Dual Role of Necroptosis in Cervical Cancer: Promoting Tumor Aggression and Modulating the Immune Microenvironment via the JAK2-STAT3 Pathway

Dual Role of Necroptosis in Cervical Cancer: Promoting Tumor Aggression and Modulating the Immune Microenvironment via the JAK2-STAT3 Pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dual Role of Necroptosis in Cervical Cancer: Promoting Tumor Aggression and Modulating the Immune Microenvironment via the JAK2-STAT3 Pathway Fangfang Xu, Yingjun Ye, Yueqing Gao, Shaohua Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4379854/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective In the dynamic landscape of cervical cancer (CC) pathophysiology, this study aimed to elucidate the role of necroptosis in modulating tumor proliferation, invasion, and the immune microenvironment in CC. Methods In this study, the impact of necroptosis on CC was evaluated through a series of bioinformatical analyses and experimental approaches. The impact of necroptosis on CC was illustrated by analyzing its effects on tumor aggression, immune responses, and the JAK2-STAT3 signaling pathway. Bevacizumab, a monoclonal antibody targeting vascular endothelial growth factor (VEGF), was also evaluated for its potential induction of necroptosis in CC cells and its interaction with necroptosis inhibitors. Additionally, the study assessed the influence of necroptosis on the immune microenvironment, particularly in T-cell-related pathways and the expression of tumor suppressor genes in CC. Results Necroptosis was found to enhance VEGFA expression through activation of the JAK2-STAT3 pathway, promoting tumor proliferative and invasive capabilities in CC. Bevacizumab induced necroptosis in CC cells, potentially leading to resistance in therapy. The combination of bevacizumab with necroptosis inhibitors attenuated VEGFA expression, suggesting a novel therapeutic strategy. Additionally, necroptosis activated T-cell-related pathways and promoted the infiltration and activation of Jurkat T cells. CD3D—a tumor suppressor gene in CC—was identified as a critical marker and its expression could be upregulated by necroptosis via the JAK2-STAT3 pathway in Jurkat T cells. Treatment of CC cells with supernatants from necroptosis-induced Jurkat cells resulted in reduced tumor cell proliferation and invasion. Conclusions This study reveals a complex interaction between necroptosis, tumor progression, and the immune response in CC. The findings propose a nuanced approach to leveraging necroptosis for therapeutic interventions, highlighting the potential of combining necroptosis inhibitors with existing therapies to improve treatment outcomes in CC. Necroptosis Cervical cancer JAK2-STAT3 signaling pathway Bevacizumab Tumor immune microenvironment T cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Cervical cancer (CC) ranks among the most frequently diagnosed malignancies in women. An estimated 604,127 females worldwide received a new diagnosis of CC in 2020, with up to 341,831 of them passing away as a result of the condition [ 1 ], posing a tremendous burden on healthcare systems. Radical hysterectomy is commonly used for early-stage CC, which has achieved great success [ 2 ]. In general, patients diagnosed with early-stage CC have a favorable prognosis [ 3 ], while outcomes for locally advanced stages, recurrent and metastatic cases are not as promising [ 4 ]. For patients with locally advanced stages, metastatic and recurrent CC, conventional treatment options include surgery, radiotherapy and chemotherapy, but these treatment options exert limited effects, the 5-year survival rate for metastatic or recurrent patients is no more than 20% [ 5 , 6 ]. Recent years, immunotherapy especially immune checkpoint inhibitors (ICIs) has emerged to be a promising strategy to treat different human malignancies. Anti-programmed cell death (PD-1) and anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) are two targets for immunotherapy under research in a range of malignancies including CC [ 7 – 9 ]. Dishearteningly, therapeutic effects of the common ICIs including PD-L1 and PD-1 in CC are limited, with a poor response rate of 10–25% [ 10 ]. Furthermore, even though the angiogenesis inhibitor bevacizumab has been employed in combination with the anti-programmed cell death ligand-1 (PD-L1) antibody atezolizumab to enhance the efficacy of CC therapy, the combination therapy only results in a modest improvement [ 11 ]. So, it is of great significance to explore more potential immunotherapy targets and cell interactions in the tumor microenvironment to improve the immunotherapy effectiveness for CC patients. Necroptosis, a form of regulated cell death, is mainly mediated by receptor interacting protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like pseudokinase (MLKL) [ 12 , 13 ]. For the past few years, an accumulating body of researches have indicated that necroptosis plays a dual role in modulating tumorigenesis and tumor progression [ 14 ]. For one thing, necroptosis is suggested to act as a contributing factor in cancer metastasis and cancer progression [ 15 , 16 ], for another, necroptosis is also reported to serve as a tumor suppressor in many malignancies [ 17 , 18 ], either promoting or defending against tumor depends on cancer subtypes and the context in which tumor cells are located. Therefore, a comprehensive investigation into necroptosis may prove beneficial in developing a framework for personalized treatment plans for patients. What’s more, necroptosis plays a role in the regulation of tumor immune microenvironment and its significance in cancer immunity has been increasingly appreciated. It was demonstrated that cancer cells undergoing necroptosis can activate cytotoxic CD8 + T cells and triggering anti-tumor immunity [ 19 , 20 ]. In addition to the regulation of T cells, necroptosis also affects other immune cells, as a key regulator of necroptosis, it was reported that the decreased level of RIPK3 was relevant to the accumulation of myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment (TME) [ 21 ]. Additionally, necroptosis is suggested to plays a negative regulatory role in tumor immunity. It was proved that pancreatic epithelial cells undergoing necroptosis induce C-X-C Motif Chemokine Ligand 1 (CXCL1), leading to the suppression of adaptive immunity and pancreatic oncogenesis [ 22 ]. To sum up, necroptosis exerts a complex regulatory influence on cancer biology and have potential applications as a source of novel drug targets. However, there are few studies on necroptosis in the immune microenvironment of CC. So, it is urgently required to investigate the impact of necroptosis in CC, which may provide valuable insights into patient stratification and precision immune treatment. This study seeks to elucidate the role of necroptosis in CC pathogenesis and its influence on the tumor immune microenvironment. Through integrated bioinformatics analysis of the TCGA-CESC cohort and functional in vitro assays, we aimed to characterize the dual role of necroptosis in CC, which on the one hand promotes the proliferation of CC cells and on the other hand can exert anti-tumor effects through eliciting activation of T cells. Its pro-cancer and anti-cancer effects are achieved through JAK2-STAT3 signaling pathway. Firstly, it exerts an anti-tumor effect by activating the JAK2-STAT3 pathway, which upregulates CD3D and then promotes T cell activation. Secondly, it exerts a pro-cancer effect by activating the JAK2-STAT3 pathway, which upregulates VEGFA. Ultimately, CD3D was determined as a biomarker for immune activation and therapeutic responsiveness in CC patients, and the role of CD3D as a biomarker of treatment response was described by exploring the relationship between CD3D expression and clinical characteristics and immune microenvironment of CC. The study extends to the potential interaction between necroptosis and bevacizumab, revealing a potential resistance mechanism of bevacizumab and offering new perspective into personalized treatment plans for CC patients. Materials and methods Public datasets acquisition The gene expression profiles and clinical data for CC cases were downloaded from The Cancer Genome Atlas (TCGA) database, accessible through https://portal.gdc.cancer.gov/ . Necroptosis-related genes (NRGs) were retrieved from MSigDB ( https://www.gsea-msigdb.org/ ) via the search term “Necroptosis”. In addition, genes related to the CD8 pathway were downloaded from MSigDB searching the keyword “CD8”. CC samples were grouped by the expression level of NRGs and defined as “high-necroptosis” and “low-necroptosis” cohorts. Subsequently, the identification of differentially expressed genes (DEGs) between two cohorts was performed using the “limma” package in R, applying the following filtering criteria: false discovery rate (FDR) 1. Then, the intersection of necroptosis-related differentially expressed genes, CD8 pathway-related genes and prognostic-related genes was identified through the R package “VennDiagram”. Immunity evaluations The ESTIMATE algorithm implemented in R software [ 23 ] was utilized to evaluate the Immune Score, Stromal Score, and overall ESTIMATE Score for each CC sample, indicating the extent of immune cell and stromal cell infiltration within the TME. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to quantify the presence of immune cells and the enrichment of immune-related pathways via “GSVA” R package [ 24 ]. The CIBERSORT algorithm was utilized to determine the composition of 22 immune cell types within the TME of each CC sample [ 25 ]. Immune checkpoint genes (ICPs) were sourced from the TISIDB database ( http://cis.hku.hk/TISIDB ) [ 26 ] and prior research papers [ 27 – 29 ]. The difference of immune components between different groups were visualized using R packages “limma”, “ggplot” and “ggplot2”. Functional enrichment analyses and GSEA Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the biological function of NRGs through R package “clusterProfiler” [ 30 ], with the outcomes visualized using the R packages “enrichplot” and “ggplot2”. A significant threshold of p-value < 0.05 and q-value < 0.05 was utilized. Additionally, gene set enrichment analysis (GSEA) was conducted between high- and low-necroptosis clusters using the HALLMARK gene sets and C2, CP, and KEGG v7.2 symbols. This analysis was performed via the gsea software (version: 4.0.3) with a significant threshold of 5% for NOM p-value and FDR q-value. Survival analysis R packages “survival” and “survminer” [ 31 ] were employed to delimit the cut-off value of all survival analyses. The survival curves were generated using the Kaplan-Meier (KM) method to display all results, with statistical significance calculated using the log-rank test. A significance level of p < 0.05 was considered to be of notable importance. The impact of gene expression on survival was also assessed using the KM Plotter online database ( http://kmplot.com ). Necroptosis Induction and Inhibition Necroptosis was induced by treating cells with a combination of TNFα (MCE, HY-P7058) and Z-VAD-FMK (MCE, HY-16658B) at concentrations of 20 µm and 50 µm. This treatment regimen aimed to inhibit caspase activity and initiate necroptotic cell death. To inhibit necroptosis, cells were exposed to necrosulfonamide (NSA) (MCE, HY-100573) or necrostatin-1 (NEC-1) (MCE, HY-15760) at concentrations of 10 µm, 20 µm, and 30 µm. NSA targets the effector protein MLKL to prevent its translocation to the plasma membrane, while NEC-1 disrupts the formation of the RIPK1-RIPK3 complex crucial for necroptotic signaling. These interventions were instrumental in elucidating the molecular mechanisms underlying necroptosis regulation in our experimental model. Cell culture and real-time qPCR (RT-qPCR) The SiHa cell lines, sourced from ATCC (Manassas, VA, USA), were cultured in high-glucose Dulbecco’s modified Eagle medium (DMEM; Servicebio, China) supplemented with 10% fetal bovine serum (FBS, Biological Industries, Israel) and 1% antibiotic (penicillin/streptomycin; New Cell & Molecular Biotech Co., China) at 37°C in a 5% CO2 incubator. Human Jurkat Clone E6-1 T cells were procured from Procell Life Science & Technology Co., Ltd (#CL-0129; Wuhan, China) and maintained in RPMI-1640 medium (Servicebio, China) with 10% FBS and 1% penicillin/streptomycin. Total RNA was isolated from SiHa and human Jurkat Clone E6-1 T cells using TRIzol reagent (Invitrogen, USA), followed by quantification of RNA concentration and purity. Reverse transcription to generate cDNA was performed using the 5× ALL-IN-One RT Master Mix kit (Applied Biological Materials Inc., Canada). RT-qPCR was carried out using the TB Green Premix Ex Taq kit (Takara, Japan), with GAPDH expression used as an internal control. The relative mRNA expression levels were determined using the 2 −△Ct method [ 32 ]. Table S1 lists all the primers used in this research. Clinical CC samples and immunohistochemistry (IHC) Ten cervical squamous cell carcinoma specimens, consisting of five early-stage and five late-stage samples, were obtained from Shanghai First Maternity and Infant Hospital. These clinical specimens underwent pathological verification according to the latest International Federation of Gynecology and Obstetrics (FIGO) guidelines. Prior to collection, informed consent was obtained from the patients, and the study was approved by the Medical Ethics Committee of Shanghai First Maternity and Infant Hospital (Approval notice: KS21264). Following fixation, paraffin embedding, dewaxing, rehydration, and antigen retrieval, all tumor tissue sections were processed for staining with a primary antibody targeting CD3D (A9770, Abclonal, China) at 4°C overnight. Subsequently, the slides underwent incubation with a secondary antibody (#PK-8501, Vector Lab, USA) for 1 hour at 37°C, followed by detection and visualization of the DAB complex using the Rabbit IgG mini-PLUS Kit (#PK-8501, Vector Lab, USA) and hematoxylin for nuclear counterstaining. Photomicrographs were captured using an optical microscope at 100× magnification. Western blotting (WB) Cell lysis was performed by adding RIPA buffer containing complete protease and phosphatase inhibitors (TargetMol, America). Protein concentrations were determined using the BCA method (Beyotime, China). The extracted proteins were subjected to a thermal treatment at 99℃ for a period of 20 minutes. Subsequently, they were separated by 10% SDS-PAGE (Servicebio, China) and transferred to polyvinylidene fluoride membranes. Next, the membranes were blocked with a 5% lipid-free milk solution for 1.5 hours. Following this, they were incubated with primary antibodies (Table S2) overnight at 4°C. Afterward, the membranes were washed and incubated with secondary antibodies (HuaBio, China) for 2 hours at room temperature. Ultimately, protein bands were visualized using an enhanced chemiluminescence reagent (Epizyme, China). Cell Counting Kit-8 (CCK-8) assay CCK-8 reagent (Vazyme, China) was utilized to detect the cell viability. CC cells and human Jurkat Clone E6-1 T cells were initially diluted to a density of 2*10 4 cells/ml and plated in 100 µl aliquots into each well of 96-well microtiter plates (Coring, NY, USA) for 96 hours. At 24-hour intervals, 10 µl of CCK-8 reagent was added to each well, followed by a 2-hour incubation period. Subsequently, optical density (OD) was measured at a wavelength of 450 nm. Transwell assay To evaluate cell migration, we initiated the procedure by adjusting the cell concentration to 2 × 10^5 cells/mL. Then, 150 µL of this cell suspension was delicately added to the upper chamber of the Transwell apparatus, while the lower chamber of each well received 800 µL of DMEM medium supplemented with 20% FBS. This setup was then placed in a controlled environment at 37°C with 5% CO2 for 24 hours. After the designated incubation period, non-migrating cells on the upper membrane surface were gently removed using sterile cotton swabs. Following this, the migrated cells were fixed with methanol for 15 minutes and then stained with crystal violet for 30 minutes. Subsequently, thorough rinsing with PBS ensured the removal of excess dye. The inserts were then subjected to microscopic examination. Five random fields of view were selected for imaging, facilitating the enumeration of migrated cells. These images provided a basis for quantifying and comparing the migratory capacities of the transfected cells. Wound healing test To conduct the wound healing assay, CC cells were first grown in six-well plates with complete medium until reaching 95% confluence over a 24-hour period. Subsequently, a scratch was made using a 200 µl sterile pipette tip, followed by washing with phosphate-buffered saline (PBS) to eliminate cell debris. The adherent cells were then cultured in serum-free DMEM and images were captured at 0, 12 and 24 h post-scratching to assess the wound area as an indicator of cell invasion ability. 5-Ethynyl-20-deoxyuridine (EdU) assay The EdU assays were performed using the BeyoClickTM EdU Cell Proliferation Kit with Alexa Fluor 555 (Beyotime, China). EdU was introduced into the cell culture medium and allowed to incubate for 2 hours. Subsequently, CC cells were fixed with 4% paraformaldehyde and permeabilized with 0.3% Triton X-100 (Servicebio, China). The Click solution was then prepared according to the manufacturer's instructions and used to incubate the cells for 30 minutes in the absence of light. The cells were stained with Hoechst 33342 and visualized using an inverted fluorescence microscope (Carl Zeiss, Germany). Statistical analysis Statistical analyses were performed utilizing R software (version 4.0.5), GraphPad Prism 8 (GraphPad, La Jolla, CA, USA), and SPSS 26.0 (IBM, SPSS, Chicago, USA). Unpaired Student’s t-tests were employed for comparing data between two groups, with statistical significance set at a threshold of P < 0.05. Each experiment was replicated a minimum of three times. Results Necroptosis promotes proliferation and invasion of CC cells We first investigated the effect of necroptosis on the proliferation and migration of CC cells by in vitro experiments. As shown by the CCK-8 results, CC cells induced to undergo necroptosis showed a higher viability compared to the normal control group (Fig. 1 A), whereas there was a decreased state with the treatment of necroptosis inhibitor (Fig. 1 B). Transwell assays were performed to assess the migration ability of CC cells and the results showed a greater migration potential in the necroptosis-inducing group (Fig. 1 C). Consistently, compared with the normal control group, necroptosis-inhibiting group exhibited a decreased migration state (Fig. 1 D). Wound healing assays were utilized to assess the migration ability of CC cells. As shown in Fig. 1 E, F, the necroptosis-inducing group tended to have a significant increased migration ability while a weaker condition was found in the necroptosis-inhibiting group. Additionally, as depicted by the EdU results, CC cells induced into necroptosis displayed elevated proliferation ability relative to the normal control group (Fig. 1 G), while treatment with a necroptosis inhibitor led to decreased proliferation (Fig. 1 H). All these results indicated necroptosis contributed to the proliferation and invasion of CC cells. Necroptosis up-regulates VEGFA via JAK2-STAT3 signaling pathway in CC cells GO enrichment analysis and KEGG analysis were performed to further explore the mechanism of necroptosis in CC cells. Results of GO enrichment analysis revealed functions about the biological process of NRGs includes receptor signaling pathway via JAK-STAT and positive regulation of interleukin-6 (IL-6) production (Fig. 2 A). Results of KEGG showed NRGs were enriched in vascular endothelial growth factor (VEGF) signaling pathway and JAK-STAT signaling pathway as well (Fig. 2 B). Notably, researches have demonstrated that activation of IL-6/IL-6R up-regulates VEGF, thereby promoting tumor angiogenesis [ 33 , 34 ]. Moreover, it was verified that the inhibition of STAT3 resulted in a decrease in VEGF [ 35 ]. In this study, the WB assay showed a significant elevation of RIPK1, phosphorylated RIPK1 (p-RIPK1), RIPK3, p-RIPK3, MLKL and p-MLKL (Fig. S1 A), indicating CC cells were undergoing necroptosis with the addition of necroptosis inducer. Results of RT-qPCR also demonstrated a similar elevation in the expression levels of RIPK1 and MLKL in CC cells in the presence of the necroptosis inducer (Fig. S1 B). Besides, it was noteworthy that CC cells undergoing necroptosis exhibited an elevated protein level of VEGFA (Fig. S1 C). Results of bioinformatic analyses indicated the expression level of VEGFA was higher in advanced CC samples (Fig. S1 D) and the high expression group demonstrated a tendency towards a worse primary therapy outcome (Fig. S1 E). In addition, CC patients with lower expression of VEGFA tended to possess a better outcome, with longer disease-free survival (DFS), overall survival (OS) and progress-free survival (PFS) (Fig. S1 F-H). Furthermore, CC patients with high expression level of VEGFA and high necroptosis were more likely to experience unfavorable outcomes (Fig. S1 I). Subsequently, the mechanism of necroptosis up-regulating VEGFA was further explored through in vitro experiments. Results of RT-qPCR showed with the induction of necroptosis, the expression levels of JAK2, STAT3 and VEGFA got significantly elevated (Fig. 2 