AGPAT4 Promotes Cervical Cancer Progression by Activating the PI3K/AKT Signaling Pathway: A Multi-Omics and Functional Study

AGPAT4 Promotes Cervical Cancer Progression by Activating the PI3K/AKT Signaling Pathway: A Multi-Omics and Functional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AGPAT4 Promotes Cervical Cancer Progression by Activating the PI3K/AKT Signaling Pathway: A Multi-Omics and Functional Study Yanlun Song, Shaofeng Huang, Jian Wang, Yannan Jiao, Jin Zhang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8592939/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To investigate the role of AGPAT4 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and its underlying mechanisms. Methods AGPAT4 expression and prognosis were analyzed using TCGA and GTEx data. Mendelian randomization (MR) was used to assess causality. Epigenetic regulation, immune microenvironment, and functional pathways were evaluated through methylation analysis, immune deconvolution, and enrichment analysis. The biological functions of AGPAT4 were validated in vitro and in vivo. Results AGPAT4 was downregulated in CESC but showed high diagnostic accuracy (AUC = 0.893). MR supported a causal link with cervical cancer risk (OR = 1.247, p = 0.008). High AGPAT4 expression was associated with worse overall, progression-free, and disease-specific survival, and served as an independent prognostic factor. Promoter hypermethylation was negatively correlated with AGPAT4 expression. AGPAT4-high tumors exhibited an immunosuppressive microenvironment and were enriched in PI3K-AKT signaling, extracellular matrix remodeling, and immune suppression pathways. Functionally, AGPAT4 overexpression promoted proliferation, migration, colony formation, and tumor growth, and activated the PI3K/AKT pathway. Conclusion AGPAT4 drives cervical cancer progression through PI3K/AKT pathway activation and immune microenvironment modulation, representing a potential diagnostic biomarker and therapeutic target. AGPAT4 Cervical cancer PI3K/AKT pathway Prognostic biomarker Immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Cervical cancer remains a leading cause of cancer-related mortality among women worldwide, with cervical squamous cell carcinoma (CSCC) accounting for 60–70% of cases and endocervical adenocarcinoma (ECA) representing another major histological subtype ( 1 , 2 ). Despite the widespread implementation of screening programs and prophylactic HPV vaccination, the prognosis for patients with advanced or recurrent disease remains poor, particularly for ECA which harbors poor prognosis and consists of heterogeneous subtypes (HPV-associated and non-HPV-associated) ( 3 ). This highlights the urgent need to identify novel molecular drivers and therapeutic targets to improve clinical management. Cancer metabolism has emerged as a hallmark of malignancy, with lipid metabolic reprogramming playing a crucial role in tumor progression, therapy resistance, and immune evasion ( 4 ). Mounting evidence demonstrates that lipid metabolism contributes to the malignancy of cancer cells and serves as an attractive target for therapeutic strategies ( 1 ). In cervical cancer specifically, lipid metabolism is necessary for tumor proliferation and metastasis, with alterations observed in various pathways including fatty acid degradation, metabolism, and cholesterol biosynthesis ( 5 – 7 ). Notably, perturbations in lipid metabolism have been implicated in regulating the tumor microenvironment (TME) and immune responses, making it a promising target for improving immunotherapy efficacy ( 2 , 3 ). AGPAT4 (1-acylglycerol-3-phosphate O-acyltransferase 4) is a key enzyme in the biosynthesis of phosphatidic acid, a central intermediate in phospholipid metabolism. Although AGPAT family members have been implicated in lipid droplet formation and membrane biosynthesis, the specific role of AGPAT4 in cancer—particularly in cervical carcinogenesis—remains largely unexplored. Recent evidence suggests that lipid metabolism enzymes can influence not only tumor cell proliferation but also the tumor immune microenvironment, as demonstrated by other lipid-related proteins like CMTM6 (a regulator of PD-L1) and FAT4 (which regulates anti-tumor immunity via the β-catenin/STT3/PD-L1 axis) ( 8 , 9 ). This raises the possibility that AGPAT4 may function as a multi-faceted regulator in cancer biology, potentially impacting both tumor cell-intrinsic metabolism and extrinsic immune modulation. Preliminary pan-cancer analyses have revealed tissue-specific dysregulation of AGPAT4, but its expression pattern, clinical relevance, and functional impact in CESC are poorly characterized. Furthermore, whether AGPAT4 expression is regulated epigenetically, how it interacts with immune cells in the tumor stroma, and through which signaling cascades it exerts its oncogenic effects are critical questions that remain unanswered. Addressing these gaps could provide new insights into cervical cancer biology and uncover actionable biomarkers for diagnosis and prognosis. In this study, we performed an integrative multi-omics investigation combined with functional validation to systematically elucidate the role of AGPAT4 in CESC. We first analyzed its expression landscape across cancers and evaluated its diagnostic and prognostic significance using large-scale transcriptomic and clinical data. To infer causality, we employed Mendelian randomization analysis using genome-wide association data. We further investigated epigenetic regulation through DNA methylation profiling and assessed the relationship between AGPAT4 expression and tumor immune infiltration. Through gene set enrichment analysis, we identified signaling pathways associated with AGPAT4. Finally, we experimentally validated its pro-tumorigenic functions in vitro and in vivo, and delineated its mechanistic link to the PI3K/AKT signaling axis. Our comprehensive approach not only establishes AGPAT4 as a clinically relevant biomarker in cervical cancer but also provides mechanistic insights into how it promotes tumor progression, thereby offering a rationale for its future exploration as a therapeutic target. Materials and Methods Data Acquisition and Preprocessing RNA sequencing data and corresponding clinical information for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were obtained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov ). The dataset comprised 304 tumor samples and 3 adjacent normal tissue samples. Transcripts Per Million (TPM) formatted data were downloaded and log2-transformed (log2[TPM + 1]) for subsequent analysis. Normal tissue expression data from multiple human organs were sourced from the Genotype-Tissue Expression (GTEx) project. All data processing and normalization were performed using R software (version 4.3.3). Expression and Prognostic Analysis of AGPAT4 The mRNA expression profile of AGPAT4 across 33 cancer types was analyzed using the xiantaozi web server ( https://www.xiantaozi.com/ ) and the GSCALite platform ( http://bioinfo.life.hust.edu.cn/web/GSCALite/ ) ( 10 ). Differential expression of AGPAT4 in CESC was validated using TCGA data. The diagnostic value of AGPAT4 was assessed by ROC analysis. The prognostic significance of AGPAT4 expression for OS, progression-free interval (PFI), and disease-specific survival (DSS) was evaluated using Kaplan-Meier survival analysis. Subgroup analyses were performed based on lymph node status (N0 vs. N1), FIGO stage (I/II vs. III/IV), and tumor grade (G1/G2 vs. G3/G4). Univariate and multivariate Cox proportional hazards regression analyses were conducted to determine whether AGPAT4 expression was an independent prognostic factor. Construction and Validation of the Clinical Nomogram A nomogram incorporating AGPAT4 expression level and key clinical parameters (pathological T stage, N stage, M stage, FIGO stage, and tumor grade) was developed to predict 1-, 3-, and 5-year OS probabilities using the 'rms' R package. The performance of the nomogram was evaluated by calibration curves, which compared the nomogram-predicted survival probabilities with the observed outcomes. Harrell's concordance index (C-index) was calculated to assess the discriminatory power of the model. Mendelian Randomization Analysis To investigate the potential causal relationship between AGPAT4 and cervical cancer, a two-sample Mendelian randomization (MR) analysis was performed. Genetic instruments for AGPAT4 exposure were obtained from expression quantitative trait loci (eQTL) data from the eQTLGen consortium (N = 31,684) and protein quantitative trait loci (pQTL) data from the UK Biobank Pharma Proteomics Project (UKB-PPP, N = 54,219). Summary statistics for cervical cancer were sourced from the GWAS catalog (ebi-a-GCST90018817; N = 239,158). The inverse-variance weighted (IVW) method was used as the primary MR analysis. Sensitivity analyses were conducted using MR-Egger, weighted median, simple mode, and weighted mode methods. Cochran's Q statistic was calculated to assess heterogeneity among instrumental variables. The MR-Egger intercept test was performed to evaluate potential horizontal pleiotropy. Leave-one-out analysis was conducted to examine if the overall causal estimate was driven by any single influential single nucleotide polymorphism (SNP). DNA Methylation Analysis The DNA methylation profile of AGPAT4 in CESC was analyzed using the UALCAN web portal ( http://ualcan.path.uab.edu ) ( 11 ). Promoter methylation levels (beta values, ranging from 0 [unmethylated] to 1 [fully methylated]) were compared between tumor tissues and normal controls. The correlation between AGPAT4 mRNA expression and its promoter methylation level was assessed using Spearman's rank correlation coefficient via the GSCA platform ( 10 ). Differential methylation analysis was further performed across various clinicopathological subgroups, including tumor stage, histological type, grade, and lymph node metastasis status. Analysis of Tumor Immune Microenvironment The immune cell infiltration landscape in CESC was characterized using two complementary algorithms. The single-sample gene set enrichment analysis (ssGSEA) algorithm was employed to estimate the relative abundance of 24 immune cell types. Additionally, the CIBERSORT algorithm ( 12 ) was used to deconvolute the expression matrix and quantify the proportions of 22 immune cell subsets. Spearman's correlation analysis was performed to examine the associations between AGPAT4 expression levels and the infiltration levels of each immune cell type. Patients were dichotomized into AGPAT4-high and AGPAT4-low groups based on the median expression value for comparative analysis. Functional Enrichment Analysis To explore the biological functions and signaling pathways associated with AGPAT4, patients were stratified into high- and low-expression groups based on the median AGPAT4 expression level. Differentially expressed genes (DEGs) between these two groups were identified using the 'limma' R package with thresholds of |log2(fold change)| > 1.5 and adjusted p-value < 0.05. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the up-regulated and down-regulated DEGs separately using the 'clusterProfiler' R package (version 4.0.3) ( 13 ). Terms and pathways with a p-value < 0.05 were considered significantly enriched. Cell Culture and Generation of Stable Cell Lines The human cervical adenocarcinoma cell line HeLa and the human embryonic kidney 293T (HEK-293T) packaging cell line was obtained from the American Type Culture Collection (ATCC). Cells were cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco) supplemented with 10% fetal bovine serum (FBS; Gibco), 100 U/mL penicillin, and 100 µg/mL streptomycin at 37°C in a humidified atmosphere with 5% CO 2 . For stable AGPAT4 overexpression, the full-length human AGPAT4 cDNA was cloned into a lentiviral expression vector (Applied Biological Materials Inc.). Lentiviral particles were produced in HEK-293T cells by co-transfecting the transfer plasmid with the packaging plasmids psPAX2 and pMD2.G using Lipofectamine 2000 (Invitrogen). HeLa cells were transduced with the viral supernatant and selected with 2 µg/mL puromycin (Sigma-Aldrich) for two weeks to establish stable polyclonal populations. Cells transduced with an empty vector served as the control (Vector). Overexpression was confirmed at the protein level by western blotting. Cell Proliferation, Colony Formation, and Migration Assays Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8; Dojindo) according to the manufacturer's instructions. Briefly, 3 × 10³ cells were seeded per well in a 96-well plate. At 0, 24, 48, 72, and 96 hours, 10 µL of CCK-8 reagent was added to each well, incubated for 2 hours, and the absorbance at 450 nm was measured. DNA synthesis was evaluated using the 5-Ethynyl-2'-deoxyuridine (EdU) incorporation assay (BeyoClick™ EdU Cell Proliferation Kit, Beyotime). Cells were incubated with 10 µM EdU for 2 hours, fixed, permeabilized, and stained. The percentage of EdU-positive cells was determined from fluorescence images. For the colony formation assay, 500 cells were seeded per well in 6-well plates and cultured for 14 days. Colonies were fixed, stained with 0.1% crystal violet, and manually counted (colonies with > 50 cells). Cell migration was evaluated using a scratch wound healing assay. A confluent monolayer was scratched with a sterile pipette tip, and images were captured at 0 and 12 hours. The wound closure area was quantified using ImageJ software (NIH). Apoptosis and Cell Cycle Analysis Apoptosis was detected using an Annexin V-FITC/PI Apoptosis Detection Kit (BD Biosciences). Cells were collected, stained with Annexin V-FITC and propidium iodide (PI), and analyzed immediately by flow cytometry (BD Accuri C6). The apoptotic rate was defined as the sum of early (Annexin V+/PI-) and late (Annexin V+/PI+) apoptotic cells. For cell cycle analysis, cells were fixed in 70% ethanol overnight at -20°C, treated with RNase A, stained with PI, and analyzed by flow cytometry. The percentages of cells in G0/G1, S, and G2/M phases were determined using ModFit LT software. Western Blot Analysis Total protein was extracted from cells or frozen tumor tissues using RIPA lysis buffer (Beyotime) supplemented with protease and phosphatase inhibitors. Protein concentration was determined using a bicinchoninic acid (BCA) assay kit (Pierce). Equal amounts of protein (30 µg) were separated by 10% SDS-PAGE and transferred onto polyvinylidene fluoride (PVDF) membranes (Millipore). Membranes were blocked with 5% non-fat milk and incubated overnight at 4°C with primary antibodies against AGPAT4 (BioDragon, BD-PT2582, 1:1000), PI3K (Proteintech, 60225-1-IG, 1:10,000), AKT (Proteintech, 10176-2-AP, 1:6000), p-AKT (Ser473) (Proteintech, 80455-1-RR, 1:5000), p21 (BioDragon, BD-PP1849, 1:1000), p53 (Proteintech, 10442-1-AP, 1:10,000), p-FOXO3A (ABclonal, AP0684, 1:1000), CDK1 (Proteintech, 10762-1-AP, 1:2000), Cyclin B2 (Proteintech, 21644-1-AP, 1:8000), PCNA (Proteintech, 10205-2-AP, 1:3000), BCL-2, BAK, and