This article elaborates in detail on the mechanisms of action of obesity-related metabolic disorders in the occurrence and development of endometrial cancer. It also reviews the potential interventional targets and clinical bariatric surgeries, aiming to provide new theoretical basis and research directions for the prevention, diagnosis, and treatment of endometrial cancer. Through a comprehensive analysis of relevant literatures, it reveals the impacts of a series of metabolic changes induced by obesity, such as hormonal metabolic imbalance, inflammatory response, insulin resistance, et al on endometrial cancer. Meanwhile, it explores the potential interventional targets and corresponding clinical treatment strategies for these mechanisms.

This article elaborates in detail on the mechanisms of action of obesity-related metabolic disorders in the occurrence and development of endometrial cancer. It also reviews the potential interventional targets and clinical bariatric surgeries, aiming to provide new theoretical basis and research directions for the prevention, diagnosis, and treatment of endometrial cancer. Through a comprehensive analysis of relevant literatures, it reveals the impacts of a series of metabolic changes induced by obesity, such as hormonal metabolic imbalance, inflammatory response, insulin resistance, et al on endometrial cancer. Meanwhile, it explores the potential interventional targets and corresponding clinical treatment strategies for these mechanisms.

Objective: This study aims to assess the impact of the metabolic risk score (MRS) on time to achieve complete remission (CR) of fertility-sparing treatments for atypical endometrial hyperplasia (AEH) and early endometrial cancer (EC) patients. Methods: Univariate and multivariate cox analyses were employed to identify independent risk factors affecting the time to CR with patients at our center. These factors were subsequently incorporated into receiver operator characteristic curve analysis and decision curve analysis to assess the predictive accuracy of time to CR. Additionally, Kaplan–Meier analysis was utilized to determine the cumulative CR rate for patients. Results: The 173 patients who achieved CR following fertility preservation treatment (FPT) were categorized into three subgroups based on their time to CR (<6, 6–9, >9 months). Body mass index (hazard ratio [HR]=0.20; 95% confidence interval [CI]=0.03, 0.38; p=0.026), MRS (HR=0.31; 95% CI=0.09, 0.52; p=0.005), insulin resistance (HR=1.83; 95% CI=0.05, 3.60; p=0.045), menstruation regularity (HR=3.77; 95% CI=1.91, 5.64; p=0.001), polycystic ovary syndrome (HR=−2.16; 95% CI=−4.03, −0.28; p=0.025), and histological type (HR=0.36;95% CI=0.10, 0.62; p=0.005) were identified as risk factors for time to CR, with MRS being the independent risk factor (HR=0.29; 95% CI=0.02, 0.56; p=0.021). The inclusion of MRS significantly enhanced the predictive accuracy of time to CR (area under the curve [AUC]=0.789 for Model 1, AUC=0.862 for Model 2, p=0.032). Kaplan–Meier survival curves revealed significant differences in the cumulative CR rate among different risk groups. Conclusion MRS emerges as a novel evaluation system that substantially enhances the predictive accuracy for the time to achieve CR in AEH and early EC patients seeking fertility preservation.

Objective: This study aims to assess the impact of the metabolic risk score (MRS) on time to achieve complete remission (CR) of fertility-sparing treatments for atypical endometrial hyperplasia (AEH) and early endometrial cancer (EC) patients. Methods: Univariate and multivariate cox analyses were employed to identify independent risk factors affecting the time to CR with patients at our center. These factors were subsequently incorporated into receiver operator characteristic curve analysis and decision curve analysis to assess the predictive accuracy of time to CR. Additionally, Kaplan–Meier analysis was utilized to determine the cumulative CR rate for patients. Results: The 173 patients who achieved CR following fertility preservation treatment (FPT) were categorized into three subgroups based on their time to CR (<6, 6–9, >9 months). Body mass index (hazard ratio [HR]=0.20; 95% confidence interval [CI]=0.03, 0.38; p=0.026), MRS (HR=0.31; 95% CI=0.09, 0.52; p=0.005), insulin resistance (HR=1.83; 95% CI=0.05, 3.60; p=0.045), menstruation regularity (HR=3.77; 95% CI=1.91, 5.64; p=0.001), polycystic ovary syndrome (HR=−2.16; 95% CI=−4.03, −0.28; p=0.025), and histological type (HR=0.36;95% CI=0.10, 0.62; p=0.005) were identified as risk factors for time to CR, with MRS being the independent risk factor (HR=0.29; 95% CI=0.02, 0.56; p=0.021). The inclusion of MRS significantly enhanced the predictive accuracy of time to CR (area under the curve [AUC]=0.789 for Model 1, AUC=0.862 for Model 2, p=0.032). Kaplan–Meier survival curves revealed significant differences in the cumulative CR rate among different risk groups. Conclusion MRS emerges as a novel evaluation system that substantially enhances the predictive accuracy for the time to achieve CR in AEH and early EC patients seeking fertility preservation.

