Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells.

BACKGROUND: Glycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC). METHODS: Data on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays. RESULTS: A total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model's prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation. CONCLUSION: The GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.

Predictive models for lymph node metastasis in endometrial cancer: A systematic review and bibliometric analysis.

BACKGROUND: Lymph node metastasis is associated with a poorer prognosis in endometrial cancer. OBJECTIVE: The objective was to synthesize and critically appraise existing predictive models for lymph node metastasis risk stratification in endometrial cancer. DESIGN: This study is a systematic review. DATA SOURCES AND METHODS: We searched the Web of Science for articles reporting models predicting lymph node metastasis in endometrial cancer, with a systematic review and bibliometric analysis conducted based upon which. Risk of bias was assessed by the Prediction model Risk Of BiAS assessment Tool (PROBAST). RESULTS: A total of 64 articles were included in the systematic review, published between 2010 and 2023. The most common articles were "development only." Traditional clinicopathological parameters remained the mainstream in models, for example, serum tumor marker, myometrial invasion and tumor grade. Also, models based upon gene-signatures, radiomics and digital histopathological images exhibited an acceptable self-reported performance. The most frequently validated models were the Mayo criteria, which reached a negative predictive value of 97.1%-98.2%. Substantial variability and inconsistency were observed through PROBAST, indicating significant between-study heterogeneity. A further bibliometric analysis revealed a relatively weak link between authors and organizations on models predicting lymph node metastasis in endometrial cancer. CONCLUSION: A number of predictive models for lymph node metastasis in endometrial cancer have been developed. Although some exhibited promising performance as they demonstrated adequate to good discrimination, few models can currently be recommended for clinical practice due to lack of independent validation, high risk of bias and low consistency in measured predictors. Collaborations between authors, organizations and countries were weak. Model updating, external validation and collaborative research are urgently needed. REGISTRATION: None.

Introduction to predictive models for lymph node metastasis in endometrial cancerLymph node metastasis of endometrial cancer is associated with a poor prognosis. There are currently many predictive models. We summarized and evaluated them in this article.

VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology.

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer.

Among gynecological cancers, endometrial cancer is the most common in developed countries. Extracellular vesicles (EVs) are cell-derived membrane-surrounded vesicles that contain proteins involved in immune response and apoptosis. A deep proteomic approach can help to identify dysregulated extracellular matrix (ECM) proteins in EVs correlated to key pathways for tumor development. In this study, we used a proteomics approach correlating the two acquisitions-data-dependent acquisition (DDA) and data-independent acquisition (DIA)-on EVs from the conditioned medium of four cell lines identifying 428 ECM proteins. After protein quantification and statistical analysis, we found significant changes in the abundance (p < 0.05) of 67 proteins. Our bioinformatic analysis identified 26 pathways associated with the ECM. Western blotting analysis on 13 patients with type 1 and type 2 EC and 13 endometrial samples confirmed an altered abundance of MMP2. Our proteomics analysis identified the dysregulated ECM proteins involved in cancer growth. Our data can open the path to other studies for understanding the interaction among cancer cells and the rearrangement of the ECM.

FIGO 2023 endometrial staging: a leap of faith into the new "prognostic based' rather than "anatomical based" staging-too fast too furious??

BACKGROUND: In 2023 FIGO revised the endometrial cancer staging system after 13 years. There is a lacuna of data regarding the performance and practicality of the revised 2023 FIGO staging schema for endometrial cancer from Low Middle-Income Countries (LMIC). OBJECTIVE: To estimate the shift of stage and adjuvant management of endometrial cancer based on the FIGO 2023 system compared to the FIGO 2009 system and assess the predictive potential of the FIGO 2023 system. MATERIAL AND METHODS: A retrospective study was conducted from 1st January 2017 to 31st December 2022. All patients with endometrial cancer were staged according to the FIGO 2023 and FIGO 2009 staging system. Follow-up of patients was done to determine recurrence. RESULTS: A total of 152 patients were included. Aggressive histology was seen in 66 (45%) patients. Eighteen (11%) had subserosal involvement. Substantial LVSI was noted in 23 (15%) of patients. Twenty-four (47%) patients of FIGO 2009 Stage IA and 26 patients (63%) of FIGO 2009 Stage IB were upstaged. Eleven (50%) patients of FIGO 2009 Stage IIIA were down staged to IA3. Overall 23 patients (15%) had a shift of stage. Fifteen out of 152 patients (15%) would have had a possible risk stratification change which would imply 23 patients (15%) would have needed a more radical treatment. Molecular classification was done in 32 patients; however, only 2 patients could afford POLE testing. Kaplan-Meier curves showed significant PFS differences in FIGO 2009 Stage IB and Stage IIIA when restaged according to the FIGO 2023 system. CONCLUSION: The FIGO 2023 endometrial staging is a more robust prognosticator; however, the practicality of molecular classification in LMICs is still a distant dream.

