AGPAT Family in Cervical Cancer: A Multi-Omics Perspective on Prognosis and Function

AGPAT Family in Cervical Cancer: A Multi-Omics Perspective on Prognosis and Function | 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 Article AGPAT Family in Cervical Cancer: A Multi-Omics Perspective on Prognosis and Function Yuexiu liang, Yuzhen Chen, Hongtao Qin, Wenting Wei, Mingyou Dong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4470497/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are the primary histological subtypes of cervical cancer. The AGPAT gene family has been implicated in disease processes across different cancer types, but its specific role and prognostic relevance in cervical cancer remain unclear. This study emphasizes the AGPAT family as a potential biomarker and therapeutic target in cervical cancer due to its significant impact on the disease's development and outcomes. Gene expression data from the AGPAT family and clinical information from 306 CESC cases and 3 control cases were collected from The Cancer Genome Atlas (TCGA) database. These data were analyzed for mRNA expression, prognostic and diagnostic value, clinical correlations, function enrichment, and ESTIMATE score. The study revealed that AGPAT2, AGPAT3, and AGPAT5 mRNA expression was elevated, while AGPAT1 and AGPAT4 expression was reduced in cervical cancer tissues. Particularly, increased levels of AGPAT3 and AGPAT4 expression were associated with a poorer prognosis in cervical cancer patients. Additionally, higher DNA methyl-ation levels of AGPAT3 were observed in CESC tissues compared to normal samples, and specific CpGs within AGPAT3 showed a strong correlation with prognosis. Moreover, AGPAT3 expression was linked to the presence of various tumor-infiltrating immune cells. Experimental evidence demonstrated that inhibiting the AGPAT3 gene led to a significant decrease in the proliferation and migration abilities of the Hela cervical cancer cell line. These results suggest that AGPAT3 could be a valuable biomarker and a promising therapeutic target for predicting the prognosis of individuals with cervical cancer. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Medical research cervical cancer prognosis immune infiltration TCGA AGPAT3 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction CESC is the predominant histologic forms of cervical cancer (CC) 1 . Despite widespread cervical cancer screening 2, 3 and HPV vaccination programs 4 , it remains a significant global health issue in terms of both incidence and mortality 5, 6 . Metastatic cervical cancer still has a poor overall survival rate 7, 8 , underscoring the need for effective predictive biomarkers to help manage its progression. Currently, surgery and radiotherapy are the most effective treatments for cervical cancer 9 , but access to these interventions is not evenly distributed globally, resulting in high morbidity and mortality rates 10 . Patients in advanced stages, or those experiencing recurrence and metastasis, have a particularly poor prognosis. Therefore, understanding the molecular mechanisms underlying cervical cancer and identifying effective prognostic biomarkers are essential for improving patient survival. AGPATs, also referred to as LPAATs, are a set of five enzymes (AGPAT1/2/3/4/5) essential for converting LPA to PA in the TAG biosynthesis pathway 11 . AGPATs play a role in cancer cell survival and growth 12 by aiding in the production of TAGs and storing lipids in LDs, which are crucial for storing energy and avoiding lipotoxicity. Additionally, AGPATs are involved in the synthesis of phospholipids necessary for new membrane formation in rapidly dividing cells and in the regulation of cell signaling pathways. Recent research indicates that AGPATs also play a role in membrane fission, vesicular transport, and communication between cancer cells and the tumor microenvironment, influencing cancer metastasis. Multiple research studies have emphasized the importance of AGPATs in different forms of cancer, demonstrating links to the proliferation of cancer cells, the growth of tumors, the spread of cancer to other parts of the body, the prognosis of patients, and the categorization of cancer types. For example, Ren et al. Discovered elevated levels of AGPAT1 expression as an unfavorable indicator in colorectal cancer, associated with an increased chance of recurrence and reduced lifespan 13 . Similarly, Song et al. Showed that inhibiting AGPAT2 led to higher levels of cell death in cisplatin-resistant osteosarcoma cell lines 14 . Moreover, variations in AGPAT expression levels between cancerous and healthy tissues, combined with computational analyses, have resulted in the incorporation of AGPAT isoforms in predictive metabolic gene profiles. The AGPAT gene family plays a vital part in the development 15 of tumors and can also act as indicators for prognosis 16 and influence the response to immunotherapy. However, their specific role in CC and potential implications for cancer prognosis require further investigation. Transcriptomic data from both CC and normal samples were collected from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) and the Genotype-Tissue Expression (GTEx, https://commonfund.nih.gov/gtex ) database for this study. Differential expression of AGPAT family genes in CC versus normal tissues was analyzed using Cox and LASSO regression models. Molecular mechanisms underlying AGPAT family gene signaling in immunotherapy and gene alterations were also investigated. The research also assessed the levels of gene expression in the risk model using different experimental methods. The results of this study may help enhance the prognosis of cervical cancer and provide guidance for treatment plans, while also identifying potential biomarkers for clinical diagnosis, prognosis, and targets for immunotherapy in patients with cervical cancer. Results Gene Expression of AGPAT Family Members The expression levels of the AGPAT family in various tissues such as adipose tissue, breast, liver, small intestine, cervix, and others were depicted in Fig. 1 A. Interestingly, AGPAT5 expression was found to be significantly upregulated in the pancreas, salivary gland, and blood vessels. Notably, in healthy cervix tissues, the expression of AGPAT3-5 was lower compared to other family members. Furthermore, among the 33 types of human tumors examined, AGPAT1-5 were found to be expressed individually (Fig. 1 B). These findings indicate a tissue-specific expression pattern of AGPAT family members. The Expression of AGPAT1-5 The TIMER database was utilized to assess the expression of the AGPAT family in both pan-cancer and normal tissues.The results showed an increase in AGPAT1-5 expression in various types of cancer compared to normal tissues, such as BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, READ, STAD, and UCEC (Fig. 3 A-E). Specifically, AGPAT2, AGPAT3 and AGPAT5 mRNA expression was found to be elevated, while AGPAT1 and AGPAT4 expression was reduced in CESC tumor tissues. These findings suggest distinct expression levels of the AGPAT family between CESC tissues and normal tissues. Genetic Alterations of AGPAT Family and Gene and Protein Network The frequencies of alterations in members of the AGPAT family were assessed in 306 CESC samples using the cBioportal tool. Figure 3 A illustrates that mutations in the AGPAT family were rare (AGPAT1, 1.8%; AGPAT2, 1.1%; AGPAT3, 1.8%; AGPAT4, 2.2%; AGPAT5, 1.1%), indicating a high level of conservation. Analysis of the gene-gene network using the GeneMANIA database identified connections between the AGPAT family and 20 possible target genes (Fig. 3 B). Following this, a network of protein-protein interactions (PPI) was created to examine the relationship between AGPAT family members through the use of the STRING platform, revealing a significant association between them (PPI enrichment p-value < 1.0e-16) (Fig. 3 C). In addition, the expression of AGPAT family genes was analyzed using TCGA data, revealing a strong positive relationship between most members, particularly AGPAT3, which exhibited a positive association with AGPAT1 and AGPAT5 (Fig. 3 D). Survival Analysis of AGPAT Family in CESC AGPAT 3 and 4 overexpression was linked to poor prognosis in CESC patients based on univariate Cox regression analysis. Additionally, in CESC patients, Multivariate Cox regression analysis revealed that elevated levels of AGPAT 3 and 4 were an autonomous predictor of overall survival (Fig. 4 A). The results were further confirmed by Kaplan-Meier analysis, which showed that CESC patients with high AGPAT3 levels had reduced survival times in terms of overall survival, disease-specific survival, and progression-free interval survival (Fig. 4 B-D). Similarly, the group with high AGPAT4 expression also exhibited shorter survival times (Fig. 4 E-G). The findings indicate that AGPAT3 and 4 could serve as indicators for forecasting unfavorable outcomes in CESC individual. Prognostic and Diagnosis Significance of AGPAT Family in CESC AGPAT3 and AGPAT4 were identified as potential prognostic biomarkers, and a nomogram was developed to predict overall survival in CESC patients by incorporating AGPAT3/4 expression and TNM stage. For CESC patients, a lower survival outcome is associated with higher points on the Nomogram model (Fig. 5 A). Figure 5 B showed a strong correlation between predicted and observed outcomes on the calibration graph. To sum up, the combination of AGPAT3/4 mRNA levels and TNM stage in a nomogram may function as a prognostic tool for overall survival in CESC, surpassing single prognostic indicators. Following this, the diagnostic effectiveness of AGPAT family members was evaluated through receiver operating characteristic (ROC) curve analysis. AGPAT1-4 displayed AUC values of 0.839, 0.688, 0.836, and 0.893, in that order (Fig. 5 C). With AUC values exceeding 0.6, AGPAT1-4 may serve as potential diagnostic biomarkers for CESC patients, with AGPAT3 and 4 demonstrating particularly promising prospects. Clinical correlation analysis The study conducted using the GSCA database revealed a significant association between AGPAT1-5 expression and survival outcomes (DSS, OS, PFS) as well as clinical stage in CESC patients. AGPAT3 and AGPAT1 were identified as strongly associated with decreased survival and late disease stage (Fig. 6 A-B). Additionally, AGPAT3 mRNA expression was notably higher in patients over 50 years old compared to those aged 50 years or younger (Fig. 6 H). Moreover, adenocarcinoma and adenosquamous patients exhibited higher AGPAT3 expression levels compared to squamous cell carcinoma patients (Fig. 6 I). The results suggest that increased levels of AGPAT3 mRNA are associated with negative prognostic factors, indicating that individuals with high AGPAT3 levels may have worse survival rates and are more likely to advance to later stages compared to those with low AGPAT3 levels. DNA Methylation Analysis of AGPAT3 in CESC Following that, the GSCA tool was implemented for the analysis of AGPAT3 methylation in CESC. As shown in Fig. 7 A, the DNA methylation levels of AGPAT3 were higher in CESC tissues, compared with normal samples. We then acquired the methylation profile of AGPAT3 from the MethSurv database. The data indicates the discovery of 38 CpG sites in AGPAT3 (Fig. 7 B). Analysis of the prognostic implications of these sites revealed that 11 were significantly linked to the prognosis of cervical cancer. Among these, 2 CpG sites had HR values exceeding 1, while the remaining 9 had HR values below 1 (Fig. 7 C). These findings imply that DNA methylation of AGPAT3 could play a role in the advancement of CESC and its prognosis in patients. Function Enrichment Analysis of AGPAT3 in CESC High levels of AGPAT3 expression were detected in CESC, which was associated with poor survival results. The impact of this heightened expression on signaling pathways in CESC remains ambiguous. A functional enrichment analysis was conducted on gene sets related to AGPAT3 to illuminate its role in CESC. Within the TCGA-CESC dataset, researchers discovered 286 genes that were expressed differently between groups with high and low levels of AGPAT3. Of these genes, 253 were found to be up-regulated and 33 were down-regulated in the high AGPAT3 expression group compared to the low expression group, as shown in Figs. 8 A and 8 B. KEGG analysis showed that up-regulated genes were linked to tuberculosis and phagosome signaling pathways (Fig. 8 C). Analysis of GO terms showed that the genes that were up-regulated were notably concentrated in pathways related to the maintenance