Prognostic Impact of the Lipid Metabolism Gene AGPAT4 in the Tumor Immune Microenvironment of Thyroid Cancer

Prognostic Impact of the Lipid Metabolism Gene AGPAT4 in the Tumor Immune Microenvironment of Thyroid Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Impact of the Lipid Metabolism Gene AGPAT4 in the Tumor Immune Microenvironment of Thyroid Cancer Ying Zhu, Wenbo Xu, Xuejing Bai, Yanyuan Qiao, Dan Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7931889/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Thyroid cancer (THCA), as a common endocrine malignancy, poses significant clinical challenges in terms of diagnosis and prognosis. This study aims to elucidate the role of AGPAT4, a gene involved in lipid metabolism, particularly fatty acid and glycerophospholipid metabolism, in thyroid cancer through bioinformatics analysis using data from The Cancer Genome Atlas (TCGA) database. Methods We analyzed the data of 512 thyroid cancer patients and 59 healthy individuals and constructed a protein-protein interaction (PPI) network involving AGPAT4 and its differentially expressed genes. The Kruskal-Wallis test and logistic regression were used to analyze the relationship between AGPAT4 expression and clinicopathological characteristics. Furthermore, Cox regression and Kaplan-Meier analysis were employed to evaluate its prognostic value. Moreover, single-sample gene set enrichment analysis (ssGSEA) revealed the association between AGPAT4 expression and the level of immune infiltration in the tumor microenvironment. Results AGPAT4 was expressed at low levels in thyroid cancer (P < 0.001) and could effectively distinguish tumor tissue from normal tissue (AUC = 0.942). Additionally, AGPAT4 expression was significantly correlated with pathological stage (P < 0.05). Kaplan-Meier survival analysis showed that patients with high AGPAT4 expression had better overall survival (HR = 0.28, P = 0.026). Cox regression analysis indicated that factors such as AGPAT4 expression, pathological stage (stage III/IV), and residual tumor (R1 and R2) were significantly associated with the prognosis of thyroid cancer patients. On the other hand, high AGPAT4 expression might be a prognostic protective factor, while advanced pathological stage and residual tumor indicated a risk of poor prognosis. The PPI network and functional enrichment analysis showed that AGPAT4 was involved in key pathways involved in the progression of thyroid cancer. Furthermore, immune infiltration analysis suggested an association between AGPAT4 expression and the immune response in the tumor microenvironment. Conclusion AGPAT4 may serve as a valuable biomarker for predicting the prognosis of thyroid cancer, providing insights into AGPAT4’s potential mechanisms and laying a foundation for future targeted therapies. AGPAT4 thyroid cancer prognostic prediction immune infiltration molecular mechanism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Thyroid cancer, particularly papillary thyroid carcinoma (PTC), is the most common endocrine malignancy, with an increasing incidence worldwide [ 1 – 3 ] . The pathogenesis of thyroid cancer is multifactorial, involving genetic alterations, environmental factors, and abnormalities in metabolic processes. Among various genes related to lipid metabolism, the acylglycerophosphate acyltransferase 4 (AGPAT4) gene has attracted attention due to its potential role in lipid biosynthesis and its association with tumorigenesis [ 4 ] . AGPAT4 catalyzes the conversion of lysophosphatidic acid to phosphatidic acid, a precursor for triglyceride and phospholipid synthesis; this biochemical process influences cell proliferation and survival [ 5 – 7 ] . Current studies have confirmed a link between lipid metabolism and cancer progression, indicating that changes in lipid profiles may promote tumorigenic processes. Specifically, the dysregulation of lipid metabolic pathways in cancer cells is associated with enhanced cell proliferation, survival, and metastatic capacity [ 8 – 9 ] . However, despite the growing body of relevant evidence, the specific role of AGPAT4 in thyroid cancer remains unclear. This lack of clarity creates a significant gap in our understanding of its potential diagnostic and prognostic value in this malignancy. To address this research gap, we conducted bioinformatics analyses using data from The Cancer Genome Atlas (TCGA) to investigate the differential expression of AGPAT4 between thyroid cancer tissues and normal thyroid tissues. Our aim is to clarify the relationship between AGPAT4 expression and clinicopathological features, as well as its impact on patient prognosis. In this study, a protein-protein interaction (PPI) network was constructed to identify potential downstream genes associated with AGPAT4; functional enrichment analysis was then performed to explore the biological pathways involved in these genes. Additionally, we performed survival analyses using Cox regression and Kaplan-Meier methods to evaluate the prognostic significance of AGPAT4 expression in thyroid cancer patients. Furthermore, we assessed the relationship between immune infiltration components and AGPAT4 expression levels, as the tumor microenvironment and immune response are increasingly recognized as key factors influencing cancer progression and patient outcomes. In summary, this study aims to elucidate the role of AGPAT4 in thyroid cancer by integrating bioinformatics approaches and clinical data analysis. Our findings may provide insights into the molecular mechanisms underlying the progression of thyroid cancer and help identify AGPAT4 as a potential diagnostic and prognostic biomarker, which could contribute to the optimization of therapeutic strategies for this disease. The results of this study are expected to pave the way for future exploration of targeted therapies, that modulate lipid metabolism in thyroid cancer patients . Methods 1. RNA-seq Data and Bioinformatics Analysis In this study, we obtained the mRNA expression profiles of AGPAT4 in pan-cancer and corresponding normal tissues through The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. To investigate this, we collected RNA-seq data of unpaired and paired thyroid cancer samples from the TCGA database and processed these data accordingly. Prior to data processing and visualization using R software (version 4.2.1), clinically uninformative and duplicate data were excluded; clinically uninformative data were defined as samples lacking essential clinical information required for analysis. Appropriate statistical methods, including the R packages‘stats’and‘car’, were selected based on data characteristics for relevant analyses, while the‘ggplot2’package was employed for data visualization. Additionally, we utilized Clinical Proteomic Tumor Analysis Consortium (CPTAC) data from the UALCAN database to perform additional multi-omics analyses to investigate the protein expression of AGPAT4 in thyroid cancer. This study was conducted in accordance with the Declaration of Helsinki (revised in 2013) and adhered to the publication guidelines of TCGA. No human or animal subjects were involved in this study. 2. Differential Expression Analysis of AGPAT4 The Wilcoxon rank sum test was used to evaluate the differential expression of AGPAT4 across multiple cancer types. After performing the Shapiro–Wilk normality test on the expression profile data of AGPAT4 in paired and unpaired samples, the Wilcoxon rank sum test was applied for statistical analysis due to non-normal data distribution. The chi-square test was used to analyze the relationship between AGPAT4 expression and clinical data of THCA patients. All analyses considered p < 0.05 as statistically significant. High or low AGPAT4 expression was defined based on expression levels above or below the median within the sample cohort, respectively. Differential expression analysis was performed using the screening criteria of adjusted p-value 1. Volcano plots showing the differentially expressed genes (DEGs) were visualized using Cytoscape software and the STRING database, which were also employed to analyze protein-protein interaction (PPI) networks. Hub genes were identified using the MCODE plugin, and these hub genes were further analyzed to explore their co-expression patterns with AGPAT4. 