Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

MR Relaxometry for Discriminating Malignant Ovarian Cystic Tumors: A Prospective Multicenter Cohort Study

BACKGROUND

Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted a retrospective study on magnetic resonance (MR) relaxometry of cyst fluid to distinguish EAOC from OE and reported that this method showed good accuracy. The purpose of this study is to evaluate the accuracy of a non-invasive method in re-evaluating pre-surgical diagnosis of malignancy by a prospective multicenter cohort study.

METHODS

After the standard diagnosis process, the R2 values were obtained using a 3T system. Data on the patients were then collected through the Case Report Form (CRF). Between December 2018 and March 2023, six hospitals enrolled 109 patients. Out of these, 81 patients met the criteria required for the study.

RESULTS

The R2 values calculated using MR relaxometry showed good discriminating ability with a cut-off of 15.74 (sensitivity 80.6%, specificity 75.0%, AUC = 0.750, p < 0.001) when considering atypical or borderline tumors as EAOC. When atypical and borderline cases were grouped as OE, EAOC could be distinguished with a cut-off of 16.87 (sensitivity 87.0%, specificity 61.1%).

CONCLUSIONS

MR relaxometry has proven to be an effective tool for discriminating EAOC from OE. Regular use of this method is expected to provide significant insights for clinical practice.

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

BACKGROUND

Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention.

METHODS

Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches.

RESULTS

We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set.

CONCLUSIONS

This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women.

IMPACT

Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.

Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

BACKGROUND

The mechanisms and risk factors underlying ovarian cancer (OC) remain under investigation, making the identification of new prognostic biomarkers and improved predictive factors critically important. Recently, circulating metabolites have shown potential in predicting survival outcomes and may be associated with the pathogenesis of OC. However, research into their genetic determinants is limited, and there are some inadequacies in understanding the distinct subtypes of OC. In this context, we conducted a Mendelian randomization study aiming to provide evidence for the relationship between genetically determined metabolites (GDMs) and the risk of OC and its subtypes.

METHODS

In this study, we consolidated genetic statistical data of GDMs with OC and its subtypes through a genome-wide association study (GWAS) and conducted a two-sample Mendelian randomization (MR) analysis. The inverse variance weighted (IVW) method served as the primary approach, with MR-Egger and weighted median methods employed for cross-validation to determine whether a causal relationship exists between the metabolites and OC risk. Moreover, a range of sensitivity analyses were conducted to validate the robustness of the results. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. False discovery rate (FDR) correction was applied to validate the findings. We also conducted a reverse MR analysis to validate whether the observed blood metabolite levels were influenced by OC risk. Additionally, metabolic pathway analysis was carried out using the MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, we discovered 18 suggestive causal associations involving 14 known metabolites, 8 metabolites as potential risk factors, and 6 as potential cancer risk reducers. In addition, three significant pathways, "caffeine metabolism," "arginine biosynthesis," and "citrate cycle (TCA cycle)" were associated with the development of mucinous ovarian cancer (MOC). The pathways "caffeine metabolism" and "alpha-linolenic acid metabolism" were associated with the onset of endometrioid ovarian cancer (OCED).

CONCLUSIONS

Our MR analysis revealed both protective and risk-associated metabolites, providing insights into the potential causal relationships between GDMs and the metabolic pathways related to OC and its subtypes. The metabolites that drive OC could be potential candidates for biomarkers.

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.

Mutational profiling of mitochondrial DNA reveals an epithelial ovarian cancer-specific evolutionary pattern contributing to high oxidative metabolism

BACKGROUND

Epithelial ovarian cancer (EOC) heavily relies on oxidative phosphorylation (OXPHOS) and exhibits distinct mitochondrial metabolic reprogramming. Up to now, the evolutionary pattern of somatic mitochondrial DNA (mtDNA) mutations in EOC tissues and their potential roles in metabolic remodelling have not been systematically elucidated.

METHODS

Based on a large somatic mtDNA mutation dataset from private and public EOC cohorts (239 and 118 patients, respectively), we most comprehensively characterised the EOC-specific evolutionary pattern of mtDNA mutations and investigated its biological implication.