of stem cell populations and the regulation of cell numbers (Fig. 8 D). Conversely, down-regulated genes were implicated in the IL-17 signaling pathway based on KEGG analysis (Fig. 8 E). GO term analysis also indicated enrichment of down-regulated genes in pathways related to skin and epidermis development (Fig. 8 F). Given the well-documented association of these pathways with immunity 17, 18, 19 , these findings suggest a potential involvement of AGPAT3 in the pathogenesis of CESC through immune regulation. Immune Infiltration Analysis of AGPAT3 in Pan-cancer AGPAT3 expression showed a significant negative correlation with stromal score, immune score, and ESTIMATEScore in several tumors including PRAD, BLCA, UCEC, LIHC, and BRCA. Conversely, contrasting results were observed in LAML, UVM, and HNSC (Fig. 9 A). We investigated the correlation between AGPAT3 levels and the presence of 24 immune cell types through Spearman analysis. AGPAT3 expression in different types of cancer showed a positive association with CD4 memory resting T cells and Macrophages M2 (Fig. 9 B), while displaying a negative correlation with plasma cells, memory B cells, and gamma delta T cells (Fig. 9 B). Relation Between AGPAT3 With immunoregulatory gene in Pan-cancer We also investigated the relationship between AGPAT3 and immunomodulatory genes, including immune checkpoints, immustimulator, immuinhibitor, chemokines and chemokine receptors 20 in 33 tumors. In most tumors, the expression of AGPAT3 was significantly positively correlated with chemokines, chemokine receptors and immustimulators (Fig. 10 A-C). Furthermore, our research revealed a positive association between AGPAT3 and immune checkpoints like PD-1, PD-L1, CTLA4, TIGIT, LAG3, HAVCR2, and PDCD1LG2 across various cancer types as shown (Fig. 10 D). AGPAT3 showed a strong positive correlation with the expression of immuinhibitors in the majority of tumors (Fig. 10 E). Thus, AGPAT3 may be a promising candidate for immunotherapy. AGPAT3 is highly expressed in CESC tissues AGPAT3 expression was analyzed in 6 CESC specimens and their corresponding paraneoplastic tissues using immunohistochemistry staining. Figure 11 A-B displayed high levels of AGPAT3 expression in the cytoplasm of the tumor cells. RNA was isolated from three sets of frozen CESC tumor tissue samples and their corresponding adjacent tissues, then underwent reverse transcription. Following this,β-actin was used as an internal control, and the level of AGPAT3 RNA expression was measured through real-time PCR. AGPAT3 mRNA expression was elevated in tumor tissue compared to paraneoplastic tissues, as shown in Fig. 11 C. AGPAT3 promotes proliferation and migration abilityof Hela cells The Hela AGPAT3-KD cell line was generated in Hela cells using electroporation technology. The knockdown efficiency was confirmed through PCR (Fig. 12 A) and Western blotting (Fig. 12 B). The CCK-8 assay demonstrated a notable decrease in the proliferation of AGPAT3-KD cells compared to Hela WT cells (Fig. 13 A). Furthermore, the colony formation ability of AGPAT3-KD cells showed a significant reduction (Fig. 13 B, C). Additionally, both Transwell experiments (Fig. 13 D, E) and cell scratch assay (Fig. 13 F, G) demonstrated a reduced migration capacity in AGPAT3 knockdown cell lines compared to wild-type cells. These findings collectively suggest that AGPAT3 plays a role in promoting the proliferation and migration ability of cervical cancer cells. Discussion Cervical cancer ranks fourth in morbidity and mortality among women worldwide, and currently patients with advanced disease or recurrence and metastasis have a poor prognosis 21 , so researchers are focused on discovering valuable biomarkers and promising therapeutic targets. The study highlights the AGPAT family as a noteworthy biomarker and potential therapeutic target for cervical cancer, given their significant role in its incidence and prognosis. Using the TCGA database, we initially discovered variations in AGPAT family expression levels between cancerous and para-cancerous tissues across various types of cancers (Figs. 1 and 2 ). Next, we performed an analysis of gene-gene networks and protein-protein interactions (PPI) networks. It was found that AGPAT3 showing a positive correlation with AGPAT1 and AGPAT5 (Fig. 3 ). By conducting survival analysis on the AGPAT family in CESC and evaluating the prognostic and diagnostic significance of AGPAT family in CESC, we have identified AGPAT3 as the most promising potential biomarker for diagnosis (Figs. 4 and 5 ). Subsequently, we conducted additional analysis on the correlation between AGPAT3 mRNA levels and clinical parameters, as well as the DNA methylation status of AGPAT3 in CESC. Our findings revealed a strong association between elevated mRNA expression and increased promoter methylation of AGPAT3 with unfavorable clinical outcomes (Figs. 6 – 7 ). Subsequently, by conducting Function Enrichment Analysis on AGPAT3 in CESC, these results indicate a possible involvement of AGPAT3 in the development of CESC through immune modulation (Fig. 8 ). Hence, an analysis of immune infiltration was carried out for AGPAT3 in various types of cancer (Fig. 9 ), revealing a connection between AGPAT3, immune checkpoints, and chemokines (Fig. 10 ). This indicates that AGPAT3 may be a promising target for immunotherapy. To confirm the reliability of the previous conjecture, Immunohistochemistry (IHC) Staining and real-time PCR and confirmed that AGPAT3 expression was higher in tumor tissue compared to paraneoplastic tissues (Fig. 11 ). Furthermore, we constructed the Hela AGPAT3-KD cell line and found that its cell proliferation ability and cell migration ability were significantly weakened compared with Hela WT cells (Figs. 12 and 13 ). So, AGPAT3 could serve as a valuable biomarker and a promising therapeutic target for cervical cancer. Many patients, particularly those in advanced stages of the disease, have a bleak outlook due to the shortcomings in diagnosing and treating cervical cancer. Extensive research is currently being conducted to find biomarkers and targets for detecting and treating cervical cancer. Yan et al. discovered that the tumor necrosis factor (TNF) family genes could serve as prognostic biomarkers for cervical cancer. The gene signature of the TNF family mainly operates in the TGF-β pathway and can impact the response to immunotherapy 22 . In a separate study, Zhang et al. showed that METTL11A, part of the methyltransferase-like gene group, enhances the movement of cervical cancer cells through an ELK3-related process 23 . AGPATs, also referred to as LPAATs, are essential in converting LPA to PA during TAG biosynthesis. AGPATs play a crucial role in the survival and growth of cancer cells 24 by facilitating TAG biosynthesis and aiding in lipid storage within LDs, which are critical for storing energy and protecting against lipotoxicity. However, the specific functional role of the methyltransferase-like gene family in cervical cancer remains unclear. This study thoroughly analyzed the AGPAT family in CESC, examining expression, mutation, diagnosis and prognosis significance, DNA methylation patterns, relationships with immune cell infiltration, and immune checkpoint involvement. The AGPAT family expression was assessed in pan-cancer and normal tissues using The TIMER database. The study revealed an up-regulation of AGPAT1-5 expression in 15, 10, 18, 16, and 15 different types of cancers when compared to normal tissues. Tumor progression is linked to the deregulation of AGPAT channels, despite the infrequency of mutations in AGPAT family genes. Genetic alterations of AGPAT family members in CESC were also examined, showing minimal mutations (approximately 7 frequencies), which did not impact the survival of CESC patients. The findings indicate that the AGPAT gene family is well-preserved, and the dysregulation in CESC is not caused by genetic mutations. Various studies have recognized AGPATs as potential biomarkers for tumor diagnosis, prognosis, and progression. Increased AGPAT1 expression has been associated with an increased likelihood of recurrence and reduced survival in individuals with colorectal cance r25, 26, 13 . AGPAT1 was discovered as a new tumor suppressor and prognostic indicator for ovarian cancer through a comprehensive analysis 27, 28 . Subsequently, the diagnostic and prognostic potential of AGPAT family members in CESC was assessed. The research's ROC analysis showed that AGPAT1/2/3/4 had excellent accuracy in differentiating CESC patients from healthy individuals (AUC > 0.6), suggesting their promise as diagnostic indicators. Furthermore, the results of survival analysis indicated that elevated levels of AGPAT3/4 were associated with a worse outcome in patients with CESC, indicating their potential as prognostic markers. Notably, due to the possible cancer-causing function of AGPAT3 in CESC, its expression was further examined in correlation with clinical factors of CESC patients. Higher levels of DNA methylation of AGPAT3 were observed in CESC tissues compared to normal samples in the study, emphasizing the significance of this epigenetic change in the development of tumorigenesist 29, 30 . To explore the biological function of AGPAT3 in CESC, DEGs were identified according to AGPAT3 expression levels, and then subjected to functional enrichment analysis. Genes that were increased due to AGPAT3 overexpression were discovered to be connected to the maturation and stimulation of immune cells, suggesting a potentially intricate function of AGPAT3 in immune control in the CESC environment. Following this, we evaluated how AGPAT3 expression influenced the makeup of immune cells that infiltrate tumors in CESC. The high AGPAT3 expression group exhibited significantly increased proportions of regulatory T cells, memory B cells, CD8 + T cells, and activated Mast cells, while the low AGPAT3 expression group showed elevated proportions of gamma delta T cells, Monocytes, M2 Macrophages, and Mast cells. Moreover, correlation analysis demonstrated a significant association between AGPAT3 expression and the accumulation of these TIICs, particularly Treg cells. Treg cells, known for their negative regulatory functions, are crucial for maintaining immune homeostasis 31, 32 . Usually not found in healthy CESC tissue, these cells increase in number in the CESC environment, encircling cancer cells and hindering the activity of effector T cells, ultimately aiding in the evasion of the immune system by the tumor 33 . Our research found that increased levels of Treg cells are linked to a worse outcome in CESC patients with high AGPAT3 expression.Conversely, in patients with low AGPAT3 expression, Treg cell levels did not significantly impact CESC prognosis. These findings suggest that AGPAT3 may serve as a potential immunomodulatory factor in CESC, and targeting AGPAT3 could potentially counteract the immunosuppressive effects of Treg cells, thereby enhancing the effectiveness of immunotherapy. To sum up, AGPAT2, AGPAT3 and AGPAT5 mRNA expression was found to be elevated, while AGPAT1 and AGPAT4 expression was reduced in CESC tumor tissues. AGPAT3/4 could serve as a promising prognostic biomarker for individuals diagnosed with CESC. Besides, Furthermore, the abnormal expression of AGPAT3 in CESC could be attributed to dysregulated DNA methylation. Notably, AGPAT3 demonstrates a strong positive correlation with Treg cell infiltration and the expression of immune checkpoints. Patient tissue samples were employed to examine previous theories, confirming elevated levels of AGPAT3 protein and RNA expression in tumor tissues relative to paraneoplastic tissue. Additionally, an AGPAT3-KD cell line was constructed in the cervical cancer cell line Hela. Growth curve and clonal formation experiments demonstrated that knocking down AGPAT3 inhibited cell proliferation. Transwell cell migration and cell scratching experiments further indicated that AGPAT3 knockdown hindered cell migration. The results indicate that increased AGPAT3 levels could promote the onset, progression, and spread of cervical cancer, making AGPAT3 a potential biomarker for detection and target for treatment. In the present study, we investigated the expression of the AGPAT family in CESC and its correlation with patient prognosis and immune microenvironment. Our findings suggest that AGPAT3 is significantly associated with the onset and progression of CESC, primarily through its influence on the immune microenvironment. In summary, we have identified AGPAT3 as a promising biomarker and potential therapeutic target for cervical