IKBIP as a prognostic biomarker and immunotherapeutic target regulates JAK-STAT3 signaling pathway to promote cervical cancer

IKBIP as a prognostic biomarker and immunotherapeutic target regulates JAK-STAT3 signaling pathway to promote cervical cancer | 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 IKBIP as a prognostic biomarker and immunotherapeutic target regulates JAK-STAT3 signaling pathway to promote cervical cancer Yan Wang, Hui Qiao, Panpan Yu, WeiRui Gao, Zouyu Zhao, Ping Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7893986/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Mar, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background Cervical cancer (CC) represents a significant threat to women's health globally. Although IKBIP has been recognized as an oncogene, although little is known about how it contributes to cancer, and it has predominantly been associated with driving malignant progression in gliomas. Further investigation into the function of IKBIP in other cancer types, including CC, is essential to fully understand its potential implications for tumorigenesis and progression in various malignancies. Methods IKBIP expression was analyzed in CC tissues using the GEPIA and GEO databases. The transcriptomic data and clinical characteristics of 306 patients with CC were obtained from TCGA, and clustering was performed using X-tile software. Additionally, to validate the prognostic significance of IKBIP, protein levels in normal and cancerous tissues were compared through IHC. The TIDE score was employed as an indicator of the response to immunotherapy. Furthermore, our research sought to investigate any possible connectionsbetween IKBIP and immunological genes, as well as their influence on the development of TMB and drug sensitivity. The impact of IKBIP on CC cells' capacity for invasion, migration, and proliferation were investigated using CCK-8, EdU, and transwell assays. To clarify IKBIP's function in controlling the JAK-STAT signaling cascade and its contribution to the progression of CC, we utilized the JAK-STAT pathway agonist Colivelin in rescue experiments. The influence of IKBIP on CC development was also validated in a xenograft tumor model. Results Our research showed that the tissues of cervical cancer overexpress IKBIP, which could be a potential oncogene associated with cervical cancer. According to nomogram creation, ROC curve analysis, and Kaplan-Meier survival analysis, IKBIP may be a biomarker for a bad prognosis in CC. Furthermore, the expression of IKBIP exhibited a strong correlation with immune infiltration, TMB and drug sensitivity in CC. In vitro experiments indicated that IKBIP functions as an oncogene, because inhibiting its expression dramatically reduced cervical cancer cells' capacity to proliferate, migrate, and invade using the CCK8, EdU, and transwell tests, respectively. Additionally, our findings suggest that IKBIP may promote cervical cancer progression by regulating the JAK-STAT signaling pathway. Rescue experiments demonstrated that the JAK-STAT pathway activator, Colivelin, could mitigate the inhibitory effects of IKBIP knockdown on cervical cancer cell behavior. We successfully constructed the cervical cancer xenograft mouse model and in vivo experiments demonstrated that the expression of IKBIP is closely correlated with the malignancy of cervical cancer, and also provided more evidence that IKBIP contributes to the advancement of cervical cancer. Conclusion In summary, this study offers novel insights for CC by establishing IKBIP as a robust prognostic indicator. Our findings suggest that IKBIP not only correlates with adverse clinical outcomes but also influences tumor immunogenicity and response to treatment. Furthermore, IKBIP is significantly correlated with the progression of CC mediated by the JAK/STAT3 signaling pathway, and it may become an effective therapeutic target. IKBIP Cervical cancer Immune infiltration Prognosis model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cervical cancer (CC) is among the most frequent cancers harming women's health. Given the complex disease mechanisms and the significant tumor heterogeneity associated with CC, there is an urgent need for a deeper understanding of its pathological aspects [ 1 ] . The primary etiological factor in CC is infection with the human papillomavirus (HPV), with more than 90% of cases linked to high-risk HPV types [ 2 – 3 ] . Although the advent of the HPV vaccine has led to a notable decrease in the incidence of CC, and early-stage diagnoses can be effectively managed, advanced-stage tumors in advanced stages are prone to metastasis and treatment resistance [ 4 – 5 ] . This often results in unfavorable prognoses for affected patients. To address the challenges posed by advanced CC, continued research is essential, focusing on the molecular mechanisms underlying tumor progression, metastasis, and treatment resistance [ 6 – 7 ] . Identifying novel therapeutic targets and Screening specific biomarkers will be critical in combating this disease and enhancing patient outcomes [ 8 ] . On human chromosome 12, I kappa B kinase interacting protein (IKBIP), often referred to as IKIP, is situated 0.5 kilobases upstream of the apoptotic protease activating factor 1 (APAF1) gene. [ 9 ] . IKBIP is recognized as one of the target genes of p53, playing a critical role in its pro-apoptotic functions. Additionally, IKBIP is considered an important regulatory factor in inflammation, significantly contributing to pathways such as the Toll-like receptor 7/8 (TLR7/8) signaling cascade and interleukin-1 family signal transduction [ 10 – 11 ] . Moreover, IKBIP has been found to be a possible biomarker for a number of cancers, including gliomas, digestive system cancers, and renal cell carcinoma [ 12 – 14 ] . Additionally, recent studies show that IKBIP contributes to the regulation of the tumor immunological milieu and is linked to CD8 T cell fatigue in hepatocellular carcinoma. [ 15 ] . However, the role of IKBIP in the pathogenesis of CC needs to be clarified. Therefore, the purpose of this research is analyze the expression of IKBIP, its correlation with clinical pathological parameters, and its association with the prognosis of CC, as well as to investigate the function of IKBIP in the development of CC to explore potential immunotherapeutic targets for this disease. In this study, we combined RNA sequencing information from the Gene Expression Omnibus (GEO) database with the Cancer Genome Atlas (TCGA). [ 16 ] , employing bioinformatics methodologies in order to assess the expression and prognosis of IKBIP in CC. Based on clinical features, we established a prognostic model for CC patients. Additionally, we examined the relationship between immune infiltration and IKBIP, tumor mutation burden, and drug sensitivity in CC [ 17 ] . Our findings indicate that IKBIP, acting as an oncogene, significantly promotes the proliferation and metastatic capabilities of CC cells. Consequently, IKBIP may serve as a potential effective biomarker for predicting prognosis in CC and contributes to the development of personalized treatment strategies for patients. Materials and Methods Database collection The expression data for IKBIP was obtained from the GENT2 database, which includes a total of 114 CC patient samples alongside 11 normal tissue samples. Additionally, gene expression array GSE63714 was retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), which comprises 28 tumor tissues and 24 normal tissues [18] . Furthermore, we accessed mRNA profiling and associated historical clinical data for 306 CC patients from The Cancer Genome Atlas (TCGA) dataset (https://portal.gdc.cancer.gov/) [19] . Using an optimal cut-off value for IKBIP expression, the X-tile program was used to divide the patients into high- and low-risk categories. [20] . atients who lacked survival time or relevant clinical features were excluded from the analysis. To evaluate overall survival (OS) differences between the distinct subgroups, Kaplan-Meier survival curves were plotted. Additionally, the effectiveness of IKBIP to predict patients' overall survival was evaluated using receiver operating characteristic (ROC) curves. This comprehensive analysis aims to elucidate the prognostic significance of IKBIP expression in CC and how it might help direct clinical judgment. Human tissues samples collection From June 2008 to December 2022, clinical pathology data were gathered from 121 patients at the First Affiliated Hospital of the Medical College of Shihezi University who had been diagnosed with CC. This cohort served as the trial group. Additionally, normal cervical epithelial tissues from 30 patients with benign conditions, such as uterine leiomyomas and adenomyosis, were gathered during the same time period to form the control group. Data collected from the CC patients included age, pathological type, tumor diameter, differentiation grade, FIGO stage, lymph node metastasis (LNM), and lymphovascular space infiltration (LVSI). All clinical stages were reassessed according to the 2018 FIGO Staging classification system [21] . The following were the requirements for CC patients to be included: a) biopsy-confirmed CC status with no pre-surgical treatment; b) absence of chemotherapy and/or radiotherapy prior to surgery; c) availability of complete clinical and pathological data; d) no history of other malignancies. Patients not meeting these criteria were excluded from the study. All subjects gave their informed permission, and the study was authorized by Shihezi University's First Affiliated Hospital's Ethics Committee (KJX2022-038-01). Construct a nomogram model Cox regression analyses using clinical information including age, grade, FIGO stage and tumor type from the TCGA were performed to confirm whether IKBIP was an individual predictor of CC. Besides, the clinical nomogram model was constructed by rms R package [22- 23] . To assess the nomogram's predictive power for OS in CC patients, independent risk indicators such as FIGO stage and IKBIP expression levels were used. The nomogram's effectiveness was shown by the calibration curve. Finally, the ROC curve was constructed for the 1-year, 3-year and 5-year risk factors and for the independent risk factors to estimate the forecasting power of the models. Analysis of immune infiltrating cells A previous publication provided a set of genes to label 28 immune cell types [24] . The variations in immune cell infiltration were measured using the ssGSEA method. Meanwhile, the CIBEROST algorithm was employed to calculate the differences in immune cell abundance between the IKBIP subgroups, and the outcomes are shown as plots [25] . Then, combining with the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm [26- 27] , the predictive efficiency for immunotherapy response was analyzed in the subgroups. Tumor mutation burden analysis (TMB) We calculated the TMB for each CC patient using the "maftools" package. We then compared the characteristics of low and high TMB groups and assessed the relationship between IKBIP expression levels and TMB scores. Drug sensitivity analysis To explore the common chemotherapeutic drug sensitivity of CC patients, the R package "pRRophetic" was used to determine each sample's half maximum inhibitory concentration (IC50) [28- 29] . Next, correlation between model gene and drug response were estimated by utilizing the R package “ggplot2.” ssGSEA enrichment analysis To explore the potential mechanisms and relevant biological processes underlying the two groups, we conducted pathway enrichment analysis using Gene Set Enrichment Analysis (GSEA) software (version 4.2.3). Cell culture Fuxiang Biotechnology Co. supplied the HeLa and SiHa cells. (Location: Shanghai, China).CC cell lines of HeLa and SiHa cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with penicillin (100 U/mL), streptomycin (100 U/mL), and 10% fetal bovine serum (FBS) at 37 °C in a 5% CO2 atmosphere. Construction of IKBIP knockdwon cell lines A lentiviral-based short hairpin RNA (shRNA) approach was implemented using three distinct shRNA sequences to downregulate IKBIP expression. These shRNA sequences were cloned into a lentiviral vector (pLKO.1) in HeLa and SiHa cells. To package the lentivirus, 293T cells were utilized.Six-well plates were seeded with HeLa and SiHa cells at 60–70% confluence, and lentiviral particle constructs were introduced to the cells in the presence of polybrene (8 μg/mL) to enhance viral transduction. After incubating the cells with the virus for 12-16 h, A brand-new culture medium was used in its place. Following a 48-hour infection period, puromycin was applied to select for successfully transduced cells. The efficacy of IKBIP knockdown was validated through Western blot and quantitative reverse transcription PCR (qRT-PCR) analyses. The resulting stable cell lines were subsequently used for downstream functional assays. qRT-PCR Total RNA was extracted from cells using TRIzol. Subsequently, 1 µg of RNA was treated with a reverse transcription kit to eliminate contaminating genomic DNA and was converted into cDNA.IKBIP primers (Forward: ATACGACGGATTTCAGGTTT and Reverse: CACTCTTTAGTTCGGTTAGCG ). H-GAPDH primers (Forward: GGGAAACTGTGGCTTGAT and Reverse: GAGTGGGTGTCGCTGTTGA ). The cDNA was then amplified using real-time PCR with the Fast Start Universal SYBR The 2−ΔΔCt technique was used to quantify target genes relative to beta-actin, which was used as the internal control. Western blot analysis Total proteins were extracted from cells using RIPA lysis buffer supplemented with 1% protease inhibitor cocktail tablets. Protein concentration was quantified using BCA assay kits after a 30-minute incubation at 37°C. A total of 30 µg of protein was denatured at 95°C for 10 minutes, separated by SDS-PAGE on 10%–12% gels, and then transferred to polyvinylidene difluoride (PVDF) membranes. Following a 2-hour blocking period with 5% nonfat milk at room temperature, the membranes were incubated overnight at 4°C with the following primary antibodies (1:5000). Afterward, Using the following primary antibodies (1:5000), the membranes were incubated for the whole night at 4°C. After that, the membranes were left at room temperature for two hours to incubate with the relevant secondary