RESULTS

Mutational profiling revealed that the mitochondrial genome of EOC tissues was highly unstable compared with non-cancerous ovary tissues. Furthermore, our data indicated the delayed heteroplasmy accumulation of mtDNA control region (mtCTR) mutations and near-complete absence of mtCTR non-hypervariable segment (non-HVS) mutations in EOC tissues, which is consistent with stringent negative selection against mtCTR mutation. Additionally, we observed a bidirectional and region-specific evolutionary pattern of mtDNA coding region mutations, manifested as significant negative selection against mutations in complex V (ATP6/ATP8) and tRNA loop regions, and potential positive selection on mutations in complex III (MT-CYB). Meanwhile, EOC tissues showed higher mitochondrial biogenesis compared with non-cancerous ovary tissues. Further analysis revealed the significant association between mtDNA mutations and both mitochondrial biogenesis and overall survival of EOC patients.

CONCLUSIONS

Our study presents a comprehensive delineation of EOC-specific evolutionary patterns of mtDNA mutations that aligned well with the specific mitochondrial metabolic remodelling, conferring novel insights into the functional roles of mtDNA mutations in EOC tumourigenesis and progression.

The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients

BACKGROUND

Recent studies have shown that pretreatment inflammatory responses can predict prognosis. However, no reports have analyzed the combined effect of the inflammatory response with pre-treatment and post-neo adjuvant chemotherapy (NACT). This retrospective study aims to identify factors predicting prognosis and create a novel predictive scoring system.

METHODS

The study was conducted at our institution between June 2006 and March 2020. Demographic and clinicopathological data were collected from patients with advanced epithelial ovarian cancer who underwent neoadjuvant chemotherapy after sample collection by laparoscopic or laparotomy surgery, followed by interval debulking surgery. We created a scoring system, called the Predictive Prognosis Score around NACT (PPSN), using factors extracted from a receiver operating characteristic curve analysis. Univariate and multivariate analyses were conducted to assess the efficacy of PPSN in predicting progression-free survival and overall survival. Kaplan-Meier and log-rank tests were used to compare the PFS or OS rate.

RESULTS

Our study included 72 patients, with a cut-off value of four for the scoring system. Our analysis showed that high PPSN (≥4) significantly predicts poor prognosis. Moreover, CD3+ and CD8+ tumor-infiltrating lymphocytes with low PPSN (<4) showed higher aggregation than those with high PPSN (≥4) cases.

CONCLUSION

Our study shows that PPSN could be a useful prognostic tool for advanced EOC patients who undergo NACT followed by IDS.

Novel proteomic technologies to address gaps in pre-clinical ovarian cancer biomarker discovery efforts

INTRODUCTION

An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

AREAS COVERED

This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

EXPERT OPINION

Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Primary Treatment Effects for High-Grade Serous Ovarian Carcinoma Evaluated by Changes in Serum Metabolites and Lipoproteins

High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest ovarian cancer subtype. Despite advances in treatment, the overall prognosis remains poor. Regardless of efforts to develop biomarkers to predict surgical outcome and recurrence risk and resistance, reproducible indicators are scarce. Exploring the complex tumor heterogeneity, serum profiling of metabolites and lipoprotein subfractions that reflect both systemic and local biological processes were utilized. Furthermore, the overall impact on the patient from the tumor and the treatment was investigated. The aim was to characterize the systemic metabolic effects of primary treatment in patients with advanced HGSOC. In total 28 metabolites and 112 lipoproteins were analyzed by nuclear magnetic resonance (NMR) spectroscopy in longitudinal serum samples (n = 112) from patients with advanced HGSOC (n = 24) from the IMPACT trial with linear mixed effect models and repeated measures ANOVA simultaneous component analysis. The serum profiling revealed treatment-induced changes in both lipoprotein subfractions and circulating metabolites. The development of a more atherogenic lipid profile throughout the treatment, which was more evident in patients with short time to recurrence, indicates an enhanced systemic inflammation and increased risk of cardiovascular disease after treatment. The findings suggest that treatment-induced changes in the metabolome reflect mechanisms behind the diversity in disease-related outcomes.