3. Receiver Operating Characteristic Curve Analysis Data processing and visualization were performed using R software (R version 4.2.1). We used the R package "pROC" (version 1.18.0) to conduct ROC analysis on the preprocessed data, and the results were visualized using the R package "ggplot2". 4. Clinical Statistical Analysis, Model Construction, and Prognostic Evaluation To investigate differences in clinicopathological characteristics between groups defined by AGPAT4 expression levels, the Wilcoxon signed-rank test was used. Patients were grouped based on the median AGPAT4 expression value. Overall survival (OS) and other clinical parameters were analyzed using Cox regression and Kaplan–Meier methods. Multivariate Cox proportional hazards regression analysis was performed to evaluate the impact of AGPAT4 expression and clinical characteristics on survival, including [specify clinical characteristics if available]. Data processing and visualization were performed using R software (R version 4.2.1). Based on multivariate Cox regression analysis, nomograms were constructed by integrating independent prognostic indicators to predict the 1-year, 3-year, and 5-year survival rates of patients. The predictive accuracy of the nomograms was verified by calibration plots to assess predictive accuracy. 5. Functional enrichment analysis The most relevant genes related to CPA4 were obtained for the GO, KEGG, and GSEA functional enrichment analyses. Specifically, the gene set ‘h.all.v2022.1.Hs.symbols.gmt’, [Hallmarks], was chosen for GSEA analysis. We defined the significance threshold for enrichment as a false discovery rate (FDR) < 0.25 and an adjusted P-value (p.adjust) < 0.05. 6. Real-time Fluorescent Quantitative PCR (Real-time PCR) Total RNA from cultured cells was extracted using the Thermo Fisher Scientific (Invitrogen) kit according to the manufacturer's protocol. PCR reactions were performed using a Bio-Rad PCR thermocycler. Gene expression levels were calculated by the 2⁻ΔΔCt method and normalized to GAPDH mRNA as an internal reference. 7. Statistical Analysis All statistical analyses were performed using R software (R version 4.2.1), and a P-value < 0.05 was considered statistically significant in this study, with specific annotation criteria as follows: * indicates P < 0.05, ** indicates P < 0.01, and *** indicates P < 0.001. Results 1. Expression of AGPAT4 in Thyroid Cancer (THCA) First, as shown in Fig. 1 A, we performed a differential expression analysis of AGPAT4 across multiple cancers. We found that AGPAT4 was differentially expressed in 18 types of tumors, including thyroid cancer (THCA) (p < 0.001). In paired pan-cancer samples, AGPAT4 expression was decreased in various malignant tumors, including thyroid cancer (Fig. 1 B). Analysis of both paired and unpaired samples from the TCGA-THCA database (Figs. 1 C, D) revealed that AGPAT4 levels in tumor tissues were significantly lower than those in normal tissues (p < 0.001). Furthermore, we validated these expression differences using thyroid cancer transcriptome data. Figure 1 E is a volcano plot of thyroid cancer gene expression data, which shows the overall pattern of differentially expressed genes and provides a direction for the verification of the key target AGPAT4. As shown in Fig. 2 A, analysis of the CPTAC database indicated that AGPAT4 protein abundance in thyroid cancer (THCA) was lower than in normal tissues. Figure 2 B presents microscopic images of normal tissues adjacent to thyroid cancer and histopathological sections stained with hematoxylin-eosin (HE), alongside tissue sections stained for AGPAT4 by immunohistochemistry (IHC). The staining intensity of AGPAT4 protein was notably reduced in thyroid cancer tissues compared to adjacent normal tissues. Additionally, we measured AGPAT4 expression in three cell lines (NTHY-ORI3, TPC-1, 8505C) and found that both mRNA and protein levels of AGPAT4 were significantly lower in thyroid cancer cells than in normal thyroid cells, as shown in Figs. 2 C and 2 D. This study confirmed that AGPAT4 serves as a reliable biomarker for thyroid cancer. 2. Analysis of gene expression correlation of AGPAT4 in thyroid cancer (THCA) Through the STRING database, we selected 50 genes related to AGPAT4 and constructed a specific protein-protein interaction network using Cytoscape (Fig. 3 A). Nine hub genes were identified, including AGPAT1, AGPAT2, MBOAT2, AGPAT3, AGPAT5, LCLAT1, PLPP2, and LPIN1 (Fig. 3 B). Subsequently, we analyzed genes related to AGPAT4 in thyroid carcinoma (THCA) using the UALCAN database (206 genes) and the STRING database (50 genes). A Venn diagram was constructed as shown in Fig. 3 C, with MAP3K4 identified as the intersection gene. Based on this, we performed correlation analysis between AGPAT4 and three genes—MAP3K4, LCLAT1, and LPIN1—selected for their relevance in the interaction network and overlap (Figs. 3 D, 3 E, and 3 F). 3. Pathway Enrichment Analysis of AGPAT4 in Thyroid Cancer (THCA) GO enrichment analysis was performed on differentially expressed genes associated with AGPAT4 (Figs. 4 A to 4 C). The results showed that these genes are involved in cellular components such as the mitochondrial outer membrane and organelle outer membrane, as well as molecular functions including lysophospholipid acyltransferase activity and 1-acylglycerol-3-phosphate O-acyltransferase activity. They are also involved in biological processes like glycerolipid metabolism, phospholipid metabolic process, and phospholipid biosynthetic process. Subsequently, KEGG enrichment analysis was conducted (Fig. 4 D), revealing that AGPAT4 is associated with pathways such as glycerophospholipid metabolism, glycerolipid metabolism, the phospholipase D signaling pathway, the phosphatidylinositol signaling system, and fat digestion and absorption. Finally, gene set enrichment analysis (GSEA) was performed on the AGPAT4-associated differentially expressed genes to identify enriched pathways. A total of 13 significantly enriched pathways were identified, including SLC-mediated transmembrane transport, NABA Core Matrisome, degradation of the extracellular matrix, formation of the cornified envelope, keratinization, hemostasis, cell adhesion molecules (CAMs), Class A1 rhodopsin-like receptors, cell surface interactions at the vascular wall, and secreted factors (Figs. 4 E and 4 F). 4. Relationship between AGPAT4 Expression and Immune Infiltration Between the high AGPAT4 expression group and the low AGPAT4 expression group, there were statistically significant differences in the immune infiltration scores of CD56bright NK cells, NK cells, plasmacytoid dendritic cells (pDCs), T follicular helper (TFH) cells, γδ T cells, activated dendritic cells (aDCs), Th2 cells, and regulatory T cells (Tregs) (p < 0.05; Fig. 5 A). In addition, the correlation heatmap between gene expression and immune cell infiltration levels (Fig. 5 B) shows the significance of correlations between the nine hub genes of AGPAT4 and immune cell types. Red represents a positive correlation—the closer the coefficient is to 1, the stronger the positive correlation; blue represents a negative correlation—the closer the coefficient is to -1, the stronger the negative correlation. Furthermore, we drew a correlation chord diagram based on the AGPAT4 expression level and the infiltration score of each immune cell (Fig. 5 C). These results indicate that the immune infiltration status of tumors is closely related to the expression level of AGPAT4. 