Targeting tumor-associated macrophage-derived CD74 improves efficacy of neoadjuvant chemotherapy in combination with PD-1 blockade for cervical cancer.

BACKGROUND: Cervical cancer has the second-highest mortality rate among malignant tumors of the female reproductive system. Immune checkpoint inhibitors such as programmed cell death protein 1 (PD-1) blockade are promising therapeutic agents, but their efficacy when combined with neoadjuvant chemotherapy (NACT) has not been fully tested, and how they alter the tumor microenvironment has not been comprehensively elucidated. METHODS: In this study, we conducted single-cell RNA sequencing using 46,950 cells from nine human cervical cancer tissues representing sequential different stages of NACT and PD-1 blockade combination therapy. We delineated the trajectory of cervical epithelial cells and identified the crucial factors involved in combination therapy. Cell-cell communication analysis was performed between tumor and immune cells. In addition, THP-1-derived and primary monocyte-derived macrophages were cocultured with cervical cancer cells and phagocytosis was detected by flow cytometry. The antitumor activity of blocking CD74 was validated in vivo using a CD74 humanized subcutaneous tumor model. RESULTS: Pathway enrichment analysis indicated that NACT activated cytokine and complement-related immune responses. Cell-cell communication analysis revealed that after NACT therapy, interaction strength between T cells and cancer cells decreased, but intensified between macrophages and cancer cells. We verified that macrophages were necessary for the PD-1 blockade to exert antitumor effects in vitro. Additionally, CD74-positive macrophages frequently interacted with the most immunoreactive epithelial subgroup 3 (Epi3) cancer subgroup during combination NACT. We found that CD74 upregulation limited phagocytosis and stimulated M2 polarization, whereas CD74 blockade enhanced macrophage phagocytosis, decreasing cervical cancer cell viability in vitro and in vivo. CONCLUSIONS: Our study reveals the dynamic cell-cell interaction network in the cervical cancer microenvironment influenced by combining NACT and PD-1 blockade. Furthermore, blocking tumor-associated macrophage-derived CD74 could augment neoadjuvant therapeutic efficacy.

The top 100 most cited articles on fertility-sparing treatments for cervical cancer: A bibliometric analysis.

Background: The primary objective of this paper was to assess and analyze the top 100 most cited articles currently cited in studies of fertility-sparing treatments for cervical cancer. Methods: Searching the Web of Science Core Collection database for the top 100 most cited articles on fertility-sparing treatments for cervical cancer, different aspects of the articles were analyzed, including countries, journals, institutions, authors, keywords and topics. Results: The search was conducted up to August 2023, and the number of citations for the top 100 articles ranged from 19 to 212. These articles originated from 28 different countries, with Professor Plante, M. from Canada and Professor Sonoda, Y. from the USA having the highest number of articles, both with 10. Professor Plante, M. was the first author of 9 articles and corresponding author of 9 articles. The Memorial Sloan Kettering Cancer Center in the USA published the most articles (21) and received a total of 258 citations. Gynecologic Oncology published 37 of the top 100 articles, with 524 citations and an average of 14.16 citations per article. Conclusions: The study concludes that the USA has made the most significant contributions to this field based on the number of articles, authors, and institutions. Additionally, keyword clustering and burst analysis revealed the research hotspots and future trends in this area.

APLN promotes the proliferation, migration, and glycolysis of cervical cancer through the PI3K/AKT/mTOR pathway.

Apelin (APLN) is an endogenous ligand of the G protein-coupled receptor APJ (APLNR). APLN has been implicated in the development of multiple tumours. Herein, we determined the effect of APLN on the biological behaviour and underlying mechanisms of cervical cancer. The expression and survival curves of APLN were determined using Gene Expression Profiling Interactive Analysis. The cellular functions of APLN were detected using CCK-8, clone formation, EdU, Transwell assays, flow cytometry, and seahorse metabolic analysis. The underlying mechanisms were elucidated using gene set enrichment analysis and Western blotting. APLN was upregulated in the samples of patients with cervical cancer and is associated with poor prognosis. APLN knockdown decreased the proliferation, migration, and glycolysis of cervical cancer cells. The opposite results were observed when APLN was overexpressed. Mechanistically, we determined that APLN was critical for activating the PI3K/AKT/mTOR pathway via APLNR. APLN receptor inhibitor ML221 reversed the effect of APLN overexpression on cervical cancer cells. Treatment with LY294002, the PI3K inhibitor, drastically reversed the oncological behaviour of APLN-overexpressing C-33A cells. APLN promoted the proliferation, migration, and glycolysis of cervical cancer cells via the PI3K/AKT/mTOR pathway.