Objective: This study aims to assess the impact of the metabolic risk score (MRS) on time to achieve complete remission (CR) of fertility-sparing treatments for atypical endometrial hyperplasia (AEH) and early endometrial cancer (EC) patients. Methods: Univariate and multivariate cox analyses were employed to identify independent risk factors affecting the time to CR with patients at our center. These factors were subsequently incorporated into receiver operator characteristic curve analysis and decision curve analysis to assess the predictive accuracy of time to CR. Additionally, Kaplan–Meier analysis was utilized to determine the cumulative CR rate for patients. Results: The 173 patients who achieved CR following fertility preservation treatment (FPT) were categorized into three subgroups based on their time to CR (<6, 6–9, >9 months). Body mass index (hazard ratio [HR]=0.20; 95% confidence interval [CI]=0.03, 0.38; p=0.026), MRS (HR=0.31; 95% CI=0.09, 0.52; p=0.005), insulin resistance (HR=1.83; 95% CI=0.05, 3.60; p=0.045), menstruation regularity (HR=3.77; 95% CI=1.91, 5.64; p=0.001), polycystic ovary syndrome (HR=−2.16; 95% CI=−4.03, −0.28; p=0.025), and histological type (HR=0.36;95% CI=0.10, 0.62; p=0.005) were identified as risk factors for time to CR, with MRS being the independent risk factor (HR=0.29; 95% CI=0.02, 0.56; p=0.021). The inclusion of MRS significantly enhanced the predictive accuracy of time to CR (area under the curve [AUC]=0.789 for Model 1, AUC=0.862 for Model 2, p=0.032). Kaplan–Meier survival curves revealed significant differences in the cumulative CR rate among different risk groups. Conclusion MRS emerges as a novel evaluation system that substantially enhances the predictive accuracy for the time to achieve CR in AEH and early EC patients seeking fertility preservation.

【Objective】 To explore the effectiveness of an image recognition system based on artificial intelligence (AI) in diagnosing benign and malignant endometrial cell clumps. 【Methods】 We selected endometrial cytological specimens from The First Affiliated Hospital of Xi’an Jiaotong University and Xi’an Daxing Hospital from August 2021 to February 2023; histopathology was used as the gold standard. We compared and analyzed the sensitivity, specificity, positive predictive value, negative predictive value, accuracy and diagnostic time of AI image recognition system (AI diagnosis) and professional pathologists’ manual diagnosis (manual diagnosis) of benign and malignant endometrial cell clumps. 【Results】 Among the 126 patients included in the analysis, the overall coincidence rate of AI diagnosis and histological diagnosis was 92.1% (116/126), which was highly consistent with histopathological results (Kappa=0.841). The overall coincidence rate of manual diagnosis and histological diagnosis was 94.4% (119/126), which was highly consistent with histopathological results (Kappa=0.889). There was no statistically significant difference between AI diagnosis and manual diagnosis methods (χ²=0.568, P=0.451). The sensitivity, specificity, positive predictive value, and negative predictive value of AI diagnosis were 91.8%, 92.3%, 91.8%, and 92.3%, respectively. There were 126 cytology sections, each of which required 6.67 minutes for manual diagnosis and 5.00 minutes for AI diagnosis. 【Conclusion】 The AI image recognition system has high diagnostic accuracy, sensitivity and specificity, which is equivalent to the manual diagnosis level of professional pathologists. Therefore, this system has application value in the diagnosis of benign and malignant endometrial cell clumps.