cancer. However, our study has certain limitations. While functional enrichment analysis and Immune Infiltration Analysis imply that AGPAT3's promotion effect on cervical cancer may involve immune regulation, this hypothesis requires further experimental validation. Furthermore, pan-cancer analysis revealed varied AGPAT family expression across different cancers and its impact on immunity, prompting the need for exploration of the AGPAT family's influence on other cancer types. Methods Data on gene expression (HTSeq-FPKM) for 306 cases of CESC and 3 control cases were obtained from the TCGA database (https //portal.gdc.cancer.gov/). Data on CESC patients' clinical information, including pathologic stage, TNM stage, histologic stage, age, and overall survival (OS). Experimental validation was conducted using clinical specimens and cell experiments. mRNA Expression AGPAT1-5 mRNA expression was detected in 3 human tissues of healthy individuals using the 'GTEx Expression' module within the GSCA database Additionally 34 , interactive body maps of AGPAT1-5 were created using the TIMER database 35 to visualize the median expression in normal tissues and cancers.Comparison of AGPAT1-5 expression variations among 33 tumors was conducted using information from the TCGA repository.A significance level of p-value < 0.05 was applied. Genetic Alterations 36 and Interaction Network of AGPAT Family Changes in AGPAT gene family members were examined in 306 CESC samples from the TCGA Firehose Legacy project through the cBioPortal database 37 . Gene regulatory networks among AGPAT gene families were predicted using the GeneMANIA database 26 . Furthermore, the AGPAT family was used to create a protein-protein interaction network in the STRING database ( https://cn.string-db.org/cgi/input.pl ), specifying parameters like organism (“Homo sapiens”), network type (“full STRING network”), and minimum interaction score (“medium confidence 0.400”). Spearman correlation analysis was used to evaluate the relationship between AGPAT family members using gene expression data from CESC samples in the TCGA database. Prognostic and Diagnostic Value Analysis The Xiantao Academic database was accessed to perform univariate and multivariate Cox regression analysis on AGPAT family members in CESC. The research analyzed the survival rates of patients with cervical squamous cell carcinoma who had high levels of AGPAT3-4 compared to those with low levels, using the Kaplan-Meier curve. Logistic regression was employed to investigate the correlation between AGPAT3 expression and clinical variables. A nomogram model was created to forecast the 1-, 3-, and 5-year overall survival rates by combining AGPAT3-4 expression data with the T, M, and N stages of CESC patients from the TCGA database. The prognostic value of the nomogram model in CESC was assessed using the 'pROC' R package. Furthermore, the diagnostic effectiveness of AGPAT members in detecting CESC in the Xiantao Academic database was assessed using the 'pROC' R package. A p-value less than 0.05 was deemed significant. The AUC of the ROC curve was utilized to summarize the diagnostic efficiency. Clinical correlation analysis The “Expression” module within the GSCA database was employed to examine the relationship between AGPAT1-5 and different survival measures (DSS, OS, PFS, DSS, and clinical stage) as well as clinical stage among CESC patients. Furthermore, the Xiantao Academic database was utilized to explore the correlation between AGPAT3 gene expression and various clinical characteristics including T Stage, N Stage, M Stage, Grade, Stage, Age, Histological type, and OS event. DNA Methylation Analysis The correlation between AGPAT3 mRNA levels and DNA methylation was examined. The UALCAN database 38 was used to analyze the differences in promoter methylation levels of AGPAT3 between normal control tissues and CESC tissues. DNA methylation levels were measured by the Beta value, which varies from 0 (unmethylated) to 1 (fully methylated), with a significant threshold established at a P-value less than 0.05. The AGPAT3 methylation pattern in CESC was acquired using the 'Gene Visualization' feature in the MethSurv database 39 . Furthermore, the MethSurv database was utilized to investigate the influence of DNA methylation on the survival of CESC patients specifically at individual CpG sites within AGPAT3 through the 'Single CpG' module. Function Enrichment Analysis Patients with CESC in the TCGA-CESC dataset were divided into two groups, AGPAT3-high and AGPAT3-low, using the median expression level of AGPAT3. The 'DESeq2' R package was employed to detect differentially expressed genes (DEGs) in the comparison of these two groups, where up-regulated DEGs were defined by log2 fold change values ≥ 1.5 and down-regulated DEGs by log2 fold change values ≤ -1.5.A significance level of P < 0.05 was set as the cutoff criteria.Following that, an analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichments was carried out with the 'ClusterProfiler' R package to explore the functional annotations of the DEGs related to AGPAT3 in CESC. Relationship between AGPAT3 expression and immunity Using the “GSVA” software package, researchers explored the relationship between AGPAT3 and 24 types of immune infiltrating cells. The ESTIMATE method, which examines stromal and immune cells in 33 tumors using gene expression information, was employed to forecast the tumor microenvironment (TME) by calculating ImmuneScore, StromalScore, and ESTIMATEScore for each tumor specimen. The relationship between AGPAT3 expression and tumor microenvironment, along with immune infiltrating cells, across 33 cancer types was explored using the 'limma' R package. A Spearman correlation heat map was generated to visualize the relationship between AGPAT3 and various immune cell types.Furthermore, the connection between AGPAT3 and genes that regulate the immune system, including the major histocompatibility complex (MHC), molecules that boost the immune response, chemotactic proteins, and receptors for chemotactic proteins, was investigated by analyzing information from the TCGA database. Additionally, the correlation between AGPAT3 expression and immune checkpoint molecules was examined through Spearman analysis. Patient tissue specimens The study included nine individuals with CESC who had surgery at the Affiliated Hospital of Youjiang Medical University for nationality in Guangxi, China between 2020 and 2023. Tumor and paracancerous tissues were collected, with six pairs being paraffin-embedded and three pairs fresh. Ethical approval for the research was granted by the hospital's Ethics Committee (license number YYFY-LL-2023HY2-06), and all participants provided written consent. The research followed the staging criteria of the International Federation of Gynecology and Obstetrics (FIGO) and the guidelines of the Declaration of Helsinki. Clinicopathological findings were derived from surgical records and pathology reports. Immunohistochemistry (IHC) Staining Ten percent formalin was used to fix six cancerous and paracancerous tissues for one week prior to embedding them in paraffin. The tissue specimens were then sectioned to 4 micrometers, deparaffinized, and underwent antigen retrieval through microwaving. Immunostaining was performed using a Polyclonal antibody (Thermo, PA5-98855, USA). In contrast, surgical samples were preserved in 4% formaldehyde, cut into 4 µm sections, and then slides were heated at 60°C for 1 hour to avoid peeling. Paraffin was removed using xylene, followed by rehydration in alcohol gradient, and then repaired with citric acid for a duration of 20 minutes.Afterward, slides were exposed to 3% hydrogen peroxide for a quarter of an hour, obstructed with 5% BSA in PBS for half an hour, and then exposed to anti-TRIM21 antibody (32520, SAB, Nanjing, China) in PBS with 5% bovine serum albumin overnight at 4°C. The standard EnVision technique from Dako in Denmark was utilized for detecting protein binding and developing color. After counterstaining with hematoxylin and sealing with neutral resin, the sections that were stained were observed using a light microscope. The IHC staining results were assessed in a double-blind manner using clinicopathological criteria. AGPAT3 was classified based on its immunoreactivity score as either negative (0), weak ( 1 ), moderate ( 2 ), or strong ( 3 ). The h-score was determined by multiplying the various levels of staining (0, 1, 2, 3) by the proportion of cells showing positivity for each level, as per the equation h-score = 1 x (% weak cells) + 2 x (% moderate cells) + 3 x (% strong cells) 40 . Quantitative Real-time PCR (qPCR) RNA was isolated from three sets of frozen CESC tumor tissue samples along with their corresponding paracancerous tissues. The extracted RNA was reverse transcribed, and β-actin expression was utilized as an endogenous control. The RNA expression level of AGPAT3 was then detected using qPCR (ToloScript ALL-in-one RT EasyMix for qpcr, TOLOBIO) with specific primers (Human-AGPAT3-F: CGCCTGTCGGACTACCCC; Human-AGPAT3-R: CTTAGCAGCCGCCACCTC). Cell line culture and conversion The HeLa cell line was acquired from the Shanghai Institute of Biochemistry and Cell Biology (SIBCB) and grown in DMEM (MeilunBio, MA0212) with the addition of 10% fetal bovine serum and 100 units/mL Penicillin-Streptomycin. The AGPAT3 Knock Down (KD) cell line was generated through electroporation. Two optimal sgRNAs (GTGCACGTGAGAAGCGTAAT, GCTAGGGTGAGCACCTTCCA) were designed using the CRISPOR website ( http://crispor.tefor.net/ ) and subsequently electrotransferred into HeLa cells along with the Cas9 protein complex. The efficiency of knock down was assessed through Western blot and PCR analysis. Western blot (WB) Cellular proteins were extracted and quantified with RIPA lysis 1×SDS Loading Buffer. After being separated using 10% SDS-PAGE, the proteins were then transferred onto a PVDF membrane (Immobilon, Millipore-p, IPVH00010, CA, USA) and subsequently blocked with 5% non-fat milk at room temperature. After that, the membrane was left to incubate overnight at 4°C with a thinned AGPAT3 antibody (PA5-98855, Thermo, USA), then incubated with a secondary antibody (Jackson ImmunoResearch, 115-035-004, USA) at room temperature.Protein bands were detected using enhanced chemiluminescence (ECL) after membrane cleaning. Validation of gene expression levels in knockdown cell lines Wild-type (WT) and AGPAT3-KD Hela cells were resuspended and then added to PCR tubes at a 1:4 ratio with a 1:200 dilution of proteinase K (Biosharp, BL628A). Incubation of the tubes in a PCR machine at 55°C for 1 hour was done, then followed by genome extraction at 95°C for 10 minutes. The extracted DNA was then combined with two Primers (CATCTTCTGCACCTCTCACG and CACTGCTCCACCCAGTACCT) and PCR Mix (Yeasen, 10157ES03), and subjected to PCR amplification as per the manufacturer's instructions. The changes in DNA length were confirmed by analyzing the product using agarose gel electrophoresis. Cell proliferation assay Cell proliferation was assessed through CCK-8 and colony formation analyses. For the CCK-8 test, wild-type and AGPAT3 knockdown HeLa cells were placed in 96-well dishes and permitted to attach prior to the introduction of CCK-8 solution (10 µL CCK-8 and 90 µL McCoy's 5 A per well) at different intervals (ranging from day 1 to day 7), then incubated for one hour. Subsequently, the OD450 was assessed with a microplate reader. To conduct the colony formation test, wild-type and AGPAT3 knockdown HeLa cells were grown in 6-well dishes with 400 cells in each well to promote colony formation. Following a two-week period, the cells were treated with anhydrous methanol, then stained with crystal violet, dried, and finally scanned. Afterwards, the quantity of cell colonies on each plate was measured. Transwell migration assay Transwell chambers with a pore size of 8 µm (Corning, Lowell, MA, USA) were placed in 24-well cell culture plates (BIOFIL, TCP010024, Guangzhou, China). These chambers were utilized for cell migration assays in the absence of matrix gel. The up-per compartment of the Transwell received 500 µL of cell suspension in serum-free medium containing 5 × 104 cells, while the lower compartment was filled with 600 µL of medium containing serum. After a 24-hour incubation period, cells that had mi-grated and adhered to the underside of the chamber were fixed using anhydrous methanol. Any non-migratory cells were gently removed with a damp cotton swab, following which the chamber was allowed to dry. Subsequently, images were captured using a Nikon Eclipse TE2000-U inverted