Glycolysis Induced by METTL14 Is Essential for Macrophage Phagocytosis and Phenotype in Cervical Cancer.

N 6-methyladenosine (m6A) is the most abundant mRNA modification in mammals and it plays a vital role in various biological processes. However, the roles of m6A on cervical cancer tumorigenesis, especially macrophages infiltrated in the tumor microenvironment of cervical cancer, are still unclear. We analyzed the abnormal m6A methylation in cervical cancer, using CaSki and THP-1 cell lines, that might influence macrophage polarization and/or function in the tumor microenvironment. In addition, C57BL/6J and BALB/c nude mice were used for validation in vivo. In this study, m6A methylated RNA immunoprecipitation sequencing analysis revealed the m6A profiles in cervical cancer. Then, we discovered that the high expression of METTL14 (methyltransferase 14, N6-adenosine-methyltransferase subunit) in cervical cancer tissues can promote the proportion of programmed cell death protein 1 (PD-1)-positive tumor-associated macrophages, which have an obstacle to devour tumor cells. Functionally, changes of METTL14 in cervical cancer inhibit the recognition and phagocytosis of macrophages to tumor cells. Mechanistically, the abnormality of METTL14 could target the glycolysis of tumors in vivo and vitro. Moreover, lactate acid produced by tumor glycolysis has an important role in the PD-1 expression of tumor-associated macrophages as a proinflammatory and immunosuppressive mediator. In this study, we revealed the effect of glycolysis regulated by METTL14 on the expression of PD-1 and phagocytosis of macrophages, which showed that METTL14 was a potential therapeutic target for treating advanced human cancers.

Development and validation of a deep learning survival model for cervical adenocarcinoma patients.

BACKGROUND: The aim was to develop a personalized survival prediction deep learning model for cervical adenocarcinoma patients and process personalized survival prediction. METHODS: A total of 2501 cervical adenocarcinoma patients from the surveillance, epidemiology and end results database and 220 patients from Qilu hospital were enrolled in this study. We created our deep learning (DL) model to manipulate the data and evaluated its performance against four other competitive models. We tried to demonstrate a new grouping system oriented by survival outcomes and process personalized survival prediction by using our DL model. RESULTS: The DL model reached 0.878 c-index and 0.09 Brier score in the test set, which was better than the other four models. In the external test set, our model achieved a 0.80 c-index and 0.13 Brier score. Thus, we developed prognosis-oriented risk grouping for patients according to risk scores computed by our DL model. Notable differences among groupings were observed. In addition, a personalized survival prediction system based on our risk-scoring grouping was developed. CONCLUSIONS: We developed a deep neural network model for cervical adenocarcinoma patients. The performance of this model proved to be superior to other models. The results of external validation supported the possibility that the model can be used in clinical work. Finally, our survival grouping and personalized prediction system provided more accurate prognostic information for patients than traditional FIGO stages.

Effect of direct surgical treatment in pregnancy-related uterine arteriovenous malformation.

BACKGROUND: Uterine arteriovenous malformation (UAVM) is a relatively rare but potentially life-threatening situations abnormal vascular connections between the uterine arterial and venous systems. Lack of recognized guidelines and clinic experience, there is a lot of clinic problems about diagnosis and treatment. By analyzing the clinical data of patients with pregnancy-related UAVM, we aim to confirm the safety of direct surgeries and the benefit of pretreatment (uterine artery embolization or medical therapy) before surgery, and to explore more optimal therapies for patients with pregnancy-related UAVM. METHODS: A total of 106 patients in Qilu Hospital of Shandong University from January 2011 to December 2021 diagnosed of pregnancy-related UAVM were involved in this study. Depending on whether preoperative intervention was performed, the patients were divided into direct surgery group and pretreatment group (uterine artery embolization or medical management). Clinical characteristics, operative related factors and prognosis were analyzed. RESULTS: The most common symptom of pregnancy-related UAVM was vaginal bleeding (82.5%), which could also be accompanied by abdominal pain. Pretreatments (uterine artery embolization or medical therapy) had no obvious benefit to the subsequent surgeries, but increased the hospital stay and hospital cost. Direct surgery group had satisfactory success rate and prognosis compared to pretreatment group. CONCLUSION: For pregnancy-related UAVM, direct surgery has good effects and high safety with shorter hospital stays and less hospital cost. What is more, without uterine artery embolization and other medical therapy, patients could remain better fertility in future.