C-E). Consistently, as results of WB assay presented, the protein levels of JAK2, STAT3, p-STAT3 and VEGFA were up-regulated with the induction of necroptosis (Fig. 2 F) whereas they were found to decrease with the treatment of NSA (Fig. 2 G). FLLL32 (MCE, HY-100544) was a specific inhibitor of JAK2-STAT3 pathway, JAK2, STAT3 and p-STAT3 were found to be inhibited in CC cells at a concentration of 10 µm (Fig. S1 J). Results of in vitro experiments demonstrated that the up-regulation of JAK2, STAT3 and VEGFA by necroptosis inducer was eliminated by FLLL32 (Fig. 2 H-K). Therefore, it could be concluded that necroptosis up-regulates VEGFA via JAK2-STAT3 signaling pathway. Bevacizumab induces the necroptosis of CC cells and activates JAK2-STAT3 signaling pathway Bevacizumab is a monoclonal antibody that can specifically target VEGF. It blocks the binding of VEGF and its receptor, inhibiting tumor neovascularization and thus playing an anti-tumor role. In this study, it has been proved that necroptosis up-regulates VEGFA via JAK2-STAT3 signaling pathway, we then further explore the interaction of necroptosis, bevacizumab and JAK2-STAT3 signaling pathway. The results of the wound healing test demonstrated that bevacizumab (Selleck, A2006) exhibited a notable inhibitory effect on the migratory capacity of CC cells (Fig. 3 A). The expression levels of RIPK1, RIPK3, MLKL and the protein levels of p-RIPK3 and p-MLKL exhibited an elevated trend when bevacizumab was added in CC cells (Fig. 3 B-E), indicating that bevacizumab induces the necroptosis of CC cells. Moreover, with the addition of bevacizumab, results of WB assay verified the VEGFA was successfully inhibited, accompanied by a significant increase in the protein levels of JAK2, STAT3 and p-STAT3 (Fig. 3 F). The elevated protein levels of JAK2, STAT3, and p-STAT3 were observed in CC cells treated with bevacizumab alone or in combination with necroptosis inducer, however the inhibitory effect of bevacizumab on VEGFA was eliminated in combination with necroptosis inducer (Fig. 3 G). Results of RT-qPCR showed the expression level of JAK2 and STAT3 got up-regulated with the addition of bevacizumab and the effect was reversed by necroptosis inhibitor NSA or NEC-1 (Fig. 3 H-I). Consistently, the impact of bevacizumab on JAK2, STAT3 and p-STAT3 was eliminated by NSA or NEC-1. A synergistic inhibition effect on VEGFA was observed while treated with bevacizumab in combination with NSA or NEC-1 (Fig. 3 J). All these results demonstrated that bevacizumab induced the necroptosis of CC cells, thus activated JAK2-STAT3 signaling pathway and up-regulated VEGFA in combination with necroptosis inducer, suggesting a potential mechanism of bevacizumab resistance. Comprehensive bioinformatical analyses revealed necroptosis could enhance T cell signaling pathways in CC In order to further investigate the role of necroptosis in TME, CC cases were first clustered into high- and low- necroptosis cohorts, with the median expression of NRGs serving as the cut-off point. Then, the correlations of TME components between the two clusters were evaluated by ESTIMATE algorithm (Fig. 4 A). As results presented, ImmuneScore exhibited the most remarkable difference, implying CC patients with higher necroptosis enjoys a heater immune microenvironment. For investigating the correlation between necroptosis and immunologic functions as well as immune cell infiltration, ssGSEA was conducted and the results demonstrated that most immune-related functions showed a remarkable difference between high- and low- necroptosis groups (Fig. 4 B). In addition, GO enrichment analysis and KEGG analysis (Fig. 4 C) were utilized to evaluate the function of necroptosis, and results indicated that genes’ functions about the biological process (BP), cellular component (CC), molecular function (MF) were all tightly centered around T cell regulation. Notably, results of KEGG revealed that necroptosis were not only involved in T cell receptor signaling pathway, but also related to JAK-STAT signaling pathway. Moreover, GSEA was performed to investigate differences of enriched pathways between these two groups. In high necroptosis cluster, pathways were significantly enriched in immunological functions including T cell receptor signaling pathway (Fig. 4 D). CIBERSORT was utilized to compare the difference of tumor-infiltrating cells proportion between different clusters. Notably, as the result exhibited, the high-necroptosis cohort tends to own a higher infiltration level of CD8 + T cells (Fig. 4 E). Moreover, anti-tumor chemokine genes including CXCL9, CXCL10, CXCL11 and CXCL13 expressed higher in high-necroptosis cohort (Fig. 4 F). All these results suggested that necroptosis could elicit immune responses and dynamically change the TME of CC, particularly T cell signaling pathway. Necroptosis could facilitate T cells activation, reduces T cell depletion and increases T cell infiltration in vitro After identifying that necroptosis was linked to immunological processes especially CD8 + T cell regulation based on bioinformatical analyses, a series of in vitro experiments were conducted to confirm it. T cells are rapidly activated after encountering antigen stimulation and undergo a series of cytological changes such as the expression of some membrane surface molecules and synthesizing cytokines. CD69 is the earliest membrane molecule expressed by activated T cells, which can be measured and reach a peak soon after half an hour of activation, thus can be the earliest activation marker for T cells [ 36 ]. The interleukin-2 receptor alpha subunit (IL-2Rɑ, CD25) is upregulated within 24 h of stimulation and remains elevated for several days, which can serve as a mid-late activation marker. Besides, CD25 plays a crucial role in the reactivity to IL-2, allowing T lymphocyte activation and further IL-2 production [ 37 ]. Activated CD8 + T cell secreted granzyme B (GZMB), Interferon gamma (IFN-γ) which can meditate target cell lysis and apoptosis [ 38 , 39 ], therefore, GZMB and IFN-γ are also important indicators of T cell activation. To verify whether necroptosis influences T cell activation, RT-qPCR was performed to evaluate the changes of T cell activation markers with or without the induction of necroptosis in Jurkat cells (Fig. 5 A-D). It was obviously that the expression levels of CD25, CD69, GZMB and IFN-γ in Jurkat cells got significantly elevated with the induction of necroptosis, suggesting necroptosis facilitates T cell activation. Moreover, cytokines secreted by T cell are involved in controlling many different cellular functions, including proliferation, differentiation and cell apoptosis. In our study, results of RT-qPCR (Fig. 5 E-G) illustrated that cytokines including IL-2, IL-6, and IL-13 were in a higher expression of Jurkat cells induced with a necroptosis-inducing agent, implying that necroptosis modulated T cell proliferation and differentiation. Chemokines play a crucial role in guiding immune cell migration, which is essential to initiate and deliver an effective anti-tumor immune response. Here, we found Jurkat cells undergoing necroptosis exhibited an up-regulated expression level of chemokine receptor genes including C-C Motif Chemokine Receptor 4 (CCR4), CCR5, CCR7, CCR9 and chemokine genes including CXCL9, CXCL10, CXCL11, CXCL13 were up-regulated (Fig. 5 H, I). We also evaluated the relationship between the expression of immune checkpoints and necroptosis, results showed CTLA4, LAG3, TIM3, VISTA, TIGIT and PD-1 were down-regulated in Jurkat cells with the induction of necroptosis (Fig. 5 J), demonstrating that necroptosis might reduce T cell depletion. In contrast, while treated with necroptosis inhibitor NSA or NEC-1, the expression of CCR4, CCR5, CCR7, CCR9, CXCL9, CXCL10, CXCL11 and CXCL13 were obviously decreased in Jurkat cells (Fig. 5 K-N). It was remarkable that the viability of Jurkat cells was not affected whether treated with necroptosis inducer or necroptosis inhibitor NEC-1 and NSA according to the results of CCK-8 experiments (Fig. S2A-C), indicating necroptosis affected functions of Jurkat cells but not proliferation. In contrast to its tumor-promoting role in CC cells, the aforementioned results provide compelling evidence to support the anti-tumor potential of necroptosis, which is achieved through the modulation of T cell activation, depletion and infiltration. In conclusion, it can be posited necroptosis plays a dual role in CC. CD3D was identified as a hub gene and emerged as a protective factor in CC As previously mentioned, bioinformatic analyses and experimental investigations identified that necroptosis has played a role in the regulation of CD8 + T cells. Subsequently, we conducted a comprehensive analysis to select genes associated with both the ‘CD8 Pathway’ and prognosis from the pool of 958 differentially expressed NRGs (Table S3). This analysis led to the identification of 6 key genes (CD2, CD247, CD3D, CD3E, CD3G, CD8A) through the intersection analysis (Fig. 6 A). Remarkably, all six genes exhibited a higher expression level in the high-necroptosis cluster (Fig. 6 B, C). Subsequent correlation analysis of the expression levels of these 6 genes with clinicopathological characteristics revealed that CD3D expression was significantly linked to stage classification, grade classification, and response to primary therapy (Fig. 6 D-G). Notably, our findings indicated a negative correlation between CD3D expression level and staging as well as grading. CC patients with a high expression level of CD3D tended to be more sensitive to the primary therapy. Hence, CD3D appears to be a promising protective indicator in CC patients. A pan-cancer analysis of CD3D expression level across various cancer types using data from TCGA was performed to investigate whether the protective effect of CD3D was limited to CC or applicable to other malignancies (Fig. S3A). The results revealed that CD3D expression was higher in tumor cases than in normal ones across most types of cancer, suggesting its potential as a therapeutic target. Besides, survival analysis was performed to assess the prognostic significance of CD3D in CC. Results demonstrated that CC patients with high expression of CD3D tended to possess a better outcome, with longer OS, DFS and PFS (Fig. S3B-D). Consistently, online database analysis also indicated better OS, DFS and PFS for CC patients with high CD3D levels compared to those with low levels (Fig. S3E-G). To further explore the correlation between CD3D protein levels and clinical stage, we collected five early-stage and five late-stage CC specimens for immunohistochemical detection of CD3D protein levels. IHC results presented in Fig. S3H indicated a decrease in CD3D expression with advanced stage classification. Consistently, correlation analysis using public databases between CD3D expression and clinicopathological characteristics revealed a lower expression level of CD3D in advanced stage and grade cases (Fig. S3I-K). These results indicate that CD3D may function as a biomarker for predicting prognosis and stage classification in CC patients. Additionally, it was noted that individuals exhibiting elevated CD3D expression levels generally experienced more favorable outcomes following primary therapy (Fig. S3L). These above results demonstrate that CD3D was a protective factor and potential therapeutic target in CC, which could forecast prognosis and foretell immunotherapy effect. Necroptosis up-regulates CD3D via JAK2-STAT3 signaling pathway in Jurkat cells We then evaluated the relationship between CD3D and necroptosis via bioinformatical analyses and in vitro experiments. Firstly, CD3D expression was positively correlated with necroptosis, and high expression group was found to exhibit a higher level of necroptosis, as is shown in Fig. 7 A. Given that CD3D is expressed on T cells, Jurkat cells, a T cell line, was used to conduct in vitro experiments. Consistently, with the induction of necroptosis, Jurkat cells exhibited a higher expression of CD3D compared with untreated control as the RT-qPCR result presented in Fig. 7 B, and the protein expression level of CD3D emerged higher with the increase of concentrations as the result of WB displayed in Fig. 7 C. Conversely, while treated with NSA (Fig. 7 D, F) or NEC-1 (Fig. 7 E, G), a decrease in CD3D expression was observed in Jurkat cells. These results provide valuable insights into the intricate interplay between CD3D expression and necroptosis dynamics, shedding light on potential therapeutic targets in CC patients. As described above, our study revealed that Jurkat cells exhibited increased CD3D expression levels upon induction of necroptosis. Subsequent mechanistic investigations were conducted, with GSEA analysis indicating enrichment of the JAK-STAT signaling pathway in the high necroptosis group (Fig. 7 H). In vitro experiments were then performed to elucidate the interplay between CD3D, the JAK-STAT signaling pathway, and necroptosis in Jurkat cells. The protein levels of JAK2, STAT3, and p-STAT3 were upregulated in response to necroptosis induction, as demonstrated by our results (Fig. 7 I), while their levels decreased in the presence of NSA (Fig. 7 J) or NEC-1 (Fig. 7 K), suggesting that necroptosis may activate the JAK2-STAT3 signaling pathway. Furthermore, western blot results (Fig. 7 L) confirmed that the protein levels of JAK2, STAT3, p-STAT3, and CD3D increased in response to necroptosis induction, and this effect was attenuated by FLLL32. RT-qPCR analysis (Fig. 7 M) indicated that CD3D expression was upregulated by necroptosis induction, an effect that was inhibited by FLLL32. Therefore, our findings suggest that necroptosis upregulates CD3D via the JAK2-STAT3 signaling pathway. Interestingly, we also observed that necroptosis downregulated the expression of common ICPs, including PD-1, TIGIT, CTLA4, TIM3, LAG3, and VISTA in Jurkat cells, an effect that was reversed by FLLL32 (Fig. 7 N). Additionally, the expression of chemokine receptors such as CCR4, CCR5, CCR7, and CCR9 in Jurkat cells was upregulated in response to necroptosis, and this effect was also abrogated by FLLL32 (Fig. 7 O). These results demonstrated that necroptosis could induce upregulation of CD3D in Jurkat cells via activation of the JAK2-STAT3 signaling pathway. We then further investigated whether the above experimental phenomenon could be found in activated T cells. Jurkat cells were firstly activated by PMA (20ng/mL, MCE, HY-18739) and lonomycin (500ng/mL, MCE, HY-13434), which was verified by the protein level of T cell activation marker CD69 using WB, and CD69 exhibited a higher level upon the induction of necroptosis (Fig. S4A), indicating necroptosis also facilitated T cell activation even though it's in activated T cells. Besides, the protein level of JAK2, STAT3, p-STAT3 and CD3D was found to increase in activated T cells with the induction of necroptosis (Fig. S4B), consistent with the inactivated T cells. Conversely, while treated with NSA (Fig. S4C) or NEC-1 (Fig. S4D), the protein level of JAK2, STAT3, p-STAT3, CD3D and CD69 in activated T cells was found to decrease. Additionally, RT-qPCR results demonstrated that CD3D was up-regulated in activated T cells with the treatment of necroptosis inducer and this effect can be inhibited by FLLL32 (Fig. S4E). WB results shown in Fig. S4F illustrated that in activated T cells, the protein level of JAK2, STAT3, p-STAT3, CD3D and CD69 was found to increase with the induction of necroptosis and this effect was eliminated by FLLL32. Therefore, it could be concluded that necroptosis up-regulates CD3D via JAK2-STAT3 signaling pathway whether in activated or inactivated T cells. Necroptosis of T cells could inhibit the viability, migration and proliferation of CC cells Knowing that necroptosis facilitates T cell activation, we excogitated the influence of T cells undergoing necroptosis on CC cells. Jurkat cells were firstly treated with or without the induction of necroptosis and the supernatant was extracted to culture SiHa CC cells respectively. The outcomes of CCK-8 assays showed a significant suppression in the viability of SiHa CC cells cocultured with the supernatant of Jurkat cells (pretreated with TNFɑ + Z-VAD-FMK at a concentration of 20 µm the day before), as evaluated by the OD values (Fig. 8 A). As illustrated by the wound healing assays (Fig. 8 B), the necroptosis group exhibited a significant suppression of migration ability in comparison to the normal control group. Additionally, the outcomes of EdU assays further confirmed that SiHa CC cells coculturing with the supernatant of Jurkat cells (pretreated with TNFɑ + Z-VAD-FMK the day before) exhibited a decrease proliferation (Fig. 8 C). Conversely, while cocultured with the supernatant of Jurkat cells (pretreated with NSA at a concentration of 10 µm the day before), the viability, migration ability and proliferation of SiHa CC cells got elevated as demonstrated by CCK-8 (Fig. 8 D), wound healing (Fig. 8 E), and EdU assays (Fig. 8 F), respectively. We then explored whether CC cells undergoing necroptosis would make some effects on T cells. We firstly treated SiHa CC cells with or without the induction of necroptosis and the supernatant was extracted to culture Jurkat cells respectively. As the RT-qPCR results shown in Fig. S5A-C, the expression of key molecules in the necroptosis pathway including MLKL, RIPK1 and RIPK3 was found to be significantly elevated in Jurkat cells cocultured with the supernatant of SiHa CC cells (pretreated with TNFɑ + Z-VAD-FMK at a concentration of 20 µm the day before) compared with the normal control group. And the protein level of RIPK1, p-RIPK1, RIPK3 and p-RIPK3 was also much higher (Fig. S5D). Also, results of RT-qPCR presented in Fig. S5E-G demonstrated that the expression of CD3D, JAK2, STAT3 was up-regulated in the necroptosis group of Jurkat cells. Besides, the protein level of CD3D, JAK2, STAT3 and p-STAT3 detected by WB exhibit an increasing trend in the necroptosis group (Fig. S5H). In sum, it could be inferred that CC cells undergoing necroptosis induced the expression of CD3D and up-regulated the JAK2-STAT3 signaling pathway in Jurkat cells, which might facilitate T cells activation and trigger anti-tumor immunity. The combination of bevacizumab with necroptosis inhibitors demonstrated an inhibitory effect on T cell function As previously demonstrated, necroptosis is a key driver of T cell activation and bevacizumab induces the necroptosis of CC cells. Subsequently, the impact of necroptosis inducer/inhibitor in conjunction with bevacizumab on TME was evaluated. In view of the fact that T cells represent the principal anti-tumor force in TME, they were selected for the following experiments. Results of WB assay indicated in comparison to bevacizumab alone, the expression of T cell activation markers including CD8 and CD3E were found to be elevated when combined with the necroptosis inducer (Fig. 9 A). In contrast, CD8, CD3E and CD3D were observed to down-regulated in combination with necroptosis inhibitor NSA or NEC-1 (Fig. 9 B). We then treated Jurkat cells with supernatant extracted from CC cells (pretreated with bevacizumab or bevacizumab in combination with necroptosis inhibitor respectively the day before). Results of RT-qPCR showed markers of T cell inhibition and depletion including CTLA4, CTSS, TIM3, TIGIT, SELPLG and VISTA exhibited a significant elevated in the combination group (bevacizumab in combination with necroptosis inhibitor) (Fig. 9 C-H), indicating the combination of bevacizumab with necroptosis inhibitors may present a challenge in the creation of an inhibitory immune microenvironment. As the survival analysis shown, CC patients with a high necroptosis level and a low cytotoxic T cells infiltration tended to possess the poorest outcome (Fig. 9 I). Besides, this group of CC patients exhibited the highest expression level of VEGFA (Fig. 9 J). All these findings offer new insights into the potential for individualized treatment of CC. Patients with high level of necroptosis and low infiltration of cytotoxic T cells are more likely to benefit from the bevacizumab and necroptosis inhibitor combination therapy. Discussion CC appears to be the one of the most lethal forms of cancer in females. Despite significant advancements in immunotherapy in recent years, the prognosis for individuals with advanced or metastatic CC continues to be unfavorable [ 11 , 40 ]. Immunotherapy represented by ICIs exerted limited effects in advanced or metastatic CC partly caused by the immunosuppressive TME [ 41 ]. Angiogenesis is a prominent characteristic of maligalancies and the utilization of bevacizumab represents a novel therapeutic approach for a spectrum of advanced cancers with a dismal prognosis including advanced and metastatic CC. The combination of bevacizumab and PD-L1 inhibitor atezolizumab offers a new option to CC treatment. Nevertheless, the utilization of bevacizumab is constrained by individual variations, the emergence of drug resistance, and alterations in the TME [ 42 – 44 ], so there is an immediate requirement to identify reliable biomarkers to stratify patients in order to guide personalized treatment and to convert this immune “cold” tumor into a “hot” tumor that responds better to targeted therapy. Current researches have revealed that the deregulation of cell death was a key factor of tumor development and progression [ 45 ]. As an indispensable type of programmed cell