Detecting metabolic signatures in endometrial cancer: potential applications of Raman spectroscopy.

Endometrial cancer (EC) is intricately linked to obesity, with metabolic reprogramming increasingly established to drive oncogenic transformation and influence treatment outcomes. Implementation of early detection strategy significantly reduces morbidity and mortality; however, screening strategies lack the required sensitivity, specificity, and accuracy to be successfully implemented in clinical practice. Current diagnostic approaches are also invasive, costly, and time-consuming, highlighting a gap in developing diagnostic and screening alternatives for EC among high-risk individuals, especially with sensitivity to capture cancer-specific changes. Raman Spectroscopy is an emerging tool in medical diagnostics. By exploiting the atomic vibrational absorption induced by the interaction of light with a biological sample, a unique spectral response namely a “metabolite fingerprint” can be generated. This nondestructive technique combined with multivariate statistical analysis can characterize metabolic discrimination between cancerous and healthy samples, demonstrating a promising role in cancer screening, diagnosis, and monitoring of treatment outcomes. This review aimed to collate available evidence on Raman’s ability to capture metabolic abnormalities, particularly cancer-specific metabolites during malignant transformation and therapeutic resistance. Given that cellular metabolism is altered in EC, this review will provide insight into its potential applications for EC screening, diagnosis, and prospects, especially for monitoring treatment outcomes among high-risk patients.

Endometrial cancer, cancer metabolism, endometrial cancer metabolic markers, Raman spectroscopy, metabolic diagnostic tool 1. Introduction Endometrial cancer (EC) is the second most prevalent gynecological malignancy affecting women globally, with nearly 417,000 new cases reported in 2020. The incidence and mortality rates of EC are rising each year, parallel to the growing prevalence of obesity, a well-recognized risk factor for EC. Obesity is also associated with poorer prognosis and treatment outcomes, with every 10% increase in BMI raising the likelihood of all-cause mortality by 9.2% [1]. EC primarily affects postmenopausal women; however, its prevalence is increasing among patients under 40 years old with morbidly higher BMI of above 38 kg/m2 [2]. The past decade has witnessed an emerging need for patient-supported priorities for EC, including the need for patient risk stratification, fertility conservation, individualized diagnostic pathways in case of abnormal uterine bleeding, and the development of minimally invasive approaches to monitor treatment and detect disease recurrence. This highlights the limitations of current EC screening and diagnostic strategies and the need for minimally invasive, rapid, and accurate alternatives. Cancer cells must rewire their metabolism to sustain the production of energy and macromolecules required for cell growth, division, and survival [3]. Aerobic glycolysis, known as the Warburg effect, has long been considered a dominant form of energy metabolism in many cancer types, including in EC [4]. Dysregulated lipid metabolism is also now recognized as a hallmark of cancer and is increasingly correlated with higher pathological stages, poorer prognosis, and treatment outcomes in EC patients [5]. Given that malignant transformation is often preceded by distinct biochemical changes, metabolomic diagnostic techniques have advanced cancer research to effectively profile metabolites involved during tumourigenesis [6]. Raman spectroscopy is an emerging optical technique that utilizes molecular-specific, inelastic scattering of photons to interrogate biological material. Its growing popularity in medical diagnostics is due to its ability to acquire molecular information in the absence of staining or labeling, where the acquisition of spectra can be performed in vitro, ex vivo, or in vivo, in each case avoiding disruption of the cellular environment [7]. Unlike mass spectrometry, the “gold standard” for metabolomic-based analysis, Raman spectroscopy is also less time-intensive [8] which can facilitate rapid metabolite screening and detection. With comparable specificity and sensitivity, Raman spectroscopy holds promise for the accurate detection of tumor-associated metabolites and EC diagnosis. However, achieving reliable outcomes requires the establishment of a comprehensive spectral database for each cancer type, considering their metabolic heterogeneity. While the application of Raman spectroscopy for metabolite characterization in EC is still in the early stages of research, Raman spectroscopy-based technologies for real-time cancer diagnosis have been clinically developed for various malignancies including brain [9,10], skin [11,12], and gastric [13,14] cancer. The feasibility of Raman spectroscopy as an established diagnostic tool for these cancers is attributed to significant structural and biochemical alterations during tumor progression, which generate distinct and reproducible Raman spectral patterns. In recent years, specific applications in gynecological cancers demonstrated its ability to characterize and distinguish metabolic alterations between non-cancerous and cancerous tissues, as well as differentiating pathological grades in gynecological malignancies including ovarian, cervical, and breast cancers [15–17]. The endometrium, however, is a highly dynamic tissue, undergoing substantial structural and metabolic fluctuations across the menstrual cycle, during pregnancy and in pathological conditions, such as endometriosis, endometrial hyperplasia and EC [18]. These unique biological variations present additional complexities in spectral analysis, necessitating dedicated studies and comprehensive analysis to optimize Raman-based approaches specifically for EC [16,19–21]. A well-curated and comprehensive spectral database that also accounts for physiological fluctuations in the constantly changing endometrium is needed, before advancing clinical implementation of Raman spectroscopy for EC diagnostics. Although numerous reviews have investigated the oncological applications of Raman spectroscopy [9,10,22], its potential for distinguishing specific metabolic signatures associated with the malignant transformation of endometrial lesions remains underexplored. This review aims to consolidate available evidence on Raman’s ability to capture metabolic abnormalities, particularly key biochemical and metabolic changes occurring in the pathophysiology of the endometrium during the benign and malignant stages. Recent advances of Raman spectroscopy in capturing the distinct metabolite profile during oncogenic transformation will be discussed, before assessing its potential and limitations for EC diagnosis, screening as well monitoring therapeutic efficacy. 2. Pathophysiological and metabolic changes during endometrial benign and malignant conditions The endometrium is one of the most dynamic tissues in the body and undergoes distinct structural changes over a female’s lifetime, governed by hormonal physiology. Specifically, differences in the morphological functional changes that occur during the menstrual cycles, and benign and malignant conditions of the endometrium can be characterized by significant molecular and cellular changes. These changes can be accompanied by apparent metabolic landscapes that likely occur much earlier before the onset of clinical symptoms. Capturing these biochemical signatures may serve as fundamental biomarker profiles for early disease classification. 2.1. Benign endometrial conditions 2.1.1. Menstrual cycle The endometrium, a complex tissue targeted by steroids, undergoes monthly shedding during menstruation in the absence of pregnancy. Histologically, the endometrium is composed of simple columnar epithelium covering a multicellular stroma that contains connective tissue components, fibroblast-like stromal cells, and numerous tubular glands connected to the luminal surface, along with arteries and immune cells [23]. The menstrual cycle, an essential biological rhythm regulating female physiology before menopause, typically occurs every 4 weeks. It involves cyclical changes in pituitary hormones (luteinizing hormone and follicle-stimulating hormone) and ovarian hormones (estrogen and progesterone). Ovarian hormones are important for maintaining lipid and glucose homeostasis. Previous studies have observed variations in metabolic parameters throughout the menstrual cycle, correlating with changes in ovarian hormone levels [24,25]. In general, circulating low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol levels were highest in the early follicular phase and decreased during the luteal phase, while HDL cholesterol peaked during the late follicular phase [24]. Throughout the menstrual cycle, estrogen and progesterone levels fluctuate, influencing metabolic pathways involved in lipid, carbohydrate, and amino acid metabolism. Metabolomic analysis from plasma and urine revealed decreased levels of 39 amino acids and derivatives, along with 18 lipid species in the luteal phase, potentially indicating an anabolic state during the progesterone peak and recovery during menstruation and the follicular phase [26]. The altered metabolite levels observed may indicate potential susceptibility to hormone-related conditions, such as premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD), in otherwise healthy women. The relative lower levels of amino acids and lipids in the luteal phase also suggest a higher utilization of fat for lipid or steroid synthesis with less need for anabolism during the follicular phase. Indeed, decreased levels of phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), phosphatidylethanolamines (PEs), and lysophosphatidylethanolamines (LPEs) align with their anabolic use for endometrial tissue thickening in preparation for pregnancy during the luteal phase [26,27]. During the follicular phase, circulating estrogen levels rise gradually, promoting lipogenesis and adipose tissue expansion, contributing to changes in lipid profiles observed during follicular development [28,29]. Plasma lipidomic study revealed alterations in lipid composition, with an increase in lipid subclasses, such as phospholipids and triglycerides [28]. While specific lipidomic changes in the endometrium during ovulation remain less characterized, the surge in estrogen levels may influence lipid metabolism, potentially affecting lipid species associated with follicular maturation and ovulatory processes. 