microscope at a total magnification of 100× (10× objective and 10× eyepiece) in 9 randomly selected regions for cell counting. Cell scratch assay WT and AGPAT3-KD HeLa cells were seeded at a 50% density in a six-well plate and incubated for 12 hours until fully adhered to the surface. Afterward, a 200 µl yellow pipette tip was utilized to generate a cell-free 'scratch' by scraping the layer of cells in a linear fashion following adhesion. After washing with PBS to eliminate debris, the cells were cultured in a serum-free medium for regeneration. Photographs were taken at 0, 24, and 48 hours with a Nikon Eclipse TE2000-U inverted microscope at 40× magnification (10× objective and 4× eyepiece) in five separate scratch locations to measure the size of the area that had not healed. Statistical analysis SPSS 25.0 software was used for statistical analysis, while Graphpad Prism 8 software was employed to generate statistical charts for comparing the two groups. The difference between the groups was assessed through independent sample t-tests or paired sample tests. In addition, the researchers performed both single-variable and multiple-variable analyses using the Cox proportional hazards regression model, and they assessed overall survival using the Kaplan-Meier method and Log-rank test. A statistical significance was determined with a P-value less than 0.05. Declarations Institutional Review Board Statement The study was approved by the Affiliated Hospital of Youjiang Medical University for nationality—Ethics Committee (license number: YYFY-LL-2023HY2-06, 8 November 2023). Informed Consent Statement: Written informed consent has been obtained from the patients to publish this paper. Author Contribution Junli Wang: Conceptualization, Methodology, Funding acquisition. Mingyou Dong: Supervision, Resources, Project administration. Yuexiu Liang: Investigation, Data curation, Writing - Original draft preparation, Writing – review & editing. Yuzhen Chen: Visualization, Investigation. Hongtao Qin: Visualization, Investigation. Wenting Wei: Visualization, Investigation. Acknowledgement We gratefully acknowledge the assistance of X.D. Zhang in preparing the gene knockout cells. We also thank Jingsheng Ao and Feng Shi for helpful discussion on experimental design and data analysis. In addition, we appreciate Guiqiong Pan’s assistance in experimental operations. Data Availability Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/projects/TCGA-CESC References Abu-Rustum, N. R. et al. NCCN Guidelines® Insights: Cervical Cancer, Version 1.2024. J. Natl. Compr. Cancer Netw. JNCCN 21, 1224–1233, DOI: https://doi.org/ 10.6004/jnccn.2023.0062 (2023). Voelker, R. A. Cervical Cancer Screening. JAMA 330, 2030, DOI: https://doi.org/ 10.1001/jama.2023.21987 (2023). Bouvard, V. et al. The IARC Perspective on Cervical Cancer Screening. N. Engl. J. Med. 385, 1908–1918, DOI:https://doi.org/ 10.1056/NEJMsr2030640 (2021). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4470497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310680177,"identity":"da6b73f2-f44e-4e07-8e8f-9db2ed8ac905","order_by":0,"name":"Yuexiu liang","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Yuexiu","middleName":"","lastName":"liang","suffix":""},{"id":310680178,"identity":"958030df-1770-4b56-8326-a55ced966ab0","order_by":1,"name":"Yuzhen Chen","email":"","orcid":"","institution":"Affiliated Hospital of Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Yuzhen","middleName":"","lastName":"Chen","suffix":""},{"id":310680179,"identity":"c54b4c06-140b-4e60-bf9c-7439e51ed353","order_by":2,"name":"Hongtao Qin","email":"","orcid":"","institution":"Affiliated Hospital of Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Hongtao","middleName":"","lastName":"Qin","suffix":""},{"id":310680180,"identity":"f6bab378-9d90-4005-ba86-2de76e5b7240","order_by":3,"name":"Wenting Wei","email":"","orcid":"","institution":"Affiliated Hospital of Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Wenting","middleName":"","lastName":"Wei","suffix":""},{"id":310680181,"identity":"27b417e5-c3a4-43b4-b719-904b379fb696","order_by":4,"name":"Mingyou Dong","email":"","orcid":"","institution":"Key Laboratory of Research on Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi of Guangxi Higher Education Institutions","correspondingAuthor":false,"prefix":"","firstName":"Mingyou","middleName":"","lastName":"Dong","suffix":""},{"id":310680182,"identity":"20e4f6d2-a666-4a3a-9445-ef400d91847c","order_by":5,"name":"Junli Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACNmaGxMd/KqA8HmK08LM3PDbgOUOKFsmeg88keNtI0WJwIzlNQnLe4WiD2w2MD962McibE9aSlmxhuO1w7oY7B5gN57YxGO5sIKglJ/FGIkjLjQQ2aaALEwwOENSS/0Hi4BywFvbfRGmR7DmQJNnYALGFmSgtwEBONmY4lp4780Zis+SccxKGGwhpAUclQ411bt+N5IMf3pTZyBO0BQqagZixAUhIEKceCOqIVjkKRsEoGAUjEAAAfv5GpZLtePMAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Junli","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-24 06:47:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4470497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4470497/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58144177,"identity":"ae5fecfc-fc02-4697-97f3-4ee86aeb50a5","added_by":"auto","created_at":"2024-06-11 18:23:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":630943,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of AGPAT family in human normal and tumor tissues. (A) Heatmap of the expression profiles of AGPAT members in diverse tissues in the GTEx dataset. (B) the expression levels of AGPAT members in Multiple types of cancer. Boxplots were used to display the median levels of expression for AGPAT1, AGPAT2, AGPAT3, AGPAT4, and AGPAT5 across various cancer types using data from the GEPIA database.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/b3603b66978f778a57e4f1b1.png"},{"id":58144182,"identity":"873a2bf4-6b71-4338-b6bd-5f05526cb87b","added_by":"auto","created_at":"2024-06-11 18:23:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1964813,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA expression of AGPAT family in Pan-Cancer and normol tissues. (A) AGPAT1, (B) AGPAT2, (C) AGPAT3, (D) AGPAT4, (E) AGPAT5 expression levels across diverse cancer types, utilizing data from TCGA database.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/37e48222422d4f0cdf71ed89.png"},{"id":58145377,"identity":"539c9807-f710-4502-a104-649f7d226530","added_by":"auto","created_at":"2024-06-11 18:31:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2561496,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic changes in AGPAT family members and the network of interactions between genes and proteins of the AGPAT family. (A) The cBioPortal database provides an overview of genomic alterations of the AGPAT family members in CESC patients. (B)The GeneMANIA database was used to analyze the gene network linked to the AGPAT family. (C) A diagram illustrating the connections among proteins produced by genes belonging to the AGPAT family. (D) Analyzing the correlation between members of the AGPAT family. Significance levels: * indicates p-value less than 0.05; ** indicates p-value less than 0.01.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/3cd5c6016572e8454cb2433a.png"},{"id":58145375,"identity":"a77b758f-d512-402f-b304-13a19b5f572a","added_by":"auto","created_at":"2024-06-11 18:31:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":431589,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the survival rate of the AGPAT gene family in CESC. (A) In CESC, AGPAT1-5 were analyzed using both univariate and multivariate Cox regression methods. (B-D) Kaplan-Meier survival analysis was conducted to compare the survival rates of CESC patients with high and low levels of AGPAT3 expression, including overall survival, disease-specific survival, and progression-free interval survival. (E-G) Kaplan-Meier survival analysis was conducted to compare the survival rates of CESC patients with high and low levels of AGPAT4 expression, including overall survival, disease-specific survival, and progression-free interval survival.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/4bcb7402c2dcf973c72a74eb.png"},{"id":58146971,"identity":"f1ec6c44-bdf1-4e45-b018-48be085a21d4","added_by":"auto","created_at":"2024-06-11 18:39:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":430653,"visible":true,"origin":"","legend":"\u003cp\u003eIn CESC, the AGPAT family has prognostic and diagnostic significance. (A) A nomogram combines the levels of AGPAT3/4 expression with TNM stages to forecast the likelihood of 1-year survival for patients with CESC. (B) The nomogram model's calibration curve. (C) ROC analysis of AGPAT family members. AGPAT1-4 exhibits strong discriminatory ability between CESC and healthy tissues, with AUC values above 0.6, suggesting they could serve as valuable cancer biomarkers for clinical use.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/f1af29053fb35cf808ca0923.png"},{"id":58144179,"identity":"edb217e2-81b8-4e6c-879c-5ceac63037d0","added_by":"auto","created_at":"2024-06-11 18:23:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":444888,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between AGPAT3 mRNA levels and Clinical Parameters. (A) (B) Examining the relationship between AGPAT1-5 expression and the pathological stage of patients with CESC. The mRNA expression of AGPAT3 in CESC based on (H) Age. Significance levels were indicated as follows: P\u0026lt;0.01 denoted by *, and P\u0026lt;0.001 denoted by ***.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/a4bb71693960727550be74cb.png"},{"id":58145378,"identity":"576976cf-8a88-419e-bae6-dec5a1542c7d","added_by":"auto","created_at":"2024-06-11 18:31:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":404245,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of DNA methylation in CESC for AGPAT3. (A) The AGPAT3 promoter methylation level was analyzed in both normal tissues and primary tumors of CESC. (B) The CESC heatmap displaying DNA methylation levels of AGPAT3 sourced from the MethSurv database. (C) The predictive significance of AGPAT3 DNA methylation in CESC varies depending on the CpG sites.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/dc9e3ba9b3bc17d9bac60602.png"},{"id":58144184,"identity":"6d3290a2-686c-43f1-9239-9c5f7eec1f51","added_by":"auto","created_at":"2024-06-11 18:23:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":7436584,"visible":true,"origin":"","legend":"\u003cp\u003eFunction enrichment analysis of AGPAT3-related gene sets in CESC. (A) In the volcano plot, DEGs were displayed for high AGPAT3 expression compared to low AGPAT3 expression in CESC. Orange dots indicated significantly up-regulated genes, while blue dots indicated significantly down-regulated genes in CESC with high AGPAT3 expression. (B) The heatmap displays the levels of expression of 286 genes that are differentially expressed in AGPAT3-high and AGPAT3-low groups. (C)Conducting enrichment analysis on the KEGG pathway for genes that are up-regulated. (D) Conducting enrichment analysis for Gene Ontology terms associated with genes that are up-regulated. (E) Conducting enrichment analysis on the KEGG pathway for genes that are down-regulated. (F) Performing enrichment analysis for Gene Ontology terms associated with genes that are down-regulated.\u003c/p\u003e","description":"","filename":"FIGURE8.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/069712c4dca6ae6c5a87167f.png"},{"id":58144189,"identity":"74bc467a-e4ad-4ed3-b201-4ced5ae007dd","added_by":"auto","created_at":"2024-06-11 18:23:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":620216,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between AGPAT3 expression and tumor micro-environment and immune infiltrating cells in 33 cancers. (A) Stromal score, immune score, estimate score and of AGPAT3 across diverse cancers based on ESTIMATE.（B）The heatmap plot shows the correlation between AGPAT3 and 22 tumor-infiltrating immune cells (TIICs) in CESC samples. Orange dots indicate cells that are significantly up-regulated, while blue dots indicate cells that are significantly down-regulated in CESC based on the expression levels of AGPAT3. Significance levels: * indicates p-value less than 0.05, ** indicates p-value less than 0.01, and *** indicates p-value less than 0.001.