Youzhong Zhang

Abnormal function of deubiquitination enzymes is implicated in cancer progression. However, the role of the 26S proteasome non-ATPase regulatory subunit 14

Objective: To explore the factors associated with elevated serum progesterone (P) levels on the day of hCG administration in patients undergoing controlled ovarian stimulation using a gonadotropin-releasing hormone antagonist (GnRH-A) protocol. Methods: A retrospective cohort study was conducted, including 1392 in vitro fertilization-embryo transfer (IVF-ET) or intracytoplasmic sperm injection (ICSI) cycles performed between January 2018 and December 2023 at the Reproductive Medicine Center of the Affiliated Hospital of Jining Medical University. Patients were stratified into a normal progesterone group (NP group, serum P ≤ 1.5 ng/mL, n = 985 cycles) and a high progesterone group (HP group, serum P > 1.5 ng/mL, n = 407 cycles) according to the cutoff value of 1.5 ng/mL on hCG day. Clinical characteristics and endocrine parameters during ovarian stimulation were compared between groups. Univariate and multivariate logistic regression analyses were applied to identify independent factors related to elevated hCG-day progesterone levels. Results: The oocyte retrieval age, BMI, basal FSH (bFSH), and hCG-day serum LH levels were significantly higher in the NP group than in the HP group, whereas basal P (bP), antral follicle count, hCG-day serum E2, AMH, and total number of oocytes retrieved were significantly lower (all P < 0.05). No significant differences were observed in basal LH, basal E2, initial Gn dose, total Gn duration, or total Gn dose between groups (all P > 0.05). Univariate logistic regression showed that oocyte retrieval age, BMI, bFSH, bP, AMH, antral follicle count, hCG-day serum LH and E2 levels, and total oocytes retrieved were significantly associated with hCG-day progesterone levels (all P < 0.05). Multivariate analysis further confirmed that BMI (OR = 0.901), bP (OR = 2.100), hCG-day serum E2 (OR = 1.225), hCG-day serum LH (OR = 1.048), and total oocytes retrieved (OR = 1.143) were independent influencing factors (all P < 0.05). Conclusion: Basal progesterone, hCG-day serum E2 and LH levels, and total number of oocytes retrieved are independent risk factors for elevated progesterone levels on hCG day, whereas BMI serves as a protective factor.

Alterations in the vaginal microbiota contribute to the pathogenesis of cervical neoplasia. However, the distinctions in microbiota changes related to different human papillomavirus (HPV) subtypes, as well as the variation in gut microbiota, have not been fully explored. In this research, we endeavored to explore the shifts in the vaginal and intestinal microbiota in correlation with the advancement of cervical neoplasia and HPV infection. A total of 578 vaginal and intestinal cross-sectional specimens were collected from 348 subjects and subjected to 16 S rRNA sequencing. Statistical analyses were performed using R language, and Student’s t-test was employed to assess the significance of differences. Both within and between, sample diversity of the vaginal and intestinal microbiota exhibited substantial alterations across cervical intraepithelial neoplasia (CIN) stages and cervical carcinoma. The vaginal genera Lactobacillus, Enterococcus, Peptoniphilus, Atobium, Anerococcus, and Veillonella were associated with different CIN stages and cervical cancer type, whereas Allisonella, Lachnospiracae, Lactobacillus, Staphylococcus, and Sellimonas were associated with varying HPV types. A Random Forest-driven classifier highlighted the predictive potential of differential bacteria in cervical neoplasia and HPV infection, with intestinal bacteria showing higher predictive accuracy in certain instances. Specifically, the accuracy of differentiating CIN I from CIN III was superior for the intestinal bacterial model compared to the vaginal bacterial model (85.52% vs. 83.33%). The model also demonstrated high accuracy in predicting HPV infection, particularly in distinguishing HPV-16 from HPV-18 and HPV-58, with AUC values of 81.61% and 83.07%, respectively, compared to less than 70% for vaginal bacteria. Our findings reveal the intricate interplay among cervical neoplasia, HPV infection, and microbiota, with potential diagnostic and therapeutic implications.