A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning.

Twenty-five percent of cervical cancers are classified as endocervical adenocarcinomas (EACs), which comprise a highly heterogeneous group of tumors. A histopathologic risk stratification system known as the Silva pattern system was developed based on morphology. However, accurately classifying such patterns can be challenging. The study objective was to develop a deep learning pipeline (Silva3-AI) that automatically analyzes whole slide image-based histopathologic images and identifies Silva patterns with high accuracy. Initially, a total of 202 patients with EACs and histopathologic slides were obtained from Qilu Hospital of Shandong University for developing and internally testing the Silva3-AI model. Subsequently, an additional 161 patients and slides were collected from seven other medical centers for independent testing. The Silva3-AI model was developed using a vision transformer and recurrent neural network architecture, utilizing multi-magnification patches, and its performance was evaluated based on a class-specific area under the receiver-operating characteristic curve. Silva3-AI achieved a class-specific area under the receiver-operating characteristic curve of 0.947 for Silva A, 0.908 for Silva B, and 0.947 for Silva C on the independent test set. Notably, the performance of Silva3-AI was consistent with that of professional pathologists with 10 years' diagnostic experience. Furthermore, the visualization of prediction heatmaps facilitated the identification of tumor microenvironment heterogeneity, which is known to contribute to variations in Silva patterns.

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.

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.

New mechanisms and biomarkers of lymph node metastasis in cervical cancer: reflections from plasma proteomics.

OBJECTIVE: Lymph node metastasis (LNM) and lymphatic vasculature space infiltration (LVSI) in cervical cancer patients indicate a poor prognosis, but satisfactory methods for diagnosing these phenotypes are lacking. This study aimed to find new effective plasma biomarkers of LNM and LVSI as well as possible mechanisms underlying LNM and LVSI through data-independent acquisition (DIA) proteome sequencing. METHODS: A total of 20 cervical cancer plasma samples, including 7 LNM-/LVSI-(NC), 4 LNM-/LVSI + (LVSI) and 9 LNM + /LVSI + (LNM) samples from a cohort, were subjected to DIA to identify differentially expressed proteins (DEPs) for LVSI and LNM. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for DEP functional annotation. Protein-protein interaction (PPI) and weighted gene coexpression network analysis (WGCNA) were used to detect new effective plasma biomarkers and possible mechanisms. RESULTS: A total of 79 DEPs were identified in the cohort. GO and KEGG analyses showed that DEPs were mainly enriched in the complement and coagulation pathway, lipid and atherosclerosis pathway, HIF-1 signal transduction pathway and phagosome and autophagy. WGCNA showed that the enrichment of the green module differed greatly between groups. Six interesting core DEPs (SPARC, HPX, VCAM1, TFRC, ERN1 and APMAP) were confirmed to be potential plasma diagnostic markers for LVSI and LNM in cervical cancer patients. CONCLUSION: Proteomic signatures developed in this study reflected the potential plasma diagnostic markers and new possible pathogenesis mechanisms in the LVSI and LNM of cervical cancer.

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.

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.

Comparison of ALA-PDT and CO2 laser treatment of low-grade vaginal intraepithelial neoplasia with high-risk HPV infection: A non-randomized controlled pilot study.