2.1.2. Uterine leiomyoma (fibroid) Uterine leiomyomas, also known as fibroids, are benign tumors that arise from the myometrium. Fibroids are considered as the most common benign neoplasm affecting women in a fertile age. It is primarily associated with abnormally high collagen production due to impaired repair processes occurring at the myometrium level, and represents a useful model to unveil pathological modifications of extracellular matrix (ECM) [30]. These tumors develop fibrosis and are enclosed by a pseudocapsule that separates the benign tumor tissue from the surrounding myometrium. Diagnostic techniques reveal that uterine fibroids are two to four times stiffer than myometrium [31–34]. The stiffness of fibroids primarily arises from their abundant ECM, which contains substantial glycosaminoglycans and, more importantly, large quantities of disordered, highly cross-linked interstitial collagens [35–38]. Numerous studies have linked the increased stiffness to altered metabolic signaling in the tumors. Plasma triglyceride levels are consistently shown to be associated with the number of fibroids [39,40], with high-circulating lipid levels potentially being a significant risk factor for the formation of uterine leiomyomas [41]. In samples of fibroids and myometrium, notable alterations in lipid levels related to the metabolism of glycerophospholipids and sphingolipids were observed [39]. Additionally, linoleic acid metabolism is significantly altered in fibroid tissues. Significantly altered levels of various phospholipids, sphingomyelins, cholesterol esters, and triglycerides were observed in the blood plasma of women with uterine fibroids and recurrent fibroids compared to healthy women [39]. Additionally, lipidomic analysis via mass spectrometry (MS) revealed that specific fatty acid composition, particularly arachidonic acid, is present at significantly higher levels in myometrial tissue than in leiomyoma tissue, while linoleic acid was higher in leiomyoma tissue [42]. Decreased levels of fatty acids, such as arachidonic acid, alpha-tocopherol, palmitic acid, and stearic acid were reported in the plasma of patients with large leiomyomas, suggesting that uterine leiomyoma tissue can be differentiated from the adjacent normal myometrial tissue using its specific fatty acid profiles [43]. 2.1.3. Benign endometrial hyperplasia Benign endometrial hyperplasia (BEH) represents a spectrum of irregular morphological alterations whereby abnormal proliferation of the endometrial glands increases the gland-to-stroma ratio when compared to endometrium from the proliferative phase of the cycle [44]. Although BEH itself is not invasive, it serves as a precursor to endometrial carcinoma, the most common gynecological malignancy in developed countries [45]. Benign endometrial hyperplasia is histologically characterized by irregular glandular remodeling, vascular thrombi, stromal breakdown, and randomly distributed cytological changes [46]. Endometrial intraepithelial neoplasia (EIN) is histopathologically defined as a monoclonal preinvasive glandular proliferation of the endometrium and serves as the direct pathological precursor to endometrioid endometrial adenocarcinoma. EIN lesions consist of aggregates of individual tubular or mildly branching glands, where the combined epithelial and luminal surface area exceeds that of the surrounding stroma [46]. Significant risk factors for BEH include obesity, chronic anovulation as seen in disorders, such as polycystic ovary syndrome (PCOS), estrogen-only hormone replacement, tamoxifen use, and Lynch syndrome. Common underlying reasons associated with the risk factors include prolonged and unopposed estrogen exposure. Patients with BEH demonstrated similar biochemical changes seen during the menstrual cycle and fibroid formation when high estrogen levels promote anabolism of endometrial tissue [47,48]. The consistent need for energy and building blocks for endometrial tissue growth can be also reflected in marked changes in plasma metabolites, specifically lipid levels [49]. While the molecular mechanisms underlying BEH is not completely understood, recent studies have implicated estrogen-driven metabolic alterations during the pathological changes observed in the endometrium A recent metabolomic study using liquid chromatography – high-resolution mass spectrometry (LC-HRMS) on plasma samples showed fatty acid metabolism in BEH was significantly dysregulated when compared to that of healthy individuals [50]. The study reported that decrease in acylcarnitine in BEH samples was linked to overactivation of fatty acid oxidation due to increased energy demand for cell proliferation. Metabolites of dietary fatty acids and acylglycine were also found accumulated in plasma of BEH patients, indicating fatty acid oxidation is upregulated in hyperplasia as a result of excess unopposed estrogen [51]. Collectively, these studies demonstrated the biological role of estrogen in the biosynthesis of endometrial tissue in BEH progression [52]. Fluctuations of the pituitary hormones (luteinizing hormone, follicle-stimulating hormone) and ovarian hormones (estrogen and progesterone) are also associated with the regulation of metabolic processes in endometrial tissue and can lead to disease progressions such as BEH, and EC if dysregulated [53,54]. 2.2. Endometrial cancer EC can be categorized into Type I and Type II according to the differences in histology and molecular characteristics. Type I EC (i.e., low-grade endometrioid type) comprises 80–90% of all sporadic EC [55]. In contrast, Type II EC comprises only 10–20% of all sporadic EC. Generally, Type II EC manifests a less favorable prognostic trajectory due to its aggressiveness. The importance of understanding the distinctions between Type I and Type II EC, however, extends beyond their epidemiological characteristics. Crucial insights into the divergent clinical behaviors of these two subtypes can be obtained by examining their histological architecture, as well as their biochemical and genetic characteristics. Histologically, Type I EC is well differentiated and can be further categorized as adenocarcinoma with or without squamous differentiation [56]. Conversely, Type II EC appears more atypical and can be further categorized into serous, clear cell, and carcinosarcoma. At the molecular level, Type I EC is significantly associated with phosphatase and tensin homolog (PTEN) mutations, whereas Type II EC is associated with TP53 mutations [57]. Both PTEN and TP53 are tumor suppressor genes regulating phosphoinositide 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway and cell proliferation. Additionally, changes at the structural level, including the ECM, are pivotal for comprehending the mechanisms underlying cancer development and progression, and subsequently lead to reduced overall survival rates in EC [58]. Furthermore, collagen, a key ECM component, forms the framework of the tumor microenvironment (TME), influencing ECM remodeling through collagen degradation and redeposition. This, in turn, fosters tumor infiltration, angiogenesis, invasion, and migration [59]. Overexpression of collagen correlates closely with the presence of invasion and metastasis in the higher-grade EC, which leads to poor prognosis [60,61]. In addition to changes in tissue composition, profound metabolic changes occur within the cancer cells and their surrounding TME during cancer progression (Figure 1). These cells exhibit a distinctive metabolic phenotype characterized by the reconfiguration of glucose, fatty acids, and amino acids utilization toward energy production and macromolecule synthesis [62]. Cancer cells from different stages of tumor growth display distinct nutrient requirements to facilitate growth, invasion, and metastasis. At the early stages of transformation, the activation of oncogenes or the loss of tumor suppressor genes leads to excessive proliferation, rendering an upsurge in nutrient uptake and biosynthesis. Distinct nutrient requirements specific to the tumor become evident, and there are even metabolic needs that are specific to subtypes. Advanced stage tumors, however, rely on novel pathways to facilitate metastasis and develop resistance to therapy [63]. EC demonstrates an addiction to glucose, with increased glucose uptake observed in EC, and even precancerous lesions, compared to the adjacent normal tissue. This high glucose consumption is likely associated with the elevated expression of the glucose transporter (GLUT). GLUT1 and GLUT6 are among the GLUT family members that are more prominent in EC [64–66], whereby GLUT1 expression increases with tumor stage [67]. The Warburg effect is observed in EC tumors, where cancer cells preferentially divert glucose metabolism toward lactate production rather than converting pyruvate to acetyl-CoA (oxidative phosphorylation), despite the presence of adequate oxygen levels [65]. The lactate dehydrogenase (LDH) activity in tissue is 1.8 and 2.8 fold higher in EC tissue compared to the normal uterine endometrium and myometrium, respectively [68]. Interestingly, taking into consideration of histological types of EC cells, the more aggressive type II EC cell lines, such as HEC-1, KLE, and AN3CA expressed lower glycolytic activity when compared to the less aggressive type I EC cell lines, such as Ishikawa, MFE-296, and MFE-319 and RL95–2 [65]. This suggests the potential of evaluating the prognosis of EC patients based on the metabolic signatures of their tumors. Furthermore, tumors also highly depend on glutamine as a building block for cell division and expansion. Present abundantly in the human body, glutamine is a non-essential amino acid, and increased metabolism of glutamine has been noted in EC cases. Glutamine transporters ASCT2 and SNAT1 are significantly upregulated in EC [69]. Glutamine is transformed into glutamate in the mitochondria, followed by conversion into α-ketoglutarate to fuel ATP production by participation in the TCA cycle. In other words, increased glutamine fuels EC growth. In EC, estrogen drives glutaminolysis, effectively blocking autophagy in estrogen-sensitive cell lines such as Ishikawa. Besides the well-known alteration in glycolytic and glutamine metabolism, cancer cells also have a dominating lipogenic metabolism with aberrant fatty acid (FAs) metabolism shown to satisfy the lipid requirements for their accelerated proliferation [70]. The Warburg effect exhibited by EC has been demonstrated to enhance glucose-derived lipogenesis [65]. As a result, FA synthase (FASN) is upregulated in EC compared to the normal tissue [64] and is directly correlated with the Ki-67 expression, a marker for cell proliferation [71]. Lipid species, particularly fatty acids, can also reliably perform as a discriminatory marker in determining the presence of EC [72]. Untargeted global metabolic profiling of EC and non-EC cases found lipid metabolic pathways constitute four out of the 12 most enriched pathways in EC cases, with FA metabolism occupying the topmost position [73]. This suggests that FA synthesis contributes to EC cell proliferation. It is also worth noting that the EC cells undergo diverse metabolic changes to facilitate the process of metastasis. EC cells exhibit a lower level of cholesterol, possibly attributable to cholesterol efflux [74]. This decrease in cholesterol levels, coupled with enhanced membrane fluidity, promotes cell migration and invasion in EC [75]. The distinct metabolic phenotypes identified in EC compared to the normal and benign endometrial tissue, along with variations observed at different cancer stages, can be leveraged to improve EC screening, diagnosis, and treatment outcomes. 