GAPDH (Proteintech, 60004-1-Ig, 1:5000). After incubation with appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies, protein bands were visualized using an enhanced chemiluminescence (ECL) substrate (Millipore). Band intensities were quantified using ImageJ software and normalized to GAPDH. Full-length, uncropped, and unprocessed scans of all western blot membranes, with clearly marked edges and labeled with corresponding figure numbers and protein markers, are provided as Supplementary Material ( Supplementary 1 ). In Vivo Tumorigenesis Assay All animal procedures were approved by the Laboratory Animal Ethics Committee of Youjiang Medical University for Nationalities (Approval No. 2024062801) and conducted in accordance with institutional guidelines. Female BALB/c nude mice (6–8 weeks old) were purchased from Vital River Laboratory Animal Technology Co., Ltd and housed under specific pathogen-free conditions. Mice were randomly divided into two groups (n = 6 per group). A suspension of 1×10⁶ AGPAT4-overexpressing or control HeLa cells in 100 µL of a 1:1 mixture of Matrigel (Corning) and serum-free DMEM was subcutaneously injected into the right flank of each mouse. Tumor dimensions were measured twice weekly with a digital caliper, and volume was calculated as (length × width²)/2. Body weight was monitored as a health indicator. After four weeks, mice were euthanized by 35% CO₂ asphyxiation. Tumors were excised, weighed, photographed, and divided for snap-freezing (for protein analysis) or fixation in 10% neutral buffered formalin (for histology). Immunohistochemistry (IHC) Formalin-fixed, paraffin-embedded tumor tissues were sectioned at 4–5 µm thickness. After deparaffinization, rehydration, and antigen retrieval, endogenous peroxidase activity was blocked. Sections were incubated overnight at 4°C with primary antibodies against AGPAT4 (1:1000), Ki-67 (Proteintech, 27309-1-AP, 1:8000), or PCNA (1:3000). After washing, sections were incubated with a biotinylated secondary antibody, followed by an HRP-streptavidin complex (Beyotime). Staining was developed with 3,3'-diaminobenzidine (DAB) substrate (ZSGB-BIO) and counterstained with hematoxylin. Images were captured using a digital slide scanner (Pannoramic MIDI, 3DHistech) and analyzed with ImageJ software and the IHC Profiler plugin. Five random fields per tumor at 200× magnification were quantified for positive staining area and intensity. Statistical Analysis All statistical analyses were performed using R software (version 4.3.3) and GraphPad Prism (version 8.0). Data are presented as mean ± standard deviation (SD) unless otherwise specified. For comparisons between two groups, Student's t-test was used for normally distributed data, and the Mann-Whitney U test was used for non-normally distributed data. One-way analysis of variance (ANOVA) followed by Tukey's post-hoc test was used for comparisons among multiple groups. Survival analyses were performed using Kaplan-Meier curves and log-rank tests. Correlation analyses were conducted using Spearman's rank correlation coefficient. A two-sided p-value < 0.05 was considered statistically significant. Specific statistical methods for bioinformatic analyses (e.g., DEG analysis, LASSO, Cox regression, MR analysis) are detailed in their respective subsections above. Results Pan-Cancer Expression Pattern and Diagnostic Value of AGPAT4 in Cervical Cancer To establish the expression profile of AGPAT4 across malignancies, we performed comprehensive differential expression analysis using TCGA datasets. Unpaired analysis revealed a cancer-specific expression pattern: AGPAT4 was significantly upregulated in cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), pheochromocytoma and paraganglioma (PCPG), and stomach adenocarcinoma (STAD), while showing significant downregulation in 11 cancer types including cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (Fig. 1 A). Paired sample analysis yielded consistent results, confirming AGPAT4 downregulation in multiple cancer types (Fig. 1 B). In CESC specifically, AGPAT4 expression was markedly lower in tumor tissues compared to adjacent normal tissues (Fig. 1 C). Notably, despite its overall downregulation in tumors, AGPAT4 demonstrated high diagnostic accuracy for distinguishing CESC from normal tissues, achieving an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI: 0.821–0.966), indicating its potential as a diagnostic biomarker (Fig. 1 D). Genetic Evidence for Causal Relationship Between AGPAT4 and Cervical Cancer We employed a bidirectional two-sample Mendelian randomization (MR) approach to investigate the causal link between genetically predicted AGPAT4 expression and cervical cancer susceptibility. The primary inverse-variance weighted (IVW) analysis revealed a significant positive association: each unit increase in genetically predicted AGPAT4 expression conferred an odds ratio (OR) of 1.247 for cervical cancer risk (95% confidence interval [CI]: 1.060–1.467; p = 0.008) (Fig. 2 A, B). Sensitivity analyses validated the robustness of this causal inference. No significant heterogeneity was detected among instrumental variables using either IVW (p = 0.312) or MR-Egger (p = 0.280) methods (Fig. 2 C, D). The MR-Egger intercept test showed no evidence of horizontal pleiotropy (intercept p = 0.312), while leave-one-out analysis confirmed that the causal estimate was not driven by any single influential single nucleotide polymorphism (SNP) (Fig. 2 E). Complementary MR methods including weighted median, simple mode, weighted mode, and MR-Egger all consistently supported AGPAT4 as a risk factor for cervical cancer. Prognostic Significance of AGPAT4 in Cervical Cancer To evaluate the clinical relevance of AGPAT4 in cervical cancer, we performed survival analysis across multiple endpoints. Higher AGPAT4 expression was significantly associated with worse overall survival (OS; p < 0.001), progression-free interval (PFI; p = 0.003), and disease-specific survival (DSS; p = 0.002) in CESC patients (Fig. 3 A-C). Subgroup analyses demonstrated that this adverse prognostic association remained significant in key clinical subsets including lymph node-negative (N0) and lymph node-positive (N1) patients (Fig. 3 D, E), as well as in early-stage (Stage I/II; p < 0.001) and advanced-stage (Stage III/IV; p = 0.007) disease (Fig. 3 F, G). Similarly, patients with both low-grade (G1/G2; p = 0.003) and high-grade (G3/G4; p = 0.005) tumors showed significantly poorer outcomes with elevated AGPAT4 expression (Fig. 3 H, I). Construction and evaluation of the Nomogram model of CESC Time-dependent receiver operating characteristic (ROC) analysis quantified the prognostic performance of AGPAT4, with AUC values for 1-year, 3-year, and 5-year OS prediction reaching 0.635, 0.682, and 0.716, respectively (Fig. 4 A). Comparable predictive accuracy was observed for PFI (AUCs: 0.691, 0.669, 0.706) and DSS (AUCs: 0.664, 0.646, 0.622) at the same timepoints (Fig. 4 B, C). Univariate Cox regression identified AGPAT4 expression, pathological T stage, and N stage as significant prognostic factors, while multivariate analysis confirmed AGPAT4 as an independent prognostic marker (hazard ratio [HR] = 2.513, 95% CI: 1.185–5.329, p = 0.016) (Table 1 ). To facilitate clinical application, we developed a nomogram integrating AGPAT4 expression with established clinical parameters (TNM stage, FIGO stage, and tumor grade) for predicting 1-, 3-, and 5-year OS probabilities (Fig. 4 D). Calibration curves demonstrated excellent agreement between nomogram-predicted and observed survival rates, confirming the model's clinical utility (Fig. 4 E). Table 1 Univariate analysis and Multivariate analysis of AGPAT4 in CESC. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic T stage 179 T1&T2 169 Reference Reference T3&T4 10 5.014 (1.914–13.136) 0.001 6.266 (2.227–17.634) < 0.001 Pathologic N stage 179 N0 123 Reference Reference N1 56 3.174 (1.574–6.403) 0.001 2.885 (1.425–5.843) 0.003 Pathologic M stage 179 M0 102 Reference M1&MX 77 1.290 (0.644–2.584) 0.473 Clinical stage 179 Stage I&Stage II 152 Reference Stage III&Stage IV 27 1.144 (0.400–3.273) 0.802 Histologic grade 179 G1&G2 98 Reference G3&G4 81 0.932 (0.459–1.893) 0.845 AGPAT4 179 Low 90 Reference Reference High 89 2.025 (0.996–4.118) 0.051 2.513 (1.185–5.329) 0.016 Epigenetic Regulation of AGPAT4 via DNA Methylation Given the observed downregulation of AGPAT4 in CESC, we investigated potential epigenetic mechanisms. Analysis of promoter methylation revealed significantly higher DNA methylation levels of AGPAT4 in CESC tissues compared to normal controls (Fig. 5 A). A strong negative correlation was observed between AGPAT4 mRNA expression and its promoter methylation level (r = -0.34, p < 0.001) (Fig. 5 B), suggesting transcriptional silencing through hypermethylation. This hypermethylation pattern was consistent across various clinical subgroups, including different disease stages (Fig. 5 C), histological subtypes (Fig. 5 D), tumor grades (Fig. 5 E), and lymph node metastasis status (Fig. 5 F), indicating that epigenetic regulation of AGPAT4 represents a common event in cervical carcinogenesis. Association Between AGPAT4 Expression and Tumor Immune Microenvironment Considering the critical role of the tumor immune microenvironment in cancer progression, we analyzed the relationship between AGPAT4 expression and immune cell infiltration patterns. Using ssGSEA algorithm, we found that high AGPAT4 expression was associated with significantly reduced infiltration of multiple anti-tumor immune cell types, including activated dendritic cells (aDC), B cells, cytotoxic cells, dendritic cells, T cells, and regulatory T cells (Tregs), while correlating with increased natural killer (NK) cell infiltration (Fig. 6 A, B). CIBERSORT analysis provided complementary insights, revealing that AGPAT4-high tumors exhibited decreased infiltration of CD8 + T cells, follicular helper T cells, regulatory T cells, M1 macrophages, and resting mast cells, but increased infiltration of CD4 + resting memory T cells and M0 macrophages (Fig. 6 C, D). These consistent findings across different analytical methods suggest that AGPAT4 expression is linked to an immunosuppressive tumor microenvironment characterized by reduced cytotoxic immune activity and altered macrophage polarization. Functional Enrichment Analysis of AGPAT4-Associated Genes To elucidate the biological pathways influenced by AGPAT4, we performed differential gene expression analysis between AGPAT4-high and AGPAT4-low CESC samples. We identified 126 upregulated and 42 downregulated genes (|log₂FC| > 1.5, adjusted p < 0.05) (Fig. 7 A, B). Gene Ontology (GO) analysis of upregulated genes revealed enrichment in extracellular matrix organization, collagen fibril organization, ossification, and osteoblast differentiation (Fig. 7 C). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated significant enrichment in cancer-related pathways including PI3K-Akt signaling, focal adhesion, and proteoglycans in cancer, as well as immune-related pathways such as IL-17 signaling, TNF signaling, NF-kappa B signaling, and cytokine-cytokine receptor interaction (Fig. 7 D). Conversely, downregulated genes were predominantly involved in immune activation processes, including interferon-gamma response, antigen processing and presentation, and T cell receptor signaling (Fig. 7 E, F). These results collectively indicate that AGPAT4 not only promotes extracellular matrix remodeling and oncogenic signaling but also contributes to immune suppression within the tumor microenvironment. AGPAT4 Promotes Malignant Phenotypes of Cervical Cancer Cells In Vitro Immunohistochemical analysis of clinical specimens confirmed AGPAT4 protein overexpression in cervical cancer tissues compared to adjacent normal epithelium, with higher expression correlating with adverse clinical outcomes (Fig. 8 A, B). To establish functional causality, we generated stable AGPAT4-overexpressing HeLa cell lines, confirmed by western blot analysis (Fig. 8 C). Ectopic AGPAT4 expression significantly enhanced cellular proliferation, as demonstrated by increased cell viability in CCK-8 assays (Fig. 8 D), elevated 5-ethynyl-2'-deoxyuridine (EdU) incorporation (Fig. 8 E, F), and upregulation of the proliferative marker PCNA and anti-apoptotic protein BCL-2, coupled with downregulation of pro-apoptotic BAK (Fig. 8 G-J). Additionally, AGPAT4 overexpression potentiated clonogenic capacity in colony formation assays (Fig. 8 K, L) and accelerated cell migration in scratch wound healing assays (Fig. 8 M, N). AGPAT4 Drives Tumor Growth In Vivo The oncogenic potential of AGPAT4 was further validated in a subcutaneous xenograft mouse model. Tumors derived from AGPAT4-overexpressing cells exhibited significantly accelerated growth throughout the experimental period (Fig. 9 A), without affecting overall mouse body weight (Fig. 9 B). At the experimental endpoint, AGPAT4-overexpressing tumors showed approximately 2.5-fold greater mass compared to control tumors (Fig. 9 C). Histological examination revealed enhanced proliferative characteristics in AGPAT4-overexpressing tumors, evidenced by significant upregulation of AGPAT4, PCNA, and Ki-67 expression (Fig. 9 D-G). These in vivo findings corroborate our in vitro results and confirm the tumor-promoting role of AGPAT4 in cervical cancer progression. Mechanistic Insights: AGPAT4 Activates PI3K/AKT Signaling Pathway Cell cycle analysis revealed that AGPAT4 overexpression induced G1 phase accumulation and G2/M phase reduction (Fig. 10 A-C), accompanied by upregulation of cyclin B and CDK1 proteins (Fig. 10 D-F). Bioinformatics analysis of differentially expressed genes between AGPAT4-high and AGPAT4-low groups confirmed enrichment in PI3K-Akt signaling pathway (Fig. 10 G-I). Based on KEGG pathway enrichment analysis indicating PI3K-Akt signaling involvement, we investigated the mechanistic link between AGPAT4 and this key oncogenic pathway. Western blot analysis confirmed that AGPAT4 overexpression robustly activated PI3K/AKT signaling, as evidenced by increased PI3K protein levels and elevated AKT phosphorylation at Ser473 (p-AKT) (Fig. 10 J, K, O). Consistent with PI3K/AKT activation, downstream effectors showed characteristic modulation: increased p21 expression and decreased levels of p53 and phosphorylated FOXO3A (p-FOXO3A) (Fig. 10 J, L-N). Collectively, these results establish that AGPAT4 promotes cervical cancer progression through activation of the PI3K/AKT signaling axis, leading to cell cycle dysregulation and enhanced proliferative capacity. Discussion This study was systematically designed to investigate the expression patterns, clinical significance, and molecular functions of AGPAT4 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Our overarching hypothesis proposed that AGPAT4, as a lipid-metabolizing enzyme, functions as a multi-faceted oncogenic driver. The integrated results substantially support this hypothesis while revealing a complex biological narrative that