Cervical cancer screening by combined cytology and HPV test has reduced the incidence of cervical cancer, but cytological screening lacks a higher sensitivity while HPV testing possesses a lower specificity. Most patients with invasive cervical cancer are treated with radiotherapy. However, insensitivity to radiotherapy leads to poor efficacy. Illumina Methylation EPIC 850k Beadchip was used for genomic screening. We detected methylation of SEPT9 and mRNA expression in different cervical tissues by using methylation-specific PCR and qRT-PCR. Then using CCK8, migration assay, and flow cytometry to detect the biological function and irradiation resistance of SEPT9 in vitro and in vivo. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and co-immunoprecipitation (CoIP) were used to find the interacting gene with SEPT9. Immunostaining of CD206 in cervical cancer and polarization of macrophages (M2) were evaluated by immunofluorescence and WB. The Cancer Genome Atlas (TCGA) database was used for screening the potential miRNAs induced by SEPT9. Hyper-methylation of SEPT9 detects cervical cancer and normal tissues, normal+CIN1 and CIN2+CIN3+cancer with high sensitivity and specificity (AUC = 0.854 and 0.797, respectively, P < 0.001). The mRNA and protein expression of SEPT9 was upregulated in cervical cancer tissues when compared to para-carcinoma tissues. SEPT9 promotes proliferation, invasion, migration, and influences the cell cycle of cervical cancer. SEPT9 interacted with HMGB1-RB axis increases irradiation resistance. Furthermore, SEPT9 mediated miR-375 via the tumor-associated macrophages (TAMs) polarization, affecting the resistance to radiotherapy in cervical cancer. These findings give us the evidence that SEPT9 methylation could be a biomarker for cervical cancer diagnoses. It promotes tumorigenesis and radioresistance of cervical cancer by targeting HMGB1-RB axis and causes polarization of macrophages by mediating miR-375. We suggest SEPT9 could be a potential screening and therapeutic biomarker 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.

Recently, it is found that T-helper (Th) 22 cells are involved in different types of autoimmune and tumor diseases. But, till now, no study has been carried out to understand the involvement of these cells in cervical cancer (CC). Flow cytometry was used to determine the expression of interferon gamma (IFN-γ), Interleukin-22 (IL-22), IL-17 in the peripheral blood of healthy controls (HC), CIN and cervical cancer patients. From peripheral blood mononuclear cells (PBMCs), mRNA expression levels of Aryl hydrocarbon receptor (AHR), RAR-related orphan receptor C (RORC), TNF-α and IL-6 were respectively determined. Using the method of ELISA, plasma concentrations of IL-22, IL-17 and TNF-α were examined. Th22 and Th17 cells were elevated in CC and CIN patients. Th1 cells and the plasma concentrations of IL-22 in CC patients were significantly increased compared with HC. In CC patients, an increased prevalence of Th22 cells was associated with lymph node metastases. There was a positive correlation between Th22 and Th17 cells, but an approximately negative correlation between Th22 and Th1 cells in CC patients. The mRNA expression of RORC, TNF-α and IL-6 was significantly high in CC patients. Our results indicate that there is a higher circulatory frequency of Th22, Th17 and Th1 cells in CC which may conjointly participate in the pathogenesis and growth of CC.

Background SLC7A11 is importantly in both ferroptosis and disulfidptosis which participated in human development and homeostasis. By utilizing bioinformatics and in vitro validation, we explored SLC7A11's role in cervical cancer. Methods From the TCGA database, we analyzed SLC7A11 expression profiles and validated its promoting role in cervical cancer by in vitro. Patients were divided into two subgroups according to SLC7A11 expression levels, and differentially expressed genes (DEGs) were identified between these groups. Then SLC7A11's mechanism was then clarified by function enrichment analysis. The association between SLC7A11 and the immune microenvironment was investigated. Finally, we explore potential drugs by oncoPredict. Results Our findings suggest that SLC7A11 could serve as a valuable prognostic biomarker in cervical cancer. Besides, SLC7A11 was positively regulated cervical cancer cells’ proliferation, migration and invasion. We identified 113 DEGs between two subgroups. Functional enrichment analysis revealed that these DEGs are linked to immune-related pathways. The SLC7A11 high expression group showed greater enrichment of resting NK cells, neutrophils, M0 macrophages, and activated mast cells, whereas the SLC7A11 low expression group had higher levels of resting dendritic cells, resting mast cells, and follicular helper T cells. Besides, there were higher TMB scores in patients with high SLC7A11 expression. Finally, we explore 37 kinds of drugs may improve prognosis. Conclusion SLC7A11, correlated with immune pathway, is a risk factor affecting the prognosis of cervical cancer. We also explore 37 kinds of drugs that may benefit cervical cancer patients.

Background Clinical staging remains the most commonly used systems for advanced cervical cancer in clinical practice and cannot provide satisfactory prognostication. Deep learning model could capture complex and nonlinear relationships of data, which has not been fully explored in cancer survival analyses. This is the first study to take advantage of the advanced cervical cancer database as well as a deep learning method to develop a novel prognostic model for such patients. Methods 1,143 advanced cervical cancer patients from inhouse (Qilu Hospital of Shandong University and Shandong First Medical University Affiliated Cancer Hospital) were enrolled in this study. The patients from inhouse were randomly split into a training set (n = 914, 80%) and testing set (n = 229, 20%). 49 selected epidemiologic, clinical and hematologic variables were used for model establishment. We developed a deep survival learning model (DSLM) and tested it for individualized survival prediction. Finally, a novel personalized prognostic model was explored. We also compared DLSM model with four other baseline models (CPH, RSF, LMTM and SVM) to prove DLSM is better. In addition, we selected 3495 patients in the SEER database for external verification. Results The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (AUROC of 0.8603 and 0.5403, respectively). Personalized survival curves for advanced cervical cancer patients also showed notably different survival slopes. Conclusion Our study developed a novel, practical, personalized prognostic model for advanced cervical cancer, this individualized survival prediction system may also help clinicians to design more balanced and reasonable clinical trials. Graphical abstract