death, necroptosis emerged as an important event that modulates tumorigenesis. An increasing number of studies have demonstrated many compounds exhibited antitumor effects by inducing or manipulating necroptosis-related molecules [ 46 – 50 ]. Moreover, the character of necroptotic cells shown to elicit adaptive immunity makes it a potential target for immunotherapy [ 51 ]. Besides, its dual role in tumors makes it possible to stratify patients and provide an important basis for individualized treatment. Therefore, a greater understanding of the necroptosis might have great use to create new strategies to control cancer. However, there are few studies on the relationship between CC and necroptosis, the role of necroptosis in the TME of CC remains unclear. Our study indicated the dual role of necroptosis in CC, promoting tumor aggression by enhancing cell proliferation through JAK2-STAT3 pathway activation while also activating the T cell activity to stimulate anti-tumor immune responses (Fig. 10 ). Based on our in vitro experiment results, necroptosis contributes to the invasion of CC cells, reflecting its pro-cancer side. Enrichment analyses revealed NRGs were involved in VEGF and JAK-STAT signaling pathway. With regard to VEGF, it has been found to be up-regulated in numerous malignant neoplasms and its combination with VEGFR has been shown to contribute to tumor development and metastasis [ 52 ]. The JAK-STAT pathway, a well-studied signaling pathway, has been shown to play a crucial role in regulating cell proliferation, differentiation, and apoptosis. Furthermore, this pathway is implicated in the development of immune disorders and tumorigenesis [ 53 ]. The primary role of STAT3 is transcriptional regulation, modulating angiogenesis and tumorigenesis [ 54 ]. Results of the experiment indicated that necroptosis could up-regulate VEGFA via the JAK2-STAT3 signaling pathway, thereby elucidating the potential mechanism underlying its role in cancer promotion. Interestingly, in this study, it was confirmed that bevacizumab can induce necroptosis of CC cells and subsequently activate JAK2-STAT3 signaling pathway. The combination of bevacizumab with the necroptosis inducer resulted in a reduction in the inhibitory effect of bevacizumab on VEGFA, which may be indicative of a resistance mechanism to bevacizumab. In contrast, when bevacizumab was combined with the necroptosis inhibitor, the inhibition of VEGFA was enhanced, suggesting a new drug combination strategy: for patients resistant to bevacizumab, the combination of bevacizumab and necroptosis inhibitors could be a better therapeutic strategy, for which could reduce VEGFA expression and reduce potential resistance due to necroptosis. In the case of tumor cells, necroptosis exerts a pro-cancer effect. However, it demonstrates a cancer-inhibiting role by activating T cells in TME. According to bioinformatic analyses, high necroptosis group mainly enriched in immune response pathway especially T cell regulation signaling pathway. In vitro experiments verified Jurkat cells undergoing necroptosis emerged to express an elevated level of cytokine-related genes including CXCL9, CXCL10, CXCL11, CXCL13, IFN-γ, IL-2, IL-6 and IL-13. As for chemokines, CXCL9/10/11-CXCR3 axis has been prove to promote recruitment of immune cells into the tumor, remodeling the tumor immune microenvironment [ 55 , 56 ], the up-regulation of CXCL9/10/11 implies necroptosis may exert an anti-tumor effect by enhancing the infiltration of immune cells in the TME. Both IL-2, a key factor of T cell proliferation [ 57 ], and IL-6, an indispensable role in the differentiation of T cell into cytotoxic cells (CTLs) [ 58 ] were up-regulated in Jurkat cells undergoing necroptosis, indicating necroptosis plays a positive regulatory role on T cells. As the main force of anti-tumor immunity, in addition to increase T cells infiltration, it is also crucial to promote T cell activation and reduce exhaustion. Immune checkpoints including PD-1, TIGIT, LAG3, CTLA4, TIM3 and VISTA have been affirmed to inhibit T cell activation and induce T cell depletion [ 59 – 62 ]. According to our results, Jurkat cells undergoing necroptosis exhibited a decreasing expression level of ICPs, implying necroptosis can defend against tumor by reducing T cell depletion. And as mentioned above, T cell activated markers including CD69, CD25, IFN-γ and GZMB got significantly elevated in Jurkat cells undergoing necroptosis. Therefore, necroptosis might facilitate T cells activation, reduce T cell depletion and increase T cell infiltration, bringing heat to TME. Besides, in our research, in vitro experiments demonstrated CC cells co-cultured with supernatant from Jurkat cells undergoing necroptosis exhibited less viability and less ability to proliferate and migrate, while necroptosis exerted less effect on Jurkat cells viability. Therefore, it could be inferred that necroptosis plays a dual role in CC, which makes it a potential indicator to classify patients in order to perform precise treatment. In this study, we obtained 6 differentially expressed NRGs related to both ‘CD8 pathway’ and prognosis. All these genes are characterized by the presence of cancer-suppressing properties. Among 6 intersected genes, CD247, CD3E, CD3G, CD3D were suggested to be involved in T cell development and the transduction of T cell activation signals [ 63 ]. As for CD2, its encoded protein interacts with LFA3 (CD58), involving in antigen recognition process [ 64 ]. It was found that CD8a was a key molecule for activation of cytotoxic T-lymphocytes (CTLs) [ 65 ]. Therefore, all these genes exhibit a positive regulatory role in the immune response, which might explain their downward trend in advanced grade and stage. Furthermore, the results in our study demonstrated that high necroptosis group exhibited a higher expression level of all these genes, suggesting necroptosis plays a vital role in immunity. Among these 6 selected genes, we found CD3D was most closely related to clinical features and therapy response of CC, so further analyses were around the relationship between CD3D and necroptosis. CD3D is a component of the TCR/CD3 complex and plays an indispensable role in T cell activation signals [ 63 ]. Recent research has suggested CD3D could be a predictive biomarker of the prognosis in some cancers [ 66 , 67 ]. In our study, bioinformatical analyses revealed CD3D was positively correlated with necroptosis, and high necroptosis level tended to own a “hotter” TME, therefore CD3D could be used as an indicator for dynamic detection of immune microenvironment. Additionally, its expression was found to have a negative correlation with both grade and stage, and survival analyses have suggested that patients had a better prognosis when it was highly expressed. Besides, patients in CD3D high expression cluster showed more sensitivity to immunotherapy. All these results indicated CD3D acted as a protective signature in CC and it could be a reliable biomarker of prognosis as well as grade and stage classification. Simultaneously, our study indicated that high necroptosis group was found to be enriched in JAK-STAT signaling pathway via GSEA analysis. In addition to tumorigenesis, STAT3 has been proved to be a key role in the development of late-stage T cell and the formation of memory T cell [ 68 , 69 ]. Another important finding in our study was that, Jurkat cells undergoing necroptosis exhibited a higher level of CD3D expression and activated the JAK2-STAT3 signaling pathway. When the JAK2-STAT3 signaling pathway was specifically inhibited by FLLL32, elevated CD3D expression via necroptosis could be rescued in Jurkat cells. Thus, we conceived that necroptosis played a positive role in immunoregulation by up-regulating CD3D through activation of JAK2-STAT3 pathway, and novel immunotherapy strategies that target this mechanism could be developed in order to improve treatment effectiveness especially in patients who are not sensitive to conventional therapies. An altered microenvironment is vital for the prognosis of CC patients. Previous studies have proved that VEGF can enhance the function and accumulation of MDSCs, thereby contributing to the formation of an immunosuppressive microenvironment [ 70 , 71 ]. In this study, it was found that bevacizumab alone or in combination with necroptosis inducer induced the expression of activation makers including CD8, CD3D and CD3E in T cells. Furthermore, co-cultured with supernatant extracted from CC cells (pretreated with bevacizumab the day before), T cells exhibited a decreased expression in depletion and inhibition markers including CTLA4, CTSS, TIM3, TIGIT, SELPLG and VISTA, indicating blocking VEGF helps activate T cells thus creates a “hot” TME in CC. On the contrary, it was observed T cells showed a decreased expression in CD8, CD3D and CD3E while treated with bevacizumab in combination with necroptosis inhibitor NSA or NEC-1. Additionally, T cells exhibited an elevation of CTLA4, CTSS, TIM3, TIGIT, SELPLG and VISTA co-cultured with supernatant extracted from CC cells (pretreated with bevacizumab in combination with necroptosis inhibitor NSA or NEC-1 the day before). The synthesis of the above results suggests that the addition of the necroptosis inhibitor could enhance the effect of bevacizumab on VEGFA blocking. However, the combination of bevacizumab and the necroptosis inhibitor might result in an immunosuppressive condition. The dual role of necroptosis foretells a potential individualized treatment strategy, CC patients with a high extent of necroptosis or an immune-depletion state are more likely to benefit from the combination of bevacizumab and the necroptosis inhibitor. Besides, both bevacizumab and necroptosis inhibitors can affect the immune microenvironment, therefore dynamic monitoring of the immune microenvironment condition is necessary, while CD3D can be served as an indicator of the degree of necroptosis and foresee the state of the immune microenvironment. Limitations of our study included the absence of in vivo validation of necroptosis in CC progression and immune modulation. Future research should focus on establishing animal models to investigate these effects, elucidating the precise mechanisms underlying necroptosis-mediated CD3D upregulation, the interaction between necroptosis and bevacizumab, and the potential effects of necroptosis inhibitors combined with bevacizumab. Additionally, further exploration of necroptosis-related biomarkers and their prognostic and therapeutic implications in CC is warranted to advance the field towards innovative and effective cancer therapies. To sum up, this study delves into the intricate role of necroptosis in the development of CC, elucidating how it stimulates an anti-tumor immune response while facilitating tumor proliferation and invasion. Moreover, a necroptosis-related molecule CD3D was identified as a protective biomarker to forecast prognosis and foretell therapeutic effects of CC. It was also demonstrated that the T cell undergoing necroptosis could elicit immune response by up-regulating CD3D through activation of JAK2-STAT3 pathway, which might provide a new perspective for the regulation of immune microenvironment of CC and might be helpful in creating novel strategies for controlling cancer. More importantly, this study proposed the potential mechanism of drug resistance to bevacizumab by exploring the interaction between necroptosis and bevacizumab, and the combination of bevacizumab with necroptosis inhibitors is expected to improve the efficacy of drug-resistant patients, providing new insights for the treatment of CC. Declarations Acknowledgements We appreciate the unrestricted use of TCGA databases. This work was supported by the Natural Science Foundation of Shanghai (grant no. 21ZR1450900), and Shanghai Science and Technology Planning Project (grant no. 21Y11907000). A uthor contributions Shaohua Xu and Yueqing Gao conceived and designed the manuscript. Fangfang Xu and Yingjun Ye wrote the paper and conceived and designed the experiments; Fangfang Xu analyzed the data; Yingjun Ye and Yueqing Gao collected and provided the sample for this study. All authors have read and approved the final submitted manuscript. Funding This study was supported by the Natural Science Foundation of Shanghai (grant no. 21ZR1450900), and Shanghai Science and Technology Planning Project (grant no. 21Y11907000). Data availability The data that supports the findings of this study are available from the corresponding author upon reasonable request. Conflict of interest The authors declare that they have no competing interests. Ethics approval The study was conducted under the approval of the Medical Ethics Committee of Shanghai First Maternity and Infant Hospital (KS21264). References Singh D, Vignat J, Lorenzoni V, Eslahi M, Ginsburg O, Lauby-Secretan B, Arbyn M, Basu P, Bray F, Vaccarella S. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. Lancet Glob Health. 2023;11(2):e197–206. Melamed A, Margul DJ, Chen L, Keating NL, Del Carmen MG, Yang J, Seagle BL, Alexander A, Barber EL, Rice LW, Wright JD, Kocherginsky M, Shahabi S, Rauh-Hain JA. Survival after Minimally Invasive Radical Hysterectomy for Early-Stage Cervical Cancer. N Engl J Med. 2018;379(20):1905–14. Wenzel HHB, Smolders RGV, Beltman JJ, Lambrechts S, Trum HW, Yigit R, Zusterzeel PLM, Zweemer RP, Mom CH, Bekkers RLM, Lemmens VEPP, Nijman HW, Van der Aa MA. Survival of patients with early-stage cervical cancer after abdominal or laparoscopic radical hysterectomy: a nationwide cohort study and literature review. Eur J Cancer. 2020;133:14–21. Wright JD, Matsuo K, Huang Y, Tergas AI, Hou JY, Khoury-Collado F, St Clair CM, Ananth CV, Neugut AI, Hershman DL. Prognostic Performance of the 2018 International Federation of Gynecology and Obstetrics Cervical Cancer Staging Guidelines. Obstet Gynecol. 2019;134(1):49–57. Adiga D, Eswaran S, Pandey D, Sharan K, Kabekkodu SP. Molecular landscape of recurrent cervical cancer. Crit Rev Oncol Hematol. 2021;157:103178. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, Forman D, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013;49(6):1374–403. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252–64. Sadreddini S, Baradaran B, Aghebati-Maleki A, Sadreddini S, Shanehbandi D, Fotouhi A, Aghebati-Maleki L. Immune checkpoint blockade opens a new way to cancer immunotherapy. J Cell Physiol. 2019;234(6):8541–9. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015;27(4):450–61. Wang R, Zhang Y, Shan F. PD-L1: Can it be a biomarker for the prognosis or a promising therapeutic target in cervical cancer? Int Immunopharmacol. 2022;103:108484. Oaknin A, Gladieff L, Martínez-García J, Villacampa G, Takekuma M, De Giorgi U, Lindemann K, Woelber L, Colombo N, Duska L, Leary A, Godoy-Ortiz A, Nishio S, Angelergues A, Rubio MJ, Fariñas-Madrid L, Yamaguchi S, Lorusso D, Ray-Coquard I, Manso L, Joly F, Alarcón J, Follana P, Romero I, Lebreton C, Pérez-Fidalgo JA, Yunokawa M, Dahlstrand H, D'Hondt V, Randall LM. ENGOT-Cx10–GEICO 68-C–JGOG1084–GOG-3030 Investigators. Atezolizumab plus bevacizumab and chemotherapy for metastatic, persistent, or recurrent cervical cancer (BEATcc): a randomised, open-label, phase 3 trial. Lancet. 2024;403(10421):31–43. Galluzzi L, Vitale I, Aaronson S, et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ. 2018;25:486–541. Khan I, Yousif A, Chesnokov M, Hong L, Chefetz I. A decade of cell death studies: Breathing new life into necroptosis. Pharmacol Ther. 2021;220:107717. Gong Y, Fan Z, Luo G, Yang C, Huang Q, Fan K, Cheng H, Jin K, Ni Q, Yu X, Liu C. The role of necroptosis in cancer biology and therapy. Mol Cancer. 2019;18(1):100. Strilic B, Yang L, Albarrán-Juárez J, Wachsmuth L, Han K, Müller UC, Pasparakis M, Offermanns S. Tumor-cell-induced endothelial cell necroptosis via death receptor 6 promotes metastasis. Nature. 2016;536(7615):215–8. Jiao D, Cai Z, Choksi S, Ma D, Choe M, Kwon HJ, Baik JY, Rowan BG, Liu C, Liu ZG. Necroptosis of tumor cells leads to tumor necrosis and promotes tumor metastasis. Cell Res. 2018;28(8):868–70. Lawlor KE, Khan N, Mildenhall A, Gerlic M, Croker BA, D'Cruz AA, Hall C, Kaur Spall S, Anderton H, Masters SL, Rashidi M, Wicks IP, Alexander WS, Mitsuuchi Y, Benetatos CA, Condon SM, Wong WW, Silke J, Vaux DL, Vince JE. RIPK3 promotes cell death and NLRP3 inflammasome activation in the absence of MLKL. Nat Commun. 2015;6:6282. Höckendorf U, Yabal M, Herold T, Munkhbaatar E, Rott S, Jilg S, Kauschinger J, Magnani G, Reisinger F, Heuser M, Kreipe H, Sotlar K, Engleitner T, Rad R, Weichert W, Peschel C, Ruland J, Heikenwalder M, Spiekermann K, Slotta-Huspenina J, Groß O, Jost PJ. RIPK3 Restricts Myeloid Leukemogenesis by Promoting Cell Death and Differentiation of Leukemia Initiating Cells. Cancer Cell. 2016;30(1):75–91. Snyder AG, Hubbard NW, Messmer MN, Kofman SB, Hagan CE, Orozco SL, Chiang K, Daniels BP, Baker D, Oberst A. Intratumoral activation of the necroptotic pathway components RIPK1 and RIPK3 potentiates antitumor immunity. Sci Immunol. 2019;4(36):eaaw2004. Aaes TL, Kaczmarek A, Delvaeye T, De Craene B, De Koker S, Heyndrickx L, Delrue I, Taminau J, Wiernicki B, De Groote P, Garg AD, Leybaert L, Grooten J, Bertrand MJ, Agostinis P, Berx G, Declercq W, Vandenabeele P, Krysko DV. Vaccination with Necroptotic Cancer Cells Induces Efficient Anti-tumor Immunity. Cell Rep. 2016;15(2):274–87. Yan G, Zhao H, Zhang Q, Zhou Y, Wu L, Lei J, Wang X, Zhang J, Zhang X, Zheng L, Du G, Xiao W, Tang B, Miao H, Li Y. A RIPK3-PGE2 Circuit Mediates Myeloid-Derived Suppressor Cell-Potentiated Colorectal Carcinogenesis. Cancer Res. 2018;78(19):5586–99. Seifert L, Werba G, Tiwari S, Giao Ly NN, Alothman S, Alqunaibit D, Avanzi A, Barilla R, Daley D, Greco SH, Torres-Hernandez A, Pergamo M, Ochi A, Zambirinis CP, Pansari M, Rendon M, Tippens D, Hundeyin M, Mani VR, Hajdu C, Engle D, Miller G. The necrosome promotes pancreatic oncogenesis via CXCL1 and Mincle-induced immune suppression. Nature. 2016;532(7598):245–9. Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW, Levine DA, Carter SL, Getz G, Stemke-Hale K, Mills GB, Verhaak RG. Inferring tumor purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7. Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, Chan NW, Zhang J. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200–2. Diesendruck Y, Benhar I. Novel immune check point inhibiting antibodies in cancer therapy-Opportunities and challenges. Drug Resist Updat. 2017;30:39–47. Postow MA, Callahan MK, Wolchok JD. Immune Checkpoint Blockade in Cancer Therapy. J Clin Oncol. 2015;33(17):1974–82. Qin S, Xu L, Yi M, Yu S, Wu K, Luo S. Novel immune checkpoint targets: moving beyond PD-1 and CTLA-4. Mol Cancer. 2019;18(1):155. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. Holleczek B, Brenner H. Model based period analysis of absolute and relative survival with R: data preparation, model fitting and derivation of survival estimates. Comput Methods Programs Biomed. 2013;110(2):192–202. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8. Bharti R, Dey G, Das AK, Mandal M. Differential expression of IL-6/IL-6R and MAO-A regulates invasion/angiogenesis in breast cancer. Br J Cancer. 2018;118(11):1442–52. Zegeye MM, Andersson B, Sirsjö A, Ljungberg LU. IL-6 trans-Signaling Impairs Sprouting Angiogenesis by Inhibiting Migration, Proliferation and Tube Formation of Human Endothelial Cells. Cells. 2020;9(6):1414. Méndez-Clemente A, Bravo-Cuellar A, González-Ochoa S, Santiago-Mercado M, Palafox-Mariscal L, Jave-Suárez L, Solorzano-Ibarra F, Villaseñor-García M, Ortiz-Lazareno P, Hernández-Flores G. Dual STAT-3 and IL-6R inhibition with stattic and tocilizumab decreases migration, invasion and proliferation of prostate cancer cells by targeting the IL-6/IL-6R/STAT-3 axis. Oncol Rep. 2022;48(2):138. Cibrián D, Sánchez-Madrid F. CD69: from activation marker to metabolic gatekeeper. Eur J Immunol. 2017;47(6):946–53. Bajnok A, Ivanova M, Rigó J Jr, Toldi G. The Distribution of Activation Markers and Selectins on Peripheral T Lymphocytes in Preeclampsia. Mediators Inflamm. 2017;2017:8045161. Cullen SP, Martin SJ. Mechanisms of granule-dependent killing. Cell Death Differ. 2008;15(2):251–62. Aqbi HF, Wallace M, Sappal S, Payne KK, Manjili MH. IFN-γ orchestrates tumor elimination, tumor dormancy, tumor escape, and progression. J Leukoc Biol 2018 Feb 22: 10.1002/JLB.5MIR0917-351R . Monk BJ, Enomoto T, Kast WM, McCormack M, Tan DSP, Wu X, González-Martín A. Integration of immunotherapy into treatment of cervical cancer: Recent data and ongoing trials. Cancer Treat Rev. 2022;106:102385. Gennigens C, Jerusalem G, Lapaille L, De Cuypere M, Streel S, Kridelka F, Ray-Coquard I. Recurrent or primary metastatic cervical cancer: current and future treatments. ESMO Open. 2022;7(5):100579. Ye W. The Complexity of Translating Anti-angiogenesis Therapy from Basic Science to the Clinic. Dev Cell. 2016;37(2):114–25. Ramjiawan RR, Griffioen AW, Duda DG. Anti-angiogenesis for cancer revisited: Is there a role for combinations with immunotherapy? Angiogenesis. 2017;20(2):185–204. Vasudev NS, Reynolds AR. Anti-angiogenic therapy for cancer: current progress, unresolved questions and future directions. Angiogenesis. 2014;17(3):471 – 94. 10.1007/s10456-014-9420-y . Epub 2014 Jan 31. Erratum in: Angiogenesis. 2014;17(3):495-7. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. Zhang SL, Tang HB, Hu JT, Zang ZL, Ding X, Li S, Yang H. PGAM5-CypD pathway is involved in bromocriptine-induced RIP3/MLKL-dependent necroptosis of prolactinoma cells. Biomed Pharmacother. 2019;111:638–48. Lin SS, Chang TM, Wei AI, Lee CW, Lin ZC, Chiang YC, Chi MC, Liu JF. Acetylshikonin induces necroptosis via the RIPK1/RIPK3-dependent pathway in lung cancer. Aging. 2023;15(24):14900–14. Basit F, Cristofanon S, Fulda S, Obatoclax. (GX15-070) triggers necroptosis by promoting the assembly of the necrosome on autophagosomal membranes. Cell Death Differ. 2013;20(9):1161–73. Chefetz I, Grimley E, Yang K, Hong L, Vinogradova EV, Suciu R, Kovalenko I, Karnak D, Morgan CA, Chtcherbinine M, Buchman C, Huddle B, Barraza S, Morgan M, Bernstein KA, Yoon E, Lombard DB, Bild A, Mehta G, Romero I, Chiang CY, Landen C, Cravatt B, Hurley TD, Larsen SD, Buckanovich RJ. A Pan-ALDH1A Inhibitor Induces Necroptosis in Ovarian Cancer Stem-like Cells. Cell Rep. 2019;26(11):3061–e30756. Coupienne I, Fettweis G, Rubio N, Agostinis P, Piette J. 5-ALA-PDT induces RIP3-dependent necrosis in glioblastoma. Photochem Photobiol Sci. 2011;10(12):1868–78. Yatim N, Jusforgues-Saklani H, Orozco S, Schulz O, Barreira da Silva R, Reis e Sousa C, Green DR, Oberst A, Albert ML. RIPK1 and NF-κB signaling in dying cells determines cross-priming of CD8⁺ T cells. Science. 2015;350(6258):328–34. Shaw P, Dwivedi SKD, Bhattacharya R, Mukherjee P, Rao G. VEGF signaling: Role in angiogenesis and beyond. Biochim Biophys Acta Rev Cancer. 2024;1879(2):189079. Owen KL, Brockwell NK, Parker BS, JAK-STAT Signaling. A Double-Edged Sword of Immune Regulation and Cancer Progression. Cancers (Basel). 2019;11(12):2002. Yang SF, Wang SN, Wu CF, Yeh YT, Chai CY, Chunag SC, Sheen MC, Lee KT. Altered p-STAT3 (tyr705) expression is associated with histological grading and intratumor microvessel density in hepatocellular carcinoma. J Clin Pathol. 2007;60(6):642–8. Tokunaga R, Zhang W, Naseem M, Puccini A, Berger MD, Soni S, McSkane M, Baba H, Lenz HJ. CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation - A target for novel cancer therapy. Cancer Treat Rev. 2018;63:40–7. Dangaj D, Bruand M, Grimm AJ, Ronet C, Barras D, Duttagupta PA, Lanitis E, Duraiswamy J, Tanyi JL, Benencia F, Conejo-Garcia J, Ramay HR, Montone KT, Powell DJ Jr, Gimotty PA, Facciabene A, Jackson DG, Weber JS, Rodig SJ, Hodi SF, Kandalaft LE, Irving M, Zhang L, Foukas P, Rusakiewicz S, Delorenzi M, Coukos G. Cooperation between Constitutive and Inducible Chemokines Enables T Cell Engraftment and Immune Attack in Solid Tumors. Cancer Cell. 2019;35(6):885–e90010. Morgan DA, Ruscetti FW, Gallo R. Selective in vitro growth of T lymphocytes from normal human bone marrows. Science. 1976;193(4257):1007–8. MacLeod MK, McKee AS, David A, Wang J, Mason R, Kappler JW, Marrack P. Vaccine adjuvants aluminum and monophosphoryl lipid A provide distinct signals to generate protective cytotoxic memory CD8 T cells. Proc Natl Acad Sci U S A. 2011;108(19):7914–9. Philip M, Schietinger A. CD8 + T cell differentiation and dysfunction in cancer. Nat Rev Immunol. 2022;22(4):209–23. Kim GR, Choi JM. Current Understanding of Cytotoxic T Lymphocyte Antigen-4 (CTLA-4) Signaling in T-Cell Biology and Disease Therapy. Mol Cells. 2022;45(8):513–21. Chiang EY, Mellman I. TIGIT-CD226-PVR axis: advancing immune checkpoint blockade for cancer immunotherapy. J Immunother Cancer. 2022;10(4):e004711. Huang X, Zhang X, Li E, Zhang G, Wang X, Tang T, Bai X, Liang T. VISTA: an immune regulatory protein checking tumor and immune cells in cancer immunotherapy. J Hematol Oncol. 2020;13(1):83. Barber EK, Dasgupta JD, Schlossman SF, Trevillyan JM, Rudd CE. The CD4 and CD8 antigens are coupled to a protein-tyrosine kinase (p56lck) that phosphorylates the CD3 complex. Proc Natl Acad Sci U S A. 1989;86(9):3277–81. de Vries JE, Yssel H, Spits H. Interplay between the TCR/CD3 complex and CD4 or CD8 in the activation of cytotoxic T lymphocytes. Immunol Rev. 1989;109:119–41. Morón G, Rueda P, Casal I, Leclerc C. CD8alpha- CD11b + dendritic cells present exogenous virus-like particles to CD8 + T cells and subsequently express CD8alpha and CD205 molecules. J Exp Med. 2002;195(10):1233–45. Wei Z, Shen Y, Zhou C, Cao Y, Deng H, Shen Z. CD3D: a prognostic biomarker associated with immune infiltration and immunotherapeutic response in head and neck squamous cell carcinoma. Bioengineered. 2022;13(5):13784–800. Zhu Z, Ye W, Wu X, Lin S, Xu J, Li L, Li J, Wang H, Huang Z. Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment. Biosci Rep. 2021;41(1):BSR20202898. Lee JK, Won C, Yi EH, Seok SH, Kim MH, Kim SJ, Chung MH, Lee HG, Ikuta K, Ye SK. Signal transducer and activator of transcription 3 (Stat3) contributes to T-cell homeostasis by regulating pro-survival Bcl-2 family genes. Immunology. 2013;140(3):288–300. Nish SA, Schenten D, Wunderlich FT, Pope SD, Gao Y, Hoshi N, Yu S, Yan X, Lee HK, Pasman L, Brodsky I, Yordy B, Zhao H, Brüning J, Medzhitov R. T cell-intrinsic role of IL-6 signaling in primary and memory responses. Elife. 2014;3:e01949. Gabrilovich D, Ishida T, Oyama T, Ran S, Kravtsov V, Nadaf S, Carbone DP. Vascular endothelial growth factor inhibits the development of dendritic cells and dramatically affects the differentiation of multiple hematopoietic lineages in vivo. Blood. 1998;92(11):4150–66. Maenhout SK, Thielemans K, Aerts JL. Location, location, location: functional and phenotypic heterogeneity between tumor-infiltrating and non-infiltrating myeloid-derived suppressor cells. Oncoimmunology. 2014;3(10):e956579. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4379854","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301836309,"identity":"0c0bc6ad-7502-4594-920f-694ae5dcfe38","order_by":0,"name":"Fangfang Xu","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Xu","suffix":""},{"id":301836313,"identity":"1d64d4be-86fd-420e-89d6-10e894d3826f","order_by":1,"name":"Yingjun Ye","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yingjun","middleName":"","lastName":"Ye","suffix":""},{"id":301836316,"identity":"a6d72a47-59fa-41bd-89d7-ff0f1450cf29","order_by":2,"name":"Yueqing Gao","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yueqing","middleName":"","lastName":"Gao","suffix":""},{"id":301836317,"identity":"783b103a-7d57-4bf9-8b0a-b0aa16df82dd","order_by":3,"name":"Shaohua Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHACNiA+wMDA3gDm8AAJAyK18BwmWYtEMlwEvxaDG+nPHvyouJPYP/P9wccFv/hkGNibt0kw1NzBqUVyRo65Yc+ZZ4kzbiczG8/sAzqM51iZBMOxZzi18EvksEnwth1ObLidzCbN2wPUIpFjJsHYcBi3RyTSn0n+BWqZf/MwVIv8G/xa+CUSzKRBtmy4wcwmzfMDZAsPfi2SPW/MpGXOHDbeeCbZ2Ji3gY2HjSet2CLhGG4tBseBDntTcVh23vGDDx/z/Dlmz89+eOONDzW4taACxrZj4GhiSCBSAxD8qSFe7SgYBaNgFIwYAADdwFERz5iNBgAAAABJRU5ErkJggg==","orcid":"","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Shaohua","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-05-07 03:09:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4379854/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4379854/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56532141,"identity":"391bdecc-0c48-4bed-8a9d-3dade996474a","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170244,"visible":true,"origin":"","legend":"\u003cp\u003eNecroptosis promotes proliferation and invasion of CC cells.\u003cstrong\u003e A \u003c/strong\u003eResults of CCK-8 assays showed that at times of 24, 48 and 72h, the SiHa cells living in the necroptosis group are always significantly greater than the normal control group (p-value: ** p \u0026lt; 0.01, * p \u0026lt;0.05). \u003cstrong\u003eB \u003c/strong\u003eResults of CCK-8 assays showed that at times of 24, 48, 72h and 96h, the SiHa cells living in the normal control group are always significantly greater than the NSA group (p-value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01). \u003cstrong\u003eC, D \u003c/strong\u003eTransewll assay was performed to determine the cell migration ability of each group of cells. Scale bar, 100 μm. \u003cstrong\u003eE\u003c/strong\u003e Wound healing assays accessed the differences on the migration ability of SiHa cells between necroptosis group and normal control group. Scale bar, 100 μm. \u003cstrong\u003eF \u003c/strong\u003eWound healing assays to access the differences on the migration ability of SiHa cells between NSA group and normal control group. Scale bar, 100 μm. \u003cstrong\u003eG, H \u003c/strong\u003eEdU assays were used to detect the proliferation ability of each group of SiHa cells. Scale bar, 200 μm.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/8c48ce7f76a0f09d0264b149.png"},{"id":56532138,"identity":"ea7ed9c3-719d-4e5c-9f24-d280d46b00d6","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53215,"visible":true,"origin":"","legend":"\u003cp\u003eNecroptosis up-regulates VEGFA via JAK2-STAT3 signaling pathway in CC cells. \u003cstrong\u003eA \u003c/strong\u003eThe result from GO enrichment analysis of NRGs.\u003cstrong\u003e B \u003c/strong\u003eThe result from KEGG analysis of NRGs. \u003cstrong\u003eC-E \u003c/strong\u003eRT-qPCR results indicated that the up-regulation of JAK2 \u003cstrong\u003e(C)\u003c/strong\u003e, STAT3 \u003cstrong\u003e(D)\u003c/strong\u003e and VEGFA \u003cstrong\u003e(E)\u003c/strong\u003e respectively with the induction of necroptosis. “SiHa-NC” means normal control, “SiHa-Necro” means treated with necroptosis inducer at a concentration of 20 μm (p-Value: **** p \u0026lt; 0.0001, *** p \u0026lt; 0.001, * p \u0026lt;0.05). \u003cstrong\u003eF \u003c/strong\u003eResults of WB assay showed an elevated protein level of JAK2, STAT3, p-STAT3 and VEGFA\u003cstrong\u003e \u003c/strong\u003ewith the induction of necroptosis. “NC” means normal control, “Necro” means treated with necroptosis inducer at a concentration of 20 μm. \u003cstrong\u003eG\u003c/strong\u003e Results of WB assay showed a decreased protein level of JAK2, STAT3, p-STAT3, VEGFA and MLKL with the treatment of a necroptosis inhibitor NSA. “NC” means normal control, “NSA” means treated with NSA. \u003cstrong\u003eH \u003c/strong\u003eResults of WB assay indicated the up-regulation of JAK2, STAT3, p-STAT3 and VEGFA was eliminated by the specific inhibitor of JAK2-STAT3 pathway FLLL32.\u003cstrong\u003e I-K\u003c/strong\u003e RT-qPCR analysis revealed that the induction of necroptosis led to an increase in the expression levels of JAK2 \u003cstrong\u003e(I)\u003c/strong\u003e, STAT3 \u003cstrong\u003e(J)\u003c/strong\u003e, and VEGFA \u003cstrong\u003e(K)\u003c/strong\u003e, which were subsequently normalized upon the introduction of FLLL32. “SiHa-NC” means normal control, “SiHa-Necro” means treated with necroptosis inducer at a concentration of 20 μm, “SiHa-Necro-Flll32” means treated with necroptosis inducer (20 μm) and FLLL32 (10 μm) (p-Value: **** p \u0026lt; 0.0001, *** p \u0026lt; 0.001, ** p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/de3bc2e84de9af414590a44c.png"},{"id":56532142,"identity":"ea254f0f-7df6-42da-8a21-1854dd1b8f61","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89816,"visible":true,"origin":"","legend":"\u003cp\u003eBevacizumab induces the necroptosis of CC cells and activates JAK2-STAT3 signaling pathway. \u003cstrong\u003eA \u003c/strong\u003eWound healing assays accessed the differences on the migration ability of SiHa cells between normal control group and Bevacizumab group. Scale bar, 100 μm. “NC” means normal control, “Bevacizumab” means treated with bevacizumab (10 μg/ml). \u003cstrong\u003eB-D\u003c/strong\u003e RT-qPCR results indicated an elevated level of RIPK1 (B), RIPK3 (C) and MLKL (D) respectively in the Bevacizumab (10 μg/ml) group. \u003cstrong\u003eE \u003c/strong\u003eResult of WB indicated an increased protein level of RIPK1, RIPK3, p-RIPK3, MLKL and p-MLKL\u003cstrong\u003e \u003c/strong\u003ewith the treatment of\u003cstrong\u003e \u003c/strong\u003ebevacizumab. \u003cstrong\u003eF \u003c/strong\u003eResult of WB showed an increased protein level of STAT3, p-STAT3 and a decreased protein level of VEGFA with the treatment of\u003cstrong\u003e \u003c/strong\u003ebevacizumab.\u003cstrong\u003e G \u003c/strong\u003eWB assay demonstrated the inhibition of VEGFA by bevacizumab (10 μg/ml) was eliminated by necroptosis inducer. \u003cstrong\u003eH, I\u003c/strong\u003e RT-qPCR showed the up-regulation of JAK2 \u003cstrong\u003e(H)\u003c/strong\u003e and STAT3 \u003cstrong\u003e(I) \u003c/strong\u003eby bevacizumab (10 μg/ml) was eliminated by the necroptosis inhibitor NSA or NEC-1 (p-Value: **** p \u0026lt; 0.0001, *** p \u0026lt; 0.001, ** p \u0026lt; 0.01). \u003cstrong\u003eJ\u003c/strong\u003e WB indicated an up-regulation protein level of JAK2, STAT3 and p-STAT3 by bevacizumab (10 μg/ml) was eliminated by the necroptosis inhibitor NSA or NEC-1, bevacizumab and necroptosis inhibitor showed synergistic effect in the inhibition of VEGFA.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/338d26913b726a972a2469cc.png"},{"id":56532140,"identity":"2e764a29-08fd-47a2-a3aa-1ef3916e9da3","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66937,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive bioinformatical analyses revealed necroptosis could enhance T cell signaling pathways in CC.\u003cstrong\u003eA\u003c/strong\u003e Box plot showed the distributions of ESTIMATE-related scores between CC tumor samples with high or low expression relative to the median of NRGs expression, and Wilcoxon rank sum was applied for the significance test. \u003cstrong\u003eB \u003c/strong\u003eHeatmap for immune responses based on ssGSEA among NRGs high- and low-expression group.\u003cstrong\u003eC\u003c/strong\u003e The result from GO and KEGG analyses of NRGs. \u003cstrong\u003eD\u003c/strong\u003e GSEA analysis was done under the high-expression cluster. \u003cstrong\u003eE\u003c/strong\u003e The CIBERSORT result was shown in the comparison between low- and high-expression groups by violin plot. \u003cstrong\u003eF\u003c/strong\u003eChemokine genes including CXCL9, CXCL10, CXCL11 and CXCL13 with significantly expressed differences are expressed higher in the cluster with high expression of NRGs, Wilcoxon rank sum was applied for the significance test.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/81c33b1fb6a459b81aa3e6a7.png"},{"id":56532879,"identity":"60b24916-3a51-4fae-979b-53fe6a721d00","added_by":"auto","created_at":"2024-05-15 12:21:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35836,"visible":true,"origin":"","legend":"\u003cp\u003eNecroptosis could facilitate T cells activation, reduces T cell depletion and increases T cell infiltration in vitro. \u003cstrong\u003eA-D\u003c/strong\u003e Inferred from RT-qPCR, T cell activation marker CD25 \u003cstrong\u003e(A)\u003c/strong\u003e, CD69 \u003cstrong\u003e(B)\u003c/strong\u003e, GZMB \u003cstrong\u003e(C)\u003c/strong\u003eand IFN-γ \u003cstrong\u003e(D)\u003c/strong\u003e showed correspondingly high expression with the induction of necroptosis. “NC” means normal control, “NECRO” means treated with necroptosis inducer at a concentration of 20 μm (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05).\u003cstrong\u003e E-G \u003c/strong\u003eRT-qPCR results showed cytokine genes IL-2 \u003cstrong\u003e(E)\u003c/strong\u003e, IL-6 \u003cstrong\u003e(F)\u003c/strong\u003e, IL-13 \u003cstrong\u003e(G)\u003c/strong\u003e were up-regulated with the induction of necroptosis (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eH-J\u003c/strong\u003e Treated with the induction of necroptosis, RT-qPCR results showed the expression of chemokine receptor genes CCR4, CCR5, CCR7, CCR9\u003cstrong\u003e (H)\u003c/strong\u003e and chemokine genes CXCL9, CXCL10, CXCL11, CXCL13 \u003cstrong\u003e(I)\u003c/strong\u003e got elevated (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003e(J) \u003c/strong\u003eRT-qPCR results indicated that cultured with the induction of necroptosis, immune checkpoint CTLA4, LAG3, TIM3, VISTA, TIGIT and PD-1 were down-regulated (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eK, L \u003c/strong\u003eResults showed the expression of chemokine receptor genes CCR4, CCR5, CCR7, CCR9\u003cstrong\u003e (K) \u003c/strong\u003eand chemokine genes CXCL9, CXCL10, CXCL11, CXCL13 \u003cstrong\u003e(L) \u003c/strong\u003ewere decreased in the condition of Necrosulfonamide (NSA) at a concentration of 10 μm. “NC” means normal control, “NSA” means treated with NSA (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eM, N\u003c/strong\u003e Results showed the expression of chemokine receptor genes CCR4, CCR5, CCR7, CCR9 \u003cstrong\u003e(M) \u003c/strong\u003eand chemokine genes CXCL9, CXCL10, CXCL11, CXCL13 \u003cstrong\u003e(N)\u003c/strong\u003e were decreased in the condition of Necrostatin-1 (NEC-1) at a concentration of 10 μm. “NC” means normal control, “NEC-1” means treated with NEC-1 (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/4837977844560335e2ad7455.png"},{"id":56532881,"identity":"d65499d5-f064-4409-bfa0-a349454e6f44","added_by":"auto","created_at":"2024-05-15 12:21:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80949,"visible":true,"origin":"","legend":"\u003cp\u003eCD3D was identified as a hub gene and emerged as a protective factor in CC. \u003cstrong\u003eA\u003c/strong\u003e Venn plot showing six genes in the intersection analysis of differentially expressed NGRs, CD8 Pathway and prognostic related genes. \u003cstrong\u003eB\u003c/strong\u003e Volcano plot for differentially expressed genes (DEGs). The green and purple dots represented the significantly downregulated and upregulated genes, respectively. FDR \u0026lt; 0.05, |log2 FC|\u0026gt; 1, and p \u0026lt; 0.05. \u003cstrong\u003eC\u003c/strong\u003e Heatmap showing the expression of six intersected genes in low necroptosis and high necroptosis clusters.\u003cstrong\u003e D \u003c/strong\u003eDistribution of gene expression in stage, by Kruskal-Wallis rank sum test (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eE\u003c/strong\u003eDistribution of gene expression in T classification, by Kruskal-Wallis rank sum test (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eF \u003c/strong\u003eDistribution of gene expression in M classification, by Kruskal-Wallis rank sum test (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eG \u003c/strong\u003eThe correlation of gene expression with primary therapy response, by Kruskal-Wallis rank sum test (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/85b8e0e6081a2917051ffd2c.png"},{"id":56532144,"identity":"afca08be-905d-4d9f-8975-ffac883d86bc","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54178,"visible":true,"origin":"","legend":"\u003cp\u003eNecroptosis up-regulates CD3D via JAK2-STAT3 signaling pathway in Jurkat cells. \u003cstrong\u003eA\u003c/strong\u003e The correlation analysis of CD3D expression with necroptosis level. \u003cstrong\u003eB \u003c/strong\u003eThe expressing amount of CD3D differed in the untreated control group and necroptosis inducer treated group with p-value \u0026lt;0.01(**). \u003cstrong\u003eC\u003c/strong\u003e The protein level of CD3D is much stronger with the induction of necroptosis and showed an increasing trend with the increase of dose. \u003cstrong\u003eD, F\u003c/strong\u003e Treated with NSA, Jurkat cells exhibited a lower level of CD3D evaluated by RT-qPCR, with p-value \u0026lt;0.0001(****) \u003cstrong\u003e(D) \u003c/strong\u003eand WB\u003cstrong\u003e (F)\u003c/strong\u003e. \u003cstrong\u003eE, G\u003c/strong\u003e Treated with Nec-1, Jurkat cells exhibited a lower level of CD3D evaluated by RT-qPCR, with p-value \u0026lt;0.01(**) \u003cstrong\u003e(E)\u003c/strong\u003e and WB \u003cstrong\u003e(G)\u003c/strong\u003e. \u003cstrong\u003eH \u003c/strong\u003eGSEA analysis revealed that the high necroptosis group was enriched in JAK-STAT signaling pathway. \u003cstrong\u003eI \u003c/strong\u003eThe protein level of JAK2, STAT3 and p-STAT3 is much stronger with the induction of necroptosis. \u003cstrong\u003eJ, K \u003c/strong\u003eThe protein level of JAK2, STAT3 and p-STAT3 exhibits to decrease in the condition of NSA\u003cstrong\u003e (J) \u003c/strong\u003eor Nec-1\u003cstrong\u003e(K)\u003c/strong\u003e.\u003cstrong\u003e L \u003c/strong\u003eThe protein level of JAK2, STAT3, p-STAT3 and CD3D was much stronger with the induction of necroptosis and this effect was eliminated by FLLL32. \u003cstrong\u003eM \u003c/strong\u003eRT-qPCR results show CD3D was up-regulated by necroptosis and this effect was inhibited by FLLL32 at a concentration of 10 μm (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eN \u003c/strong\u003eRT-qPCR showed the down-regulation of PD-1, TIGIT, CTLA4, TIM3, LAG3 and VISTA in the condition of TNFɑ + Z-VAD-FMK and this effect was inhibited by FLLL32 (p-Value: **** p \u0026lt; 0.0001, *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eO \u003c/strong\u003eRT-qPCR showed the up-regulation of CCR4, CCR5, CCR7 and CCR9 with the treatment of TNFɑ + Z-VAD-FMK and this effect was eliminated by FLLL32 (p-Value: *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/f76f5ebf0fbb0dd8c24581b8.png"},{"id":56532149,"identity":"e73e33f6-b372-462b-9af2-b7266bad67f8","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":152438,"visible":true,"origin":"","legend":"\u003cp\u003eNecroptosis of T cells could inhibit the viability, migration and proliferation of CC cells.