antibodies. The blots were examined densitometrically using e-BLOT software and imaged using an ECL chemiluminescence system (Bioground). β-actin functioned as the internal regulator. CCK8 assay Cell proliferation capacity was evaluated using CCK8 kits. A density of 1,000 cells per well was used to seed transduced cells in 96-well plates. CCK8 solution was added after the cells had had time to adhere, and the cells were then grown for two more hours. The absorbance of each well was then measured at 450 nm to establish the baseline value. Subsequent absorbance measurements were performed daily for five days, ensuring consistency in the timing of each measurement. EdU assay HeLa and SiHa cells were cultured in 24-well plates and incubated overnight. The following day, the cells were treated with 50 μM EdU for 2 h. After the incubation, the cells were fixed with 4% paraformaldehyde for 15 minutes and then permeabilized using 0.5% Triton X-100 for 20 minutes. EdU incorporation was evaluated using the EdU Assay Kit (Invitrogen Click-iT™ EdU Imaging Kit) according to the manufacturer’s instructions. Subsequently, the cells were stained with Hoechst 33342 to visualize the nuclei, and the percentage of EdU-positive cells was determined using fluorescence microscopy. Transwell assay HeLa and SiHa cells (2 × 10 ^5 ) were resuspended in 500 µL of serum-free DMEM and added to the upper chamber of a transwell system, whereas 10% FBS was added to DMEM in the lower chamber. The non-migratory cells in the top chamber were carefully removed following a predetermined incubation time. After migrating to the bottom chamber, the cells were fixed with methanol and then stained for 15 min with 0.1% crystal violet dye. Following this, neutral resin was applied for sealing. Using an inverted microscope, the number of stained cells in six randomly chosen areas at 20X magnification was counted. Xenograft mouse model Male BALB/c nude mice weighing 18–22 g and aged 4-6 weeks were kept in conventional settings with ad libitum access to food and water. These conditions included a 12-hour light/dark cycle, 20–24°C, and 50–60% humidity. To induce tumors, HeLa cells (4.0 x 10⁷ cells/mL) transfected with either IKBIP-targeting shRNA or an empty vector were suspended in PBS and injected subcutaneously into the dorsal cervical area. On day 40 post-inoculation, Mice were given 2% pentobarbital sodium (50 mg/kg) intraperitoneally to induce anesthesia, and they were then put to sleep by cervical dislocation. Tumors were excised and weighed for comparative analysis. The Institutional Animal Care and Use Committee of Shihezi University School of Medicine's First Affiliated Hospital gave its approval to all experimental operations (Protocol No.:A2023-223-01). Hematoxylin and eosin (HE) staining HE staining was utilized to assess the histological features of CC tissue sections obtained from subcutaneous tumors. The CC tissues were embedded in paraffin blocks and preserved in 10% formalin, and sectioned into five-micrometer-thick slices that were mounted on glass slides. Following rehydration and deparaffinization, the slices were counterstained with eosin to bring out the cytoplasmic features and stained with hematoxylin to see the nuclei. The stained sections were then examined microscopically, and images were captured for further analysis. Immunohistochemistry (IHC) Formalin-fixed human CC tissue specimens, each measuring 4 µm in thickness, were prepared, encompassing 121 CC samples and 30 non-cancerous cervical tissues. The tissue sections, as well as mice tissues were post-embedded at 60°C for 12 h, followed by the mice's tissues being deparaffinized in xylene and then dehydrated using a series of graded ethanol. Antigen retrieval was performed using citrate solution. The sections were coated with the IKBIP antibody (dilution 1:100, Rabbit, Abmart) and left to incubate overnight at 4°C. The slices were treated for 30 min at 37°C with a suitable secondary antibody the next day. Subsequently, the specimens were stained with diaminobenzidine, counterstained with hematoxylin, and dehydrated through a series of graded alcohols. Two seasoned pathologists who were blinded to the patients' clinical information scored the results. The proportion of positive cells was used to determine the staining extent, and scores were given as follows: 1 = 0%–10%, 2 = 10%–25%, 3 = 50%–75%, and 4 = 75%–100%. There were four categories for staining intensity: 0 denoted no staining, 1 mild staining, 2 moderate staining, and 3 high staining. The extent and intensity ratings were added up to determine the total IHC score. A score of ≤6 indicated low expression of IKBIP, while a score of >6 indicated high expression. Statistical Analysis The most of statistical analysis were utilized the R studio software (version 4.1.2). The two expression groups were compared using the Wilcoxon signed rank test. The log-rank test and the K-M technique were used to evaluate differences in survival curves. The correlation between the target gene and clinical features was assessed using the chi-squared test. To determine if IKBIP expression levels were an independent predictive factor associated with overall survival (OS) and relapse-free survival (RFS), univariate and multivariate Cox analyses were conducted. P<0.05 was deemed to distinguish the differences. Additionally, GraphPad Prism 9.0 software was used to evaluate the experimental data from Cell. Every experiment was carried out in triplicate, and the mean ± standard deviation (SD) is used to represent the results. A t-test was used to compare the two groups statistically, and a p-value of less than 0.05 was considered statistically significant. Every experiment was conducted at least three times in order to guarantee repeatability. Results IKBIP is a prognostic biomarker of CC Utilizing the online application GEPIA2, we conducted an analysis of IKBIP expression levels in CC and corresponding normal cervical tissues. As illustrated in Figure 1A, we observed a modest elevation in IKBIP expression in CC. Additionally, a significant increase in IKBIP expression was noted in CC samples from the GSE63714 dataset. Further, we retrieved clinical data from 306 patients within the TCGA database and employed XILE software to stratify these patients into high and low IKBIP expression cohorts. Comparative survival analysis revealed substantial prognostic differences between these groups, whereby the high IKBIP expression cohort exhibited markedly poorer clinical outcomes compared to their low expression counterparts (Figure 1B) . The prognostic utility of IKBIP expression was further assessed through ROC analysis, yielding area under the curve (AUC) values of 0.618, 0.559, and 0.562, indicating moderate predictive capacity (Figure 1C) . Subsequently, we performed Cox regression analyses, adjusting for IKBIP expression alongside various clinical factors, including age, FIGO stage, tumor type, and grade . The results indicated that IKBIP levels and clinical stage are independent prognostic variables for CC prognosis (Figure 1D) . Consequently, we developed a prognostic nomogram integrating IKBIP expression and clinical stage, which serves as a quantitative tool for predicting patient outcomes (Figure 1E) . At 1-, 3-, and 5-year intervals, calibration curves showed a high degree of agreement between nomogram forecasts and actual observed survival rates (Figure 1F) . Moreover, ROC curves indicated improved accuracy in predicting OS at 1, 3, and 5 years, with respective AUC values of 0.718, 0.643, and 0.630 (Figure 1G) . Importantly, the AUC analysis revealed that the nomogram exhibited greater sensitivity and specificity compared to individual prognostic factors such as age, stage, grade, and IKBIP expression (Figure 1H) . In summary, our findings suggested that IKBIP could be a strong predictor of CC. The expression of IKBIP is associated with a severer degree of CC. To assess the protein expression levels of IKBIP, we obtained cancerous tissue samples from 121 patients with CC and 30 normal cervical tissues for IHC analysis. Figure 2A presents representative images of HE and IHC staining, contrasting IKBIP expression between specimens of cervical squamous cell carcinoma and adenocarcinoma, alongside non-tumor cervical epithelial tissues. HE staining confirmed that the excised tissues were indeed representative of paracancerous and cancerous states. It was observed that IKBIP localized predominantly to the cytoplasm of the cancerous tissues. Figure 2B highlights representative images showcasing varying intensities of IHC staining for IKBIP across different expression levels in CC tissues. The results indicated significantly elevated levels of IKBIP expression in CC samples compared to non-cancerous tissues (Figure 2C-D) . Ultimately, KM analysis demonstrated that patients exhibiting high IKBIP expression experienced poorer OS and RFS (Figure 2E-F) . In addition, our findings revealed a strong association between IKBIP expression levels in CC tissue and FIGO stage and grade (Table 1) . Furthermore, univariate analyses indicated that FIGO stage, LNM, and IKBIP expression were significant prognostic indicators for both OS and RFS (Table 2) . These risk factors identified in the univariate analysis were subsequently incorporated as covariates in a multivariate Cox proportional hazards model, which confirmed that both FIGO stage and IKBIP expression serve as independent predictors for OS and RFS (Table 3) . Correlation between the expression of IKBIP with immune infiltration and drug sensitivity We used the ssGSEA to evaluate the associations with 28 immune cell types obtained from the TCGA database in order to further examine the link between IKBIP expression levels and the immune milieu in CC patients. The findings showed that IKBIP expression was negatively correlated with CD56 dim natural killer cells (Figure 3A) and strongly positively correlated with a number of immune cells, including macrophages, effector memory CD4 T cells, Type 2 T helper cells, natural killer T cells, central memory CD4 T cells, CD56 bright natural killer cells, and regulatory T cells. Subsequently, we next used the CIBERSORT method to examine the proportions of immune cell types. According to our research, the group with high IKBIP expression had much fewer resting mast cells, Tregs, and follicular helper T cells than the group with low IKBIP expression. Conversely, the levels of M0 macrophages were reduced in the low IKBIP expression group (Figure 3B-E) . Additionally, we obtained four relevant scores for each CC sample using the TIDE tool, including TIDE score, MSI score, dysfunction score, and exclusion score. Our analysis showed that both the TIDE score and exclusion score were substantially higher in the high IKBIP expression group, whereas the MSI score was significantly elevated in the low IKBIP expression group. This suggests that CC patients classified within the low IKBIP group may have a greater likelihood of benefitting from immunotherapy (Figure 3F-3I). Chemotherapy and targeted drug therapy are recognized as essential strategies in the clinical management of CC patients. Consequently, it is crucial to investigate the differences in drug sensitivity among groups with varying levels of IKBIP expression. According to our research, the group with low IKBIP expression was more susceptible to widely used chemotherapeutic drugs, with the exception of Dasatinib (Figure 3J) . This finding suggests that patients with low IKBIP expression are more likely to benefit from these conventional chemotherapeutic treatments. These findings collectively suggest that IKBIP expression might be a useful diagnostic for forecasting how well different pharmaceutical therapies would work for CC patients potentially guiding more personalized therapeutic approaches. Correlation between the expression of IKBIP and TMB There is increasing evidence supporting the notion that tumorigenesis is closely linked to the accumulation of genetic mutations. To elucidate the distributions of somatic mutations, we constructed waterfall plots for CC patients stratified by IKBIP expression levels. The upper bars in these plots demonstrated that TMB was significantly lower in the high IKBIP expression group compared to low IKBIP expression group. Within the high-IKBIP cluster, we identified TTN, PIK3CA, DMD, KMT2D, and MUC16 as the five most frequently mutated genes. Conversely, the low-IKBIP cluster exhibited a different mutation profile, highlighting PIK3CA, TTN, KMT2C, MUC16, and EP300 as commonly mutated genes (Figure 4A-B) . The violin plot also showed that the group with low IKBIP expression had a substantially greater TMB dispersion, confirming a correlation between IKBIP levels and mutational burden (Figure 4C) . To define an optimal cutoff for TMB, we utilized X-tile software, which enabled us to divide patients into groups with high and low TMB. Our findings revealed that CC patients with lower TMB were associated with poorer OS outcomes (Figure 4D) . Furthermore, a significant negative correlation was observed, indicating that increased IKBIP expression was associated with lower TMB levels (Figure 4E) . Notably, patients classified as IKBIP-high and TMB-low showed significantly inferior OS, while those in the IKBIP-low and TMB-high group demonstrated superior OS outcomes (Figure 4F) . These results underscore the potential role of IKBIP and TMB as biomarkers in evaluating prognosis in CC patients. 