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

Enhancing endometrial cancer detection: Blood serum intrinsic fluorescence data processing and machine learning application

Endometrial cancer (EC) is the most prevalent cancer within the female reproductive system in developed countries. Despite its high incidence, there is currently no established laboratory screening test for EC, making early detection challenging. This study introduces an innovative, minimally invasive, and cost-effective method utilizing three-dimensional fluorescence analysis combined with machine learning algorithms to enhance early EC detection. Intrinsic fluorescence of blood serum samples was measured using a luminescence spectrophotometer, which captured fluorescence spectra as synchronous excitation spectra and visualized them through wavelength contour matrices. The spectral data were processed using machine learning algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), along with exploratory techniques such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Fluorescence ratios R300/330 and R360/490, indicative of altered tryptophan metabolism and redox state changes, were identified as fluorescent spectral markers and represent key metabolic biomarkers. These ratios demonstrated high diagnostic efficacy with AUC values of 0.88 and 0.91, respectively. Among the ML algorithms, LR and RF exhibited high sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), showing significant promise for clinical application. After optimization, LR achieved a sensitivity of 0.94, specificity of 0.89, and an impressive AUC value of 0.94. The application of this novel approach in laboratory diagnostics has the potential to significantly enhance early detection and improve prognosis for EC patients.

Screening Strategies to Improve Early Diagnosis in Endometrial Cancer

Endometrial cancer is the most common gynecological malignancy in high-income countries and the sixth most common cancer in women. Overall incidence has risen in the last few decades as a consequence of the increase in the prevalence of its risk factors, mainly obesity and the aging of the population, and although diagnoses have increased across all age groups, the incidence rates have doubled in women under the age of 40 years. The survival rates of endometrial cancer are highly dependent on its stage at diagnosis, bringing to the fore the importance of early diagnosis. The aim of a screening strategy in this type of tumor should be to detect the disease in the pre-invasive or early stage (before developing myometrial invasion), which would improve cure rates, reduce the morbidity associated with aggressive treatment and offer uterus-sparing management options for younger women. The ideal screening tool in this scenario would be a minimally invasive, inexpensive and easy-to-perform test or auto-test, which could be implemented in a routine gynecologic checkup of patients at-risk or in the general adult population. In this comprehensive review, we aim to define the populations at higher risk of developing endometrial cancer, to assess the performance of current diagnostic tools when used in a screening setting and to discuss the accuracy of new molecular screening strategies.

Cervicovaginal Metabolome and Tumor Characteristics for Endometrial Cancer Detection and Risk Stratification

PURPOSE

Endometrial cancer is highly prevalent and lacking noninvasive diagnostic techniques. Diagnosis depends on histological investigation of biopsy samples. Serum biomarkers for endometrial cancer have lacked sensitivity and specificity. The objective of this study was to investigate the cervicovaginal environment to improve the understanding of metabolic reprogramming related to endometrial cancer and identify potential biomarker candidates for noninvasive diagnostic and prognostic tests.

EXPERIMENTAL DESIGN

Cervicovaginal lavages were collected from 192 participants with endometrial cancer (n = 66) and non-malignant conditions (n = 108), and global untargeted metabolomics was performed. Using the metabolite data (n = 920), we completed a multivariate biomarker discovery analysis.

RESULTS

We analyzed grade 1/2 endometrioid carcinoma (n = 53) and other endometrial cancer subtypes (n = 13) to identify shared and unique metabolic signatures between the subtypes. When compared to non-malignant conditions, downregulation of proline (P < 0.0001), tryptophan (P < 0.0001), and glutamate (P < 0.0001) was found among both endometrial cancer groups, relating to key hallmarks of cancer including immune suppression and redox balance. Upregulation (q < 0.05) of sphingolipids, fatty acids, and glycerophospholipids was observed in endometrial cancer in a type-specific manner. Furthermore, cervicovaginal metabolites related to tumor characteristics, including tumor size and myometrial invasion.

CONCLUSIONS

Our findings provide insights into understanding the endometrial cancer metabolic landscape and improvement in diagnosis. The metabolic dysregulation described in this article linked specific metabolites and pathophysiological mechanisms including cellular proliferation, energy supply, and invasion of neighboring tissues. Furthermore, cervicovaginal metabolite levels related to tumor characteristics, which are used for risk stratification. Overall, development of noninvasive diagnostics can improve both the acceptability and accessibility of diagnosis.

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

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

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

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

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

OBJECTIVE

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

METHOD

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

RESULTS

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

CONCLUSION

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

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.

A Systematic Review on Metabolomics Analysis in Hearing Impairment: Is It a Possible Tool in Understanding Auditory Pathologies?

With more than 466 million people affected, hearing loss represents the most common sensory pathology worldwide. Despite its widespread occurrence, much remains to be explored, particularly concerning the intricate pathogenic mechanisms underlying its diverse phenotypes. In this context, metabolomics emerges as a promising approach. Indeed, lying downstream from molecular biology's central dogma, the metabolome reflects both genetic traits and environmental influences. Furthermore, its dynamic nature facilitates well-defined changes during disease states, making metabolomic analysis a unique lens into the mechanisms underpinning various hearing impairment forms. Hence, these investigations may pave the way for improved diagnostic strategies, personalized interventions and targeted treatments, ultimately enhancing the clinical management of affected individuals. In this comprehensive review, we discuss findings from 20 original articles, including human and animal studies. Existing literature highlights specific metabolic changes associated with hearing loss and ototoxicity of certain compounds. Nevertheless, numerous critical issues have emerged from the study of the current state of the art, with the lack of standardization of methods, significant heterogeneity in the studies and often small sample sizes being the main limiting factors for the reliability of these findings. Therefore, these results should serve as a stepping stone for future research aimed at addressing the aforementioned challenges.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Challenges and Opportunities for Data Science in Women's Health

The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Survival outcomes and the prognostic significance of clinicopathological features in patients with endometrial clear cell carcinoma: a 35-year single-center retrospective study

BACKGROUND

To evaluate the oncological outcomes and the impact of clinicopathological factors on endometrial clear cell carcinoma (ECCC) outcomes.

