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

Using the online application Gene Expression Profiling Interactive Analysis, we analyzed IKBIP expression levels in CC and corresponding normal cervical tissues. As illustrated in Fig.  1 A, we observed a modest elevation in IKBIP expression in CC ( p  = 7.6e-05). Additionally, a significant increase in IKBIP expression was observed in CC samples from the GSE63714 dataset ( p  = 0.0014). Furthermore, we retrieved clinical data from 306 patients within TCGA database and used the X-tile software to stratify these patients into high- and low-IKBIP expression cohorts. Comparative survival analysis revealed substantial prognostic differences between these groups; the high-IKBIP expression group was associated with poor clinical outcomes compared with the low-IKBIP expression group ( p  = 4.448e-03, Fig.  1 B). The prognostic utility of IKBIP expression was further assessed using the ROC curve analysis, which yielded AUC values of 0.618, 0.559, and 0.562 at 1, 3, and 5 years, respectively, suggesting a moderate predictive association (Fig.  1 C). Subsequently, we performed Cox regression analyses, adjusting for IKBIP expression and various clinical factors, including age, FIGO stage, tumor type, and grade. The results indicated that IKBIP levels and clinical stage were independently associated with the prognosis of CC (Fig.  1 D). Consequently, we developed a prognostic nomogram integrating IKBIP expression and clinical stage, which served as a quantitative tool for predicting patient outcomes (Fig.  1 E). At 1-, 3-, and 5-year intervals, the calibration curves showed a high degree of agreement between the nomogram forecasts and the actual observed survival rates (Fig. 1 F). Moreover, the ROC curves indicated improved accuracy in predicting OS at 1, 3, and 5 years, with AUC values of 0.718, 0.643, and 0.630, respectively (Fig. 1 G). Importantly, AUC analysis revealed that the nomogram exhibited greater sensitivity and specificity than individual prognostic factors such as age, stage, grade, and IKBIP expression (Fig. 1 H). In summary, these analyses suggest that IKBIP expression is associated with the prognosis of CC. Fig. 1 Prognostic significance of IKBIP expression in CC. ( 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. OS, overall survival; AUC, area under the curve Prognostic significance of IKBIP expression in CC. ( 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. OS, overall survival; AUC, area under the curve To assess IKBIP protein expression levels, we obtained cancerous tissue samples from 121 patients with CC and 30 normal cervical tissues for IHC analysis. Figure 2 A presents representative images of HE and IHC staining contrasting IKBIP expression between specimens of cervical squamous cell carcinoma and adenocarcinoma, along with non-tumor cervical epithelial tissues. HE staining confirmed that the excised tissues were representative of para-cancerous and cancerous states. IKBIP predominantly localized to the cytoplasm of cancerous tissues. Figure 2 B shows representative images of varying intensities of IKBIP IHC staining across different expression levels in CC tissues. The results indicated significantly elevated levels of IKBIP expression in CC samples compared with non-cancerous tissues ( p  < 0.05, Fig. 2 C-D). Kaplan–Meier analysis demonstrated that patients with high IKBIP expression had worse OS and recurrence-free survival (RFS) than those with low IKBIP expression ( p  < 0.05, Fig. 2 E-F). In addition, our findings revealed a strong association between IKBIP expression levels in CC tissues and FIGO stage and grade (Table  1 ). Furthermore, univariate analyses indicated that the FIGO stage, lymph node metastasis, and IKBIP expression were significant prognostic indicators for both OS and RFS (Table  2 ). The risk factors identified in the univariate analysis were subsequently incorporated as covariates in a multivariate Cox proportional hazards model, which confirmed that both the FIGO stage and IKBIP expression were independent predictors of OS and RFS (Table  3 ). These observations further support the clinical correlation between IKBIP expression and disease progression. Fig. 2 IKBIP expression is upregulated in cervical cancer and correlates with poor prognosis. ( A ) HE staining and IHC analysis of IKBIP expression in normal cervical squamous epithelium, cervical SCC, normal glandular epithelium, and cervical AC. IHC staining shows increased IKBIP protein levels in tumor tissues compared to corresponding normal tissues. ( B ) Representative IHC images of IKBIP expression at ×100 and ×400 magnification in cervical SCC and AC, showing both weak and strong positive staining patterns. Brown staining indicates IKBIP positivity, primarily localized in the nucleus and cytoplasm. ( C ) Distribution of IKBIP expression levels (high vs. low) in cervical cancer tissues, with high expression observed in a significant proportion of cases. ( D ) Boxplot comparing IKBIP expression levels between normal and tumor tissues, showing significantly higher expression in tumors. ( E - F ) Kaplan-Meier survival curves demonstrating that high IKBIP expression is associated with significantly worse overall survival in both cervical squamous cell carcinoma ( E ) and adenocarcinoma ( F ) cohorts. HR and 95% CI are shown. HE, hematoxylin and eosin; IHC, immunohistochemistry; SCC, squamous cell carcinoma; AC, adenocarcinoma; IKBIP, Inhibitor of κB protein interactor IKBIP expression is upregulated in cervical cancer and correlates with poor prognosis. ( A ) HE staining and IHC analysis of IKBIP expression in normal cervical squamous epithelium, cervical SCC, normal glandular epithelium, and cervical AC. IHC staining shows increased IKBIP protein levels in tumor tissues compared to corresponding normal tissues. ( B ) Representative IHC images of IKBIP expression at ×100 and ×400 magnification in cervical SCC and AC, showing both weak and strong positive staining patterns. Brown staining indicates IKBIP positivity, primarily localized in the nucleus and cytoplasm. ( C ) Distribution of IKBIP expression levels (high vs. low) in cervical cancer tissues, with high expression observed in a significant proportion of cases. ( D ) Boxplot comparing IKBIP expression levels between normal and tumor tissues, showing significantly higher expression in tumors. ( E - F ) Kaplan-Meier survival curves demonstrating that high IKBIP expression is associated with significantly worse overall survival in both cervical squamous cell carcinoma ( E ) and adenocarcinoma ( F ) cohorts. HR and 95% CI are shown. HE, hematoxylin and eosin; IHC, immunohistochemistry; SCC, squamous cell carcinoma; AC, adenocarcinoma; IKBIP, Inhibitor of κB protein interactor Table 1 The relationship between IKBIP protein expression and clinical features in cervical cancer Characteristics N IKBIP protein expression χ 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 The relationship between IKBIP protein expression and clinical features in cervical cancer 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 The univariate and multivariate cox analysis for OS in CC ( n  = 111) Age (<50 vs. ≥50) Histological Type (Adenocarcinoma vs. SCC) FIGO Stage (≤ IB1 vs.≥IB2) Grade (Low vs. High) LNM (Positive vs. Negative) LVSI (Positive vs. Negative) IKBIP (High vs. Low) 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 The univariate and multivariate cox analysis for RFS in CC ( n  = 111) Age (<50 vs. ≥50) Histological Type (Adenocarcinoma vs. SCC) FIGO Stage (≤ IB1 vs.≥IB2) Grade (Low vs. High) LNM (Positive vs. Negative) LVSI (Positive vs. Negative) IKBIP (High vs. Low) SCC, squamous cell carcinoma; LNM, lymph node metastasis; LVSI, lymphovascular space invasion We used single-sample GSEA to evaluate the associations with 28 immune cell types obtained from TCGA database to further examine the link between IKBIP expression levels and the immune milieu in patients with CC. The findings showed that IKBIP expression was negatively correlated with CD56 dim natural killer cells (Fig. 3 A) and strongly positively correlated with several 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 used CIBERSORT to examine the proportions of immune cell types. In our study, the group with high IKBIP expression had fewer resting mast cells, regulatory T cells, and follicular helper T cells than the group with low IKBIP expression. Conversely, the number of M0 macrophages was reduced in the low-IKBIP expression group ( p  < 0.05, Fig.  3 B-E). Additionally, we obtained four relevant scores for each CC sample using the TIDE tool: TIDE, microsatellite instability, dysfunction, and exclusion scores. Our analysis showed that both the TIDE and exclusion scores were substantially higher in the high-IKBIP expression group than in the low-IKBIP expression group ( p  < 0.05), whereas the microsatellite instability score was significantly higher in the low-IKBIP expression group than in the high-IKBIP expression group ( p  = 0.0013). This pattern suggests a potential association between low IKBIP expression and a tumor microenvironment that may be highly responsive to immunotherapy (Fig. 3 F-I). Chemotherapy and targeted drug therapy are recognized as essential strategies for the clinical management of patients with CC. Consequently, it is crucial to investigate differences in drug sensitivity among groups with varying levels of IKBIP expression. According to our results, the group with low IKBIP expression was more susceptible to widely used chemotherapeutic drugs, except for dasatinib, than the high-IKBIP expression group (Fig. 3 J-M). This observation indicates an association between low IKBIP expression and increased sensitivity to conventional chemotherapy. Collectively, these findings suggest a correlation between IKBIP expression and immune profiles or drug responses, which may inform future hypothesis-driven studies. Fig. 3 Correlation between the expression of IKBIP and immune infiltration and drug sensitivity. ( 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. TIDE, Tumor Immune Dysfunction and Exclusion; MSI, microsatellite instabilit Correlation between the expression of IKBIP and immune infiltration and drug sensitivity. ( 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. TIDE, Tumor Immune Dysfunction and Exclusion; MSI, microsatellite instabilit Increasing evidence supports the notion that tumorigenesis is closely linked to accumulation of genetic mutations. To elucidate the distribution of somatic mutations, we constructed waterfall plots for patients with CC stratified according to IKBIP expression levels. The upper bars in these plots indicated that the TMB was significantly lower in the high-IKBIP expression group than in the low-IKBIP expression group. Within the high-IKBIP cluster, TTN , PIK3CA , DMD , KMT2D , and MUC16 were identified 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 (Fig. 4 A-B). The violin plot also showed that the group with low IKBIP expression had substantially greater TMB dispersion than the high-IKBIP expression group, confirming a correlation between IKBIP levels and mutational burden ( p  = 0.0012, Fig.  4 C). To define an optimal cutoff for TMB, we utilized the X-tile software, which enabled us to divide the patients into groups with high and low TMB. Our findings revealed that patients with CC with a low TMB had worse OS outcomes than those with a high TMB ( p  = 4.51e-02, Fig.  4 D). Furthermore, a significant negative correlation was observed, indicating that increased IKBIP expression was associated with a low TMB ( p  = 0.000159, Fig. 4 E). Notably, patients classified as IKBIP-high and TMB-low showed significantly inferior OS, whereas those in the IKBIP-low and TMB-high group demonstrated superior OS ( p  = 4.304e-03, Fig. 4 F). These results indicate an association between IKBIP and TMB and their utility as potential biomarkers for the prognostic evaluation of CC. Fig. 4 Correlation between the expression of IKBIP and tumor mutation burden. ( 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 ) Kaplan–Meier survival curves of patient subgroups stratified by combined IKBIP expression and TMB status. TMB, tumor mutation burden Correlation between the expression of IKBIP and tumor mutation burden. ( 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 ) Kaplan–Meier survival curves of patient subgroups stratified by combined IKBIP expression and TMB status. TMB, tumor mutation burden To investigate the biological function of IKBIP in CC development, we generated stable IKBIP knockdown HeLa and SiHa cell lines. IKBIP knockdown efficiency was assessed by measuring mRNA levels using quantitative reverse transcription polymerase chain reaction, and protein expression was analyzed using western blotting. In both HeLa and SiHa cells, we found that the shIKBIP-1 and shIKBIP-2 groups had significantly low IKBIP mRNA and protein levels compared with the control group (Fig.  5 A-B). Subsequently, we used these knockdown cell lines to perform phenotypic assays. The CCK-8 assay revealed that IKBIP knockdown markedly inhibited cell proliferation. After 24 h of culture, the proliferation rate of the IKBIP knockdown group was significantly lower than that of the control group. Notably, the OD values in the shIKBIP-1 and shIKBIP-2 knockdown groups decreased to over 40% of those in the control group (Fig. 5 C-D). Additionally, the EdU incorporation assay demonstrated a decreased proliferation rate in the IKBIP-knockdown group. The shIKBIP group showed a statistically significant reduction in EdU-positive cell rate compared with the control (shNC) group (Fig. 5 E-F). Furthermore, transwell assay results showed that the capacity of CC cell lines to migrate and invade was markedly reduced when IKBIP was knocked down. Specifically, significantly fewer cells migrated and invaded in the shIKBIP-1 and shIKBIP-2 groups following IKBIP knockdown than in the control group (shNC) (Fig. 5 G-H). Collectively, these results indicate that IKBIP knockdown is associated with suppressed proliferation, migration, and invasion of CC cells, suggesting its potential role in CC progression. Fig. 5 Effect of silencing IKBIP on cervical cancer cells in vitro. ( 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. * p  < 0.05; ** p  < 0.01; *** p  < 0.001.error bars represent mean ± SD. NC, negative control Effect of silencing IKBIP on cervical cancer cells in vitro. ( 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. * p  < 0.05; ** p  < 0.01; *** p  < 0.001.error bars represent mean ± SD. NC, negative control To investigate the underlying differences in biological functions and signaling pathways between distinct risk clusters, we performed GSEA using GO, KEGG, and Hallmark gene set analyses. The GO analysis indicated that the Biological Process categories were significantly enriched for basement membrane organization, collagen metabolic processes, and endodermal cell differentiation. Cellular Component analysis revealed that the enriched terms primarily included the endoplasmic reticulum lumen, collagen-containing extracellular matrix, and basement membrane. Additionally, Molecular Function analysis indicated a notable enrichment in collagen binding, extracellular matrix binding, and laminin binding (Fig. 6 A), suggesting a potential association between IKBIP expression and the epithelial–mesenchymal transition (EMT) process in 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 (Fig. 6 B). In addition, these pathways participate in the process of EMT in CC. Furthermore, the results of the Hallmark analysis demonstrated that the high-risk group showed enrichment in tumorigenic pathways, including EMT and TNF-α signaling via NF-κB (Fig. 6 C). Conversely, a subset of metabolic pathways, particularly in the low-risk group, showed high levels of fatty acid and bile acid metabolism (Fig. 6 D). To further elucidate the potential mechanisms regulated by IKBIP that underlie CC proliferation and metastasis, enrichment analysis was performed, which indicated that IKBIP expression may be associated with the JAK-STAT signaling pathway in CC. To validate this association at the transcriptomic level, we performed GSVA (Gene Set Variation Analysis) to quantify the activity of the JAK-STAT3 signaling pathway in CC samples, and the generated scatter plot revealed a significant positive correlation between IKBIP expression and JAK-STAT3 pathway activity ( p  < 0.001, Supplementary Figure S1 ). Therefore, we investigated whether IKBIP is linked to the JAK-STAT signaling pathway in CC progression. Western blot analysis confirmed that IKBIP knockdown significantly reduced p-JAK2 and p-STAT3 levels in both HeLa and SiHa cells but did not affect