Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer

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Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE7803, GSE9750, GSE63514 and GSE67522. The correlation between the expression of pyroptosis-related genes in normal cervical tissue and cervical cancer tissue was analyzed through the TCGA database. Using LASSO regression algorithm to establish a prediction model for the obtained genes related to pyroptosis. Exploring the functions of differentially expressed genes through GO and KEGG pathway analysis. Using PPI network analysis to screen hub genes, using CIBERSORT method for immune infiltration analysis of prognostic genes, and finally predicting drug-gene interactions in DGIdb database. A total of 19 pyroptosis-related genes were screened from the GEO dataset of cervical cancer tissues, revealing their regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production, and cellular response to environmental stimuli. LASSO regression algorithm was used to establish prediction models for 7 of these genes, and 3 pyroptosis-related genes (SPP1, VEGFA, and CXCL8) closely associated to the prognosis of cervical cancer were identified. qRT-PCR confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P<0.05). SPP1, VEGFA, and CXCL8 are most closely related to macrophages, Th2, and neutrophils, respectively. 148 potential targeted drugs targeting key genes were predicted, providing a possible basis for predicting the prognosis and treatment of cervical cancer. Knocking down SPP1 can inhibit cell proliferation and migration in cervical cancer cells in vitro. In conclusion, our study has identified key genes related to pyroptosis in cervical cancer, which potentially become effective clinical prognostic biomarkers, and further research is needed to explore their underlying mechanisms. cervical cancer cell pyroptosis Lasso regression enrichment analysis immune infiltration drug gene prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction In the ranking of incidence rate and mortality of female malignant tumors in 2022, cervical cancer ranks fourth after breast cancer, lung cancer and colorectal cancer. It is estimated that there will be about 660000 new cases and 350000 deaths worldwide in 2022, which is one of the most harmful malignant tumors to women [1]. 85% of cervical cancer cases occur in developing countries, and this data should be taken seriously by the healthcare departments of these countries. Long term persistent human papillomavirus (HPV) infection is the main risk factor for cervical cancer, but many other genetic factors have also been found to have an impact on the onset of cervical cancer. In 2020, WHO updated the classification of female genital tumors based on new pathological findings, dividing cervical cancer into HPV related and HPV independent tumors. Squamous cell carcinoma is the most common in the former, while it is very rare in the latter. At present, people are committed to searching for biomarkers and targeted drugs that can be used for diagnosis and treatment of cervical cancer at various stages based on the continuously accumulated and updated information in oncology [4]. Cellular pyroptosis is an inflammatory programmed cell death characterized by cell swelling, membrane perforation, and release of cellular contents such as IL-1 β and IL-18. This cell death process is triggered by the activation of caspase-1/4/5/11 proteins by the inflammatory body, especially caspase-1, which can cleave gasdermin (GSDMD) protein, leading to the formation of pores on the cell membrane [5,6]. Cellular pyroptosis has shown great potential in cancer treatment, as it not only promotes anti-tumor immune responses and inhibits tumor growth, but also certain drugs and compounds can induce cancer cell pyroptosis, providing new strategies for cancer treatment [7]. However, excessive activation of cell pyroptosis may lead to inflammatory diseases and tumor progression, thus requiring precise control of its activation and regulatory mechanisms [8]. In recent years, an increasing number of studies have shown a close relationship between cervical cancer and pyroptosis. Some studies focus on exploring the prognostic features of pyroptosis in cervical cancer [9], while others attempt to predict the prognosis and immune microenvironment of cervical cancer through pyroptosis related mechanisms [10]. Therefore, our research aims to identify key targets related to cervical cancer necrosis and predict potential therapeutic drugs. Further research on cell pyroptosis may help discover a new treatment strategy for cervical cancer. This study determined the differential expression profiles of apoptosis related genes in normal cervical tissue and cervical cancer tissue through bioinformatics analysis, and found that the differential genes were enriched in pathways such as inflammation regulation. In addition, we also predicted and analyzed the prognostic genes related to cervical cancer necrosis, and based on these genes, predicted drug gene interactions to screen candidate drugs for cervical cancer treatment. The detailed schematic diagram of the workflow in this study is shown in Figure 1. In summary, our research may provide more evidence for the diagnosis, prognosis, and treatment of cervical cancer. 2 Materials and methods 2.1 Acquisition and Preprocessing of Cervical Cancer Dataset This study obtained gene expression data of cervical cancer patients from the GEO database (http://www.ncbi.nlm.nih.gov/geo). A total of 4 datasets (GSE7803, GSE9750, GSE63514, and GSE67522) were selected for subsequent analysis. These 4 datasets were the results of transcriptome analysis of cervical tissue samples from cervical cancer patients. Detailed information on the 4 selected datasets is shown in Table 1. Table 1 GSE datasets referenced in this study GSE dataset Organism Sample number PMID GSE7803 Homo sapiens Normal：10 Tumor：21 17974957 GSE9750 Homo sapiens Normal：24 Tumor：42 18506748 GSE63514 Homo sapiens Normal：24 Tumor：28 26056290 GSE67522 Homo sapiens Normal：22 Tumor：20 28402266 2.2 Identification of differentially expressed genes related to apoptosis In addition, this study also obtained information from the GeneCards database (https://www.genecards.org) for research. By retrieving genes related to apoptosis, we found 831 related genes. By using the R package limma, we screened out 55224156 and 55 differentially expressed genes in four cervical cancer data sets (GSE7803, GSE9750, GSE63514, GSE67522) using the P≤0.05 and |logFC|>1 methods, respectively. Using the Venn diagram intersection method, we screened out 19 differentially expressed genes from the four data sets, which were related to cervical cancer necrosis. 2.3 Correlation analysis The Pearson correlation analysis was used to calculate and obtain the expression correlation of 19 apoptosis-related DEGs in all samples and cervical cancer samples. The data used for the analysis came from the TCGA database. The process was completed using the rcorr function in the R package hmisc and visualized using the R package corrplot. 2.4 LASSO Regression The prediction model of cervical cancer apoptosis-related genes was constructed using the minimum absolute value shrinkage and selection operator regression algorithm. In the penalty function constructed in this study, as the λ value increases, the regression coefficient continues to converge and finally converges to 0. This study uses λ 1. - se. as the final equation selection standard to obtain the coefficient, and the selected 7 genes are used for prognostic analysis through the TCGA dataset. 2.5 Functional Enrichment Analysis This study performed Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, which was implemented by the "clusterProfiler (3.18.1)" R package. In this analysis, the biological processes of cross-talk genes and apoptosis-related genes in four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522) were evaluated. 2.6 protein-protein interaction (PPI) network analysis The initial protein interaction network (PPI) can be obtained by importing the differentially expressed genes into the interaction gene search tool (STRING, www.string-db.org). These initial images are not beautiful, so this study uses Cytoscape software (version 3.10.2) to visualize the protein interaction network, and further uses the cytoHubba plug-in of the software to sort the differentially expressed genes in the PPI network by degree centrality value, which increases the information content of the PPI network. 2.7 RNA extraction and real-time fluorescence quantitative PCR (qRT-PCR) The RNA of 3 cervical cancer tissues and adjacent tissues was extracted using TRIzol reagent (TaKaRa Company) and reverse transcribed (HiScript® III RT SuperMix Vazyme). The primers were synthesized by Qingke Biotechnology Co., Ltd., and the sequences are shown in Table 2. qRT-PCR was performed on the Step-One Plus real-time fluorescence quantitative PCR instrument (Thermo Fisher Company) using qPCR SYBR Green premix (Vazyme Company). Three biological replicates were performed. Table 2 Sequences of primer used for qRT-PCR Gene Forward primer sequences Reverse primer sequences SPP1 CTCCATTGACTCCAACGACTC CAGGTCTGCGAAACTTCTTAGAT VEGFA TGGCTCACTGGCTTGCTCTA ATCCAACTGCACCGTCACAG CXCL8 TTGGCAGCCTTCCTGATTT TCAAAAACTTCTCCACAACCC β-actin CGGGAAATCGTCCGTGAC CCGTGTTGGCGTAGAGGT 2.8 Immune infiltration analysis of prognostic genes The CIBERSORT method was used to analyze the immune infiltration of prognostic genes. This method is a computational method that quantifies the relative proportions of different immune cell subsets in tumor tissues based on gene expression data. After standardizing the prognostic gene data related to cervical cancer necrosis, a feature matrix containing marker genes of specific cell types was constructed. The gene expression data and feature matrix were then input into the CIBERSORT software for computational analysis to estimate the abundance of each immune cell subset in the sample, thereby analyzing the immune infiltration of the tumor. 