METHODS

Medical records of patients with primary ECCC treated at our center between 1985 and December 2020 were reviewed. Overall survival (OS) and progression-free survival (PFS) were the endpoints. The Kaplan-Meier method and Cox regression analysis were used.

RESULTS

In total, 156 patients were included, of whom 59% and 41% had early- and advanced-stage ECCC, respectively. The median age of onset was 61 years, and 80.8% of the patients were postmenopausal. Ninety-two (59%) and 64 (41%) patients had pure ECCC and mixed endometrial carcinoma with clear cell carcinoma (CCC) components, respectively. Mixed pathological components, elevated cancer antigen 125 levels, positive lymphovascular space invasion, deep myometrial invasion, and malignant peritoneal washing cytology (PWC) were more frequently observed in the advanced stage. Thirty-nine patients (25%) experienced relapse and 32 patients (20.5%) died. The 5-year PFS and OS rates for the entire cohort were 72.6% and 79%, respectively. Multivariate analysis showed that advanced-stage disease and positive PWC significantly decreased PFS, while advanced-stage disease and older age (> 61 years) significantly decreased OS.

CONCLUSIONS

ECCC is a rare and aggressive type II endometrial carcinoma that is common in older women and patients with advanced-stage disease. Positive PWC was associated with decreased PFS, although its presence did not influence the stage. Positive PWC, and advanced stage and older age were independent negative prognostic factors.

Minimally invasive approaches for the early detection of endometrial cancer

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

Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

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

BACKGROUND

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

MATERIALS AND METHODS

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

RESULT

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Synaptotagmin-like protein 1 is a potential diagnostic and prognostic biomarker in endometrial cancer based on bioinformatics and experiments

Endometrial cancer (EC) is one of the most common gynecologic malignancies. Identification of potential EC biomarkers is essential to improve the prognosis and development of therapies against EC. Synaptotagmin-like protein 1 (SYTL1), as a small GTPase Rab27 effector, mainly plays a role in vesicle trafficking and cytotoxic granule exocytosis in lymphocytes. However the role of SYTL1 in EC remains uncertain. We performed a comprehensive assessment of the relationship between SYTL1 and patient diagnosis and prognosis by analysis of EC patients' data from TCGA. We employed the LinkedOmics and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to analyze the biological function of SYTL1 in EC. In addition, the correlation between SYTL1 expression and its DNA methylation was performed by using cBioportal, UALCAN, TCGA Wanderer and MethSurv databases. We further assessed the link between SYTL1 and tumor-infiltrating immune cells by using gene set variation analysis (GSVA).Results We found that SYTL1 was highly expressed in EC patients and cell lines. And increased expression of SYTL1 was associated with age, clinical stage, histological type, histological grade and good overall survival (OS).SYTL1 DNA methylation is negatively associated with SYTL1 expression and UCEC patients' OS. SYTL1 expression is closely correlated with immune infiltration. Furthermore, we carried out in vitro experiments to verify the results of bioinformatic analysis.Conclusion Our results demonstrated that the elevation of SYTL1 expression is associated with good OS and SYTL1 might be a potential diagnostic and prognostic marker in EC.

tRF-20-S998LO9D inhibits endometrial carcinoma by upregulating SESN2

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

Construction of Oxidative Stress-Related Genes Risk Model Predicts the Prognosis of Uterine Corpus Endometrial Cancer Patients

Oxidative stress contributes significantly to cancer development. Recent studies have demonstrated that oxidative stress could alter the epigenome and, in particular, DNA methylation. This study aimed to explore the potential link between oxidative stress and uterine corpus endometrial carcinoma (UCEC). An analysis of RNA-seq data and relevant clinical information was conducted with data from The Cancer Genome Atlas (TCGA), and oxidative stress genes were obtained from Gene Set Enrichment Analysis (GSEA). Differentially expressed genes (DEGs) in normal and tumor groups of UCEC were analyzed using GO and KEGG enrichment analysis. As a result of survival analysis, Lasso regression analysis of DEGs, a risk score model of oxidative stress-related genes (OSRGs) was constructed. Moreover, this study demonstrated that OSRGs are associated with immune cell infiltration in UCEC, suggesting oxidative stress may play a role in UCEC development by activating immune cells. We discovered 136 oxidative stress-related DEGs in UCEC, from which we screened 25 prognostic genes significantly related to the overall survival of UCEC patients. BCL2A1, CASP6, GPX2, HIC1, IL19, MSX1, RNF183, SFN, TRPM2 and HIST1H3C are associated with a good prognosis while CDKN2A, CHAC1, E2F1, GSDME, HMGA1, ITGA7, MCM4, MYBL2, PPIF, S100A1, S100A9, STK26 and TRIB3 are involved in a poor prognosis in UCEC. A 7-OSRGs-based risk score (H3C1, CDKN2A, STK26, TRPM2, E2F1, CHAC1, MSX1) was generated by Lasso regression. Further, an association was found between H3C1, CDKN2A, STK26, TRPM2, E2F1, CHAC1 and MSX1 expression levels and the immune infiltrating cells, including CD8 T cells, NK cells, and mast cells in UCEC. NFYA and RFX5 were speculated as common transcription factors of CDKN2A, TRPM2, E2F1, CHAC1, and MSX1 in UCEC.