\u003cstrong\u003e A \u003c/strong\u003eResults of CCK-8 assays showed that at times of 24, 48, 72 and 96 h, the SiHa cells living in the normal control group are always significantly greater than the necroptosis group (p-value: *** p \u0026lt; 0.001, ****p \u0026lt;0.0001). \u003cstrong\u003eB\u003c/strong\u003e Wound healing assays accessed the differences on the migration ability of SiHa cells between necroptosis group and normal control group. Scale bar, 100 μm. \u003cstrong\u003eC \u003c/strong\u003eEdU assays were used to detect the proliferation ability of each group of SiHa cells. Scale bar, 200 μm. \u003cstrong\u003eD\u003c/strong\u003e Results of CCK-8 assays showed that at times of 24, 48, 72 and 96 h, the SiHa cells living in the NSA group are always significantly greater than the normal control group (p-value: **p \u0026lt;0.01, ****p \u0026lt;0.0001). \u003cstrong\u003eE\u003c/strong\u003e Wound healing assays to access the differences on the migration ability of SiHa cells between NSA group and normal control group. Scale bar, 100 μm. \u003cstrong\u003eF\u003c/strong\u003e EdU assays were used to detect the proliferation ability of each group of SiHa cells. Scale bar, 200 μm.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/8dd574a959e53ca79b5f6c7b.png"},{"id":56532146,"identity":"06cf0b55-995f-4c81-b32e-2bddf99d545f","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":39708,"visible":true,"origin":"","legend":"\u003cp\u003eThe combination of bevacizumab with necroptosis inhibitors demonstrated an inhibitory effect on T cell function. \u003cstrong\u003eA \u003c/strong\u003eThe protein level of CD8, CD3E and CD3D was stronger with the treatment of necroptosis inducer alone or bevacizumab (10 μg/ml) alone or treated with necroptosis inducer in combination with bevacizumab. \u003cstrong\u003eB\u003c/strong\u003e WB assay indicated the elevation of CD8, CD3E and CD3D by bevacizumab was mitigated by the necroptosis inhibitor NSA or NEC-1. \u003cstrong\u003eC-H \u003c/strong\u003eRT-qPCR showed the inhibition effect of\u003cstrong\u003e \u003c/strong\u003ebevacizumab on CTLA4 \u003cstrong\u003e(C)\u003c/strong\u003e, CTSS \u003cstrong\u003e(D)\u003c/strong\u003e, TIM3 \u003cstrong\u003e(E)\u003c/strong\u003e, TIGIT \u003cstrong\u003e(F)\u003c/strong\u003e, SELPLG \u003cstrong\u003e(G)\u003c/strong\u003e and VISTA\u003cstrong\u003e(H) \u003c/strong\u003ewas abrogated by the necroptosis inhibitor NSA or NEC-1 (p-Value: **** p \u0026lt; 0.0001, *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt;0.05, ns p \u0026gt; 0.05). \u003cstrong\u003eI \u003c/strong\u003eSurvival analysis revealed CC patients with high\u003cstrong\u003e \u003c/strong\u003enecroptosis level and low infiltration of cytotoxic T cells tended to have the worst outcome.\u003cstrong\u003e J \u003c/strong\u003eBioinformatic analysis indicated high necroptosis level and low infiltration of cytotoxic T cells group owns the highest expression level of VEGFA.\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/a96a4f3649017fdfa9318458.png"},{"id":56532880,"identity":"2f9057fc-b27f-451f-badf-e90bc5dd1685","added_by":"auto","created_at":"2024-05-15 12:21:18","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":22710,"visible":true,"origin":"","legend":"\u003cp\u003eThe dual functionality of necroptosis in CC patients. Necroptosis plays a dichotomous role in the progression of CC. On one hand, necroptosis facilitates the proliferation of CC cells by upregulating the JAK2-STAT3 signaling pathway and augmenting VEGFA expression. Treatment with bevacizumab in CC cells induces necroptosis, and concurrent administration of necroptosis inhibitors synergistically attenuates VEGFA expression. Conversely, necroptosis elicits T cell activation and bolsters anti-tumor immune responses through the activation of the JAK2-STAT3 signaling pathway.\u003c/p\u003e","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/7cd88d79f4b03e8ce2ca9162.png"},{"id":56733300,"identity":"856d2f63-7c65-469a-89dc-3ecf6da4d436","added_by":"auto","created_at":"2024-05-19 14:01:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1897442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/87205fee-9cdf-4cad-b2a3-dd808d4d2780.pdf"},{"id":56532143,"identity":"d87ba09a-ab81-46df-969f-eae21b75e82e","added_by":"auto","created_at":"2024-05-15 12:13:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1743147,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4379854/v1/f2522627d46913a118c8cbf1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual Role of Necroptosis in Cervical Cancer: Promoting Tumor Aggression and Modulating the Immune Microenvironment via the JAK2-STAT3 Pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) ranks among the most frequently diagnosed malignancies in women. An estimated 604,127 females worldwide received a new diagnosis of CC in 2020, with up to 341,831 of them passing away as a result of the condition [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], posing a tremendous burden on healthcare systems. Radical hysterectomy is commonly used for early-stage CC, which has achieved great success [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In general, patients diagnosed with early-stage CC have a favorable prognosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while outcomes for locally advanced stages, recurrent and metastatic cases are not as promising [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For patients with locally advanced stages, metastatic and recurrent CC, conventional treatment options include surgery, radiotherapy and chemotherapy, but these treatment options exert limited effects, the 5-year survival rate for metastatic or recurrent patients is no more than 20% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent years, immunotherapy especially immune checkpoint inhibitors (ICIs) has emerged to be a promising strategy to treat different human malignancies. Anti-programmed cell death (PD-1) and anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) are two targets for immunotherapy under research in a range of malignancies including CC [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Dishearteningly, therapeutic effects of the common ICIs including PD-L1 and PD-1 in CC are limited, with a poor response rate of 10\u0026ndash;25% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, even though the angiogenesis inhibitor bevacizumab has been employed in combination with the anti-programmed cell death ligand-1 (PD-L1) antibody atezolizumab to enhance the efficacy of CC therapy, the combination therapy only results in a modest improvement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. So, it is of great significance to explore more potential immunotherapy targets and cell interactions in the tumor microenvironment to improve the immunotherapy effectiveness for CC patients.\u003c/p\u003e \u003cp\u003eNecroptosis, a form of regulated cell death, is mainly mediated by receptor interacting protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like pseudokinase (MLKL) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For the past few years, an accumulating body of researches have indicated that necroptosis plays a dual role in modulating tumorigenesis and tumor progression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For one thing, necroptosis is suggested to act as a contributing factor in cancer metastasis and cancer progression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], for another, necroptosis is also reported to serve as a tumor suppressor in many malignancies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], either promoting or defending against tumor depends on cancer subtypes and the context in which tumor cells are located. Therefore, a comprehensive investigation into necroptosis may prove beneficial in developing a framework for personalized treatment plans for patients. What\u0026rsquo;s more, necroptosis plays a role in the regulation of tumor immune microenvironment and its significance in cancer immunity has been increasingly appreciated. It was demonstrated that cancer cells undergoing necroptosis can activate cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells and triggering anti-tumor immunity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition to the regulation of T cells, necroptosis also affects other immune cells, as a key regulator of necroptosis, it was reported that the decreased level of RIPK3 was relevant to the accumulation of myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment (TME) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, necroptosis is suggested to plays a negative regulatory role in tumor immunity. It was proved that pancreatic epithelial cells undergoing necroptosis induce C-X-C Motif Chemokine Ligand 1 (CXCL1), leading to the suppression of adaptive immunity and pancreatic oncogenesis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To sum up, necroptosis exerts a complex regulatory influence on cancer biology and have potential applications as a source of novel drug targets. However, there are few studies on necroptosis in the immune microenvironment of CC. So, it is urgently required to investigate the impact of necroptosis in CC, which may provide valuable insights into patient stratification and precision immune treatment.\u003c/p\u003e \u003cp\u003eThis study seeks to elucidate the role of necroptosis in CC pathogenesis and its influence on the tumor immune microenvironment. Through integrated bioinformatics analysis of the TCGA-CESC cohort and functional in vitro assays, we aimed to characterize the dual role of necroptosis in CC, which on the one hand promotes the proliferation of CC cells and on the other hand can exert anti-tumor effects through eliciting activation of T cells. Its pro-cancer and anti-cancer effects are achieved through JAK2-STAT3 signaling pathway. Firstly, it exerts an anti-tumor effect by activating the JAK2-STAT3 pathway, which upregulates CD3D and then promotes T cell activation. Secondly, it exerts a pro-cancer effect by activating the JAK2-STAT3 pathway, which upregulates VEGFA. Ultimately, CD3D was determined as a biomarker for immune activation and therapeutic responsiveness in CC patients, and the role of CD3D as a biomarker of treatment response was described by exploring the relationship between CD3D expression and clinical characteristics and immune microenvironment of CC. The study extends to the potential interaction between necroptosis and bevacizumab, revealing a potential resistance mechanism of bevacizumab and offering new perspective into personalized treatment plans for CC patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePublic datasets acquisition\u003c/h2\u003e \u003cp\u003eThe gene expression profiles and clinical data for CC cases were downloaded from The Cancer Genome Atlas (TCGA) database, accessible through \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Necroptosis-related genes (NRGs) were retrieved from MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the search term \u0026ldquo;Necroptosis\u0026rdquo;. In addition, genes related to the CD8 pathway were downloaded from MSigDB searching the keyword \u0026ldquo;CD8\u0026rdquo;. CC samples were grouped by the expression level of NRGs and defined as \u0026ldquo;high-necroptosis\u0026rdquo; and \u0026ldquo;low-necroptosis\u0026rdquo; cohorts. Subsequently, the identification of differentially expressed genes (DEGs) between two cohorts was performed using the \u0026ldquo;limma\u0026rdquo; package in R, applying the following filtering criteria: false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log2 fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1. Then, the intersection of necroptosis-related differentially expressed genes, CD8 pathway-related genes and prognostic-related genes was identified through the R package \u0026ldquo;VennDiagram\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eImmunity evaluations\u003c/h2\u003e \u003cp\u003eThe ESTIMATE algorithm implemented in R software [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was utilized to evaluate the Immune Score, Stromal Score, and overall ESTIMATE Score for each CC sample, indicating the extent of immune cell and stromal cell infiltration within the TME. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to quantify the presence of immune cells and the enrichment of immune-related pathways via \u0026ldquo;GSVA\u0026rdquo; R package [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The CIBERSORT algorithm was utilized to determine the composition of 22 immune cell types within the TME of each CC sample [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Immune checkpoint genes (ICPs) were sourced from the TISIDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and prior research papers [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The difference of immune components between different groups were visualized using R packages \u0026ldquo;limma\u0026rdquo;, \u0026ldquo;ggplot\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analyses and GSEA\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the biological function of NRGs through R package \u0026ldquo;clusterProfiler\u0026rdquo; [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], with the outcomes visualized using the R packages \u0026ldquo;enrichplot\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo;. A significant threshold of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was utilized. Additionally, gene set enrichment analysis (GSEA) was conducted between high- and low-necroptosis clusters using the HALLMARK gene sets and C2, CP, and KEGG v7.2 symbols. This analysis was performed via the gsea software (version: 4.0.3) with a significant threshold of 5% for NOM p-value and FDR q-value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eR packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo; [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] were employed to delimit the cut-off value of all survival analyses. The survival curves were generated using the Kaplan-Meier (KM) method to display all results, with statistical significance calculated using the log-rank test. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be of notable importance. The impact of gene expression on survival was also assessed using the KM Plotter online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com\u003c/span\u003e\u003cspan address=\"http://kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNecroptosis Induction and Inhibition\u003c/h2\u003e \u003cp\u003eNecroptosis was induced by treating cells with a combination of TNFα (MCE, HY-P7058) and Z-VAD-FMK (MCE, HY-16658B) at concentrations of 20 \u0026micro;m and 50 \u0026micro;m. This treatment regimen aimed to inhibit caspase activity and initiate necroptotic cell death. To inhibit necroptosis, cells were exposed to necrosulfonamide (NSA) (MCE, HY-100573) or necrostatin-1 (NEC-1) (MCE, HY-15760) at concentrations of 10 \u0026micro;m, 20 \u0026micro;m, and 30 \u0026micro;m. NSA targets the effector protein MLKL to prevent its translocation to the plasma membrane, while NEC-1 disrupts the formation of the RIPK1-RIPK3 complex crucial for necroptotic signaling. These interventions were instrumental in elucidating the molecular mechanisms underlying necroptosis regulation in our experimental model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and real-time qPCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eThe SiHa cell lines, sourced from ATCC (Manassas, VA, USA), were cultured in high-glucose Dulbecco\u0026rsquo;s modified Eagle medium (DMEM; Servicebio, China) supplemented with 10% fetal bovine serum (FBS, Biological Industries, Israel) and 1% antibiotic (penicillin/streptomycin; New Cell \u0026amp; Molecular Biotech Co., China) at 37\u0026deg;C in a 5% CO2 incubator. Human Jurkat Clone E6-1 T cells were procured from Procell Life Science \u0026amp; Technology Co., Ltd (#CL-0129; Wuhan, China) and maintained in RPMI-1640 medium (Servicebio, China) with 10% FBS and 1% penicillin/streptomycin.\u003c/p\u003e \u003cp\u003eTotal RNA was isolated from SiHa and human Jurkat Clone E6-1 T cells using TRIzol reagent (Invitrogen, USA), followed by quantification of RNA concentration and purity. Reverse transcription to generate cDNA was performed using the 5\u0026times; ALL-IN-One RT Master Mix kit (Applied Biological Materials Inc., Canada). RT-qPCR was carried out using the TB Green Premix Ex Taq kit (Takara, Japan), with GAPDH expression used as an internal control. The relative mRNA expression levels were determined using the 2\u003csup\u003e\u0026minus;△Ct\u003c/sup\u003e method [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e lists all the primers used in this research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical CC samples and immunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eTen cervical squamous cell carcinoma specimens, consisting of five early-stage and five late-stage samples, were obtained from Shanghai First Maternity and Infant Hospital. These clinical specimens underwent pathological verification according to the latest International Federation of Gynecology and Obstetrics (FIGO) guidelines. Prior to collection, informed consent was obtained from the patients, and the study was approved by the Medical Ethics Committee of Shanghai First Maternity and Infant Hospital (Approval notice: KS21264).\u003c/p\u003e \u003cp\u003eFollowing fixation, paraffin embedding, dewaxing, rehydration, and antigen retrieval, all tumor tissue sections were processed for staining with a primary antibody targeting CD3D (A9770, Abclonal, China) at 4\u0026deg;C overnight. Subsequently, the slides underwent incubation with a secondary antibody (#PK-8501, Vector Lab, USA) for 1 hour at 37\u0026deg;C, followed by detection and visualization of the DAB complex using the Rabbit IgG mini-PLUS Kit (#PK-8501, Vector Lab, USA) and hematoxylin for nuclear counterstaining. Photomicrographs were captured using an optical microscope at 100\u0026times; magnification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting (WB)\u003c/h2\u003e \u003cp\u003eCell lysis was performed by adding RIPA buffer containing complete protease and phosphatase inhibitors (TargetMol, America). Protein concentrations were determined using the BCA method (Beyotime, China). The extracted proteins were subjected to a thermal treatment at 99℃ for a period of 20 minutes. Subsequently, they were separated by 10% SDS-PAGE (Servicebio, China) and transferred to polyvinylidene fluoride membranes. Next, the membranes were blocked with a 5% lipid-free milk solution for 1.5 hours. Following this, they were incubated with primary antibodies (Table S2) overnight at 4\u0026deg;C. Afterward, the membranes were washed and incubated with secondary antibodies (HuaBio, China) for 2 hours at room temperature. Ultimately, protein bands were visualized using an enhanced chemiluminescence reagent (Epizyme, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Counting Kit-8 (CCK-8) assay\u003c/h2\u003e \u003cp\u003eCCK-8 reagent (Vazyme, China) was utilized to detect the cell viability. CC cells and human Jurkat Clone E6-1 T cells were initially diluted to a density of 2*10\u003csup\u003e4\u003c/sup\u003e cells/ml and plated in 100 \u0026micro;l aliquots into each well of 96-well microtiter plates (Coring, NY, USA) for 96 hours. At 24-hour intervals, 10 \u0026micro;l of CCK-8 reagent was added to each well, followed by a 2-hour incubation period. Subsequently, optical density (OD) was measured at a wavelength of 450 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranswell assay\u003c/h2\u003e \u003cp\u003eTo evaluate cell migration, we initiated the procedure by adjusting the cell concentration to 2 \u0026times; 10^5 cells/mL. Then, 150 \u0026micro;L of this cell suspension was delicately added to the upper chamber of the Transwell apparatus, while the lower chamber of each well received 800 \u0026micro;L of DMEM medium supplemented with 20% FBS. This setup was then placed in a controlled environment at 37\u0026deg;C with 5% CO2 for 24 hours. After the designated incubation period, non-migrating cells on the upper membrane surface were gently removed using sterile cotton swabs. Following this, the migrated cells were fixed with methanol for 15 minutes and then stained with crystal violet for 30 minutes. Subsequently, thorough rinsing with PBS ensured the removal of excess dye. The inserts were then subjected to microscopic examination. Five random fields of view were selected for imaging, facilitating the enumeration of migrated cells. These images provided a basis for quantifying and comparing the migratory capacities of the transfected cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWound healing test\u003c/h2\u003e \u003cp\u003eTo conduct the wound healing assay, CC cells were first grown in six-well plates with complete medium until reaching 95% confluence over a 24-hour period. Subsequently, a scratch was made using a 200 \u0026micro;l sterile pipette tip, followed by washing with phosphate-buffered saline (PBS) to eliminate cell debris. The adherent cells were then cultured in serum-free DMEM and images were captured at 0, 12 and 24 h post-scratching to assess the wound area as an indicator of cell invasion ability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5-Ethynyl-20-deoxyuridine (EdU) assay\u003c/h2\u003e \u003cp\u003eThe EdU assays were performed using the BeyoClickTM EdU Cell Proliferation Kit with Alexa Fluor 555 (Beyotime, China). EdU was introduced into the cell culture medium and allowed to incubate for 2 hours. Subsequently, CC cells were fixed with 4% paraformaldehyde and permeabilized with 0.3% Triton X-100 (Servicebio, China). The Click solution was then prepared according to the manufacturer's instructions and used to incubate the cells for 30 minutes in the absence of light. The cells were stained with Hoechst 33342 and visualized using an inverted fluorescence microscope (Carl Zeiss, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed utilizing R software (version 4.0.5), GraphPad Prism 8 (GraphPad, La Jolla, CA, USA), and SPSS 26.0 (IBM, SPSS, Chicago, USA). Unpaired Student\u0026rsquo;s t-tests were employed for comparing data between two groups, with statistical significance set at a threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Each experiment was replicated a minimum of three times.