Detecting Endometrial Cancer by Blood Spectroscopy: A Diagnostic Cross-Sectional Study

Infrared Spectroscopy in Gynecological Oncology: A Comprehensive Review of Diagnostic Potentials and Challenges

The early detection of gynecological cancers, which is critical for improving patient survival rates, is challenging because of the vague early symptoms and the diagnostic limitations of current approaches. This comprehensive review delves into the game-changing potential of infrared (IR) spectroscopy, a noninvasive technology used to transform the landscape of cancer diagnosis in gynecology. By collecting the distinctive vibrational frequencies of chemical bonds inside tissue samples, Fourier-transform infrared (FTIR) spectroscopy provides a 'molecular fingerprint' that outperforms existing diagnostic approaches. We highlight significant advances in this field, particularly the identification of discrete biomarker bands in the mid- and near-IR spectra. Proteins, lipids, carbohydrates, and nucleic acids exhibited different absorption patterns. These spectral signatures not only serve to distinguish between malignant and benign diseases, but also provide additional information regarding the cellular changes associated with cancer. To underscore the practical consequences of these findings, we examined studies in which IR spectroscopy demonstrated exceptional diagnostic accuracy. This review supports the use of IR spectroscopy in normal clinical practice, emphasizing its capacity to detect and comprehend the intricate molecular underpinnings of gynecological cancers.

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

OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSION

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

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

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

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

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

OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSION

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

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.

Blood cancer diagnosis using hyperspectral imaging combined with the forward searching method and machine learning

Hyperspectral imaging (HSI), a widely used biosensing technique, has been applied to tumor detection. Rapid, accurate, and low-cost detection of blood cancer using hyperspectral technology remains a challenge. We developed a new method to discriminate blood cancer using hyperspectral imaging (HSI) and the forward searching method (FSM). Four commonly used classification models are applied for four types of blood cancer spectra recognition. The support vector machine (SVM) model with the highest recognition accuracy (94.5%) combined with HSI achieves high-precision tumor identification. For higher recognition accuracy and lower hardware barriers, based on the selection probabilities of spectral lines calculated by a multi-objective atomic orbital search method, the FSM is proposed for HSI feature selection. With the proposed method, the wavelength band range of the spectrum is reduced by at least 50%. Compared with the traditional dimensionality reduction methods, the FSM can obtain a higher accuracy rate with lower hardware requirements. These results show that our proposed method can achieve non-invasive rapid screening of blood cancers with lower hardware requirements. Therefore, HSI assisted with FSM and SVM hybrid models can be a powerful and promising tool for blood cancer detection.

ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab "dip" test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab "dip" test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme.

FTIR- based serum structure analysis in molecular diagnostics of essential thrombocythemia disease

Essential thrombocythemia (ET) reflects the transformation of a multipotent hematopoietic stem cell, but its molecular pathogenesis remains obscure. Nevertheless, tyrosine kinase, especially Janus kinase 2 (JAK2), has been implicated in myeloproliferative disorders other than chronic myeloid leukaemia. FTIR analysis was performed on the blood serum of 86 patients and 45 healthy volunteers as control with FTIR spectra-based machine learning methods and chemometrics. Thus, the study aimed to determine biomolecular changes and separation of ET and healthy control groups illustration by applying chemometrics and ML techniques to spectral data. The FTIR-based results showed that in ET disease with JAK2 mutation, there are alterations in functional groups associated with lipids, proteins and nucleic acids significantly. Moreover, in ET patients the lower amount of proteins with simultaneously higher amount of lipids was noted in comparison with the control one. Furthermore, the SVM-DA model showed 100% accuracy in calibration sets in both spectral regions and 100.0% and 96.43% accuracy in prediction sets for the 800-1800 cm^-1 and 2700-3000 cm^-1 spectral regions, respectively. While changes in the dynamic spectra showed that CH₂ bending, amide II and CO vibrations could be used as a spectroscopy marker of ET. Finally, it was found a positive correlation between FTIR peaks and first bone marrow fibrosis degree, as well as the absence of JAK2 V617F mutation. The findings of this study contribute to a better understanding of the molecular pathogenesis of ET and identifying biomolecular changes and may have implications for early diagnosis and treatment of this disease.