Analysis of prognostic factors for cervical mucinous adenocarcinoma and establishment and validation a nomogram: a SEER-based study.

Up to now, there are no relevant studies on prognostic factors of cervical mucinous adenocarcinoma. Therefore, we explored the prognostic factors for cervical mucinous adenocarcinoma, and established and validated the prognostic model using the SEER database. We selected the independent factors through univariate and multivariate analyses. LASSO regression analysis was conducted to identify potential risk factors. In conjunction with LASSO and multivariate analysis, the nomogram incorporated three variables, including age, tumour size, and AJCC stage for OS. The c-index was 0.794 and 0.831 in development and validated cohorts, indicating that this prediction model showed adequate discriminative ability in the development cohort. Besides, calibration curves showed good concordance for the development cohort, as well as the validation cohort. We constructed a first-of-its-kind nomogram to predict cervical mucinous adenocarcinomas OS and it showed better performance than AJCC and FIGO stages. Patients with cervical mucinous adenocarcinoma might benefit from using this model to develop tailored treatments.IMPACT STATEMENTWhat is already known on this subject? Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different.What do the results of this study add? We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma.What are the implications of these findings for clinical practice and/or further research? Through the analysis of the SEER dataset, the prognostic factors affecting cervical mucinous adenocarcinoma were identified, and the first predictive model was created to predict the prognosis to help doctors develop individualised treatment plans and follow-up plans.

The roles of m6A methylation in cervical cancer: functions, molecular mechanisms, and clinical applications.

Cervical cancer (CC) is a gynecological neoplasm with the highest incidence rate, primarily attributed to the persistent infection of high-risk Human papillomavirus (HPV). Despite extensive research, the pathogenesis of CC remains unclear. N6-methyladenosine (m6A) methylation, the most prevalent form of epigenetic modification in RNA, is intricately linked to cell proliferation, metastasis, metabolism, and therapeutic resistance within the tumor microenvironment (TME) of CC. The involvement of the writer, reader, and eraser in m6A modification impacts the advancement of tumors through the regulation of RNA stability, nuclear export, translation efficiency, and RNA degradation. Here, we discuss the biogenesis of m6A, the atypical expressions of m6A regulators, the mechanisms of molecular interactions, and their functions in CC. Furthermore, we elucidate m6A modification of non-coding RNA. In the context of precision medicine, and with the advancements of genomics, proteomics, and high-throughput sequencing technologies, we summarize the application of m6A in the clinical diagnosis and treatment of CC. Additionally, new perspectives on detection methods, immune regulation, and nano-drug development are presented, which lay the foundation for further research of m6A and provide new ideas for the clinical treatment of CC.

Construction of a prognostic model based on eight ubiquitination-related genes via machine learning and potential therapeutics analysis for cervical cancer.

Introduction: Ubiquitination is involved in many biological processes and its predictive value for prognosis in cervical cancer is still unclear. Methods: To further explore the predictive value of the ubiquitination-related genes we obtained URGs from the Ubiquitin and Ubiquitin-like Conjugation Database, analyzed datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases, and then selected differentially expressed ubiquitination-related genes between normal and cancer tissues. Then, DURGs significantly associated with overall survival were selected through univariate Cox regression. Machine learning was further used to select the DURGs. Then, we constructed and validated a reliable prognostic gene signature by multivariate analysis. In addition, we predicted the substrate proteins of the signature genes and did a functional analysis to further understand the molecular biology mechanisms. The study provided new guidelines for evaluating cervical cancer prognosis and also suggested new directions for drug development. Results: By analyzing 1,390 URGs in GEO and TCGA databases, we obtained 175 DURGs. Our results showed 19 DURGs were related to prognosis. Finally, eight DURGs were identified via machine learning to construct the first ubiquitination prognostic gene signature. Patients were stratified into high-risk and low-risk groups and the prognosis was worse in the high-risk group. In addition, these gene protein levels were mostly consistent with their transcript level. According to the functional analysis of substrate proteins, the signature genes may be involved in cancer development through the transcription factor activity and the classical P53 pathway ubiquitination-related signaling pathways. Additionally, 71 small molecular compounds were identified as potential drugs. Conclusion: We systematically studied the influence of ubiquitination-related genes on prognosis in cervical cancer, established a prognostic model through a machine learning algorithm, and verified it. Also, our study provides a new treatment strategy for cervical cancer.