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNecroptosis promotes proliferation and invasion of CC cells\u003c/h2\u003e \u003cp\u003eWe first investigated the effect of necroptosis on the proliferation and migration of CC cells by in vitro experiments. As shown by the CCK-8 results, CC cells induced to undergo necroptosis showed a higher viability compared to the normal control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), whereas there was a decreased state with the treatment of necroptosis inhibitor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Transwell assays were performed to assess the migration ability of CC cells and the results showed a greater migration potential in the necroptosis-inducing group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Consistently, compared with the normal control group, necroptosis-inhibiting group exhibited a decreased migration state (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Wound healing assays were utilized to assess the migration ability of CC cells. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F, the necroptosis-inducing group tended to have a significant increased migration ability while a weaker condition was found in the necroptosis-inhibiting group. Additionally, as depicted by the EdU results, CC cells induced into necroptosis displayed elevated proliferation ability relative to the normal control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG), while treatment with a necroptosis inhibitor led to decreased proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). All these results indicated necroptosis contributed to the proliferation and invasion of CC cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eNecroptosis up-regulates VEGFA via JAK2-STAT3 signaling pathway in CC cells\u003c/h2\u003e \u003cp\u003eGO enrichment analysis and KEGG analysis were performed to further explore the mechanism of necroptosis in CC cells. Results of GO enrichment analysis revealed functions about the biological process of NRGs includes receptor signaling pathway via JAK-STAT and positive regulation of interleukin-6 (IL-6) production (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Results of KEGG showed NRGs were enriched in vascular endothelial growth factor (VEGF) signaling pathway and JAK-STAT signaling pathway as well (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, researches have demonstrated that activation of IL-6/IL-6R up-regulates VEGF, thereby promoting tumor angiogenesis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, it was verified that the inhibition of STAT3 resulted in a decrease in VEGF [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, the WB assay showed a significant elevation of RIPK1, phosphorylated RIPK1 (p-RIPK1), RIPK3, p-RIPK3, MLKL and p-MLKL (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA), indicating CC cells were undergoing necroptosis with the addition of necroptosis inducer. Results of RT-qPCR also demonstrated a similar elevation in the expression levels of RIPK1 and MLKL in CC cells in the presence of the necroptosis inducer (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Besides, it was noteworthy that CC cells undergoing necroptosis exhibited an elevated protein level of VEGFA (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Results of bioinformatic analyses indicated the expression level of VEGFA was higher in advanced CC samples (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD) and the high expression group demonstrated a tendency towards a worse primary therapy outcome (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE). In addition, CC patients with lower expression of VEGFA tended to possess a better outcome, with longer disease-free survival (DFS), overall survival (OS) and progress-free survival (PFS) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF-H). Furthermore, CC patients with high expression level of VEGFA and high necroptosis were more likely to experience unfavorable outcomes (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eI). Subsequently, the mechanism of necroptosis up-regulating VEGFA was further explored through in vitro experiments. Results of RT-qPCR showed with the induction of necroptosis, the expression levels of JAK2, STAT3 and VEGFA got significantly elevated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-E). Consistently, as results of WB assay presented, the protein levels of JAK2, STAT3, p-STAT3 and VEGFA were up-regulated with the induction of necroptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) whereas they were found to decrease with the treatment of NSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). FLLL32 (MCE, HY-100544) was a specific inhibitor of JAK2-STAT3 pathway, JAK2, STAT3 and p-STAT3 were found to be inhibited in CC cells at a concentration of 10 \u0026micro;m (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eJ). Results of in vitro experiments demonstrated that the up-regulation of JAK2, STAT3 and VEGFA by necroptosis inducer was eliminated by FLLL32 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH-K). Therefore, it could be concluded that necroptosis up-regulates VEGFA via JAK2-STAT3 signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBevacizumab induces the necroptosis of CC cells and activates JAK2-STAT3 signaling pathway\u003c/h2\u003e \u003cp\u003eBevacizumab is a monoclonal antibody that can specifically target VEGF. It blocks the binding of VEGF and its receptor, inhibiting tumor neovascularization and thus playing an anti-tumor role. In this study, it has been proved that necroptosis up-regulates VEGFA via JAK2-STAT3 signaling pathway, we then further explore the interaction of necroptosis, bevacizumab and JAK2-STAT3 signaling pathway. The results of the wound healing test demonstrated that bevacizumab (Selleck, A2006) exhibited a notable inhibitory effect on the migratory capacity of CC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The expression levels of RIPK1, RIPK3, MLKL and the protein levels of p-RIPK3 and p-MLKL exhibited an elevated trend when bevacizumab was added in CC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-E), indicating that bevacizumab induces the necroptosis of CC cells. Moreover, with the addition of bevacizumab, results of WB assay verified the VEGFA was successfully inhibited, accompanied by a significant increase in the protein levels of JAK2, STAT3 and p-STAT3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The elevated protein levels of JAK2, STAT3, and p-STAT3 were observed in CC cells treated with bevacizumab alone or in combination with necroptosis inducer, however the inhibitory effect of bevacizumab on VEGFA was eliminated in combination with necroptosis inducer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Results of RT-qPCR showed the expression level of JAK2 and STAT3 got up-regulated with the addition of bevacizumab and the effect was reversed by necroptosis inhibitor NSA or NEC-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH-I). Consistently, the impact of bevacizumab on JAK2, STAT3 and p-STAT3 was eliminated by NSA or NEC-1. A synergistic inhibition effect on VEGFA was observed while treated with bevacizumab in combination with NSA or NEC-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). All these results demonstrated that bevacizumab induced the necroptosis of CC cells, thus activated JAK2-STAT3 signaling pathway and up-regulated VEGFA in combination with necroptosis inducer, suggesting a potential mechanism of bevacizumab resistance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive bioinformatical analyses revealed necroptosis could enhance T cell signaling pathways in CC\u003c/h2\u003e \u003cp\u003eIn order to further investigate the role of necroptosis in TME, CC cases were first clustered into high- and low- necroptosis cohorts, with the median expression of NRGs serving as the cut-off point. Then, the correlations of TME components between the two clusters were evaluated by ESTIMATE algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As results presented, ImmuneScore exhibited the most remarkable difference, implying CC patients with higher necroptosis enjoys a heater immune microenvironment. For investigating the correlation between necroptosis and immunologic functions as well as immune cell infiltration, ssGSEA was conducted and the results demonstrated that most immune-related functions showed a remarkable difference between high- and low- necroptosis groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In addition, GO enrichment analysis and KEGG analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) were utilized to evaluate the function of necroptosis, and results indicated that genes\u0026rsquo; functions about the biological process (BP), cellular component (CC), molecular function (MF) were all tightly centered around T cell regulation. Notably, results of KEGG revealed that necroptosis were not only involved in T cell receptor signaling pathway, but also related to JAK-STAT signaling pathway. Moreover, GSEA was performed to investigate differences of enriched pathways between these two groups. In high necroptosis cluster, pathways were significantly enriched in immunological functions including T cell receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). CIBERSORT was utilized to compare the difference of tumor-infiltrating cells proportion between different clusters. Notably, as the result exhibited, the high-necroptosis cohort tends to own a higher infiltration level of CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Moreover, anti-tumor chemokine genes including CXCL9, CXCL10, CXCL11 and CXCL13 expressed higher in high-necroptosis cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). All these results suggested that necroptosis could elicit immune responses and dynamically change the TME of CC, particularly T cell signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNecroptosis could facilitate T cells activation, reduces T cell depletion and increases T cell infiltration in vitro\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter identifying that necroptosis was linked to immunological processes especially CD8\u0026thinsp;+\u0026thinsp;T cell regulation based on bioinformatical analyses, a series of in vitro experiments were conducted to confirm it. T cells are rapidly activated after encountering antigen stimulation and undergo a series of cytological changes such as the expression of some membrane surface molecules and synthesizing cytokines. CD69 is the earliest membrane molecule expressed by activated T cells, which can be measured and reach a peak soon after half an hour of activation, thus can be the earliest activation marker for T cells [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The interleukin-2 receptor alpha subunit (IL-2Rɑ, CD25) is upregulated within 24 h of stimulation and remains elevated for several days, which can serve as a mid-late activation marker. Besides, CD25 plays a crucial role in the reactivity to IL-2, allowing T lymphocyte activation and further IL-2 production [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Activated CD8\u0026thinsp;+\u0026thinsp;T cell secreted granzyme B (GZMB), Interferon gamma (IFN-γ) which can meditate target cell lysis and apoptosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], therefore, GZMB and IFN-γ are also important indicators of T cell activation. To verify whether necroptosis influences T cell activation, RT-qPCR was performed to evaluate the changes of T cell activation markers with or without the induction of necroptosis in Jurkat cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). It was obviously that the expression levels of CD25, CD69, GZMB and IFN-γ in Jurkat cells got significantly elevated with the induction of necroptosis, suggesting necroptosis facilitates T cell activation. Moreover, cytokines secreted by T cell are involved in controlling many different cellular functions, including proliferation, differentiation and cell apoptosis. In our study, results of RT-qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-G) illustrated that cytokines including IL-2, IL-6, and IL-13 were in a higher expression of Jurkat cells induced with a necroptosis-inducing agent, implying that necroptosis modulated T cell proliferation and differentiation. Chemokines play a crucial role in guiding immune cell migration, which is essential to initiate and deliver an effective anti-tumor immune response. Here, we found Jurkat cells undergoing necroptosis exhibited an up-regulated expression level of chemokine receptor genes including C-C Motif Chemokine Receptor 4 (CCR4), CCR5, CCR7, CCR9 and chemokine genes including CXCL9, CXCL10, CXCL11, CXCL13 were up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH, I). We also evaluated the relationship between the expression of immune checkpoints and necroptosis, results showed CTLA4, LAG3, TIM3, VISTA, TIGIT and PD-1 were down-regulated in Jurkat cells with the induction of necroptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ), demonstrating that necroptosis might reduce T cell depletion. In contrast, while treated with necroptosis inhibitor NSA or NEC-1, the expression of CCR4, CCR5, CCR7, CCR9, CXCL9, CXCL10, CXCL11 and CXCL13 were obviously decreased in Jurkat cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK-N). It was remarkable that the viability of Jurkat cells was not affected whether treated with necroptosis inducer or necroptosis inhibitor NEC-1 and NSA according to the results of CCK-8 experiments (Fig. S2A-C), indicating necroptosis affected functions of Jurkat cells but not proliferation. In contrast to its tumor-promoting role in CC cells, the aforementioned results provide compelling evidence to support the anti-tumor potential of necroptosis, which is achieved through the modulation of T cell activation, depletion and infiltration. In conclusion, it can be posited necroptosis plays a dual role in CC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCD3D was identified as a hub gene and emerged as a protective factor in CC\u003c/h2\u003e \u003cp\u003eAs previously mentioned, bioinformatic analyses and experimental investigations identified that necroptosis has played a role in the regulation of CD8\u0026thinsp;+\u0026thinsp;T cells. Subsequently, we conducted a comprehensive analysis to select genes associated with both the \u0026lsquo;CD8 Pathway\u0026rsquo; and prognosis from the pool of 958 differentially expressed NRGs (Table S3). This analysis led to the identification of 6 key genes (CD2, CD247, CD3D, CD3E, CD3G, CD8A) through the intersection analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Remarkably, all six genes exhibited a higher expression level in the high-necroptosis cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C). Subsequent correlation analysis of the expression levels of these 6 genes with clinicopathological characteristics revealed that CD3D expression was significantly linked to stage classification, grade classification, and response to primary therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-G). Notably, our findings indicated a negative correlation between CD3D expression level and staging as well as grading. CC patients with a high expression level of CD3D tended to be more sensitive to the primary therapy. Hence, CD3D appears to be a promising protective indicator in CC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA pan-cancer analysis of CD3D expression level across various cancer types using data from TCGA was performed to investigate whether the protective effect of CD3D was limited to CC or applicable to other malignancies (Fig. S3A). The results revealed that CD3D expression was higher in tumor cases than in normal ones across most types of cancer, suggesting its potential as a therapeutic target. Besides, survival analysis was performed to assess the prognostic significance of CD3D in CC. Results demonstrated that CC patients with high expression of CD3D tended to possess a better outcome, with longer OS, DFS and PFS (Fig. S3B-D). Consistently, online database analysis also indicated better OS, DFS and PFS for CC patients with high CD3D levels compared to those with low levels (Fig. S3E-G). To further explore the correlation between CD3D protein levels and clinical stage, we collected five early-stage and five late-stage CC specimens for immunohistochemical detection of CD3D protein levels. IHC results presented in Fig. S3H indicated a decrease in CD3D expression with advanced stage classification. Consistently, correlation analysis using public databases between CD3D expression and clinicopathological characteristics revealed a lower expression level of CD3D in advanced stage and grade cases (Fig. S3I-K). These results indicate that CD3D may function as a biomarker for predicting prognosis and stage classification in CC patients. Additionally, it was noted that individuals exhibiting elevated CD3D expression levels generally experienced more favorable outcomes following primary therapy (Fig. S3L). These above results demonstrate that CD3D was a protective factor and potential therapeutic target in CC, which could forecast prognosis and foretell immunotherapy effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eNecroptosis up-regulates CD3D via JAK2-STAT3 signaling pathway in Jurkat cells\u003c/h2\u003e \u003cp\u003eWe then evaluated the relationship between CD3D and necroptosis via bioinformatical analyses and in vitro experiments. Firstly, CD3D expression was positively correlated with necroptosis, and high expression group was found to exhibit a higher level of necroptosis, as is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA. Given that CD3D is expressed on T cells, Jurkat cells, a T cell line, was used to conduct in vitro experiments. Consistently, with the induction of necroptosis, Jurkat cells exhibited a higher expression of CD3D compared with untreated control as the RT-qPCR result presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, and the protein expression level of CD3D emerged higher with the increase of concentrations as the result of WB displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC. Conversely, while treated with NSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, F) or NEC-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, G), a decrease in CD3D expression was observed in Jurkat cells. These results provide valuable insights into the intricate interplay between CD3D expression and necroptosis dynamics, shedding light on potential therapeutic targets in CC patients.\u003c/p\u003e \u003cp\u003eAs described above, our study revealed that Jurkat cells exhibited increased CD3D expression levels upon induction of necroptosis. Subsequent mechanistic investigations were conducted, with GSEA analysis indicating enrichment of the JAK-STAT signaling pathway in the high necroptosis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). In vitro experiments were then performed to elucidate the interplay between CD3D, the JAK-STAT signaling pathway, and necroptosis in Jurkat cells. The protein levels of JAK2, STAT3, and p-STAT3 were upregulated in response to necroptosis induction, as demonstrated by our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI), while their levels decreased in the presence of NSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ) or NEC-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK), suggesting that necroptosis may activate the JAK2-STAT3 signaling pathway. Furthermore, western blot results (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL) confirmed that the protein levels of JAK2, STAT3, p-STAT3, and CD3D increased in response to necroptosis induction, and this effect was attenuated by FLLL32. RT-qPCR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eM) indicated that CD3D expression was upregulated by necroptosis induction, an effect that was inhibited by FLLL32. Therefore, our findings suggest that necroptosis upregulates CD3D via the JAK2-STAT3 signaling pathway. Interestingly, we also observed that necroptosis downregulated the expression of common ICPs, including PD-1, TIGIT, CTLA4, TIM3, LAG3, and VISTA in Jurkat cells, an effect that was reversed by FLLL32 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eN). Additionally, the expression of chemokine receptors such as CCR4, CCR5, CCR7, and CCR9 in Jurkat cells was upregulated in response to necroptosis, and this effect was also abrogated by FLLL32 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eO). These results demonstrated that necroptosis could induce upregulation of CD3D in Jurkat cells via activation of the JAK2-STAT3 signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then further investigated whether the above experimental phenomenon could be found in activated T cells. Jurkat cells were firstly activated by PMA (20ng/mL, MCE, HY-18739) and lonomycin (500ng/mL, MCE, HY-13434), which was verified by the protein level of T cell activation marker CD69 using WB, and CD69 exhibited a higher level upon the induction of necroptosis (Fig. S4A), indicating necroptosis also facilitated T cell activation even though it's in activated T cells. Besides, the protein level of JAK2, STAT3, p-STAT3 and CD3D was found to increase in activated T cells with the induction of necroptosis (Fig. S4B), consistent with the inactivated T cells. Conversely, while treated with NSA (Fig. S4C) or NEC-1 (Fig. S4D), the protein level of JAK2, STAT3, p-STAT3, CD3D and CD69 in activated T cells was found to decrease. Additionally, RT-qPCR results demonstrated that CD3D was up-regulated in activated T cells with the treatment of necroptosis inducer and this effect can be inhibited by FLLL32 (Fig. S4E). WB results shown in Fig. S4F illustrated that in activated T cells, the protein level of JAK2, STAT3, p-STAT3, CD3D and CD69 was found to increase with the induction of necroptosis and this effect was eliminated by FLLL32. Therefore, it could be concluded that necroptosis up-regulates CD3D via JAK2-STAT3 signaling pathway whether in activated or inactivated T cells.