Objective:To analyze the lympho-vascular space invasion (LVSI) in different molecular subtypes of the cancer genome atlas (TCGA) molecular subtypes of endometrial cancer (EC) and to evaluate the prognostic value of LVSI in EC patients with different molecular subtypes.Methods:A total of 258 patients diagnosed EC undergoing surgery in Peking University People′s Hospital from January 2016 to June 2022 were analyzed retrospectively. Among 258 patients, 14 cases were classified as POLE-ultramutated subtype, 43 as high-microsatellite instability (MSI-H) subtype, 155 as copy-number low (CNL) subtype, and 46 as copy-number high (CNH) subtype. Fifty-four patients were positive for LVSI, while 203 tested negative.Results:(1) The incidence of LVSI was found to be highest in the CNH subtype (32.6%,15/46), followed by the MSI-H subtype (27.9%, 12/43), the CNL subtype (16.9%, 26/154), and the POLE-ultramutated subtype (1/14), with statistically significant differences ( χ2=7.79, P=0.044). (2) Staging and deep myometrial invasion were higher in the LVSI positive group than those in the LVSI negative group (all P<0.05), except for the POLE-ultramutated subtype. The grade, lymph node metastasis, and the expression of nuclear antigen associated with cell proliferation (Ki-67) were significantly higher in LVSI positive patients than those in LVSI negative EC patients with both MSI-H and CNL subtypes (all P<0.05). In CNL subtypes patients, LVSI was also associated with age, histology subtype,and progesterone receptor (PR; all P<0.05). (3) Of the 257 EC patients, 25 cases recurred during the follow-up period, with a recurrence rate of 9.7% (25/257); among them, the recurrence rate of LVSI positive patients was 22.2% (12/54), which was significantly higher than those with LVSI negative (6.4%, 13/203; χ2=12.15, P<0.001). During the follow-up period, none of the 14 patients with POLE-ultramutated had recurrence; among CNL patients, the recurrence rate was 19.2% (5/26) in LVSI positive patients, which was significantly higher than that in LVSI negative ones (5.5%, 7/128; χ2=3.94, P=0.047); where as no difference were found in both MSI-H [recurrence rates in LVSI positive and negative patients were 2/12 and 9.7% (3/31), respectively] and CNH subtype [recurrence rates between LVSI positive and negative patients were 5/15 and 9.7% (3/31), respectively] EC patients (both P>0.05). After log-rank test, the 3-year recurrence free survival (RFS) rate were significantly lower in LVSI positive patients from CNL subtype and CNH subtype than those in LVSI negative patients (CNL: 80.8% vs 94.5%; CNH: 66.7% vs 90.3%; both P<0.05). (4) Lymph node metastasis ( HR=6.93, 95% CI: 1.15-41.65; P=0.034) had a significant effect on the 3-year RFS rate of EC patients with MSI-H subtype. Multivariate analysis revealed that PR expression ( HR=0.04, 95% CI: 0.01-0.14; P<0.001) was significantly associated with the 3-year RFS rate of CNL subtype patients. Conclusions:LVSI has the highest positivity rate in CNH subtype, followed by MSI-H subtype, CNL subtype, and the lowest positivity rate in POLE-ultramutated subtype. LVSI is significantly associated with poor prognosis in CNL subtype patients and may affect the prognosis of CNH subtype patients. However, LVSI is not an independent risk factor for recurrence across all four TCGA molecular subtypes.

Objective: To evaluate the impact of bleomycin/etoposide/cisplatin (BEP) and paclitaxel/carboplatin (PC) chemotherapy regimens on the fertility and prognostic outcomes in malignant ovarian germ cell tumor (MOGCT) patients who underwent fertility-sparing surgery (FSS). Methods: A propensity score matching algorithm was performed between the BEP and PC groups. The χ2 test and the Kaplan-Meier method were used to compare the fertility outcome, disease-free survival (DFS) and overall survival (OS). The Cox proportional hazards regression analysis was used to identify risk factor of DFS. Results: We included 213 patients, 185 (86.9%) underwent BEP chemotherapy, and 28 (13.1%) underwent PC chemotherapy. The median age was 22 years (range, 8–44 years), and the median follow-up period was 63 months (range, 2–191 months). Fifty-one (29.3%) patients had a pregnancy plan, and 35 (85.4%) delivered successfully. In the before and after propensity score matching cohorts, there were no significant differences in spontaneous abortion, selective termination of pregnancy, during-pregnancy status, and live birth between the BEP and PC groups (p>0.05). Fourteen (6.6%) patients experienced recurrence, including 11 (5.9%) in the BEP group and 3 (10.7%) in the PC group. Four (1.9%) patients in the BEP group died. Kaplan-Meier analysis revealed no significant differences in DFS (p=0.328) and OS (p=0.446) between the BEP and PC groups, and the same survival results were observed in the after matching cohort. Conclusion The PC regimen is as safe as the BEP regimen for MOGCT patients with fertility preservation treatment, and no differences were observed in fertility and clinical prognosis.