Tailoring postoperative management through sentinel lymph node biopsy in low- and intermediate-risk endometrial cancer - the SENTRY clinical trial.

BACKGROUND: While total hysterectomy and bilateral salpingo-oophorectomy without lymph node staging are standard for low- and intermediate-risk endometrial cancer, certain histopathologic factors revealed after surgery can necessitate additional interventions. Our study assessed the influence of sentinel lymph node biopsy on postoperative decision-making. MATERIALS AND METHODS: In the SENTRY trial (July 2021 - February 2023), we enrolled patients with International Federation of Gynaecology and Obstetrics (FIGO) stage IA-IB low-grade endometrioid endometrial cancer. Laparoscopic sentinel lymph node mapping using indocyanine green was performed alongside total hysterectomy with bilateral salpingo-oophorectomy. Subsequent management changes based on sentinel lymph node biopsy results were evaluated. The trial was registered at ClinicalTrials.gov (NCT04972682). RESULTS: Of the 100 enrolled participants, a bilateral detection rate of 91% was observed with a median detection time of 10 min (interquartile range 8-13 min). Sentinel lymph node metastases were found in 8% (N = 8) of participants. Postoperative FIGO staging increased in 15% (N = 15) and decreased in 5% (N = 5) of patients. Sentinel lymph node biopsy results altered the adjuvant treatment plan for 20% (N = 20): external beam radiotherapy was omitted in 12% (N = 12) while 6% (N = 6) had external beam radiotherapy +/- systemic chemotherapy added due to sentinel lymph node metastases. In 2% (N = 2), the external beam radiotherapy field was expanded with the paraaortic region. No intraoperative complications were reported and no 30-day major morbidity and mortality occurred. Throughout a median follow-up of 14 (95% CI 12-15 months, neither patient-reported lymphedema nor pelvic recurrence surfaced in the cohort. CONCLUSIONS: Sentinel lymph node biopsy using indocyanine green is a safe procedure and allows tailoring adjuvant therapy in presumed low- and intermediate-risk endometrial cancer. It assists in avoiding external beam radiotherapy overtreatment and introducing additional modalities when necessary.

Delineating genotype and parent-of-origin effect on the phenotype in MSH6-associated Lynch syndrome.

BACKGROUND: This study investigates the potential influence of genotype and parent-of-origin effects (POE) on the clinical manifestations of Lynch syndrome (LS) within families carrying (likely) disease-causing MSH6 germline variants. PATIENTS AND METHODS: A cohort of 1615 MSH6 variant carriers (310 LS families) was analyzed. Participants were categorized based on RNA expression and parental inheritance of the variant. Hazard ratios (HRs) were calculated using weighted Cox regression, considering external information to address ascertainment bias. The findings were cross-validated using the Prospective Lynch Syndrome Database (PLSD) for endometrial cancer (EC). RESULTS: No significant association was observed between genotype and colorectal cancer (CRC) risk (HR = 1.06, 95% confidence interval [CI]: 0.77-1.46). Patients lacking expected RNA expression exhibited a reduced risk of EC (Reference Cohort 1: HR = 0.68, 95% CI: 0.43-1.03; Reference Cohort 2: HR = 0.63, 95% CI: 0.46-0.87). However, these results could not be confirmed in the PLSD. Moreover, no association was found between POE and CRC risk (HR = 0.78, 95% CI: 0.52-1.17) or EC risk (Reference Cohort 1: HR = 0.93, 95% CI: 0.65-1.33; Reference Cohort 2: HR = 0.8, 95% CI: 0.64-1.19). DISCUSSION AND CONCLUSION: No evidence of POE was detected in MSH6 families. While RNA expression may be linked to varying risks of EC, further investigation is required to explore this observation.

Histological assessment of endometrial polyps resected by hysteroscopy.