the total protein levels of JAK2 and STAT3 (Fig. 6 E). These data suggest a potential regulatory relationship between IKBIP and JAK/STAT3 signaling. We also investigated whether the inhibitory effects of IKBIP knockdown were mediated through the JAK/STAT3 signaling pathway. Following treatment with the STAT3 activator colivelin, both HeLa and SiHa cells showed elevated levels of p-JAK2 and p-STAT3 (Fig. 6 F). This result suggests a potential connection between IKBIP and the JAK/STAT3 axis in CC cells and further implies that the functional effects observed upon IKBIP knockdown may be associated with JAK/STAT3 pathway activation. Fig. 6 Effect of downregulation of IKBIP expression on JAK/STAT3 processes in cervical cancer cells. ( 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. * p  < 0.05; ** p  < 0.01; *** p  < 0.001.Error bars represent mean ± SD. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NC, negative control Effect of downregulation of IKBIP expression on JAK/STAT3 processes in cervical cancer cells. ( 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. * p  < 0.05; ** p  < 0.01; *** p  < 0.001.Error bars represent mean ± SD. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NC, negative control Given that IKBIP may be associated with the regulation of the proliferation and metastatic capabilities of CC cells through the JAK-STAT signaling pathway, we further explored the impact of this pathway using colivelin. Initially, we added colivelin to the IKBIP-knockdown stable cell lines, and the assay results of CCK-8 showed that it significantly counteracted the inhibitory effects of IKBIP knockdown (Fig. 7 A). Consistent findings were observed in the EdU incorporation assay, which showed that colivelin treatment effectively restored cell proliferation (Fig. 7 B). Moreover, transwell migration and invasion experiments demonstrated that colivelin treatment partially mitigated the reduction in CC cell invasion and migration induced by IKBIP knockdown (Fig. 7 C-D). These results suggest that colivelin may exert protective effects by modulating JAK-STAT signaling, thereby influencing the migratory and invasive properties of CC cells affected by IKBIP knockdown. Fig. 7 Colivelin reversed the inhibitory effects of silencing IKBIP on cervical cancer cells. ( 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. * p  < 0.05; ** p  < 0.01; *** p  < 0.001.Error bars represent mean ± SD. NC, negative control Colivelin reversed the inhibitory effects of silencing IKBIP on cervical cancer cells. ( 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. * p  < 0.05; ** p  < 0.01; *** p  < 0.001.Error bars represent mean ± SD. NC, negative control To better understand the possible effects of IKBIP on CC progression in vivo, we inoculated HeLa cells transfected with negative control (NC) or shIKBIP into nude mice to construct an animal xenotransplantation model. The tumor volume in the shIKBIP group was considerably lower than that in the NC group after the tenth day of subcutaneous injection. After 40 days, the nude mice were euthanized, and the tumor tissues were excised and assessed for size and weight, which revealed a substantial decrease in tumor volume and weight in the shIKBIP group (Fig. 8 A-D). Paraffin embedding was performed on the tumor tissues, followed by HE and IHC staining. Analysis of the stained sections revealed that IKBIP expression in the tumor tissues was noticeably lower than that in the NC group (Fig.  8 E). Overall, IKBIP knockdown was associated with the inhibition of tumor growth in vivo, supporting its potential relevance to CC progression. Fig. 8 Silencing IKBIP inhibits cervical cancer tumor growth in vivo. ( 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. * p  < 0.05, ** p  < 0.01, *** p  < 0.001. Error bars represent mean ± SD. HE, hematoxylin and eosin; IHC, immunohistochemistry Silencing IKBIP inhibits cervical cancer tumor growth in vivo. ( 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. * p  < 0.05, ** p  < 0.01, *** p  < 0.001. Error bars represent mean ± SD. HE, hematoxylin and eosin; IHC, immunohistochemistry

IKBIP expression data were obtained from Gene Expression database of Normal and Tumor tissues 2 (GENT2), which included 114 CC samples and 11 normal tissue samples. Additionally, the gene expression array GSE63714 was retrieved from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ), which comprised 28 tumor tissues and 24 normal tissues [ 18 ]. Raw data from GSE63714 were normalized using the “limma” R package. Furthermore, we assessed mRNA profiles and associated historical clinical data for 306 patients with CC from TCGA dataset ( https://portal.gdc.cancer.gov/ ) [ 19 ]. TCGA RNA-Seq data (FPKM format) were log2-transformed and normalized for downstream analysis. Using an optimal cutoff value for IKBIP expression, the X-tile program (version 3.6.1) was used to divide the patients into high- and low-risk categories [ 20 ]. Patients lacking survival time or relevant clinical features were excluded from the analysis. To evaluate overall survival (OS) differences between the subgroups, Kaplan–Meier survival curves were plotted, and statistical significance was assessed using the log-rank test, with a p -value < 0.05 considered significant. The prognostic ability of IKBIP was assessed by constructing time-dependent receiver operating characteristic (ROC) curves at 1, 3, and 5 years, and the corresponding area under the curve (AUC) values were calculated. Between June 2008 and December 2022, clinical pathology data were collected from 121 patients diagnosed with CC at the First Affiliated Hospital of the Medical College of Shihezi University. 