5. The effect of silencing IKBIP on CC cells in vitro In HeLa and SiHa cells, we created stable knockdown IKBIP cell lines in order to examine the biological function of IKBIP in the development of CC. The knockdown efficiency of IKBIP was assessed by measuring mRNA levels using qRT-PCR and analyzing protein expression through Western blotting. In both HeLa and SiHa cells, we found that the shIKBIP-1 and shIKBIP-2 groups had significantly lower levels of IKBIP mRNA and protein (Figure 5A-B) . Subsequently, we utilized these knockdown cell lines to performe phenotype assays. CCK-8 assays, which revealed that IKBIP knockdown markedly inhibited cell proliferation. After 24 h of culture, the proliferation rate in the IKBIP knockdown groups was significantly lower compared to the control group. Notably, OD values in shIKBIP-1 and ShIKBIP-2 interference groups declined to over 40% of those in the control group (Figure 5C-D) . Additionaly, EdU incorporation assay demonstrated a decreased proliferation rate in the IKBIP knockdown group. The reduction in the EdU-positive cell rate in the sh-IKBIP group was statistically significant compared to the sh-NC group (Figure 5E-F) . Furthermore, transwell test results showed that CC cell lines' capacity to migrate and invade was markedly reduced when IKBIP was knocked down. Specifically, There were significantly fewer cells that migrated and invaded as opposed to the control group (sh-NC) following IKBIP knockdown in the shIKBIP-1 and shIKBIP-2 groups (Figure 5G-H) . Collectively, these results indicate that IKBIP functions as an oncogene promote the proliferation, migratory and invasive properties of CC cells. This implies that IKBIP plays a crucial part in the development of CC and emphasizes its potential as a therapeutic target. IKBIP regulates the JAK/STAT3 pathway in cervical cancer cells. To investigate the underlying differences in biological functions and signaling pathways between distinct risk clusters, we performed GSEA utilizing GO, KEGG, and Hallmark gene set and KEGG analyses. The GO analysis indicated that BP categories identified significant enrichments in basement membrane organization, collagen metabolic processes, and endodermal cell differentiation. The CC analysis revealed that the enriched terms primarily included the endoplasmic reticulum lumen, collagen-containing extracellular matrix, and the basement membrane. Additionally, the MF analysis indicated notable enrichments in collagen binding, extracellular matrix binding, and laminin binding (Figure 6A), which suggested the expression of IKBIP could regulate the EMT process of CC. Subsequently, the KEGG pathway analysis highlighted several cancer proliferation-related pathways, with prominent involvement of the JAK-STAT signaling pathway, TGF-β signaling pathway, and various pathways associated with cancer (Figure 6B) . In addition , these pathways also participate to regulate the EMT process of CC. Furthermore, the results from the Hallmark analysis demonstrated that high-risk group showed enrichment in tumorigenic pathways, including epithelial-mesenchymal transition (EMT) and TNF-α signaling via NF-κB (Figure 6C) . Conversely, a subset of metabolic pathways, particularly the low-risk group had higher levels of those involved in fatty acid and bile acid metabolism in particular. (Figure 6D) . To further elucidate the potential mechanisms regulated by IKBIP that underlie CC proliferation and metastasis, enrichment analysis indicates that the expression of IKBIP may influence JAK-STAT signaling pathway in CC. As a result, we aim to investigate whether IKBIP uses the JAK-STAT signaling pathway to control the development of cervical cancer. Western blot analysis confirmed that IKBIP knockdown significantly reduced the p-JAK2 and p-STAT3 in both HeLa and SiHa cells, while not affecting the total protein levels of JAK2 and STAT3 (Figure 6E) . Additionally, we investigated whether the inhibitory effects of IKBIP knockdown may be mediated through the JAK/STAT3 signaling pathway. Upon treatment with Colivelin, an activator of STAT3，both HeLa and SiHa cells exhibited increased levels of p-JAK2 and p-STAT3 proteins (Figure 6F) , indicating that the negative impact of IKBIP knockdown on CC cells might be regulated by the JAK/STAT3 axis. These findings imply that activating the JAK/STAT3 pathway may play an important role in counteracting the consequences of IKBIP knockdown, thus contributing to the modulation of CC cell behavior. Colivelin reversed the inhibitory effects of silencing IKBIP on CC cells. Given that IKBIP may regulate the proliferation and metastatic capabilities of CC through the JAK-STAT signaling pathway, we further explored the impact of this pathway by using Colivelin. Initially, we added Colivelin to the IKBIP knockdown stable cell lines, and the assay results of CCK-8 showed that Colivelin significantly counteracted the inhibitory effects resulting from IKBIP knockdown (Figure 7A) . Consistent findings were also observed in the EdU incorporation assay, which showed that Colivelin treatment effectively restored cell proliferation (Figure 7B) . Moreover, Transwell migration and invasion experiments demonstrated that Colivelin treatment partially mitigated the reductions in CC cell invasion and migration induced by IKBIP inhibition (Figure 7C-D) . Thes results suggest that the Colivelin play a protective role by modulating JAK-STAT signaling, thereby enhancing the migratory and invasive properties of CC cells affected by IKBIP knockdown. 8. The effect of silencing IKBIP on CC cells in vivo To better understand the possible effect of IKBIP on CC progression in vivo, we inoculated HeLa cells transfected with NC and shIKBIP into nude mice to construct an animal model of xenotransplantation. The tumor volume in the shIKBIP group was considerably lower than in the NC group after the tenth day of subcutaneous injection. Over a period of forty days, the naked mice were euthanized, and the tumor tissue was then excised and assessed for size and weight, revealing a substantial decrease in tumor volume and weight in the shIKBIP group ( Figure 8A-D ). Paraffin embedding was performed on the tumor tissues, HE and IHC staining were performed. The stained sections' data analysis revealed that the tumor tissues' IKBIP expression was noticeably lower than that of the NC group ( Figure 8E ). Overall, knockdown of IKBIP can inhibit the occurrence of CC in vivo. Discussion This study elucidates a critical role for IKBIP in the context of CC, highlighting its potential as a therapeutic target as well as a predictive biomarker Our findings indicate that IKBIP is overexpressed in CC tissues and correlates significantly with adverse clinical outcomes, immune infiltration, tumor mutation burden, and drug sensitivity. The association with immune infiltration is particularly noteworthy, because it is consistent with the increasing understanding of the critical role the tumor microenvironment plays in the development of cancer and its response to treatment. The strong expression of IKBIP and its characterization as an oncogene extend the understanding of CC pathogenesis beyond traditional factors such as HPV infection. This adds a new layer of complexity to the molecular landscape of CC, suggesting that while HPV serves as the primary etiologic agent, downstream molecular pathways involving IKBIP could be essential to the development of tumors and the course of illness. In our investigations, silencing IKBIP in CC cell lines led to a marked decrease in cell proliferation, migration, and invasion abilities, reinforcing the notion that IKBIP functions as a key driver of cancer cell aggressiveness. These findings are consistent with studies on other malignancies such as ESCC, glioblastoma where IKBIP has been implicated in enhancing tumor cell survival and migration, indicating a broader role for this protein in oncogenesis[ 30,14 ]. Additionally, IKBIP's involvement in key signaling pathways, including the NF-kB and JAK-STAT3 pathways, represents another important aspect of its role in CC. The NF-kB pathway is a central regulator of inflammation, immune responses, and cell survival. It is essential for promoting the growth of tumors, immune evasion, and resistance to apoptosis in many cancers [31- 32] . Moreover, the identification of IKBIP's role in promoting the JAK-STAT signaling pathway further delineates its mechanism of action in CC, our study highlights the significant role of the JAK-STAT3 pathway in IKBIP-mediated tumor progression. The JAK-STAT3 pathway is known to be frequently activated in many types of cancer, including CC [33- 34] . It plays a pivotal role in regulating a wide range of cellular processes, including proliferation, survival, immune modulation, and metastasis [35] . We demonstrated that the high expression of IKBIP was associated with the activation of JAK-STAT3 signaling, as evidenced by the increased phosphorylation of JAK2 and STAT3 in CC cells. The inhibition of IKBIP expression led to a significant reduction in p-JAK2 and p-STAT3 levels, which impaired CC cell proliferation, migration, and invasion [36] . These results point to IKBIP's potential as a therapeutic target for blocking this crucial signaling axis in CC by indicating that it partially promotes tumor growth via activating the JAK-STAT3 pathway. The use of Colivelin, a JAK-STAT pathway agonist, in rescue experiments highlighted the potential for IKBIP to modulate signaling cascades critical for tumor progression, opening avenues for targeted interventions that could disrupt these pathways. Additionally, IKBIP overexpression increases the AKT signaling pathway's activation, which improves ESCC cells' capacity for migration and proliferation [14] , further underscores the critical role of IKBIP in promoting tumor progression through the regulation of key signaling pathways. Tumor formation and treatment outcomes are significantly influenced by the immunological microenvironment of CC [37] . Studies have shown that cervical cancer is often associated with local immune suppression, featuring various types of immune cells within its microenvironment, including TILs, macrophages, and Tregs. By secreting cytokines and chemokines, these cells encourage the development and spread of tumors [38-39] . Interestingly, TAMs mostly display an M2 phenotype, facilitating tissue repair and immune suppression, which aids tumor escape from host immune surveillance [40] . Furthermore, the immune microenvironment in CC is closely linked to HPV infection, which influences tumorigenesis and progression by modulating immune responses and apoptosis [41] . Moreover, our study's integration of bioinformatics approaches, specifically the analysis of transcriptomic data from TCGA and GEO databases, supports the robustness of our findings. The construction of a prognostic model for CC patients based on IKBIP expression levels provides a valuable tool for clinical decision-making. The correlation of IKBIP with immune infiltration in CC poses interesting implications for immunotherapies. Our data indicate that IKBIP may alter CD8 T cell dynamics in the tumor microenvironment, making it a viable target in the search to develop immune-based therapy for CC. Furthermore, Li et colleagues discovered that IKBIP expression is strongly related with immunosuppressive cells in pan-cancer samples from TCGA. Tumor-associated fibroblasts, regulatory T cells, and tumor-associated macrophages are examples of these immunosuppressive cells. Furthermore, the expression of immunosuppressive genes and immune checkpoints is positively correlated with IKBIP expression in several tumor types, including cervical cancer [42] . Importantly, our study also addressed the clinical implications of IKBIP as a predictor of chemotherapy response. Despite the progress in the treatment of CC, chemotherapy resistance remains a significant obstacle to effective treatment. Platinum-based chemotherapeutic agents, such as oxaliplatin, are commonly used to treat advanced CC [43] . However, a large number of CC cells become resistant to these medications, which results in treatment failure and subpar clinical results [44] . The IKBIP-related model constructed through bioinformatics analysis showed significant correlations with various drugs, indicating that the expression of IKBIP plays a crucial role in guiding the use of clinical medications. This suggests that targeting IKBIP could not only inhibit tumor progression but also improve the efficacy of chemotherapy, offering a potential therapeutic strategy to overcome chemotherapy resistance in CC [45- 46] . The limitations of our study should also be acknowledged. While our results from in vitro and in vivo experiments provide significant insights, the mechanistic interplay between IKBIP expression, immune modulation, and cancer biology warrants further investigation. Longitudinal studies in larger patient cohorts are needed to validate the prognostic significance of IKBIP. Additionally, the functional studies exploring IKBIP's interactions with other oncogenic pathways and immune mediators should be expanded to illuminate more comprehensive therapeutic strategies. Conclusions Our research demonstrates that IKBIP is essential to the development of CC, poor clinical outcomes, such as advanced tumor stage, metastasis, and decreased survival, are strongly linked to high IKBIP expression. We showed that silencing IKBIP suppresses key tumorigenic processes, including cell proliferation, migration, invasion, and modulating JAK-STAT3 signaling pathways. Additionally, IKBIP inhibition enhances chemotherapy sensitivity, providing a promising therapeutic strategy for overcoming chemotherapy resistance in CC. These results highlight the potential of IKBIP as a useful therapeutic target and predictive biomarker in CC. In order to enhance patient outcomes in CC, more clinical research is required to confirm these results and investigate the therapeutic implications of focusing on IKBIP in conjunction with other therapies. Abbreviations HPV Human papillomavirus ICI Immune checkpoint inhibition TME Tumor microenvironment IKBIP I kappa B kinase-interacting protein GEO Gene Expression Omnibus TCGA The Cancer Genome Atlas KM Kaplan–Meier surve ROC Receiver operating characteristic AUC Area Under Curve IHC Immunohistochemistry HE Hematoxylin and eosin TIDE Tumor immune dysfunction and exclusion MSI microsatellite instability TMB Tumor mutational burden FIGO Federation of International of Gynecologists and Obstetricians LNM Lymph node metastasis LVSI Lymphovascular space infiltration OS Overall survival RFS Relapse-free survival IC50 The half maximal inhibitory concentration GO Gene ontology BP Biological process CC Cellular components MF Molecular function KEGG Kyoto encyclopedia of genes and genomes GSEA Gene set enrichment analysis EMT Epithelial mesenchymal transition WHO The World Health Organization CCK8 Cell Counting Kit-8 TILs tumor-infiltrating lymphocytes Tregs regulatory T cells TAMs tumor-associated macrophages Declarations Acknowledgements Not Applicable. Authors' contributions PY and YW conceived and supervised the study. YW and ZYZ designed the experiments. YWand HQ carried out the experiment. The data were analyzed by YW and HQ. YW and WRG carried out the samples collection. YW, ZYZ and PPY performed the experiments, analyzed the data and chart organization. YW drafted the manuscript. PY critically revised the manuscript. All authors read and approved the final version of the manuscript. Funding This work is supported by the National Natural Science Foundation of China (Grant No. 82072893) ；Tianshan Talent Program for Leading Scientific and Technological Innovation Talents-High level Leading Talents (CA001201) ；Key Science and Technology Project of Key Areas of Xinjiang Production and Construction Corps (2023AB055) ；Autonomous region science and technology department key research and development projects ( 2022B03018-1) ；Youth Fund Projrct of the Institute (QN202204) . Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The current study approved by the First Affiliated Hospital of Shihezi University (KJX2022-038-01 and A2023-223-01). All tissue samples were collected with written informed consents. Consent for publication All authors approved the current version of the manuscript and gave consent for submission and publication. Competing interests The authors declare no competing interests. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68 : 394-424. Crosbie EJ, Einstein MH, Franceschi S, Kitchener HC. Human papillomavirus and cervical cancer. Lancet. 2013;382 : 889-899. Olusola P, Banerjee HN, Philley JV, Dasgupta S. 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RAVE-FRäNK M, SCHMIDBERGER H, CHRISTIANSEN H, et al. Comparison of the combined action of oxaliplatin or cisplatin and radiation in cervical and lung cancer cells [J]. International journal of radiation biology, 2007, 83(1): 41-7. Wang W, Zhang J, Wang Y, Xu Y, Zhang S. Identifies microtubule-binding protein CSPP1 as a novel cancer biomarker associated with ferroptosis and tumor microenvironment. Computational and Structural Biotechnology Journal. 2022;20 : 3322-3335. Cheng X, Wang X, Nie K, Cheng L, Zhang Z, et al. Systematic Pan-Cancer Analysis Identifies TREM2 as an Immunological and Prognostic Biomarker. Frontiers in Immunology. 2021;12. Jiang L, Hua Y, Shen Q. Role of mast cells in gynecological neoplasms. Frontiers in Bioscience. 2013 : 773-781. Tables Table 1 The relationship between IKBIP protein expression and clinical features in cervical cancer. Characteristics N IKBIP protein expression c 2 P value Low, n (%) High, n (%) Age (years) 2.333 0.127 <50 55 24(43.60) 31(56.40) ≥50 66 38(57.60) 28(42.40) Histological Type 0.998 0.318 SCC 91 49(53.80) 42(46.20) Adenocarcinoma 30 13(43.30) 17(56.70) Grade 5.669 0.017 Low 15 12(80.00) 3(20.00) High 106 50(47.20) 56(52.60) FIGO Stage 17.405 <0.001 ≤IB1 52 38(73.10) 14(26.90) ≥IB2 69 24(34.80) 45(65.20) LNM 2.782 0.095 Negative 110 59(53.60) 51(46.40) Positive 11 3(27.30) 8(72.70) LVSI 2.164 0.141 Negative 95 52(54.70) 43(45.30) Positive 26 10(38.50) 16(61.50) SCC, squamous cell carcinoma; LNM, lymph node metastasis; LVSI, lymphovascular space invasion. Table 2 The univariate and multivariate Cox analysis for OS in CC(n=111）. Characteristics Univariate Cox analysis Multivariate Cox analysis HR 95%CI P HR 95%CI P Age (＜50 vs. ≥50) 1.014 0.408-2.520 0.976 Histological Type (Adenocarcinoma vs. SCC) 1.736 0.682-4.415 0.247 FIGO Stage ( ≤IB1 vs.≥IB2) 7.631 1.758-33.134 0.007 4.615 1.008-21.131 0.049 Grade ( Low vs. High) 2.891 0.384-21.790 0.303 LNM (Positive vs. Negative) 3.597 1.292-10.017 0.014 1.958 0.688-5.569 0.208 LVSI (Positive vs. Negative) 2.416 0.971-6.012 0.058 IKBIP (High vs. Low) 6.102 1.772-21.014 0.004 4.258 1.211-14.976 0.024 SCC, squamous cell carcinoma; LNM, lymph node metastasis; LVSI, lymphovascular space invasion. Table 3 The univariate and multivariate Cox analysis for RFS in CC(n=111）. Characteristics Univariate Cox analysis Multivariate Cox analysis HR 95%CI P HR 95%CI P Age (＜50 vs. ≥50) 1.113 0.455-2.718 0.815 Histological Type (Adenocarcinoma vs. SCC) 1.631 0.650-4.094 0.297 FIGO Stage ( ≤IB1 vs.≥IB2) 8.379 1.937-36.249 0.004 5.064 1.115-22.997 0.036 Grade ( Low vs. High) 3.026 0.402-22.762 0.282 LNM (Positive vs. Negative) 3.73 1.352-10.291 0.011 2.026 0.719-5.714 0.182 LVSI (Positive vs. Negative) 2.249 0.919-5.506 0.076 IKBIP (High vs. Low) 6.827 1.992-23.402 0.002 4.853 1.390-16.942 0.013 SCC, squamous cell carcinoma; LNM, lymph node metastasis; LVSI, lymphovascular space invasion. 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1","display":"","copyAsset":false,"role":"figure","size":600060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIKBIP is a prognostic biomarker of cervical cancer\u003c/strong\u003e. (A) The expression of IKBIP in CC patients and normal samples were detected through GENT-GPL570 and GSE63714 dataset. (B) KM curves of IKBIP subgroups in TCGA cohort. (C) ROC analysis of IKBIP subgroups for TCGA cohort; (D) Cox regression analysis in both univariate and multivariate settings. (E) Construction of nomogram involving different stage and the expression of IKBIP. (F) Calibration curves of nomogram. (G-H) The ROC curves for 1-, 3-,5-years predict the ability of nomogram and different clinical characteristics.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/3e9813d67af32a0a1a2b9c6d.jpg"},{"id":96652664,"identity":"08a1bc7f-176a-4c7d-bfca-b7158971f848","added_by":"auto","created_at":"2025-11-24 16:35:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":654792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of IKBIP is associated with a severer degree of CC.\u003c/strong\u003e(A) HE staining, alongside IHC analysis, was performed to compare IKBIP expression levels between specimens of CC and adenocarcinoma, as well as normal tissue samples. (B) The IHC staining demonstrated varying intensities of IKBIP expression in CC tissues. (C-D) A comparative analysis revealed distinct differences in IKBIP expression levels between cervical cancer tissues and normal tissues. Furthermore, Kaplan-Meier curves illustrating OS. (E-F) RFS curves were generated to assess differences in survival outcomes across subgroups.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/5db041898b6db384cba127ee.jpg"},{"id":96710568,"identity":"1581c783-2432-48ee-8bad-868f2f59a410","added_by":"auto","created_at":"2025-11-25 10:10:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":400398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the expression of IKBIP with immune infiltrationimmune infiltration and drug sensitivity.\u003c/strong\u003e (A) The correlation between IKBIP expression and 28 immune cell types was analyzed using the single-sample ssGSEA algorithm. (B-E) The composition of immune cell types in various subtypes was determined utilizing the CIBERSORT algorithm. (F-I) The differences in TIDE scores, MSI levels, and exclusion and dysfunction scores between the two distinct subtypes were also assessed.(J) Comparative analysis of chemotherapy drug sensitivity between two distinct risk groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/70b0247c86076781ccd4c21e.png"},{"id":96710299,"identity":"2234a1d0-7404-46b3-b9a0-9569f485ec35","added_by":"auto","created_at":"2025-11-25 10:10:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":359676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the expression of IKBIP and TMB\u003c/strong\u003e. (A-B) Waterfall plots illustrating the somatic mutations present in high and low IKBIP expression groups are presented. (C) The comparison of TMB between the two groups indicates significant differences. (D) Kaplan-Meier survival curves delineate the survival outcomes between high and low TMB groups. (E) The correlation between IKBIP expression and TMB is also depicted, highlighting their relationship. (F) Survival curves are provided for different subgroups based on specific criteria, further illustrating variability in survival outcomes.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/bdb0c7f595978670cfdcbaa7.png"},{"id":96709356,"identity":"f5a9eb6b-7e1f-42e5-ac10-91d8fe7107b2","added_by":"auto","created_at":"2025-11-25 10:08:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":894128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effect of silencing IKBIP on CC cells in vitro.\u003c/strong\u003e(A) qRT-PCR analysis was employed to confirm the down-regulation of IKBIP expression levels. (B) Western blot analysis to assess the protein levels of IKBIP, accompanied by statistical analysis of the gray values in histograms. (C-D) Evaluation of the impact of IKBIP down-regulation on cell proliferation, as measured by CCK-8 assay. (E-F) Assessment of cell proliferation effects resulting from IKBIP down-regulation using EdU assay. (G-H) Transwell migration and invasion assays were conducted to evaluate the effects of IKBIP down-regulation on cellular migration and invasion capabilities. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/4317b90c13efa542e98d1c1e.png"},{"id":96652678,"identity":"b817189a-8ced-49d5-96d7-93f349a699c9","added_by":"auto","created_at":"2025-11-24 16:35:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":627140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of down-regulation of IKBIP on JAK/STAT3 processes in CC cells.\u003c/strong\u003e(A) GO term enrichment analysis for the two groups; (B) KEGG pathway enrichment analysis for both groups. (C-D) GSEA based on Hallmark gene sets, conducted for the high-risk and low-risk groups. (E) Western blot analysis was conducted to assess the expression of JAK/STAT3 signaling pathway proteins following IKBIP down-regulation. (F) Comparison of the protein expression levels associated with the JAK2/STAT3 pathway in HeLa and SiHa cells, both subjected to IKBIP knockdown and treated with colivelin, was performed using Western blotting. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/25c02f986f4cdc242c16e69e.png"},{"id":96652672,"identity":"129ce643-08a1-48de-bf9b-a56fcf68270c","added_by":"auto","created_at":"2025-11-24 16:35:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1066804,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eColivelin reversed the inhibitory effects of silencing IKBIP on CC cells. \u003c/strong\u003e(A) Evaluation of the impact of IKBIP down-regulation treated with Colivelin on cell proliferation, as measured by CCK-8 assay. (B) Assessment of cell proliferation effects resulting from IKBIP down-regulation treated with Colivelin using EdU assay. (C-D) Transwell migration and invasion assays were conducted to evaluate the effects of IKBIP down-regulation treated with Colivelin on cellular migration and invasion capabilities. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/e17ad26a550158f325c34d70.png"},{"id":96652673,"identity":"be1950da-f2e7-4c5b-a4e6-0a3ee68a323d","added_by":"auto","created_at":"2025-11-24 16:35:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":816757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSilencing IKBIP inhibits CC tumor growth in vivo.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative images of xenograft tumors in mice (n = 6). (B) Tumor weight of mice in different groups. (C) Tumor volume growth curves of different groups. (D) Analysis of tumour volume differences in mice after 40 days. (E) Representative images of HE and IHC staining of tumor tissues. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/2ed14ee32648ec6639c77bc3.png"},{"id":105224157,"identity":"00e40474-8a39-41da-aa2d-22105920af42","added_by":"auto","created_at":"2026-03-23 16:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6238411,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7893986/v1/06069df0-116b-43af-b41e-effe5af2fc0b.pdf"}],"financialInterests":"","formattedTitle":"IKBIP as a prognostic biomarker and immunotherapeutic target regulates JAK-STAT3 signaling pathway to promote cervical cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) is among the most frequent cancers harming women's health. Given the complex disease mechanisms and the significant tumor heterogeneity associated with CC, there is an urgent need for a deeper understanding of its pathological aspects\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The primary etiological factor in CC is infection with the human papillomavirus (HPV), with more than 90% of cases linked to high-risk HPV types\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Although the advent of the HPV vaccine has led to a notable decrease in the incidence of CC, and early-stage diagnoses can be effectively managed, advanced-stage tumors in advanced stages are prone to metastasis and treatment resistance\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. This often results in unfavorable prognoses for affected patients. To address the challenges posed by advanced CC, continued research is essential, focusing on the molecular mechanisms underlying tumor progression, metastasis, and treatment resistance\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Identifying novel therapeutic targets and Screening specific biomarkers will be critical in combating this disease and enhancing patient outcomes\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOn human chromosome 12, I kappa B kinase interacting protein (IKBIP), often referred to as IKIP, is situated 0.5 kilobases upstream of the apoptotic protease activating factor 1 (APAF1) gene.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. IKBIP is recognized as one of the target genes of p53, playing a critical role in its pro-apoptotic functions. Additionally, IKBIP is considered an important regulatory factor in inflammation, significantly contributing to pathways such as the Toll-like receptor 7/8 (TLR7/8) signaling cascade and interleukin-1 family signal transduction\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Moreover, IKBIP has been found to be a possible biomarker for a number of cancers, including gliomas, digestive system cancers, and renal cell carcinoma\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Additionally, recent studies show that IKBIP contributes to the regulation of the tumor immunological milieu and is linked to CD8 T cell fatigue in hepatocellular carcinoma.