OBJECTIVE: This study aimed to analyze the clinical data and pathologic aspects of endometrial polyps (EMPs) excised completely during surgical hysteroscopy and assess the connection between premalignant and malignant EMPs. PATIENTS AND METHODS: This retrospective study includes 489 participants who underwent hysteroscopy due to endometrial polyps, and the clinical features and histological findings of the resected polyps analyzed. RESULTS: Participants with EMPs were divided into six groups according to histologic findings. The histologic finding of most cases was simple benign endometrial polyp [397 patients (81.2%)]. Malignant polyp was detected in 3 patients (0.6%). The histologic findings according to age, menopausal status, and menstrual bleeding patterns at the time of presentation to the outpatient clinic were compared; however, no significant difference was observed. 237 patients were observed to have menometrorrhagia, which was the most prevalent symptom reported. The distribution of polyp sizes observed at hysteroscopy according to histologic findings was compared, but no significant difference was observed. CONCLUSIONS: EMPs are often benign but can include premalignant or malignant tissue changes. Hysteroscopy is used for direct observation of the uterine cervix and resection of existing polyps, considering the increasing frequency of its use as a diagnostic and treatment tool.

A new perspective on Endometrial Carcinoma classification and management strategies in context of molecular subtypes.

Endometrial cancer is the most common gynecological cancer and the second most prevalent female malignancy in the developed world. It is typically diagnosed in postmenopausal women, presenting with the characteristic clinical symptom of uterine abnormal bleeding. In the past, only two histological types were considered. However, it has become increasingly evident that endometrial cancer is a clinically heterogeneous disease, and this heterogeneity is closely associated with the diversity of underlying molecular alterations. The Cancer Genome Atlas classification has significantly advanced the diagnosis, risk stratification, and management of endometrial cancer by categorizing it into four molecular subgroups, each characterized by distinct mutational burdens and copy number alterations.

New staging of endometrial carcinoma - FIGO 2023.

AIM: To review the changes in the new version of the FIGO 2023 staging system for endometrial cancer. METHODS AND RESULTS: The new FIGO 2023 endometrial cancer staging system provides key updates for the diagnosis and treatment of endometrial cancer. An important step in diagnosis is molecular classification, which allows more accurate risk stratification for recurrence and the identification of targeted therapies. The new staging system, based on the recommendations of the international societies ESGO, ESTRO and ESP, incorporates not only the description of the pathological and anatomical extent of the disease, but also the histopathological characteristics of the tumour, including the histological type and the presence of lymphovascular space invasion. In addition, the staging system uses molecular testing to classify endometrial cancers into four prognostic groups: POLEmut, MMRd, NSMP and p53abn. Each group has its own specific characteristics and prognosis. The most significant changes have occurred in stages I and II, in which the sub-staging better reflects the biological behaviour of the tumour. This update increases the accuracy of prognosis and improves individualized treatment options for patients with endometrial cancer. CONCLUSION: The updated FIGO staging of endometrial cancer for 2023 incorporates different histologic types, tumour features, and molecular classifications to better reflect the current improved understanding of the complex nature of several endometrial cancer types and their underlying bio logic behaviour. The aim of the new endometrial cancer staging system is to better define stages with similar prognosis, allowing for more precise indication of individualised adjuvant radiation or systemic treatment, including the use of immunotherapy.

Sesamolin serves as an MYH14 inhibitor to sensitize endometrial cancer to chemotherapy and endocrine therapy via suppressing MYH9/GSK3ß/ß-catenin signaling.

BACKGROUND: Endometrial cancer (EC) is one of the most common gynecological cancers. Herein, we aimed to define the role of specific myosin family members in EC because this protein family is involved in the progression of various cancers. METHODS: Bioinformatics analyses were performed to reveal EC patients' prognosis-associated genes in patients with EC. Furthermore, colony formation, immunofluorescence, cell counting kit 8, wound healing, and transwell assays as well as coimmunoprecipitation, cycloheximide chase, luciferase reporter, and cellular thermal shift assays were performed to functionally and mechanistically analyze human EC samples, cell lines, and a mouse model, respectively. RESULTS: Machine learning techniques identified MYH14, a member of the myosin family, as the prognosis-associated gene in patients with EC. Furthermore, bioinformatics analyses based on public databases showed that MYH14 was associated with EC chemoresistance. Moreover, immunohistochemistry validated MYH14 upregulation in EC cases compared with that in normal controls and confirmed that MYH14 was an independent and unfavorable prognostic indicator of EC. MYH14 impaired cell sensitivity to carboplatin, paclitaxel, and progesterone, and increased cell proliferation and metastasis in EC. The mechanistic study showed that MYH14 interacted with MYH9 and impaired GSK3ß-mediated ß-catenin ubiquitination and degradation, thus facilitating the Wnt/ß-catenin signaling pathway and epithelial-mesenchymal transition. Sesamolin, a natural compound extracted from Sesamum indicum (L.), directly targeted MYH14 and attenuated EC progression. Additionally, the compound disrupted the interplay between MYH14 and MYH9 and repressed MYH9-regulated Wnt/ß-catenin signaling. The in vivo study further verified sesamolin as a therapeutic drug without side effects. CONCLUSIONS: Herein, we identified that EC prognosis-associated MYH14 was independently responsible for poor overall survival time of patients, and it augmented EC progression by activating Wnt/ß-catenin signaling. Targeting MYH14 by sesamolin, a cytotoxicity-based approach, can be applied synergistically with chemotherapy and endocrine therapy to eventually mitigate EC development. This study emphasizes MYH14 as a potential target and sesamolin as a valuable natural drug for EC therapy.