2.9 Prediction of Drug Gene Interactions Given that the Drug-Gene Interaction Database (DGIdb, http://www.dgidb.org/) is rich in content and is a resource that integrates drug-gene interaction information from multiple sources, we used it to predict drug-gene interactions. DGIdb version 5.0 contains up to 12 drug data sources (ChemIDplus, HemOnc, NCIt, Drugs@FDA, HGNC, RxNorm, ChEMBL, Ensembl, Guide to Pharmacology, CIViC, PharmGKB, Wikidata), aggregating a large amount of drug-gene interaction information from different data sources. Because it provides data on the relationship between specific gene variants and drug responses, researchers can quickly identify potential drug targets and biomarkers. Prognostic genes associated with cervical cancer necrosis were imported into DGIdb for analysis to explore drugs or small molecule organic compounds associated with them. 2.10 Cell culture The human cervical cancer cell lines (HeLa and SiHa) were purchased from the China Type Culture and Preservation Center (Wuhan University, Wuhan, China) and cultured in RPMI-1640 medium (Gibco, USA), which contained 10% fetal bovine serum. Among them, HeLa is a human cervical adenocarcinoma cell line, and SiHa is a human cervical squamous cell carcinoma cell line. All cells were identified by short tandem repeat analysis and maintained in a culture state under a humid environment of 37 °C and 5% CO2. 2.11 siRNA transfection SPP1 small interfering RNA (SPP1-siRNA) was provided by Shanghai GenePharma Co.,Ltd. The transfection process was carried out with Lipofectamine 3000 (Invitrogen, USA) in accordance with the manufacturer's protocol. The transfected cells were harvested and used for subsequent experiments. 2.12 Cell proliferation detection The MTT assay kit (Beyotime, Shanghai, China) is used to determine cell viability. Cells in logarithmic growth phase were inoculated at a density of 1000 cells per well into a 96 well plate and cultured for 24, 48, and 72 hours. Then, MTT solution (0.5 mg/μL) was added and incubated at 37 °C for 4 hours. Subsequently, 100 μL of dimethyl sulfoxide (DMSO) was added to separate the crystals and the plate was stirred at 37 °C for 20 minutes. The absorbance is measured using an enzyme-linked immunosorbent assay (ELISA) reader at 490 nm. 2.13 Wound healing and Transwell assay In the wound healing assay, cells were seeded at a density of 3 × 10^5 cells per well in 6-well plates. After siRNA transfection, the cells were cultured for 48 h. After the cells formed a confluent monolayer, a 200 μL pipette tip was used to scratch the cell surface. Images of the wound area were taken at 0 and 24 h using an inverted optical microscope (Olympus, Japan). The wound healing rate was quantified using ImageJ software (version 1.51), and the migration ability of cancer cells was evaluated. In the Transwell assay, 200 μL of serum-free medium containing 5 × 10^4 cells was added to the upper chamber that was not coated (migration assay) or coated with Matrigel (invasion assay), while 600 μL of complete medium was added to the lower chamber. After incubation at 37°C for 24 h, cells that migrated or invaded through the 8μm pore size membrane were fixed with 4% formaldehyde and stained with 0.1% Safranin O. The number of migrated or invaded cells was counted in five random fields for each sample at a magnification of ×200. All experiments were repeated three times. 2.14 Western blot The total protein was extracted using the previous method and a protein blotting experiment was conducted [11]. The specific antibodies used in this study were rabbit anti-SPP1 antibody (1:1000, ProteTek, Wuhan, China) and mouse anti-GAPDH antibody (1:1000, Cell Signaling Company). GAPDH was used as the sample control. The band intensities were quantitatively analyzed using Image J software. 2.15 Statistical Analysis All statistical analyses were conducted using R version 4.1.1 and GraphPad Prism 8 software. Univariate and multivariate logistic regression analyses were employed to evaluate the diagnostic value of the predictive model. All statistical tests were two-tailed, and a P value < 0.05 indicated a statistically significant difference. 3 Results 3.1 Definition of pyroptosis-related-genes in cervical cancer We obtained it from the GEO database（https://www.ncbi.nlm.nih.gov/geo/）The CCP has selected four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522）， Differential genes were screened using P ≤ 0.05 and |LogFC| ≥ 1. GSE7803 identified 864 differentially expressed genes, GSE63514 identified 3687 differentially expressed genes, and GSE67522 identified 1131 differentially expressed genes (Figure 2A). These genes were then analyzed using the GeneCards database (https://www.genecards.org) by searching for genes related to apoptosis, 831 genes related to apoptosis were identified. Four cervical cancer datasets were intersected with genes related to apoptosis, resulting in 55,224,156 and 55 potential cancer genes (Figure 2B). 3.2 Characteristics and differences of pyroptosis-related-genes in cervical cancer We used four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522) to screen for 19 pyroptosis-related-genes, including DNMT1, MELK, POLA1, SMC4, EZH2, CDK1, BRCA1, KLF4, AQP1, DEPTOR, KIF23, AIM2, SPP1, KIF11, S100A12, CITED2, IGF2BP3, VEGFA, CXCL8 (Figure 3A) and shows the chromosomal positions of these 19 cervical cancer apoptosis related genes (Figure 3B). In the TCGA database, we further validated the expression of 19 genes in normal cervical tissue and cervical cancer tissue. The results showed that except for S100A12 and VEGFA, the expression of other genes was significant. Among them, 13 genes including DNMT1, MELK, POLA1, SMC4, EZH2, CDK1, BRCA1, KIF23, AIM2, SPP1, KIF11, IGF2BP3, and CXCL8 are highly expressed in cervical cancer tissues, while 4 genes including KLF4, AQP1, DEPTOR, and CITED2 are lowly expressed in cervical cancer tissues (Figure 3C). We further plotted heatmaps of 19 genes using the TCGA database (Figure 3D), and then conducted PPI analysis on these 19 genes using the STRING platform to further explore their interactions with cervical cancer pyroptosis-related-genes(Figure3E). 3.3 Enrichment analysis of pyroptosis-related-genes in cervical cancer In order to explore the differences in the biological processes of pyroptosis-related-genes in cervical cancer, we performed GO and KEGG enrichment (gene set enrichment analysis) on samples obtained from the intersection of four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522) and necrosis genes, revealing the regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production, and cellular response to environmental stimuli (Figure 4A to 4D). 3.4 Correlation analysis of pyroptosis-related-genes in cervical cancer Further calculate and analyze the expression correlation of 19 pyroptosis-related-genes in cervical cancer related DEGs in normal cervical tissue and cervical cancer samples through the TCGA database (Figure 5A and 5B). 3.5 Predicting pyroptosis-associated prognostic genes in cervical cancer We applied 19 genes to the LASSO regression algorithm and included 7 genes in the prediction model based on the optimal lambda value (Figures 6A, 6B). The risk score is as follows: Risk score=(-0.18106680996544 * POLA1 exp.)+(-0.123503509447933 * KLF4 exp.)+ (-0.0457937381093486 * AQP1 exp.)+4 (0.14098424549269 * PP1 exp.)+5 (0.0909218549036766 * CITED2 exp.)+8 (0.1657192995788403 * VEGFA exp.)+0.1537547581067 * CXCL8 exp.). Prognostic analysis using the TCGA dataset on the seven genes mentioned above showed that SPP1, VEGFA, and CXCL8 are closely related to prognosis. The overall survival (OS) and progression free survival (PFS) of cervical cancer patients with high expression of SPP1 were significantly reduced (Figure 6C, P=0.022 and P=0.039, respectively). The OS and disease-specific survival rate (DFS) of cervical cancer patients with high expression of VEGFA were significantly reduced (Figure 6D, P=0.002 and P=0.010, respectively). The OS, DFS, and PFS of cancer patients with high expression of CXCL8 were significantly reduced (Figure 6E, P ≤ 0.001, P<0.001, and P<0.008, respectively). 3.6 Immune infiltration analysis of pyroptosis-associated prognostic genes in cervical cancer We identified the differences in immune infiltration results between high and low expression groups of SPP1, VEGFA, and CXCL8 in the TCGA database (Figures 7A-C). The correlation between SPP1, VEGFA, and CXCL8 in immune infiltrating cells (Figure 7D). The results showed that SPP1, VEGFA, and CXCL8 were most closely related to macrophages, Th2, and neutrophils, respectively (Figure 7E-7G). 3.7 Structural model diagram and drug gene interaction prediction based on pyroptosis-associated prognostic genes in cervical cancer. Through the UniProt database (https://www.uniprot.org/) retrieve the amino acid sequences of SPP1, VEGFA, and CXCL8, and use the SWISS-MODEL method to predict the folding structure models of SPP1, VEGFA, and CXCL8 (Figure 8A-8C). In DGIdb database ( http://dgidb.org/). The analysis of drug gene interactions revealed 148 drugs/compounds targeting SPP1, VEGFA, and CXCL8. The top 30 targeted drugs/compounds and their order based on the "interaction group score" in the DGIdb database(Figure.8D). 