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eNecroptosis of T cells could inhibit the viability, migration and proliferation of CC cells\u003c/h2\u003e \u003cp\u003eKnowing that necroptosis facilitates T cell activation, we excogitated the influence of T cells undergoing necroptosis on CC cells. Jurkat cells were firstly treated with or without the induction of necroptosis and the supernatant was extracted to culture SiHa CC cells respectively. The outcomes of CCK-8 assays showed a significant suppression in the viability of SiHa CC cells cocultured with the supernatant of Jurkat cells (pretreated with TNFɑ + Z-VAD-FMK at a concentration of 20 \u0026micro;m the day before), as evaluated by the OD values (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). As illustrated by the wound healing assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), the necroptosis group exhibited a significant suppression of migration ability in comparison to the normal control group. Additionally, the outcomes of EdU assays further confirmed that SiHa CC cells coculturing with the supernatant of Jurkat cells (pretreated with TNFɑ + Z-VAD-FMK the day before) exhibited a decrease proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Conversely, while cocultured with the supernatant of Jurkat cells (pretreated with NSA at a concentration of 10 \u0026micro;m the day before), the viability, migration ability and proliferation of SiHa CC cells got elevated as demonstrated by CCK-8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), wound healing (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE), and EdU assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then explored whether CC cells undergoing necroptosis would make some effects on T cells. We firstly treated SiHa CC cells with or without the induction of necroptosis and the supernatant was extracted to culture Jurkat cells respectively. As the RT-qPCR results shown in Fig. S5A-C, the expression of key molecules in the necroptosis pathway including MLKL, RIPK1 and RIPK3 was found to be significantly elevated in Jurkat cells cocultured with the supernatant of SiHa CC cells (pretreated with TNFɑ + Z-VAD-FMK at a concentration of 20 \u0026micro;m the day before) compared with the normal control group. And the protein level of RIPK1, p-RIPK1, RIPK3 and p-RIPK3 was also much higher (Fig. S5D). Also, results of RT-qPCR presented in Fig. S5E-G demonstrated that the expression of CD3D, JAK2, STAT3 was up-regulated in the necroptosis group of Jurkat cells. Besides, the protein level of CD3D, JAK2, STAT3 and p-STAT3 detected by WB exhibit an increasing trend in the necroptosis group (Fig. S5H). In sum, it could be inferred that CC cells undergoing necroptosis induced the expression of CD3D and up-regulated the JAK2-STAT3 signaling pathway in Jurkat cells, which might facilitate T cells activation and trigger anti-tumor immunity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eThe combination of bevacizumab with necroptosis inhibitors demonstrated an inhibitory effect on T cell function\u003c/h2\u003e \u003cp\u003eAs previously demonstrated, necroptosis is a key driver of T cell activation and bevacizumab induces the necroptosis of CC cells. Subsequently, the impact of necroptosis inducer/inhibitor in conjunction with bevacizumab on TME was evaluated. In view of the fact that T cells represent the principal anti-tumor force in TME, they were selected for the following experiments. Results of WB assay indicated in comparison to bevacizumab alone, the expression of T cell activation markers including CD8 and CD3E were found to be elevated when combined with the necroptosis inducer (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). In contrast, CD8, CD3E and CD3D were observed to down-regulated in combination with necroptosis inhibitor NSA or NEC-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). We then treated Jurkat cells with supernatant extracted from CC cells (pretreated with bevacizumab or bevacizumab in combination with necroptosis inhibitor respectively the day before). Results of RT-qPCR showed markers of T cell inhibition and depletion including CTLA4, CTSS, TIM3, TIGIT, SELPLG and VISTA exhibited a significant elevated in the combination group (bevacizumab in combination with necroptosis inhibitor) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-H), indicating the combination of bevacizumab with necroptosis inhibitors may present a challenge in the creation of an inhibitory immune microenvironment. As the survival analysis shown, CC patients with a high necroptosis level and a low cytotoxic T cells infiltration tended to possess the poorest outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI). Besides, this group of CC patients exhibited the highest expression level of VEGFA (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eJ). All these findings offer new insights into the potential for individualized treatment of CC. Patients with high level of necroptosis and low infiltration of cytotoxic T cells are more likely to benefit from the bevacizumab and necroptosis inhibitor combination therapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCC appears to be the one of the most lethal forms of cancer in females. Despite significant advancements in immunotherapy in recent years, the prognosis for individuals with advanced or metastatic CC continues to be unfavorable [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Immunotherapy represented by ICIs exerted limited effects in advanced or metastatic CC partly caused by the immunosuppressive TME [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Angiogenesis is a prominent characteristic of maligalancies and the utilization of bevacizumab represents a novel therapeutic approach for a spectrum of advanced cancers with a dismal prognosis including advanced and metastatic CC. The combination of bevacizumab and PD-L1 inhibitor atezolizumab offers a new option to CC treatment. Nevertheless, the utilization of bevacizumab is constrained by individual variations, the emergence of drug resistance, and alterations in the TME [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], so there is an immediate requirement to identify reliable biomarkers to stratify patients in order to guide personalized treatment and to convert this immune \u0026ldquo;cold\u0026rdquo; tumor into a \u0026ldquo;hot\u0026rdquo; tumor that responds better to targeted therapy.\u003c/p\u003e \u003cp\u003eCurrent researches have revealed that the deregulation of cell death was a key factor of tumor development and progression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. As an indispensable type of programmed cell death, necroptosis emerged as an important event that modulates tumorigenesis. An increasing number of studies have demonstrated many compounds exhibited antitumor effects by inducing or manipulating necroptosis-related molecules [\u003cspan additionalcitationids=\"CR47 CR48 CR49\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Moreover, the character of necroptotic cells shown to elicit adaptive immunity makes it a potential target for immunotherapy [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Besides, its dual role in tumors makes it possible to stratify patients and provide an important basis for individualized treatment. Therefore, a greater understanding of the necroptosis might have great use to create new strategies to control cancer. However, there are few studies on the relationship between CC and necroptosis, the role of necroptosis in the TME of CC remains unclear. Our study indicated the dual role of necroptosis in CC, promoting tumor aggression by enhancing cell proliferation through JAK2-STAT3 pathway activation while also activating the T cell activity to stimulate anti-tumor immune responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on our in vitro experiment results, necroptosis contributes to the invasion of CC cells, reflecting its pro-cancer side. Enrichment analyses revealed NRGs were involved in VEGF and JAK-STAT signaling pathway. With regard to VEGF, it has been found to be up-regulated in numerous malignant neoplasms and its combination with VEGFR has been shown to contribute to tumor development and metastasis [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The JAK-STAT pathway, a well-studied signaling pathway, has been shown to play a crucial role in regulating cell proliferation, differentiation, and apoptosis. Furthermore, this pathway is implicated in the development of immune disorders and tumorigenesis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The primary role of STAT3 is transcriptional regulation, modulating angiogenesis and tumorigenesis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Results of the experiment indicated that necroptosis could up-regulate VEGFA via the JAK2-STAT3 signaling pathway, thereby elucidating the potential mechanism underlying its role in cancer promotion. Interestingly, in this study, it was confirmed that bevacizumab can induce necroptosis of CC cells and subsequently activate JAK2-STAT3 signaling pathway. The combination of bevacizumab with the necroptosis inducer resulted in a reduction in the inhibitory effect of bevacizumab on VEGFA, which may be indicative of a resistance mechanism to bevacizumab. In contrast, when bevacizumab was combined with the necroptosis inhibitor, the inhibition of VEGFA was enhanced, suggesting a new drug combination strategy: for patients resistant to bevacizumab, the combination of bevacizumab and necroptosis inhibitors could be a better therapeutic strategy, for which could reduce VEGFA expression and reduce potential resistance due to necroptosis.\u003c/p\u003e \u003cp\u003eIn the case of tumor cells, necroptosis exerts a pro-cancer effect. However, it demonstrates a cancer-inhibiting role by activating T cells in TME. According to bioinformatic analyses, high necroptosis group mainly enriched in immune response pathway especially T cell regulation signaling pathway. In vitro experiments verified Jurkat cells undergoing necroptosis emerged to express an elevated level of cytokine-related genes including CXCL9, CXCL10, CXCL11, CXCL13, IFN-γ, IL-2, IL-6 and IL-13. As for chemokines, CXCL9/10/11-CXCR3 axis has been prove to promote recruitment of immune cells into the tumor, remodeling the tumor immune microenvironment [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], the up-regulation of CXCL9/10/11 implies necroptosis may exert an anti-tumor effect by enhancing the infiltration of immune cells in the TME. Both IL-2, a key factor of T cell proliferation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], and IL-6, an indispensable role in the differentiation of T cell into cytotoxic cells (CTLs) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] were up-regulated in Jurkat cells undergoing necroptosis, indicating necroptosis plays a positive regulatory role on T cells. As the main force of anti-tumor immunity, in addition to increase T cells infiltration, it is also crucial to promote T cell activation and reduce exhaustion. Immune checkpoints including PD-1, TIGIT, LAG3, CTLA4, TIM3 and VISTA have been affirmed to inhibit T cell activation and induce T cell depletion [\u003cspan additionalcitationids=\"CR60 CR61\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. According to our results, Jurkat cells undergoing necroptosis exhibited a decreasing expression level of ICPs, implying necroptosis can defend against tumor by reducing T cell depletion. And as mentioned above, T cell activated markers including CD69, CD25, IFN-γ and GZMB got significantly elevated in Jurkat cells undergoing necroptosis. Therefore, necroptosis might facilitate T cells activation, reduce T cell depletion and increase T cell infiltration, bringing heat to TME. Besides, in our research, in vitro experiments demonstrated CC cells co-cultured with supernatant from Jurkat cells undergoing necroptosis exhibited less viability and less ability to proliferate and migrate, while necroptosis exerted less effect on Jurkat cells viability. Therefore, it could be inferred that necroptosis plays a dual role in CC, which makes it a potential indicator to classify patients in order to perform precise treatment.\u003c/p\u003e \u003cp\u003eIn this study, we obtained 6 differentially expressed NRGs related to both \u0026lsquo;CD8 pathway\u0026rsquo; and prognosis. All these genes are characterized by the presence of cancer-suppressing properties. Among 6 intersected genes, CD247, CD3E, CD3G, CD3D were suggested to be involved in T cell development and the transduction of T cell activation signals [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. As for CD2, its encoded protein interacts with LFA3 (CD58), involving in antigen recognition process [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. It was found that CD8a was a key molecule for activation of cytotoxic T-lymphocytes (CTLs) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Therefore, all these genes exhibit a positive regulatory role in the immune response, which might explain their downward trend in advanced grade and stage. Furthermore, the results in our study demonstrated that high necroptosis group exhibited a higher expression level of all these genes, suggesting necroptosis plays a vital role in immunity. Among these 6 selected genes, we found CD3D was most closely related to clinical features and therapy response of CC, so further analyses were around the relationship between CD3D and necroptosis.\u003c/p\u003e \u003cp\u003eCD3D is a component of the TCR/CD3 complex and plays an indispensable role in T cell activation signals [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Recent research has suggested CD3D could be a predictive biomarker of the prognosis in some cancers [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In our study, bioinformatical analyses revealed CD3D was positively correlated with necroptosis, and high necroptosis level tended to own a \u0026ldquo;hotter\u0026rdquo; TME, therefore CD3D could be used as an indicator for dynamic detection of immune microenvironment. Additionally, its expression was found to have a negative correlation with both grade and stage, and survival analyses have suggested that patients had a better prognosis when it was highly expressed. Besides, patients in CD3D high expression cluster showed more sensitivity to immunotherapy. All these results indicated CD3D acted as a protective signature in CC and it could be a reliable biomarker of prognosis as well as grade and stage classification. Simultaneously, our study indicated that high necroptosis group was found to be enriched in JAK-STAT signaling pathway via GSEA analysis. In addition to tumorigenesis, STAT3 has been proved to be a key role in the development of late-stage T cell and the formation of memory T cell [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Another important finding in our study was that, Jurkat cells undergoing necroptosis exhibited a higher level of CD3D expression and activated the JAK2-STAT3 signaling pathway. When the JAK2-STAT3 signaling pathway was specifically inhibited by FLLL32, elevated CD3D expression via necroptosis could be rescued in Jurkat cells. Thus, we conceived that necroptosis played a positive role in immunoregulation by up-regulating CD3D through activation of JAK2-STAT3 pathway, and novel immunotherapy strategies that target this mechanism could be developed in order to improve treatment effectiveness especially in patients who are not sensitive to conventional therapies.\u003c/p\u003e \u003cp\u003eAn altered microenvironment is vital for the prognosis of CC patients. Previous studies have proved that VEGF can enhance the function and accumulation of MDSCs, thereby contributing to the formation of an immunosuppressive microenvironment [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In this study, it was found that bevacizumab alone or in combination with necroptosis inducer induced the expression of activation makers including CD8, CD3D and CD3E in T cells. Furthermore, co-cultured with supernatant extracted from CC cells (pretreated with bevacizumab the day before), T cells exhibited a decreased expression in depletion and inhibition markers including CTLA4, CTSS, TIM3, TIGIT, SELPLG and VISTA, indicating blocking VEGF helps activate T cells thus creates a \u0026ldquo;hot\u0026rdquo; TME in CC. On the contrary, it was observed T cells showed a decreased expression in CD8, CD3D and CD3E while treated with bevacizumab in combination with necroptosis inhibitor NSA or NEC-1. Additionally, T cells exhibited an elevation of CTLA4, CTSS, TIM3, TIGIT, SELPLG and VISTA co-cultured with supernatant extracted from CC cells (pretreated with bevacizumab in combination with necroptosis inhibitor NSA or NEC-1 the day before). The synthesis of the above results suggests that the addition of the necroptosis inhibitor could enhance the effect of bevacizumab on VEGFA blocking. However, the combination of bevacizumab and the necroptosis inhibitor might result in an immunosuppressive condition. The dual role of necroptosis foretells a potential individualized treatment strategy, CC patients with a high extent of necroptosis or an immune-depletion state are more likely to benefit from the combination of bevacizumab and the necroptosis inhibitor. Besides, both bevacizumab and necroptosis inhibitors can affect the immune microenvironment, therefore dynamic monitoring of the immune microenvironment condition is necessary, while CD3D can be served as an indicator of the degree of necroptosis and foresee the state of the immune microenvironment.\u003c/p\u003e \u003cp\u003eLimitations of our study included the absence of in vivo validation of necroptosis in CC progression and immune modulation. Future research should focus on establishing animal models to investigate these effects, elucidating the precise mechanisms underlying necroptosis-mediated CD3D upregulation, the interaction between necroptosis and bevacizumab, and the potential effects of necroptosis inhibitors combined with bevacizumab. Additionally, further exploration of necroptosis-related biomarkers and their prognostic and therapeutic implications in CC is warranted to advance the field towards innovative and effective cancer therapies.\u003c/p\u003e \u003cp\u003eTo sum up, this study delves into the intricate role of necroptosis in the development of CC, elucidating how it stimulates an anti-tumor immune response while facilitating tumor proliferation and invasion. Moreover, a necroptosis-related molecule CD3D was identified as a protective biomarker to forecast prognosis and foretell therapeutic effects of CC. It was also demonstrated that the T cell undergoing necroptosis could elicit immune response by up-regulating CD3D through activation of JAK2-STAT3 pathway, which might provide a new perspective for the regulation of immune microenvironment of CC and might be helpful in creating novel strategies for controlling cancer. More importantly, this study proposed the potential mechanism of drug resistance to bevacizumab by exploring the interaction between necroptosis and bevacizumab, and the combination of bevacizumab with necroptosis inhibitors is expected to improve the efficacy of drug-resistant patients, providing new insights for the treatment of CC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the unrestricted use of TCGA databases. This work was supported by the Natural Science Foundation of Shanghai (grant no. 21ZR1450900), and Shanghai Science and Technology Planning Project (grant no. 21Y11907000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003euthor contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShaohua Xu and Yueqing Gao conceived and designed the manuscript. Fangfang Xu and Yingjun Ye wrote the paper and conceived and designed the experiments; Fangfang Xu analyzed the data; Yingjun Ye and Yueqing Gao collected and provided the sample for this study. All authors have read and approved the final submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Shanghai (grant no. 21ZR1450900), and Shanghai Science and Technology Planning Project (grant no. 21Y11907000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted under the approval of the Medical Ethics Committee of Shanghai First Maternity and Infant Hospital (KS21264).