Analysis of CDO1, PITX2, and CDH13 Gene Methylation in Early Endometrial Cancer for Prediction of Medical Treatment Outcomes.

An observational cohort study of patients diagnosed with endometrial cancer (EC) stage IA G1, or atypical endometrial hyperplasia (AEH), undergoing organ-preserving treatment, was conducted. OBJECTIVE OF THE STUDY: To determine CDO1, PITX2, and CDH13 gene methylation levels in early endometrial cancer and atypical hyperplasia specimens obtained before organ-preserving treatment in the patients with adequate response and with insufficient response to hormonal treatment. MATERIALS AND METHODS: A total of 41 endometrial specimens obtained during diagnostic uterine curettage in women with EC (n = 28) and AEH (n = 13), willing to preserve reproductive function, were studied; 18 specimens of uterine cancer IA stage G1 from peri- and early postmenopausal women (comparison group) were included in the study. The control group included 18 endometrial specimens from healthy women obtained by diagnostic curettage for missed abortion and/or intrauterine adhesions. Methylation levels were analyzed using the modified MS-HRM method. RESULTS: All 13 women with AEH had a complete response (CR) to medical treatment. In the group undergoing organ-preserving treatment for uterine cancer IA stage G1 (n = 28), 14 patients had a complete response (EC CR group) and 14 did not (EC non-CR group). It was found that all groups had statistically significant differences in CDO1 gene methylation levels compared to the control group (p < 0.001) except for the EC CR group (p = 0.21). The p-value for the difference between EC CR and EC non-CR groups was <0.001. The differences in PITX2 gene methylation levels between the control and study groups were also significantly different (p < 0.001), except for the AEH group (p = 0.21). For the difference between EC CR and EC non-CR groups, the p-value was 0.43. For CDH13 gene methylation levels, statistically significant differences were found between the control and EC non-CR groups (p < 0.001), and the control and EC comparison groups (p = 0.005). When comparing the EC CR group with EC non-CR group, the p-value for this gene was <0.001. The simultaneous assessment of CDO1 and CDH13 genes methylation allowed for an accurate distinction between EC CR and EC non-CR groups (AUC = 0.96). CONCLUSION: The assessment of CDO1 and CDH13 gene methylation in endometrial specimens from patients with endometrial cancer (IA stage G1), scheduled for medical treatment, can predict the treatment outcome.

Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq.

BACKGROUND: The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered. METHODS: UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types. RESULTS: A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm. CONCLUSION: CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

Development of a long term, ex vivo, patient-derived explant model of endometrial cancer.

Incidence of endometrial cancer (EC) is rising in the developed world. The current standard of care, hysterectomy, is often infeasible for younger patients and those with high body mass index. There are limited non-surgical treatment options and a lack of biologically relevant research models to investigate novel alternatives to surgery for EC. The aim of the present study was to develop a long-term, patient-derived explant (PDE) model of early-stage EC and demonstrate its use for investigating predictive biomarkers for a current non-surgical treatment option, the levonorgestrel intra-uterine system (LNG-IUS). Fresh tumour specimens were obtained from patients with early-stage endometrioid EC. Tumours were cut into explants, cultured on media-soaked gelatin sponges for up to 21 days and treated with LNG. Formalin-fixed, paraffin embedded (FFPE) blocks were generated for each explant after 21 days in culture. Tumour architecture and integrity were assessed by haematoxylin and eosin (H&E) and immunohistochemistry (IHC). IHC was additionally performed for the expression of five candidate biomarkers of LNG resistance. The developed ex vivo PDE model is capable of culturing explants from early-stage EC tumours long-term (21 Days). This model can complement existing models and may serve as a tool to validate results obtained in higher-throughput in vitro studies. Our study provides the foundation to validate the extent to which EC PDEs reflect patient response in future research.

Endometrial Cancer Detection by DNA Methylation Analysis in Cervical Papanicolaou Brush Samples.