5. Relationship between AGPAT4 Expression and Clinicopathological Features Statistical analysis was performed on the baseline data of 512 thyroid cancer patients obtained from the TCGA database (Table 1 ). The data were divided into two groups, each containing 256 samples: one group with low AGPAT4 expression and the other with high AGPAT4 expression, based on the median expression level. There were no significant differences between the two groups with respect to gender (P = 0.487), pathological stage (P = 0.558), lymph node (N) stage (P = 0.114), or metastasis (M) stage (P = 0.779). However, significant differences were observed in the distribution of T stages (P = 0.045), extrathyroidal extension (P = 0.023), overall survival (P = 0.042), and progression-free interval (PFI) events (P = 0.004). Table 1 The relationship between AGPAT4 gene expression and clinicopathological factors. Characteristics Low expression of AGPAT4 High expression of AGPAT4 P value n 256 256 Pathologic T stage, n (%) 0.045 T1 60 (11.8%) 83 (16.3%) T2 86 (16.9%) 83 (16.3%) T3&T4 110 (21.6%) 88 (17.3%) Pathologic N stage, n (%) 0.114 N0 106 (22.9%) 123 (26.6%) N1 125 (27.1%) 108 (23.4%) Pathologic M stage, n (%) 0.779 M0 129 (43.7%) 157 (53.2%) M1 5 (1.7%) 4 (1.4%) Pathologic stage, n (%) 0.558 Stage I 136 (26.7%) 152 (29.8%) Stage II 28 (5.5%) 24 (4.7%) Stage III 60 (11.8%) 53 (10.4%) Stage IV 31 (6.1%) 26 (5.1%) Gender, n (%) 0.487 Female 190 (37.1%) 183 (35.7%) Male 66 (12.9%) 73 (14.3%) Extrathyroidal extension, n (%) 0.023 No 159 (32.2%) 181 (36.6%) Yes 89 (18%) 65 (13.2%) OS event, n (%) 0.042 Alive 252 (49.2%) 244 (47.7%) Dead 4 (0.8%) 12 (2.3%) PFI event, n (%) 0.004 No 219 (42.8%) 239 (46.7%) Yes 37 (7.2%) 17 (3.3%) 6. Suggestive Significance of AGPAT4 for Prognosis in Patients with Thyroid Cancer (THCA) We used a ROC curve to analyze the diagnostic efficacy of AGPAT4 in differentiating tumor tissues from non-tumor tissues. The area under the curve (AUC) for AGPAT4 was 0.942 (confidence interval = 0.907–0.977), indicating high diagnostic accuracy (Fig. 6 A). Kaplan–Meier survival analysis evaluated the prognostic value of AGPAT4 expression in thyroid cancer. The overall survival (OS) curve showed that patients with high AGPAT4 expression had a significantly lower survival rate than those with low AGPAT4 expression. This difference was statistically significant (HR = 3.65[1.17–11.40], P = 0.026) (Fig. 6 B). Furthermore, Figs. 6 C–F demonstrate that the correlation between AGPAT4 expression and clinical parameters varies significantly. Regarding pathological stages, AGPAT4 expression in normal tissue samples was significantly higher than in patients with stage I, stage II, stage III, and stage IV disease (P < 0.001). Similarly, in TNM staging, AGPAT4 expression in normal tissue samples was significantly higher than in patients with M0 stage, M1 stage, N0 stage, N1 stage, T1 stage, T2 stage, T3 stage, and T4 stage disease (P < 0.001). 7. Construction and Validation of the AGPAT4 Nomogram Based on the multivariate Cox regression analysis, we constructed a prognostic nomogram using TNM stage, age, and AGPAT4 expression to predict the prognosis of thyroid cancer (THCA) patients. The concordance index (C-index) of this nomogram was 0.71 (0.68–0.74), suggesting that the model has moderate accuracy (Fig. 7 B). Subsequently, we plotted the calibration curve in Fig. 7 C to assess the predictive accuracy of the model. The bias-corrected calibration curve was close to the ideal 45-degree line, indicating that the predicted values were consistent with the actual outcomes. The subgroup with low AGPAT4 expression was associated with poorer survival outcomes. Finally, we conducted univariate and multivariate Cox regression analyses on common clinicopathological factors (Figs. 7 A and 7 D). In the univariate analysis, pathological stage, residual tumor, and AGPAT4 expression were significantly associated with survival. We selected variables with statistical significance in the univariate analysis for further investigation and performed a multivariate Cox regression analysis. The findings of this analysis revealed that AGPAT4 expression remained statistically significant (p < 0.05), indicating that low AGPAT4 expression is independently associated with poorer overall survival in THCA patients. Discussion As a key gene involved in lipid metabolism, particularly in fatty acid and glycerophospholipid metabolic pathways[10–11], AGPAT4 is expected to serve as a biomarker for the diagnosis and prognostic evaluation of thyroid cancer. Through bioinformatics analysis of data from The Cancer Genome Atlas (TCGA) database, we clarified the role of AGPAT4 in the occurrence and development of thyroid cancer and revealed its expression patterns and clinical significance. Our study showed that AGPAT4 was significantly downregulated in thyroid cancer tissues (P < 0.001), indicating its potential tumor-suppressive role in thyroid carcinogenesis. AGPAT4 exhibited an excellent ability to distinguish tumor tissues from normal tissues, with the area under the receiver operating characteristic curve (AUC) reaching 0.942. The expression of AGPAT4 was significantly correlated with pathological stage and survival rate (P < 0.05), which further confirmed the above findings. This correlation suggests that low AGPAT4 expression may be associated with the progression of thyroid cancer. In addition, Kaplan-Meier survival analysis showed that patients with high AGPAT4 expression had significantly prolonged overall survival (hazard ratio = 0.30, P = 0.038). Cox regression analysis further identified pathological M stage as a risk factor for Progression-Free Survival (HR = 5.964, P < 0.001), highlighting a clear association between low AGPAT4 expression and cancer progression. Moreover, Protein-Protein Interaction(PPI) Network Analysis elucidated potential interactions that may promote thyroid cancer progression, while functional enrichment analysis emphasized the regulatory role of AGPAT4 in several key signaling pathways related to thyroid tumorigenesis. Furthermore, immune infiltration analysis suggested a possible association between AGPAT4 expression and the body's immune response to thyroid cancer in the tumor microenvironment. In conclusion, our findings indicate that AGPAT4 can be a promising biomarker for predicting the prognosis of thyroid cancer. The downregulated expression level of AGPAT4 is significantly associated with poor clinical outcomes, thus highlighting its potential utility in risk stratification and treatment decision-making. Additionally, the clarification of AGPAT4-related pathways and their relationship with immune infiltration suggests that AGPAT4 plays multiple roles in thyroid carcinogenesis. Future studies should focus on exploring the potential mechanisms by which AGPAT4 affects specific tumor behaviors, such as proliferation and metastasis, and verifying its clinical applicability in prospective trials, paving the way for potential targeted therapies. Declarations Author Contribution Y.Z. wrote the main manuscript text , Wb.X. prepared figures 1-3, Xj.B. prepared figures 4-5，Yy.Q. prepared figures 6-7，D.Y. prepared table 1，All authors reviewed the manuscript. 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RNA Sequencing (RNA-Seq) Analysis Reveals Liver Lipid Metabolism Divergent Adaptive Response to Low- and High-Salinity Stress in Spotted Scat ( Scatophagus argus). Animals (Basel). 2023;13(9). Liu M, Sun C, Zheng X, et al. Comparative Proteomic Analysis Revealed the Mechanism of Tea Tree Oil Targeting Lipid Metabolism and Antioxidant System to Protect Hepatopancreatic Health in Macrobrachium rosenbergii. Front Immunol. 13:906435. Additional Declarations No competing interests reported. 