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, the role of IKBIP in the pathogenesis of CC needs to be clarified. Therefore, the purpose of this research is analyze the expression of IKBIP, its correlation with clinical pathological parameters, and its association with the prognosis of CC, as well as to investigate the function of IKBIP in the development of CC to explore potential immunotherapeutic targets for this disease.\u003c/p\u003e\u003cp\u003eIn this study, we combined RNA sequencing information from the Gene Expression Omnibus (GEO) database with the Cancer Genome Atlas (TCGA).\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, employing bioinformatics methodologies in order to assess the expression and prognosis of IKBIP in CC. Based on clinical features, we established a prognostic model for CC patients. Additionally, we examined the relationship between immune infiltration and IKBIP, tumor mutation burden, and drug sensitivity in CC\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Our findings indicate that IKBIP, acting as an oncogene, significantly promotes the proliferation and metastatic capabilities of CC cells. Consequently, IKBIP may serve as a potential effective biomarker for predicting prognosis in CC and contributes to the development of personalized treatment strategies for patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eDatabase collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression data for IKBIP was obtained from the GENT2 database, which includes a total of 114 CC patient samples alongside 11 normal tissue samples. Additionally, gene expression array GSE63714 was retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), which comprises 28 tumor tissues and 24 normal tissues\u003csup\u003e[18]\u003c/sup\u003e. Furthermore, we accessed mRNA profiling and associated historical clinical data for 306 CC patients from The Cancer Genome Atlas (TCGA) dataset (https://portal.gdc.cancer.gov/)\u003csup\u003e[19]\u003c/sup\u003e. Using an optimal cut-off value for IKBIP expression, the X-tile program was used to divide the patients into high- and low-risk categories.\u003csup\u003e[20]\u003c/sup\u003e. atients who lacked survival time or relevant clinical features were excluded from the analysis. To evaluate overall survival (OS) differences between the distinct subgroups, Kaplan-Meier survival curves were plotted. Additionally, the effectiveness of IKBIP to predict patients\u0026apos; overall survival was evaluated using receiver operating characteristic (ROC) curves. This comprehensive analysis aims to elucidate the prognostic significance of IKBIP expression in CC and how it might help direct clinical judgment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman tissues samples collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom June 2008 to December 2022, clinical pathology data were gathered from 121 patients at the First Affiliated Hospital of the Medical College of Shihezi University who had been diagnosed with CC. This cohort served as the trial group. Additionally, normal cervical epithelial tissues from 30 patients with benign conditions, such as uterine leiomyomas and adenomyosis, were gathered during the same time period to form the control group. Data collected from the CC patients included age, pathological type, tumor diameter, differentiation grade, FIGO stage, lymph node metastasis (LNM), and lymphovascular space infiltration (LVSI). All clinical stages were reassessed according to the 2018 FIGO Staging classification system\u003csup\u003e[21]\u003c/sup\u003e. The following were the requirements for CC patients to be included: a) biopsy-confirmed CC status with no pre-surgical treatment; b) absence of chemotherapy and/or radiotherapy prior to surgery; c) availability of complete clinical and pathological data; d) no history of other malignancies. Patients not meeting these criteria were excluded from the study. All subjects gave their informed permission, and the study was authorized by Shihezi University\u0026apos;s First Affiliated Hospital\u0026apos;s Ethics Committee (KJX2022-038-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruct a nomogram model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCox regression analyses using clinical information including age, grade, FIGO stage and tumor type from the TCGA were performed to confirm whether IKBIP was an individual predictor of CC. Besides, the clinical nomogram model was constructed by rms R package\u003csup\u003e[22-\u003c/sup\u003e\u003csup\u003e23]\u003c/sup\u003e. To assess the nomogram\u0026apos;s predictive power for OS in CC patients, independent risk indicators such as FIGO stage and IKBIP expression levels were used. The nomogram\u0026apos;s effectiveness was shown by the calibration curve. Finally, the ROC curve was constructed for the 1-year, 3-year and 5-year risk factors and for the independent risk factors to estimate the forecasting power of the models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of immune infiltrating cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA previous publication provided a set of genes to label 28 immune cell types\u003csup\u003e[24]\u003c/sup\u003e. The variations in immune cell infiltration were measured using the ssGSEA method. Meanwhile, the CIBEROST algorithm was employed to calculate the differences in immune cell abundance between the IKBIP subgroups, and the outcomes are shown as plots\u003csup\u003e[25]\u003c/sup\u003e. Then, combining with the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm\u003csup\u003e[26-\u003c/sup\u003e\u003csup\u003e27]\u003c/sup\u003e, the predictive efficiency for immunotherapy response was analyzed in the subgroups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor mutation burden analysis (TMB)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated the TMB for each CC patient using the \u0026quot;maftools\u0026quot; package. We then compared the characteristics of low and high TMB groups and assessed the relationship between IKBIP expression levels and TMB scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the common chemotherapeutic drug sensitivity of CC patients, the R package \u0026quot;pRRophetic\u0026quot; was used to determine each sample\u0026apos;s half maximum inhibitory concentration (IC50)\u003csup\u003e[28-\u003c/sup\u003e\u003csup\u003e29]\u003c/sup\u003e. Next, correlation between model gene and drug response were estimated by utilizing the R package \u0026ldquo;ggplot2.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003essGSEA enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the potential mechanisms and relevant biological processes underlying the two groups, we conducted pathway enrichment analysis using Gene Set Enrichment Analysis (GSEA) software (version 4.2.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuxiang Biotechnology Co. supplied the HeLa and SiHa cells. (Location: Shanghai, China).CC cell lines of HeLa and SiHa cells were cultured in Dulbecco\u0026apos;s Modified Eagle Medium (DMEM) supplemented with penicillin (100 U/mL), streptomycin (100 U/mL), and 10% fetal bovine serum (FBS) at 37 \u0026deg;C in a 5% CO2 atmosphere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of IKBIP knockdwon cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA lentiviral-based short hairpin RNA (shRNA) approach was implemented using three distinct shRNA sequences to downregulate IKBIP expression. These shRNA sequences were cloned into a lentiviral vector (pLKO.1) in HeLa and SiHa cells. To package the lentivirus, 293T cells were utilized.Six-well plates were seeded with HeLa and SiHa cells at 60\u0026ndash;70% confluence, and lentiviral particle constructs were introduced to the cells in the presence of polybrene (8 \u0026mu;g/mL) to enhance viral transduction. After incubating the cells with the virus for 12-16 h, A brand-new culture medium was used in its place. Following a 48-hour infection period, puromycin was applied to select for successfully transduced cells. The efficacy of IKBIP knockdown was validated through Western blot and quantitative reverse transcription PCR (qRT-PCR) analyses. The resulting stable cell lines were subsequently used for downstream functional assays.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eqRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from cells using TRIzol. Subsequently, 1 \u0026micro;g of RNA was treated with a reverse transcription kit to eliminate contaminating genomic DNA and was converted into cDNA.IKBIP primers (Forward: ATACGACGGATTTCAGGTTT and Reverse: CACTCTTTAGTTCGGTTAGCG ). H-GAPDH primers (Forward: GGGAAACTGTGGCTTGAT and Reverse: GAGTGGGTGTCGCTGTTGA ). The cDNA was then amplified using real-time PCR with the Fast Start Universal SYBR The 2\u0026minus;\u0026Delta;\u0026Delta;Ct technique was used to quantify target genes relative to beta-actin, which was used as the internal control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blot analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal proteins were extracted from cells using RIPA lysis buffer supplemented with 1% protease inhibitor cocktail tablets. Protein concentration was quantified using BCA assay kits after a 30-minute incubation at 37\u0026deg;C. A total of 30 \u0026micro;g of protein was denatured at 95\u0026deg;C for 10 minutes, separated by SDS-PAGE on 10%\u0026ndash;12% gels, and then transferred to polyvinylidene difluoride (PVDF) membranes. Following a 2-hour blocking period with 5% nonfat milk at room temperature, the membranes were incubated overnight at 4\u0026deg;C with the following primary antibodies (1:5000). Afterward, Using the following primary antibodies (1:5000), the membranes were incubated for the whole night at 4\u0026deg;C. After that, the membranes were left at room temperature for two hours to incubate with the relevant secondary antibodies. The blots were examined densitometrically using e-BLOT software and imaged using an ECL chemiluminescence system (Bioground). \u0026beta;-actin functioned as the internal regulator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCCK8 assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell proliferation capacity was evaluated using CCK8 kits. A density of 1,000 cells per well was used to seed transduced cells in 96-well plates. CCK8 solution was added after the cells had had time to adhere, and the cells were then grown for two more hours. The absorbance of each well was then measured at 450 nm to establish the baseline value. Subsequent absorbance measurements were performed daily for five days, ensuring consistency in the timing of each measurement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEdU assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeLa and SiHa cells were cultured in 24-well plates and incubated overnight. The following day, the cells were treated with 50 \u0026mu;M EdU for 2 h. After the incubation, the cells were fixed with 4% paraformaldehyde for 15 minutes and then permeabilized using 0.5% Triton X-100 for 20 minutes. EdU incorporation was evaluated using the EdU Assay Kit (Invitrogen Click-iT\u0026trade; EdU Imaging Kit) according to the manufacturer\u0026rsquo;s instructions. Subsequently, the cells were stained with Hoechst 33342 to visualize the nuclei, and the percentage of EdU-positive cells was determined using fluorescence microscopy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranswell assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeLa and SiHa cells (2 \u0026times; 10\u003csup\u003e^5\u003c/sup\u003e) were resuspended in 500 \u0026micro;L of serum-free DMEM and added to the upper chamber of a transwell system, whereas 10% FBS was added to DMEM in the lower chamber. The non-migratory cells in the top chamber were carefully removed following a predetermined incubation time. \u0026nbsp;After migrating to the bottom chamber, the cells were fixed with methanol and then stained for 15 min with 0.1% crystal violet dye. Following this, neutral resin was applied for sealing. Using an inverted microscope, the number of stained cells in six randomly chosen areas at 20X magnification was counted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXenograft mouse model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale BALB/c nude mice weighing 18\u0026ndash;22 g and aged 4-6 weeks were kept in conventional settings with ad libitum access to food and water. \u0026nbsp; \u0026nbsp;These conditions included a 12-hour light/dark cycle, 20\u0026ndash;24\u0026deg;C, and 50\u0026ndash;60% humidity. To induce tumors, HeLa cells (4.0 x 10⁷ cells/mL) transfected with either IKBIP-targeting shRNA or an empty vector were suspended in PBS and injected subcutaneously into the dorsal cervical area. On day 40 post-inoculation, Mice were given 2% pentobarbital sodium (50 mg/kg) intraperitoneally to induce anesthesia, and they were then put to sleep by cervical dislocation. Tumors were excised and weighed for comparative analysis. The Institutional Animal Care and Use Committee of Shihezi University School of Medicine\u0026apos;s First Affiliated Hospital gave its approval to all experimental operations (Protocol No.:A2023-223-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHematoxylin and eosin (HE) staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHE staining was utilized to assess the histological features of CC tissue sections obtained from subcutaneous tumors. The CC tissues were embedded in paraffin blocks and preserved in 10% formalin, and sectioned into five-micrometer-thick slices that were mounted on glass slides. Following rehydration and deparaffinization, the slices were counterstained with eosin to bring out the cytoplasmic features and stained with hematoxylin to see the nuclei. The stained sections were then examined microscopically, and images were captured for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry (IHC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormalin-fixed human CC tissue specimens, each measuring 4 \u0026micro;m in thickness, were prepared, encompassing 121 CC samples and 30 non-cancerous cervical tissues. The tissue sections, as well as mice tissues were post-embedded at 60\u0026deg;C for 12 h, followed by the mice\u0026apos;s tissues being deparaffinized in xylene and then dehydrated using a series of graded ethanol. Antigen