extends beyond a simple linear model of oncogene activation. A pivotal and novel finding is the identification of AGPAT4 as a potent modulator of the tumor immune microenvironment (TIME), a critical aspect that profoundly influences its overall biological impact in cervical cancer ( 14 – 16 ). The initial observation of AGPAT4 mRNA downregulation in tumor tissues, coupled with its strong diagnostic and prognostic power, presents an intriguing paradox. This suggests a model where functional activity (enzymatic output or specific lipid products) rather than absolute transcript abundance determines its oncogenic role. The epigenetic silencing via promoter hypermethylation provides a plausible mechanism for transcriptional repression, aligning AGPAT4 with the well-established paradigm of epigenetic dysregulation in cancer ( 17 ). Importantly, the Mendelian Randomization analysis demonstrating a causal link between genetically predicted AGPAT4 expression and increased cervical cancer risk provides robust evidence supporting its pro-tumorigenic function independent of confounding factors ( 18 ). The consistent prognostic significance of AGPAT4 across all clinical endpoints and subgroups underscores its value as a biomarker of intrinsic tumor aggressiveness. Its status as an independent prognostic factor suggests it captures fundamental biological processes not fully reflected by traditional staging systems ( 19 , 20 ). This clinical relevance is further emphasized by the successful development of a predictive nomogram, which incorporates AGPAT4 expression along with other clinicopathological parameters to improve risk stratification. The molecular characterization revealed that AGPAT4 modulates key oncogenic pathways including cell proliferation, invasion, and immune evasion mechanisms - particularly through its impact on the tumor immune microenvironment ( 21 , 22 ). Notably, our findings position AGPAT4 at the intersection of metabolic reprogramming and immune modulation in cervical cancer, similar to other metabolic enzymes like FABP4 ( 23 ) and SLC25A ( 24 ) that have been shown to influence tumor progression through both cell-autonomous and microenvironmental mechanisms. The observed effects on immune cell infiltration patterns and cytokine networks suggest AGPAT4 may serve as a molecular switch coordinating metabolic adaptation and immune escape - a feature increasingly recognized as critical in cervical cancer pathogenesis ( 25 , 26 ). These comprehensive insights into AGPAT4's multifaceted roles provide a strong rationale for targeting this molecule as a therapeutic strategy, potentially in combination with existing immunotherapies. A pivotal and extended contribution of this study lies in the comprehensive analysis of the tumor immune microenvironment. Our data reveal that high AGPAT4 expression is associated with a profoundly immunosuppressive landscape, consistent with findings in cervical cancer where the tumor microenvironment (TME) shows high infiltration of immunosuppressive cell types ( 27 , 28 ). This is evidenced by the significant reduction in the infiltration of key anti-tumor immune effectors, such as activated dendritic cells (PD-1 + DCs) 1 and cytotoxic T cells (CD8 + T cells) ( 29 , 30 ), as quantified by both ssGSEA and CIBERSORT algorithms. Simultaneously, we observed alterations in macrophage polarization (particularly toward M2-type TAMs) ( 31 ) and an increase in immunosuppressive cell subsets, mirroring the immune evasion mechanisms observed in cervical cancer ( 32 ). This finding establishes a critical logical connection: AGPAT4 not only drives cell-autonomous proliferation and survival but also actively sculpts a permissive microenvironment by suppressing adaptive anti-tumor immunity, similar to how lipid metabolism reprogramming contributes to immune evasion in cervical cancer ( 2 , 33 ). The underlying mechanism may involve AGPAT4-derived lipid metabolites functioning as signaling molecules or chemotactic factors that alter immune cell recruitment, activation, or function—a burgeoning concept in the field of immunometabolism that has been demonstrated in cervical cancer TME studies ( 34 ). This aligns with our functional enrichment analysis, which showed downregulation of immune activation pathways (e.g., interferon-gamma response, antigen presentation) in AGPAT4-high tumors, paralleling findings in immunosuppressive cervical cancer microenvironments ( 27 ). This immunomodulatory role significantly deepens the mechanistic understanding of AGPAT4, positioning it as a critical node linking tumor cell metabolism to systemic immune evasion ( 35 ). Our in vitro and in vivo functional experiments provided direct causal validation of AGPAT4's tumor-promoting capabilities. The observed enhancements in proliferation, clonogenicity, migration, and in vivo tumor growth are classic hallmarks of an oncogene, similar to the tumorigenic effects mediated by the PI3K/AKT pathway in cervical cancer ( 36 ). These experiments bridge the gap between observational association and functional causality. Mechanistically, the study converges on the activation of the PI3K/AKT signaling pathway as a central downstream effector of AGPAT4, which is particularly relevant given the established role of PI3K/AKT signaling in cervical cancer progression ( 37 ). This provides a coherent explanatory framework for the observed cellular phenotypes, including dysregulated cell cycle progression and inhibition of apoptosis. The convergence of bioinformatic pathway enrichment predictions with experimental protein-level validation strengthens this mechanistic argument. While PI3K/AKT activation is a common theme in cancer, our study uniquely identifies AGPAT4, a lipid metabolism enzyme, as a specific upstream regulator in cervical cancer. Conclusion This multi-omics and functional study establishes AGPAT4 as a significant contributor to cervical cancer pathogenesis through a dual mechanism: driving intrinsic tumor cell proliferation via PI3K/AKT signaling and fostering an extrinsic immunosuppressive microenvironment. Academically, this work elevates AGPAT4 from a metabolic enzyme to a key regulator at the interface of cancer cell signaling and immunology. Practically, it presents AGPAT4 as a promising biomarker for prognosis and patient stratification. Furthermore, the elucidated AGPAT4-PI3K/AKT-immunosuppression axis reveals a novel therapeutic vulnerability. Targeting this axis, potentially through AGPAT4 inhibition combined with immunotherapeutic strategies to reverse the associated immune-cold phenotype, could represent a innovative approach for treating aggressive cervical cancer. Future research should focus on identifying the specific immunomodulatory lipid species involved and testing combinatorial therapeutic strategies in preclinical models. Abbreviations AGPAT4 1-acylglycerol-3-phosphate O-acyltransferase 4 CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma TCGA The Cancer Genome Atlas GTEx Genotype-Tissue Expression MR Mendelian Randomization PI3K/AKT Phosphoinositide 3-kinase/Protein kinase B TME Tumor microenvironment TIME Tumor immune microenvironment OS Overall survival PFI Progression-free interval DSS Disease-specific survival IHC Immunohistochemistry WB Western blot DEGs Differentially expressed genes GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes Declarations Ethics approval and consent to participate All procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the ethics committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Approval No. 2025010501). Informed consent was obtained from all individual participants and/or their legal guardians. Consent for publication Not applicable. Competing Interests The authors declare that they have no competing interests. Funding This study was supported by the Science Foundation for Youths of Guang Xi (No.2023JJB140064), the Natural Science Foundation of Guangxi Province (No. 2025GXNSFHA069073; 2025GXNSFHA069187), Innovation Project of Guangxi Graduate Education (No. YCSW2024532), the Scientific Research and Technology Development Plan of Baise (No. 20241541), and the Project to Improve the Basic Research Ability of Young and Middle-Aged Teachers at Guangxi Universities (No. 2025KY0568). The work here was also supported by the Project of Baise Scientific Research and Technology Development Plan in 2025 (Grant No. Baike202537042). Author Contribution Yanlun Song and Shaofeng Huang contributed equally to this work. They participated in study design, data analysis, and manuscript drafting. Jian Wang and Yannan Jiao performed bioinformatics analysis and data validation. Jin Zhang and Zongyun Lin conducted experimental investigations. Yuehua Huang and Rong Wang contributed to sample collection and resource provision. Yihua Yang, Junli Wang, and Mingyou Dong conceived and supervised the study, secured funding, and revised the manuscript critically. All authors read and approved the final manuscript. Acknowledgement All authors would like to thank all publicly available data used in the study. Data Availability The datasets generated and/or analyzed during the current study are available in the TCGA repository (https://portal.gdc.cancer.gov/) and GTEx portal (https://gtexportal.org/). The original western blot images and other supplementary materials supporting the findings of this study are included in the Supplementary Material file. Further inquiries can be directed to the corresponding authors. References Castelli S, Ciccarone F, Tavian D, Ciriolo MR. ROS-dependent HIF1alpha activation under forced lipid catabolism entails glycolysis and mitophagy as mediators of higher proliferation rate in cervical cancer cells. J Exp Clin Cancer Res. 2021;40:94. 10.1186/s13046-021-01887-w . Bai G, Chen F, Qiu J, Hua K. Construction of a lipid metabolism-based prognostic gene signature in cervical squamous cell carcinoma and validation of LIPG's oncogenic role. Cancer Cell Int. 2025;25:349. 10.1186/s12935-025-03991-9 . Liang HY, Chen SL, Cai SH, Zhang SW, Yang X, Wei LJ, et al. CMTM6 recruits T cells within the endocervical adenocarcinoma microenvironment and suppresses cell proliferation via the p53 pathway. J Med Virol. 2023;95:e28605. 10.1002/jmv.28605 . Shi K, Pan B, Zheng R, Liu K, Song J, Wang X, et al. Bidirectional regulation of lipid metabolism and the tumor microenvironment: new perspectives from mechanism to therapy. Front Immunol. 2025;16:1696102. 10.3389/fimmu.2025.1696102 . Han C, Hu C, Liu T, Sun Y, Hu F, He Y, et al. IGF2BP3 enhances lipid metabolism in cervical cancer by upregulating the expression of SCD. Cell Death Dis. 2024;15:138. 10.1038/s41419-024-06520-0 . Liu H, Liu Y, Zhou Y, Chen X, Pan S, Zhou Q, et al. TM7SF2-induced lipid reprogramming promotes cell proliferation and migration via CPT1A/Wnt/beta-Catenin axis in cervical cancer cells. Cell Death Discov. 2024;10:207. 10.1038/s41420-024-01975-8 . Li N, Li N, Wen S, Li B, Zhang Y, Liu Q et al. HSP60 Regulates Lipid Metabolism in Human Ovarian Cancer. Oxid Med Cell Longev (2021) 2021: 6610529. 10.1155/2021/6610529 Yin B, Chen C, Huang B, Ding J, Hu H, Zhou H, et al. Oncogenic CMTM6 drives M2a macrophages formation and fuels cervical cancer progression. Front Immunol. 2025;16:1621816. 10.3389/fimmu.2025.1621816 . Wang D, Wu S, He J, Sun L, Zhu H, Zhang Y, et al. FAT4 overexpression promotes antitumor immunity by regulating the beta-catenin/STT3/PD-L1 axis in cervical cancer. J Exp Clin Cancer Res. 2023;42:222. 10.1186/s13046-023-02758-2 . Liu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 2018;34:3771–72. 10.1093/bioinformatics/bty411 . Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18–27. 10.1016/j.neo.2022.01.001 . Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol. 2018;1711:243–59. 10.1007/978-1-4939-7493-1_12 . Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov (Camb). 2021;2:100141. 10.1016/j.xinn.2021.100141 . Zhang Z, Liu M, An Y, Gao C, Wang T, Zhang Z, et al. Targeting immune microenvironment in cervical cancer: current research and advances. J Transl Med. 2025;23:888. 10.1186/s12967-025-06896-3 . Huang H, Pan Y, Mai Q, Zhang C, Du Q, Liao Y, et al. Targeting CDCP1 boost CD8 + T cells-mediated cytotoxicity in cervical cancer via the JAK/STAT signaling pathway. J Immunother Cancer. 2024;12. 10.1136/jitc-2024-009416 . Li Y, Deng J, Liu Y, Yu S. HPV infection and the immune microenvironment in cervical cancer. Front Immunol. 2025;16:1645019. 10.3389/fimmu.2025.1645019 . Tian X, Lin R, Bao J, Pan Y, Liu J, Zhou H, et al. Integrated single-cell, spatial, and bulk transcriptomics reveal a chromatin regulator-TME prognostic framework guiding precision therapy in cervical cancer. J Transl Med. 2025;23:1235. 10.1186/s12967-025-07085-y . Lu X, Ying Y, Zhang W, Zhang J, Li R, Wang W. Prognosis, immune microenvironment, and personalized treatment prediction in Rho GTPase-activating protein 4-mutant cervical cancer: Computer strategies for precision oncology. Life Sci. 2023;315:121360. 10.1016/j.lfs.2022.121360 . Yang Y, Li Y, Qi R, Zhang L. Constructe a novel 5 hypoxia genes signature for cervical cancer. Cancer Cell Int. 2021;21:345. 10.1186/s12935-021-02050-3 . Zhang J, Meng S, Wang X, Wang J, Fan X, Sun H, et al. Sequential gene expression analysis of cervical malignant transformation identifies RFC4 as a novel diagnostic and prognostic biomarker. Bmc Med. 2022;20:437. 10.1186/s12916-022-02630-8 . Li L, Bo Y, Xue D, Qin L. Ferroptosis-immune crosstalk in cervical cancer: mechanisms and therapeutic implications. Front Immunol. 2025;16:1657905. 10.3389/fimmu.2025.1657905 . Peng X, Zhu Y, Han Y, Cai C. Enhancing anti-tumor immunity by targeting BATF and the STAT1/PD-L1 pathway in cervical carcinoma. Cell Mol Life Sci. 2025;82:353. 10.1007/s00018-025-05792-9 . Li G, Wu Q, Gong L, Xu X, Cai J, Xu L, et al. FABP4 is an independent risk factor for lymph node metastasis and poor prognosis in patients with cervical cancer. Cancer Cell Int. 2021;21:568. 10.1186/s12935-021-02273-4 . Ma C, Lu X, Ni C, Gao Y, Yang F, Chen S, et al. SLC25A10 promotes cisplatin resistance by inhibiting ferroptosis in cervical cancer. Cell Death Discov. 2025;11:447. 10.1038/s41420-025-02712-5 . Yao S, Zhao L, Chen S, Wang H, Gao Y, Shao NY, et al. Cervical cancer immune infiltration microenvironment identification, construction of immune scores, assisting patient prognosis and immunotherapy. Front Immunol. 2023;14:1135657. 10.3389/fimmu.2023.1135657 . De Nola R, Loizzi V, Cicinelli E, Cormio G. Dynamic crosstalk within the tumor microenvironment of uterine cervical carcinoma: baseline network, iatrogenic alterations, and translational implications. Crit Rev Oncol Hematol. 2021;162:103343. 10.1016/j.critrevonc.2021.103343 . Wang YM, Qiu JJ, Qu XY, Peng J, Lu C, Zhang M, et al. Accumulation of dysfunctional tumor-infiltrating PD-1 + DCs links PD-1/PD-L1 blockade immunotherapeutic response in cervical cancer. Oncoimmunology. 2022;11:2034257. 