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 collected during the same period to form the control group. Data collected from patients with CC included age, pathological type, tumor diameter, differentiation grade, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and lymphovascular space infiltration. All the clinical stages were reassessed according to the 2018 FIGO Staging classification system [ 21 ]. The inclusion criteria for patients with CC were as follows: (a) biopsy-confirmed CC status with no presurgical treatment, (b) absence of chemotherapy and/or radiotherapy before surgery, (c) availability of complete clinical and pathological data, and (d) no history of other malignancies. Patients who did not meet these criteria were excluded. All participants provided informed consent, and the study was authorized by the Ethics Committee of Shihezi University’s First Affiliated Hospital (KJX2022-038-01). Cox regression analyses using clinical information such as age, grade, FIGO stage, and tumor type from TCGA were performed to confirm whether IKBIP is an independent predictor of CC. Both univariate and multivariate Cox proportional hazards models were fitted, and variables with p < 0.05 in the multivariate analysis were retained as independent prognostic factors. In addition, a clinical nomogram model was constructed using the “rms” R package [ 22 , 23 ]. Independent risk indicators, such as the FIGO stage and IKBIP expression levels, were used to assess the predictive power of the nomogram for OS in patients with CC. The effectiveness of the nomogram was demonstrated using the calibration curve. Finally, ROC curves were constructed for the 1-year, 3-year, and 5-year risk predictions as well as for the independent risk factors to estimate the predictive performance of the models. The immune infiltration analysis was performed using two complementary algorithms to ensure robustness. First, single‑sample gene‑set enrichment analysis (GSEA) was used to quantify the relative abundance of 28 immune cell types based on a previously published gene signature [ 24 ]. The single-sample GSEA scores were computed using the “GSVA” R package with default parameters. The association between IKBIP expression and immune cell scores was assessed using Spearman’s correlation, p-values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR); FDR < 0.05 was considered statistically significant. Second, the CIBERSORT algorithm was applied with the LM22 signature matrix and 1,000 permutations to estimate the proportion of definitive immune cell types. Differences in immune cell abundance between the IKBIP subgroups were compared using the Wilcoxon rank-sum test [ 25 ]. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm [ 26 , 27 ], was employed to evaluate the predictive efficiency of immunotherapy response in the subgroups. Somatic mutation data (MAF files) for patients with CC were downloaded from the TCGA database. TMB was calculated as the total number of nonsynonymous mutations per megabase of coding region using the “maftools” R package. Patients were stratified into high- and low-TMB groups based on the median TMB values. The relationship between IKBIP expression and the TMB score was assessed using Spearman’s correlation. Survival differences between the TMB subgroups were evaluated using Kaplan–Meier analysis and the log-rank test. To explore the common chemotherapeutic drug sensitivity of patients with CC, the R package “pRRophetic” was used to predict the half‑maximal inhibitory concentration (IC₅₀) for each sample based on the Genomics of Drug Sensitivity in Cancer database [ 28 , 29 ]. Differences in IC₅₀ between the IKBIP expression groups were compared using the Wilcoxon rank‑sum test, and p-values were adjusted for multiple testing via the Benjamini–Hochberg method. Significant associations (adjusted p < 0.05) were visualized using the “ggplot2” R package. The GSEA software (version 4.2.3) was used to characterize the functional differences between CC samples stratified by IKBIP expression levels. Using predefined gene sets from the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and MSigDB Hallmark databases, we systematically analyzed the enriched biological processes and signaling pathways. Enrichment scores were calculated using 1,000 permutations, and gene sets with a normalized enrichment score absolute value > 1 and an FDR q-value < 0.05 were considered significantly enriched. HeLa and SiHa cells were obtained from Fuxiang Biotechnology Co. (Shanghai, China). CC cell lines (HeLa and SiHa) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with penicillin (100 U/mL), streptomycin (100 U/mL), and 10% fetal bovine serum at 37 °C in a 5% CO2 atmosphere. We implemented a lentivirus-based short hairpin RNA (shRNA) approach using three distinct shRNA sequences to downregulate IKBIP expression. The 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 into 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, fresh culture medium was added. Following a 48-h infection period, puromycin was used to select the successfully transduced cells. The efficacy of IKBIP knockdown was validated using western blotting and quantitative reverse transcription polymerase chain reaction. The resulting stable cell lines were used for the downstream functional assays. Total RNA was extracted from the cells using the TRIzol reagent. Subsequently, 1 µg of RNA was treated with a reverse transcription kit to eliminate contaminating genomic