Metabolomics in oncology

BACKGROUND

Oncogenic transformation alters intracellular metabolism and contributes to the growth of malignant cells. Metabolomics, or the study of small molecules, can reveal insight about cancer progression that other biomarker studies cannot. Number of metabolites involved in this process have been in spotlight for cancer detection, monitoring, and therapy.

RECENT FINDINGS

In this review, the "Metabolomics" is defined in terms of current technology having both clinical and translational applications. Researchers have shown metabolomics can be used to discern metabolic indicators non-invasively using different analytical methods like positron emission tomography, magnetic resonance spectroscopic imaging etc. Metabolomic profiling is a powerful and technically feasible way to track changes in tumor metabolism and gauge treatment response across time. Recent studies have shown metabolomics can also predict individual metabolic changes in response to cancer treatment, measure medication efficacy, and monitor drug resistance. Its significance in cancer development and treatment is summarized in this review.

CONCLUSION

Although in infancy, metabolomics can be used to identify treatment options and/or predict responsiveness to cancer treatments. Technical challenges like database management, cost and methodical knowhow still persist. Overcoming these challenges in near further can help in designing new treatment régimes with increased sensitivity and specificity.

The prognosis predictive score around primary debulking surgery (PPSP) improves diagnostic efficacy in predicting the prognosis of ovarian cancer

In recent years, the pretreatment inflammatory responses have proven to predict the prognosis, but no report exists analyzing the combined inflammatory response of the pre- and postsurgical treatment. The current study aims to extract the factors predicting the recurrence and create novel predictive scoring. This retrospective study was conducted at our institution between November 2006 and December 2020, with follow-up until September 2022. Demographic and clinicopathological data were collected from women who underwent primary debulking surgery. We created the scoring system named the prognosis predictive score around primary debulking surgery(PPSP) for progression-free survival(PFS). Univariate and multivariate analyses were performed to assess its efficacy in predicting PFS and overall survival(OS). Cox regression analyses were used to assess its time-dependent efficacy. Kaplan-Meier and the log-rank test were used to compare the survival rate. A total of 235 patients were included in the current study. The cut-off value of the scoring system was six. Multivariate analyses revealed that an advanced International Federation of Gynecology and Obstetrics(FIGO) stage (p < 0.001 for PFS; p = 0.038 for OS), the decreased white blood cell count difference (p = 0.026 for PFS) and the high-PPSP (p = 0.004 for PFS; p = 0.002 for OS) were the independent prognostic factors. Cox regression analysis also supported the above results. The PPSP showed good prognostic efficacy not only in predicting the PFS but also OS of ovarian cancer patients comparable to FIGO staging.

The Endometriotic Neoplasm Algorithm for Risk Assessment (e-NARA) Index Sheds Light on the Discrimination of Endometriosis-Associated Ovarian Cancer from Ovarian Endometrioma

BACKGROUND

Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate endometriosis-associated ovarian cancer (EAOC) from ovarian endometrioma (OE) with high accuracy. However, this method has a limitation in discriminating malignancy in clinical use because the R2 value depends on the device manufacturer and repeated imaging is unrealistic. The current study aimed to reassess the diagnostic accuracy of MR relaxometry and investigate a more powerful tool to distinguish EAOC from OE.

METHODS

This retrospective study was conducted at our institution from December, 2012, to May, 2022. A total of 150 patients were included in this study. Patients with benign ovarian tumors (n = 108) mainly received laparoscopic surgery, and cases with suspected malignancy (n = 42) underwent laparotomy. Information from a chart review of the patients' medical records was collected.

RESULTS

A multiple regression analysis revealed that the age, the tumor diameter, and the R2 value were independent malignant predicting factors. The endometriotic neoplasm algorithm for risk assessment (e-NARA) index provided high accuracy (sensitivity, 85.7%; specificity, 87.0%) to discriminate EAOC from OE.

CONCLUSIONS

The e-NARA index is a reliable tool to assess the probability of malignant transformation of endometrioma.