10.1080/2162402X.2022.2034257 . Dang T, You Y, Wei L, Li Q, Sun H, Sun M, et al. ICAT drives lactylation of tumor-associated macrophages via the c-Myc-ENO1 axis to promote cervical cancer progression. Free Radic Biol Med. 2025;241:316–29. 10.1016/j.freeradbiomed.2025.09.031 . Yu L, Lanqing G, Huang Z, Xin X, Minglin L, Fa-Hui L, et al. T cell immunotherapy for cervical cancer: challenges and opportunities. Front Immunol. 2023;14:1105265. 10.3389/fimmu.2023.1105265 . Liu L, Wang A, Liu X, Han S, Sun Y, Zhang J, et al. Blocking TIGIT/CD155 signalling reverses CD8(+) T cell exhaustion and enhances the antitumor activity in cervical cancer. J Transl Med. 2022;20:280. 10.1186/s12967-022-03480-x . Wang Q, Sudan K, Schmoeckel E, Kost BP, Kuhn C, Vattai A et al. CCL22-Polarized TAMs to M2a Macrophages in Cervical Cancer In Vitro Model. Cells (2022) 11. 10.3390/cells11132027 Wang Z, Wang B, Feng Y, Ye J, Mao Z, Zhang T, et al. Targeting tumor-associated macrophage-derived CD74 improves efficacy of neoadjuvant chemotherapy in combination with PD-1 blockade for cervical cancer. J Immunother Cancer. 2024;12. 10.1136/jitc-2024-009024 . Zhao D, Wu L, Li Y. Targeting lipid metabolism to enhance cancer immunotherapy. Biochim Biophys Acta Rev Cancer. 2025;1880:189416. 10.1016/j.bbcan.2025.189416 . Marelli G, Morina N, Portale F, Pandini M, Iovino M, Di Conza G, et al. Lipid-loaded macrophages as new therapeutic target in cancer. J Immunother Cancer. 2022;10. 10.1136/jitc-2022-004584 . Liu F, Xu J, Yang R, Liu S, Hu S, Yan M, et al. New light on treatment of cervical cancer: Chinese medicine monomers can be effective for cervical cancer by inhibiting the PI3K/Akt signaling pathway. Biomed Pharmacother. 2023;157:114084. 10.1016/j.biopha.2022.114084 . Tan B, Wikan N, Lin S, Thaklaewphan P, Potikanond S, Nimlamool W. Inhibitory actions of oxyresveratrol on the PI3K/AKT signaling cascade in cervical cancer cells. Biomed Pharmacother. 2024;170:115982. 10.1016/j.biopha.2023.115982 . Yang WT, Chen M, Xu R, Zheng PS. PRDM4 inhibits cell proliferation and tumorigenesis by inactivating the PI3K/AKT signaling pathway through targeting of PTEN in cervical carcinoma. Oncogene. 2021;40:3318–30. 10.1038/s41388-021-01765-x . Additional Declarations No competing interests reported. Supplementary Files Supplementary1.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 Mar, 2026 Editor assigned by journal 19 Jan, 2026 Editor invited by journal 19 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 14 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8592939","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602866521,"identity":"3befdc85-5468-42f1-b125-2a68eba79e39","order_by":0,"name":"Yanlun Song","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Yanlun","middleName":"","lastName":"Song","suffix":""},{"id":602866522,"identity":"f719e4ee-97e7-4b75-b451-4f26ca561827","order_by":1,"name":"Shaofeng Huang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Shaofeng","middleName":"","lastName":"Huang","suffix":""},{"id":602866523,"identity":"633d92b5-07df-4f81-97f9-ac10901c3584","order_by":2,"name":"Jian Wang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""},{"id":602866524,"identity":"d9252655-89a6-431e-b625-20df278bb482","order_by":3,"name":"Yannan Jiao","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Yannan","middleName":"","lastName":"Jiao","suffix":""},{"id":602866525,"identity":"4358b88e-14b3-4242-9908-2a11d2fef8f3","order_by":4,"name":"Jin Zhang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zhang","suffix":""},{"id":602866526,"identity":"f0f7df65-d25c-4bf6-a6c8-2efff08712ce","order_by":5,"name":"Zongyun Lin","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Zongyun","middleName":"","lastName":"Lin","suffix":""},{"id":602866528,"identity":"3594d6aa-1bcb-499c-aed9-4ba4ba75ebe2","order_by":6,"name":"Yuehua Huang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Yuehua","middleName":"","lastName":"Huang","suffix":""},{"id":602866531,"identity":"8e7e05fd-6470-41fa-b8bc-f2ab1f19d669","order_by":7,"name":"Rong Wang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wang","suffix":""},{"id":602866533,"identity":"5d83e3b4-b414-43c6-a423-dbc9f1986c59","order_by":8,"name":"Yihua Yang","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yihua","middleName":"","lastName":"Yang","suffix":""},{"id":602866535,"identity":"b6a965af-5ad2-4c9f-b396-fe0409ada0c3","order_by":9,"name":"Junli Wang","email":"","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Junli","middleName":"","lastName":"Wang","suffix":""},{"id":602866536,"identity":"05d0ff11-3fa5-4e09-9a8d-b1c94e2ba155","order_by":10,"name":"Mingyou Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACNmbGxsd/KqA8HmK08LMzNxvwnCFFi2Q/e5sEbxspWgwOMzYbSM47HG1wu4Hxwds2BnlzIrQ0PjDcdjh3w50DzIZz2xgMdzYQY0siSMuNBDZpoAsTDA4Q1tImcXAOWAv7b6K0SDYztkk2NkBsYSZKCz8zY7Mxw7H03Jk3Epsl55yTMNxASAsb//GHjxlqrHP7biQf/PCmzEaeoC1Q0AzEjA1AQoI49UBQR7TKUTAKRsEoGIEAAP0vQzW7GA0oAAAAAElFTkSuQmCC","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mingyou","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2026-01-13 14:08:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8592939/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8592939/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104406048,"identity":"d553713c-c802-49a0-8292-84905e363f4f","added_by":"auto","created_at":"2026-03-11 12:24:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":621100,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression analysis of AGPAT4 in pan-cancer and its diagnostic value in cervical cancer. \u003c/strong\u003e(A) Unpaired differential expression analysis of AGPAT4 across various cancer types in the TCGA database. (B) Paired differential expression analysis of AGPAT4 across cancers. (C) AGPAT4 expression is significantly downregulated in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tissues compared to adjacent normal tissues (unpaired analysis). (D) Receiver operating characteristic (ROC) curve demonstrating the diagnostic efficacy of AGPAT4 expression for distinguishing CESC from normal tissues. AUC, area under the curve.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/edc3aa923ebb4e00315b963f.png"},{"id":104334220,"identity":"5aeb6ca0-a1a8-4df4-922f-cd199aa19e41","added_by":"auto","created_at":"2026-03-10 15:35:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBidirectional two-sample Mendelian randomization analysis assessing the causal relationship between AGPAT4 and cervical cancer. \u003c/strong\u003e(A) Forest plot of the two-sample MR analysis with AGPAT4 as the exposure and cervical cancer as the outcome. nsnp, number of single nucleotide polymorphisms; pval, \u003cem\u003eP\u003c/em\u003e-value; OR, odds ratio. (B-C) Assessment of heterogeneity among instrumental variables using the (B) inverse-variance weighted (IVW) and (C) MR-Egger methods. (D) Causal estimates for AGPAT4 as a risk factor for cervical cancer using five complementary MR methods. The dashed line indicates the significance threshold (P \u0026lt; 0.05). (E) Leave-one-out sensitivity analysis evaluating the influence of individual SNPs on the overall MR estimate.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/2cb57b299ab66c4ce62e8892.jpeg"},{"id":104334210,"identity":"c6f30692-d46e-405b-a370-163176055776","added_by":"auto","created_at":"2026-03-10 15:35:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":609151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic value of AGPAT4 expression in cervical cancer. \u003c/strong\u003e(A-C) Kaplan-Meier survival curves comparing overall survival (OS, A), progression-free interval (PFI, B), and disease-specific survival (DSS, C) between CESC patients with high versus low AGPAT4 expression. (D-I) Subgroup Kaplan-Meier analyses for OS based on lymph node status (N0, D; N1, E), disease stage (Stage I/II, F; Stage III/IV, G), and tumor grade (G1/G2, H; G3/G4, I).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/66f5135e6c41d333eff071b0.png"},{"id":104334209,"identity":"282f97d7-6c7c-47be-bdcd-a09a5707771b","added_by":"auto","created_at":"2026-03-10 15:35:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":524110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of a nomogram model integrating AGPAT4 for survival prediction in CESC. \u003c/strong\u003e(A-C) Time-dependent ROC curves showing the prognostic performance of AGPAT4 expression for predicting (A) OS, (B) PFI, and (C) DSS at 1, 3, and 5 years. AUC values are indicated. (D) Nomogram developed to predict the probability of 1-, 3-, and 5-year OS by integrating AGPAT4 expression level, TNM stage, FIGO stage, and tumor grade. (E) Calibration curves of the nomogram for 1-, 3-, and 5-year OS prediction. The 45-degree dotted line represents perfect prediction.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/7c5a68c5013a9911f2847312.png"},{"id":104406049,"identity":"407544fe-1eb0-4d5a-8794-50b0e4f32285","added_by":"auto","created_at":"2026-03-11 12:24:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":491538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of AGPAT4 promoter DNA methylation in cervical cancer. \u003c/strong\u003e(A) Promoter methylation level of AGPAT4 in CESC tissues compared to normal controls (analyzed via UALCAN). (B) Scatter plot showing a significant negative correlation (Spearman) between AGPAT4 mRNA expression and its DNA methylation level in CESC (analyzed via GSCA). (C-F) Comparison of AGPAT4 promoter methylation levels between normal tissues and CESC subgroups stratified by (C) clinical stage, (D) tumor histology, (E) tumor grade, and (F) lymph node metastasis status. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/f198c9b0238e4be3160dbe6c.png"},{"id":104334215,"identity":"a7347a41-54d6-4460-a3d6-61ac16a49b9d","added_by":"auto","created_at":"2026-03-10 15:35:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":819111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between AGPAT4 expression and immune cell infiltration in the cervical cancer microenvironment. \u003c/strong\u003e(A) Differences in the infiltration levels of 24 immune cell types (estimated by ssGSEA) between AGPAT4-high and AGPAT4-low CESC groups. (B) Bar plot showing the correlation coefficients (Spearman) between AGPAT4 expression and the infiltration levels of the 24 immune cell types. (C) Differences in the infiltration levels of 21 immune cell subsets (estimated by CIBERSORT) between AGPAT4-high and AGPAT4-low groups. (D) Bar plot showing the correlation coefficients between AGPAT4 expression and the infiltration levels of the 21 immune cell subsets. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/33af576d17014326269f4a3b.png"},{"id":104334211,"identity":"b26bdd24-bfe3-4a8b-8a7b-29627a2999ed","added_by":"auto","created_at":"2026-03-10 15:35:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":719522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of genes associated with AGPAT4 expression in cervical cancer. \u003c/strong\u003e(A) Volcano plot of differentially expressed genes (DEGs) between AGPAT4-high and AGPAT4-low CESC samples. Red and blue dots represent significantly upregulated and downregulated genes, respectively (|log₂(fold change)| \u0026gt; 1.5, adjusted p-value \u0026lt; 0.05). (B) Heatmap of the top significantly dysregulated DEGs, showing distinct expression profiles between the two groups. (C, D) Gene Ontology (GO, C) and Kyoto Encyclopedia of Genes and Genomes (KEGG, D) pathway enrichment analyses for genes upregulated in the AGPAT4-high group. (E, F) GO (E) and KEGG (F) pathway enrichment analyses for genes downregulated in the AGPAT4-high group.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/9d3d81a1ff13fc2423a18e37.png"},{"id":104334216,"identity":"3406ae23-7fa5-4173-bb2b-51dc48efbd50","added_by":"auto","created_at":"2026-03-10 15:35:32","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":208916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAGPAT4 overexpression promotes malignant phenotypes of cervical cancer cells in vitro. \u003c/strong\u003e(A) Representative immunohistochemical (IHC) staining images of AGPAT4 protein in clinical cervical cancer and adjacent normal tissues. Scale bar, 100 μm. (B) Quantification of AGPAT4 IHC staining intensity in clinical samples. (C) Western blot confirming AGPAT4 protein overexpression in stable HeLa cell lines. GAPDH served as the loading control. (D) Cell proliferation assessed by CCK-8 assay over 96 hours. (E, F) Representative images (E) and quantification (F) of the EdU incorporation assay. Scale bar, 100 μm. (G) Western blot analysis of proliferation- and apoptosis-related proteins (PCNA, BAK, BCL-2). (H-J) Densitometric quantification of PCNA (H), BAK (I), and BCL-2 (J) protein levels normalized to GAPDH. (K, L) Representative images (K) and quantification (L) of the colony formation assay. (M, N) Representative images (M) and quantification (N) of the scratch wound healing assay at 0 and 12 hours. Scale bar, 200 μm. Data are presented as mean ± SD; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001 vs. Vector group.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/4bd8697fb98cc7dd9e400b88.jpeg"},{"id":104334213,"identity":"c8e69894-81de-4a92-901b-41fd0e84e64e","added_by":"auto","created_at":"2026-03-10 15:35:31","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":247107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAGPAT4 overexpression promotes tumor growth in vivo. \u003c/strong\u003e(A) Representative photographs of subcutaneous tumors resected from nude mice injected with AGPAT4-overexpressing or control HeLa cells at the experimental endpoint (4 weeks). (B) Body weight curves of mice during the experiment. (C) Tumor weights at the endpoint. (D) Representative images of hematoxylin and eosin (H\u0026amp;E) staining and immunohistochemical (IHC) staining for AGPAT4, PCNA, and Ki-67 in xenograft tumor sections. Scale bars: H\u0026amp;E, 200 μm; IHC, 100 μm. (E-G) Quantitative analysis of the positive staining area for AGPAT4 (E), PCNA (F), and Ki-67 (G) in tumor tissues. Data are presented as mean ± SD; **p \u0026lt; 0.01, ***p \u0026lt; 0.001 vs. Vector group.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/a042d28d7ea65a7f32a3c96e.jpeg"},{"id":104405607,"identity":"992769eb-740d-4b30-82fc-641fea186e2d","added_by":"auto","created_at":"2026-03-11 12:23:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":405180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAGPAT4 drives cervical cancer progression by activating the PI3K/AKT signaling pathway. \u003c/strong\u003e(A) Flow cytometry analysis of cell cycle distribution in AGPAT4-overexpressing and control HeLa cells. (B, C) Quantification of the percentage of cells in G1 (B) and G2/M (C) phases. (D) Western blot analysis of cell cycle regulators CDK1 and cyclin B. (E, F) Densitometric quantification of CDK1 (E) and cyclin B (F) protein levels normalized to GAPDH. (G) Volcano plot of differentially expressed genes (DEGs) between AGPAT4-high and -low groups from bioinformatics analysis. (H) Heatmap of the top DEGs. (I) KEGG pathway enrichment analysis of the DEGs, highlighting the PI3K-Akt signaling pathway. (J) Western blot analysis of key proteins in the PI3K/AKT pathway and its downstream effectors. (K-O) Densitometric quantification of p-AKT/AKT ratio (K), PI3K (L), p21 (M), p53 (N), and p-FOXO3A (O) protein levels normalized to GAPDH. Data are presented as mean ± SD; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001 vs. Vector group.