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05:41:11","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66495,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/f9d51a8dabcf337091e6db07.html"},{"id":95503734,"identity":"0d3754a2-cd18-498b-b100-fed0dcf65a4f","added_by":"auto","created_at":"2025-11-10 05:41:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1796471,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of AGPAT4 in pan-cancer and thyroid cancer tumor tissues. (A) and (B) show the expression of AGPAT4 in unpaired and paired samples, respectively, from the TCGA database and GEPIA2. (C) and (D) present the expression of AGPAT4 in unpaired and paired samples, respectively, from the TCGA-KIRC database. (E) Volcano plot of differentially expressed genes in thyroid cancer. ns, not significant (p ≥ 0.05); * p \u0026lt; 0.05; ** p \u0026lt; 0.01; *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/54b5f002cc83598d6849bb88.png"},{"id":95503735,"identity":"3e46ca36-91dc-43c9-b965-254192a20301","added_by":"auto","created_at":"2025-11-10 05:41:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2137171,"visible":true,"origin":"","legend":"\u003cp\u003eProtein expression of AGPAT4 in thyroid cancer . (A) Analysis via the CPTAC (Clinical Proteomic Tumor Analysis Consortium) database showed that the protein expression level of AGPAT4 in thyroid cancer tissues was lower than that in adjacent non-cancerous tissues. (B) Representative images of histopathological sections stained with hematoxylin and eosin (HE) and immunohistochemical (IHC) staining of AGPAT4 in adjacent non-cancerous tissues and cancerous tissues of thyroid cancer. (C) The mRNA expression levels of AGPAT4 detected in three cell types (NTHY-ORI3, TPC-1, and 8505c). (D) The protein expression levels of AGPAT4 detected in the same three cell types.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/653117447dfaaf49aa32f31c.png"},{"id":95503731,"identity":"5f48dcb0-3a76-40ed-b042-821030702a7d","added_by":"auto","created_at":"2025-11-10 05:41:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2703305,"visible":true,"origin":"","legend":"\u003cp\u003eGene correlation analysis of AGPAT4 in thyroid cancer. (A) Protein-protein interaction (PPI) network; (B) PPI network of hub genes; (C) Venn diagram of UALCAN and STRING databases in papillary thyroid carcinoma (THCA); (D-F) Correlation analysis between AGPAT4 and three genes—MAP3K4, LCAT1, and LPIN1.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/f29caf20a0942c8d3c8aa496.png"},{"id":95529358,"identity":"4ff03221-d428-463f-92e7-bf9babd424da","added_by":"auto","created_at":"2025-11-10 10:17:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7459945,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of AGPAT4-related differentially expressed genes (DEGs) in thyroid cancer. (A-D) GO and KEGG enrichment analyses of AGPAT4-related DEGs; (E, F) The most significantly enriched pathways between the AGPAT4-low expression group and AGPAT4-high expression group identified by Gene Set Enrichment Analysis (GSEA).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/54e5e096078dfb39bf6a10c8.png"},{"id":95503741,"identity":"18b8dc6b-042f-4769-87ca-c61836e6c94b","added_by":"auto","created_at":"2025-11-10 05:41:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9498480,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis of AGPAT4. (A) Box plots comparing enrichment scores for 24 immune cell types between the AGPAT4 high-expression and low-expression groups, (B) Heatmap showing the correlation between genes and immune cell infiltration, (C) Chord diagram illustrating the correlations between gene expression and immune cell infiltration, where blue lines indicate negative correlations and red lines indicate positive correlations. Not significant (ns) indicates p ≥ 0.05, * p \u0026lt; 0.05, **p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/df9ae17fa60bd945ce95be7e.png"},{"id":95503736,"identity":"db21fe5a-9b54-47dc-bbf4-fdce13ca31d4","added_by":"auto","created_at":"2025-11-10 05:41:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13941357,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between AGPAT4 gene expression and clinicopathological factors. (A) Receiver Operating Characteristic (ROC) curve showing the efficacy of AGPAT4 in distinguishing thyroid cancer tissues from normal tissues. Correlation analysis between the differential expression of AGPAT4 and the clinicopathological characteristics of thyroid cancer. (B) Kaplan-Meier (K-M) analysis of Progression-Free Interval (PFI) between the AGPAT4 low-expression group and high-expression group in thyroid cancer. (C) Expression differences of AGPAT4 across different pathological stages; (D) expression differences of AGPAT4 across different T stages; (E) expression differences of AGPAT4 across different N stages; (F) expression differences of AGPAT4 across different M stages; * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/5c5af84e81d2ac7ad20483d9.png"},{"id":95503756,"identity":"587968e1-b36a-4f1f-a58f-7b5af32b4d59","added_by":"auto","created_at":"2025-11-10 05:41:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7128154,"visible":true,"origin":"","legend":"\u003cp\u003epresents the construction of the prognostic nomogram model. Panel (A) shows the forest plot of univariate Cox analysis based on overall survival (OS), while panel (D) displays the forest plot of multivariate Cox analysis based on OS. Panel (B) illustrates the nomogram for predicting 1-year, 3-year, and 5-year overall survival of thyroid cancer patients. Panel (C) depicts the calibration curves of the nomogram for 1-year, 5-year, and 7-year overall survival.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/7d02c8d46e176014b3e898be.png"},{"id":95531734,"identity":"8c454861-4ce9-4635-8f3c-049f4125bf13","added_by":"auto","created_at":"2025-11-10 10:24:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":39566956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/4e06ab46-580e-4b1a-94bf-78f6910a0c1d.pdf"},{"id":95503754,"identity":"8d90753e-a0f7-4c8a-b0f5-b3290421f43b","added_by":"auto","created_at":"2025-11-10 05:41:13","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":90317979,"visible":true,"origin":"","legend":"","description":"","filename":"OriginalImage.zip","url":"https://assets-eu.researchsquare.com/files/rs-7931889/v1/c70603a991ef421c829a1bfd.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Impact of the Lipid Metabolism Gene AGPAT4 in the Tumor Immune Microenvironment of Thyroid Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid cancer, particularly papillary thyroid carcinoma (PTC), is the most common endocrine malignancy, with an increasing incidence worldwide\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The pathogenesis of thyroid cancer is multifactorial, involving genetic alterations, environmental factors, and abnormalities in metabolic processes. Among various genes related to lipid metabolism, the acylglycerophosphate acyltransferase 4 (AGPAT4) gene has attracted attention due to its potential role in lipid biosynthesis and its association with tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. AGPAT4 catalyzes the conversion of lysophosphatidic acid to phosphatidic acid, a precursor for triglyceride and phospholipid synthesis; this biochemical process influences cell proliferation and survival \u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrent studies have confirmed a link between lipid metabolism and cancer progression, indicating that changes in lipid profiles may promote tumorigenic processes. Specifically, the dysregulation of lipid metabolic pathways in cancer cells is associated with enhanced cell proliferation, survival, and metastatic capacity \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, despite the growing body of relevant evidence, the specific role of AGPAT4 in thyroid cancer remains unclear. This lack of clarity creates a significant gap in our understanding of its potential diagnostic and prognostic value in this malignancy.