Background: Endometrial cancer (EC) is the leading gynecological cancer worldwide, yet current EC screening approaches are not satisfying. The purpose of this retrospective study was to evaluate the feasibility and capability of DNA methylation analysis in cervical Papanicolaou (Pap) brush samples for EC detection. Methods: We used quantitative methylation-sensitive PCR (qMS-PCR) to determine the methylation status of candidate genes in EC tissue samples, as well as cervical Pap brushes. The ability of RASSF1A and HIST1H4F to serve as diagnostic markers for EC was then examined in cervical Pap brush samples from women with endometrial lesions of varying degrees of severity. Results: Methylated RASSF1A and HIST1H4F were found in EC tissues. Further, methylation of the two genes was also observed in cervical Pap smear samples from EC patients. Methylation levels of RASSF1A and HIST1H4F increased as endometrial lesions progressed, and cervical Pap brush samples from women affected by EC exhibited significantly higher levels of methylated RASSF1A and HIST1H4F compared to noncancerous controls (P < .001). Receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses revealed RASSF1A and HIST1H4F methylation with a combined AUC of 0.938 and 0.951 for EC/pre-EC detection in cervical Pap brush samples, respectively. Conclusion: These findings demonstrate that DNA methylation analysis in cervical Pap brush samples may be helpful for EC detection, broadening the scope of the commonly used cytological screening. Our proof-of-concept study provides new insights into the field of clinical EC diagnosis.

Upregulation of ESPL1 is associated with poor prognostic outcomes in endometrial cancer.

BACKGROUND: Extra spindle pole bodies-like 1 (ESPL1) is known to play a crucial role in the segregation of sister chromatids during mitosis. Overexpression of ESPL1 is considered to have oncogenic effects in various human cancers. However, the specific biological function of ESPL1 in endometrial cancer (EC) remains unclear. METHODS: The TCGA and GEO databases were utilized to assess the expression of ESPL1 in EC. Immunohistochemistry was utilized to detect separase expression in EC samples. Kaplan-Meier survival analysis and Cox regression analysis were performed to evaluate the diagnostic and prognostic significance of ESPL1 in EC. Gene Set Enrichment Analysis (GSEA) was employed to explore the potential signaling pathway of ESPL1 in EC. Cell proliferation and colony formation ability were analyzed using CCK-8 and colony formation assay. RESULTS: Our analysis revealed that ESPL1 is significantly upregulated in EC, and its overexpression is associated with advanced clinical characteristics and unfavourable prognostic outcomes. Suppression of ESPL1 attenuated proliferation of EC cell line. CONCLUSION: The upregulation of ESPL1 is associated with advanced disease and poor prognosis in EC patients. These findings suggest that ESPL1 has the potential to serve as a diagnostic and prognostic biomarker in EC, highlighting its significance in the management of EC patients.

The expression of ESPL1 was higher in EC tissue than normal endometrial tissue.ESPL1 could be a potential prognostic marker for EC.

Development and Expansion of Patient-Derived Xenografts for Endometrial Cancer.

Patient-Derived Xenografts (PDXs) are established by implanting a fragment of a patient tumor into rodents either subcutaneously or orthotopically. PDX models faithfully recapitulate the histologic and molecular profile of the donor patient's cancer and are regarded as authentic preclinical models for drug testing, understanding of tumor biology and biomarker discovery. This Chapter describes the detailed method for establishing robust PDXs for endometrial cancer and provide important notes for users of the protocol to consider during PDXs development.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification.

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

Proteogenomic insights into early-onset endometrioid endometrial carcinoma: predictors for fertility-sparing therapy response.

Endometrial carcinoma remains a public health concern with a growing incidence, particularly in younger women. Preserving fertility is a crucial consideration in the management of early-onset endometrioid endometrial carcinoma (EEEC), particularly in patients under 40 who maintain both reproductive desire and capacity. To illuminate the molecular characteristics of EEEC, we undertook a large-scale multi-omics study of 215 patients with endometrial carcinoma, including 81 with EEEC. We reveal an unexpected association between exposome-related mutational signature and EEEC, characterized by specific CTNNB1 and SIGLEC10 hotspot mutations and disruption of downstream pathways. Interestingly, SIGLEC10Q144K mutation in EEECs resulted in aberrant SIGLEC-10 protein expression and promoted progestin resistance by interacting with estrogen receptor alpha. We also identified potential protein biomarkers for progestin response in fertility-sparing treatment for EEEC. Collectively, our study establishes a proteogenomic resource of EEECs, uncovering the interactions between exposome and genomic susceptibilities that contribute to the development of primary prevention and early detection strategies for EEECs.