2.3. Metabolite changes during the benign transformation of endometrial tissue to EC progression The distinct metabolic phenotypes during the pathogenesis of EC are increasingly captured via mass spectrometry-Based metabolomics, the “gold standard” of omics-based analytical technique. The primary focus of metabolomics research is to identify metabolic biomarkers that can distinguish cancerous from healthy tissue [76]. In the context of endometrial tissue, metabolic rewiring associated with pathophysiological changes from benign conditions toward EC progression is generally reflected in the signature change of metabolites derived from abnormal utilization of glucose, lipids, and amino acids (Table 1). Evidently, distinct nutrient requirements specific to tumor progression have been consistently reported, with advanced-stage tumors relying on specific metabolic pathways to facilitate growth, metastasize, and evade therapy [77–79]. Table 1. | Refs | Endometrium Conditions | Sample type | Sample Size | Main Findings/Summary | Sample preparation | Metabolomic Techniques | Analysis Method | Limitations | Sensitivity | Specificity | |---|---|---|---|---|---|---|---|---|---|---| | [27] | Menstrual Cycle | Plasma and urine | 34 | The levels of glutamine, glycine, alanine, lysine, serine, and creatinine significantly decrease, while the levels of acetoacetate and very low-density lipoprotein (VLDL) significantly increase during the luteal phase. These metabolic changes highlight the need to control for such variations in the design of future metabolomic studies involving premenopausal women, especially in studies collecting multiple timepoints | Urine samples were prepared by adding 300 ml of phosphate buffer (0.2 M KH2PO4, 0.8 M K2HPO4) to 500 ml of urine. After centrifuging at 7155 g for 5 minutes, 10 ml of TSP and 50 ml of D2O were added to 500 ml of the supernatant. | 500 MHz DRX Nuclear magnetic resonance (NMR) spectrometer | XWIN NMR (version 3.5) | null | null | | | [29] | Plasma | 21 | 12.6% of the total detected ions significantly changed throughout the menstrual cycle, including lipids, amino acids, and citric acid. Many metabolites peaked during either the follicular or luteal phase and returned to baseline levels during menstruation, fluctuating periodically with hormonal changes. | A 50 μL serum sample was added to each well of a 96-well filter plate and mixed with 200 μL methanol. The 96-well filter plates were then vortexed for 10 minutes at room temperature and covered with aluminum foil. Protein was removed via positive-pressure filtration. The filtrate was freeze-dried and stored at − 80 °C. Before analysis, each sample was redissolved in 50 μL of a methanol/water (1:4) solvent and vortexed for 30 minutes. | ultra-performance liquid chromatography (UPLC) coupled to a Triple TOF 5600 mass spectrometer system | MarkerView software | |||| | [26] | Plasma and urine | 34 | During the luteal phase, 39 amino acids and derivatives, as well as 18 lipid species, showed a decrease. This trend may suggest an anabolic state coinciding with the progesterone peak, with recovery occurring during menstruation and the follicular phase. | not mentioned in literature | LC-MS and GC-MS | MultiQuant Software for Quantitative Analysis (AB SCIEX, Version 3.0.2) and Agilent MassHunter Quantitative Analysis software (Agilent, Version B.04.00) | The study’s vitamin differences detection was limited by missing values; more time points could enhance diagnostic sensitivity | null | null | | | [38] | Uterine Leiomyoma (fibroid) | Tissue | 42 | Uterine Leiomyomas (MED12 subtype) exhibited lower levels of vitamin A, various membrane lipids, and amino acids, along with disrupted vitamin C metabolism. | Tissue stored fresh at −80c | Two reverse phase/ultra-performance liquid chromatography – tandem mass spectroscopy (RP/UPLC-MS/MS) | null | null | || | [43] | blood | 75 | In patients with large leiomyomas, fatty acids such as arachidonic acid, alpha-tocopherol, palmitic acid, and stearic acid were generally reduced, while alpha-linolenic acid levels increased. | Metabolic and lipid fractions were extracted following the GC-MS protocol. Plasma (50 μL) or standard was mixed with 50 μL of acetonitrile, left to stand for 5 minutes, and centrifuged at 15,400×g for 10 minutes at 4 °C. The upper phase (50 μL) was transferred to a GC vial and evaporated to dryness in a Speedvac Concentrator. Ten microliters of O-methoxyamine hydrochloride (15 mg/mL) in pyridine was added, mixed for 5 minutes, and left at room temperature for 16 hours. After adding 10 μL of BSTFA with 1% TMCS, the mixture was silylated at 70 °C for 1 hour. Finally, 100 μL of heptane with 10 ppm of C16:0 methyl ester (IS) was added and mixed for 2 minutes before GC analysis. | Gas chromatography system coupled to quadrupole mass spectrometry (GCMS-QP2020 NX) | GCMS Solution (v.3.30), NIST 17 MASS (v.1.00.1), and GCMS Smart Metabolite (v.3.01) software | Plasma metabolites may be subject to other metabolic confounding factors (eg, dietary supplements, physical activity, environmental variables, stage of menstrual cycle) that could affect the interpretation of metabolomic profiles | null | null | | | [50] | Benign endometrialhyperplasia and Endometrial cancer | plasma | 59 | Endometrial hyperplasia samples showed similar metabolic changes to endometrial cancer, except for differences in triglyceride levels, 7a,12b-dihydroxy-5b-cholan-24-oic acid, and hept-2-enedioyl carnitine levels. Specific markers for hyperplasia include N-heptanoyl glycine, (methylthio)-2,3-isopentyl phosphate, and formimino glutamic acid, while dimethyl phosphatidyl ethanolamine and 8-isoprostaglandin E2 are specific to EC. The metabolite changes suggest that EC cells adopt alternative energy production strategies to support their proliferation. | 100 µL of plasma was combined with 900 µL of 50% extraction solvent (acetonitrile). The mixture was vigorously mixed using a Thermomixer (Eppendorf, Hamburg, Germany) set at 600 rpm and 4 °C for one hour. After vortexing, the samples underwent centrifugation at 16,000 rpm for 10 minutes at 4 °C (Eppendorf, SE, Germany). The resulting supernatants were transferred to Eppendorf tubes and concentrated using a vacuum evaporator (SpeedVac; Christ, Germany). To reconstitute the dried samples, 100 µL of a 1:1 mixture of mobile phase A (0.1% formic acid in dH2O) and B (0.1% formic acid in 50% methanol and acetonitrile) was added.” | Waters ACQUITY ultrahigh-pressure liquid chromatography (UPLC) system coupled with a Xevo G2-S QTOF mass spectrometer equipped with an electrospray ionization source (ESI) in positive and negative modes (ESI+, ESI−) | Progenesis QI v.3.0 software | small sample size and potential external factors such as lifestyle and diet were acknowledged. Statistical significance was rigorously assessed, and sensitivity analyses were performed to discern the impact of various parameters on model performance. | null | null | | [86] | Endometrial Cancer | plasma | 126 | In patients with endometrial cancer, three specific phosphatidylcholines were found at significantly decreased levels. A diagnostic model was established based on the ratios of acylcarnitine C16 to phosphatidylcholine PCae C40:1, proline to tyrosine, and two phosphatidylcholines PCaa C42:0 to PCae C44:5. | The samples were centrifuged at 3213×g for 10 minutes at 4°C. Following centrifugation, plasma was carefully aspirated and aliquots of 200 µL were transferred into 1.8 mL cryotubes, then stored at −80°C. Upon thawing at room temperature, the plasma samples were vortexed and subsequently centrifuged at 2750×g for 5 minutes at 4°C. | API 4000 triple quadrupole system equipped with a HTC PAL auto sampler | Analyst 1.6.2 | used targeted approach focusing only on acylcarnitines, phophatidylcholines (PCs) and sphingolipids | 85.25% | 69.23% | | For deep myometrial invasion, the model included the ratio of two hydroxysphingomyelins (SMOH C14:1 to SMOH C24:1) and two phosphatidylcholines (PCaa C40:2 to PCaa C42:6). | 81.25% | 86.36% | |||||||| | For lymphovascular invasion, the model included the ratio of two phosphatidylcholines (PCaa C34:4 to PCae C38:3) and the ratio of acylcarnitine C16:2 to phosphatidylcholine PCaa C38:1. | 88.89% | 84.31% | |||||||| | [73] | plasma | 54 | Levels of free fatty acids linoleic acid (C18:2) and myristic acid (C14:0) were lower in EC cases but higher levels of conjugated fatty acids such as acylcholines, acylcarnitines, and monoacylglycerols. Monoacylglycerol 1-oleoylglycerol (18:1) was strongly elevated in EC recurrence cases and could represent a marker of EC recurrence. | Lipids were extracted from serum samples by a heptane/ethyl acetate mixture after addition of a butanol/methanol solution. Phase separation was induced by addition of aqueous acetic acid and centrifugation | Shimadzu LC with nano PEEK tubing coupled to a Sciex SelexIon-5500 QTRAP | Sample Size: The study included a limited number of prospective endometrial cancer (EC) cases, although sample matching was used to reduce variations. HRT Impact: There is uncertainty about whether the 3-week period without hormone replacement therapy (HRT) before blood draw was enough to normalize metabolite levels affected by HRT. However, similar HRT use across groups likely minimized this potential bias. Biomarker Validation: The identified biomarkers (Strand et al., 2019)need validation in larger cohorts with quantitative methods. Their specificity for EC must be confirmed, particularly against other gynecological cancers and benign conditions, to ensure clinical applicability. | null | null | || | [89] | plasma | 40 | Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in EC patients. Methionine is an essential amino acid and a precursor for succinyl-CoA, homocysteine, cysteine, creatine, and carnitine. Consequently, this amino acid affects the lipid metabolic pathway | EDTA blood was obtained before primary surgery. The samples were centrifuged at 1600× g for 15 min and the plasma was stored at − 80 °C until analysis was performed. | AB SCIEX 4000 OTrap® mass spectrometer | MetaboAnalyst 4.0 software | small cohort size | null | null | | | [82] | plasma | 1706 | Variation in levels of glycine, serine, sphingomyelin C18:0 and free carnitine may represent specific pathways linked to endometrial cancer development. levels of free fatty acids linoleic acid (C18:2) and myristic acid (C14:0) were lower in EC cases as compared with control women | 1290 Agilent UPLC coupled to a QTRAP 4000 SCIEX mass spectrometer. | null | null | |||| | [94] | cervicovaginal fluid | 54 | Phosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy | NMR spectrometer equipped with a cryoprobe | 78% | 75%-80% | |||| | [84] | Tissue | 18 | Lipid metabolism was severely dysregulated and lipid metabolites (unsaturated lipids and fatty acids) were most highly upregulated in endometrial cancer with various amino acids, inositols, nucleobases, and glutathione also altered. Among the most important lipid-related alterations were increased phosphocholine levels (increased 70% in endometrial cancer) | Cell samples were treated with cold methanol to halt metabolism, followed by extraction using a dual-phase method. Dried methanol-quenched samples were reconstituted with chloroform/methanol (300 μL, 2:1, v/v), vortexed briefly (30 seconds), and centrifuged (16,000×g, 10 minutes). Ultrapure water (300 μL) was added to the resulting mixture, followed by vortexing and centrifugation as previously described. The aqueous and organic phases were carefully transferred to new sample tubes. To ensure maximal recovery of metabolites, the extraction process was repeated, and the resulting samples were combined and dried. Blank samples were also prepared to control for potential contamination from the extraction solvents | Bruker DRX600 spectrometer operating at 600.13-MHz 1 H NMR | TopSpin software | menopausal status of the patients studied was not known | null | null | | | Tissue: Approximately 5 mm3 (5–18.5 mg) of endometrial tissue was thawed and washed with a small amount of 0.9% saline solution (90 mg NaCl in 10 mL D₂O) before being transferred into a zirconia rotor (Bruker Biospin) | |||||||||| | [90] | Tissue | 74 | increased amino acids 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 frozen tissue samples were trimmed to fit 30 µL disposable Kel-F inserts, which were filled with a 5 µL cold solution containing 25 mM sodium formate in D₂O, used for shimming and locking | High resolution (400 MHz) magic angle spinning (HR-MAS) proton spectroscopy | TopSpin software | null | null | || | [85] | Tissue | 37 | stearamide and monoolein levels are increased in EC samples. 3 metabolites belonging to purine metabolism (inosine, xanthine, hypoxanthine) which are increased at the invasive front in EC. | Tissue samples were homogenized in a buffer containing 180 mM KCl, 5 mM3-[N-morpholino-propanesulfonicacid, 2 mM EDTA, 1 mMdiethylenetriaminepentaaceticacid, and 1 mM butylated hydroxytoluene at pH 7.3 using a Potter – Elvehjem device at 4°C. Protein concentration was measured using the Lowry assay with bovine serum albumin as a standard. Each sample (100 µ g protein) was resuspended in 30 µL of homogenization buffer and vortexed. | Agilent 1290 LC system coupled to an ESI-Q-TOF MS/MS 6520 instrument | null | null | ||| | [83] | Tissue | 56 | Glycerophospholipid class of metabolites was found to be upregulated in tumor tissues, including PCs, PEs, and PIs. Downregulation of the acylamido analogs of endocannabinoids such as palmitamide, stearamide and oleamide in EC. Significant changes in the lipidome including PC (16:0/20:5), PC (16:0/22:6), PE (16:0/22:6), PE (22:6/P-18:1) and PE (18:1/22:6) at different stages of cancer progression, as well as a significant increase in the levels of UDP-N-acetyl-D-galactosamine and arachidonic acid at advanced stages of EC. | To extract metabolites, each sample was supplemented with 90 µL of ice-cold methanol, then incubated at −20°C for 1 hour. Following centrifugation at 12,000 g for 3 minutes at room temperature, the resulting supernatants were collected, evaporated using a Speed Vac, and reconstituted in water containing 0.4% acetic acid and 2 ng/mL of deuterium-labeled docosahexaenoic acid (d4-DHA) in methanol as an internal standard (1:1, v/v). | Ultra Performance Liquid Chromatography coupled to Quadrupole Time-Of-Flight Mass Spectrometry | XCMS software | null | null | Correspondingly, lipid metabolism is predominantly dysregulated in EC tissues. Using mass-spectrometry, metabolomics analysis reveals significant upregulation of unsaturated lipids and FAs in EC compared to benign and healthy tissues derived from the endometrium [49,79–82]. Specific lipid species, including phosphatidylcholines (PCs) levels, were shown to be upregulated as much as 70%, while other classes of phospholipids, such as phosphatidylethanolamines (PEs) and phosphatidylinositols (PIs), were also reported to be higher in EC tumor tissues compared to healthy tissues [83–85]. Ultra Performance Liquid Chromatography coupled to Quadrupole Time-Of-Flight Mass Spectrometry (UPLC-QTOF/MS) analysis on plasma samples, however, three specific PCs (C40:1, C42:5 and C42:6) were reported to be significantly decreased [83,86]. Additionally, when compared to normal endometrial tissues, EC tissues show higher levels of fatty acid [84] and conjugated FAs (acylcholines, acylcarnitines, and monoacylglycerols) [73]. However, the plasma levels of FAs including linoleic acid (C18:2) and myristic acid (C14:0) are lower in EC patients compared to normal individuals. This suggests a potential mechanism whereby PCs and FAs are taken up from plasma into EC tissue for cancer cells to enhance membrane synthesis and hence their proliferation rate [87,88]. Amino acid-derived metabolites are also altered in EC samples compared to benign endometrial samples [89,90]. Mass spectrometry analysis reported increase in plasma methionine sulfoxide is strongly linked to poor survival rates in EC patients [89]. Methionine is also an essential amino acid and precursor for compounds like succinyl-CoA, homocysteine, cysteine, creatine, and carnitine, essential for regulating lipid metabolism [91]. Metabolomic study revealed that the differences between less and highly aggressive tumors are primarily linked to alterations in other derivatives of amino acid metabolism, including betaine, serine, and glycine. Across all levels of EC aggressiveness, elevated concentrations of valine, isoleucine, leucine, serine, and lysine are also consistently observed compared to non-cancerous tissue [90]. Metabolomic approaches have also been shown to discriminate EC grades besides its utility to identify cancerous lesions from healthy endometrial tissues. Metabolic diversity among various EC histological grades and stages in clinical samples has been demonstrated based on metabolite characterization and quantification [83,90,92]. For instance, Grade 1 EC tumors exhibit increased PC metabolism and gradually decreased with the aggressiveness of the tumor grade (between grade 2 and grade 3). Conversely, PE biosynthesis is increased in all grades of EC tumors, but highest in grade 3 tumors [90]. Advanced EC stages are also reported to be associated with high levels of UDP-N-acetyl-D-galactosamine and arachidonic acid [83]. As critical components of biological membranes, these metabolites play vital roles in processes, such as membrane fusion and cell division. Furthermore, they regulate the membrane curvature, promoting tumor proliferation and invasion in EC [93]. Leveraging the sensitivity of metabolomic tools to detect changes in metabolites in EC tissue grades attempts to formulate diagnostic models to characterize the aggressiveness and metastatic potentials of ECs have been reported [86]. Metabolite ratios include acylcarnitine (C16) to phosphatidylcholine (PCae C40:1), proline to tyrosine, and phosphatidylcholines PCaa C42:0 to PCae C44:5 were suggested as key biomarkers for general EC diagnosis. Reportedly, the ratios of hydroxysphingomyelins C14:1 to C24:1 and phosphatidylcholines PCaa C40:2 to PCaa C42:6 were employed to successfully identify deeper tumor invasion. For assessing lymphovascular invasion, the ratios of phosphatidylcholines PCaa C34:4 to PCae C38:3 and acylcarnitine C16:2 to phosphatidylcholine PCaa C38:1 were shown to assist in indicating more aggressive disease [86]. Evidently, metabolomic studies emphasize the importance of specific lipid metabolites as potential diagnostic and prognostic biomarkers in EC, with advanced analytical models improving the accuracy of EC detection and monitoring. Mass spectrometry remains the most established for omics-based analysis that can provide quantitative insights into metabolic reprogramming during malignant transformation. Unlike Raman spectroscopy, which offers real-time, label-free metabolic profiling, mass spectrometry excels in comprehensive molecular characterization, revealing intricate biochemical pathways involved in cancer progression. However, several limitations must be considered. First, the sensitivity and specificity ranges are not particularly high, the sensitivity ranges from 78% to 88.89%, and the specificity ranges from 69.23% to 86.36% [86,94]. Second, the reproducibility of data is challenging due to complex sample preparation processes and numerous variables, making the process time-consuming and inefficient for real-life screening purposes. Further, plasma metabolites can be influenced by various metabolic confounding factors, such as menstrual cycle stage, dietary habits, physical activity, and environmental conditions, which can potentially impact the interpretation of metabolomic profiles. 