DNA, which was then converted into cDNA. The following IKBIP primers were used: Forward: 5′ATACGACGGATTTCAGGTTT3′ and Reverse: 5′CACTCTTTAGTTCGGTTAGCG3′. The following H-GAPDH primers were used: Forward: 5′GGGAAACTGTGGCTTGAT3′ and Reverse: 5′GAGTGGGTGTCGCTGTTGA3′. The cDNA was then amplified using real-time polymerase chain reaction 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. Total protein was extracted from cells using RIPA lysis buffer supplemented with 1% protease inhibitor cocktail tablets. Protein concentration was quantified using bicinchoninic acid assay kits after 30 min of incubation at 37 °C. A total of 30 µg of protein was denatured at 95 °C for 10 min, separated using sodium dodecyl sulfate–polyacrylamide gel electrophoresis on 10–12% gels, and then transferred to polyvinylidene difluoride membranes. Following a 2-h blocking period with 5% nonfat milk at room temperature, the membranes were incubated overnight at 4 °C with the following primary antibodies (1:5000). The membranes were then incubated with appropriate secondary antibodies at room temperature for 2 h. The blots were examined densitometrically using the e-BLOT software and imaged using an ECL chemiluminescence system (Bioground). β-actin functioned as the internal regulator. The cell proliferation capacity was evaluated using the CCK8 kits. Cells were seeded at a density of 1,000 cells/well in 96-well plates. CCK8 solution was added after the cells had adhered, and the cells were then incubated for 2 h. The absorbance of each well was measured at 450 nm to determine the baseline values. Subsequent absorbance measurements were performed daily for 5 days to ensure consistency in the timing of each measurement. HeLa and SiHa cells were cultured overnight in 24-well plates. The following day, the cells were treated with 50 µM EdU for 2 h. After incubation, the cells were fixed with 4% paraformaldehyde for 15 min and permeabilized using 0.5% Triton X-100 for 20 min. 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 33,342 to visualize the nuclei, and the percentage of EdU-positive cells was determined using fluorescence microscopy. 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% fetal bovine serum was added to DMEM in the lower chamber. Non-migratory cells in the top chamber were carefully removed after a predetermined incubation period. After migration to the bottom chamber, the cells were fixed with methanol and stained with 0.1% crystal violet for 15 min. Subsequently, neutral resin was applied for sealing. Using an inverted microscope, the number of stained cells in six randomly chosen areas at ×20 magnification was counted. Male BALB/c nude mice, weighing 18–22 g and aged 4–6 weeks, were housed in conventional settings with ad libitum access to food and water. The conditions included a 12-h light/dark cycle, 20–24 °C, and 50–60% humidity. To induce tumors, HeLa cells (4.0 × 10⁷ cells/mL) transfected with either IKBIP-targeting shRNA or an empty vector were suspended in phosphate-buffered saline and injected subcutaneously into the dorsal cervical area. On day 40 post-inoculation, the mice were intraperitoneally administered 2% pentobarbital sodium (50 mg/kg) to induce anesthesia and were then euthanized by cervical dislocation. The 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 approved all experimental procedures (protocol no.: A2023-223-01). Hematoxylin and eosin (HE) staining was used to assess the histological features of CC tissue sections obtained from subcutaneous tumors. The CC tissues were embedded in paraffin blocks, preserved in 10% formalin, and sectioned into five-micrometer-thick slices that were mounted on glass slides. Following rehydration and deparaffinization, the slices were stained with hematoxylin to observe the nuclei and counterstained with eosin to identify cytoplasmic features. The stained sections were examined under a microscope, and images were captured for further analysis. 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. Subsequently, the mice tissues were deparaffinized in xylene and dehydrated using a series of graded ethanol. Antigen retrieval was performed using a citrate solution. The sections were then incubated overnight at 4 °C with the IKBIP antibody (dilution 1:100, Rabbit, Abmart). The slices were incubated for 30 min at 37 °C with a suitable secondary antibody the following day. Subsequently, the specimens were stained with diaminobenzidine, counterstained with hematoxylin, and dehydrated using a series of graded alcohol solutions. Two pathologists blinded to the patients’ clinical information scored the results. The proportion of positive cells was used to determine the staining extent, and scores were assigned as follows: 1 = 0–10%, 2 = 10–25%, 3 = 50–75%, and 4 = 75–100%. There were four categories of staining intensity: 0, no staining; 1, mild staining; 2, moderate staining; and 3, high staining intensity. Extent and intensity ratings were used to determine the total immunohistochemistry (IHC) score. A score of ≤ 6 indicated low expression of IKBIP, whereas a score of > 6 indicated high expression. Most statistical analyses were performed using R studio software (version 4.1.2). The GraphPad Prism software (version 9.0) was used to evaluate the experimental data. All experiments were performed in triplicate, and the results are presented as the mean ± standard deviation. A t -test was used to statistically compare the two groups, and a p -value < 0.05 was considered statistically significant. Every experiment was performed at least three times to guarantee repeatability.