OBJECTIVE: To evaluate the efficacy and safety of 5-aminolevulinic acid-mediated photodynamic therapy (ALA-PDT) and CO2 laser therapy of low-grade vaginal intraepithelial neoplasia (VAIN1) combined with high-risk human papillomavirus (hr-HPV) infection. METHODS: A total of 163 patients with VAIN1 and hr-HPV infection were divided into PDT Group (n = 83) and CO2 laser Group (n = 80). The PDT Group received six times of ALA-PDT treatments and the CO2 laser Group received once CO2 laser treatment. HPV types, cytology, colposcopy, and pathological examinations were carried out before and after treatment. The differences in HPV clearance rate, VAIN1 regression rate, and adverse reactions between the two groups were analyzed during 6-month follow-up. RESULTS: The overall HPV clearance rate of the PDT Group was significantly higher than that of the CO2 laser Group (65.06% vs 38.75%, P = 0.0008) although similar result was obtained for 16/18-related HPV infection patients (54.55% vs 43.48%, P = 0.4578). The VAIN1 regression rate of the PDT Group was significantly higher than that of the CO2 laser Group (95.18% vs 83.75%, P = 0.0170). In patients ≥ 50 years old, ALA-PDT showed better HPV clearance rate and VAIN1 regression rate than CO2 laser therapy (P < 0.05). The adverse reactions in the PDT Group were significantly lower than that in the CO2 laser Group (P > 0.05). CONCLUSIONS: The efficacy of ALA-PDT appears better than CO2 laser for VAIN1 patients. However, the long-term effect of ALA-PDT for VAIN1 still needs to be explored. As a non-invasive treatment, ALA-PDT is a highly effective therapeutic procedure for VAIN1 with hr-HPV infection.

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.

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.

Analysis of systemic inflammatory and coagulation biomarkers in advanced cervical cancer: Prognostic and predictive significance.

OBJECTIVE: Peripheral systemic inflammatory, nutritional, and coagulation biomarkers have prognostic and predictive value in various malignancies. We evaluated the prognostic and predictive roles of systemic inflammatory, nutritional, and coagulation biomarkers in the circulating blood of patients with advanced cervical cancer. METHODS: A retrospective study of 795 patients with cervical cancer who received concurrent chemoradiation therapy was performed. Overall survival was evaluated by the Kaplan-Meier estimator. Univariate and multivariate Cox regression models were used to determine prognostic factors associated with overall survival. RESULTS: The median follow-up time was 76 months. In the univariate analysis, overall survival showed positive prognostic value in patients with a platelet-to-lymphocyte ratio (PLR) <164.29 (P = 0.010), and a plasma fibrinogen (FIB) level <4 g/L(P = 0.012). In the multivariate analysis, the PLR (P = 0.036), and FIB level (P = 0.047) maintained their significance for overall survival. Therefore, the PLR and FIB levels are independent prognostic factors in patients with advanced cervical cancer. CONCLUSIONS: Systemic inflammatory and coagulation biomarkers could help to understand survival differences in the clinical treatment of advanced cervical cancer. The PLR and FIB levels are independent prognostic factors of poor survival in patients with advanced cervical cancer.

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.

Adjuvant chemotherapy versus adjuvant concurrent chemoradiotherapy after radical surgery for early-stage cervical cancer: a randomized, non-inferiority, multicenter trial.

We conducted a prospective study to assess the non-inferiority of adjuvant chemotherapy alone versus adjuvant concurrent chemoradiotherapy (CCRT) as an alternative strategy for patients with early-stage (FIGO 2009 stage IB-IIA) cervical cancer having risk factors after surgery. The condition was assessed in terms of prognosis, adverse effects, and quality of life. This randomized trial involved nine centers across China. Eligible patients were randomized to receive adjuvant chemotherapy or CCRT after surgery. The primary end-point was progression-free survival (PFS). From December 2012 to December 2014, 337 patients were subjected to randomization. Final analysis included 329 patients, including 165 in the adjuvant chemotherapy group and 164 in the adjuvant CCRT group. The median follow-up was 72.1 months. The three-year PFS rates were both 91.9%, and the five-year OS was 90.6% versus 90.0% in adjuvant chemotherapy and CCRT groups, respectively. No significant differences were observed in the PFS or OS between groups. The adjusted HR for PFS was 0.854 (95% confidence interval 0.415-1.757; P = 0.667) favoring adjuvant chemotherapy, excluding the predefined non-inferiority boundary of 1.9. The chemotherapy group showed a tendency toward good quality of life. In comparison with post-operative adjuvant CCRT, adjuvant chemotherapy treatment showed non-inferior efficacy in patients with early-stage cervical cancer having pathological risk factors. Adjuvant chemotherapy alone is a favorable alternative post-operative treatment.

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.

Effect of Maternal Body Mass Index on the Transcriptomic Network of Human First-Trimester Chorionic Villi.