retrieval was performed using citrate solution. The sections were coated with the IKBIP antibody (dilution 1:100, Rabbit, Abmart) and left to incubate overnight at 4\u0026deg;C. The slices were treated for 30 min at 37\u0026deg;C with a suitable secondary antibody the next day. Subsequently, the specimens were stained with diaminobenzidine, counterstained with hematoxylin, and dehydrated through a series of graded alcohols. Two seasoned pathologists who were blinded to the patients\u0026apos; clinical information scored the results. The proportion of positive cells was used to determine the staining extent, and scores were given as follows: 1 = 0%\u0026ndash;10%, 2 = 10%\u0026ndash;25%, 3 = 50%\u0026ndash;75%, and 4 = 75%\u0026ndash;100%. There were four categories for staining intensity: 0 denoted no staining, 1 mild staining, 2 moderate staining, and 3 high staining. The extent and intensity ratings were added up to determine the total IHC score. A score of \u0026le;6 indicated low expression of IKBIP, while a score of \u0026gt;6 indicated high expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe most of statistical analysis were utilized the R studio software (version 4.1.2). The two expression groups were compared using the Wilcoxon signed rank test. \u0026nbsp; The log-rank test and the K-M technique were used to evaluate differences in survival curves. The correlation between the target gene and clinical features was assessed using the chi-squared test. To determine if IKBIP expression levels were an independent predictive factor associated with overall survival (OS) and relapse-free survival (RFS), univariate and multivariate Cox analyses were conducted. P\u0026lt;0.05 was deemed to distinguish the differences. Additionally, GraphPad Prism 9.0 software was used to evaluate the experimental data from Cell. Every experiment was carried out in triplicate, and the mean \u0026plusmn; standard deviation (SD) is used to represent the results. A t-test was used to compare the two groups statistically, and a p-value of less than 0.05 was considered statistically significant. Every experiment was conducted at least three times in order to guarantee repeatability.\u003c/p\u003e"},{"header":"Results","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eIKBIP is a prognostic biomarker of CC\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUtilizing the online application GEPIA2, we conducted an analysis of IKBIP expression levels in CC and corresponding normal cervical tissues. As illustrated in \u003cstrong\u003eFigure 1A,\u003c/strong\u003e we observed a modest elevation in IKBIP expression in CC. Additionally, a significant increase in IKBIP expression was noted in CC samples from the GSE63714 dataset. Further, we retrieved clinical data from 306 patients within the TCGA database and employed XILE software to stratify these patients into high and low IKBIP expression cohorts. Comparative survival analysis revealed substantial prognostic differences between these groups, whereby the high IKBIP expression cohort exhibited markedly poorer clinical outcomes compared to their low expression counterparts \u003cstrong\u003e(Figure 1B)\u003c/strong\u003e. The prognostic utility of IKBIP expression was further assessed through ROC analysis, yielding area under the curve (AUC) values of 0.618, 0.559, and 0.562, indicating moderate predictive capacity \u003cstrong\u003e(Figure 1C)\u003c/strong\u003e. Subsequently, we performed Cox regression analyses, adjusting for IKBIP expression alongside various clinical factors, including age, FIGO stage, tumor type, and grade\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e. The results indicated that IKBIP levels and clinical stage are independent prognostic variables for CC prognosis \u003cstrong\u003e(Figure 1D)\u003c/strong\u003e. Consequently, we developed a prognostic nomogram integrating IKBIP expression and clinical stage, which serves as a quantitative tool for predicting patient outcomes\u003cstrong\u003e\u0026nbsp;(Figure 1E)\u003c/strong\u003e. At 1-, 3-, and 5-year intervals, calibration curves showed a high degree of agreement between nomogram forecasts and actual observed survival rates \u003cstrong\u003e(Figure 1F)\u003c/strong\u003e. Moreover, ROC curves indicated improved accuracy in predicting OS at 1, 3, and 5 years, with respective AUC values of 0.718, 0.643, and 0.630 \u003cstrong\u003e(Figure 1G)\u003c/strong\u003e. Importantly, the AUC analysis revealed that the nomogram exhibited greater sensitivity and specificity compared to individual prognostic factors such as age, stage, grade, and IKBIP expression \u003cstrong\u003e(Figure 1H)\u003c/strong\u003e. In summary, our findings suggested that IKBIP could be a strong predictor of CC.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eThe expression of IKBIP is associated with a severer degree of CC.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo assess the protein expression levels of IKBIP, we obtained cancerous tissue samples from 121 patients with CC and 30 normal cervical tissues for IHC analysis. \u003cstrong\u003eFigure 2A\u003c/strong\u003e presents representative images of HE and IHC staining, contrasting IKBIP expression between specimens of cervical squamous cell carcinoma and adenocarcinoma, alongside non-tumor cervical epithelial tissues. HE staining confirmed that the excised tissues were indeed representative of paracancerous and cancerous states. It was observed that IKBIP localized predominantly to the cytoplasm of the cancerous tissues. \u003cstrong\u003eFigure 2B\u003c/strong\u003e highlights representative images showcasing varying intensities of IHC staining for IKBIP across different expression levels in CC tissues. The results indicated significantly elevated levels of IKBIP expression in CC samples compared to non-cancerous tissues \u003cstrong\u003e(Figure 2C-D)\u003c/strong\u003e. Ultimately, KM analysis demonstrated that patients exhibiting high IKBIP expression experienced poorer OS and RFS \u003cstrong\u003e(Figure 2E-F)\u003c/strong\u003e. In addition, our findings revealed a strong association between IKBIP expression levels in CC tissue and FIGO stage and grade \u003cstrong\u003e(Table 1)\u003c/strong\u003e. Furthermore, univariate analyses indicated that FIGO stage, LNM, and IKBIP expression were significant prognostic indicators for both OS and RFS\u003cstrong\u003e\u0026nbsp;(Table 2)\u003c/strong\u003e. These risk factors identified in the univariate analysis were subsequently incorporated as covariates in a multivariate Cox proportional hazards model, which confirmed that both FIGO stage and IKBIP expression serve as independent predictors for OS and RFS \u003cstrong\u003e(Table 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eCorrelation between the expression of IKBIP with immune infiltration and drug sensitivity\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe used the ssGSEA to evaluate the associations with 28 immune cell types obtained from the TCGA database in order to further examine the link between IKBIP expression levels and the immune milieu in CC patients. The findings showed that IKBIP expression was negatively correlated with CD56 dim natural killer cells \u003cstrong\u003e(Figure 3A)\u003c/strong\u003e and strongly positively correlated with a number of immune cells, including macrophages, effector memory CD4 T cells, Type 2 T helper cells, natural killer T cells, central memory CD4 T cells, CD56 bright natural killer cells, and regulatory T cells. Subsequently, we next used the CIBERSORT method to examine the proportions of immune cell types. \u0026nbsp; \u0026nbsp;According to our research, the group with high IKBIP expression had much fewer resting mast cells, Tregs, and follicular helper T cells than the group with low IKBIP expression. Conversely, the levels of M0 macrophages were reduced in the low IKBIP expression group \u003cstrong\u003e(Figure 3B-E)\u003c/strong\u003e. Additionally, we obtained four relevant scores for each CC sample using the TIDE tool, including TIDE score, MSI score, dysfunction score, and exclusion score. Our analysis showed that both the TIDE score and exclusion score were substantially higher in the high IKBIP expression group, whereas the MSI score was significantly elevated in the low IKBIP expression group. This suggests that CC patients classified within the low IKBIP group may have a greater likelihood of benefitting from immunotherapy \u003cstrong\u003e(Figure 3F-3I).\u0026nbsp;\u003c/strong\u003eChemotherapy and targeted drug therapy are recognized as essential strategies in the clinical management of CC patients. Consequently, it is crucial to investigate the differences in drug sensitivity among groups with varying levels of IKBIP expression. According to our research, the group with low IKBIP expression was more susceptible to widely used chemotherapeutic drugs, with the exception of Dasatinib\u003cstrong\u003e\u0026nbsp;(Figure 3J)\u003c/strong\u003e. This finding suggests that patients with low IKBIP expression are more likely to benefit from these conventional chemotherapeutic treatments. These findings collectively suggest that IKBIP expression might be a useful diagnostic for forecasting how well different pharmaceutical therapies would work for CC patients potentially guiding more personalized therapeutic approaches.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eCorrelation between the expression of IKBIP and TMB\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThere is increasing evidence supporting the notion that tumorigenesis is closely linked to the accumulation of genetic mutations. To elucidate the distributions of somatic mutations, we constructed waterfall plots for CC patients stratified by IKBIP expression levels. The upper bars in these plots demonstrated that TMB was significantly lower in the high IKBIP expression group compared to low IKBIP expression group. Within the high-IKBIP cluster, we identified TTN, PIK3CA, DMD, KMT2D, and MUC16 as the five most frequently mutated genes. Conversely, the low-IKBIP cluster exhibited a different mutation profile, highlighting PIK3CA, TTN, KMT2C, MUC16, and EP300 as commonly mutated genes \u003cstrong\u003e(Figure 4A-B)\u003c/strong\u003e. The violin plot also showed that the group with low IKBIP expression had a substantially greater TMB dispersion, confirming a correlation between IKBIP levels and mutational burden \u003cstrong\u003e(Figure 4C)\u003c/strong\u003e. To define an optimal cutoff for TMB, we utilized X-tile software, which enabled us to divide patients into groups with high and low TMB. Our findings revealed that CC patients with lower TMB were associated with poorer OS outcomes \u003cstrong\u003e(Figure 4D)\u003c/strong\u003e. Furthermore, a significant negative correlation was observed, indicating that increased IKBIP expression was associated with lower TMB levels \u003cstrong\u003e(Figure 4E)\u003c/strong\u003e. Notably, patients classified as IKBIP-high and TMB-low showed significantly inferior OS, while those in the IKBIP-low and TMB-high group demonstrated superior OS outcomes \u003cstrong\u003e(Figure 4F)\u003c/strong\u003e. These results underscore the potential role of IKBIP and TMB as biomarkers in evaluating prognosis in CC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u003c/strong\u003e \u003cstrong\u003eThe effect of silencing IKBIP on CC cells in vitro\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn HeLa and SiHa cells, we created stable knockdown IKBIP cell lines in order to examine the biological function of IKBIP in the development of CC. The knockdown efficiency of IKBIP was assessed by measuring mRNA levels using qRT-PCR and analyzing protein expression through Western blotting. In both HeLa and SiHa cells, we found that the shIKBIP-1 and shIKBIP-2 groups had significantly lower levels of IKBIP mRNA and protein \u003cstrong\u003e(Figure 5A-B)\u003c/strong\u003e. Subsequently, we utilized these knockdown cell lines to performe phenotype assays. CCK-8 assays, which revealed that IKBIP knockdown markedly inhibited cell proliferation. After 24 h of culture, the proliferation rate in the IKBIP knockdown groups was significantly lower compared to the control group. Notably, OD values in shIKBIP-1 and ShIKBIP-2 interference groups declined to over 40% of those in the control group \u003cstrong\u003e(Figure 5C-D)\u003c/strong\u003e. Additionaly, EdU incorporation assay demonstrated a decreased proliferation rate in the IKBIP knockdown group. The reduction in the EdU-positive cell rate in the sh-IKBIP group was statistically significant compared to the sh-NC group\u003cstrong\u003e\u0026nbsp;(Figure 5E-F)\u003c/strong\u003e. \u0026nbsp;Furthermore, transwell test results showed that CC cell lines\u0026apos; capacity to migrate and invade was markedly reduced when IKBIP was knocked down. Specifically, There were significantly fewer cells that migrated and invaded as opposed to the control group (sh-NC) following IKBIP knockdown in the shIKBIP-1 and shIKBIP-2 groups\u003cstrong\u003e(Figure 5G-H)\u003c/strong\u003e. Collectively, these results indicate that IKBIP functions as an oncogene promote the proliferation, migratory and invasive properties of CC cells. This implies that IKBIP plays a crucial part in the development of CC and emphasizes its potential as a therapeutic target.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n \u003cli\u003e\u003cstrong\u003eIKBIP regulates the JAK/STAT3 pathway in cervical cancer cells.