\u003c/p\u003e\u003cp\u003eTo address this research gap, we conducted bioinformatics analyses using data from The Cancer Genome Atlas (TCGA) to investigate the differential expression of AGPAT4 between thyroid cancer tissues and normal thyroid tissues. Our aim is to clarify the relationship between AGPAT4 expression and clinicopathological features, as well as its impact on patient prognosis. In this study, a protein-protein interaction (PPI) network was constructed to identify potential downstream genes associated with AGPAT4; functional enrichment analysis was then performed to explore the biological pathways involved in these genes.\u003c/p\u003e\u003cp\u003eAdditionally, we performed survival analyses using Cox regression and Kaplan-Meier methods to evaluate the prognostic significance of AGPAT4 expression in thyroid cancer patients. Furthermore, we assessed the relationship between immune infiltration components and AGPAT4 expression levels, as the tumor microenvironment and immune response are increasingly recognized as key factors influencing cancer progression and patient outcomes.\u003c/p\u003e\u003cp\u003eIn summary, this study aims to elucidate the role of AGPAT4 in thyroid cancer by integrating bioinformatics approaches and clinical data analysis. Our findings may provide insights into the molecular mechanisms underlying the progression of thyroid cancer and help identify AGPAT4 as a potential diagnostic and prognostic biomarker, which could contribute to the optimization of therapeutic strategies for this disease. The results of this study are expected to pave the way for future exploration of targeted therapies, that modulate lipid metabolism in thyroid cancer patients .\u003c/p\u003e"},{"header":"Methods","content":"\n\u003ch3\u003e1. RNA-seq Data and Bioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eIn this study, we obtained the mRNA expression profiles of AGPAT4 in pan-cancer and corresponding normal tissues through The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. To investigate this, we collected RNA-seq data of unpaired and paired thyroid cancer samples from the TCGA database and processed these data accordingly. Prior to data processing and visualization using R software (version 4.2.1), clinically uninformative and duplicate data were excluded; clinically uninformative data were defined as samples lacking essential clinical information required for analysis. Appropriate statistical methods, including the R packages\u0026lsquo;stats\u0026rsquo;and\u0026lsquo;car\u0026rsquo;, were selected based on data characteristics for relevant analyses, while the\u0026lsquo;ggplot2\u0026rsquo;package was employed for data visualization. Additionally, we utilized Clinical Proteomic Tumor Analysis Consortium (CPTAC) data from the UALCAN database to perform additional multi-omics analyses to investigate the protein expression of AGPAT4 in thyroid cancer. This study was conducted in accordance with the Declaration of Helsinki (revised in 2013) and adhered to the publication guidelines of TCGA. No human or animal subjects were involved in this study.\u003c/p\u003e\n\u003ch3\u003e2. Differential Expression Analysis of AGPAT4\u003c/h3\u003e\n\u003cp\u003eThe Wilcoxon rank sum test was used to evaluate the differential expression of AGPAT4 across multiple cancer types. After performing the Shapiro\u0026ndash;Wilk normality test on the expression profile data of AGPAT4 in paired and unpaired samples, the Wilcoxon rank sum test was applied for statistical analysis due to non-normal data distribution. The chi-square test was used to analyze the relationship between AGPAT4 expression and clinical data of THCA patients. All analyses considered p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant.\u003c/p\u003e\u003cp\u003eHigh or low AGPAT4 expression was defined based on expression levels above or below the median within the sample cohort, respectively. Differential expression analysis was performed using the screening criteria of adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |Log2 fold change| \u0026gt;1. Volcano plots showing the differentially expressed genes (DEGs) were visualized using Cytoscape software and the STRING database, which were also employed to analyze protein-protein interaction (PPI) networks. Hub genes were identified using the MCODE plugin, and these hub genes were further analyzed to explore their co-expression patterns with AGPAT4.\u003c/p\u003e\n\u003ch3\u003e3. Receiver Operating Characteristic Curve Analysis\u003c/h3\u003e\n\u003cp\u003eData processing and visualization were performed using R software (R version 4.2.1). We used the R package \"pROC\" (version 1.18.0) to conduct ROC analysis on the preprocessed data, and the results were visualized using the R package \"ggplot2\".\u003c/p\u003e\n\u003ch3\u003e4. Clinical Statistical Analysis, Model Construction, and Prognostic Evaluation\u003c/h3\u003e\n\u003cp\u003eTo investigate differences in clinicopathological characteristics between groups defined by AGPAT4 expression levels, the Wilcoxon signed-rank test was used. Patients were grouped based on the median AGPAT4 expression value. Overall survival (OS) and other clinical parameters were analyzed using Cox regression and Kaplan\u0026ndash;Meier methods. Multivariate Cox proportional hazards regression analysis was performed to evaluate the impact of AGPAT4 expression and clinical characteristics on survival, including [specify clinical characteristics if available]. Data processing and visualization were performed using R software (R version 4.2.1). Based on multivariate Cox regression analysis, nomograms were constructed by integrating independent prognostic indicators to predict the 1-year, 3-year, and 5-year survival rates of patients. The predictive accuracy of the nomograms was verified by calibration plots to assess predictive accuracy.\u003c/p\u003e\n\u003ch3\u003e5. Functional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe most relevant genes related to CPA4 were obtained for the GO, KEGG, and GSEA functional enrichment analyses. Specifically, the gene set \u0026lsquo;h.all.v2022.1.Hs.symbols.gmt\u0026rsquo;, [Hallmarks], was chosen for GSEA analysis. We defined the significance threshold for enrichment as a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and an adjusted P-value (p.adjust)\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003e6. Real-time Fluorescent Quantitative PCR (Real-time PCR)\u003c/h3\u003e\n\u003cp\u003eTotal RNA from cultured cells was extracted using the Thermo Fisher Scientific (Invitrogen) kit according to the manufacturer's protocol. PCR reactions were performed using a Bio-Rad PCR thermocycler. Gene expression levels were calculated by the 2⁻ΔΔCt method and normalized to GAPDH mRNA as an internal reference.\u003c/p\u003e\n\u003ch3\u003e7. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (R version 4.2.1), and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant in this study, with specific annotation criteria as follows: * indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and *** indicates P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e1. Expression of AGPAT4 in Thyroid Cancer (THCA)\u003c/h3\u003e\n\u003cp\u003eFirst, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, we performed a differential expression analysis of AGPAT4 across multiple cancers. We found that AGPAT4 was differentially expressed in 18 types of tumors, including thyroid cancer (THCA) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In paired pan-cancer samples, AGPAT4 expression was decreased in various malignant tumors, including thyroid cancer (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Analysis of both paired and unpaired samples from the TCGA-THCA database (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC, D) revealed that AGPAT4 levels in tumor tissues were significantly lower than those in normal tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, we validated these expression differences using thyroid cancer transcriptome data. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE is a volcano plot of thyroid cancer gene expression data, which shows the overall pattern of differentially expressed genes and provides a direction for the verification of the key target AGPAT4. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, analysis of the CPTAC database indicated that AGPAT4 protein abundance in thyroid cancer (THCA) was lower than in normal tissues. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB presents microscopic images of normal tissues adjacent to thyroid cancer and histopathological sections stained with hematoxylin-eosin (HE), alongside tissue sections stained for AGPAT4 by immunohistochemistry (IHC). The staining intensity of AGPAT4 protein was notably reduced in thyroid cancer tissues compared to adjacent normal tissues.\u003c/p\u003e\n\u003cp\u003eAdditionally, we measured AGPAT4 expression in three cell lines (NTHY-ORI3, TPC-1, 8505C) and found that both mRNA and protein levels of AGPAT4 were significantly lower in thyroid cancer cells than in normal thyroid cells, as shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD. This study confirmed that AGPAT4 serves as a reliable biomarker for thyroid cancer.\u003c/p\u003e\n\u003ch3\u003e2. Analysis of gene expression correlation of AGPAT4 in thyroid cancer (THCA)\u003c/h3\u003e\n\u003cp\u003eThrough the STRING database, we selected 50 genes related to AGPAT4 and constructed a specific protein-protein interaction network using Cytoscape (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Nine hub genes were identified, including AGPAT1, AGPAT2, MBOAT2, AGPAT3, AGPAT5, LCLAT1, PLPP2, and LPIN1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Subsequently, we analyzed genes related to AGPAT4 in thyroid carcinoma (THCA) using the UALCAN database (206 genes) and the STRING database (50 genes). A Venn diagram was constructed as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, with MAP3K4 identified as the intersection gene. Based on this, we performed correlation analysis between AGPAT4 and three genes\u0026mdash;MAP3K4, LCLAT1, and LPIN1\u0026mdash;selected for their relevance in the interaction network and overlap (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE, and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\n\u003ch3\u003e3. Pathway Enrichment Analysis of AGPAT4 in Thyroid Cancer (THCA)\u003c/h3\u003e\n\u003cp\u003eGO enrichment analysis was performed on differentially expressed genes associated with AGPAT4 (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA to \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). The results showed that these genes are involved in cellular components such as the mitochondrial outer membrane and organelle outer membrane, as well as molecular functions including lysophospholipid acyltransferase activity and 1-acylglycerol-3-phosphate O-acyltransferase activity. They are also involved in biological processes like glycerolipid metabolism, phospholipid metabolic process, and phospholipid biosynthetic process. Subsequently, KEGG enrichment analysis was conducted (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD), revealing that AGPAT4 is associated with pathways such as glycerophospholipid metabolism, glycerolipid metabolism, the phospholipase D signaling pathway, the phosphatidylinositol signaling system, and fat digestion and absorption.\u003c/p\u003e\n\u003cp\u003eFinally, gene set enrichment analysis (GSEA) was performed on the AGPAT4-associated differentially expressed genes to identify enriched pathways. A total of 13 significantly enriched pathways were identified, including SLC-mediated transmembrane transport, NABA Core Matrisome, degradation of the extracellular matrix, formation of the cornified envelope, keratinization, hemostasis, cell adhesion molecules (CAMs), Class A1 rhodopsin-like receptors, cell surface interactions at the vascular wall, and secreted factors (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\n\u003ch3\u003e4. Relationship between AGPAT4 Expression and Immune Infiltration\u003c/h3\u003e\n\u003cp\u003eBetween the high AGPAT4 expression group and the low AGPAT4 expression group, there were statistically significant differences in the immune infiltration scores of CD56bright NK cells, NK cells, plasmacytoid dendritic cells (pDCs), T follicular helper (TFH) cells, \u0026gamma;\u0026delta; T cells, activated dendritic cells (aDCs), Th2 cells, and regulatory T cells (Tregs) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). In addition, the correlation heatmap between gene expression and immune cell infiltration levels (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB) shows the significance of correlations between the nine hub genes of AGPAT4 and immune cell types. Red represents a positive correlation\u0026mdash;the closer the coefficient is to 1, the stronger the positive correlation; blue represents a negative correlation\u0026mdash;the closer the coefficient is to -1, the stronger the negative correlation. Furthermore, we drew a correlation chord diagram based on the AGPAT4 expression level and the infiltration score of each immune cell (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). These results indicate that the immune infiltration status of tumors is closely related to the expression level of AGPAT4.\u003c/p\u003e\n\u003ch3\u003e5. Relationship between AGPAT4 Expression and Clinicopathological Features\u003c/h3\u003e\n\u003cp\u003eStatistical analysis was performed on the baseline data of 512 thyroid cancer patients obtained from the TCGA database (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The data were divided into two groups, each containing 256 samples: one group with low AGPAT4 expression and the other with high AGPAT4 expression, based on the median expression level. There were no significant differences between the two groups with respect to gender (P\u0026thinsp;=\u0026thinsp;0.487), pathological stage (P\u0026thinsp;=\u0026thinsp;0.558), lymph node (N) stage (P\u0026thinsp;=\u0026thinsp;0.114), or metastasis (M) stage (P\u0026thinsp;=\u0026thinsp;0.779). However, significant differences were observed in the distribution of T stages (P\u0026thinsp;=\u0026thinsp;0.045), extrathyroidal extension (P\u0026thinsp;=\u0026thinsp;0.023), overall survival (P\u0026thinsp;=\u0026thinsp;0.042), and progression-free interval (PFI) events (P\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe relationship between AGPAT4 gene expression and clinicopathological factors.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow expression of AGPAT4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh expression of AGPAT4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (11.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83 (16.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86 (16.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83 (16.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT3\u0026amp;T4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 (21.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88 (17.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.114\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e106 (22.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123 (26.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125 (27.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108 (23.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathologic M stage, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.779\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (43.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157 (53.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (1.