3. Raman spectroscopy Due to the ability to obtain molecular fingerprints, vibrational spectroscopy approaches, such as Raman spectroscopy, are now recognized as a powerful technique for analyzing biochemical changes at the molecular level. Raman spectroscopy enables the determination and characterization of chemical properties, allowing for the identification of molecules, examination of intramolecular bonds, and quantification of vibrational frequencies of chemical bonds. Its advantageous outputs have attracted significant interest in industries like geology, mineralogy, and electronics, and its accessibility ex vivo and in vivo makes it widely used for biomedical research. Raman spectroscopy has been extensively employed to diagnose diseases by discriminating diseased tissues from healthy ones based on biomolecular changes. In the context of endometrial cancer detection and diagnosis, Raman spectroscopy is increasingly employed in many emerging studies (Table 2). Raman spectroscopy probes the unique vibrational modes of proteins, carbohydrates, lipids, and nucleic acids in different cells and tissues, offering valuable structural and compositional insights. This technique has been shown to provide specific biochemical and metabolic changes associated with the onset of malignant transformation. Table 2. | Ref | Year | Authors/Research Group | Cancer/Disease | Sample | Instrument settings | Analysis Techniques | Performance | Main Findings | |||||| |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| | Sample type | Sample Size | Preparation | Substrate | Laser Intensity | Laser Wavelength | Microscope | Sensitivity | Specificity | |||||| | [99] | 2011 | Imran I. Patel | Endometrial Cancer | Tissue, ex-vivo | 2 benign endometrium, 2 EC (stage 1A & stage IIA) | Snap-frozen, cryo-sectioned to 20 um | Gold-coated (50 nm-thick) slides | 785 nm | PCA HCA MCR-ALS PCA-LDA | The combination of vibrational spectroscopy techniques with sophisticated computational analyses has the potential to provide novel insights into the structures of biomolecules in vivo. | |||| | [100] | 2020 | Edyta Barnas(Depciuch’s Group) | Endometrial Cancer | Tissue, ex-vivo | 16 EC, 12 atypical hyperplasia, 17 normal endometrium | 10 um FFPE sections | CaF2 slides | 500 mW | 1064 nm | PCA HCA | The employment of spectroscopy methods enables the discrimination between atypical hyperplasia and EC tissues from the normal endometrial tissue. | ||| | [105] | 2021 | Joanna Depciuch (Depciuch’s Group) | Endometrial Cancer | Tissue, ex-vivo | 16 EC, 6 endometrial polyp, 8 atypical hyperplasia, 12 atrophic endometrium, 17 normal endometrium | 10 um FFPE sections | CaF2 slides | 500 mW | 1064 nm | PCA HCA PLS RF C5.0 | The obtained Raman spectra demonstrated statistically significant variations in the chemical structure and compositions of tissues between EC samples and control samples across all analyzed Raman ranges, indicating a distinct correlation with the development of EC. | ||| | [109] | 2023 | Tze Hua Yeu | Endometrial Cancer | Cells, ex-vivo | 9 obese EC CAFs, 5 non-obese EC CAFs | Cultured and fixed with 3.7 %formaldehyde | Silicon wafer | 5 mW | 532 nm | 50x objectivemicroscope | PCA | Combining Raman spectroscopy with chemometric analysis offers a dependable method for characterizing metabolic changes in clinical samples, thereby offering valuable insights into obesity-related modifications in cancer-associated fibroblasts (CAFs), a crucial stromal component involved in EC tumourigenesis. | || | [104] | 2019 | Ugur Parlatan | Endometriosis | Bloodserum, ex-vivo | 49 patients, 45 healthy | Fresh blood serum, 0.5 mL | Quartzcuvette | 100 mW | 785 nm | 60x water immersion microscope objective | PCA | The integration of Raman spectroscopy technique with PCA and classification algorithms holds promise as a potential noninvasive diagnostic approach for endometriosis. | || | kNN | 78–80.5 % (training) 87.5–100% (test) | 84.6–89.7% (training) 100% (test) | |||||||||||| | SVM | 75.6% (training) 87.5% (test) | 84.6–87.1% (training) 100% (test) | |||||||||||| | [103] | 2022 | Zozan Guleken (Depciuch’s Group) | Endometriomas | Blood serum, ex-vivo | 50 patients, 50 healthy | Frozen blood serum, 4 uL | Goldsupport | 500 mW | 1064 nm | PCA HCA PLS | Raman spectroscopy emerges as a valuable tool in the diagnosis of endometrioma, and the vibrational characteristics of lipids present potential spectroscopic markers | ||| | kNN | 100% | 100% | |||||||||||| | C5.0 | 98% | 100% | |||||||||||| | RF | 100% | 100% | 3.1. Principles of Raman spectroscopy Raman spectroscopy is a nondestructive method for analyzing molecular vibrations based on the Raman effect, involving the interaction of light with molecules in a material. This technique utilizes monochromatic laser radiation to induce inelastic scattered-photon analysis. In contrast to Rayleigh elastic scattering, where there is no change in the energy of detected photons, Raman inelastic scattering results in either energy loss (stokes) or energy gain (anti-stokes) by the scattered photons. These shifts in photon energy reflect changes in the original and final vibrational energy states of the molecular interaction [95]. Roughly 1 in 107 photons are expected to undergo Raman inelastic scattering, while the remaining photons experience Rayleigh elastic scattering. In general, the more intense Stokes photons are favored for detection over anti-stokes photons due to higher signal intensity, detecting these inelastically scattered photons produces a spectrum of peaks. Therefore, the Raman spectrum illustrates the intensity of inelastically scattered light as a function of the Raman shift, expressed in wavenumbers [96]. The specific location, frequency, and spacing of these Raman peaks offer insights into various vibrational levels, providing information about molecular configuration, secondary structure, and interactions, including the chemical micro-environment of molecular subgroups [97]. Raman spectroscopy offers numerous advantages compared to other diagnostic tools. It rapidly provides real-time diagnostic results with significantly improved signal-to-noise ratios. This technique excels in characterizing tissues at a molecular level with high sensitivity and specificity, making it extensively used for biomedical sample diagnosis [98]. The key components of a Raman spectroscopic setup include an excitation source, often a monochromatic laser, an optical path to efficiently direct light onto the specimen while minimizing power loss, an isolation system that eliminates Rayleigh elastic scattered photons and efficiently collects Raman scattered photons, and a detector. Figure 2 illustrates the schematic of Raman spectroscopy, depicting the optical pathways and isolation mechanisms involved. 3.2. Metabolite changes in endometrial cancer measured by Raman spectroscopy Recent studies increasingly report metabolic alterations across healthy, precancerous, and cancerous endometrial tissues. This paves the way for Raman spectroscopy-based analysis contributions to a comprehensive reference library with spectral biomarkers unique to EC. This section discusses existing studies which report the ability of Raman spectroscopy to analyze EC progression, its utility to identify precancerous lesions, and expanding its applicability across diverse EC-related research contexts. Raman spectroscopy was first employed to generate Raman images of endometrial tissue architectures (e.g., glandular epithelium, stroma, glandular lumen, and myometrium) in 2011 [99]. In this study, commonly used multivariate analysis methods, including principal component analysis (PCA), and hierarchical cluster analysis (HCA), were compared to the multivariate curve resolution-alternating least squares (MCR-ALS) for their ability to provide the best bio-molecular contract images with clear and defined margins of histological features. MCR-ALS provides the best clearly defined Raman images of endometrial tissues that correlated to standard H&E-stained tissue sections. Additionally, the study implies Raman spectroscopy to characterize different stages of EC tissue samples. Specific metabolite changes including amide, proline, and nucleic acids were highlighted to be associated with more aggressive EC tissues (Table 3). This study highlights the potential utility of Raman spectroscopy in the diagnosis and staging of EC based on altered metabolites. Table 3. | Raman Shift (cm−1) | Vibration Assignment | Changes to peaks in pathological endometrial tissues compared to normal | Refs | |---|---|---|---| | 1337 | Adenine | Shifted to higher frequency | [99] | | 1654, 1659, 1685 | C = C groups in unsaturated fatty acids, Amide I/lipid | Reduced intensity in obese EC samples | [99,109] | | 1436, 1447 | CH2 bending from lipids and proteins | Shifted to higher frequency | [99,100] | | 1003 | Phenylalanine | Shifted to higher frequency | [99] | | 784 | Cytosil/uracil | Shifted to higher frequency | [99] | | 1620 | Tyrosine and Tryptophan | Biomarker identified by PCA loading | [99] | | 1650, 1654, 1668,1685 | Amide I | Shifted to lower frequency, biomarker for more advanced EC stage | [100,105] | | 812, 821 | Proline, PO2− stretching from nucleic acids | Shifted to higher frequency | [105] | | 953 | Valine | Biomarker identified by PCA loading | [99] | | 1359–1374 | Tryptophan | Shifted to higher frequency | [100,105] | | 1776,1792 | C = O vibrations from lipids | Shifted to lower frequency | [99] | | 2852, 2871, 2873 | CH2 vibrations from lipids | Absence in normal tissue | [100] | | 2758–2798 | CH3 vibrations from lipids | Shifted to lower frequency | [100,105] | | 876 | C-C stretching from proline and hydroxyproline | Shifted to lower frequency | [105] | | 1248, 1302 | Amide III (collagen assignment) | Shifted to higher frequency | [105,109] | | 1005 | C = CH bending vibration of ground state beta carotene | Reduced intensity | [104] | | 1156 | C-C stretching bond vibrations of beta carotene | Reduced intensity | [104] | | 1520 | C = C stretching