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo investigate the underlying differences in biological functions and signaling pathways between distinct risk clusters, we performed GSEA utilizing GO, KEGG, and Hallmark gene set and KEGG analyses. The GO analysis indicated that BP categories identified significant enrichments in basement membrane organization, collagen metabolic processes, and endodermal cell differentiation. The CC analysis revealed that the enriched terms primarily included the endoplasmic reticulum lumen, collagen-containing extracellular matrix, and the basement membrane. Additionally, the MF analysis indicated notable enrichments in collagen binding, extracellular matrix binding, and laminin binding \u003cstrong\u003e(Figure 6A),\u0026nbsp;\u003c/strong\u003ewhich suggested the expression of IKBIP could regulate the EMT process of CC. Subsequently, the KEGG pathway analysis highlighted several cancer proliferation-related pathways, with prominent involvement of the JAK-STAT signaling pathway, TGF-\u0026beta; signaling pathway, and various pathways associated with cancer\u003cstrong\u003e\u0026nbsp;(Figure 6B)\u003c/strong\u003e. In addition , these pathways also participate to regulate the EMT process of CC. Furthermore, the results from the Hallmark analysis demonstrated that high-risk group showed enrichment in tumorigenic pathways, including epithelial-mesenchymal transition (EMT) and TNF-\u0026alpha; signaling via NF-\u0026kappa;B \u003cstrong\u003e(Figure 6C)\u003c/strong\u003e. Conversely, a subset of metabolic pathways, particularly the low-risk group had higher levels of those involved in fatty acid and bile acid metabolism in particular. \u003cstrong\u003e(Figure 6D)\u003c/strong\u003e. To further elucidate the potential mechanisms regulated by IKBIP that underlie CC proliferation and metastasis,\u0026nbsp;enrichment analysis indicates that the expression of IKBIP may influence JAK-STAT signaling pathway in CC. As a result, we aim to investigate whether IKBIP uses the JAK-STAT signaling pathway to control the development of cervical cancer. Western blot analysis confirmed that IKBIP knockdown significantly reduced the p-JAK2 and p-STAT3 in both HeLa and SiHa cells, while not affecting the total protein levels of JAK2 and STAT3\u003cstrong\u003e\u0026nbsp;(Figure 6E)\u003c/strong\u003e. Additionally, we investigated whether the inhibitory effects of IKBIP knockdown may be mediated through the JAK/STAT3 signaling pathway. Upon treatment with Colivelin, an activator of STAT3，both HeLa and SiHa cells exhibited increased levels of p-JAK2 and p-STAT3 proteins \u003cstrong\u003e(Figure 6F)\u003c/strong\u003e, indicating that the negative impact of IKBIP knockdown on CC cells might be regulated by the JAK/STAT3 axis. These findings imply that activating the JAK/STAT3 pathway may play an important role in counteracting the consequences of IKBIP knockdown, thus contributing to the modulation of CC cell behavior.\u003c/p\u003e\n\u003col start=\"7\"\u003e\n \u003cli\u003e\u003cstrong\u003eColivelin reversed the inhibitory effects of silencing IKBIP on CC cells.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eGiven that IKBIP may regulate the proliferation and metastatic capabilities of CC through the JAK-STAT signaling pathway, we further explored the impact of this pathway by using Colivelin. Initially, we added Colivelin to the IKBIP knockdown stable cell lines, and the assay results of CCK-8 showed that Colivelin significantly counteracted the inhibitory effects resulting from IKBIP knockdown \u003cstrong\u003e(Figure 7A)\u003c/strong\u003e. Consistent findings were also observed in the EdU incorporation assay, which showed that Colivelin treatment effectively restored cell proliferation \u003cstrong\u003e(Figure 7B)\u003c/strong\u003e. Moreover, Transwell migration and invasion experiments demonstrated that Colivelin treatment partially mitigated the reductions in CC cell invasion and migration induced by IKBIP inhibition \u003cstrong\u003e(Figure 7C-D)\u003c/strong\u003e. Thes results suggest that the Colivelin play a protective role by modulating JAK-STAT signaling, thereby enhancing the migratory and invasive properties of CC cells affected by IKBIP knockdown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.\u003c/strong\u003e \u003cstrong\u003eThe effect of silencing IKBIP on CC cells in vivo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the possible effect of IKBIP on CC progression in vivo, we inoculated HeLa cells transfected with NC and shIKBIP into nude mice to construct an animal model of xenotransplantation. The tumor volume in the shIKBIP group was considerably lower than in the NC group after the tenth day of subcutaneous injection. Over a period of forty days, the naked mice were euthanized, and the tumor tissue was then excised and assessed for size and weight, revealing a substantial decrease in tumor volume and weight in the shIKBIP group (\u003cstrong\u003eFigure 8A-D\u003c/strong\u003e). Paraffin embedding was performed on the tumor tissues, HE and IHC staining were performed. The stained sections\u0026apos; data analysis revealed that the tumor tissues\u0026apos; IKBIP expression was noticeably lower than that of the NC group (\u003cstrong\u003eFigure 8E\u003c/strong\u003e). Overall, knockdown of IKBIP can inhibit the occurrence of CC in vivo.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study elucidates a critical role for IKBIP in the context of CC, highlighting its potential as a therapeutic target as well as a predictive biomarker Our findings indicate that IKBIP is overexpressed in CC tissues and correlates significantly with adverse clinical outcomes, immune infiltration, tumor mutation burden, and drug sensitivity. The association with immune infiltration is particularly noteworthy, because it is consistent with the increasing understanding of the critical role the tumor microenvironment plays in the development of cancer and its response to treatment. The strong expression of IKBIP and its characterization as an oncogene extend the understanding of CC pathogenesis beyond traditional factors such as HPV infection. This adds a new layer of complexity to the molecular landscape of CC, suggesting that while HPV serves as the primary etiologic agent, downstream molecular pathways involving IKBIP could be essential to the development of tumors and the course of illness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our investigations, silencing IKBIP in CC cell lines led to a marked decrease in cell proliferation, migration, and invasion abilities, reinforcing the notion that IKBIP functions as a key driver of cancer cell aggressiveness. These findings are consistent with studies on other malignancies such as ESCC, glioblastoma where IKBIP has been implicated in enhancing tumor cell survival and migration, indicating a broader role for this protein in oncogenesis[\u003csup\u003e30,14\u003c/sup\u003e]. Additionally, IKBIP\u0026apos;s involvement in key signaling pathways, including the NF-kB and JAK-STAT3 pathways, represents another important aspect of its role in CC. The NF-kB pathway is a central regulator of inflammation, immune responses, and cell survival. It is essential for promoting the growth of tumors, immune evasion, and resistance to apoptosis in many cancers\u003csup\u003e[31-\u003c/sup\u003e\u003csup\u003e32]\u003c/sup\u003e. Moreover, the identification of IKBIP\u0026apos;s role in promoting the JAK-STAT signaling pathway further delineates its mechanism of action in CC, our study highlights the significant role of the JAK-STAT3 pathway in IKBIP-mediated tumor progression. The JAK-STAT3 pathway is known to be frequently activated in many types of cancer, including CC\u003csup\u003e[33-\u003c/sup\u003e\u003csup\u003e34]\u003c/sup\u003e. It plays a pivotal role in regulating a wide range of cellular processes, including proliferation, survival, immune modulation, and metastasis\u003csup\u003e[35]\u003c/sup\u003e. We demonstrated that the high expression of IKBIP was associated with the activation of JAK-STAT3 signaling, as evidenced by the increased phosphorylation of JAK2 and STAT3 in CC cells. The inhibition of IKBIP expression led to a significant reduction in p-JAK2 and p-STAT3 levels, which impaired CC cell proliferation, migration, and invasion\u003csup\u003e[36]\u003c/sup\u003e. These results point to IKBIP\u0026apos;s potential as a therapeutic target for blocking this crucial signaling axis in CC by indicating that it partially promotes tumor growth via activating the JAK-STAT3 pathway. The use of Colivelin, a JAK-STAT pathway agonist, in rescue experiments highlighted the potential for IKBIP to modulate signaling cascades critical for tumor progression, opening avenues for targeted interventions that could disrupt these pathways. Additionally, IKBIP overexpression increases the AKT signaling pathway\u0026apos;s activation, which improves ESCC cells\u0026apos; capacity for migration and proliferation\u003csup\u003e[14]\u003c/sup\u003e, further underscores the critical role of IKBIP in promoting tumor progression through the regulation of key signaling pathways.\u003c/p\u003e\n\u003cp\u003eTumor formation and treatment outcomes are significantly influenced by the immunological microenvironment of CC\u003csup\u003e[37]\u003c/sup\u003e. Studies have shown that cervical cancer is often associated with local immune suppression, featuring various types of immune cells within its microenvironment, including TILs, macrophages, and Tregs. By secreting cytokines and chemokines, these cells encourage the development and spread of tumors\u003csup\u003e[38-39]\u003c/sup\u003e. Interestingly, TAMs mostly display an M2 phenotype, facilitating tissue repair and immune suppression, which aids tumor escape from host immune surveillance\u003csup\u003e[40]\u003c/sup\u003e. Furthermore, the immune microenvironment in CC is closely linked to HPV infection, which influences tumorigenesis and progression by modulating immune responses and apoptosis\u003csup\u003e[41]\u003c/sup\u003e. Moreover, our study\u0026apos;s integration of bioinformatics approaches, specifically the analysis of transcriptomic data from TCGA and GEO databases, supports the robustness of our findings. The construction of a prognostic model for CC patients based on IKBIP expression levels provides a valuable tool for clinical decision-making. The correlation of IKBIP with immune infiltration in CC poses interesting implications for immunotherapies. Our data indicate that IKBIP may alter CD8 T cell dynamics in the tumor microenvironment, making it a viable target in the search to develop immune-based therapy for CC. Furthermore, Li et colleagues discovered that IKBIP expression is strongly related with immunosuppressive cells in pan-cancer samples from TCGA. Tumor-associated fibroblasts, regulatory T cells, and tumor-associated macrophages are examples of these immunosuppressive cells. Furthermore, the expression of immunosuppressive genes and immune checkpoints is positively correlated with IKBIP expression in several tumor types, including cervical cancer\u003csup\u003e[42]\u003c/sup\u003e. Importantly, our study also addressed the clinical implications of IKBIP as a predictor of chemotherapy response. Despite the progress in the treatment of CC, chemotherapy resistance remains a significant obstacle to effective treatment. Platinum-based chemotherapeutic agents, such as oxaliplatin, are commonly used to treat advanced CC\u003csup\u003e[43]\u003c/sup\u003e. However, a large number of CC cells become resistant to these medications, which results in treatment failure and subpar clinical results\u003csup\u003e[44]\u003c/sup\u003e. The IKBIP-related model constructed through bioinformatics analysis showed significant correlations with various drugs, indicating that the expression of IKBIP plays a crucial role in guiding the use of clinical medications. This suggests that targeting IKBIP could not only inhibit tumor progression but also improve the efficacy of chemotherapy, offering a potential therapeutic strategy to overcome chemotherapy resistance in CC\u003csup\u003e[45-\u003c/sup\u003e\u003csup\u003e46]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe limitations of our study should also be acknowledged. While our results from in vitro and in vivo experiments provide significant insights, the mechanistic interplay between IKBIP expression, immune modulation, and cancer biology warrants further investigation. Longitudinal studies in larger patient cohorts are needed to validate the prognostic significance of IKBIP. Additionally, the functional studies exploring IKBIP\u0026apos;s interactions with other oncogenic pathways and immune mediators should be expanded to illuminate more comprehensive therapeutic strategies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur research demonstrates that IKBIP is essential to the development of CC, poor clinical outcomes, such as advanced tumor stage, metastasis, and decreased survival, are strongly linked to high IKBIP expression. We showed that silencing IKBIP suppresses key tumorigenic processes, including cell proliferation, migration, invasion, and modulating JAK-STAT3 signaling pathways. Additionally, IKBIP inhibition enhances chemotherapy sensitivity, providing a promising therapeutic strategy for overcoming chemotherapy resistance in CC. These results highlight the potential of IKBIP as a useful therapeutic target and predictive biomarker in CC.