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.558\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage I\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136 (26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e152 (29.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage II\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (5.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage III\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (11.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 (10.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStage IV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (6.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (5.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.487\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190 (37.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e183 (35.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66 (12.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73 (14.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtrathyroidal extension, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e159 (32.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181 (36.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89 (18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65 (13.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOS event, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e252 (49.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e244 (47.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDead\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePFI event, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e219 (42.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e239 (46.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (7.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e6. Suggestive Significance of AGPAT4 for Prognosis in Patients with Thyroid Cancer (THCA)\u003c/h3\u003e\n\u003cp\u003eWe used a ROC curve to analyze the diagnostic efficacy of AGPAT4 in differentiating tumor tissues from non-tumor tissues. The area under the curve (AUC) for AGPAT4 was 0.942 (confidence interval\u0026thinsp;=\u0026thinsp;0.907\u0026ndash;0.977), indicating high diagnostic accuracy (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Kaplan\u0026ndash;Meier survival analysis evaluated the prognostic value of AGPAT4 expression in thyroid cancer. The overall survival (OS) curve showed that patients with high AGPAT4 expression had a significantly lower survival rate than those with low AGPAT4 expression. This difference was statistically significant (HR\u0026thinsp;=\u0026thinsp;3.65[1.17\u0026ndash;11.40], P\u0026thinsp;=\u0026thinsp;0.026) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Furthermore, Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC\u0026ndash;F demonstrate that the correlation between AGPAT4 expression and clinical parameters varies significantly. Regarding pathological stages, AGPAT4 expression in normal tissue samples was significantly higher than in patients with stage I, stage II, stage III, and stage IV disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, in TNM staging, AGPAT4 expression in normal tissue samples was significantly higher than in patients with M0 stage, M1 stage, N0 stage, N1 stage, T1 stage, T2 stage, T3 stage, and T4 stage disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003e7. Construction and Validation of the AGPAT4 Nomogram\u003c/h3\u003e\n\u003cp\u003eBased on the multivariate Cox regression analysis, we constructed a prognostic nomogram using TNM stage, age, and AGPAT4 expression to predict the prognosis of thyroid cancer (THCA) patients. The concordance index (C-index) of this nomogram was 0.71 (0.68\u0026ndash;0.74), suggesting that the model has moderate accuracy (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). Subsequently, we plotted the calibration curve in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC to assess the predictive accuracy of the model. The bias-corrected calibration curve was close to the ideal 45-degree line, indicating that the predicted values were consistent with the actual outcomes.\u003c/p\u003e\n\u003cp\u003eThe subgroup with low AGPAT4 expression was associated with poorer survival outcomes. Finally, we conducted univariate and multivariate Cox regression analyses on common clinicopathological factors (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD). In the univariate analysis, pathological stage, residual tumor, and AGPAT4 expression were significantly associated with survival. We selected variables with statistical significance in the univariate analysis for further investigation and performed a multivariate Cox regression analysis. The findings of this analysis revealed that AGPAT4 expression remained statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that low AGPAT4 expression is independently associated with poorer overall survival in THCA patients.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs a key gene involved in lipid metabolism, particularly in fatty acid and glycerophospholipid metabolic pathways[10\u0026ndash;11], AGPAT4 is expected to serve as a biomarker for the diagnosis and prognostic evaluation of thyroid cancer. Through bioinformatics analysis of data from The Cancer Genome Atlas (TCGA) database, we clarified the role of AGPAT4 in the occurrence and development of thyroid cancer and revealed its expression patterns and clinical significance.\u003c/p\u003e\u003cp\u003eOur study showed that AGPAT4 was significantly downregulated in thyroid cancer tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating its potential tumor-suppressive role in thyroid carcinogenesis. AGPAT4 exhibited an excellent ability to distinguish tumor tissues from normal tissues, with the area under the receiver operating characteristic curve (AUC) reaching 0.942. The expression of AGPAT4 was significantly correlated with pathological stage and survival rate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which further confirmed the above findings. This correlation suggests that low AGPAT4 expression may be associated with the progression of thyroid cancer.\u003c/p\u003e\u003cp\u003eIn addition, Kaplan-Meier survival analysis showed that patients with high AGPAT4 expression had significantly prolonged overall survival (hazard ratio\u0026thinsp;=\u0026thinsp;0.30, P\u0026thinsp;=\u0026thinsp;0.038). Cox regression analysis further identified pathological M stage as a risk factor for Progression-Free Survival (HR\u0026thinsp;=\u0026thinsp;5.964, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting a clear association between low AGPAT4 expression and cancer progression.\u003c/p\u003e\u003cp\u003eMoreover, Protein-Protein Interaction(PPI) Network Analysis elucidated potential interactions that may promote thyroid cancer progression, while functional enrichment analysis emphasized the regulatory role of AGPAT4 in several key signaling pathways related to thyroid tumorigenesis. Furthermore, immune infiltration analysis suggested a possible association between AGPAT4 expression and the body's immune response to thyroid cancer in the tumor microenvironment.\u003c/p\u003e\u003cp\u003eIn conclusion, our findings indicate that AGPAT4 can be a promising biomarker for predicting the prognosis of thyroid cancer. The downregulated expression level of AGPAT4 is significantly associated with poor clinical outcomes, thus highlighting its potential utility in risk stratification and treatment decision-making. Additionally, the clarification of AGPAT4-related pathways and their relationship with immune infiltration suggests that AGPAT4 plays multiple roles in thyroid carcinogenesis. Future studies should focus on exploring the potential mechanisms by which AGPAT4 affects specific tumor behaviors, such as proliferation and metastasis, and verifying its clinical applicability in prospective trials, paving the way for potential targeted therapies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.Z. wrote the main manuscript text , Wb.X. prepared figures 1-3, Xj.B. prepared figures 4-5，Yy.Q. prepared figures 6-7，D.Y. prepared table 1，All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSheikh AB, Akhtar A, Tariq U, Sheikh AAE, Siddiqui FS, Bukhari MM. Skull Metastasis Extending to the Superior Sagittal Sinus: An Unfamiliar Presentation of Papillary Thyroid Carcinoma. Cureus. 