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingh D, Vignat J, Lorenzoni V, Eslahi M, Ginsburg O, Lauby-Secretan B, Arbyn M, Basu P, Bray F, Vaccarella S. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. Lancet Glob Health. 2023;11(2):e197\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelamed A, Margul DJ, Chen L, Keating NL, Del Carmen MG, Yang J, Seagle BL, Alexander A, Barber EL, Rice LW, Wright JD, Kocherginsky M, Shahabi S, Rauh-Hain JA. Survival after Minimally Invasive Radical Hysterectomy for Early-Stage Cervical Cancer. N Engl J Med. 2018;379(20):1905\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenzel HHB, Smolders RGV, Beltman JJ, Lambrechts S, Trum HW, Yigit R, Zusterzeel PLM, Zweemer RP, Mom CH, Bekkers RLM, Lemmens VEPP, Nijman HW, Van der Aa MA. Survival of patients with early-stage cervical cancer after abdominal or laparoscopic radical hysterectomy: a nationwide cohort study and literature review. Eur J Cancer. 2020;133:14\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JD, Matsuo K, Huang Y, Tergas AI, Hou JY, Khoury-Collado F, St Clair CM, Ananth CV, Neugut AI, Hershman DL. Prognostic Performance of the 2018 International Federation of Gynecology and Obstetrics Cervical Cancer Staging Guidelines. Obstet Gynecol. 2019;134(1):49\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdiga D, Eswaran S, Pandey D, Sharan K, Kabekkodu SP. Molecular landscape of recurrent cervical cancer. Crit Rev Oncol Hematol. 2021;157:103178.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, Forman D, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013;49(6):1374\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadreddini S, Baradaran B, Aghebati-Maleki A, Sadreddini S, Shanehbandi D, Fotouhi A, Aghebati-Maleki L. Immune checkpoint blockade opens a new way to cancer immunotherapy. J Cell Physiol. 2019;234(6):8541\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTopalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015;27(4):450\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang R, Zhang Y, Shan F. PD-L1: Can it be a biomarker for the prognosis or a promising therapeutic target in cervical cancer? Int Immunopharmacol. 2022;103:108484.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOaknin A, Gladieff L, Mart\u0026iacute;nez-Garc\u0026iacute;a J, Villacampa G, Takekuma M, De Giorgi U, Lindemann K, Woelber L, Colombo N, Duska L, Leary A, Godoy-Ortiz A, Nishio S, Angelergues A, Rubio MJ, Fari\u0026ntilde;as-Madrid L, Yamaguchi S, Lorusso D, Ray-Coquard I, Manso L, Joly F, Alarc\u0026oacute;n J, Follana P, Romero I, Lebreton C, P\u0026eacute;rez-Fidalgo JA, Yunokawa M, Dahlstrand H, D'Hondt V, Randall LM. ENGOT-Cx10\u0026ndash;GEICO 68-C\u0026ndash;JGOG1084\u0026ndash;GOG-3030 Investigators. Atezolizumab plus bevacizumab and chemotherapy for metastatic, persistent, or recurrent cervical cancer (BEATcc): a randomised, open-label, phase 3 trial. Lancet. 2024;403(10421):31\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalluzzi L, Vitale I, Aaronson S, et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ. 2018;25:486\u0026ndash;541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan I, Yousif A, Chesnokov M, Hong L, Chefetz I. A decade of cell death studies: Breathing new life into necroptosis. Pharmacol Ther. 2021;220:107717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong Y, Fan Z, Luo G, Yang C, Huang Q, Fan K, Cheng H, Jin K, Ni Q, Yu X, Liu C. The role of necroptosis in cancer biology and therapy. Mol Cancer. 2019;18(1):100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrilic B, Yang L, Albarr\u0026aacute;n-Ju\u0026aacute;rez J, Wachsmuth L, Han K, M\u0026uuml;ller UC, Pasparakis M, Offermanns S. Tumor-cell-induced endothelial cell necroptosis via death receptor 6 promotes metastasis. Nature. 2016;536(7615):215\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiao D, Cai Z, Choksi S, Ma D, Choe M, Kwon HJ, Baik JY, Rowan BG, Liu C, Liu ZG. Necroptosis of tumor cells leads to tumor necrosis and promotes tumor metastasis. Cell Res. 2018;28(8):868\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawlor KE, Khan N, Mildenhall A, Gerlic M, Croker BA, D'Cruz AA, Hall C, Kaur Spall S, Anderton H, Masters SL, Rashidi M, Wicks IP, Alexander WS, Mitsuuchi Y, Benetatos CA, Condon SM, Wong WW, Silke J, Vaux DL, Vince JE. RIPK3 promotes cell death and NLRP3 inflammasome activation in the absence of MLKL. Nat Commun. 2015;6:6282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026ouml;ckendorf U, Yabal M, Herold T, Munkhbaatar E, Rott S, Jilg S, Kauschinger J, Magnani G, Reisinger F, Heuser M, Kreipe H, Sotlar K, Engleitner T, Rad R, Weichert W, Peschel C, Ruland J, Heikenwalder M, Spiekermann K, Slotta-Huspenina J, Gro\u0026szlig; O, Jost PJ. RIPK3 Restricts Myeloid Leukemogenesis by Promoting Cell Death and Differentiation of Leukemia Initiating Cells. Cancer Cell. 2016;30(1):75\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder AG, Hubbard NW, Messmer MN, Kofman SB, Hagan CE, Orozco SL, Chiang K, Daniels BP, Baker D, Oberst A. Intratumoral activation of the necroptotic pathway components RIPK1 and RIPK3 potentiates antitumor immunity. Sci Immunol. 2019;4(36):eaaw2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAaes TL, Kaczmarek A, Delvaeye T, De Craene B, De Koker S, Heyndrickx L, Delrue I, Taminau J, Wiernicki B, De Groote P, Garg AD, Leybaert L, Grooten J, Bertrand MJ, Agostinis P, Berx G, Declercq W, Vandenabeele P, Krysko DV. Vaccination with Necroptotic Cancer Cells Induces Efficient Anti-tumor Immunity. Cell Rep. 2016;15(2):274\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan G, Zhao H, Zhang Q, Zhou Y, Wu L, Lei J, Wang X, Zhang J, Zhang X, Zheng L, Du G, Xiao W, Tang B, Miao H, Li Y. A RIPK3-PGE2 Circuit Mediates Myeloid-Derived Suppressor Cell-Potentiated Colorectal Carcinogenesis. Cancer Res. 2018;78(19):5586\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeifert L, Werba G, Tiwari S, Giao Ly NN, Alothman S, Alqunaibit D, Avanzi A, Barilla R, Daley D, Greco SH, Torres-Hernandez A, Pergamo M, Ochi A, Zambirinis CP, Pansari M, Rendon M, Tippens D, Hundeyin M, Mani VR, Hajdu C, Engle D, Miller G. The necrosome promotes pancreatic oncogenesis via CXCL1 and Mincle-induced immune suppression. Nature. 2016;532(7598):245\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara K, Shahmoradgoli M, Mart\u0026iacute;nez E, Vegesna R, Kim H, Torres-Garcia W, Trevi\u0026ntilde;o V, Shen H, Laird PW, Levine DA, Carter SL, Getz G, Stemke-Hale K, Mills GB, Verhaak RG. Inferring tumor purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRu B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, Chu KC, Wong CY, Lau CY, Chen I, Chan NW, Zhang J. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiesendruck Y, Benhar I. Novel immune check point inhibiting antibodies in cancer therapy-Opportunities and challenges. Drug Resist Updat. 2017;30:39\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePostow MA, Callahan MK, Wolchok JD. Immune Checkpoint Blockade in Cancer Therapy. J Clin Oncol. 2015;33(17):1974\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin S, Xu L, Yi M, Yu S, Wu K, Luo S. Novel immune checkpoint targets: moving beyond PD-1 and CTLA-4. Mol Cancer. 2019;18(1):155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolleczek B, Brenner H. Model based period analysis of absolute and relative survival with R: data preparation, model fitting and derivation of survival estimates. Comput Methods Programs Biomed. 2013;110(2):192\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBharti R, Dey G, Das AK, Mandal M. Differential expression of IL-6/IL-6R and MAO-A regulates invasion/angiogenesis in breast cancer. Br J Cancer. 2018;118(11):1442\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZegeye MM, Andersson B, Sirsj\u0026ouml; A, Ljungberg LU. IL-6 trans-Signaling Impairs Sprouting Angiogenesis by Inhibiting Migration, Proliferation and Tube Formation of Human Endothelial Cells. Cells. 2020;9(6):1414.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026eacute;ndez-Clemente A, Bravo-Cuellar A, Gonz\u0026aacute;lez-Ochoa S, Santiago-Mercado M, Palafox-Mariscal L, Jave-Su\u0026aacute;rez L, Solorzano-Ibarra F, Villase\u0026ntilde;or-Garc\u0026iacute;a M, Ortiz-Lazareno P, Hern\u0026aacute;ndez-Flores G. Dual STAT-3 and IL-6R inhibition with stattic and tocilizumab decreases migration, invasion and proliferation of prostate cancer cells by targeting the IL-6/IL-6R/STAT-3 axis. Oncol Rep. 2022;48(2):138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCibri\u0026aacute;n D, S\u0026aacute;nchez-Madrid F. CD69: from activation marker to metabolic gatekeeper. Eur J Immunol. 2017;47(6):946\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBajnok A, Ivanova M, Rig\u0026oacute; J Jr, Toldi G. The Distribution of Activation Markers and Selectins on Peripheral T Lymphocytes in Preeclampsia. Mediators Inflamm. 2017;2017:8045161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCullen SP, Martin SJ. Mechanisms of granule-dependent killing. Cell Death Differ. 2008;15(2):251\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAqbi HF, Wallace M, Sappal S, Payne KK, Manjili MH. IFN-γ orchestrates tumor elimination, tumor dormancy, tumor escape, and progression. J Leukoc Biol 2018 Feb 22:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/JLB.5MIR0917-351R\u003c/span\u003e\u003cspan address=\"10.1002/JLB.5MIR0917-351R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonk BJ, Enomoto T, Kast WM, McCormack M, Tan DSP, Wu X, Gonz\u0026aacute;lez-Mart\u0026iacute;n A. Integration of immunotherapy into treatment of cervical cancer: Recent data and ongoing trials. Cancer Treat Rev. 2022;106:102385.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGennigens C, Jerusalem G, Lapaille L, De Cuypere M, Streel S, Kridelka F, Ray-Coquard I. Recurrent or primary metastatic cervical cancer: current and future treatments. ESMO Open. 2022;7(5):100579.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe W. The Complexity of Translating Anti-angiogenesis Therapy from Basic Science to the Clinic. Dev Cell. 2016;37(2):114\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamjiawan RR, Griffioen AW, Duda DG. Anti-angiogenesis for cancer revisited: Is there a role for combinations with immunotherapy? Angiogenesis. 2017;20(2):185\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasudev NS, Reynolds AR. Anti-angiogenic therapy for cancer: current progress, unresolved questions and future directions. Angiogenesis. 2014;17(3):471\u0026thinsp;\u0026ndash;\u0026thinsp;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10456-014-9420-y\u003c/span\u003e\u003cspan address=\"10.1007/s10456-014-9420-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2014 Jan 31. Erratum in: Angiogenesis. 2014;17(3):495-7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang SL, Tang HB, Hu JT, Zang ZL, Ding X, Li S, Yang H. PGAM5-CypD pathway is involved in bromocriptine-induced RIP3/MLKL-dependent necroptosis of prolactinoma cells. Biomed Pharmacother. 2019;111:638\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin SS, Chang TM, Wei AI, Lee CW, Lin ZC, Chiang YC, Chi MC, Liu JF. Acetylshikonin induces necroptosis via the RIPK1/RIPK3-dependent pathway in lung cancer. Aging. 2023;15(24):14900\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasit F, Cristofanon S, Fulda S, Obatoclax. (GX15-070) triggers necroptosis by promoting the assembly of the necrosome on autophagosomal membranes. Cell Death Differ. 2013;20(9):1161\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChefetz I, Grimley E, Yang K, Hong L, Vinogradova EV, Suciu R, Kovalenko I, Karnak D, Morgan CA, Chtcherbinine M, Buchman C, Huddle B, Barraza S, Morgan M, Bernstein KA, Yoon E, Lombard DB, Bild A, Mehta G, Romero I, Chiang CY, Landen C, Cravatt B, Hurley TD, Larsen SD, Buckanovich RJ. A Pan-ALDH1A Inhibitor Induces Necroptosis in Ovarian Cancer Stem-like Cells. Cell Rep. 2019;26(11):3061\u0026ndash;e30756.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoupienne I, Fettweis G, Rubio N, Agostinis P, Piette J. 5-ALA-PDT induces RIP3-dependent necrosis in glioblastoma. Photochem Photobiol Sci. 2011;10(12):1868\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYatim N, Jusforgues-Saklani H, Orozco S, Schulz O, Barreira da Silva R, Reis e Sousa C, Green DR, Oberst A, Albert ML. RIPK1 and NF-κB signaling in dying cells determines cross-priming of CD8⁺ T cells. Science. 2015;350(6258):328\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaw P, Dwivedi SKD, Bhattacharya R, Mukherjee P, Rao G. VEGF signaling: Role in angiogenesis and beyond. Biochim Biophys Acta Rev Cancer. 2024;1879(2):189079.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwen KL, Brockwell NK, Parker BS, JAK-STAT Signaling. A Double-Edged Sword of Immune Regulation and Cancer Progression. Cancers (Basel). 2019;11(12):2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang SF, Wang SN, Wu CF, Yeh YT, Chai CY, Chunag SC, Sheen MC, Lee KT. Altered p-STAT3 (tyr705) expression is associated with histological grading and intratumor microvessel density in hepatocellular carcinoma. J Clin Pathol. 2007;60(6):642\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTokunaga R, Zhang W, Naseem M, Puccini A, Berger MD, Soni S, McSkane M, Baba H, Lenz HJ. CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation - A target for novel cancer therapy. Cancer Treat Rev. 2018;63:40\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDangaj D, Bruand M, Grimm AJ, Ronet C, Barras D, Duttagupta PA, Lanitis E, Duraiswamy J, Tanyi JL, Benencia F, Conejo-Garcia J, Ramay HR, Montone KT, Powell DJ Jr, Gimotty PA, Facciabene A, Jackson DG, Weber JS, Rodig SJ, Hodi SF, Kandalaft LE, Irving M, Zhang L, Foukas P, Rusakiewicz S, Delorenzi M, Coukos G. Cooperation between Constitutive and Inducible Chemokines Enables T Cell Engraftment and Immune Attack in Solid Tumors. Cancer Cell. 2019;35(6):885\u0026ndash;e90010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgan DA, Ruscetti FW, Gallo R. Selective in vitro growth of T lymphocytes from normal human bone marrows. Science. 1976;193(4257):1007\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacLeod MK, McKee AS, David A, Wang J, Mason R, Kappler JW, Marrack P. Vaccine adjuvants aluminum and monophosphoryl lipid A provide distinct signals to generate protective cytotoxic memory CD8 T cells. Proc Natl Acad Sci U S A. 2011;108(19):7914\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilip M, Schietinger A. CD8\u0026thinsp;+\u0026thinsp;T cell differentiation and dysfunction in cancer. Nat Rev Immunol. 2022;22(4):209\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim GR, Choi JM. Current Understanding of Cytotoxic T Lymphocyte Antigen-4 (CTLA-4) Signaling in T-Cell Biology and Disease Therapy. Mol Cells. 2022;45(8):513\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiang EY, Mellman I. TIGIT-CD226-PVR axis: advancing immune checkpoint blockade for cancer immunotherapy. J Immunother Cancer. 2022;10(4):e004711.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Zhang X, Li E, Zhang G, Wang X, Tang T, Bai X, Liang T. VISTA: an immune regulatory protein checking tumor and immune cells in cancer immunotherapy. J Hematol Oncol. 2020;13(1):83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarber EK, Dasgupta JD, Schlossman SF, Trevillyan JM, Rudd CE. The CD4 and CD8 antigens are coupled to a protein-tyrosine kinase (p56lck) that phosphorylates the CD3 complex. Proc Natl Acad Sci U S A. 1989;86(9):3277\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Vries JE, Yssel H, Spits H. Interplay between the TCR/CD3 complex and CD4 or CD8 in the activation of cytotoxic T lymphocytes. Immunol Rev. 1989;109:119\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMor\u0026oacute;n G, Rueda P, Casal I, Leclerc C. CD8alpha- CD11b\u0026thinsp;+\u0026thinsp;dendritic cells present exogenous virus-like particles to CD8\u0026thinsp;+\u0026thinsp;T cells and subsequently express CD8alpha and CD205 molecules. J Exp Med. 2002;195(10):1233\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Z, Shen Y, Zhou C, Cao Y, Deng H, Shen Z. CD3D: a prognostic biomarker associated with immune infiltration and immunotherapeutic response in head and neck squamous cell carcinoma. Bioengineered. 2022;13(5):13784\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Ye W, Wu X, Lin S, Xu J, Li L, Li J, Wang H, Huang Z. Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment. Biosci Rep. 2021;41(1):BSR20202898.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JK, Won C, Yi EH, Seok SH, Kim MH, Kim SJ, Chung MH, Lee HG, Ikuta K, Ye SK. Signal transducer and activator of transcription 3 (Stat3) contributes to T-cell homeostasis by regulating pro-survival Bcl-2 family genes. Immunology. 2013;140(3):288\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNish SA, Schenten D, Wunderlich FT, Pope SD, Gao Y, Hoshi N, Yu S, Yan X, Lee HK, Pasman L, Brodsky I, Yordy B, Zhao H, Br\u0026uuml;ning J, Medzhitov R. T cell-intrinsic role of IL-6 signaling in primary and memory responses. Elife. 2014;3:e01949.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabrilovich D, Ishida T, Oyama T, Ran S, Kravtsov V, Nadaf S, Carbone DP. Vascular endothelial growth factor inhibits the development of dendritic cells and dramatically affects the differentiation of multiple hematopoietic lineages in vivo. Blood. 1998;92(11):4150\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaenhout SK, Thielemans K, Aerts JL. Location, location, location: functional and phenotypic heterogeneity between tumor-infiltrating and non-infiltrating myeloid-derived suppressor cells. Oncoimmunology. 2014;3(10):e956579.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Necroptosis, Cervical cancer, JAK2-STAT3 signaling pathway, Bevacizumab, Tumor immune microenvironment, T cells","lastPublishedDoi":"10.21203/rs.3.rs-4379854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4379854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eIn the dynamic landscape of cervical cancer (CC) pathophysiology, this study aimed to elucidate the role of necroptosis in modulating tumor proliferation, invasion, and the immune microenvironment in CC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, the impact of necroptosis on CC was evaluated through a series of bioinformatical analyses and experimental approaches. The impact of necroptosis on CC was illustrated by analyzing its effects on tumor aggression, immune responses, and the JAK2-STAT3 signaling pathway. Bevacizumab, a monoclonal antibody targeting vascular endothelial growth factor (VEGF), was also evaluated for its potential induction of necroptosis in CC cells and its interaction with necroptosis inhibitors. Additionally, the study assessed the influence of necroptosis on the immune microenvironment, particularly in T-cell-related pathways and the expression of tumor suppressor genes in CC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNecroptosis was found to enhance VEGFA expression through activation of the JAK2-STAT3 pathway, promoting tumor proliferative and invasive capabilities in CC. Bevacizumab induced necroptosis in CC cells, potentially leading to resistance in therapy. The combination of bevacizumab with necroptosis inhibitors attenuated VEGFA expression, suggesting a novel therapeutic strategy. Additionally, necroptosis activated T-cell-related pathways and promoted the infiltration and activation of Jurkat T cells. CD3D\u0026mdash;a tumor suppressor gene in CC\u0026mdash;was identified as a critical marker and its expression could be upregulated by necroptosis via the JAK2-STAT3 pathway in Jurkat T cells. Treatment of CC cells with supernatants from necroptosis-induced Jurkat cells resulted in reduced tumor cell proliferation and invasion.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study reveals a complex interaction between necroptosis, tumor progression, and the immune response in CC. The findings propose a nuanced approach to leveraging necroptosis for therapeutic interventions, highlighting the potential of combining necroptosis inhibitors with existing therapies to improve treatment outcomes in CC.\u003c/p\u003e","manuscriptTitle":"Dual Role of Necroptosis in Cervical Cancer: Promoting Tumor Aggression and Modulating the Immune Microenvironment via the JAK2-STAT3 Pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 12:13:13","doi":"10.21203/rs.3.rs-4379854/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d112ae2-2060-4e60-a4d9-4d280ba0fa2e","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-19T13:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 12:13:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4379854","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4379854","identity":"rs-4379854","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}