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/e09a8eed99b1626968942605.png"},{"id":104409555,"identity":"01d2a7e8-d0b7-4d32-88f7-9b6b1ba904d8","added_by":"auto","created_at":"2026-03-11 12:45:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6374048,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/23d8e234-dce9-4d2a-982e-c00743f555de.pdf"},{"id":104334218,"identity":"06d1fde7-95d4-4020-8feb-6acfbde95d5c","added_by":"auto","created_at":"2026-03-10 15:35:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4396117,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8592939/v1/2f1887318838ad0ef4fd01ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AGPAT4 Promotes Cervical Cancer Progression by Activating the PI3K/AKT Signaling Pathway: A Multi-Omics and Functional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer remains a leading cause of cancer-related mortality among women worldwide, with cervical squamous cell carcinoma (CSCC) accounting for 60\u0026ndash;70% of cases and endocervical adenocarcinoma (ECA) representing another major histological subtype (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite the widespread implementation of screening programs and prophylactic HPV vaccination, the prognosis for patients with advanced or recurrent disease remains poor, particularly for ECA which harbors poor prognosis and consists of heterogeneous subtypes (HPV-associated and non-HPV-associated) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This highlights the urgent need to identify novel molecular drivers and therapeutic targets to improve clinical management.\u003c/p\u003e \u003cp\u003eCancer metabolism has emerged as a hallmark of malignancy, with lipid metabolic reprogramming playing a crucial role in tumor progression, therapy resistance, and immune evasion (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Mounting evidence demonstrates that lipid metabolism contributes to the malignancy of cancer cells and serves as an attractive target for therapeutic strategies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In cervical cancer specifically, lipid metabolism is necessary for tumor proliferation and metastasis, with alterations observed in various pathways including fatty acid degradation, metabolism, and cholesterol biosynthesis (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Notably, perturbations in lipid metabolism have been implicated in regulating the tumor microenvironment (TME) and immune responses, making it a promising target for improving immunotherapy efficacy (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAGPAT4 (1-acylglycerol-3-phosphate O-acyltransferase 4) is a key enzyme in the biosynthesis of phosphatidic acid, a central intermediate in phospholipid metabolism. Although AGPAT family members have been implicated in lipid droplet formation and membrane biosynthesis, the specific role of AGPAT4 in cancer\u0026mdash;particularly in cervical carcinogenesis\u0026mdash;remains largely unexplored. Recent evidence suggests that lipid metabolism enzymes can influence not only tumor cell proliferation but also the tumor immune microenvironment, as demonstrated by other lipid-related proteins like CMTM6 (a regulator of PD-L1) and FAT4 (which regulates anti-tumor immunity via the β-catenin/STT3/PD-L1 axis) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This raises the possibility that AGPAT4 may function as a multi-faceted regulator in cancer biology, potentially impacting both tumor cell-intrinsic metabolism and extrinsic immune modulation.\u003c/p\u003e \u003cp\u003ePreliminary pan-cancer analyses have revealed tissue-specific dysregulation of AGPAT4, but its expression pattern, clinical relevance, and functional impact in CESC are poorly characterized. Furthermore, whether AGPAT4 expression is regulated epigenetically, how it interacts with immune cells in the tumor stroma, and through which signaling cascades it exerts its oncogenic effects are critical questions that remain unanswered. Addressing these gaps could provide new insights into cervical cancer biology and uncover actionable biomarkers for diagnosis and prognosis.\u003c/p\u003e \u003cp\u003eIn this study, we performed an integrative multi-omics investigation combined with functional validation to systematically elucidate the role of AGPAT4 in CESC. We first analyzed its expression landscape across cancers and evaluated its diagnostic and prognostic significance using large-scale transcriptomic and clinical data. To infer causality, we employed Mendelian randomization analysis using genome-wide association data. We further investigated epigenetic regulation through DNA methylation profiling and assessed the relationship between AGPAT4 expression and tumor immune infiltration. Through gene set enrichment analysis, we identified signaling pathways associated with AGPAT4. Finally, we experimentally validated its pro-tumorigenic functions in vitro and in vivo, and delineated its mechanistic link to the PI3K/AKT signaling axis. Our comprehensive approach not only establishes AGPAT4 as a clinically relevant biomarker in cervical cancer but also provides mechanistic insights into how it promotes tumor progression, thereby offering a rationale for its future exploration as a therapeutic target.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eRNA sequencing data and corresponding clinical information for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were obtained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The dataset comprised 304 tumor samples and 3 adjacent normal tissue samples. Transcripts Per Million (TPM) formatted data were downloaded and log2-transformed (log2[TPM\u0026thinsp;+\u0026thinsp;1]) for subsequent analysis. Normal tissue expression data from multiple human organs were sourced from the Genotype-Tissue Expression (GTEx) project. All data processing and normalization were performed using R software (version 4.3.3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExpression and Prognostic Analysis of AGPAT4\u003c/h3\u003e\n\u003cp\u003eThe mRNA expression profile of AGPAT4 across 33 cancer types was analyzed using the xiantaozi web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.xiantaozi.com/\u003c/span\u003e\u003cspan address=\"https://www.xiantaozi.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the GSCALite platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/web/GSCALite/\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/web/GSCALite/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Differential expression of AGPAT4 in CESC was validated using TCGA data. The diagnostic value of AGPAT4 was assessed by ROC analysis. The prognostic significance of AGPAT4 expression for OS, progression-free interval (PFI), and disease-specific survival (DSS) was evaluated using Kaplan-Meier survival analysis. Subgroup analyses were performed based on lymph node status (N0 vs. N1), FIGO stage (I/II vs. III/IV), and tumor grade (G1/G2 vs. G3/G4). Univariate and multivariate Cox proportional hazards regression analyses were conducted to determine whether AGPAT4 expression was an independent prognostic factor.\u003c/p\u003e\n\u003ch3\u003eConstruction and Validation of the Clinical Nomogram\u003c/h3\u003e\n\u003cp\u003eA nomogram incorporating AGPAT4 expression level and key clinical parameters (pathological T stage, N stage, M stage, FIGO stage, and tumor grade) was developed to predict 1-, 3-, and 5-year OS probabilities using the 'rms' R package. The performance of the nomogram was evaluated by calibration curves, which compared the nomogram-predicted survival probabilities with the observed outcomes. Harrell's concordance index (C-index) was calculated to assess the discriminatory power of the model.\u003c/p\u003e\n\u003ch3\u003eMendelian Randomization Analysis\u003c/h3\u003e\n\u003cp\u003eTo investigate the potential causal relationship between AGPAT4 and cervical cancer, a two-sample Mendelian randomization (MR) analysis was performed. Genetic instruments for AGPAT4 exposure were obtained from expression quantitative trait loci (eQTL) data from the eQTLGen consortium (N\u0026thinsp;=\u0026thinsp;31,684) and protein quantitative trait loci (pQTL) data from the UK Biobank Pharma Proteomics Project (UKB-PPP, N\u0026thinsp;=\u0026thinsp;54,219). Summary statistics for cervical cancer were sourced from the GWAS catalog (ebi-a-GCST90018817; N\u0026thinsp;=\u0026thinsp;239,158). The inverse-variance weighted (IVW) method was used as the primary MR analysis. Sensitivity analyses were conducted using MR-Egger, weighted median, simple mode, and weighted mode methods. Cochran's Q statistic was calculated to assess heterogeneity among instrumental variables. The MR-Egger intercept test was performed to evaluate potential horizontal pleiotropy. Leave-one-out analysis was conducted to examine if the overall causal estimate was driven by any single influential single nucleotide polymorphism (SNP).\u003c/p\u003e\n\u003ch3\u003eDNA Methylation Analysis\u003c/h3\u003e\n\u003cp\u003eThe DNA methylation profile of AGPAT4 in CESC was analyzed using the UALCAN web portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Promoter methylation levels (beta values, ranging from 0 [unmethylated] to 1 [fully methylated]) were compared between tumor tissues and normal controls. The correlation between AGPAT4 mRNA expression and its promoter methylation level was assessed using Spearman's rank correlation coefficient via the GSCA platform (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Differential methylation analysis was further performed across various clinicopathological subgroups, including tumor stage, histological type, grade, and lymph node metastasis status.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Tumor Immune Microenvironment\u003c/h2\u003e \u003cp\u003eThe immune cell infiltration landscape in CESC was characterized using two complementary algorithms. The single-sample gene set enrichment analysis (ssGSEA) algorithm was employed to estimate the relative abundance of 24 immune cell types. Additionally, the CIBERSORT algorithm (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) was used to deconvolute the expression matrix and quantify the proportions of 22 immune cell subsets. Spearman's correlation analysis was performed to examine the associations between AGPAT4 expression levels and the infiltration levels of each immune cell type. Patients were dichotomized into AGPAT4-high and AGPAT4-low groups based on the median expression value for comparative analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eTo explore the biological functions and signaling pathways associated with AGPAT4, patients were stratified into high- and low-expression groups based on the median AGPAT4 expression level. Differentially expressed genes (DEGs) between these two groups were identified using the 'limma' R package with thresholds of |log2(fold change)| \u0026gt; 1.5 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the up-regulated and down-regulated DEGs separately using the 'clusterProfiler' R package (version 4.0.3) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Terms and pathways with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e\n\u003ch3\u003eCell Culture and Generation of Stable Cell Lines\u003c/h3\u003e\n\u003cp\u003eThe human cervical adenocarcinoma cell line HeLa and the human embryonic kidney 293T (HEK-293T) packaging cell line was obtained from the American Type Culture Collection (ATCC). Cells were cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco) supplemented with 10% fetal bovine serum (FBS; Gibco), 100 U/mL penicillin, and 100 \u0026micro;g/mL streptomycin at 37\u0026deg;C in a humidified atmosphere with 5% CO\u003csub\u003e2\u003c/sub\u003e. For stable AGPAT4 overexpression, the full-length human AGPAT4 cDNA was cloned into a lentiviral expression vector (Applied Biological Materials Inc.). Lentiviral particles were produced in HEK-293T cells by co-transfecting the transfer plasmid with the packaging plasmids psPAX2 and pMD2.G using Lipofectamine 2000 (Invitrogen). HeLa cells were transduced with the viral supernatant and selected with 2 \u0026micro;g/mL puromycin (Sigma-Aldrich) for two weeks to establish stable polyclonal populations. Cells transduced with an empty vector served as the control (Vector). Overexpression was confirmed at the protein level by western blotting.