We explored the critical role of IKBIP in CC, suggesting its potential as a therapeutic target and predictive biomarker. Our findings indicated that IKBIP is overexpressed in CC tissues and is significantly associated with adverse clinical outcomes, immune infiltration, TMB, and drug sensitivity. The association with immune infiltration is particularly noteworthy because it is consistent with the increasing understanding of the critical role of the tumor microenvironment in the development of cancer and its response to treatment. The strong expression of IKBIP and its characterization as an oncogene extend our 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 etiological agent, downstream molecular pathways involving IKBIP may contribute to tumor development and progression. In our study, silencing IKBIP in CC cell lines led to a marked decrease in cell proliferation, migration, and invasion, supporting the hypothesis that IKBIP functions as a driver of cancer cell aggressiveness. These findings are consistent with studies on other malignancies, such as esophageal squamous cell carcinoma and glioblastoma, in which IKBIP has been implicated in enhancing tumor cell survival and migration, indicating a broad role of this protein in oncogenesis [ 14 , 30 ]. Additionally, the potential involvement of IKBIP in key signaling pathways, including the NF-κB and JAK-STAT3 pathways, represents another important aspect of its proposed role in CC. The NF-κB pathway is a central regulator of inflammation, immune responses, and cell survival and is often associated with promoting tumor growth, immune evasion, and apoptosis resistance in many cancers [ 31 , 32 ]. Moreover, our data suggest that IKBIP potentially influences the JAK-STAT signaling pathway, which may partially delineate its mechanism of action in CC. The JAK-STAT3 pathway is frequently activated in many types of cancers, including CC [ 33 , 34 ]. It plays a pivotal role in the regulation of a wide range of cellular processes such as proliferation, survival, immune modulation, and metastasis [ 35 ]. We observed that high IKBIP expression was associated with the activation of JAK-STAT3 signaling, as evidenced by 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 indicate a potential link between IKBIP and this signaling axis in CC, suggesting that IKBIP may partially promote tumor growth via activation of the JAK-STAT3 pathway. The use of colivelin, a JAK-STAT pathway agonist, in rescue experiments further supported the potential of IKBIP to modulate signaling cascades critical for tumor progression, suggesting avenues for targeted interventions. Additionally, the finding that IKBIP overexpression increased the activation of the AKT signaling pathway in esophageal squamous cell carcinoma cells [ 14 ] further underscores the potential significance 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 CC is often associated with local immune suppression, involving various types of immune cells within the microenvironment, including tumor-infiltrating lymphocytes, macrophages, and regulatory T cells. By secreting cytokines and chemokines, these cells promote tumor development and spread [ 38 , 39 ]. Tumor-associated macrophages mostly display an M2 phenotype, facilitating tissue repair and immune suppression, which aids in tumor escape from host immune surveillance [ 40 ]. Furthermore, the immune microenvironment in CC is closely linked to HPV infection, which influences tumorigenesis and cancer progression by modulating immune response and apoptosis [ 41 ]. Moreover, our integration of bioinformatic 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 patients with CC based on IKBIP expression levels provides a valuable tool for clinical decision making. The correlation between IKBIP and immune infiltration in CC has implications for immunotherapy. Our data indicate that IKBIP may alter CD8 T-cell dynamics in the tumor microenvironment, making it a viable target for the development of immune-based therapies for CC. Furthermore, Li et al. discovered that IKBIP expression was strongly associated with immunosuppressive cells in pan-cancer samples from TCGA. Tumor-associated fibroblasts, regulatory T cells, and tumor-associated macrophages are examples of immunosuppressive cells. Furthermore, the expression of immunosuppressive genes and immune checkpoints positively correlated with IKBIP expression in several tumor types, including CC [ 12 ]. Importantly, our study addressed the potential clinical implications of IKBIP as a predictor of response to chemotherapy. Despite 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 [ 42 ]. However, many CC cells become resistant to these medications, resulting in treatment failure and poor clinical outcomes [ 43 ]. The IKBIP-related model constructed through bioinformatic analysis showed significant correlations with various drugs, suggesting that IKBIP expression may play a role in guiding clinical medication choices in future studies. This implies that targeting IKBIP may inhibit tumor progression and improve the efficacy of chemotherapy, thereby offering a therapeutic strategy to overcome chemotherapy resistance in CC; however, preclinical and clinical investigations remain pending [ 44 , 45 ]. This study has some limitations. Although our in vitro and in vivo results provide significant preclinical insights, the mechanistic interplay between IKBIP expression, immune modulation, and cancer biology warrants further investigation and rigorous validation through clinical trials. Longitudinal studies with larger patient cohorts are required to validate the prognostic significance of IKBIP. Additionally, functional studies exploring the interactions of IKBIP with other oncogenic pathways and immune mediators should be conducted to determine comprehensive therapeutic strategies.