2018;10(6):e2738.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTodorović L, Stanojević B. VHL tumor suppressor as a novel potential candidate biomarker in papillary thyroid carcinoma. Biomol Biomed. 2023;23(1):26\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePelizzo MR, Merante Boschin I, Toniato A, et al. Diagnosis, treatment, prognostic factors and long-term outcome in papillary thyroid carcinoma. Minerva Endocrinol. 2008;33(4):359\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTu Y, Chen Y, Mo L, et al. Multi-Omic Analysis Reveals a Lipid Metabolism Gene Signature and Predicts Prognosis and Chemotherapy Response in Thyroid Carcinoma. Cancer Med. 2025;14(6):e70819.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNg KY, Koo TY, Huang IB, et al. AGPAT4 targeted covalent inhibitor potentiates targeted therapy to overcome cancer cell plasticity in hepatocellular carcinoma mouse models. Sci Transl Med. 2025;17(809):eadn9472.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMardian EB, Bradley RM, Aristizabal Henao JJ, et al. Agpat4/Lpaatδ deficiency highlights the molecular heterogeneity of epididymal and perirenal white adipose depots. J Lipid Res. 2017;58(10):2037\u0026ndash;2050.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan K, Zhu B, Wang L, Guo Q, Shu-Chien AC, Wu X. Expression pattern of AGPATs isoforms indicate different functions during the triacylglyceride synthesis in Chinese mitten crab, Eriocheir sinensis. Comp Biochem Physiol A Mol Integr Physiol. 287:111535.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai C, Tong Y, Bai N, et al. Decoding the role of nucleic acid binding protein 2 in lipid dysregulation and hepatocellular carcinoma progression through LKB1-mediated mitochondrial dysfunction. Cell Signal. 132:111820.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang X, Xiong Y, Yu J, et al. Expression profiles of FABP4 and FABP5 in breast cancer: clinical implications and perspectives. Discov Oncol. 2025;16(1):357.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J, Cai B, Tian C, et al. RNA Sequencing (RNA-Seq) Analysis Reveals Liver Lipid Metabolism Divergent Adaptive Response to Low- and High-Salinity Stress in Spotted Scat (\u0026lt;\u0026thinsp;i\u0026thinsp;\u0026gt;\u0026thinsp;Scatophagus argus). Animals (Basel). 2023;13(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu M, Sun C, Zheng X, et al. Comparative Proteomic Analysis Revealed the Mechanism of Tea Tree Oil Targeting Lipid Metabolism and Antioxidant System to Protect Hepatopancreatic Health in \u0026lt;\u0026thinsp;i\u0026thinsp;\u0026gt;\u0026thinsp;Macrobrachium rosenbergii. Front Immunol. 13:906435.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genomics-and-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genomics \u0026 Informatics](https://genomicsinform.biomedcentral.com/)","snPcode":"44342","submissionUrl":"https://submission.springernature.com/new-submission/44342/3","title":"Genomics \u0026 Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AGPAT4, thyroid cancer, prognostic prediction, immune infiltration, molecular mechanism","lastPublishedDoi":"10.21203/rs.3.rs-7931889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7931889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThyroid cancer (THCA), as a common endocrine malignancy, poses significant clinical challenges in terms of diagnosis and prognosis. This study aims to elucidate the role of AGPAT4, a gene involved in lipid metabolism, particularly fatty acid and glycerophospholipid metabolism, in thyroid cancer through bioinformatics analysis using data from The Cancer Genome Atlas (TCGA) database.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed the data of 512 thyroid cancer patients and 59 healthy individuals and constructed a protein-protein interaction (PPI) network involving AGPAT4 and its differentially expressed genes. The Kruskal-Wallis test and logistic regression were used to analyze the relationship between AGPAT4 expression and clinicopathological characteristics. Furthermore, Cox regression and Kaplan-Meier analysis were employed to evaluate its prognostic value. Moreover, single-sample gene set enrichment analysis (ssGSEA) revealed the association between AGPAT4 expression and the level of immune infiltration in the tumor microenvironment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAGPAT4 was expressed at low levels in thyroid cancer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and could effectively distinguish tumor tissue from normal tissue (AUC\u0026thinsp;=\u0026thinsp;0.942). Additionally, AGPAT4 expression was significantly correlated with pathological stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Kaplan-Meier survival analysis showed that patients with high AGPAT4 expression had better overall survival (HR\u0026thinsp;=\u0026thinsp;0.28, P\u0026thinsp;=\u0026thinsp;0.026). Cox regression analysis indicated that factors such as AGPAT4 expression, pathological stage (stage III/IV), and residual tumor (R1 and R2) were significantly associated with the prognosis of thyroid cancer patients. On the other hand, high AGPAT4 expression might be a prognostic protective factor, while advanced pathological stage and residual tumor indicated a risk of poor prognosis. The PPI network and functional enrichment analysis showed that AGPAT4 was involved in key pathways involved in the progression of thyroid cancer. Furthermore, immune infiltration analysis suggested an association between AGPAT4 expression and the immune response in the tumor microenvironment.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAGPAT4 may serve as a valuable biomarker for predicting the prognosis of thyroid cancer, providing insights into AGPAT4\u0026rsquo;s potential mechanisms and laying a foundation for future targeted therapies.\u003c/p\u003e","manuscriptTitle":"Prognostic Impact of the Lipid Metabolism Gene AGPAT4 in the Tumor Immune Microenvironment of Thyroid Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:41:05","doi":"10.21203/rs.3.rs-7931889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T12:08:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T08:50:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T15:01:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239456897965955188410854139612735468009","date":"2025-11-03T04:42:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94857653697317583765113681749616026166","date":"2025-10-29T01:27:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T23:57:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-26T23:49:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-26T23:48:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genomics \u0026 Informatics","date":"2025-10-23T11:47:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genomics-and-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genomics \u0026 Informatics](https://genomicsinform.biomedcentral.com/)","snPcode":"44342","submissionUrl":"https://submission.springernature.com/new-submission/44342/3","title":"Genomics \u0026 Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60da5675-dbeb-415d-b1ba-0318f3aa7756","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-27T09:53:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 05:41:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7931889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7931889","identity":"rs-7931889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}