BACKGROUND: Steroid receptor-associated and regulated protein (SRARP) suppresses tumor progression and modulates steroid receptor signaling by interacting with estrogen receptors and androgen receptors in breast cancer. In endometrial cancer (EC), progesterone receptor (PR) signaling is crucial for responsiveness to progestin therapy. The aim of this study was to investigate the role of SRARP in tumor progression and PR signaling in EC. METHODS: Ribonucleic acid sequencing data from the Cancer Genome Atlas, Clinical Proteomic Tumor Analysis Consortium, and Gene Expression Omnibus were used to analyze the clinical significance of SRARP and its correlation with PR expression in EC. The correlation between SRARP and PR expression was validated in EC samples obtained from Peking University People's Hospital. SRARP function was investigated by lentivirus-mediated overexpression in Ishikawa and HEC-50B cells. Cell Counting Kit-8 assays, cell cycle analyses, wound healing assays, and Transwell assays were used to evaluate cell proliferation, migration, and invasion. Western blotting and quantitative real-time polymerase chain reaction were used to evaluate gene expression. The effects of SRARP on the regulation of PR signaling were determined by co-immunoprecipitation, PR response element (PRE) luciferase reporter assay, and PR downstream gene detection. RESULTS: Higher SRARP expression was significantly associated with better overall survival and disease-free survival and less aggressive EC types. SRARP overexpression suppressed growth, migration, and invasion in EC cells, increased E-cadherin expression, and decreased N-cadherin and Wnt family member 7A ( WNT7A ) expression. SRARP expression was positively correlated with PR expression in EC tissues. In SRARP -overexpressing cells, PR isoform B (PRB) was upregulated and SRARP bound to PRB. Significant increases in PRE-based luciferase activity and expression levels of PR target genes were observed in response to medroxyprogesterone acetate. CONCLUSIONS This study illustrates that SRARP exerts a tumor-suppressive effect by inhibiting the epithelial-mesenchymal transition via Wnt signaling in EC. In addition, SRARP positively modulates PR expression and interacts with PR to regulate PR downstream target genes.
Female ; Humans ; Receptors, Progesterone/metabolism* ; Proteomics ; Cell Line, Tumor ; Endometrial Neoplasms/metabolism* ; Cell Proliferation/genetics* ; Luciferases/pharmacology* ; Gene Expression Regulation, Neoplastic/genetics*

Purpose: The unique chromosomal rearrangements of endometrial stromal sarcoma (ESS) make it possible to distinguish high-grade ESS (HGESS) and low-grade ESS (LGESS) from the molecular perspective. Analysis of ESS at the genomic and transcriptomic levels can help us achieve accurate diagnosis of ESS and provide potential therapy options for ESS patients. Materials and Methods: A total of 36 ESS patients who conducted DNA- and/or RNA-based next-generation sequencing were retrospectively enrolled in this study. The molecular characteristics of ESS at genomic and transcriptomic levels, including mutational spectrum, fusion profiles, gene expression and pathway enrichment analysis and features about immune microenvironment were comprehensively explored. Results: TP53 and DNMT3A mutations were the most frequent mutations. The classical fusions frequently found in HGESS (ZC3H7B-BCOR and NUTM2B-YWHAE) and LGESS (JAZF1-SUZ12) were detected in our cohort. CCND1 was significantly up-regulated in HGESS, while the expression of GPER1 and PGR encoding estrogen receptor (ER) and progesterone receptor (PR) did not differ significantly between HGESS and LGESS. Actionable mutations enriched in homologous recombination repair, cell cycle, and phosphoinositide 3-kinase/AKT/mammalian target of rapamycin pathways were detected in 60% of HGESS patients. Genes with up-regulated expression in HGESS were significantly enriched in five immune-related pathways. Most HGESS patients (85.7%) had positive predictors of immunotherapy efficacy. Moreover, immune microenvironment analysis showed that HGESS had relatively high immune infiltration. The degree of immune infiltration in HGESS patients with ZC3H7B-BCOR fusion was relatively higher than that of those with NUTM2B-YWHAE fusion. Conclusion This study investigated the molecular characteristics of ESS patients at the genomic and transcriptomic levels and revealed the potentially high sensitivity of targeted therapy and immunotherapy in a subset of HGESS with specific molecular features, providing a basis for guiding decision-making of treatment and the design of future clinical trials on precision therapy.