3.8 Verification of the crucial pyroptosis-associated prognostic genes in cervical cancer in the signature Using qRT-PCR further confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P<0.05) (Figure 9A). It is reported that SPP1 plays an important role in epithelial colorectal cancer, gastric cancer and other tumors [12-14]. Therefore, we focus on further validation of hub genes (SPP1). To validate the biological function of SPP1 in cervical cancer cells, we knocked down SPP1 using siRNA in SiHa and HeLa cells (Figure 9B). Knockdown of SPP1 by MTT can significantly reduce the proliferation of cervical cancer cells (Figure 9C). The wound healing rate of SiHa and HeLa cells was significantly inhibited by SPP1 knockdown, as indicated by the results of the wound-healing assay (Figure 9D). The invasion ability of SiHa and HeLa cells was significantly inhibited in transwell assay following SPP1 knockdown (Figure 9E). In summary, the results are consistent with our bioinformatics analysis. 4 Discussion Pyroptosis is also known as cellular inflammatory necrosis. GSDMD-NT (cleavage product of Gasdermin D) plays a central role in the process of pyroptosis. Activated caspase-1, caspase-3 and granzyme B (GZMB) can catalyze the enzymatic cleavage of GSDMD[15]. Studies have shown that HPV E7 can inhibit the expression of inflammasomes through the E3 protein ligase TRIM21 and promote the degradation of IFI16 inflammasomes, which is not conducive to cell apoptosis and thus promotes the malignant transformation of cervical cells[16]. On the contrary, GZMB and activated caspase-3 can activate pyroptosis, which may be a good strategy for tumor immunotherapy. In addition, since advanced cervical cancer is associated with high expression of granzyme B (GZMB) and IL-1β, this provides a reference for the prognostic assessment of cervical cancer[17]. However, the role of apoptosis-related genes in cervical cancer has not been fully elucidated, and our study aims to reveal this role. This study is the first to clarify the expression pattern of apoptosis-related genes in cervical cancer tissues. We selected four cervical cancer datasets (GSE7803, GSE9750, GSE63514, and GSE67522) from the GEO database, further screened and identified 19 genes related to apoptosis in cervical cancer cells, and verified them in the TCGA database. Compared with normal cervical tissues, 13 genes (including DNMT1, MELK, POLA1, SMC4, EZH2, CDK1, BRCA1, KIF23, AIM2, SPP1, KIF11, IGF2BP3, and CXCL8) were expressed at higher levels in cervical cancer tissues, and 4 genes (including KLF4, AQP1, DEPTOR, and CITED2) were expressed at lower levels in cervical cancer tissues. We performed protein interaction analysis on these 19 apoptosis-related genes and found that the core genes included E2H2, BRCA1, DNMT1, and CDK1. To further explore the differences in biological processes of these 19 cervical cancer necrosis-related genes, we performed GO and KEGG enrichment analysis, and the results revealed the enrichment of differentially expressed genes in the regulation of endopeptidase activity, inflammatory response, positive regulation of cytokine production, and cell response to environmental stimuli. The results showed that the enrichment pathways of pyroptosis clusters in different cervical cancer patients were different, which suggests that we should take targeted treatment measures for patients with different expression levels of these pyroptosis-related genes. Among the many treatment strategies for malignant tumors, inducing tumor cell necroptosis has attracted widespread attention due to its good effectiveness. In this treatment process, the immune components in the tumor microenvironment play a crucial regulatory role. Tumor cell necroptosis can be induced by regulating and changing the functions of immune cells such as cytotoxic T lymphocytes, natural killer cells, tumor-associated macrophages and neutrophils. In cancer immunotherapy of cancer cells and tumor cells, cytotoxic CD8+T cells play a major role. They induce tumor cell necroptosis through the granzyme pathway, thereby triggering immune activation. Activated cytotoxic lymphocytes release GzmA/B to tumor cells through perforin, directly or indirectly triggering GSDM-dependent necroptosis, thereby triggering immune activation. Activated natural killer cells induce tumor cell apoptosis, effectively triggering the release of intracellular proinflammatory cytokines, and rapidly inducing tumor inflammation[20]. Subsequently, natural killer cells play a key role in inducing tumor cell necroptosis through GSDME[21]. One of the important sources of tumor necrosis factor α (TNF-α) in the tumor microenvironment (TME) is macrophages, which can promote cell necrosis by secreting TNF-α and activating caspase-8[22]. Specifically, under hypoxic conditions, the formation of the nPD-L1/p-Stat3 complex leads to increased expression of Gasdermin C (GSDMC), which is cleaved by TNF-α-activated caspase-8, thereby inducing cell necrosis[23]. We used the LASSO regression algorithm to construct a prediction model for these 19 differentially expressed genes, and finally used 7 of them to establish a prediction model to calculate the risk score. Subsequently, we used the TCGA dataset to perform prognostic analysis on these 7 genes and found that 3 of them, namely SPP1, VEGFA, and CXCL8, were closely related to the prognosis of cervical cancer. Secretory phosphoprotein 1 (SPP1), also known as osteopontin (OPN), early T lymphocyte activation protein 1 (ETA-1), or rickettsial resistance protein (Ric), belongs to the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. SPP1 can specifically bind to and activate matrix metalloproteinases (MMPs), a property that has been confirmed by Su et al. (2020) [24]. Studies have confirmed that in macrophages, the SPP1 gene expression level is high and interacts with cervical epithelial cells through the SPP1-CD44 axis, which plays a key role in tumor progression and is closely related to the poor prognosis of cervical cancer [25-26]. In the process of promoting tumor growth, VEGF-A is involved in the proliferation, migration and survival of new capillary endothelial cells. Specifically, VEGF-A participates in regulating the invasion and migration ability of tumors by binding to vascular endothelial growth factor receptor 1 (VEGFR-1, also known as Flt-1) and VEGFR-2 (also known as Flk-1) [27]. In cervical cancer tissues, the overexpression of CXCL8 is regulated by E6/E7 of HPV 16 and 18, and its expression is positively correlated with the expression of SERPINB3. It mainly forms the CXCL8/CXCR1 and CXCL8/CXCR2 signaling axes by binding to CXCR1 and CXCR2 of the GPCR family, thereby playing an active role in tumor growth, proliferation, metastasis and drug resistance [28-29]. At present, in addition to the first-line treatment drugs cisplatin, carboplatin and fluorouracil, the second-line treatment drugs gemcitabine, pemetrexed, topotecan, vinorelbine and irinotecan, as well as the immunotherapy drugs pembrolizumab, sepilizumab-vedotin-tftv and bevacizumab are all used in the clinical treatment of cervical cancer. Among them, the first two drugs are mainly used for cervical cancer patients with positive PD-L1 and PD-1, while the latter two drugs target tissue factor and anti-angiogenesis respectively to exert therapeutic effects. [30] This study identified three major apoptosis-related genes (SPP1, VEGF-A, CXCL8) associated with the prognosis of cervical cancer, and obtained the drug-gene interaction results of these three genes (SPP1, VEGF-A, CXCL8) by searching the DGIdb database, with a total of 148 targeted drugs. Knockdown of SPP1 can inhibit the proliferation and migration of cervical cancer cells in vitro. In summary, through systematic and comprehensive bioinformatics analysis, we identified key genes associated with cell pyroptosis in cervical cancer patients and predicted up to 148 potential targeted drugs/compounds for these genes. We believe that further exploration of the therapeutic effects of these drugs/compounds on cervical cancer may have positive significance. Due to the limitations of some objective conditions, this study does have certain limitations and shortcomings. First, this study did not consider factors that may be related to gene expression, such as the age, incidence, and pathological grade of cervical cancer patients; second, this study should expand the number of genes studied and increase related in vivo and in vitro experiments. Declarations Ethics statement The study was conducted according to the principles expressed in the Declaration of Helsinki and was approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (No: 2021-S353). Written informed consent was obtained from all patients. Consent for publication Not applicable. Availability of data and material The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Funding This work was supported by the Open Research Fund of Hubei Province Key Laboratory of Precision Radiation Oncology（No.2024ZLJZFL003）, Key Laboratory of Anesthesiology and Resuscitation (Huazhong University of Science and Technology), Ministry of Education (No.2024MZFS010) and Knowledge Innovation Program of Wuhan-Shuguang Project (2023020201020563). Authors’ contributions Zongchen Hou: Writing – review & editing, Writing – original draft, Software, Investigation, Formal analysis, Data curation. Guiju Tang: Validation, Formal analysis. Hang Chu : Resources, Project administration. Zhengxi Wang: Supervision, Conceptualization. Lufang Wang: Funding acquisition, Conceptualization. Competing interests The authors declare that they have no competing interests. Acknowledgements We would like to acknowledge the GEO (GSE7803, GSE9750, GSE63514 and GSE67522) network for providing data. References Bray F, Laversanne M, Sung H, et al. 