bond vibrations of beta carotene | Reduced intensity | [104] | | 1446, 1450,1455 | CH2 bending vibrations of lipids | Increased intensity in obese EC samples | [109] | | 2931–2935 | CH3 symmetric stretch of proteins | Reduced intensity in obese EC samples | [109] | The capability of biospectroscopies, Fourier Transform Infrared (FTIR), and Raman spectroscopy to identify and differentiate EC, atypical hyperplasia, and normal endometrial tissues were consistently compared to highlight their potential utility in the field [100]. Consistent with heightened FA synthesis [64] and oncogenic protein biosynthesis [101] in EC cells, significant shifts of peaks corresponding to lipids and amide I in EC tissues are observed when comparing atypical hyperplasia and normal endometrial tissues. Particularly, the absence of peak 1011 cm−1 (stretching vibrations of CO, CC, and OCH from the ring of polysaccharides and pectin) is observed in pathological endometrial tissues in comparison with normal endometrial tissues. It is worth noting that only Raman spectroscopy, not FTIR, is capable of detecting significant lipid metabolite changes (Raman shifts at 1723 cm−1, 2758 cm−1, and 2871 cm−1) due to the difference in physical principles of both techniques. Additional PCA and HCA analysis indicate that Raman spectroscopy can exclusively distinguish normal endometrial tissues from atypical hyperplasia and cancer tissues [100]. Hence, the coupling of Raman spectroscopy with multivariate statistical methods may provide indispensable metabolite profiling for discriminating benign and malignant endometrial lesions. Endometriosis is associated with an increased risk of EC [102]. Interestingly, Raman spectroscopy demonstrated its potential utility to distinguish precancerous lesions that could lead to EC carcinogenesis, such as endometriosis and endometriomas [103,104]. The first Raman spectroscopy-based classification model as a noninvasive diagnostic technique for endometriosis was reported by Parlatan’s group [104]. By analyzing the shifts in 1005 cm−1 (C = CH bending vibration of ground state beta carotene content), 1156 cm−1, and 1520 cm−1 bands corresponding to C–C and C=C stretching bond vibrations, beta carotene was identified as a potential biomarker for endometriosis. Raman spectroscopy was also used as an alternative platform to assess the volume of endometriomas, a cyst formed in the ovary due to the accumulation of old, brown blood after menstruation, which is also an indicator of severe endometriosis [103]. Raman shifts between 2956 cm−1 (CH2 lipid vibrations) and 2840 cm−1 (CH3 lipid vibrations), both corresponding to lipid vibrations, were able to differentiate the serum collected from healthy and unhealthy women. Specifically, 2929 cm−1, originating from CH2 group of lipids, is strongly correlated to the volume of endometriomas, and this was proposed as a spectroscopic marker of endometriosis. Apart from the ability of Raman spectroscopy to detect metabolite changes between benign and malignant lesions, Raman spectroscopy captures the transformation process of normal to cancerous endometrial tissues, with the inclusion of more pathological tissue samples (atrophic endometrium and endometrial polyp) [105]. Raman spectra of EC tissues exhibited the greatest number of peaks having significant shifts compared to the other diseased endometrial tissue samples, such as endometrial polyp, atypical hyperplasia, and atrophic endometrium [105]. Specifically, peaks originating from 821 cm−1 (proline, hydroxyproline, tyrosine, PO2− stretching from nucleic acids), 876 cm−1 (C–C stretching from proline and hydroxyproline), 1302 cm−1 (Amide III collagen assignment), 1376 cm−1 (tryptophan), 1685 cm−1 (Amide I), and 1792 cm−1 (C = O stretching from lipids) are among the statistically significant shifted peaks in Raman spectra of EC compared to Raman spectra from normal endometrial tissue. Notably, these shifts were more significant for samples with a more developed carcinogenesis process, consistent to previous study reporting similar findings [99]. Additionally, Raman peak shifts in different endometrial tissues are exclusive in Raman spectroscopy analysis but not in FTIR with an accuracy ranging as high as 76.36% to 92.73% [105]. These findings further emphasize the unique value of Raman spectroscopy, among other biospectroscopies, as a revelatory tool for uncovering cancer progression, especially in EC. It is well established that obesity, characterized with elevated plasma-free FAs, can augment the lipogenic phenotype exhibited by cancer cells [106,107]. In EC, we reported that obesity causes treatment resistance by modulating metabolic activities in cancer-associated fibroblasts (CAFs), a key pro-tumoral components in the tumor microenvironment (TME) [108]. Obesity-driven lipid phenotype in CAFs was captured via Raman spectroscopy for the first time in our recent findings [109]. Alterations in lipid metabolism of obese CAFs are reflected in the Raman spectra between 2875 cm−1 and 1446 cm−1, corresponding to lipids and saturated lipid bands, respectively. This study underscores the impact of obesity on the EC TME. However, gaps remain in understanding how obesity drives metabolic reprogramming in EC and Raman spectroscopy presents a promising tool to bridge this gap. Future research leveraging Raman spectroscopy to predict treatment responses, particularly among high-risk obese EC patients, could revolutionize its clinical application in EC management. Collectively, Raman spectroscopy demonstrated a diagnostic capability comparable to mass spectrometry in distinguishing EC from other precancerous lesions through minor changes in Raman spectral associated with metabolite changes, such as changes in nucleic acids, lipids, and proteins/amide. Furthermore, Raman spectroscopy demonstrates superior sensitivity and specificity compared to FTIR, as it can detect crucial spectral peaks that FTIR fails to resolve. These advantages position Raman spectroscopy as a highly promising diagnostic tool for EC, offering a rapid, nondestructive, and highly sensitive approach that could enhance early detection and improve clinical outcomes. Despite the large amount of data generated from Raman spectra of different preparations of endometrial tissue samples, there is a lack of uniformity on the protocols employed across studies. The difference of preparation workflow for the same sample may lead to significantly different Raman spectra and even result in the discovery of false biomarkers. Therefore, it is of paramount importance to optimize and standardize sample preparation workflows for different types of biological samples, such as bodily fluids, tissues, and primary cells. 3.3. Independency of Raman spectroscopy on sample types The sensitivity and specificity of Raman spectroscopy are important metrics for clinical evaluation, which can be influenced by several factors, such as sample preparation and instrumentation. Various sample preparation methods were employed based on the sample type to ensure high-quality Raman spectra acquisition [100,103–105,109]. The wavelength of excitation lasers is one of the most critical aspects to be considered in experimental design for Raman spectroscopy analysis for biological samples. Typical wavelengths available are laser-specific and fall between mid-UV (>200 nm) and the near-infrared (NIR) (< 1.1 μm). Fluorescence contribution to the Raman spectrum can be avoided if the sample is excited with a wavelength that falls outside its profile, hence generating a cleaner Raman spectrum. However, higher laser wavelengths have lower photon energy, which consequently leads to a reduced sensitivity [110]. Furthermore, optimizing laser power and exposure time is also important to improve sensitivity while minimizing photodamage and phototoxicity. Hence, lasers with NIR wavelength, typically 785 nm, have been extensively used in biological studies as they can effectively avoid interference of fluorescence contribution, and the relatively lower photon energy which avoids photodamage [111]. Another key factor that contributes to the final Raman spectra of a biological sample is the sample preparation process. The tissue sample is the most widely used for Raman spectroscopy analysis of EC [99,105]. Tissue samples were processed differently, by either snap-frozen in liquid nitrogen and cryo-sectioned or processed using the formalin fixation and paraffin-embedded (FFPE) method. Substrate used for sample analysis is also different, including gold-coated slides and CaF2 slides. Depending on the resource on-site, both tissue sample preparation methods are widely used for the Raman spectroscopy-based pathological diagnosis [112,113]. Fresh-snap frozen tissues are preferred as they preserve more comprehensive qualitative and quantitative compositional information with no disturbance from the organic solvents and paraffin [114]. Disadvantages of this method when analyzing large amounts of frozen tissue samples are time and the risk associated with thawing cycles of the tissues and, hence, causing loss of molecular information. Organic solvents and paraffins used in FFPE could induce coagulation of globular proteins that exist in the cytoplasm and cause degradation or loss of some cellular components [115]. However, FFPE samples are more accessible in the hospital tissue bank and stable for Raman spectroscopy analysis. Nevertheless, by using appropriate chemical or digital dewaxing methods, paraffin peaks could be removed to extract a metabolic fingerprint that is comparable to fresh frozen tissue [116,117]. Another challenge of analyzing live tissues is the inherently weak Raman signal, which can be compromised by the background emanating from the substrate and the sample itself. Hence, substrate selection is a critical during sample preparation for Raman spectroscopy to prevent unwanted background signals and cosmic background that would interfere with the sensitivity and specificity of Raman analysis. Expensive substrates such as gold-coated slides, CaF2 slides, and quartz cuvette were commonly used [100,105,118–120]. Notably, Yeu et al. had demonstrated sample preparation of cancer cells on silicon wafers as a potential cost-effective single-use sterile substrate for Raman spectroscopy analysis [109]. Existing studies on Raman spectroscopy in endometrial samples have employed diverse sample preparation methods, substrates, and instrument settings. These variations significantly influence the sensitivity and specificity of the spectral data acquired, potentially leading to discrepancies in findings and interpretations. 