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/ddff42f19e5eb4da9b1f7421.png"},{"id":58144186,"identity":"62a07338-c434-45d2-8df1-83b5f8be1d1a","added_by":"auto","created_at":"2024-06-11 18:23:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1543327,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of AGPAT3 and immunomodulatory genes in pan-cancer. (A) Correlation between AGPAT3 and chemokine receptors. (B) Association of AGPAT3 with chemokines. (C) Association of AGPAT3 with immustimulators. (D) Association of AGPAT3 with checkpoints. (E) Correlation between AGPAT3 and immuinhibitors. * p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/95df74729aa1712eebade6bf.png"},{"id":58144188,"identity":"1448fe26-203d-4b16-9d8e-ccbbbb722933","added_by":"auto","created_at":"2024-06-11 18:23:15","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":5252943,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of AGPAT3 in CESC specimens and their paraneoplastic tissues. (A) Typical images of HE and AGPAT3 immunohistochemistry staining of cancerous and paracancerous tissues in CECS patient samples. (B) The h-scores for AGPAT3 immunohistochemistry staining were calculated in cancerous and paracancerous tissues from 6 CESC patients by using the formula h-score = 1 x (% weak cells) + 2 x (% moderate cells) + 3 x (% strong cells). (C) The RNA levels of AGPAT3 were measured in 3 sets of frozen CESC tumor tissue samples and their corresponding adjacent tissues.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/2f00047e50c5081a9f989adb.png"},{"id":58145379,"identity":"ef91b8c8-e5c3-453a-bfc2-9c84fa2d1028","added_by":"auto","created_at":"2024-06-11 18:31:15","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":465063,"visible":true,"origin":"","legend":"\u003cp\u003eThe knockout effectiveness of AGPAT3 in Hela cells was confirmed through PCR and Western blotting analysis. (A) An image displaying PCR amplification products of AGPAT3 gene fragments from Hela WT and Hela AGPAT3-KD cells on an agarose gel electrophoresis. (B) Western blotting assay showing the expression level of AGPAT3 in Hela WT and Hela AGPAT3-KD cells, with Vinculin used as an internal control.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/7c10498458ad938cbec55e63.png"},{"id":58144190,"identity":"4cfc68a7-a2c5-4f0f-aadf-ece4eaf183d5","added_by":"auto","created_at":"2024-06-11 18:23:15","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":4710084,"visible":true,"origin":"","legend":"\u003cp\u003eThe cell proliferation and migration ability of Hela cells were inhibited after AGPAT3 gene was knocked down. (A) CCK-8 assay was used to detect growth curves of Hela WT and Hela AGPAT3-KD cells. (B) Colony formation assay of Hela WT and Hela AGPAT3-KD cells. (C) Quantitative statistical results of Colony formation units of Hela WT and Hela AGPAT3-KD cells, from three independent experiments. (D) Transwell migration assay of WT and AGPAT3-KD cells. After seeding cells in Transwell, they were allowed to migrate for 24 hours. Following this, photographs were taken and the number of cells was counted; scale bar was set at 100 micrometers. (E) Quantitation of the transwell migration assay results. (F) Scratch assay of WT and AGPAT3-KD cells. Microscopy images were captured at 0, 24, and 48 hours with a scale bar of 100 micrometers. (G) Quantitation of the scratch assay results. A t-test was used for statistical analysis. A t-test was used for statistical analysis. Not significant, P greater than 0.05; *, P less than 0.05; **, P less than 0.01; and ***, P less than 0.001.\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/ea53f6a41f10b7a2b31c42ee.png"},{"id":59731788,"identity":"a0a79304-a8f8-4907-9f8d-9ef02c247f65","added_by":"auto","created_at":"2024-07-05 12:18:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32264784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4470497/v1/b0f8c128-5b7d-4e68-9180-79ce7ec68e3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AGPAT Family in Cervical Cancer: A Multi-Omics Perspective on Prognosis and Function","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCESC is the predominant histologic forms of cervical cancer (CC)\u003csup\u003e1\u003c/sup\u003e. Despite widespread cervical cancer screening\u003csup\u003e2, 3\u003c/sup\u003e and HPV vaccination programs\u003csup\u003e4\u003c/sup\u003e, it remains a significant global health issue in terms of both incidence and mortality\u003csup\u003e5, 6\u003c/sup\u003e. Metastatic cervical cancer still has a poor overall survival rate\u003csup\u003e7, 8\u003c/sup\u003e, underscoring the need for effective predictive biomarkers to help manage its progression. Currently, surgery and radiotherapy are the most effective treatments for cervical cancer\u003csup\u003e9\u003c/sup\u003e, but access to these interventions is not evenly distributed globally, resulting in high morbidity and mortality rates\u003csup\u003e10\u003c/sup\u003e. Patients in advanced stages, or those experiencing recurrence and metastasis, have a particularly poor prognosis. Therefore, understanding the molecular mechanisms underlying cervical cancer and identifying effective prognostic biomarkers are essential for improving patient survival.\u003c/p\u003e \u003cp\u003eAGPATs, also referred to as LPAATs, are a set of five enzymes (AGPAT1/2/3/4/5) essential for converting LPA to PA in the TAG biosynthesis pathway\u003csup\u003e11\u003c/sup\u003e. AGPATs play a role in cancer cell survival and growth\u003csup\u003e12\u003c/sup\u003e by aiding in the production of TAGs and storing lipids in LDs, which are crucial for storing energy and avoiding lipotoxicity. Additionally, AGPATs are involved in the synthesis of phospholipids necessary for new membrane formation in rapidly dividing cells and in the regulation of cell signaling pathways. Recent research indicates that AGPATs also play a role in membrane fission, vesicular transport, and communication between cancer cells and the tumor microenvironment, influencing cancer metastasis. Multiple research studies have emphasized the importance of AGPATs in different forms of cancer, demonstrating links to the proliferation of cancer cells, the growth of tumors, the spread of cancer to other parts of the body, the prognosis of patients, and the categorization of cancer types. For example, Ren et al. Discovered elevated levels of AGPAT1 expression as an unfavorable indicator in colorectal cancer, associated with an increased chance of recurrence and reduced lifespan\u003csup\u003e13\u003c/sup\u003e. Similarly, Song et al. Showed that inhibiting AGPAT2 led to higher levels of cell death in cisplatin-resistant osteosarcoma cell lines\u003csup\u003e14\u003c/sup\u003e. Moreover, variations in AGPAT expression levels between cancerous and healthy tissues, combined with computational analyses, have resulted in the incorporation of AGPAT isoforms in predictive metabolic gene profiles.\u003c/p\u003e \u003cp\u003eThe AGPAT gene family plays a vital part in the development\u003csup\u003e15\u003c/sup\u003e of tumors and can also act as indicators for prognosis\u003csup\u003e16\u003c/sup\u003e and influence the response to immunotherapy. However, their specific role in CC and potential implications for cancer prognosis require further investigation. Transcriptomic data from both CC and normal samples were collected from The Cancer Genome Atlas (TCGA, \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) and the Genotype-Tissue Expression (GTEx, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://commonfund.nih.gov/gtex\u003c/span\u003e\u003cspan address=\"https://commonfund.nih.gov/gtex\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database for this study. Differential expression of AGPAT family genes in CC versus normal tissues was analyzed using Cox and LASSO regression models. Molecular mechanisms underlying AGPAT family gene signaling in immunotherapy and gene alterations were also investigated. The research also assessed the levels of gene expression in the risk model using different experimental methods. The results of this study may help enhance the prognosis of cervical cancer and provide guidance for treatment plans, while also identifying potential biomarkers for clinical diagnosis, prognosis, and targets for immunotherapy in patients with cervical cancer.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGene Expression of AGPAT Family Members\u003c/h2\u003e \u003cp\u003eThe expression levels of the AGPAT family in various tissues such as adipose tissue, breast, liver, small intestine, cervix, and others were depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Interestingly, AGPAT5 expression was found to be significantly upregulated in the pancreas, salivary gland, and blood vessels. Notably, in healthy cervix tissues, the expression of AGPAT3-5 was lower compared to other family members. Furthermore, among the 33 types of human tumors examined, AGPAT1-5 were found to be expressed individually (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These findings indicate a tissue-specific expression pattern of AGPAT family members.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eThe Expression of AGPAT1-5\u003c/h2\u003e \u003cp\u003eThe TIMER database was utilized to assess the expression of the AGPAT family in both pan-cancer and normal tissues.The results showed an increase in AGPAT1-5 expression in various types of cancer compared to normal tissues, such as BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, READ, STAD, and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-E). Specifically, AGPAT2, AGPAT3 and AGPAT5 mRNA expression was found to be elevated, while AGPAT1 and AGPAT4 expression was reduced in CESC tumor tissues. These findings suggest distinct expression levels of the AGPAT family between CESC tissues and normal tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Alterations of AGPAT Family and Gene and Protein Network\u003c/h2\u003e \u003cp\u003eThe frequencies of alterations in members of the AGPAT family were assessed in 306 CESC samples using the cBioportal tool. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates that mutations in the AGPAT family were rare (AGPAT1, 1.8%; AGPAT2, 1.1%; AGPAT3, 1.8%; AGPAT4, 2.2%; AGPAT5, 1.1%), indicating a high level of conservation. Analysis of the gene-gene network using the GeneMANIA database identified connections between the AGPAT family and 20 possible target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Following this, a network of protein-protein interactions (PPI) was created to examine the relationship between AGPAT family members through the use of the STRING platform, revealing a significant association between them (PPI enrichment p-value\u0026thinsp;\u0026lt;\u0026thinsp;1.0e-16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In addition, the expression of AGPAT family genes was analyzed using TCGA data, revealing a strong positive relationship between most members, particularly AGPAT3, which exhibited a positive association with AGPAT1 and AGPAT5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis of AGPAT Family in CESC\u003c/h2\u003e \u003cp\u003eAGPAT 3 and 4 overexpression was linked to poor prognosis in CESC patients based on univariate Cox regression analysis. Additionally, in CESC patients, Multivariate Cox regression analysis revealed that elevated levels of AGPAT 3 and 4 were an autonomous predictor of overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The results were further confirmed by Kaplan-Meier analysis, which showed that CESC patients with high AGPAT3 levels had reduced survival times in terms of overall survival, disease-specific survival, and progression-free interval survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D). Similarly, the group with high AGPAT4 expression also exhibited shorter survival times (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-G). The findings indicate that AGPAT3 and 4 could serve as indicators for forecasting unfavorable outcomes in CESC individual.