Point-of-Care Diagnosis of Endometrial Cancer Using the Surgical Intelligent Knife (iKnife)-A Prospective Pilot Study of Diagnostic Accuracy

Introduction: Delays in the diagnosis and treatment of endometrial cancer negatively impact patient survival. The aim of this study was to establish whether rapid evaporative ionisation mass spectrometry using the iKnife can accurately distinguish between normal and malignant endometrial biopsy tissue samples in real time, enabling point-of-care (POC) diagnoses. Methods: Pipelle biopsy samples were obtained from consecutive women needing biopsies for clinical reasons. A Waters G2-XS Xevo Q-Tof mass spectrometer was used in conjunction with a modified handheld diathermy (collectively called the 'iKnife'). Each tissue sample was processed with diathermy, and the resultant surgical aerosol containing ionic lipid species was then analysed, producing spectra. Principal component analyses and linear discriminant analyses were performed to determine variance in spectral signatures. Leave-one-patient-out cross-validation was used to test the diagnostic accuracy. Results: One hundred and fifty patients provided Pipelle biopsy samples (85 normal, 59 malignant, 4 hyperplasia and 2 insufficient), yielding 453 spectra. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differential phospholipid spectra. Cross-validation revealed a diagnostic accuracy of 89% with sensitivity, specificity, positive predictive value and negative predictive value of 85%, 93%, 94% and 85%, respectively. Conclusions: This study is the first to use the iKnife to identify cancer in endometrial Pipelle biopsy samples. These results are highly encouraging and suggest that the iKnife could be used in the clinic to provide a POC diagnosis.

Diagnostic Accuracy of Liquid Biomarkers for the Non-Invasive Diagnosis of Endometrial Cancer: A Systematic Review and Meta-Analysis

Endometrial cancer rates are increasing annually due to an aging population and rising rates of obesity. Currently there is no widely available, accurate, non-invasive test that can be used to triage women for diagnostic biopsy whilst safely reassuring healthy women without the need for invasive assessment. The aim of this systematic review and meta-analysis is to evaluate studies assessing blood and urine-based biomarkers as a replacement test for endometrial biopsy or as a triage test in symptomatic women. For each primary study, the diagnostic accuracy of different biomarkers was assessed by sensitivity, specificity, likelihood ratio and area under ROC curve. Forest plots of summary statistics were constructed for biomarkers which were assessed by multiple studies using data from a random-effect models. All but one study was of blood-based biomarkers. In total, 15 studies reported 29 different exosomal biomarkers; 34 studies reported 47 different proteomic biomarkers. Summary statistic meta-analysis was reported for micro-RNAs, cancer antigens, hormones, and other proteomic markers. Metabolites and circulating tumor materials were also summarized. For the majority of biomarkers, no meta-analysis was possible. There was a low number of small, heterogeneous studies for the majority of evaluated index tests. This may undermine the reliability of summary estimates from the meta-analyses. At present there is no liquid biopsy that is ready to be used as a replacement test for endometrial biopsy. However, to the best of our knowledge this is the first study to report and meta-analyze the diagnostic accuracy of different classes of blood and urine biomarkers for detection of endometrial cancer. This review may thus provide a reference guide for those wishing to explore candidate biomarkers for further research.

Urine CA125 and HE4 for the Triage of Symptomatic Women with Suspected Endometrial Cancer

A simple, noninvasive and accurate detection tool that can triage women with suspected endometrial cancer for definitive testing will transform patient care. The aim of this study was to evaluate urine CA125 and HE4 levels for the detection of endometrial cancer in symptomatic women. This was a cross-sectional diagnostic accuracy study of 153 symptomatic women who underwent urgent diagnostic investigations for suspected endometrial cancer at a large gynecological cancer center. Urine samples were collected prior to routine clinical procedures. Urine CA125 and HE4 levels were determined using automated chemiluminescent enzyme immunoassays. Univariate and multivariable receiver operating characteristic (ROC) curve analyses were performed. Urine CA125 and HE4 were discovered to be significantly elevated in women with endometrial cancer, compared to controls (p < 0.001 and p = 0.01, respectively). Urine CA125 and HE4 detected endometrial cancer with an area under the ROC curve (AUC) of 0.89 (0.81, 0.98) and 0.69 (0.55, 0.83), respectively. CA125 exhibited good discriminatory potential for Type I and early-stage tumors (AUC 0.93 and 0.90, respectively). A diagnostic model that combined urine CA125 and transvaginal ultrasound-measured endometrial thickness predicted endometrial cancer with an AUC of 0.96 (0.91, 1.00). Urine CA125 displays potential as a diagnostic tool for symptomatic women with suspected endometrial cancer. When combined with transvaginal ultrasound-measured endometrial thickness, this patient-friendly, urine-based test could help triage women for invasive diagnostics or safe reassurance, reducing costs and improving patient experience.

Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.

Non-invasive Technology Advances in Cancer-A Review of the Advances in the Liquid Biopsy for Endometrial and Ovarian Cancers

Improving cancer survival rates globally requires improvements in disease detection and monitoring, with the aim of improving early diagnosis and prediction of disease relapse. Traditional means of detecting and monitoring cancers rely largely on imaging and, where possible, blood-based protein biomarkers, many of which are non-specific. Treatments are being improved by identification of inherited and acquired genomic aberrations in tumors, some of which can be targeted by newly developed therapeutic interventions. Treatment of gynecological malignancy is progressively moving toward personalized therapy, as exemplified by application of PARP-inhibition for patients with BRCA-deficient tubo-ovarian cancers, or checkpoint inhibition in patients with mismatch repair-deficient disease. However, the more recent discovery of a group of biomarkers described under the umbrella term of “liquid biopsy” promises significant improvement in our ability to detect and monitor cancers. The term “liquid biopsy” is used to describe an array of tumor-derived material found in blood plasma and other bodily fluids such as ascites, pleural fluid, saliva, and urine. It includes circulating tumors cells (CTCs), circulating nucleic acids including DNA, messenger RNA and micro RNAs, and extracellular vesicles (EVs). In this review, we discuss recent advancements in liquid biopsy for biomarker detection to help in diagnosis, prognosis, and planning of treatment of ovarian and endometrial cancer.

Iodine excess induces hepatic, renal and pancreatic injury in female mice as determined by attenuated total reflection Fourier-transform infrared spectrometry

Limited knowledge of the long-term effects of excessive iodine (EI) intake on biomolecular signatures in the liver/pancreas/kidney prompted this study. Herein, following 6 months of exposure in mice to 300, 600, 1200 or 2400 μg/L iodine, the biochemical signature of alterations to the liver/pancreas/kidney was profiled using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with principal component analysis-linear discriminant analysis (PCA-LDA). Our research showed that serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (Scr), insulin, blood glucose levels and homeostasis model assessment for insulin resistance (HOMA-IR) index in the 1200 and 2400 μg/L iodine-treated groups were significantly increased compared with those in the control group. Moreover, histological analysis showed that the liver/kidney/pancreas tissues of mice exposed to EI treatment displayed substantial morphological abnormalities, such as a loss of hepatic architecture, glomerular cell vacuolation and pancreatic neutrophilic infiltration. Notably, EI treatment caused distinct biochemical signature segregation between EI-exposed versus the control liver/pancreas/kidney. The main biochemical alterations between EI-exposed and control groups were observed for protein phosphorylation, protein secondary structures and lipids. The ratios of amide I-to-amide II (1674 cm^-1 /1570 cm^-1 ), α-helix-to-β-sheet (1657 cm^-1 /1635 cm^-1 ), glycogen-to-phosphate (1030 cm^-1 /1086 cm^-1 ) and the peptide aggregation (1 630 cm^-1 /1650 cm^-1 ) level of EI-treated groups significantly differed from the control group. Our study demonstrated that EI induced hepatic, renal and pancreatic injury by disturbing the structure, metabolism and function of the cell membrane. This finding provides the new method and implication for human health assessment regarding long-term EI intake.