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Proliferation, Colony Formation, and Migration Assays\u003c/h2\u003e \u003cp\u003eCell proliferation was assessed using the Cell Counting Kit-8 (CCK-8; Dojindo) according to the manufacturer's instructions. Briefly, 3 \u0026times; 10\u0026sup3; cells were seeded per well in a 96-well plate. At 0, 24, 48, 72, and 96 hours, 10 \u0026micro;L of CCK-8 reagent was added to each well, incubated for 2 hours, and the absorbance at 450 nm was measured. DNA synthesis was evaluated using the 5-Ethynyl-2'-deoxyuridine (EdU) incorporation assay (BeyoClick\u0026trade; EdU Cell Proliferation Kit, Beyotime). Cells were incubated with 10 \u0026micro;M EdU for 2 hours, fixed, permeabilized, and stained. The percentage of EdU-positive cells was determined from fluorescence images. For the colony formation assay, 500 cells were seeded per well in 6-well plates and cultured for 14 days. Colonies were fixed, stained with 0.1% crystal violet, and manually counted (colonies with \u0026gt;\u0026thinsp;50 cells). Cell migration was evaluated using a scratch wound healing assay. A confluent monolayer was scratched with a sterile pipette tip, and images were captured at 0 and 12 hours. The wound closure area was quantified using ImageJ software (NIH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eApoptosis and Cell Cycle Analysis\u003c/h2\u003e \u003cp\u003eApoptosis was detected using an Annexin V-FITC/PI Apoptosis Detection Kit (BD Biosciences). Cells were collected, stained with Annexin V-FITC and propidium iodide (PI), and analyzed immediately by flow cytometry (BD Accuri C6). The apoptotic rate was defined as the sum of early (Annexin V+/PI-) and late (Annexin V+/PI+) apoptotic cells. For cell cycle analysis, cells were fixed in 70% ethanol overnight at -20\u0026deg;C, treated with RNase A, stained with PI, and analyzed by flow cytometry. The percentages of cells in G0/G1, S, and G2/M phases were determined using ModFit LT software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot Analysis\u003c/h2\u003e \u003cp\u003eTotal protein was extracted from cells or frozen tumor tissues using RIPA lysis buffer (Beyotime) supplemented with protease and phosphatase inhibitors. Protein concentration was determined using a bicinchoninic acid (BCA) assay kit (Pierce). Equal amounts of protein (30 \u0026micro;g) were separated by 10% SDS-PAGE and transferred onto polyvinylidene fluoride (PVDF) membranes (Millipore). Membranes were blocked with 5% non-fat milk and incubated overnight at 4\u0026deg;C with primary antibodies against AGPAT4 (BioDragon, BD-PT2582, 1:1000), PI3K (Proteintech, 60225-1-IG, 1:10,000), AKT (Proteintech, 10176-2-AP, 1:6000), p-AKT (Ser473) (Proteintech, 80455-1-RR, 1:5000), p21 (BioDragon, BD-PP1849, 1:1000), p53 (Proteintech, 10442-1-AP, 1:10,000), p-FOXO3A (ABclonal, AP0684, 1:1000), CDK1 (Proteintech, 10762-1-AP, 1:2000), Cyclin B2 (Proteintech, 21644-1-AP, 1:8000), PCNA (Proteintech, 10205-2-AP, 1:3000), BCL-2, BAK, and GAPDH (Proteintech, 60004-1-Ig, 1:5000). After incubation with appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies, protein bands were visualized using an enhanced chemiluminescence (ECL) substrate (Millipore). Band intensities were quantified using ImageJ software and normalized to GAPDH. Full-length, uncropped, and unprocessed scans of all western blot membranes, with clearly marked edges and labeled with corresponding figure numbers and protein markers, are provided as Supplementary Material (\u003cb\u003eSupplementary 1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIn Vivo Tumorigenesis Assay\u003c/h2\u003e \u003cp\u003e All animal procedures were approved by the Laboratory Animal Ethics Committee of Youjiang Medical University for Nationalities (Approval No. 2024062801) and conducted in accordance with institutional guidelines. Female BALB/c nude mice (6\u0026ndash;8 weeks old) were purchased from Vital River Laboratory Animal Technology Co., Ltd and housed under specific pathogen-free conditions. Mice were randomly divided into two groups (n\u0026thinsp;=\u0026thinsp;6 per group). A suspension of 1\u0026times;10⁶ AGPAT4-overexpressing or control HeLa cells in 100 \u0026micro;L of a 1:1 mixture of Matrigel (Corning) and serum-free DMEM was subcutaneously injected into the right flank of each mouse. Tumor dimensions were measured twice weekly with a digital caliper, and volume was calculated as (length \u0026times; width\u0026sup2;)/2. Body weight was monitored as a health indicator. After four weeks, mice were euthanized by 35% CO₂ asphyxiation. Tumors were excised, weighed, photographed, and divided for snap-freezing (for protein analysis) or fixation in 10% neutral buffered formalin (for histology).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eFormalin-fixed, paraffin-embedded tumor tissues were sectioned at 4\u0026ndash;5 \u0026micro;m thickness. After deparaffinization, rehydration, and antigen retrieval, endogenous peroxidase activity was blocked. Sections were incubated overnight at 4\u0026deg;C with primary antibodies against AGPAT4 (1:1000), Ki-67 (Proteintech, 27309-1-AP, 1:8000), or PCNA (1:3000). After washing, sections were incubated with a biotinylated secondary antibody, followed by an HRP-streptavidin complex (Beyotime). Staining was developed with 3,3'-diaminobenzidine (DAB) substrate (ZSGB-BIO) and counterstained with hematoxylin. Images were captured using a digital slide scanner (Pannoramic MIDI, 3DHistech) and analyzed with ImageJ software and the IHC Profiler plugin. Five random fields per tumor at 200\u0026times; magnification were quantified for positive staining area and intensity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.3.3) and GraphPad Prism (version 8.0). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) unless otherwise specified. For comparisons between two groups, Student's t-test was used for normally distributed data, and the Mann-Whitney U test was used for non-normally distributed data. One-way analysis of variance (ANOVA) followed by Tukey's post-hoc test was used for comparisons among multiple groups. Survival analyses were performed using Kaplan-Meier curves and log-rank tests. Correlation analyses were conducted using Spearman's rank correlation coefficient. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Specific statistical methods for bioinformatic analyses (e.g., DEG analysis, LASSO, Cox regression, MR analysis) are detailed in their respective subsections above.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePan-Cancer Expression Pattern and Diagnostic Value of AGPAT4 in Cervical Cancer\u003c/h2\u003e \u003cp\u003eTo establish the expression profile of AGPAT4 across malignancies, we performed comprehensive differential expression analysis using TCGA datasets. Unpaired analysis revealed a cancer-specific expression pattern: AGPAT4 was significantly upregulated in cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), pheochromocytoma and paraganglioma (PCPG), and stomach adenocarcinoma (STAD), while showing significant downregulation in 11 cancer types including cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Paired sample analysis yielded consistent results, confirming AGPAT4 downregulation in multiple cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In CESC specifically, AGPAT4 expression was markedly lower in tumor tissues compared to adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Notably, despite its overall downregulation in tumors, AGPAT4 demonstrated high diagnostic accuracy for distinguishing CESC from normal tissues, achieving an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI: 0.821\u0026ndash;0.966), indicating its potential as a diagnostic biomarker (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Evidence for Causal Relationship Between AGPAT4 and Cervical Cancer\u003c/h2\u003e \u003cp\u003eWe employed a bidirectional two-sample Mendelian randomization (MR) approach to investigate the causal link between genetically predicted AGPAT4 expression and cervical cancer susceptibility. The primary inverse-variance weighted (IVW) analysis revealed a significant positive association: each unit increase in genetically predicted AGPAT4 expression conferred an odds ratio (OR) of 1.247 for cervical cancer risk (95% confidence interval [CI]: 1.060\u0026ndash;1.467; p\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Sensitivity analyses validated the robustness of this causal inference. No significant heterogeneity was detected among instrumental variables using either IVW (p\u0026thinsp;=\u0026thinsp;0.312) or MR-Egger (p\u0026thinsp;=\u0026thinsp;0.280) methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). The MR-Egger intercept test showed no evidence of horizontal pleiotropy (intercept p\u0026thinsp;=\u0026thinsp;0.312), while leave-one-out analysis confirmed that the causal estimate was not driven by any single influential single nucleotide polymorphism (SNP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Complementary MR methods including weighted median, simple mode, weighted mode, and MR-Egger all consistently supported AGPAT4 as a risk factor for cervical cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Significance of AGPAT4 in Cervical Cancer\u003c/h2\u003e \u003cp\u003eTo evaluate the clinical relevance of AGPAT4 in cervical cancer, we performed survival analysis across multiple endpoints. Higher AGPAT4 expression was significantly associated with worse overall survival (OS; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), progression-free interval (PFI; p\u0026thinsp;=\u0026thinsp;0.003), and disease-specific survival (DSS; p\u0026thinsp;=\u0026thinsp;0.002) in CESC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Subgroup analyses demonstrated that this adverse prognostic association remained significant in key clinical subsets including lymph node-negative (N0) and lymph node-positive (N1) patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E), as well as in early-stage (Stage I/II; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and advanced-stage (Stage III/IV; p\u0026thinsp;=\u0026thinsp;0.007) disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, G). Similarly, patients with both low-grade (G1/G2; p\u0026thinsp;=\u0026thinsp;0.003) and high-grade (G3/G4; p\u0026thinsp;=\u0026thinsp;0.005) tumors showed significantly poorer outcomes with elevated AGPAT4 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH, I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and evaluation of the Nomogram model of CESC\u003c/h2\u003e \u003cp\u003eTime-dependent receiver operating characteristic (ROC) analysis quantified the prognostic performance of AGPAT4, with AUC values for 1-year, 3-year, and 5-year OS prediction reaching 0.635, 0.682, and 0.716, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Comparable predictive accuracy was observed for PFI (AUCs: 0.691, 0.669, 0.706) and DSS (AUCs: 0.664, 0.646, 0.622) at the same timepoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). Univariate Cox regression identified AGPAT4 expression, pathological T stage, and N stage as significant prognostic factors, while multivariate analysis confirmed AGPAT4 as an independent prognostic marker (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;2.513, 95% CI: 1.185\u0026ndash;5.329, p\u0026thinsp;=\u0026thinsp;0.016) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To facilitate clinical application, we developed a nomogram integrating AGPAT4 expression with established clinical parameters (TNM stage, FIGO stage, and tumor grade) for predicting 1-, 3-, and 5-year OS probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Calibration curves demonstrated excellent agreement between nomogram-predicted and observed survival rates, confirming the model's clinical utility (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis and Multivariate analysis of AGPAT4 in CESC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.014 (1.914\u0026ndash;13.136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.266 (2.227\u0026ndash;17.634)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.174 (1.574\u0026ndash;6.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.885 (1.425\u0026ndash;5.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u0026amp;MX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.290 (0.644\u0026ndash;2.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.144 (0.400\u0026ndash;3.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u0026amp;G2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u0026amp;G4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.932 (0.459\u0026ndash;1.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGPAT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.025 (0.996\u0026ndash;4.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.513 (1.185\u0026ndash;5.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eEpigenetic Regulation of AGPAT4 via DNA Methylation\u003c/h2\u003e \u003cp\u003eGiven the observed downregulation of AGPAT4 in CESC, we investigated potential epigenetic mechanisms. Analysis of promoter methylation revealed significantly higher DNA methylation levels of AGPAT4 in CESC tissues compared to normal controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). A strong negative correlation was observed between AGPAT4 mRNA expression and its promoter methylation level (r = -0.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), suggesting transcriptional silencing through hypermethylation. This hypermethylation pattern was consistent across various clinical subgroups, including different disease stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), histological subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), tumor grades (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), and lymph node metastasis status (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), indicating that epigenetic regulation of AGPAT4 represents a common event in cervical carcinogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between AGPAT4 Expression and Tumor Immune Microenvironment\u003c/h2\u003e \u003cp\u003eConsidering the critical role of the tumor immune microenvironment in cancer progression, we analyzed the relationship between AGPAT4 expression and immune cell infiltration patterns. Using ssGSEA algorithm, we found that high AGPAT4 expression was associated with significantly reduced infiltration of multiple anti-tumor immune cell types, including activated dendritic cells (aDC), B cells, cytotoxic cells, dendritic cells, T cells, and regulatory T cells (Tregs), while correlating with increased natural killer (NK) cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). CIBERSORT analysis provided complementary insights, revealing that AGPAT4-high tumors exhibited decreased infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells, follicular helper T cells, regulatory T cells, M1 macrophages, and resting mast cells, but increased infiltration of CD4\u0026thinsp;+\u0026thinsp;resting memory T cells and M0 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D). These consistent findings across different analytical methods suggest that AGPAT4 expression is linked to an immunosuppressive tumor microenvironment characterized by reduced cytotoxic immune activity and altered macrophage polarization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eFunctional Enrichment Analysis of AGPAT4-Associated Genes\u003c/h2\u003e \u003cp\u003eTo elucidate the biological pathways influenced by AGPAT4, we performed differential gene expression analysis between AGPAT4-high and AGPAT4-low CESC samples. We identified 126 upregulated and 42 downregulated genes (|log₂FC| \u0026gt; 1.5, adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Gene Ontology (GO) analysis of upregulated genes revealed enrichment in extracellular matrix organization, collagen fibril organization, ossification, and osteoblast differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated significant enrichment in cancer-related pathways including PI3K-Akt signaling, focal adhesion, and proteoglycans in cancer, as well as immune-related pathways such as IL-17 signaling, TNF signaling, NF-kappa B signaling, and cytokine-cytokine receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Conversely, downregulated genes were predominantly involved in immune activation processes, including interferon-gamma response, antigen processing and presentation, and T cell receptor signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F). These results collectively indicate that AGPAT4 not only promotes extracellular matrix remodeling and oncogenic signaling but also contributes to immune suppression within the tumor microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eAGPAT4 Promotes Malignant Phenotypes of Cervical Cancer Cells In Vitro\u003c/h2\u003e \u003cp\u003eImmunohistochemical analysis of clinical specimens confirmed AGPAT4 protein overexpression in cervical cancer tissues compared to adjacent normal epithelium, with higher expression correlating with adverse clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). To establish functional causality, we generated stable AGPAT4-overexpressing HeLa cell lines, confirmed by western blot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Ectopic AGPAT4 expression significantly enhanced cellular proliferation, as demonstrated by increased cell viability in CCK-8 assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), elevated 5-ethynyl-2'-deoxyuridine (EdU) incorporation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE, F), and upregulation of the proliferative marker PCNA and anti-apoptotic protein BCL-2, coupled with downregulation of pro-apoptotic BAK (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-J). Additionally, AGPAT4 overexpression potentiated clonogenic capacity in colony formation assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eK, L) and accelerated cell migration in scratch wound healing assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eM, N).