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic and Diagnosis Significance of AGPAT Family in CESC\u003c/h2\u003e \u003cp\u003eAGPAT3 and AGPAT4 were identified as potential prognostic biomarkers, and a nomogram was developed to predict overall survival in CESC patients by incorporating AGPAT3/4 expression and TNM stage. For CESC patients, a lower survival outcome is associated with higher points on the Nomogram model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB showed a strong correlation between predicted and observed outcomes on the calibration graph. To sum up, the combination of AGPAT3/4 mRNA levels and TNM stage in a nomogram may function as a prognostic tool for overall survival in CESC, surpassing single prognostic indicators. Following this, the diagnostic effectiveness of AGPAT family members was evaluated through receiver operating characteristic (ROC) curve analysis. AGPAT1-4 displayed AUC values of 0.839, 0.688, 0.836, and 0.893, in that order (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). With AUC values exceeding 0.6, AGPAT1-4 may serve as potential diagnostic biomarkers for CESC patients, with AGPAT3 and 4 demonstrating particularly promising prospects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical correlation analysis\u003c/h2\u003e \u003cp\u003eThe study conducted using the GSCA database revealed a significant association between AGPAT1-5 expression and survival outcomes (DSS, OS, PFS) as well as clinical stage in CESC patients. AGPAT3 and AGPAT1 were identified as strongly associated with decreased survival and late disease stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Additionally, AGPAT3 mRNA expression was notably higher in patients over 50 years old compared to those aged 50 years or younger (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Moreover, adenocarcinoma and adenosquamous patients exhibited higher AGPAT3 expression levels compared to squamous cell carcinoma patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). The results suggest that increased levels of AGPAT3 mRNA are associated with negative prognostic factors, indicating that individuals with high AGPAT3 levels may have worse survival rates and are more likely to advance to later stages compared to those with low AGPAT3 levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDNA Methylation Analysis of AGPAT3 in CESC\u003c/h2\u003e \u003cp\u003eFollowing that, the GSCA tool was implemented for the analysis of AGPAT3 methylation in CESC. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, the DNA methylation levels of AGPAT3 were higher in CESC tissues, compared with normal samples. We then acquired the methylation profile of AGPAT3 from the MethSurv database. The data indicates the discovery of 38 CpG sites in AGPAT3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Analysis of the prognostic implications of these sites revealed that 11 were significantly linked to the prognosis of cervical cancer. Among these, 2 CpG sites had HR values exceeding 1, while the remaining 9 had HR values below 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). These findings imply that DNA methylation of AGPAT3 could play a role in the advancement of CESC and its prognosis in patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFunction Enrichment Analysis of AGPAT3 in CESC\u003c/h2\u003e \u003cp\u003eHigh levels of AGPAT3 expression were detected in CESC, which was associated with poor survival results. The impact of this heightened expression on signaling pathways in CESC remains ambiguous. A functional enrichment analysis was conducted on gene sets related to AGPAT3 to illuminate its role in CESC. Within the TCGA-CESC dataset, researchers discovered 286 genes that were expressed differently between groups with high and low levels of AGPAT3. Of these genes, 253 were found to be up-regulated and 33 were down-regulated in the high AGPAT3 expression group compared to the low expression group, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB. KEGG analysis showed that up-regulated genes were linked to tuberculosis and phagosome signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Analysis of GO terms showed that the genes that were up-regulated were notably concentrated in pathways related to the maintenance of stem cell populations and the regulation of cell numbers (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Conversely, down-regulated genes were implicated in the IL-17 signaling pathway based on KEGG analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). GO term analysis also indicated enrichment of down-regulated genes in pathways related to skin and epidermis development (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). Given the well-documented association of these pathways with immunity\u003csup\u003e17, 18, 19\u003c/sup\u003e, these findings suggest a potential involvement of AGPAT3 in the pathogenesis of CESC through immune regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmune Infiltration Analysis of AGPAT3 in Pan-cancer\u003c/h2\u003e \u003cp\u003eAGPAT3 expression showed a significant negative correlation with stromal score, immune score, and ESTIMATEScore in several tumors including PRAD, BLCA, UCEC, LIHC, and BRCA. Conversely, contrasting results were observed in LAML, UVM, and HNSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). We investigated the correlation between AGPAT3 levels and the presence of 24 immune cell types through Spearman analysis. AGPAT3 expression in different types of cancer showed a positive association with CD4 memory resting T cells and Macrophages M2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), while displaying a negative correlation with plasma cells, memory B cells, and gamma delta T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelation Between AGPAT3 With immunoregulatory gene in Pan-cancer\u003c/h2\u003e \u003cp\u003eWe also investigated the relationship between AGPAT3 and immunomodulatory genes, including immune checkpoints, immustimulator, immuinhibitor, chemokines and chemokine receptors\u003csup\u003e20\u003c/sup\u003e in 33 tumors. In most tumors, the expression of AGPAT3 was significantly positively correlated with chemokines, chemokine receptors and immustimulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C). Furthermore, our research revealed a positive association between AGPAT3 and immune checkpoints like PD-1, PD-L1, CTLA4, TIGIT, LAG3, HAVCR2, and PDCD1LG2 across various cancer types as shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). AGPAT3 showed a strong positive correlation with the expression of immuinhibitors in the majority of tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). Thus, AGPAT3 may be a promising candidate for immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAGPAT3 is highly expressed in CESC tissues\u003c/h2\u003e \u003cp\u003eAGPAT3 expression was analyzed in 6 CESC specimens and their corresponding paraneoplastic tissues using immunohistochemistry staining. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA-B displayed high levels of AGPAT3 expression in the cytoplasm of the tumor cells. RNA was isolated from three sets of frozen CESC tumor tissue samples and their corresponding adjacent tissues, then underwent reverse transcription. Following this,β-actin was used as an internal control, and the level of AGPAT3 RNA expression was measured through real-time PCR. AGPAT3 mRNA expression was elevated in tumor tissue compared to paraneoplastic tissues, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAGPAT3 promotes proliferation and migration abilityof Hela cells\u003c/h2\u003e \u003cp\u003eThe Hela AGPAT3-KD cell line was generated in Hela cells using electroporation technology. The knockdown efficiency was confirmed through PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA) and Western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). The CCK-8 assay demonstrated a notable decrease in the proliferation of AGPAT3-KD cells compared to Hela WT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). Furthermore, the colony formation ability of AGPAT3-KD cells showed a significant reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB, C). Additionally, both Transwell experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eD, E) and cell scratch assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eF, G) demonstrated a reduced migration capacity in AGPAT3 knockdown cell lines compared to wild-type cells. These findings collectively suggest that AGPAT3 plays a role in promoting the proliferation and migration ability of cervical cancer cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCervical cancer ranks fourth in morbidity and mortality among women worldwide, and currently patients with advanced disease or recurrence and metastasis have a poor prognosis\u003csup\u003e21\u003c/sup\u003e, so researchers are focused on discovering valuable biomarkers and promising therapeutic targets. The study highlights the AGPAT family as a noteworthy biomarker and potential therapeutic target for cervical cancer, given their significant role in its incidence and prognosis. Using the TCGA database, we initially discovered variations in AGPAT family expression levels between cancerous and para-cancerous tissues across various types of cancers (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Next, we performed an analysis of gene-gene networks and protein-protein interactions (PPI) networks. It was found that AGPAT3 showing a positive correlation with AGPAT1 and AGPAT5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). By conducting survival analysis on the AGPAT family in CESC and evaluating the prognostic and diagnostic significance of AGPAT family in CESC, we have identified AGPAT3 as the most promising potential biomarker for diagnosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Subsequently, we conducted additional analysis on the correlation between AGPAT3 mRNA levels and clinical parameters, as well as the DNA methylation status of AGPAT3 in CESC. Our findings revealed a strong association between elevated mRNA expression and increased promoter methylation of AGPAT3 with unfavorable clinical outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e–\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Subsequently, by conducting Function Enrichment Analysis on AGPAT3 in CESC, these results indicate a possible involvement of AGPAT3 in the development of CESC through immune modulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Hence, an analysis of immune infiltration was carried out for AGPAT3 in various types of cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), revealing a connection between AGPAT3, immune checkpoints, and chemokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). This indicates that AGPAT3 may be a promising target for immunotherapy. To confirm the reliability of the previous conjecture, Immunohistochemistry (IHC) Staining and real-time PCR and confirmed that AGPAT3 expression was higher in tumor tissue compared to paraneoplastic tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Furthermore, we constructed the Hela AGPAT3-KD cell line and found that its cell proliferation ability and cell migration ability were significantly weakened compared with Hela WT cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). So, AGPAT3 could serve as a valuable biomarker and a promising therapeutic target for cervical cancer.\u003c/p\u003e \u003cp\u003eMany patients, particularly those in advanced stages of the disease, have a bleak outlook due to the shortcomings in diagnosing and treating cervical cancer. Extensive research is currently being conducted to find biomarkers and targets for detecting and treating cervical cancer. Yan et al. discovered that the tumor necrosis factor (TNF) family genes could serve as prognostic biomarkers for cervical cancer. The gene signature of the TNF family mainly operates in the TGF-β pathway and can impact the response to immunotherapy\u003csup\u003e22\u003c/sup\u003e. In a separate study, Zhang et al. showed that METTL11A, part of the methyltransferase-like gene group, enhances the movement of cervical cancer cells through an ELK3-related process\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAGPATs, also referred to as LPAATs, are essential in converting LPA to PA during TAG biosynthesis. AGPATs play a crucial role in the survival and growth of cancer cells\u003csup\u003e24\u003c/sup\u003e by facilitating TAG biosynthesis and aiding in lipid storage within LDs, which are critical for storing energy and protecting against lipotoxicity. However, the specific functional role of the methyltransferase-like gene family in cervical cancer remains unclear. This study thoroughly analyzed the AGPAT family in CESC, examining expression, mutation, diagnosis and prognosis significance, DNA methylation patterns, relationships with immune cell infiltration, and immune checkpoint involvement. The AGPAT family expression was assessed in pan-cancer and normal tissues using The TIMER database. The study revealed an up-regulation of AGPAT1-5 expression in 15, 10, 18, 16, and 15 different types of cancers when compared to normal tissues. Tumor progression is linked to the deregulation of AGPAT channels, despite the infrequency of mutations in AGPAT family genes. Genetic alterations of AGPAT family members in CESC were also examined, showing minimal mutations (approximately 7 frequencies), which did