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Jaime-Sánchez P, Catalán E, Uranga-Murillo I, Aguiló N, Santiago LP ML, et al. Antigen-specific primed cytotoxic T cells eliminate tumour cells in vivo and prevent tumour development, regardless of the presence of anti-apoptotic mutations conferring drug resistance. Cell Death Differ. 2018;25(9):1536–48. Zhou Z, He H, Wang K, Shi X, Wang Y, Su Y, et al. Granzyme A from cytotoxic lymphocytes cleaves GSDMB to trigger pyroptosis in target cells. Science(New York, NY). 2020;368(6494):eaaz7548. Tsuchiya K. Switching from apoptosis to pyroptosis: gasdermin-elicited inflammation and antitumor immunity. Int J Mol Sci. 2021;22(1):426. Tang R, Xu J, Zhang B, Liu J, Liang C, Hua J, et al. Ferroptosis, necroptosis, and pyroptosis in anticancer immunity. J Hematol Oncol. 2020;13(1):110. Ji X, Huang X, Li C, Guan N, Pan T, Dong J, et al. Effect of tumor-associated macrophages on the pyroptosis of breast cancer tumor cells. Cell Commun Signal. 2023;21(1):197. Murdoch C, Muthana M, Coffelt SB, Lewis CE. The role of myeloid cells in the promotion of tumour angiogenesis. Nat Rev Cancer. 2008;8(8):618–31. Su X, Xu BH, Zhou DL, et al. Polymorphisms in matricellular SPP1 and SPARC contribute to susceptibility to papillary thyroid cancer. Genomics. 2020;112(6):4959-4967. Sheng B, Pan S, Ye M, et al. Single-cell RNA sequencing of cervical exfoliated cells reveals potential biomarkers and cellular pathogenesis in cervical carcinogenesis. Cell Death Dis. 2024;15(2):130. Song JY, Lee JK, Lee NW, Jung HH, Kim SH, Lee KW. Microarray analysis of normal cervix, carcinoma in situ, and invasive cervical cancer: identification of candidate genes in pathogenesis of invasion in cervical cancer. Int J Gynecol Cancer. 2008;18(5):1051-1059. Hicklin DJ, Ellis LM. Role of the vascular endothelial growth factor pathway in tumor growth and angiogenesis. J Clin Oncol. 2005;23(5):1011-1027. Brat DJ, Bellail AC, Van Meir EG. The role of interleukin-8 and its receptors in gliomagenesis and tumoral angiogenesis. Neuro Oncol. 2005;7(2):122-133. Fernandez-Avila L, Castro-Amaya AM, Molina-Pineda A, Hernández-Gutiérrez R, Jave-Suarez LF, Aguilar-Lemarroy A. The Value of CXCL1, CXCL2, CXCL3, and CXCL8 as Potential Prognosis Markers in Cervical Cancer: Evidence of E6/E7 from HPV16 and 18 in Chemokines Regulation. Biomedicines. 2023;11(10):2655. Abu-Rustum NR, Yashar CM, Arend R, et al. NCCN Guidelines® Insights: Cervical Cancer, Version 1.2024. J Natl Compr Canc Netw. 2023;21(12):1224-1233. Additional Declarations No competing interests reported. Supplementary Files GAPDH.jpg SPP1.jpg Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in BMC Medical Genomics → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers invited by journal 07 Jul, 2025 Editor assigned by journal 25 Jun, 2025 Editor invited by journal 25 Jun, 2025 Submission checks completed at journal 24 Jun, 2025 First submitted to journal 24 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6932347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482090717,"identity":"4800c98a-46c3-4b34-b216-3379f744f414","order_by":0,"name":"Zongchen Hou","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zongchen","middleName":"","lastName":"Hou","suffix":""},{"id":482090721,"identity":"60ecbf6e-d144-441f-8f96-8388c7f2dcec","order_by":1,"name":"Guiju Tang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guiju","middleName":"","lastName":"Tang","suffix":""},{"id":482090722,"identity":"debd61e3-d2ea-46da-a036-f142222479b2","order_by":2,"name":"Hang Chu","email":"","orcid":"","institution":"Huazhong University of Science and Technology，Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Chu","suffix":""},{"id":482090724,"identity":"b1c2d999-f702-4be5-a2d0-f4ebae0bbe11","order_by":3,"name":"Zhengxi Wang","email":"","orcid":"","institution":"Hubei University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhengxi","middleName":"","lastName":"Wang","suffix":""},{"id":482090727,"identity":"7387e704-201f-45b8-9f0f-d4c42326f1aa","order_by":4,"name":"Lufang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3OsWrDMBCAYQmBvJzr9YyL+wpXDJkMfpWagicX+gg2gsvS7M3Qh+jSZmwItIseIGNCwFOWLIWAhyahW2q73TroRwhO8KETwuX6hwVK1itZIWil3r/fbvpJODaGZJVeXnhcHGYaJmQ/GGVVpDHY0e+IWOYm8WcL0Fh+bqBtReCVJPazbiEf83o9tUdy95r4TCJ82JKc2G6iMDe04xN5ifyKBC1LUpK7icacD+e0WBNBSyIbIgBzxh0XoMHqCPThFxwg6NXmesopaI9H4RMngLa5n096SLbw1hufMbsyqsFtG8fB+PZ5te8h55ser7c/AJfL5XL90Bf6rkv6AwBPWgAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Lufang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-19 14:53:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6932347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6932347/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12920-025-02260-y","type":"published","date":"2025-11-14T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86344412,"identity":"8ab2fbb8-442f-4f01-932c-7e101bb89734","added_by":"auto","created_at":"2025-07-09 14:45:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":575657,"visible":true,"origin":"","legend":"\u003cp\u003eSchema of the study\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/fb9637d1b421b2cf4a7645cb.png"},{"id":86343476,"identity":"72b4962a-5b88-4dd8-b1e0-f5784828dce1","added_by":"auto","created_at":"2025-07-09 14:37:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2003521,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of pyroptosis-related-genes in cervical cancer.A Volcanic map of differentially expressed genes in GSE7803, GSE9750, GSE63514, and GSE67522. The red is the up-regulated differential gene, the blue is the down-regulated differential gene, and the gray is the non-significant gene. B Venn related gene map.\u003c/p\u003e\n\u003cp\u003e3.2 Characteristics and differences of pyroptosis-related-genes in cervical cancer\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/d20c852ba4826387c6be2f28.png"},{"id":86343483,"identity":"e7724bd3-cf26-414d-8a72-42b20eb0c39b","added_by":"auto","created_at":"2025-07-09 14:37:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4350794,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics and differences of pyroptosis-related-genes incervical cancer. A Venn diagram of the intersection of four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522) with pyroptosis-related-genes. BThe positions of 19 cervical cancer pyroptosis-related-genes on 23 chromosomes. C Box plot of the expression of 19 cervical cancer necrosis related genes in normal cervical tissue and cervical cancer tissue in the TCGA database. D Heat map of the expression of 19 cervical cancer necrosis related genes in normal cervical tissue and cervical cancer tissue. E The PPI network displays the interaction of genes related to cervical cancer necrosis in TCGA database.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/3165245a9974592d4e01ce52.png"},{"id":86343486,"identity":"23fe2c79-4dbd-42c4-9afa-910134b80bd1","added_by":"auto","created_at":"2025-07-09 14:37:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1883724,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of pyroptosis-related-genes in cervical cancer. KEGG pathway enrichment analysis of genes related to pyroptosis, including GSE7803 (A), GSE9750 (B), GSE63514 (C), and GSE67522 (D).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/c90ee365fb99834481791ed6.png"},{"id":86343478,"identity":"e8f05bad-2f38-4198-af30-5d9bf3ebb2be","added_by":"auto","created_at":"2025-07-09 14:37:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2121525,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of pyroptosis-related-genes in cervical cancer. A Correlation analysis and string plot of 19 pyroptosis-related-genes in cervical cancer. B Correlation heatmap of 19 pyroptosis-related-genes in cervical cancer.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/5ae9c94198c253b57bfe0584.png"},{"id":86344410,"identity":"7d968b25-d0ad-4ef9-aadc-4f9185c4b653","added_by":"auto","created_at":"2025-07-09 14:45:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2038945,"visible":true,"origin":"","legend":"\u003cp\u003ePredicting pyroptosis-associated prognostic genes in cervical cancer. A LASSO regression of DEGs related to pyroptosis-associated prognostic genesin cervical cancer. B Cross validation is used to adjust parameter selection in LASSO regression. The overall survival rate, disease-specific survival rate, and progression free survival rate of SPP1 (C), VEGFA (D), and CXCL8 (E).\u003cem\u003e P\u003c/em\u003evalues were shown as: *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, n = 3.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/7c8414cfd033fb6ea59ff26f.png"},{"id":86344411,"identity":"c412fafa-ca8b-4602-bad1-2fd3b49a5b19","added_by":"auto","created_at":"2025-07-09 14:45:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4579784,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis of pyroptosis-associated prognostic genes in cervical cancer. The difference in immune infiltration results between high and low expression groups of SPP1 (A), VEGFA (B), and CXCL8 (C) in stacked bar charts Correlation heatmap of immune infiltrating cell expression of SPP1, VEGFA, and CXCL8. Rod plot of immune infiltrating cell expression of E-G SPP1, VEGFA, and CXCL8.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/56039596f3324de5b6921071.png"},{"id":86343487,"identity":"ebe1dac9-1b63-4b1b-8dc1-5ee3cc7e7dad","added_by":"auto","created_at":"2025-07-09 14:37:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1993758,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model diagram and drug gene interaction prediction based on pyroptosis-associated prognostic genes in cervical cancer. Using SWISS-MODEL to predict the folding structure models of SPP1 (A), VEGFA (B), and CXCL8 (C); D. Prediction of drug gene interactions in hub genes.