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eAGPAT4 Drives Tumor Growth In Vivo\u003c/h2\u003e \u003cp\u003eThe oncogenic potential of AGPAT4 was further validated in a subcutaneous xenograft mouse model. Tumors derived from AGPAT4-overexpressing cells exhibited significantly accelerated growth throughout the experimental period (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), without affecting overall mouse body weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). At the experimental endpoint, AGPAT4-overexpressing tumors showed approximately 2.5-fold greater mass compared to control tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Histological examination revealed enhanced proliferative characteristics in AGPAT4-overexpressing tumors, evidenced by significant upregulation of AGPAT4, PCNA, and Ki-67 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD-G). These in vivo findings corroborate our in vitro results and confirm the tumor-promoting role of AGPAT4 in cervical cancer progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMechanistic Insights: AGPAT4 Activates PI3K/AKT Signaling Pathway\u003c/h2\u003e \u003cp\u003eCell cycle analysis revealed that AGPAT4 overexpression induced G1 phase accumulation and G2/M phase reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C), accompanied by upregulation of cyclin B and CDK1 proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD-F). Bioinformatics analysis of differentially expressed genes between AGPAT4-high and AGPAT4-low groups confirmed enrichment in PI3K-Akt signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eG-I). Based on KEGG pathway enrichment analysis indicating PI3K-Akt signaling involvement, we investigated the mechanistic link between AGPAT4 and this key oncogenic pathway. Western blot analysis confirmed that AGPAT4 overexpression robustly activated PI3K/AKT signaling, as evidenced by increased PI3K protein levels and elevated AKT phosphorylation at Ser473 (p-AKT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eJ, K, O). Consistent with PI3K/AKT activation, downstream effectors showed characteristic modulation: increased p21 expression and decreased levels of p53 and phosphorylated FOXO3A (p-FOXO3A) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eJ, L-N). Collectively, these results establish that AGPAT4 promotes cervical cancer progression through activation of the PI3K/AKT signaling axis, leading to cell cycle dysregulation and enhanced proliferative capacity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study was systematically designed to investigate the expression patterns, clinical significance, and molecular functions of AGPAT4 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Our overarching hypothesis proposed that AGPAT4, as a lipid-metabolizing enzyme, functions as a multi-faceted oncogenic driver. The integrated results substantially support this hypothesis while revealing a complex biological narrative that extends beyond a simple linear model of oncogene activation. A pivotal and novel finding is the identification of AGPAT4 as a potent modulator of the tumor immune microenvironment (TIME), a critical aspect that profoundly influences its overall biological impact in cervical cancer (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe initial observation of AGPAT4 mRNA downregulation in tumor tissues, coupled with its strong diagnostic and prognostic power, presents an intriguing paradox. This suggests a model where functional activity (enzymatic output or specific lipid products) rather than absolute transcript abundance determines its oncogenic role. The epigenetic silencing via promoter hypermethylation provides a plausible mechanism for transcriptional repression, aligning AGPAT4 with the well-established paradigm of epigenetic dysregulation in cancer (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Importantly, the Mendelian Randomization analysis demonstrating a causal link between genetically predicted AGPAT4 expression and increased cervical cancer risk provides robust evidence supporting its pro-tumorigenic function independent of confounding factors (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe consistent prognostic significance of AGPAT4 across all clinical endpoints and subgroups underscores its value as a biomarker of intrinsic tumor aggressiveness. Its status as an independent prognostic factor suggests it captures fundamental biological processes not fully reflected by traditional staging systems (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This clinical relevance is further emphasized by the successful development of a predictive nomogram, which incorporates AGPAT4 expression along with other clinicopathological parameters to improve risk stratification. The molecular characterization revealed that AGPAT4 modulates key oncogenic pathways including cell proliferation, invasion, and immune evasion mechanisms - particularly through its impact on the tumor immune microenvironment (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, our findings position AGPAT4 at the intersection of metabolic reprogramming and immune modulation in cervical cancer, similar to other metabolic enzymes like FABP4 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and SLC25A (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) that have been shown to influence tumor progression through both cell-autonomous and microenvironmental mechanisms. The observed effects on immune cell infiltration patterns and cytokine networks suggest AGPAT4 may serve as a molecular switch coordinating metabolic adaptation and immune escape - a feature increasingly recognized as critical in cervical cancer pathogenesis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). These comprehensive insights into AGPAT4's multifaceted roles provide a strong rationale for targeting this molecule as a therapeutic strategy, potentially in combination with existing immunotherapies.\u003c/p\u003e \u003cp\u003eA pivotal and extended contribution of this study lies in the comprehensive analysis of the tumor immune microenvironment. Our data reveal that high AGPAT4 expression is associated with a profoundly immunosuppressive landscape, consistent with findings in cervical cancer where the tumor microenvironment (TME) shows high infiltration of immunosuppressive cell types (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This is evidenced by the significant reduction in the infiltration of key anti-tumor immune effectors, such as activated dendritic cells (PD-1\u0026thinsp;+\u0026thinsp;DCs) 1 and cytotoxic T cells (CD8\u0026thinsp;+\u0026thinsp;T cells) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), as quantified by both ssGSEA and CIBERSORT algorithms. Simultaneously, we observed alterations in macrophage polarization (particularly toward M2-type TAMs) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and an increase in immunosuppressive cell subsets, mirroring the immune evasion mechanisms observed in cervical cancer (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis finding establishes a critical logical connection: AGPAT4 not only drives cell-autonomous proliferation and survival but also actively sculpts a permissive microenvironment by suppressing adaptive anti-tumor immunity, similar to how lipid metabolism reprogramming contributes to immune evasion in cervical cancer (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The underlying mechanism may involve AGPAT4-derived lipid metabolites functioning as signaling molecules or chemotactic factors that alter immune cell recruitment, activation, or function\u0026mdash;a burgeoning concept in the field of immunometabolism that has been demonstrated in cervical cancer TME studies (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This aligns with our functional enrichment analysis, which showed downregulation of immune activation pathways (e.g., interferon-gamma response, antigen presentation) in AGPAT4-high tumors, paralleling findings in immunosuppressive cervical cancer microenvironments (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This immunomodulatory role significantly deepens the mechanistic understanding of AGPAT4, positioning it as a critical node linking tumor cell metabolism to systemic immune evasion (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur in vitro and in vivo functional experiments provided direct causal validation of AGPAT4's tumor-promoting capabilities. The observed enhancements in proliferation, clonogenicity, migration, and in vivo tumor growth are classic hallmarks of an oncogene, similar to the tumorigenic effects mediated by the PI3K/AKT pathway in cervical cancer (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These experiments bridge the gap between observational association and functional causality.\u003c/p\u003e \u003cp\u003eMechanistically, the study converges on the activation of the PI3K/AKT signaling pathway as a central downstream effector of AGPAT4, which is particularly relevant given the established role of PI3K/AKT signaling in cervical cancer progression (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This provides a coherent explanatory framework for the observed cellular phenotypes, including dysregulated cell cycle progression and inhibition of apoptosis. The convergence of bioinformatic pathway enrichment predictions with experimental protein-level validation strengthens this mechanistic argument. While PI3K/AKT activation is a common theme in cancer, our study uniquely identifies AGPAT4, a lipid metabolism enzyme, as a specific upstream regulator in cervical cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis multi-omics and functional study establishes AGPAT4 as a significant contributor to cervical cancer pathogenesis through a dual mechanism: driving intrinsic tumor cell proliferation via PI3K/AKT signaling and fostering an extrinsic immunosuppressive microenvironment. Academically, this work elevates AGPAT4 from a metabolic enzyme to a key regulator at the interface of cancer cell signaling and immunology. Practically, it presents AGPAT4 as a promising biomarker for prognosis and patient stratification. Furthermore, the elucidated AGPAT4-PI3K/AKT-immunosuppression axis reveals a novel therapeutic vulnerability. Targeting this axis, potentially through AGPAT4 inhibition combined with immunotherapeutic strategies to reverse the associated immune-cold phenotype, could represent a innovative approach for treating aggressive cervical cancer. Future research should focus on identifying the specific immunomodulatory lipid species involved and testing combinatorial therapeutic strategies in preclinical models.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAGPAT4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e1-acylglycerol-3-phosphate O-acyltransferase 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCESC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGTEx\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenotype-Tissue Expression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian Randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI3K/AKT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphoinositide 3-kinase/Protein kinase B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor immune microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgression-free interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisease-specific survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWestern blot\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eAll procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the ethics committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Approval No. 2025010501). Informed consent was obtained from all individual participants and/or their legal guardians.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Science Foundation for Youths of Guang Xi (No.2023JJB140064), the Natural Science Foundation of Guangxi Province (No. 2025GXNSFHA069073; 2025GXNSFHA069187), Innovation Project of Guangxi Graduate Education (No. YCSW2024532), the Scientific Research and Technology Development Plan of Baise (No. 20241541), and the Project to Improve the Basic Research Ability of Young and Middle-Aged Teachers at Guangxi Universities (No. 2025KY0568). The work here was also supported by the Project of Baise Scientific Research and Technology Development Plan in 2025 (Grant No. Baike202537042).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYanlun Song and Shaofeng Huang contributed equally to this work. They participated in study design, data analysis, and manuscript drafting. Jian Wang and Yannan Jiao performed bioinformatics analysis and data validation. Jin Zhang and Zongyun Lin conducted experimental investigations. Yuehua Huang and Rong Wang contributed to sample collection and resource provision. Yihua Yang, Junli Wang, and Mingyou Dong conceived and supervised the study, secured funding, and revised the manuscript critically. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAll authors would like to thank all publicly available data used in the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the TCGA repository (https://portal.gdc.cancer.gov/) and GTEx portal (https://gtexportal.org/). The original western blot images and other supplementary materials supporting the findings of this study are included in the Supplementary Material file. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCastelli S, Ciccarone F, Tavian D, Ciriolo MR. ROS-dependent HIF1alpha activation under forced lipid catabolism entails glycolysis and mitophagy as mediators of higher proliferation rate in cervical cancer cells. J Exp Clin Cancer Res. 2021;40:94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13046-021-01887-w\u003c/span\u003e\u003cspan address=\"10.1186/s13046-021-01887-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai G, Chen F, Qiu J, Hua K. Construction of a lipid metabolism-based prognostic gene signature in cervical squamous cell carcinoma and validation of LIPG's oncogenic role. Cancer Cell Int. 2025;25:349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12935-025-03991-9\u003c/span\u003e\u003cspan address=\"10.1186/s12935-025-03991-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang HY, Chen SL, Cai SH, Zhang SW, Yang X, Wei LJ, et al. CMTM6 recruits T cells within the endocervical adenocarcinoma microenvironment and suppresses cell proliferation via the p53 pathway. J Med Virol. 