not impact the survival of CESC patients. The findings indicate that the AGPAT gene family is well-preserved, and the dysregulation in CESC is not caused by genetic mutations. Various studies have recognized AGPATs as potential biomarkers for tumor diagnosis, prognosis, and progression. Increased AGPAT1 expression has been associated with an increased likelihood of recurrence and reduced survival in individuals with colorectal cance\u003csup\u003er25, 26, 13\u003c/sup\u003e. AGPAT1 was discovered as a new tumor suppressor and prognostic indicator for ovarian cancer through a comprehensive analysis\u003csup\u003e27, 28\u003c/sup\u003e. Subsequently, the diagnostic and prognostic potential of AGPAT family members in CESC was assessed. The research's ROC analysis showed that AGPAT1/2/3/4 had excellent accuracy in differentiating CESC patients from healthy individuals (AUC \u0026gt; 0.6), suggesting their promise as diagnostic indicators. Furthermore, the results of survival analysis indicated that elevated levels of AGPAT3/4 were associated with a worse outcome in patients with CESC, indicating their potential as prognostic markers. Notably, due to the possible cancer-causing function of AGPAT3 in CESC, its expression was further examined in correlation with clinical factors of CESC patients. Higher levels of DNA methylation of AGPAT3 were observed in CESC tissues compared to normal samples in the study, emphasizing the significance of this epigenetic change in the development of tumorigenesist\u003csup\u003e29, 30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e To explore the biological function of AGPAT3 in CESC, DEGs were identified according to AGPAT3 expression levels, and then subjected to functional enrichment analysis. Genes that were increased due to AGPAT3 overexpression were discovered to be connected to the maturation and stimulation of immune cells, suggesting a potentially intricate function of AGPAT3 in immune control in the CESC environment. Following this, we evaluated how AGPAT3 expression influenced the makeup of immune cells that infiltrate tumors in CESC. The high AGPAT3 expression group exhibited significantly increased proportions of regulatory T cells, memory B cells, CD8 + T cells, and activated Mast cells, while the low AGPAT3 expression group showed elevated proportions of gamma delta T cells, Monocytes, M2 Macrophages, and Mast cells. Moreover, correlation analysis demonstrated a significant association between AGPAT3 expression and the accumulation of these TIICs, particularly Treg cells. Treg cells, known for their negative regulatory functions, are crucial for maintaining immune homeostasis\u003csup\u003e31, 32\u003c/sup\u003e. Usually not found in healthy CESC tissue, these cells increase in number in the CESC environment, encircling cancer cells and hindering the activity of effector T cells, ultimately aiding in the evasion of the immune system by the tumor\u003csup\u003e33\u003c/sup\u003e. Our research found that increased levels of Treg cells are linked to a worse outcome in CESC patients with high AGPAT3 expression.Conversely, in patients with low AGPAT3 expression, Treg cell levels did not significantly impact CESC prognosis. These findings suggest that AGPAT3 may serve as a potential immunomodulatory factor in CESC, and targeting AGPAT3 could potentially counteract the immunosuppressive effects of Treg cells, thereby enhancing the effectiveness of immunotherapy. To sum up, AGPAT2, AGPAT3 and AGPAT5 mRNA expression was found to be elevated, while AGPAT1 and AGPAT4 expression was reduced in CESC tumor tissues. AGPAT3/4 could serve as a promising prognostic biomarker for individuals diagnosed with CESC. Besides, Furthermore, the abnormal expression of AGPAT3 in CESC could be attributed to dysregulated DNA methylation. Notably, AGPAT3 demonstrates a strong positive correlation with Treg cell infiltration and the expression of immune checkpoints.\u003c/p\u003e \u003cp\u003ePatient tissue samples were employed to examine previous theories, confirming elevated levels of AGPAT3 protein and RNA expression in tumor tissues relative to paraneoplastic tissue. Additionally, an AGPAT3-KD cell line was constructed in the cervical cancer cell line Hela. Growth curve and clonal formation experiments demonstrated that knocking down AGPAT3 inhibited cell proliferation. Transwell cell migration and cell scratching experiments further indicated that AGPAT3 knockdown hindered cell migration. The results indicate that increased AGPAT3 levels could promote the onset, progression, and spread of cervical cancer, making AGPAT3 a potential biomarker for detection and target for treatment.\u003c/p\u003e \u003cp\u003eIn the present study, we investigated the expression of the AGPAT family in CESC and its correlation with patient prognosis and immune microenvironment. Our findings suggest that AGPAT3 is significantly associated with the onset and progression of CESC, primarily through its influence on the immune microenvironment. In summary, we have identified AGPAT3 as a promising biomarker and potential therapeutic target for cervical cancer. However, our study has certain limitations. While functional enrichment analysis and Immune Infiltration Analysis imply that AGPAT3's promotion effect on cervical cancer may involve immune regulation, this hypothesis requires further experimental validation. Furthermore, pan-cancer analysis revealed varied AGPAT family expression across different cancers and its impact on immunity, prompting the need for exploration of the AGPAT family's influence on other cancer types.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \n\n "},{"header":"Methods","content":"\u003cp\u003eData on gene expression (HTSeq-FPKM) for 306 cases of CESC and 3 control cases were obtained from the TCGA database (https //portal.gdc.cancer.gov/). Data on CESC patients' clinical information, including pathologic stage, TNM stage, histologic stage, age, and overall survival (OS). Experimental validation was conducted using clinical specimens and cell experiments.\u003c/p\u003e\u003ch2\u003emRNA Expression\u003c/h2\u003e\u003cp\u003eAGPAT1-5 mRNA expression was detected in 3 human tissues of healthy individuals using the 'GTEx Expression' module within the GSCA database Additionally\u003csup\u003e34\u003c/sup\u003e, interactive body maps of AGPAT1-5 were created using the TIMER database\u003csup\u003e35\u003c/sup\u003e to visualize the median expression in normal tissues and cancers.Comparison of AGPAT1-5 expression variations among 33 tumors was conducted using information from the TCGA repository.A significance level of p-value \u0026lt; 0.05 was applied.\u003c/p\u003e\u003cp\u003e \u003cb\u003eGenetic Alterations\u003c/b\u003e \u003csup\u003e36\u003c/sup\u003e \u003cb\u003eand Interaction Network of AGPAT Family\u003c/b\u003e\u003c/p\u003e\u003cp\u003eChanges in AGPAT gene family members were examined in 306 CESC samples from the TCGA Firehose Legacy project through the cBioPortal database\u003csup\u003e37\u003c/sup\u003e. Gene regulatory networks among AGPAT gene families were predicted using the GeneMANIA database\u003csup\u003e26\u003c/sup\u003e. Furthermore, the AGPAT family was used to create a protein-protein interaction network in the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/cgi/input.pl\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/cgi/input.pl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), specifying parameters like organism (“Homo sapiens”), network type (“full STRING network”), and minimum interaction score (“medium confidence 0.400”). Spearman correlation analysis was used to evaluate the relationship between AGPAT family members using gene expression data from CESC samples in the TCGA database.\u003c/p\u003e\u003ch2\u003ePrognostic and Diagnostic Value Analysis\u003c/h2\u003e\u003cp\u003eThe Xiantao Academic database was accessed to perform univariate and multivariate Cox regression analysis on AGPAT family members in CESC. The research analyzed the survival rates of patients with cervical squamous cell carcinoma who had high levels of AGPAT3-4 compared to those with low levels, using the Kaplan-Meier curve. Logistic regression was employed to investigate the correlation between AGPAT3 expression and clinical variables. A nomogram model was created to forecast the 1-, 3-, and 5-year overall survival rates by combining AGPAT3-4 expression data with the T, M, and N stages of CESC patients from the TCGA database. The prognostic value of the nomogram model in CESC was assessed using the 'pROC' R package. Furthermore, the diagnostic effectiveness of AGPAT members in detecting CESC in the Xiantao Academic database was assessed using the 'pROC' R package. A p-value less than 0.05 was deemed significant. The AUC of the ROC curve was utilized to summarize the diagnostic efficiency.\u003c/p\u003e\u003ch2\u003eClinical correlation analysis\u003c/h2\u003e\u003cp\u003eThe “Expression” module within the GSCA database was employed to examine the relationship between AGPAT1-5 and different survival measures (DSS, OS, PFS, DSS, and clinical stage) as well as clinical stage among CESC patients. Furthermore, the Xiantao Academic database was utilized to explore the correlation between AGPAT3 gene expression and various clinical characteristics including T Stage, N Stage, M Stage, Grade, Stage, Age, Histological type, and OS event.\u003c/p\u003e\u003ch2\u003eDNA Methylation Analysis\u003c/h2\u003e\u003cp\u003eThe correlation between AGPAT3 mRNA levels and DNA methylation was examined. The UALCAN database\u003csup\u003e38\u003c/sup\u003e was used to analyze the differences in promoter methylation levels of AGPAT3 between normal control tissues and CESC tissues. DNA methylation levels were measured by the Beta value, which varies from 0 (unmethylated) to 1 (fully methylated), with a significant threshold established at a P-value less than 0.05. The AGPAT3 methylation pattern in CESC was acquired using the 'Gene Visualization' feature in the MethSurv database\u003csup\u003e39\u003c/sup\u003e. Furthermore, the MethSurv database was utilized to investigate the influence of DNA methylation on the survival of CESC patients specifically at individual CpG sites within AGPAT3 through the 'Single CpG' module.\u003c/p\u003e\u003ch2\u003eFunction Enrichment Analysis\u003c/h2\u003e\u003cp\u003ePatients with CESC in the TCGA-CESC dataset were divided into two groups, AGPAT3-high and AGPAT3-low, using the median expression level of AGPAT3. The 'DESeq2' R package was employed to detect differentially expressed genes (DEGs) in the comparison of these two groups, where up-regulated DEGs were defined by log2 fold change values ≥ 1.5 and down-regulated DEGs by log2 fold change values ≤ -1.5.A significance level of P \u0026lt; 0.05 was set as the cutoff criteria.Following that, an analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichments was carried out with the 'ClusterProfiler' R package to explore the functional annotations of the DEGs related to AGPAT3 in CESC.\u003c/p\u003e\u003ch2\u003eRelationship between AGPAT3 expression and immunity\u003c/h2\u003e\u003cp\u003eUsing the “GSVA” software package, researchers explored the relationship between AGPAT3 and 24 types of immune infiltrating cells. The ESTIMATE method, which examines stromal and immune cells in 33 tumors using gene expression information, was employed to forecast the tumor microenvironment (TME) by calculating ImmuneScore, StromalScore, and ESTIMATEScore for each tumor specimen. The relationship between AGPAT3 expression and tumor microenvironment, along with immune infiltrating cells, across 33 cancer types was explored using the 'limma' R package. A Spearman correlation heat map was generated to visualize the relationship between AGPAT3 and various immune cell types.Furthermore, the connection between AGPAT3 and genes that regulate the immune system, including the major histocompatibility complex (MHC), molecules that boost the immune response, chemotactic proteins, and receptors for chemotactic proteins, was investigated by analyzing information from the TCGA database. Additionally, the correlation between AGPAT3 expression and immune checkpoint molecules was examined through Spearman analysis.