Screening of key biomarkers and immune infiltration in Pulmonary Arterial Hypertension via integrated bioinformatics analysis

This study aimed to screen key biomarkers and investigate immune infiltration in pulmonary arterial hypertension (PAH) based on integrated bioinformatics analysis. The Gene Expression Omnibus (GEO) database was used to download three mRNA expression profiles comprising 91 PAH lung specimens and 49 normal lung specimens. Three mRNA expression datasets were combined, and differentially expressed genes (DEGs) were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the protein-protein interaction (PPI) network of DEGs were performed using the STRING and DAVID databases, respectively. The diagnostic value of hub gene expression in PAH was also analyzed. Finally, the infiltration of immune cells in PAH was analyzed using the CIBERSORT algorithm. Total 182 DEGs (117 upregulated and 65 downregulated) were identified, and 15 hub genes were screened. These 15 hub genes were significantly associated with immune system functions such as myeloid leukocyte migration, neutrophil migration, cell chemotaxis, Toll-like receptor signaling pathway, and NF-κB signaling pathway. A 7-gene-based model was constructed and had a better diagnostic value in identifying PAH tissues compared with normal controls. The immune infiltration profiles of the PAH and normal control samples were significantly different. High proportions of resting NK cells, activated mast cells, monocytes, and neutrophils were found in PAH samples, while high proportions of resting T cells CD4 memory and Macrophages M1 cell were found in normal control samples. Functional enrichment of DEGs and immune infiltration analysis between PAH and normal control samples might help to understand the pathogenesis of PAH.

Detection of ovarian cancer (± neo-adjuvant chemotherapy effects) via ATR-FTIR spectroscopy: comparative analysis of blood and urine biofluids in a large patient cohort

Ovarian cancer remains the most lethal gynaecological malignancy, as its timely detection at early stages remains elusive. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy of biofluids has been previously applied in pilot studies for ovarian cancer diagnosis, with promising results. Herein, these initial findings were further investigated by application of ATR-FTIR spectroscopy in a large patient cohort. Spectra were obtained by measurements of blood plasma and serum, as well as urine, from 116 patients with ovarian cancer and 307 patients with benign gynaecological conditions. A preliminary chemometric analysis revealed significant spectral differences in ovarian cancer patients without previous chemotherapy (n = 71) and those who had received neo-adjuvant chemotherapy-NACT (n = 45), so these groups were compared separately with benign controls. Classification algorithms with blind predictive model validation demonstrated that serum was the best biofluid, achieving 76% sensitivity and 98% specificity for ovarian cancer detection, whereas urine exhibited poor performance. A drop in sensitivities for the NACT ovarian cancer group in plasma and serum indicates the potential of ATR-FTIR spectroscopy to identify chemotherapy-related spectral changes. Comparisons of regression coefficient plots for identification of biomarkers suggest that glycoproteins (such as CA125) are the main classifiers for ovarian cancer detection and responsible for smaller differences in spectra between NACT patients and benign controls. This study confirms the capacity of biofluids' ATR-FTIR spectroscopy (mainly blood serum) to diagnose ovarian cancer with high accuracy and demonstrates its potential in monitoring response to chemotherapy, which is reported for the first time. ATR-FTIR spectroscopy of blood serum achieves good segregation of ovarian cancers from benign controls, with attenuation of differences following neo-adjuvant chemotherapy.

Spectroscopic evaluation of carcinogenesis in endometrial cancer

Carcinogenesis is a multifaceted process of cancer formation. The transformation of normal cells into cancerous ones may be difficult to determine at a very early stage. Therefore, methods enabling identification of initial changes caused by cancer require novel approaches. Although physical spectroscopic methods such as FT-Raman and Fourier Transform InfraRed (FTIR) are used to detect chemical changes in cancer tissues, their potential has not been investigated with respect to carcinogenesis. The study aimed to evaluate the usefulness of FT-Raman and FTIR spectroscopy as diagnostic methods of endometrial cancer carcinogenesis. The results indicated development of endometrial cancer was accompanied with chemical changes in nucleic acid, amide I and lipids in Raman spectra. FTIR spectra showed that tissues with development of carcinogenesis were characterized by changes in carbohydrates and amides vibrations. Principal component analysis and hierarchical cluster analysis of Raman spectra demonstrated similarity of tissues with cancer cells and lesions considered precursor of cancer (complex atypical hyperplasia), however they differed from the control samples. Pearson correlation test showed correlation between cancer and complex atypical hyperplasia tissues and between non-cancerous tissue samples. The results of the study indicate that Raman spectroscopy is more effective in assessing the development of carcinogenesis in endometrial cancer than FTIR.

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.

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm⁻¹ and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans.

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

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