2023;95:e28605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmv.28605\u003c/span\u003e\u003cspan address=\"10.1002/jmv.28605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi K, Pan B, Zheng R, Liu K, Song J, Wang X, et al. Bidirectional regulation of lipid metabolism and the tumor microenvironment: new perspectives from mechanism to therapy. Front Immunol. 2025;16:1696102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1696102\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1696102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan C, Hu C, Liu T, Sun Y, Hu F, He Y, et al. IGF2BP3 enhances lipid metabolism in cervical cancer by upregulating the expression of SCD. Cell Death Dis. 2024;15:138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41419-024-06520-0\u003c/span\u003e\u003cspan address=\"10.1038/s41419-024-06520-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Liu Y, Zhou Y, Chen X, Pan S, Zhou Q, et al. TM7SF2-induced lipid reprogramming promotes cell proliferation and migration via CPT1A/Wnt/beta-Catenin axis in cervical cancer cells. Cell Death Discov. 2024;10:207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41420-024-01975-8\u003c/span\u003e\u003cspan address=\"10.1038/s41420-024-01975-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi N, Li N, Wen S, Li B, Zhang Y, Liu Q et al. HSP60 Regulates Lipid Metabolism in Human Ovarian Cancer. Oxid Med Cell Longev (2021) 2021: 6610529. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/6610529\u003c/span\u003e\u003cspan address=\"10.1155/2021/6610529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin B, Chen C, Huang B, Ding J, Hu H, Zhou H, et al. Oncogenic CMTM6 drives M2a macrophages formation and fuels cervical cancer progression. Front Immunol. 2025;16:1621816. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1621816\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1621816\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Wu S, He J, Sun L, Zhu H, Zhang Y, et al. FAT4 overexpression promotes antitumor immunity by regulating the beta-catenin/STT3/PD-L1 axis in cervical cancer. J Exp Clin Cancer Res. 2023;42:222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13046-023-02758-2\u003c/span\u003e\u003cspan address=\"10.1186/s13046-023-02758-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 2018;34:3771\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/bty411\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bty411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neo.2022.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.neo.2022.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol. 2018;1711:243\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4939-7493-1_12\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4939-7493-1_12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov (Camb). 2021;2:100141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.xinn.2021.100141\u003c/span\u003e\u003cspan address=\"10.1016/j.xinn.2021.100141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Liu M, An Y, Gao C, Wang T, Zhang Z, et al. Targeting immune microenvironment in cervical cancer: current research and advances. J Transl Med. 2025;23:888. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-025-06896-3\u003c/span\u003e\u003cspan address=\"10.1186/s12967-025-06896-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Pan Y, Mai Q, Zhang C, Du Q, Liao Y, et al. Targeting CDCP1 boost CD8\u0026thinsp;+\u0026thinsp;T cells-mediated cytotoxicity in cervical cancer via the JAK/STAT signaling pathway. J Immunother Cancer. 2024;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jitc-2024-009416\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2024-009416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Deng J, Liu Y, Yu S. HPV infection and the immune microenvironment in cervical cancer. Front Immunol. 2025;16:1645019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1645019\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1645019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian X, Lin R, Bao J, Pan Y, Liu J, Zhou H, et al. Integrated single-cell, spatial, and bulk transcriptomics reveal a chromatin regulator-TME prognostic framework guiding precision therapy in cervical cancer. J Transl Med. 2025;23:1235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-025-07085-y\u003c/span\u003e\u003cspan address=\"10.1186/s12967-025-07085-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu X, Ying Y, Zhang W, Zhang J, Li R, Wang W. Prognosis, immune microenvironment, and personalized treatment prediction in Rho GTPase-activating protein 4-mutant cervical cancer: Computer strategies for precision oncology. Life Sci. 2023;315:121360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lfs.2022.121360\u003c/span\u003e\u003cspan address=\"10.1016/j.lfs.2022.121360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Li Y, Qi R, Zhang L. Constructe a novel 5 hypoxia genes signature for cervical cancer. Cancer Cell Int. 2021;21:345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12935-021-02050-3\u003c/span\u003e\u003cspan address=\"10.1186/s12935-021-02050-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Meng S, Wang X, Wang J, Fan X, Sun H, et al. Sequential gene expression analysis of cervical malignant transformation identifies RFC4 as a novel diagnostic and prognostic biomarker. Bmc Med. 2022;20:437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12916-022-02630-8\u003c/span\u003e\u003cspan address=\"10.1186/s12916-022-02630-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Bo Y, Xue D, Qin L. Ferroptosis-immune crosstalk in cervical cancer: mechanisms and therapeutic implications. Front Immunol. 2025;16:1657905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1657905\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1657905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng X, Zhu Y, Han Y, Cai C. Enhancing anti-tumor immunity by targeting BATF and the STAT1/PD-L1 pathway in cervical carcinoma. Cell Mol Life Sci. 2025;82:353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00018-025-05792-9\u003c/span\u003e\u003cspan address=\"10.1007/s00018-025-05792-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Wu Q, Gong L, Xu X, Cai J, Xu L, et al. FABP4 is an independent risk factor for lymph node metastasis and poor prognosis in patients with cervical cancer. Cancer Cell Int. 2021;21:568. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12935-021-02273-4\u003c/span\u003e\u003cspan address=\"10.1186/s12935-021-02273-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa C, Lu X, Ni C, Gao Y, Yang F, Chen S, et al. SLC25A10 promotes cisplatin resistance by inhibiting ferroptosis in cervical cancer. Cell Death Discov. 2025;11:447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41420-025-02712-5\u003c/span\u003e\u003cspan address=\"10.1038/s41420-025-02712-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao S, Zhao L, Chen S, Wang H, Gao Y, Shao NY, et al. Cervical cancer immune infiltration microenvironment identification, construction of immune scores, assisting patient prognosis and immunotherapy. Front Immunol. 2023;14:1135657. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1135657\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1135657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Nola R, Loizzi V, Cicinelli E, Cormio G. Dynamic crosstalk within the tumor microenvironment of uterine cervical carcinoma: baseline network, iatrogenic alterations, and translational implications. Crit Rev Oncol Hematol. 2021;162:103343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.critrevonc.2021.103343\u003c/span\u003e\u003cspan address=\"10.1016/j.critrevonc.2021.103343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YM, Qiu JJ, Qu XY, Peng J, Lu C, Zhang M, et al. Accumulation of dysfunctional tumor-infiltrating PD-1\u0026thinsp;+\u0026thinsp;DCs links PD-1/PD-L1 blockade immunotherapeutic response in cervical cancer. Oncoimmunology. 2022;11:2034257. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/2162402X.2022.2034257\u003c/span\u003e\u003cspan address=\"10.1080/2162402X.2022.2034257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang T, You Y, Wei L, Li Q, Sun H, Sun M, et al. ICAT drives lactylation of tumor-associated macrophages via the c-Myc-ENO1 axis to promote cervical cancer progression. Free Radic Biol Med. 2025;241:316\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.freeradbiomed.2025.09.031\u003c/span\u003e\u003cspan address=\"10.1016/j.freeradbiomed.2025.09.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu L, Lanqing G, Huang Z, Xin X, Minglin L, Fa-Hui L, et al. T cell immunotherapy for cervical cancer: challenges and opportunities. Front Immunol. 2023;14:1105265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1105265\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1105265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Wang A, Liu X, Han S, Sun Y, Zhang J, et al. Blocking TIGIT/CD155 signalling reverses CD8(+) T cell exhaustion and enhances the antitumor activity in cervical cancer. J Transl Med. 2022;20:280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-022-03480-x\u003c/span\u003e\u003cspan address=\"10.1186/s12967-022-03480-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Sudan K, Schmoeckel E, Kost BP, Kuhn C, Vattai A et al. CCL22-Polarized TAMs to M2a Macrophages in Cervical Cancer In Vitro Model. Cells (2022) 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells11132027\u003c/span\u003e\u003cspan address=\"10.3390/cells11132027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Wang B, Feng Y, Ye J, Mao Z, Zhang T, et al. Targeting tumor-associated macrophage-derived CD74 improves efficacy of neoadjuvant chemotherapy in combination with PD-1 blockade for cervical cancer. J Immunother Cancer. 2024;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jitc-2024-009024\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2024-009024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao D, Wu L, Li Y. Targeting lipid metabolism to enhance cancer immunotherapy. Biochim Biophys Acta Rev Cancer. 2025;1880:189416. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbcan.2025.189416\u003c/span\u003e\u003cspan address=\"10.1016/j.bbcan.2025.189416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarelli G, Morina N, Portale F, Pandini M, Iovino M, Di Conza G, et al. Lipid-loaded macrophages as new therapeutic target in cancer. J Immunother Cancer. 2022;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jitc-2022-004584\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2022-004584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu F, Xu J, Yang R, Liu S, Hu S, Yan M, et al. New light on treatment of cervical cancer: Chinese medicine monomers can be effective for cervical cancer by inhibiting the PI3K/Akt signaling pathway. Biomed Pharmacother. 2023;157:114084. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopha.2022.114084\u003c/span\u003e\u003cspan address=\"10.1016/j.biopha.2022.114084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan B, Wikan N, Lin S, Thaklaewphan P, Potikanond S, Nimlamool W. Inhibitory actions of oxyresveratrol on the PI3K/AKT signaling cascade in cervical cancer cells. Biomed Pharmacother. 2024;170:115982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopha.2023.115982\u003c/span\u003e\u003cspan address=\"10.1016/j.biopha.2023.115982\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang WT, Chen M, Xu R, Zheng PS. PRDM4 inhibits cell proliferation and tumorigenesis by inactivating the PI3K/AKT signaling pathway through targeting of PTEN in cervical carcinoma. Oncogene. 2021;40:3318\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41388-021-01765-x\u003c/span\u003e\u003cspan address=\"10.1038/s41388-021-01765-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AGPAT4, Cervical cancer, PI3K/AKT pathway, Prognostic biomarker, Immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-8592939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8592939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the role of AGPAT4 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and its underlying mechanisms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAGPAT4 expression and prognosis were analyzed using TCGA and GTEx data. Mendelian randomization (MR) was used to assess causality. Epigenetic regulation, immune microenvironment, and functional pathways were evaluated through methylation analysis, immune deconvolution, and enrichment analysis. The biological functions of AGPAT4 were validated in vitro and in vivo.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAGPAT4 was downregulated in CESC but showed high diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.893). MR supported a causal link with cervical cancer risk (OR\u0026thinsp;=\u0026thinsp;1.247, p\u0026thinsp;=\u0026thinsp;0.008). High AGPAT4 expression was associated with worse overall, progression-free, and disease-specific survival, and served as an independent prognostic factor. Promoter hypermethylation was negatively correlated with AGPAT4 expression. AGPAT4-high tumors exhibited an immunosuppressive microenvironment and were enriched in PI3K-AKT signaling, extracellular matrix remodeling, and immune suppression pathways. Functionally, AGPAT4 overexpression promoted proliferation, migration, colony formation, and tumor growth, and activated the PI3K/AKT pathway.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAGPAT4 drives cervical cancer progression through PI3K/AKT pathway activation and immune microenvironment modulation, representing a potential diagnostic biomarker and therapeutic target.\u003c/p\u003e","manuscriptTitle":"AGPAT4 Promotes Cervical Cancer Progression by Activating the PI3K/AKT Signaling Pathway: A Multi-Omics and Functional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 15:35:26","doi":"10.21203/rs.3.rs-8592939/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-05T12:24:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T11:35:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T10:10:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T13:37:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-14T13:15:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f90e1744-9193-4115-b3d0-6df82f80af84","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T15:35:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 15:35:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8592939","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8592939","identity":"rs-8592939","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}