Metabolomics of ischemic stroke: insights into risk prediction and mechanisms

Ischemic stroke (IS) is the most prevalent type of stroke. The early diagnosis and prognosis of IS are crucial for successful therapy and early intervention. Metabolomics, a tool in systems biology based on several innovative technologies, can be used to identify disease biomarkers and unveil underlying pathophysiological processes. Accordingly, in recent years, an increasing number of studies have identified metabolites from cerebral ischemia patients and animal models that could improve the diagnosis of IS and prediction of its outcome. In this paper, metabolomic research is comprehensively reviewed with a focus on describing the metabolic changes and related pathways associated with IS. Most clinical studies use biofluids (e.g., blood or plasma) because their collection is minimally invasive and they are ideal for analyzing changes in metabolites in patients of IS. We review the application of animal models in metabolomic analyses aimed at investigating potential mechanisms of IS and developing novel therapeutic approaches. In addition, this review presents the strengths and limitations of current metabolomic studies on IS, providing a reference for future related studies.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Chemoresistant Cancer Cell Lines Are Characterized by Migratory, Amino Acid Metabolism, Protein Catabolism and IFN1 Signalling Perturbations

Simple Summary

While chemoresistance remains a major barrier to improving the outcomes for patients with ovarian cancer, the molecular features, and associated biological functions, which underpin chemoresistance in ovarian cancer remain poorly understood. In this study we aimed to provide insight into the proteins and metabolites, and their associated biological pathways, which play a role in conferring chemoresistance to ovarian cancer. Through mass spectrometry analysis comparing the proteome and metabolome of chemosensitive vs chemoresistant ovarian cancer cell lines we revealed numerous perturbations in signalling and metabolic pathways in chemoresistant cells. Further comparison to primary cells taken from patients with chemoresistant or chemosensitive disease identified a shared dysregulation in cytokine and type 1 interferon signalling. Our research sets the foundation for a deeper understanding of the proteomic and metabolomic features of chemoresistance and identifies type 1 interferon signalling as a common feature of chemoresistance.

Abstract

Chemoresistance remains the major barrier to effective ovarian cancer treatment. The molecular features and associated biological functions of this phenotype remain poorly understood. We developed carboplatin-resistant cell line models using OVCAR5 and CaOV3 cell lines with the aim of identifying chemoresistance-specific molecular features. Chemotaxis and CAM invasion assays revealed enhanced migratory and invasive potential in OVCAR5-resistant, compared to parental cell lines. Mass spectrometry analysis was used to analyse the metabolome and proteome of these cell lines, and was able to separate these populations based on their molecular features. It revealed signalling and metabolic perturbations in the chemoresistant cell lines. A comparison with the proteome of patient-derived primary ovarian cancer cells grown in culture showed a shared dysregulation of cytokine and type 1 interferon signalling, potentially revealing a common molecular feature of chemoresistance. A comprehensive analysis of a larger patient cohort, including advanced in vitro and in vivo models, promises to assist with better understanding the molecular mechanisms of chemoresistance and the associated enhancement of migration and invasion.

The Comparison of Three Predictive Indexes to Discriminate Malignant Ovarian Tumors from Benign Ovarian Endometrioma: The Characteristics and Efficacy

This study aimed to evaluate the prediction efficacy of malignant transformation of ovarian endometrioma (OE) using the Copenhagen Index (CPH-I), the risk of ovarian malignancy algorithm (ROMA), and the R2 predictive index. This retrospective study was conducted at the Department of Gynecology, Nara Medical University Hospital, from January 2008 to July 2021. A total of 171 patients were included in the study. In the current study, cases were divided into three cohorts: pre-menopausal, post-menopausal, and a combined cohort. Patients with benign ovarian tumor mainly received laparoscopic surgery, and patients with suspected malignant tumors underwent laparotomy. Information from a review chart of the patients’ medical records was collected. In the combined cohort, a multivariate analysis confirmed that the ROMA index, the R2 predictive index, and tumor laterality were extracted as independent factors for predicting malignant tumors (hazard ratio (HR): 222.14, 95% confidence interval (CI): 22.27−2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90−33.13, p < 0.001; HR: 0.15, 95% CI: 0.03−0.75, p = 0.021, respectively). In the pre-menopausal cohort, a multivariate analysis confirmed that the CPH index and the R2 predictive index were extracted as independent factors for predicting malignant tumors (HR: 6.45, 95% CI: 1.47−28.22, p = 0.013; HR: 31.19, 95% CI: 8.48−114.74, p < 0.001, respectively). Moreover, the R2 predictive index was only extracted as an independent factor for predicting borderline tumors (HR: 45.00, 95% CI: 7.43−272.52, p < 0.001) in the combined cohort. In pre-menopausal cases or borderline cases, the R2 predictive index is useful; while, in post-menopausal cases, the ROMA index is better than the other indexes.