3.4. Sensitivity and specificity of Raman spectroscopy in endometrial cancer The sensitivity and specificity of Raman spectroscopy are crucial factors to consider for its application as a reliable diagnostic tool for EC. Several studies reported a high degree of sensitivity and specificity of Raman spectroscopy in gynecological cancer diagnosis, with 100% sensitivity and 85% specificity, 98.5% sensitivity, and 99% specificity in ovarian and cervix cancer, respectively [20,121]. Hence, in this section, we review the efficacy of Raman spectroscopy in classifying and distinguishing pathogenic endometrial tissues from healthy samples. To assess the sensitivity and specificity of Raman spectroscopy, various machine learning algorithms were employed. Most commonly used machine learning algorithms were C5.0 decision tree algorithm, Random Forest (RF), k-Nearest Neighbours (kNN) and Support Vector Machines (SVM) [122]. Ugur et al. used kNN and SVM to evaluate the performance of Raman spectroscopy in differentiating samples from patients with endometriosis and healthy individuals [120]. The authors analyzed 80 measurements for training and 14 measurements for test set. For kNN method, the sensitivity and specificity for the training set were reported to be 78–80.5% and 84.6–89.7%, respectively, while these values were increased for the test set by up to 87.5–100% and 100% sensitivity and specificity. For SVM method, these values were slightly lower or the same whereby the sensitivity and specificity for the training set were 75.6% and 84.6–87.1%, and for the test set were 87.5% and 100%. It was worth noting that both sensitivity and specificity for the test set were higher compared to that of the training set. The three machine learning models, RF, C5.0, and kNN have also been previously applied to obtain information about the sensitivity and specificity of Raman spectroscopy in discriminating endometrioma and healthy endometrial tissue [103]. The obtained classification quality presented in Table 1 was almost perfect for each machine-learning model. Additionally, C5.0 algorithm was able to build a decision tree to distinguish patients with endometrioma and healthy individuals solely on one Raman shift, 1641 cm−1(Amide I). Collectively, machine learning algorithms, including kNN and SVM can enhance the sensitivity and specificity of Raman spectroscopy in predicting diseased tissues, making Raman spectroscopy a promising diagnostic tool for EC. 4. Future prospect of Raman spectroscopy for endometrial cancer screening and treatment monitoring 4.1. Screening and diagnosing EC A major challenge in achieving early and effective EC screening, particularly in obese patients and those with overlapping benign and malignant symptoms, lies in the nonspecific nature of clinical presentations, such as irregular uterine bleeding and pelvic pain [123]. These symptoms are common across a spectrum of endometrial pathologies, including endometrial hyperplasia, polyps, and benign endometriosis, making differential diagnosis challenging. Currently, initial screening and diagnostic approaches for EC rely primarily on structural imaging modalities, such as transvaginal ultrasound and hysteroscopy, alongside histopathological evaluation via endometrial biopsy [124–126]. The “gold standard,” biopsy [127], is time-consuming, highly susceptible to human error [128] and lacks standardized criteria, leading to high variability and diagnostic delays [129]. Additionally, biopsy adequacy is affected by menopausal status, endometrial thickness, and endometrial lesion type [130]. In addition, while biopsy with conventional histopathology is effective in diagnosing EC, its reliability in differentiating EC and endometrial hyperplasia is low [131]. Other imaging methods, such as ultrasound, hysteroscopy, and magnetic resonance imaging (MRI) also have drawbacks. Ultrasound relies on operator expertise and in obese patients, excess adipose tissue can disrupt sound wave transmission, hence reducing image quality [123]. Similar to biopsy, ultrasound is unable to identify whether increased endometrial thickness is due to benign lesions or malignant disease [126]. Alternatively, although hysteroscopy provides direct endoscopic visualization of the endometrial cavity, but it is invasive, often causing patient discomfort and complications, such as bleeding, infection, and uterine damage after this invasive procedure. Advanced imaging techniques like MRI, aid in disease staging and monitoring but are not routinely used for early diagnosis. These limitations highlight the urgent need for metabolite-based screening and diagnostic tools, which offer greater specificity and accuracy in detecting EC compared to conventional structural assessment methods. As highlighted, Raman spectroscopy serves as an excellent potential candidate as an efficient in vivo tool to probe metabolite changes alongside cancer development. In cancers with established spectra databases, clinical trial integrating Raman spectroscopy in screening and diagnosis had been reported. Hand-held single-point Raman spectroscopy probe has been employed in several pilot clinical studies and shown efficacy in diagnosis of breast [15], prostate [132], lung [133] and ovarian [134] cancer. Notably, various ongoing clinical trials integrating Raman spectroscopy as a biomarker prediction tool to detect early-stage cancer and monitor cancer immunotherapy reported encouraging results [135]. Several challenges must be addressed to fully its clinical potential in EC. Furthermore, metabolite profiling provided by Raman spectroscopy can be integrated with other omics technologies for improving the screening and detection of EC. Combining multi-omics approaches with Raman spectroscopy could harness the benefits of both methods. Omics technologies can offer high specificity and comprehensive information, while Raman spectroscopy provides high sensitivity and detailed spatiotemporal data. Various omics approaches have successfully revealed the intracellular dysregulation that initiates and progresses EC, contributing toward screening, diagnosis, recurrence identification, and the associated beneficial treatment pathway [136]. An excellent example is the integration of TCGA-based molecular prognostic groups of EC into ESGO-ESTRO-ESP guidelines. The POLE-mutant, mismatch repair (MMR)-deficient, p53-abnormal, and “no specific molecular profile” (NSMP) groups have distinguished mutational load and distinctive molecular surrogate markers [137]. Future Raman studies could explore metabolites associated with each of the TCGA EC molecular subtypes, potentially providing more precise prognostic discrimination than traditional histological subtyping of EC [93]. Additionally, considering that EC prevalence is shifting toward a younger age, evidently driven by obesity and metabolic dysregulation, complementing the TCGA-based markers with metabolite profiles from Raman spectroscopy could further refine the prognosis based on the phenotype of interest. Indeed, compared with other TCGA groups, POLE-mutant ECs are associated with younger age (mean 58.6 years), lower BMI (mean 27.2), an earlier stage of EC and highly favorable prognosis [137], highlighting the potential corresponding metabolic signature that could be detected by Raman in early diagnosis. Multi-omics approach has also identified several targets for early diagnosis (TMEFF2, RNF183) as well as prognostic biomarkers for EC (ZBTB7A, BTG1, PTGDS). Several genes/proteins have also been correlated with EC progression, including HOXB9, LGR5, SST, ZNF558, and PTGDS [138]. Raman can be combined with this diagnostic or prognostic transcriptomics to comprehensively evaluate and quantify the impact of gene expression changes on the biochemical composition of cells. Such interdisciplinary approaches offer a robust and comprehensive understanding of disease phenotypes, enhancing the capabilities of each omics method while addressing their individual limitations to comprehend disease heterogeneity [139]. Notably, sample preparation for Raman spectroscopy is less complicated and hence imposes lesser human error, although a standardized sample preparation procedure is warranted for different types of samples. The straightforward nature of sample preparation for Raman analysis significantly decreases the time required from patient sample collection to diagnostic results, thereby enhancing the efficiency of screening and diagnosis processes. Unlike imaging techniques, Raman spectroscopy result is not affected by patient conditions (pre- or post-menstrual cycle, endometrium thickness, etc.) as Raman spectroscopy measures metabolite changes instead of pathophysiological structural changes [103]. 