The microbiome present throughout the human body serves a variety of functions. In this study, 16S rRNA sequencing is employed to uncover differences in the abundance of Lactobacillus within the vaginal microbiota between individuals with cervical cancer and those with healthy cervixes. The research further identifies that the metabolite of Lactobacillus crispatus can induce ferroptosis in cervical cancer cells. This conclusion is reached through targeted bacterial culture, patient‐derived organoids (PDO) and single‐cell RNA sequencing. Erucic acid, identified as a primary metabolite via untargeted metabolomics, acts as a ligand for PPARδ receptor. It has the capacity to activate PPARδ pathway and subsequently trigger downstream fatty acid oxidation (FAO). Excessive enhancement of FAO can generate large amounts of H ₂ O ₂ and O ₂ ‐, known as ROS. Utilizing PDO, cell lines and cervical cancer xenograft (CDX) models, the study demonstrate both in vitro and in vivo that the metabolite of L. crispatus , erucic acid, can modulate the proliferation, migration and invasion of cervical cancer by activating the PPAR‐δ pathway. This activation leads to fatty acid oxidation, release ROS, and ultimately induces ferroptosis. Therefore, L. crispatus and erucic acid show potential as novel adjuvant therapeutic agents in the treatment of cervical cancer.

Purpose: This study aimed to identify reliable risk factors for the development of Vaginal intraepithelial neoplasia (VaIN) within two years after the conization for high-grade squamous intraepithelial lesions (HSIL). We developed a prediction model to predict the risk of VaIN based on preoperative and follow-up data. Methods: We collected 5358 patients who underwent conization for HSIL, of whom 99 developed VaIN within two years after conization. We selected 495 patients as the control group by randomly pairing them 1:5, and were randomly divided into development and validation cohorts at a ratio 7:3. Random Forest (RF), Lasso, and Extreme Gradient Boosting (XGBoost) were employed to identify the most influential variables in the model development dataset. The optimal variables selected through this process were then used for model construction. Subsequently, four machine learning models were developed, and their performance was evaluated using metrics including sensitivity, specificity, accuracy, area under the curve (AUC), and the F1 score. To enhance interpretability, the prediction process was visualized using Shapley Additive Explanations (SHAP). Finally, the model was deployed as a web-based clinical decision support system for practical clinical applications. Results: Five key clinical predictive variables were identified: age, transformation zone (TZ) type, presence of VaIN before conization, follow-up cytology after conization, and follow-up HPV after conization. The optimal model demonstrated strong predictive performance, achieving AUC of 0.910 (95% CI: 0.854– 0.966) in the internal validation cohort and 0.905 (95% CI: 0.859– 0.951) in the external validation cohort. Conclusion: We established a practical and accurate prediction model deployed in the network application to predict the occurrence of VaIN within two years after conization in patients with HSIL. This tool can facilitate targeted clinical decision-making for clinicians.

Subsequently to the publication of the above paper, an interested reader drew to the authors' attention that, for the cell migration and invasion assay experiments shown in Fig. 2 on p. 6322, the 'HeLa/miR‑378 inhibitor' panels in Fig. 2B and C appeared to contain overlapping sections of data, such that these data panels were apparently derived from the same original source to show the results of purportedly different experiments. The authors have re‑examined their original data, and realize that Fig. 2C was inadvertently assembled incorrectly. The revised version of Fig. 2, now containing the correct data for the 'HeLa/miR‑378 inhibitor' experiment in Fig. 2C, is shown on the next page. Note that this error did not adversely affect either the results or the overall conclusions reported in this study. All the authors agree with the publication of this corrigendum, and are grateful to the Editor of Molecular Medicine Reports for allowing them the opportunity to publish this. They also wish to apologize to the readership of the Journal for any inconvenience caused. [Molecular Medicine Reports 17: 6319‑6326, 2018; DOI: 10.3892/mmr.2018.8645].

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.

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.

Background: This study was designed to investigate the roles of RASAL2 in cervical cancer (CC). Methods: Fifty-four CC tissues and 33 adjacent tissues were obtained from CC patients admitted to our hospital between March 2012 and June 2014. Real-time polymerase chain reaction and western blotting were performed to analyze the expression of RASAL2 mRNA and protein in these tissues, CC cell lines, and normal cervical cells. Over-expression and silencing of RASAL2 were induced after transfection, and the migration, invasion, and proliferation of the CC cell lines were examined. Results: RASAL2 mRNA and protein expressions were significantly down-regulated in CC tissues and cell lines than in adjacent tissues and normal cervical cells, respectively. While low RASAL2 expression correlated with advanced stage and metastasis of CC, its over-expression significantly inhibited proliferation and metastasis of CC cells and induced apoptosis. Under in vitro conditions, silencing of RASAL2 expression could significantly increase the proliferation, invasion, and migration of CC cells. Conclusion: RASAL2 functioned as a tumor suppressor in CC, and was down-regulated in CC tissue samples and cell lines. tumor suppressor in CC, and was down-regulated in CC tissue samples and cell lines.