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/883870b0c3565e4152c2b839.png"},{"id":86343484,"identity":"c1fea970-d3e3-4e4f-8e8e-8e2c81f72ee8","added_by":"auto","created_at":"2025-07-09 14:37:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":8993052,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the crucial ferroptosis-associated prognostic genes in cervical cancer in the signature. A. Using qRT-PCR to detect mRNA levels of SPP1, VEGFA, and CXCL8. B. Validate the efficiency of SPP1 siRNA by using western blotting. C. MTT assay to determine the effect of the proliferation of cervical cancer cells SiHa and HeLa knocked down SPP1. D. A scratch assay was used to determine the impact of knockdown of SPP1 on the migration ability of SiHa and HeLa cells. E. Transwell assays were used to determine the impact of SPP1 knockdown on the migration ability of SiHa and HeLa cells.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/cbaf7983f2fc93e12521e931.png"},{"id":96105022,"identity":"2ab462a2-32ac-4090-ba64-445cff661b2b","added_by":"auto","created_at":"2025-11-17 16:07:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26700791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/59db4cd1-3f16-422b-802b-6949b0d9b13d.pdf"},{"id":86343479,"identity":"e3d6762a-5c75-490f-acfd-2405813a2e9a","added_by":"auto","created_at":"2025-07-09 14:37:41","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1432100,"visible":true,"origin":"","legend":"","description":"","filename":"GAPDH.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/c1bbd8ae70c64bbaed264dd5.jpg"},{"id":86343482,"identity":"a650bc20-03ea-4b79-9f78-68f387619dde","added_by":"auto","created_at":"2025-07-09 14:37:41","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1303614,"visible":true,"origin":"","legend":"","description":"","filename":"SPP1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6932347/v1/f22640f860df6d10a6d7bc6e.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the ranking of incidence rate and mortality of female malignant tumors in 2022, cervical cancer ranks fourth after breast cancer, lung cancer and colorectal cancer. It is estimated that there will be about 660000 new cases and 350000 deaths worldwide in 2022, which is one of the most harmful malignant tumors to women [1]. 85% of cervical cancer cases occur in developing countries, and this data should be taken seriously by the healthcare departments of these countries. Long term persistent human papillomavirus (HPV) infection is the main risk factor for cervical cancer, but many other genetic factors have also been found to have an impact on the onset of cervical cancer. In 2020, WHO updated the classification of female genital tumors based on new pathological findings, dividing cervical cancer into HPV related and HPV independent tumors. Squamous cell carcinoma is the most common in the former, while it is very rare in the latter. At present, people are committed to searching for biomarkers and targeted drugs that can be used for diagnosis and treatment of cervical cancer at various stages based on the continuously accumulated and updated information in oncology [4].\u003c/p\u003e\n\u003cp\u003eCellular pyroptosis is an inflammatory programmed cell death characterized by cell swelling, membrane perforation, and release of cellular contents such as IL-1 \u0026beta; and IL-18. This cell death process is triggered by the activation of caspase-1/4/5/11 proteins by the inflammatory body, especially caspase-1, which can cleave gasdermin (GSDMD) protein, leading to the formation of pores on the cell membrane [5,6]. Cellular pyroptosis has shown great potential in cancer treatment, as it not only promotes anti-tumor immune responses and inhibits tumor growth, but also certain drugs and compounds can induce cancer cell pyroptosis, providing new strategies for cancer treatment [7]. However, excessive activation of cell pyroptosis may lead to inflammatory diseases and tumor progression, thus requiring precise control of its activation and regulatory mechanisms [8]. In recent years, an increasing number of studies have shown a close relationship between cervical cancer and pyroptosis. Some studies focus on exploring the prognostic features of pyroptosis in cervical cancer [9], while others attempt to predict the prognosis and immune microenvironment of cervical cancer through pyroptosis related mechanisms [10].\u0026nbsp;Therefore, our research aims to identify key targets related to cervical cancer necrosis and predict potential therapeutic drugs. Further research on cell pyroptosis may help discover a new treatment strategy for cervical cancer.\u003c/p\u003e\n\u003cp\u003eThis study determined the differential expression profiles of apoptosis related genes in normal cervical tissue and cervical cancer tissue through bioinformatics analysis, and found that the differential genes were enriched in pathways such as inflammation regulation. In addition, we also predicted and analyzed the prognostic genes related to cervical cancer necrosis, and based on these genes, predicted drug gene interactions to screen candidate drugs for cervical cancer treatment. The detailed schematic diagram of the workflow in this study is shown in Figure 1. In summary, our research may provide more evidence for the diagnosis, prognosis, and treatment of cervical cancer.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003e2.1 Acquisition and Preprocessing of Cervical Cancer Dataset\u003c/p\u003e\n\u003cp\u003eThis study obtained gene expression data of cervical cancer patients from the GEO database (http://www.ncbi.nlm.nih.gov/geo). A total of 4 datasets (GSE7803, GSE9750, GSE63514, and GSE67522) were selected for subsequent analysis. These 4 datasets were the results of transcriptome analysis of cervical tissue samples from cervical cancer patients. Detailed information on the 4 selected datasets is shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 GSE datasets referenced in this study\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSE dataset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePMID\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGSE7803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNormal：10\u003c/p\u003e\n \u003cp\u003eTumor：21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e17974957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGSE9750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNormal：24\u003c/p\u003e\n \u003cp\u003eTumor：42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e18506748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGSE63514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNormal：24\u003c/p\u003e\n \u003cp\u003eTumor：28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e26056290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eGSE67522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNormal：22\u003c/p\u003e\n \u003cp\u003eTumor：20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e28402266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.2 Identification of differentially expressed genes related to apoptosis\u003c/p\u003e\n\u003cp\u003eIn addition, this study also obtained information from the GeneCards database (https://www.genecards.org) for research. By retrieving genes related to apoptosis, we found 831 related genes. By using the R package limma, we screened out 55224156 and 55 differentially expressed genes in four cervical cancer data sets (GSE7803, GSE9750, GSE63514, GSE67522) using the P\u0026le;0.05 and |logFC|\u0026gt;1 methods, respectively. Using the Venn diagram intersection method, we screened out 19 differentially expressed genes from the four data sets, which were related to cervical cancer necrosis.\u003c/p\u003e\n\u003cp\u003e2.3 Correlation analysis\u003c/p\u003e\n\u003cp\u003eThe Pearson correlation analysis was used to calculate and obtain the expression correlation of 19 apoptosis-related DEGs in all samples and cervical cancer samples. The data used for the analysis came from the TCGA database. The process was completed using the rcorr function in the R package hmisc and visualized using the R package corrplot.\u003c/p\u003e\n\u003cp\u003e2.4 LASSO Regression\u003c/p\u003e\n\u003cp\u003eThe prediction model of cervical cancer apoptosis-related genes was constructed using the minimum absolute value shrinkage and selection operator regression algorithm. In the penalty function constructed in this study, as the \u0026lambda; value increases, the regression coefficient continues to converge and finally converges to 0. This study uses \u0026lambda; 1. - se. as the final equation selection standard to obtain the coefficient, and the selected 7 genes are used for prognostic analysis through the TCGA dataset.\u003c/p\u003e\n\u003cp\u003e2.5 Functional Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eThis study performed Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, which was implemented by the \u0026quot;clusterProfiler (3.18.1)\u0026quot; R package. In this analysis, the biological processes of cross-talk genes and apoptosis-related genes in four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522) were evaluated.\u003c/p\u003e\n\u003cp\u003e2.6 protein-protein interaction (PPI) network analysis\u003c/p\u003e\n\u003cp\u003eThe initial protein interaction network (PPI) can be obtained by importing the differentially expressed genes into the interaction gene search tool (STRING, www.string-db.org). These initial images are not beautiful, so this study uses Cytoscape software (version 3.10.2) to visualize the protein interaction network, and further uses the cytoHubba plug-in of the software to sort the differentially expressed genes in the PPI network by degree centrality value, which increases the information content of the PPI network.