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

Unraveling Autocrine Signaling Pathways through Metabolic Fingerprinting in Serous Ovarian Cancer Cells

Focusing on defining metabolite-based inter-tumoral heterogeneity in ovarian cancer, we investigated the metabolic diversity of a panel of high-grade serous ovarian carcinoma (HGSOC) cell-lines using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis established the heterogeneity of the HGSOC cells by clustering them into five distinct metabolic groups compared to the fallopian tube epithelial cell line control. An overall increase in the metabolites associated with aerobic glycolysis and phospholipid metabolism were observed in the majority of the cancer cells. A preponderant increase in the levels of metabolites involved in trans-sulphuration and glutathione synthesis was also observed. More significantly, subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, γ-aminobutyrate, or glutamate. Additionally, 5-hydroxytryptamin synthesis inhibitor as well as antagonists of γ-aminobutyrate and glutamate receptors prohibited the proliferation of HGSOC cells, pointing to their potential roles as oncometabolites and ligands for receptor-mediated autocrine signaling in cancer cells. Consistent with this role, 5-Hydroxytryptamine synthesis inhibitor as well as receptor antagonists of γ-aminobutyrate and Glutamate-receptors inhibited the proliferation of HGSOC cells. These antagonists also inhibited the three-dimensional spheroid growth of TYKNU cells, a representative HGSOC cell-line. These results identify 5-HT, GABA, and Glutamate as putative oncometabolites in ovarian cancer metabolic sub-type and point to them as therapeutic targets in a metabolomic fingerprinting-based therapeutic strategy.

Metabolomic Analysis of Actinic Keratosis and SCC Suggests a Grade-Independent Model of Squamous Cancerization

Simple Summary

Actinic keratoses (AKs) are the most common sun-induced precancerous lesions that can progress to squamocellular carcinoma (SCC). AK I have been considered low-risk lesions, often evolving into AK II, the AK grade II and III have the potential to evolve to SCC. This research has assessed the metabolomic fingerprints of AK I, AK II, AK III and SCC by HR-MAS NMR spectroscopy, with the aim of evaluating the hypothesis of grade-association AK to SCC. The association between AKs and SCCs has also been evaluated by histopathology. Our findings support the notion that AK I are different from healthy skin and share different features with SCCs, indeed, they are metabolically active lesions with metabolic profiles similar to high-grade AKs and to SCC. The negative association of AKs with parakeratosis and the positive association with hypertrophy also suggested a similar behavior between AKs and SCCs. Therefore, all AKs should be treated independently from their clinical appearance or histological grade, since it is not possible to predict their potential evolution to SCC.

Abstract

Background—Actinic keratoses (AKs) are the most common sun-induced precancerous lesions that can progress to squamocellular carcinoma (SCC). Recently, the grade-independent association between AKs and SCC has been suggested; however, the molecular bases of this potential association have not been investigated. This study has assessed the metabolomic fingerprint of AK I, AK II, AK III and SCC using high resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy in order to evaluate the hypothesis of grade-independent association between AK and SCC. Association between AKs and SCCs has also been evaluated by histopathology. Methods—Metabolomic data were obtained through HR-MAS NMR spectroscopy. The whole spectral profiles were analyzed through multivariate statistical analysis using MetaboAnalyst 5.0. Histologic examination was performed on sections stained with hematoxylin and eosin; statistical analysis was performed using STATA software version 14. Results—A group of 35 patients affected by AKs and/or SCCs and 10 healthy controls were enrolled for metabolomics analysis. Histopathological analysis was conducted on 170 specimens of SCCs and AKs (including the ones that underwent metabolomic analysis). SCCs and AK I were found to be significantly associated in terms of the content of some metabolites. Moreover, in the logistic regression model, the presence of parakeratosis in AKs appeared to be less frequently associated with SCCs, while AKs with hypertrophy had a two-fold higher risk of being associated with SCC. Conclusions—Our findings, derived from metabolomics and histopathological data, support the notion that AK I are different from healthy skin and share some different features with SCCs. This may further support the expanding notion that all AKs should be treated independently from their clinical appearance or histological grade because they may be associated with SCC.