The relationship between fertility and maternal body weight is shaped like an inverted "U," meaning that fertility is negatively affected in overweight or underweight women. Timely and appropriate maternal-fetal interaction is a crucial part of successful pregnancy. However, it is not clear how body weight affects maternal-fetal interaction. Placental villi are the bridge for maternal-fetal interaction. Therefore, we collected villi from pregnant women with different body mass indexes (BMI), who voluntarily underwent induced abortion, to construct a molecular network via RNA-seq. Surprisingly, based on global and significant gene network analysis, we found that dysregulation of inflammatory reaction, cell adhesion, and immune response were the most significantly enriched pathways. We also conducted dynamic gene expression analysis with BMI as a variable, and identified several distinct clusters. Among them, cluster 9 showed an inverted "U" shape and genes in it were mainly enriched in chemical synaptic transmission and cell-cell adhesion via plasma-membrane adhesion molecules. Additionally, genes in the "U" shaped cluster (cluster 5) were enriched in regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains and negative regulation of immune response. We thus conclude that maternal body weight can affect maternal-fetal interaction through alterations or aberrant activation of inflammatory reaction and immune response. Regulating inflammatory reaction may be a potential therapeutic strategy to improve fertility of overweight and underweight people.

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.

Background: Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a novel prognostic model to predict the prognosis for operable cervical cancer patients with better accuracy than clinical staging system. Methods: A total of 13,952 operable cervical cancer patients were retrospectively enrolled in this study. The whole dataset was randomly split into a training set (n = 9,068, 65%), validation set (n = 2,442, 17.5%), and testing set (n = 2,442, 17.5%). Cox proportional hazard (CPH) model and random survival forest (RSF) model were used as baseline models for the prediction of overall survival (OS). Then, a deep survival learning model (DSLM) was developed for OS prediction. Finally, a novel prognostic model was explored based on this DSLM. Results: The C-indexes for the CPH and RSF model were 0.731 and 0.753, respectively. DSLM, which had four layers that had 50 neurons in each layer, achieved a C-index of 0.782 in the validation set and a C-index of 0.758 in the testing set. The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (area under receiver operating curves were 0.826 and 0.689, respectively). Personalized survival curves for individual patient using this novel model also showed notably different survival slopes. Conclusions: Our study developed a novel, practical, personalized prognostic model for operable cervical cancer patients. This novel prognostic model may have the potential to provide a more prognostic information to oncologists.

The Plasma DIA-Based Quantitative Proteomics Reveals the Pathogenic Pathways and New Biomarkers in Cervical Cancer and High Grade Squamous Intraepithelial Lesion.

OBJECTIVE: The process of normal cervix changing into high grade squamous intraepithelial lesion (HSIL) and invasive cervical cancer is long and the mechanisms are still not completely clear. This study aimed to reveal the protein profiles related to HSIL and cervical cancer and find the diagnostic and prognostic molecular changes. METHODS: Data-independent acquisition (DIA) analysis was performed to identify 20 healthy female volunteers, 20 HSIL and 20 cervical patients in a cohort to screen differentially expressed proteins (DEPs) for the HSIL and cervical cancer. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used for functional annotation of DEPs; the protein-protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) were performed for detection of key molecular modules and hub proteins. They were validated using the Enzyme-Linked Immunosorbent Assay (ELISA). RESULTS: A total of 243 DEPs were identified in the study groups. GO and KEGG analysis showed that DEPs were mainly enriched in the complement and coagulation pathway, cholesterol metabolism pathway, the IL-17 signaling pathway as well as the viral protein interaction with cytokine and cytokine receptor pathway. Subsequently, the WGCNA analysis showed that the green module was highly correlated with the cervical cancer stage. Additionally, six interesting core DEPs were verified by ELISA, APOF and ORM1, showing nearly the same expression pattern with DIA. The area under the curve (AUC) of 0.978 was obtained by using ORM1 combined with APOF to predict CK and HSIL+CC, and in the diagnosis of HSIL and CC, the AUC can reach to 0.982. The high expression of ORM1 is related to lymph node metastasis and the clinical stage of cervical cancer patients as well as the poor prognosis. CONCLUSION: DIA-ELSIA combined analysis screened and validated two previously unexplored but potentially useful biomarkers for early diagnosis of HSIL and cervical cancer, as well as possible new pathogenic pathways and therapeutic targets.

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.