\u003c/p\u003e\n\u003cp\u003e2.7 RNA extraction and real-time fluorescence quantitative PCR (qRT-PCR)\u003c/p\u003e\n\u003cp\u003eThe RNA of 3 cervical cancer tissues and adjacent tissues was extracted using TRIzol reagent (TaKaRa Company) and reverse transcribed (HiScript\u0026reg; III RT SuperMix Vazyme). The primers were synthesized by Qingke Biotechnology Co., Ltd., and the sequences are shown in Table 2. qRT-PCR was performed on the Step-One Plus real-time fluorescence quantitative PCR instrument (Thermo Fisher Company) using qPCR SYBR Green premix (Vazyme Company). Three biological replicates were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSequences of primer used for qRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward primer sequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse primer sequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSPP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCTCCATTGACTCCAACGACTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCAGGTCTGCGAAACTTCTTAGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eVEGFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eTGGCTCACTGGCTTGCTCTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eATCCAACTGCACCGTCACAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eCXCL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eTTGGCAGCCTTCCTGATTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eTCAAAAACTTCTCCACAACCC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026beta;-actin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 254px;\"\u003e\n \u003cp\u003eCGGGAAATCGTCCGTGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCCGTGTTGGCGTAGAGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.8 Immune infiltration analysis of prognostic genes\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT method was used to analyze the immune infiltration of prognostic genes. This method is a computational method that quantifies the relative proportions of different immune cell subsets in tumor tissues based on gene expression data. After standardizing the prognostic gene data related to cervical cancer necrosis, a feature matrix containing marker genes of specific cell types was constructed. The gene expression data and feature matrix were then input into the CIBERSORT software for computational analysis to estimate the abundance of each immune cell subset in the sample, thereby analyzing the immune infiltration of the tumor.\u003c/p\u003e\n\u003cp\u003e2.9 Prediction of Drug Gene Interactions\u003c/p\u003e\n\u003cp\u003eGiven that the Drug-Gene Interaction Database (DGIdb, http://www.dgidb.org/) is rich in content and is a resource that integrates drug-gene interaction information from multiple sources, we used it to predict drug-gene interactions. DGIdb version 5.0 contains up to 12 drug data sources (ChemIDplus, HemOnc, NCIt, Drugs@FDA, HGNC, RxNorm, ChEMBL, Ensembl, Guide to Pharmacology, CIViC, PharmGKB, Wikidata), aggregating a large amount of drug-gene interaction information from different data sources. Because it provides data on the relationship between specific gene variants and drug responses, researchers can quickly identify potential drug targets and biomarkers. Prognostic genes associated with cervical cancer necrosis were imported into DGIdb for analysis to explore drugs or small molecule organic compounds associated with them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Cell culture\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human cervical cancer cell lines (HeLa and SiHa) were purchased from the China Type Culture and Preservation Center (Wuhan University, Wuhan, China) and cultured in RPMI-1640 medium (Gibco, USA), which contained 10% fetal bovine serum. Among them, HeLa is a human cervical adenocarcinoma cell line, and SiHa is a human cervical squamous cell carcinoma cell line. All cells were identified by short tandem repeat analysis and maintained in a culture state under a humid environment of 37 \u0026deg;C and 5% CO2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 siRNA transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPP1 small interfering RNA (SPP1-siRNA) was provided by Shanghai GenePharma Co.,Ltd. The transfection process was carried out with Lipofectamine 3000 (Invitrogen, USA) in accordance with the manufacturer\u0026apos;s protocol. The transfected cells were harvested and used for subsequent experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Cell proliferation detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MTT assay kit (Beyotime, Shanghai, China) is used to determine cell viability. Cells in logarithmic growth phase were inoculated at a density of 1000 cells per well into a 96 well plate and cultured for 24, 48, and 72 hours. Then, MTT solution (0.5 mg/\u0026mu;L) was added and incubated at 37 \u0026deg;C for 4 hours. Subsequently, 100 \u0026mu;L of dimethyl sulfoxide (DMSO) was added to separate the crystals and the plate was stirred at 37 \u0026deg;C for 20 minutes. The absorbance is measured using an enzyme-linked immunosorbent assay (ELISA) reader at 490 nm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.13 Wound healing and Transwell assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the wound healing assay, cells were seeded at a density of 3 \u0026times; 10^5 cells per well in 6-well plates. After siRNA transfection, the cells were cultured for 48 h. After the cells formed a confluent monolayer, a 200 \u0026mu;L pipette tip was used to scratch the cell surface. Images of the wound area were taken at 0 and 24 h using an inverted optical microscope (Olympus, Japan). The wound healing rate was quantified using ImageJ software (version 1.51), and the migration ability of cancer cells was evaluated. In the Transwell assay, 200 \u0026mu;L of serum-free medium containing 5 \u0026times; 10^4 cells was added to the upper chamber that was not coated (migration assay) or coated with Matrigel (invasion assay), while 600 \u0026mu;L of complete medium was added to the lower chamber. After incubation at 37\u0026deg;C for 24 h, cells that migrated or invaded through the 8\u0026mu;m pore size membrane were fixed with 4% formaldehyde and stained with 0.1% Safranin O. The number of migrated or invaded cells was counted in five random fields for each sample at a magnification of \u0026times;200. All experiments were repeated three times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.14 Western blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total protein was extracted using the previous method and a protein blotting experiment was conducted [11]. The specific antibodies used in this study were rabbit anti-SPP1 antibody (1:1000, ProteTek, Wuhan, China) and mouse anti-GAPDH antibody (1:1000, Cell Signaling Company). GAPDH was used as the sample control. The band intensities were quantitatively analyzed using Image J software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.15 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using R version 4.1.1 and GraphPad Prism 8 software. Univariate and multivariate logistic regression analyses were employed to evaluate the diagnostic value of the predictive model. All statistical tests were two-tailed, and a P value \u0026lt; 0.05 indicated a statistically significant difference.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 Definition of pyroptosis-related-genes in cervical cancer\u003c/p\u003e\n\u003cp\u003eWe obtained it from the GEO database（https://www.ncbi.nlm.nih.gov/geo/）The CCP has selected four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522）， Differential genes were screened using P \u0026le; 0.05 and |LogFC| \u0026ge; 1. GSE7803 identified 864 differentially expressed genes, GSE63514 identified 3687 differentially expressed genes, and GSE67522 identified 1131 differentially expressed genes (Figure 2A). These genes were then analyzed using the GeneCards database (https://www.genecards.org) by searching for genes related to apoptosis, 831 genes related to apoptosis were identified. Four cervical cancer datasets were intersected with genes related to apoptosis, resulting in 55,224,156 and 55 potential cancer genes (Figure 2B).\u003c/p\u003e\n\u003cp\u003e3.2 Characteristics and differences of pyroptosis-related-genes in cervical cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522) to screen for 19 pyroptosis-related-genes, including DNMT1, MELK, POLA1, SMC4, EZH2, CDK1, BRCA1, KLF4, AQP1, DEPTOR, KIF23, AIM2, SPP1, KIF11, S100A12, CITED2, IGF2BP3, VEGFA, CXCL8 (Figure 3A) and shows the chromosomal positions of these 19 cervical cancer apoptosis related genes (Figure 3B). In the TCGA database, we further validated the expression of 19 genes in normal cervical tissue and cervical cancer tissue. The results showed that except for S100A12 and VEGFA, the expression of other genes was significant. Among them, 13 genes including DNMT1, MELK, POLA1, SMC4, EZH2, CDK1, BRCA1, KIF23, AIM2, SPP1, KIF11, IGF2BP3, and CXCL8 are highly expressed in cervical cancer tissues, while 4 genes including KLF4, AQP1, DEPTOR, and CITED2 are lowly expressed in cervical cancer tissues (Figure 3C). We further plotted heatmaps of 19 genes using the TCGA database (Figure 3D), and then conducted PPI analysis on these 19 genes using the STRING platform to further explore their interactions with cervical cancer pyroptosis-related-genes(Figure3E).\u003c/p\u003e\n\u003cp\u003e3.3 Enrichment analysis of pyroptosis-related-genes in cervical cancer\u003c/p\u003e\n\u003cp\u003eIn order to explore the differences in the biological processes of pyroptosis-related-genes in cervical cancer, we performed GO and KEGG enrichment (gene set enrichment analysis) on samples obtained from the intersection of four cervical cancer datasets (GSE7803, GSE9750, GSE63514, GSE67522) and necrosis genes, revealing the regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production, and cellular response to environmental stimuli (Figure 4A to 4D).