\u003c/p\u003e\u003cp\u003eIn order to enhance patient outcomes in CC, more clinical research is required to confirm these results and investigate the therapeutic implications of focusing on IKBIP in conjunction with other therapies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHPV Human papillomavirus\u003c/p\u003e\n\u003cp\u003eICI Immune checkpoint inhibition\u003c/p\u003e\n\u003cp\u003eTME Tumor microenvironment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIKBIP I kappa B kinase-interacting protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEO Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eTCGA The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eKM Kaplan\u0026ndash;Meier surve\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC Area Under Curve\u003c/p\u003e\n\u003cp\u003eIHC Immunohistochemistry\u003c/p\u003e\n\u003cp\u003eHE Hematoxylin and eosin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTIDE Tumor immune dysfunction and exclusion\u003c/p\u003e\n\u003cp\u003eMSI microsatellite instability\u003c/p\u003e\n\u003cp\u003eTMB Tumor mutational burden\u003c/p\u003e\n\u003cp\u003eFIGO Federation of International of Gynecologists and Obstetricians\u003c/p\u003e\n\u003cp\u003eLNM Lymph node metastasis\u003c/p\u003e\n\u003cp\u003eLVSI Lymphovascular space infiltration\u003c/p\u003e\n\u003cp\u003eOS Overall survival\u003c/p\u003e\n\u003cp\u003eRFS Relapse-free survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIC50 The half maximal inhibitory concentration \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO Gene ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBP Biological process\u003c/p\u003e\n\u003cp\u003eCC Cellular components\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMF Molecular function\u003c/p\u003e\n\u003cp\u003eKEGG Kyoto encyclopedia of genes and genomes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA Gene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEMT Epithelial mesenchymal transition\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWHO The World Health Organization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCCK8 Cell Counting Kit-8\u003c/p\u003e\n\u003cp\u003eTILs tumor-infiltrating lymphocytes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTregs regulatory T cells\u003c/p\u003e\n\u003cp\u003eTAMs tumor-associated macrophages\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePY and YW conceived and supervised the study. YW and ZYZ designed the experiments. YWand HQ carried out the experiment. The data were analyzed by YW and HQ. YW and WRG carried out the samples collection. YW, ZYZ and PPY performed the experiments, analyzed the data and chart organization. YW drafted the manuscript. PY critically revised the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the National Natural Science Foundation of China (Grant No. 82072893)\u0026nbsp;；Tianshan Talent Program for Leading Scientific and Technological Innovation Talents-High level Leading Talents (CA001201)\u0026nbsp;；Key Science and Technology Project of Key Areas of Xinjiang Production and Construction Corps (2023AB055)\u0026nbsp;；Autonomous region science and technology department key research and development projects ( 2022B03018-1)\u0026nbsp;；Youth Fund Projrct of the Institute (QN202204) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study approved by the First Affiliated Hospital of Shihezi University (KJX2022-038-01 and A2023-223-01). All tissue samples were collected with written informed consents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the current version of the manuscript and gave consent for submission and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68\u003cstrong\u003e:\u003c/strong\u003e394-424.\u003c/li\u003e\n\u003cli\u003eCrosbie EJ, Einstein MH, Franceschi S, Kitchener HC. Human papillomavirus and cervical cancer. 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IKBIP is a Predictive Biomarker Related to Immunosuppressive Microenvironment in Digestive System Malignancies. Discov Med. 2023 Feb 1;35(174):57-72.\u003c/li\u003e\n\u003cli\u003eGUTI\u0026eacute;RREZ-HOYA A, SOTO-CRUZ I. Role of the JAK/STAT Pathway in Cervical Cancer: Its Relationship with HPV E6/E7 Oncoproteins [J]. Cells, 2020, 9(10).\u003c/li\u003e\n\u003cli\u003eRAVE-FR\u0026auml;NK M, SCHMIDBERGER H, CHRISTIANSEN H, et al. Comparison of the combined action of oxaliplatin or cisplatin and radiation in cervical and lung cancer cells [J]. International journal of radiation biology, 2007, 83(1): 41-7.\u003c/li\u003e\n\u003cli\u003eWang W, Zhang J, Wang Y, Xu Y, Zhang S. Identifies microtubule-binding protein CSPP1 as a novel cancer biomarker associated with ferroptosis and tumor microenvironment. Computational and Structural Biotechnology Journal. 2022;20\u003cstrong\u003e:\u003c/strong\u003e3322-3335.\u003c/li\u003e\n\u003cli\u003eCheng X, Wang X, Nie K, Cheng L, Zhang Z, et al. 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Frontiers in Bioscience. 2013\u003cstrong\u003e:\u003c/strong\u003e773-781.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e The relationship between IKBIP protein expression and clinical features in cervical cancer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eIKBIP protein expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLow, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eHigh, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e24(43.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e31(56.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026ge;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e38(57.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e28(42.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eHistological Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e49(53.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e42(46.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e13(43.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e17(56.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e5.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e12(80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3(20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e50(47.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e56(52.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e17.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026le;IB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e38(73.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e14(26.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026ge;IB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e24(34.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e45(65.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e59(53.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e51(46.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3(27.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8(72.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e52(54.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e43(45.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e10(38.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e16(61.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 544px;\"\u003e\n \u003cp\u003eSCC,\u0026nbsp;squamous cell carcinoma; LNM, lymph node metastasis; LVSI, lymphovascular space invasion.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 631px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e The univariate and multivariate Cox analysis for OS in CC(n=111）.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eUnivariate Cox analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eMultivariate Cox analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e(＜50 vs.\u0026nbsp;\u0026ge;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.408-2.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eHistological Type\u003c/p\u003e\n \u003cp\u003e(Adenocarcinoma vs. SCC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.682-4.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003cp\u003e( \u0026le;IB1 vs.\u0026ge;IB2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e7.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.758-33.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.008-21.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003cp\u003e( Low vs. High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.384-21.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003cp\u003e(Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e3.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.292-10.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.688-5.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003cp\u003e(Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e2.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.971-6.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eIKBIP\u003c/p\u003e\n \u003cp\u003e(High vs. Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e6.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1.772-21.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e1.211-14.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 631px;\"\u003e\n \u003cp\u003eSCC, squamous cell carcinoma; LNM, lymph node metastasis; LVSI, lymphovascular space invasion.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 658px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e The univariate and multivariate Cox analysis for RFS in CC(n=111）.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eUnivariate Cox analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 220px;\"\u003e\n \u003cp\u003eMultivariate Cox analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e(＜50 vs.\u0026nbsp;\u0026ge;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.455-2.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eHistological Type\u003c/p\u003e\n \u003cp\u003e(Adenocarcinoma vs. SCC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.650-4.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003cp\u003e( \u0026le;IB1 vs.\u0026ge;IB2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.937-36.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.115-22.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003cp\u003e( Low vs. High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.402-22.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003cp\u003e(Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.352-10.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.719-5.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003cp\u003e(Positive vs. Negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.919-5.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eIKBIP\u003c/p\u003e\n \u003cp\u003e(High vs. Low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.992-23.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.390-16.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 658px;\"\u003e\n \u003cp\u003eSCC, squamous cell carcinoma; LNM, lymph node metastasis; LVSI, lymphovascular space invasion.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"IKBIP, Cervical cancer, Immune infiltration, Prognosis model","lastPublishedDoi":"10.21203/rs.3.rs-7893986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7893986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCervical cancer (CC) represents a significant threat to women's health globally. Although IKBIP has been recognized as an oncogene, although little is known about how it contributes to cancer, and it has predominantly been associated with driving malignant progression in gliomas. Further investigation into the function of IKBIP in other cancer types, including CC, is essential to fully understand its potential implications for tumorigenesis and progression in various malignancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIKBIP expression was analyzed in CC tissues using the GEPIA and GEO databases.\u003c/p\u003e\n\u003cp\u003eThe transcriptomic data and clinical characteristics of 306 patients with CC were obtained from TCGA, and clustering was performed using X-tile software. Additionally, to validate the prognostic significance of IKBIP, protein levels in normal and cancerous tissues were compared through IHC. The TIDE score was employed as an indicator of the response to immunotherapy. Furthermore, our research sought to investigate any possible connectionsbetween IKBIP and immunological genes, as well as their influence on the development of TMB and drug sensitivity. The impact of IKBIP on CC cells' capacity for invasion, migration, and proliferation were investigated using CCK-8, EdU, and transwell assays. To clarify IKBIP's function in controlling the JAK-STAT signaling cascade and its contribution to the progression of CC, we utilized the JAK-STAT pathway agonist Colivelin in rescue experiments. The influence of IKBIP on CC development was also validated in a xenograft tumor model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research showed that the tissues of cervical cancer overexpress IKBIP, which could be a potential oncogene associated with cervical cancer. According to nomogram creation, ROC curve analysis, and Kaplan-Meier survival analysis, IKBIP may be a biomarker for a bad prognosis in CC. Furthermore, the expression of IKBIP exhibited a strong correlation with immune infiltration, TMB and drug sensitivity in CC. In vitro experiments indicated that IKBIP functions as an oncogene, because inhibiting its expression dramatically reduced cervical cancer cells' capacity to proliferate, migrate, and invade using the CCK8, EdU, and transwell tests, respectively. Additionally, our findings suggest that IKBIP may promote cervical cancer progression by regulating the JAK-STAT signaling pathway. Rescue experiments demonstrated that the JAK-STAT pathway activator, Colivelin, could mitigate the inhibitory effects of IKBIP knockdown on cervical cancer cell behavior. We successfully constructed the cervical cancer xenograft mouse model and in vivo experiments demonstrated that the expression of IKBIP is closely correlated with the malignancy of cervical cancer, and also provided more evidence that IKBIP contributes to the advancement of cervical cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, this study offers novel insights for CC by establishing IKBIP as a robust prognostic indicator. Our findings suggest that IKBIP not only correlates with adverse clinical outcomes but also influences tumor immunogenicity and response to treatment. Furthermore, IKBIP is significantly correlated with the progression of CC mediated by the JAK/STAT3 signaling pathway, and it may become an effective therapeutic target.\u003c/p\u003e","manuscriptTitle":"IKBIP as a prognostic biomarker and immunotherapeutic target regulates JAK-STAT3 signaling pathway to promote cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 16:35:06","doi":"10.21203/rs.3.rs-7893986/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-12T10:45:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T10:25:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T14:38:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-10-18T10:20:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef85fa83-5163-4750-93d3-4e07bc9427e9","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:08:48+00:00","versionOfRecord":{"articleIdentity":"rs-7893986","link":"https://doi.org/10.1186/s12967-026-08015-2","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-03-20 15:58:57","publishedOnDateReadable":"March 20th, 2026"},"versionCreatedAt":"2025-11-24 16:35:06","video":"","vorDoi":"10.1186/s12967-026-08015-2","vorDoiUrl":"https://doi.org/10.1186/s12967-026-08015-2","workflowStages":[]},"version":"v1","identity":"rs-7893986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7893986","identity":"rs-7893986","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}