Recent Progress in Intelligent Wearable Sensors for Health Monitoring and Wound Healing Based on Biofluids

The intelligent wearable sensors promote the transformation of the health care from a traditional hospital-centered model to a personal portable device-centered model. There is an urgent need of real-time, multi-functional, and personalized monitoring of various biochemical target substances and signals based on the intelligent wearable sensors for health monitoring, especially wound healing. Under this background, this review article first reviews the outstanding progress in the development of intelligent, wearable sensors designed for continuous, real-time analysis, and monitoring of sweat, blood, interstitial fluid, tears, wound fluid, etc. Second, this paper reports the advanced status of intelligent wound monitoring sensors designed for wound diagnosis and treatment. The paper highlights some smart sensors to monitor target analytes in various wounds. Finally, this paper makes conservative recommendations regarding future development of intelligent wearable sensors.

Prediction and evaluation of the effect of pre-centrifugation sample management on the measurable untargeted LC-MS plasma metabolome

Optimal handling is the most important means to ensure adequate sample quality. We aimed to investigate whether pre-centrifugation delay time and temperature could be accurately predicted and to what extent variability induced by pre-centrifugation management can be adjusted for. We used untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to predict and evaluate the influence of pre-centrifugation temperature and delayed time on plasma samples. Pre-centrifugation temperature (4, 25 and 37 °C; classification rate 87%) and time (5-210 min; Q² = 0.82) were accurately predicted using Random Forest (RF). Metabolites uniquely reflecting temperature and temperature-time interactions were discovered using a combination of RF and generalized linear models. Time-related metabolite profiles suggested a perturbed stability of the metabolome at all temperatures in the investigated time period (5-210 min), and the variation at 4 °C was observed in particular before 90 min. Fourteen and eight metabolites were selected and validated for accurate prediction of pre-centrifugation temperature (classification rate 94%) and delay time (Q² = 0.90), respectively. In summary, the metabolite profile was rapidly affected by pre-centrifugation delay at all temperatures and thus the pre-centrifugation delay should be as short as possible for metabolomics analysis. The metabolite panels provided accurate predictions of pre-centrifugation delay time and temperature in healthy individuals in a separate validation sample. Such predictions could potentially be useful for assessing legacy samples where relevant metadata is lacking. However, validation in larger populations and different phenotypes, including disease states, is needed.

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.

Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is a fatal gynecologic cancer, and its poor prognosis is mainly due to delayed diagnosis. Therefore, biomarker identification and prognosis prediction are crucial in EOC. Altered cell metabolism is a characteristic feature of cancers, and metabolomics reflects an individual’s current phenotype. In particular, plasma metabolome analyses can be useful for biomarker identification. In this study, we analyzed 624 metabolites, including uremic toxins (UTx) in plasma derived from 80 patients with EOC using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Compared with the healthy control, we detected 77 significantly increased metabolites and 114 significantly decreased metabolites in EOC patients. Especially, decreased concentrations of lysophosphatidylcholines and phosphatidylcholines and increased concentrations of triglycerides were observed, indicating a metabolic profile characteristic of EOC patients. After calculating the parameters of each metabolic index, we found that higher ratios of kynurenine to tryptophan correlates with worse prognosis in EOC patients. Kynurenine, one of the UTx, can affect the prognosis of EOC. Our results demonstrated that plasma metabolome analysis is useful not only for the diagnosis of EOC, but also for predicting prognosis with the variation of UTx and evaluating response to chemotherapy.