\u003c/p\u003e\n\u003cp\u003e3.4 Correlation analysis of pyroptosis-related-genes in cervical cancer\u003c/p\u003e\n\u003cp\u003eFurther calculate and analyze the expression correlation of 19 pyroptosis-related-genes in cervical cancer related DEGs in normal cervical tissue and cervical cancer samples through the TCGA database (Figure 5A and 5B).\u003c/p\u003e\n\u003cp\u003e3.5 Predicting pyroptosis-associated prognostic genes in cervical cancer\u003c/p\u003e\n\u003cp\u003eWe applied 19 genes to the LASSO regression algorithm and included 7 genes in the prediction model based on the optimal lambda value (Figures 6A, 6B). The risk score is as follows: Risk score=(-0.18106680996544 * POLA1 exp.)+(-0.123503509447933 * KLF4 exp.)+ (-0.0457937381093486 * AQP1 exp.)+4 (0.14098424549269 * PP1 exp.)+5 (0.0909218549036766 * CITED2 exp.)+8 (0.1657192995788403 * VEGFA exp.)+0.1537547581067 * CXCL8 exp.). Prognostic analysis using the TCGA dataset on the seven genes mentioned above showed that SPP1, VEGFA, and CXCL8 are closely related to prognosis. The overall survival (OS) and progression free survival (PFS) of cervical cancer patients with high expression of SPP1 were significantly reduced (Figure 6C, P=0.022 and P=0.039, respectively). The OS and disease-specific survival rate (DFS) of cervical cancer patients with high expression of VEGFA were significantly reduced (Figure 6D, P=0.002 and P=0.010, respectively). The OS, DFS, and PFS of cancer patients with high expression of CXCL8 were significantly reduced (Figure 6E, P \u0026le; 0.001, P\u0026lt;0.001, and P\u0026lt;0.008, respectively).\u003c/p\u003e\n\u003cp\u003e3.6 Immune infiltration analysis of pyroptosis-associated prognostic genes in cervical cancer\u003c/p\u003e\n\u003cp\u003eWe identified the differences in immune infiltration results between high and low expression groups of SPP1, VEGFA, and CXCL8 in the TCGA database (Figures 7A-C). The correlation between SPP1, VEGFA, and CXCL8 in immune infiltrating cells (Figure 7D). The results showed that SPP1, VEGFA, and CXCL8 were most closely related to macrophages, Th2, and neutrophils, respectively (Figure 7E-7G).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.7 Structural model diagram and drug gene interaction prediction based on pyroptosis-associated prognostic genes in cervical cancer.\u003c/p\u003e\n\u003cp\u003eThrough the UniProt database (https://www.uniprot.org/) retrieve the amino acid sequences of SPP1, VEGFA, and CXCL8, and use the SWISS-MODEL method to predict the folding structure models of SPP1, VEGFA, and CXCL8 (Figure 8A-8C). In DGIdb database ( http://dgidb.org/). The analysis of drug gene interactions revealed 148 drugs/compounds targeting SPP1, VEGFA, and CXCL8. The top 30 targeted drugs/compounds and their order based on the \u0026quot;interaction group score\u0026quot; in the DGIdb database(Figure.8D).\u003c/p\u003e\n\u003cp\u003e3.8 Verification of the crucial pyroptosis-associated\u0026nbsp;prognostic genes in cervical cancer in the signature\u003c/p\u003e\n\u003cp\u003eUsing qRT-PCR further confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P\u0026lt;0.05) (Figure 9A). It is reported that SPP1 plays an important role in epithelial colorectal cancer, gastric cancer and other tumors [12-14]. Therefore, we focus on further validation of hub genes (SPP1). To validate the biological function of SPP1 in cervical cancer cells, we knocked down SPP1 using siRNA in SiHa and HeLa cells (Figure 9B). Knockdown of SPP1 by MTT can significantly reduce the proliferation of cervical cancer cells (Figure 9C). The wound healing rate of SiHa and HeLa cells was significantly inhibited by SPP1 knockdown, as indicated by the results of the wound-healing assay (Figure 9D). The invasion ability of SiHa and HeLa cells was significantly inhibited in transwell assay following SPP1 knockdown (Figure 9E). In summary, the results are consistent with our bioinformatics analysis.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003ePyroptosis is also known as cellular inflammatory necrosis. GSDMD-NT (cleavage product of Gasdermin D) plays a central role in the process of pyroptosis. Activated caspase-1, caspase-3 and granzyme B (GZMB) can catalyze the enzymatic cleavage of GSDMD[15]. Studies have shown that HPV E7 can inhibit the expression of inflammasomes through the E3 protein ligase TRIM21 and promote the degradation of IFI16 inflammasomes, which is not conducive to cell apoptosis and thus promotes the malignant transformation of cervical cells[16]. On the contrary, GZMB and activated caspase-3 can activate pyroptosis, which may be a good strategy for tumor immunotherapy. In addition, since advanced cervical cancer is associated with high expression of granzyme B (GZMB) and IL-1β, this provides a reference for the prognostic assessment of cervical cancer[17]. However, the role of apoptosis-related genes in cervical cancer has not been fully elucidated, and our study aims to reveal this role.\u003c/p\u003e\n\u003cp\u003eThis study is the first to clarify the expression pattern of apoptosis-related genes in cervical cancer tissues. We selected four cervical cancer datasets (GSE7803, GSE9750, GSE63514, and GSE67522) from the GEO database, further screened and identified 19 genes related to apoptosis in cervical cancer cells, and verified them in the TCGA database. Compared with normal cervical tissues, 13 genes (including DNMT1, MELK, POLA1, SMC4, EZH2, CDK1, BRCA1, KIF23, AIM2, SPP1, KIF11, IGF2BP3, and CXCL8) were expressed at higher levels in cervical cancer tissues, and 4 genes (including KLF4, AQP1, DEPTOR, and CITED2) were expressed at lower levels in cervical cancer tissues. We performed protein interaction analysis on these 19 apoptosis-related genes and found that the core genes included E2H2, BRCA1, DNMT1, and CDK1. To further explore the differences in biological processes of these 19 cervical cancer necrosis-related genes, we performed GO and KEGG enrichment analysis, and the results revealed the enrichment of differentially expressed genes in the regulation of endopeptidase activity, inflammatory response, positive regulation of cytokine production, and cell response to environmental stimuli. The results showed that the enrichment pathways of pyroptosis clusters in different cervical cancer patients were different, which suggests that we should take targeted treatment measures for patients with different expression levels of these pyroptosis-related genes.\u003c/p\u003e\n\u003cp\u003eAmong the many treatment strategies for malignant tumors, inducing tumor cell necroptosis has attracted widespread attention due to its good effectiveness. In this treatment process, the immune components in the tumor microenvironment play a crucial regulatory role. Tumor cell necroptosis can be induced by regulating and changing the functions of immune cells such as cytotoxic T lymphocytes, natural killer cells, tumor-associated macrophages and neutrophils. In cancer immunotherapy of cancer cells and tumor cells, cytotoxic CD8+T cells play a major role. They induce tumor cell necroptosis through the granzyme pathway, thereby triggering immune activation. Activated cytotoxic lymphocytes release GzmA/B to tumor cells through perforin, directly or indirectly triggering GSDM-dependent necroptosis, thereby triggering immune activation. Activated natural killer cells induce tumor cell apoptosis, effectively triggering the release of intracellular proinflammatory cytokines, and rapidly inducing tumor inflammation[20]. Subsequently, natural killer cells play a key role in inducing tumor cell necroptosis through GSDME[21]. One of the important sources of tumor necrosis factor α (TNF-α) in the tumor microenvironment (TME) is macrophages, which can promote cell necrosis by secreting TNF-α and activating caspase-8[22]. Specifically, under hypoxic conditions, the formation of the nPD-L1/p-Stat3 complex leads to increased expression of Gasdermin C (GSDMC), which is cleaved by TNF-α-activated caspase-8, thereby inducing cell necrosis[23].\u003c/p\u003e\n\u003cp\u003eWe used the LASSO regression algorithm to construct a prediction model for these 19 differentially expressed genes, and finally used 7 of them to establish a prediction model to calculate the risk score. Subsequently, we used the TCGA dataset to perform prognostic analysis on these 7 genes and found that 3 of them, namely SPP1, VEGFA, and CXCL8, were closely related to the prognosis of cervical cancer. Secretory phosphoprotein 1 (SPP1), also known as osteopontin (OPN), early T lymphocyte activation protein 1 (ETA-1), or rickettsial resistance protein (Ric), belongs to the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. SPP1 can specifically bind to and activate matrix metalloproteinases (MMPs), a property that has been confirmed by Su et al. (2020) [24]. Studies have confirmed that in macrophages, the SPP1 gene expression level is high and interacts with cervical epithelial cells through the SPP1-CD44 axis, which plays a key role in tumor progression and is closely related to the poor prognosis of cervical cancer [25-26]. In the process of promoting tumor growth, VEGF-A is involved in the proliferation, migration and survival of new capillary endothelial cells. Specifically, VEGF-A participates in regulating the invasion and migration ability of tumors by binding to vascular endothelial growth factor receptor 1 (VEGFR-1, also known as Flt-1) and VEGFR-2 (also known as Flk-1) [27]. In cervical cancer tissues, the overexpression of CXCL8 is regulated by E6/E7 of HPV 16 and 18, and its expression is positively correlated with the expression of SERPINB3. It mainly forms the CXCL8/CXCR1 and CXCL8/CXCR2 signaling axes by binding to CXCR1 and CXCR2 of the GPCR family, thereby playing an active role in tumor growth, proliferation, metastasis and drug resistance [28-29].