4.2. Treatment evaluation and monitoring of EC Early detection significantly enhances the effectiveness of EC treatment, resulting in excellent survival rates. Surgery and radiation therapy aim to halt disease progression, while chemotherapy helps minimize the risk of metastasis. Pharmacological treatments, including neoadjuvants and adjuvants, are administered based on the disease stage to optimize therapeutic outcomes [140]. These treatments are administered orally or intravenously over a long period, reducing the possibility of cancer recurrence [141]. Hence, there is a pressing need for the development of a straightforward, noninvasive, and high-throughput analytical method to assess the efficacy of drug candidates and predict acquired drug resistance. While clinical utility specifically for monitoring treatment outcomes in EC is yet to be explored, in other cancer types, Raman spectroscopy demonstrated to be valuable for monitoring and follow-up treatments. For instance, Raman spectroscopy findings showed that triple-negative MDA-MB-231 breast cancer cells responded to Trametinib, an ERK pathway inhibitor while showing no response to Alpelisib, an mTOR pathway inhibitor [142]. This response was associated with a collective decrease in DNA, membrane phospholipids, amino acids, lipids, and FAs. In contrast, estrogen receptor-positive MCF-7 breast cancer cells displayed resistance to Trametinib, exhibiting minimal metabolic changes and no clear classification between treatment groups in PCA analysis. Additionally, Raman spectroscopy enables early, label-free detection of metabolic changes in non-small cell lung cancer following radiation, with measurable effects seen as soon as 2 h post-irradiation, corresponding to increased tumor hypoxia [143]. It also detected radiation-induced glycogen accumulation from 1 to 10 days post-irradiation, reflecting metabolic changes linked to tissue reoxygenation [144]. In the surgical management of EC, minimally invasive surgery (MIS), including laparoscopic and robotic approaches, is associated with shorter hospital stays, reduced postoperative analgesia requirements, faster recovery, and enhanced quality of life [145]. However, achieving adequate resection margins to ensure oncological safety remains a significant challenge [146]. Raman spectroscopy has been implemented to guide surgical margins in oncological surgeries [147–149]. Real-time Raman intraoperative systems have been shown to discriminate cancerous tissue from normal breast tissue with a sensitivity of 100% and specificity of 100% [150]. Furthermore, the real-time intraoperative Raman system successfully distinguished dense and low-density cancer infiltration from benign brain tissue with a sensitivity of 93% and specificity of 91%, while the patient was undergoing the surgery [151]. Ultimately, the development of portable Raman devices and fiber-optic probes [152] may facilitate real-time, noninvasive screening, offering a practical alternative to traditional biopsy methods. Raman spectroscopy’s capability to detect subtle molecular changes could improve treatment monitoring by predicting therapeutic responses and emerging drug resistance, leading to more adaptive interventions. With ongoing advancements in optical technology, AI-driven spectral analysis and machine learning, Raman-based diagnostics will become more accessible and integrated into routine clinical workflows. 4.3. Key challenges in advancing Raman spectroscopy for EC Raman spectroscopy remains limited for metabolomics due to its qualitative nature. Unlike mass spectrometry or nuclear magnetic resonance, Raman also lacks comprehensive and validated metabolite libraries. Many metabolite reference spectra are missing or poorly characterized, limiting confident peak assignments. Hence, a major challenge in the clinical translation of Raman spectroscopy is the establishment of an extensive spectral database and a standardized analytical framework specific to EC. Variability in spectral data due to differences in spectrometer configurations presents a significant hurdle, necessitating strategies to minimize inconsistencies across different instruments and experimental setups. A critical limitation also lies in optimizing Raman spectral quality while reducing acquisition time. The accuracy and reproducibility of Raman spectra are highly influenced by background noise, laser stability, and instrument variability [153]. Additionally, factors, such as laser wavelength, power settings, integration time, and sample preparation methods significantly impact spectral consistency and reproducibility. To mitigate these challenges, future research should focus on optimizing data acquisition protocols to enhance spectral resolution, minimize signal-to-noise ratio, and develop standardized sampling procedures for diverse biological specimens, including tissue, plasma, bodily fluids, and cells. Improving instrument calibration and detection stability is also important to ensure consistency across different Raman platforms. Data analysis of Raman spectra remains a significant challenge, particularly in identifying subtle spectral variations and ensuring high-accuracy spectral interpretation. The vast amount of spectral data generated necessitates robust and precise computational approaches. Recent advances in artificial intelligence (AI) [135,154] and deep learning (DL) [155,156] have shown substantial promise in analyzing Raman spectral and imaging data in various cancers. 5. Conclusion Raman spectroscopy has the potential to revolutionize EC diagnostics and treatment monitoring by enabling real-time, label-free metabolic profiling. Its ability to detect biochemical changes in lipids, proteins, and nucleic acids provides a powerful approach for identifying metabolic shifts associated with disease progression and therapeutic response. For broader clinical application, Raman-based technology must accurately distinguish key metabolic signatures of EC subtypes, precancerous states, and normal tissue variations. The development of a refined workflow integrating Raman spectroscopy with existing omics-based technologies could enhance disease classification based on metabolic changes. Additionally, standardization and optimization are essential for clinical adoption, alongside artificial intelligence and machine learning to streamline spectral data interpretation. Despite these challenges, Raman spectroscopy remains a promising metabolomic tool with the potential to significantly advance EC detection, classification, and treatment strategies. 6. Future perspectives Over the next decade, Raman spectroscopy is expected to revolutionize the diagnosis and treatment monitoring of EC. Its integration with advanced imaging techniques, such as MRI, could improve surgical precision by providing real-time biochemical data. A recent pilot study involving 18 prostate cancer patients demonstrated the feasibility of this approach, where preoperative multiparametric MRI combined with intraoperative transrectal ultrasound guided a Raman spectroscopy needle to detect metabolic changes in situ before brachytherapy, enabling precise cancer lesion identification [132]. Integrating AI-driven algorithms with Raman spectroscopy could enhance automation, diagnostic accuracy, and spectral reproducibility, potentially reducing reliance on traditional histopathological assessments. Future research should focus on refining machine learning models for Raman-based EC diagnostics, ensuring their robustness, interpretability, and clinical validation. Given the increasing prevalence of obesity, a major risk factor for EC, future research should focus on utilizing Raman spectroscopy to investigate obesity-driven metabolic alterations in cancer cells. This could provide deeper insights into disease progression and identify novel therapeutic targets, further advancing personalized medicine in EC management. Acknowledgments The authors would like to thank the staffs of the Translational Core Laboratory (TCL lab) for their assistance and support. Funding Statement This work was funded by the Malaysian Ministry of Higher Education (MOHE) Fundamental Research Grant Scheme (FRGS) [FRGS/1/2021/SKK03/UM/02/1]. Article highlights Endometrial cancer (EC) is closely linked to obesity, with metabolic reprogramming influencing both cancer progression and treatment outcomes. The clinical application of Raman spectroscopy in EC necessitates a comprehensive spectral database, standardized workflows, and large-scale clinical validation. Current screening methods for EC lack the required sensitivity, specificity, and accuracy, while diagnostic approaches are invasive and costly. Metabolomic studies have demonstrated the ability to differentiate EC based on metabolic profiles; however, they are limited by low reproducibility and complex sample preparation requirements. Raman Spectroscopy offers a noninvasive approach by generating “metabolite fingerprints,” showing promise for EC screening and diagnosis. Raman Spectroscopy is increasingly employed to detect treatment-induced metabolite changes in cancers, positioning it as a promising tool for monitoring treatment outcomes in EC patients. Abbreviations - EC Endometrial cancer - BMI Body mass index - OXPHOS Oxidative phosphorylation - LDL Low density lipoprotein - HDL High density lipoprotein - PMS Premenstrual syndrome - PMDD Premenstrual dysphoric disorder - PC Phosphatidylcholines - LPC Lysophosphatidylcholines - PE Phosphatidylethanolamines - LPE Lysophosphatidylethanolamines - ECM Extracellular matrix - MS Mass spectrometry - BEH Benign endometrial hyperplasia - EIN Endometrial intraepithelial neoplasia - PCOS Polycystic ovary syndrome - LC-HRMS Liquid chromatography – high resolution mass spectrometry - PTEN Phosphatase and tensin homolog - PI3K Phosphatidylinositol-3 kinase - mTOR Mammalian target of rapamycin - ITGA7 Integrin α7 - TME Tumor microenvironment - GLUT Glucose transporter - GLUT1 Glucose transporter 1 - GLUT6 Glucose transporter 6 - LDH Lactate dehydrogenase - ATP Adenosine triphosphate - TCA Tricarboxylic acid cycle - FA Fatty acid - FASN Fatty acid synthase - PI Phosphatidylinositols - VLDL Very low-density lipoprotein - NMR Nuclear magnetic resonance - UPLC Ultra-performance liquid chromatography - TOF Time of flight - GC Gas chromatography - GCMS Gas chromatography – mass spectrometry - SMOH Hydroxysphingomyelins - PCA Principal component analysis - HCA Hierarchal cluster analysis - MCR-ALS Multivariate curve resolution-alternating least squares - FFPE Formalin fixation and paraffin-embedded - PLS Partial least squares - RF Random forest - CAF Cancer-associated fibroblasts - kNN K-nearest neighbors - SVM Support vector machines - FTIR Fourier transform infrared - NIR Near infrared - CT Computed tomography - MRI Magnetic resonance imaging - WHO World health organisation - NSCLC Non-small cell lung cancer - MIS Minimally invasive surgery Author contribution Tze Hua Yeu, Amira Hajirah Abd Jamil, and Ivy Chung were involved in the conceptualization, organizing, and writing the manuscript, Tze Hua Yeu prepared the tables. Intan Sofia Omar, Soke Chee Kwong, Nur Akmarina B M Said, and SF Abdul Sani wrote and reviewed the manuscript. Amira Hajirah Abd Jamil and Ivy Chung reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Disclosure statement The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership, or options, expert testimony, grants, or patents received or pending, or royalties Reviewer disclosure Peer reviewers on this manuscript have no relevant financial or other relationships to disclose Writing disclosure No writing assistance was utilized in the production of this manuscript. Ethical conduct of research The authors state that they have obtained appropriate institutional review board approval and/or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

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