Cesarean Scar Ectopic Pregnancy Clinical Classification System With Recommended Surgical Strategy.

OBJECTIVE: To establish a new cesarean scar ectopic pregnancy clinical classification system with recommended individual surgical strategy and to evaluate its clinical efficacy in treatment of cesarean scar ectopic pregnancy. METHODS: This retrospective cohort study included patients with cesarean scar ectopic pregnancy in Qilu Hospital in Shandong, China. From 2008 to 2015, patients with cesarean scar ectopic pregnancy were included to determine risk factors for intraoperative hemorrhage during cesarean scar ectopic pregnancy treatment. Univariable analysis and multivariable logistic regression analyses were used to explore the independent risk factors for hemorrhage (300 mL or greater) during a cesarean scar ectopic pregnancy surgical procedure. The model was internally validated with a separate cohort. Receiver operating characteristic curve methodology was used to identify optimal thresholds for the identified risk factors to further classify cesarean scar ectopic pregnancy risk, and the recommended operative treatment was established for each classification group by expert consensus. A final cohort of patients from 2014 to 2022 were classified according to the new classification system, and the recommended surgical procedure and clinical outcomes were abstracted from the medical record. RESULTS: Overall, 955 patients with first-trimester cesarean scar ectopic pregnancy were included; 273 were used to develop a model to predict intraoperative hemorrhage with cesarean scar ectopic pregnancy, and 118 served as an internal validation group for the model. Anterior myometrium thickness at the scar (adjusted odds ratio [aOR] 0.51, 95% CI 0.36-0.73) and average diameter of the gestational sac or mass (aOR 1.10, 95% CI 1.07-1.14) were independent risk factors for intraoperative hemorrhage of cesarean scar ectopic pregnancy. Five clinical classifications of cesarean scar ectopic pregnancy were established on the basis of the thickness and gestational sac diameter, and the optimal surgical option for each type was recommended by clinical experts. When the classification system was applied to a separate cohort of 564 patients with cesarean scar ectopic pregnancy, the overall success rate of recommended first-line treatment with the new classification grouping was 97.5% (550/564). No patients needed to undergo hysterectomy. Eighty-five percent of patients had a negative serum ß-hCG level within 3 weeks after the surgical procedure; 95.2% of patients resumed their menstrual cycles within 8 weeks. CONCLUSION: Anterior myometrium thickness at the scar and the diameter of the gestational sac were confirmed to be independent risk factors for intraoperative hemorrhage during cesarean scar ectopic pregnancy treatment. A new clinical classification system based on these factors with recommended surgical strategy resulted in high treatment success rates with minimal complications.

Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images.

BACKGROUND: Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM. METHODS: A deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole-slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set. RESULTS: In the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM. CONCLUSION: DL-based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision-making for patients diagnosed with cervical cancer.

Use of Nomogram to Predict the Risk of Lymph Node Metastasis among Patients with Cervical Adenocarcinoma.

Background: The objective of this study was to develop a nomogram that can predict lymph node metastasis (LNM) in patients with cervical adenocarcinoma (cervical AC). Methods: A total of 219 patients with cervical AC who had undergone radical hysterectomy and lymphadenopathy between 2005 and 2021 were selected for this study. Both univariate and multivariate logistic regression analyses were performed to analyze the selected key clinicopathologic features and develop a nomogram and underwent internal validation to predict the probability of LNM. Results: Lymphovascular invasion (LVI), tumor size ≥ 4 cm, and depth of cervical stromal infiltration were independent predictors of LNM in cervical AC. However, the Silva pattern was not found to be a significant predictor in the multivariate model. The Silva pattern was still included in the model based on the improved predictive performance of the model observed in the previous studies. The concordance index (C-index) of the model increased from 0.786 to 0.794 after the inclusion of the Silva pattern. The Silva pattern was found to be the strongest predictor of LNM among all the pathological factors investigated, with an OR of 4.37 in the nomogram model. The nomogram developed by incorporation of these four predictors performed well in terms of discrimination and calibration capabilities (C - index = 0.794; 95% confidence interval (CI), 0.727-0.862; Brier score = 0.127). Decision curve analysis demonstrated that the nomogram was clinically effective in the prediction of LNM. Conclusion: In this study, a nomogram was developed based on the pathologic features, which helped to screen individuals with a higher risk of occult LNM. As a result, this tool may be specifically useful in the management of individuals with cervical AC and help gynecologists to guide clinical individualized treatment plan.