\u003c/p\u003e\n\u003cp\u003eAt present, in addition to the first-line treatment drugs cisplatin, carboplatin and fluorouracil, the second-line treatment drugs gemcitabine, pemetrexed, topotecan, vinorelbine and irinotecan, as well as the immunotherapy drugs pembrolizumab, sepilizumab-vedotin-tftv and bevacizumab are all used in the clinical treatment of cervical cancer. Among them, the first two drugs are mainly used for cervical cancer patients with positive PD-L1 and PD-1, while the latter two drugs target tissue factor and anti-angiogenesis respectively to exert therapeutic effects. [30] This study identified three major apoptosis-related genes (SPP1, VEGF-A, CXCL8) associated with the prognosis of cervical cancer, and obtained the drug-gene interaction results of these three genes (SPP1, VEGF-A, CXCL8) by searching the DGIdb database, with a total of 148 targeted drugs. Knockdown of SPP1 can inhibit the proliferation and migration of cervical cancer cells in vitro.\u003c/p\u003e\n\u003cp\u003eIn summary, through systematic and comprehensive bioinformatics analysis, we identified key genes associated with cell pyroptosis in cervical cancer patients and predicted up to 148 potential targeted drugs/compounds for these genes. We believe that further exploration of the therapeutic effects of these drugs/compounds on cervical cancer may have positive significance. Due to the limitations of some objective conditions, this study does have certain limitations and shortcomings. First, this study did not consider factors that may be related to gene expression, such as the age, incidence, and pathological grade of cervical cancer patients; second, this study should expand the number of genes studied and increase related in vivo and in vitro experiments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the principles expressed in the Declaration of Helsinki and was approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (No: 2021-S353). Written informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This work was supported by the Open Research Fund of Hubei Province Key Laboratory of Precision Radiation Oncology（No.2024ZLJZFL003）, Key Laboratory of Anesthesiology and Resuscitation (Huazhong University of Science and Technology), Ministry of Education (No.2024MZFS010) and Knowledge Innovation Program of Wuhan-Shuguang Project (2023020201020563).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZongchen Hou:\u003c/strong\u003e Writing – review \u0026amp; editing, Writing – original draft, Software, Investigation, Formal analysis, Data curation. \u003cstrong\u003eGuiju Tang:\u003c/strong\u003e Validation, Formal analysis. \u003cstrong\u003eHang Chu\u003c/strong\u003e: Resources, Project administration. \u003cstrong\u003eZhengxi Wang:\u0026nbsp;\u003c/strong\u003eSupervision, Conceptualization.\u003cstrong\u003e\u0026nbsp;Lufang Wang:\u003c/strong\u003e Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the GEO (GSE7803, GSE9750, GSE63514 and GSE67522) network for providing data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBray F, Laversanne M, Sung H, et al. 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Polymorphisms in matricellular SPP1 and SPARC contribute to susceptibility to papillary thyroid cancer. Genomics. 2020;112(6):4959-4967.\u003c/li\u003e\n \u003cli\u003eSheng B, Pan S, Ye M, et al. Single-cell RNA sequencing of cervical exfoliated cells reveals potential biomarkers and cellular pathogenesis in cervical carcinogenesis. Cell Death Dis. 2024;15(2):130.\u003c/li\u003e\n \u003cli\u003eSong JY, Lee JK, Lee NW, Jung HH, Kim SH, Lee KW. Microarray analysis of normal cervix, carcinoma in situ, and invasive cervical cancer: identification of candidate genes in pathogenesis of invasion in cervical cancer. Int J Gynecol Cancer. 2008;18(5):1051-1059.\u003c/li\u003e\n \u003cli\u003eHicklin DJ, Ellis LM. Role of the vascular endothelial growth factor pathway in tumor growth and angiogenesis. J Clin Oncol. 2005;23(5):1011-1027.\u003c/li\u003e\n \u003cli\u003eBrat DJ, Bellail AC, Van Meir EG. The role of interleukin-8 and its receptors in gliomagenesis and tumoral angiogenesis. Neuro Oncol. 2005;7(2):122-133.\u003c/li\u003e\n \u003cli\u003eFernandez-Avila L, Castro-Amaya AM, Molina-Pineda A, Hern\u0026aacute;ndez-Guti\u0026eacute;rrez R, Jave-Suarez LF, Aguilar-Lemarroy A. The Value of CXCL1, CXCL2, CXCL3, and CXCL8 as Potential Prognosis Markers in Cervical Cancer: Evidence of E6/E7 from HPV16 and 18 in Chemokines Regulation. Biomedicines. 2023;11(10):2655.\u003c/li\u003e\n \u003cli\u003eAbu-Rustum NR, Yashar CM, Arend R, et al. NCCN Guidelines\u0026reg; Insights: Cervical Cancer, Version 1.2024. J Natl Compr Canc Netw. 2023;21(12):1224-1233.\u003c/li\u003e\n\u003c/ol\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cervical cancer, cell pyroptosis, Lasso regression, enrichment analysis, immune infiltration, drug gene prediction","lastPublishedDoi":"10.21203/rs.3.rs-6932347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6932347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE7803, GSE9750, GSE63514 and GSE67522. The correlation between the expression of pyroptosis-related genes in normal cervical tissue and cervical cancer tissue was analyzed through the TCGA database. Using LASSO regression algorithm to establish a prediction model for the obtained genes related to pyroptosis. Exploring the functions of differentially expressed genes through GO and KEGG pathway analysis. Using PPI network analysis to screen hub genes, using CIBERSORT method for immune infiltration analysis of prognostic genes, and finally predicting drug-gene interactions in DGIdb database. A total of 19 pyroptosis-related genes were screened from the GEO dataset of cervical cancer tissues, revealing their regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production, and cellular response to environmental stimuli. LASSO regression algorithm was used to establish prediction models for 7 of these genes, and 3 pyroptosis-related genes (SPP1, VEGFA, and CXCL8) closely associated to the prognosis of cervical cancer were identified. qRT-PCR confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P\u003c0.05). SPP1, VEGFA, and CXCL8 are most closely related to macrophages, Th2, and neutrophils, respectively. 148 potential targeted drugs targeting key genes were predicted, providing a possible basis for predicting the prognosis and treatment of cervical cancer. Knocking down SPP1 can inhibit cell proliferation and migration in cervical cancer cells in vitro. In conclusion, our study has identified key genes related to pyroptosis in cervical cancer, which potentially become effective clinical prognostic biomarkers, and further research is needed to explore their underlying mechanisms.","manuscriptTitle":"Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 14:37:37","doi":"10.21203/rs.3.rs-6932347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T12:18:45+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"223044776827429230371388954132475597533","date":"2025-09-19T10:03:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35178811618139433350929968556146096601","date":"2025-09-19T07:53:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T18:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132538391693300886717672246325271642267","date":"2025-09-17T08:30:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309639100752639537026945920421224771085","date":"2025-09-16T08:19:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13735517784451401763190651083707242569","date":"2025-09-15T11:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191368924516961287146790807485456808290","date":"2025-09-15T11:45:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268583146223514801183655453879770318120","date":"2025-08-25T17:53:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T12:08:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51100450866227144100183837089065549024","date":"2025-07-30T12:34:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T05:07:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145688330311786312533411134869244323667","date":"2025-07-28T23:58:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317685842052373998062095948404930968089","date":"2025-07-28T19:36:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-07T09:56:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-25T17:16:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-25T16:25:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-24T16:26:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Genomics","date":"2025-06-24T16:22:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7299cf92-5e21-4d8b-a965-9dcf16980c91","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:00:47+00:00","versionOfRecord":{"articleIdentity":"rs-6932347","link":"https://doi.org/10.1186/s12920-025-02260-y","journal":{"identity":"bmc-medical-genomics","isVorOnly":false,"title":"BMC Medical Genomics"},"publishedOn":"2025-11-14 15:57:38","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-07-09 14:37:37","video":"","vorDoi":"10.1186/s12920-025-02260-y","vorDoiUrl":"https://doi.org/10.1186/s12920-025-02260-y","workflowStages":[]},"version":"v1","identity":"rs-6932347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6932347","identity":"rs-6932347","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}