\u003c/p\u003e\u003ch2\u003ePatient tissue specimens\u003c/h2\u003e\u003cp\u003eThe study included nine individuals with CESC who had surgery at the Affiliated Hospital of Youjiang Medical University for nationality in Guangxi, China between 2020 and 2023. Tumor and paracancerous tissues were collected, with six pairs being paraffin-embedded and three pairs fresh. Ethical approval for the research was granted by the hospital's Ethics Committee (license number YYFY-LL-2023HY2-06), and all participants provided written consent. The research followed the staging criteria of the International Federation of Gynecology and Obstetrics (FIGO) and the guidelines of the Declaration of Helsinki. Clinicopathological findings were derived from surgical records and pathology reports.\u003c/p\u003e\u003ch2\u003eImmunohistochemistry (IHC) Staining\u003c/h2\u003e\u003cp\u003eTen percent formalin was used to fix six cancerous and paracancerous tissues for one week prior to embedding them in paraffin. The tissue specimens were then sectioned to 4 micrometers, deparaffinized, and underwent antigen retrieval through microwaving. Immunostaining was performed using a Polyclonal antibody (Thermo, PA5-98855, USA). In contrast, surgical samples were preserved in 4% formaldehyde, cut into 4 µm sections, and then slides were heated at 60°C for 1 hour to avoid peeling. Paraffin was removed using xylene, followed by rehydration in alcohol gradient, and then repaired with citric acid for a duration of 20 minutes.Afterward, slides were exposed to 3% hydrogen peroxide for a quarter of an hour, obstructed with 5% BSA in PBS for half an hour, and then exposed to anti-TRIM21 antibody (32520, SAB, Nanjing, China) in PBS with 5% bovine serum albumin overnight at 4°C. The standard EnVision technique from Dako in Denmark was utilized for detecting protein binding and developing color. After counterstaining with hematoxylin and sealing with neutral resin, the sections that were stained were observed using a light microscope. The IHC staining results were assessed in a double-blind manner using clinicopathological criteria. AGPAT3 was classified based on its immunoreactivity score as either negative (0), weak (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), moderate (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), or strong (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The h-score was determined by multiplying the various levels of staining (0, 1, 2, 3) by the proportion of cells showing positivity for each level, as per the equation h-score = 1 x (% weak cells) + 2 x (% moderate cells) + 3 x (% strong cells)\u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eQuantitative Real-time PCR (qPCR)\u003c/h2\u003e\u003cp\u003eRNA was isolated from three sets of frozen CESC tumor tissue samples along with their corresponding paracancerous tissues. The extracted RNA was reverse transcribed, and β-actin expression was utilized as an endogenous control. The RNA expression level of AGPAT3 was then detected using qPCR (ToloScript ALL-in-one RT EasyMix for qpcr, TOLOBIO) with specific primers (Human-AGPAT3-F: CGCCTGTCGGACTACCCC; Human-AGPAT3-R: CTTAGCAGCCGCCACCTC).\u003c/p\u003e\u003ch2\u003eCell line culture and conversion\u003c/h2\u003e\u003cp\u003eThe HeLa cell line was acquired from the Shanghai Institute of Biochemistry and Cell Biology (SIBCB) and grown in DMEM (MeilunBio, MA0212) with the addition of 10% fetal bovine serum and 100 units/mL Penicillin-Streptomycin. The AGPAT3 Knock Down (KD) cell line was generated through electroporation. Two optimal sgRNAs (GTGCACGTGAGAAGCGTAAT, GCTAGGGTGAGCACCTTCCA) were designed using the CRISPOR website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://crispor.tefor.net/\u003c/span\u003e\u003cspan address=\"http://crispor.tefor.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and subsequently electrotransferred into HeLa cells along with the Cas9 protein complex. The efficiency of knock down was assessed through Western blot and PCR analysis.\u003c/p\u003e\u003ch2\u003eWestern blot (WB)\u003c/h2\u003e\u003cp\u003eCellular proteins were extracted and quantified with RIPA lysis 1×SDS Loading Buffer. After being separated using 10% SDS-PAGE, the proteins were then transferred onto a PVDF membrane (Immobilon, Millipore-p, IPVH00010, CA, USA) and subsequently blocked with 5% non-fat milk at room temperature. After that, the membrane was left to incubate overnight at 4°C with a thinned AGPAT3 antibody (PA5-98855, Thermo, USA), then incubated with a secondary antibody (Jackson ImmunoResearch, 115-035-004, USA) at room temperature.Protein bands were detected using enhanced chemiluminescence (ECL) after membrane cleaning.\u003c/p\u003e\u003ch2\u003eValidation of gene expression levels in knockdown cell lines\u003c/h2\u003e\u003cp\u003eWild-type (WT) and AGPAT3-KD Hela cells were resuspended and then added to PCR tubes at a 1:4 ratio with a 1:200 dilution of proteinase K (Biosharp, BL628A). Incubation of the tubes in a PCR machine at 55°C for 1 hour was done, then followed by genome extraction at 95°C for 10 minutes. The extracted DNA was then combined with two Primers (CATCTTCTGCACCTCTCACG and CACTGCTCCACCCAGTACCT) and PCR Mix (Yeasen, 10157ES03), and subjected to PCR amplification as per the manufacturer's instructions. The changes in DNA length were confirmed by analyzing the product using agarose gel electrophoresis.\u003c/p\u003e\u003ch2\u003eCell proliferation assay\u003c/h2\u003e\u003cp\u003eCell proliferation was assessed through CCK-8 and colony formation analyses. For the CCK-8 test, wild-type and AGPAT3 knockdown HeLa cells were placed in 96-well dishes and permitted to attach prior to the introduction of CCK-8 solution (10 µL CCK-8 and 90 µL McCoy's 5 A per well) at different intervals (ranging from day 1 to day 7), then incubated for one hour. Subsequently, the OD450 was assessed with a microplate reader. To conduct the colony formation test, wild-type and AGPAT3 knockdown HeLa cells were grown in 6-well dishes with 400 cells in each well to promote colony formation. Following a two-week period, the cells were treated with anhydrous methanol, then stained with crystal violet, dried, and finally scanned. Afterwards, the quantity of cell colonies on each plate was measured.\u003c/p\u003e\u003ch3\u003eTranswell migration assay\u003c/h3\u003e\u003cp\u003eTranswell chambers with a pore size of 8 µm (Corning, Lowell, MA, USA) were placed in 24-well cell culture plates (BIOFIL, TCP010024, Guangzhou, China). These chambers were utilized for cell migration assays in the absence of matrix gel. The up-per compartment of the Transwell received 500 µL of cell suspension in serum-free medium containing 5 × 104 cells, while the lower compartment was filled with 600 µL of medium containing serum. After a 24-hour incubation period, cells that had mi-grated and adhered to the underside of the chamber were fixed using anhydrous methanol. Any non-migratory cells were gently removed with a damp cotton swab, following which the chamber was allowed to dry. Subsequently, images were captured using a Nikon Eclipse TE2000-U inverted microscope at a total magnification of 100× (10× objective and 10× eyepiece) in 9 randomly selected regions for cell counting.\u003c/p\u003e\u003ch2\u003eCell scratch assay\u003c/h2\u003e\u003cp\u003eWT and AGPAT3-KD HeLa cells were seeded at a 50% density in a six-well plate and incubated for 12 hours until fully adhered to the surface. Afterward, a 200 µl yellow pipette tip was utilized to generate a cell-free 'scratch' by scraping the layer of cells in a linear fashion following adhesion. After washing with PBS to eliminate debris, the cells were cultured in a serum-free medium for regeneration. Photographs were taken at 0, 24, and 48 hours with a Nikon Eclipse TE2000-U inverted microscope at 40× magnification (10× objective and 4× eyepiece) in five separate scratch locations to measure the size of the area that had not healed.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eSPSS 25.0 software was used for statistical analysis, while Graphpad Prism 8 software was employed to generate statistical charts for comparing the two groups. The difference between the groups was assessed through independent sample t-tests or paired sample tests. In addition, the researchers performed both single-variable and multiple-variable analyses using the Cox proportional hazards regression model, and they assessed overall survival using the Kaplan-Meier method and Log-rank test. A statistical significance was determined with a P-value less than 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the Affiliated Hospital of Youjiang Medical University for nationality\u0026mdash;Ethics Committee (license number: YYFY-LL-2023HY2-06, 8 November 2023).\u003c/p\u003e\n\u003ch2\u003eInformed Consent Statement:\u003c/h2\u003e\n\u003cp\u003eWritten informed consent has been obtained from the patients to publish this paper.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJunli Wang: Conceptualization, Methodology, Funding acquisition. Mingyou Dong: Supervision, Resources, Project administration. Yuexiu Liang: Investigation, Data curation, Writing - Original draft preparation, Writing \u0026ndash; review \u0026amp; editing. Yuzhen Chen: Visualization, Investigation. Hongtao Qin: Visualization, Investigation. Wenting Wei: Visualization, Investigation.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe gratefully acknowledge the assistance of X.D. Zhang in preparing the gene knockout cells. We also thank Jingsheng Ao and Feng Shi for helpful discussion on experimental design and data analysis. In addition, we appreciate Guiqiong Pan\u0026rsquo;s assistance in experimental operations.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/projects/TCGA-CESC\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbu-Rustum, N. R. et al. NCCN Guidelines\u0026reg; Insights: Cervical Cancer, Version 1.2024. J. Natl. Compr. Cancer Netw. JNCCN 21, 1224\u0026ndash;1233, DOI: https://doi.org/ 10.6004/jnccn.2023.0062 (2023).\u003c/li\u003e\n\u003cli\u003eVoelker, R. A. Cervical Cancer Screening. 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Cytochem. 54, 21\u0026ndash;29, DOI: https://doi.org/10.1267/ahc.20-00032 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cervical cancer, prognosis, immune infiltration, TCGA, AGPAT3","lastPublishedDoi":"10.21203/rs.3.rs-4470497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4470497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are the primary histological subtypes of cervical cancer. The AGPAT gene family has been implicated in disease processes across different cancer types, but its specific role and prognostic relevance in cervical cancer remain unclear. This study emphasizes the AGPAT family as a potential biomarker and therapeutic target in cervical cancer due to its significant impact on the disease's development and outcomes. Gene expression data from the AGPAT family and clinical information from 306 CESC cases and 3 control cases were collected from The Cancer Genome Atlas (TCGA) database. These data were analyzed for mRNA expression, prognostic and diagnostic value, clinical correlations, function enrichment, and ESTIMATE score. The study revealed that AGPAT2, AGPAT3, and AGPAT5 mRNA expression was elevated, while AGPAT1 and AGPAT4 expression was reduced in cervical cancer tissues. Particularly, increased levels of AGPAT3 and AGPAT4 expression were associated with a poorer prognosis in cervical cancer patients. Additionally, higher DNA methyl-ation levels of AGPAT3 were observed in CESC tissues compared to normal samples, and specific CpGs within AGPAT3 showed a strong correlation with prognosis. Moreover, AGPAT3 expression was linked to the presence of various tumor-infiltrating immune cells. Experimental evidence demonstrated that inhibiting the AGPAT3 gene led to a significant decrease in the proliferation and migration abilities of the Hela cervical cancer cell line. These results suggest that AGPAT3 could be a valuable biomarker and a promising therapeutic target for predicting the prognosis of individuals with cervical cancer.\u003c/p\u003e","manuscriptTitle":"AGPAT Family in Cervical Cancer: A Multi-Omics Perspective on Prognosis and Function","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 18:23:09","doi":"10.21203/rs.3.rs-4470497/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9aba8e99-92dc-4c63-8005-c5ac556bfb7c","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32832510,"name":"Biological sciences/Cancer"},{"id":32832511,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":32832512,"name":"Biological sciences/Immunology"},{"id":32832513,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-07-05T12:09:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 18:23:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4470497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4470497","identity":"rs-4470497","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}