancer prediction (Topic 2, 3, 10, 11) will be heavily focused in the future. From the topic visuallization and evolution trends, metabolomics of breast cancer (Topic 9), pharmacogenomics (Topic 15), genome-guided therapy regimens (Topic 16), and microRNAs target genes (Topic 17) could have more rapidly developed in the study of cancer treatment effect and recurrence prediction.

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.

Metabolic Drug Response Phenotyping in Colorectal Cancer Organoids by LC-QTOF-MS

As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) facing the challenge of capturing metabolic information from a minimal sample amount (<500 cells/injection) in the presence of an extracellular matrix (ECM). The best procedure of the tested protocols included ultrasonic metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v/v) without ECM removal. To eliminate ECM-derived background signals, we implemented a data filtering procedure based on the p-value and fold change cut-offs, which retained features with signal intensities >120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2′-deoxyuridine, 2′-O-methylcytidine, inosine and 1-methyladenosine and depletion of 2′-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models.

Urinary Biomarkers and Their Potential for the Non-Invasive Detection of Endometrial Cancer

Endometrial cancer is the most common malignancy of the female genital tract and its incidence is rising in parallel with the mounting prevalence of obesity. Early diagnosis has great potential to improve outcomes as treatment can be curative, especially for early stage disease. Current tests and procedures for diagnosis are limited by insufficient accuracy in some and unacceptable levels of invasiveness and discomfort in others. There has, therefore, been a growing interest in the search for sensitive and specific biomarkers for endometrial cancer detection based on non-invasive sampling methodologies. Urine, the prototype non-invasive sample, is attractive for biomarker discovery as it is easily accessible and can be collected repeatedly and in quantity. Identification of urinary biomarkers for endometrial cancer detection relies on the excretion of systemic biomarkers by the kidneys or urinary contamination by biomarkers shed from the uterus. In this review, we present the current standing of the search for endometrial cancer urinary biomarkers based on cytology, genomic, transcriptomic, proteomic, and metabolomic platforms. We summarize the biomarker candidates and highlight the challenges inherent in urinary biomarker discovery. We review the various technologies with promise for biomarker detection and assess these novel approaches for endometrial cancer biomarker research.