Our research demonstrated that IKBIP is essential for the development of CC and that poor clinical outcomes, such as advanced tumor stage, metastasis, and decreased survival, are strongly associated with high IKBIP expression. We showed that silencing IKBIP suppressed key tumorigenic processes, including cell proliferation, migration, invasion, and modulation of the JAK-STAT3 signaling pathway. Additionally, IKBIP inhibition enhanced chemotherapy sensitivity, providing a promising therapeutic strategy for overcoming chemotherapy resistance in CC. These results highlight the potential of IKBIP as a therapeutic target and predictive biomarker in CC. To enhance patient outcomes in CC, further clinical research is required to confirm these results and investigate the therapeutic implications of focusing on IKBIP in conjunction with other therapies.

This study has several limitations that should be acknowledged. First, although our in vitro and in vivo experiments support a functional role for IKBIP in cervical cancer progression, the precise molecular mechanisms by which IKBIP regulates JAK/STAT3 signaling and immune modulation remain incompletely defined. Second, the prognostic and immunological associations of IKBIP were primarily derived from retrospective transcriptomic datasets (TCGA and GEO), and validation in larger, independent, and prospective clinical cohorts is required to confirm its clinical utility. Third, while bioinformatic analyses suggested a potential relationship between IKBIP expression and chemotherapeutic response, direct experimental validation of IKBIP-mediated drug sensitivity and resistance was not performed. Finally, the complex interactions between IKBIP, HPV-related oncogenic processes, and the tumor immune microenvironment warrant further investigation to fully elucidate the translational potential of targeting IKBIP in cervical cancer.

Cervical cancer (CC) is one of the most common cancers affecting women. Given the complex disease mechanisms and significant tumor heterogeneity associated with CC, there is an urgent need for a deeper understanding of its pathological aspects [ 1 ]. The primary etiological factor for CC is human papillomavirus (HPV) infection, 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 are prone to metastasis and treatment resistance [ 4 , 5 ]. This often results in unfavorable prognoses for the affected patients. To address these challenges, further research is required to elucidate the molecular mechanisms underlying tumor progression, metastasis, and treatment resistance [ 6 , 7 ]. Identifying novel therapeutic targets and screening for reliable biomarkers are critical for 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 a target gene of p53 and plays a critical role in its proapoptotic functions. Additionally, IKBIP is considered an important regulatory factor in inflammation, significantly contributing to pathways such as the Toll-like receptor 7/8 signaling cascade and interleukin-1 family signal transduction [ 10 , 11 ]. Moreover, IKBIP has been identified as a potential biomarker for several cancers, including gliomas, digestive system cancers, and renal cell carcinoma [ 12 – 14 ]. Recent studies also indicate that IKBIP contributes to the regulation of the tumor immune microenvironment and is associated with CD8 T-cell exhaustion in hepatocellular carcinoma [ 15 ]. However, the role of IKBIP in the pathogenesis of CC remains unclear. Therefore, we aimed to analyze IKBIP expression, its correlation with clinicopathological parameters, and its association with the prognosis in CC. We investigated the function of IKBIP in CC development to identify potential immunotherapeutic targets. In this study, we combined RNA sequencing information from the Gene Expression Omnibus (GEO) database with the Cancer Genome Atlas (TCGA) [ 16 ] using bioinformatic methodologies to assess the expression of IKBIP and prognosis in CC. Based on these clinical features, we established a prognostic model for patients with CC. Additionally, we examined the relationship between IKBIP expression and immune cell infiltration, tumor mutation burden (TMB), and drug sensitivity in CC [ 17 ]. Our findings indicated that IKBIP, acting as an oncogene, significantly promoted the proliferation and metastasis of CC cells. Consequently, IKBIP may serve as a potentially effective biomarker for predicting the prognosis of CC and contribute to the development of personalized treatment strategies for patients.

Below is the link to the electronic supplementary material. Supplementary Material 1: Supplementary Figure S1 Correlation between IKBIP expression and JAK-STAT3 signaling pathway activity in cervical cancer Supplementary Material 1: Supplementary Figure S1 Correlation between IKBIP expression and JAK-STAT3 signaling pathway activity in cervical cancer