Expression of Lipid Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Endometrial Carcinoma

Expression of Lipid Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Endometrial Carcinoma | 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 Expression of Lipid Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Endometrial Carcinoma Huang Chen, Ye Chen, Xiaoli Liu, Ling Weng, Yongping Zeng, Yanying Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3885090/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 Objective Endometrial carcinoma (EC) is one of the most prevalent types of gynecologic cancer. The purpose of this work was to identify the metabolic-related biological characteristics of endometrial cancer and to investigate the immune-related molecular pathways of carcinogenesis in endometrial cancer. Methods Data from The Cancer Genome Atlas (TCGA) were utilized to identify lipid metabolism-related genes (LMRGs) with significant correlations to the prognosis of EC patients. Enrichment of functional pathways within the LMRGs was studied. LASSO and Cox regression analysis were conducted to identify LMRGs that were significantly associated with the prognosis of EC patients. We created a prognostic signature and proved its effectiveness in both training and validation groups. In addition, we constructed a complete nomogram consisting of risk models and clinical variables to estimate the survival probability of EC patients. Results ACOT11, CYP1A2, GDPD5, MOGAT3, OLAH, PIASS4, PIP5K1C, PLPP2, and SRD5A1 were discovered to be strongly associated with the clinical outcomes of EC patients. On the basis of these nine LMRGs, we generated and validated our predictive signature using the training and validation cohorts. In addition to being independent of other clinical factors, the nine-LMRG signature distinguished between patients at high- and low-risk for EC and predict EC patient's probability of survival. Statistically, the nomogram exhibited a high correlation between survival forecasts and observations. In the high-risk group, immune/stromal scores were lower and there was a higher density of several kinds of immune cells. Conclusions The LMRG's prognostic model and comprehensive nomogram could guide therapeutic choices in clinical practice, and explore the underlying mechanisms involved in EC progression. endometrial cancer (EC) Lipid metabolism-related genes nomogram immune infiltration risk model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Endometrial cancer is a prevalent genital malignancy among women in China and worldwide. [ 1 ] . Symptoms of endometrial cancer usually manifest early in life and include excessive bleeding after menopause or abnormally heavy bleeding prior to menopause. Approximately 85% of women survive for 5 years after diagnosis. However, 13–25% of patients with advanced EC experience recurrence and metastasis. [ 2 ] . Thus, identifying biological ways to improve prognosis or exploring significant molecular functions in EC is necessary. Understanding its pathogenesis and better treating endometrial cancer might be possible. The development and prognosis of EC are affected by various factors including genomic and clinical factors. Unfortunately, the current classification system cannot accurately predict EC patient survival outcomes [ 3 ] . Consequently, it is imperative to investigate prognostic factors further and develop prognostic models combining gene expression profiles with traditional clinical features. Current information from clinical and academic research shows that lipid metabolism problems play a crucial role in carcinogenesis, tumor development, and therapy [ 4 ] . Su et al. [ 5 ] revealed that increased lipid metabolism is required for the initiation and development of tumor-associated macrophages. According to Niemi et al. [ 6 ] showed that abnormal lipid metabolism intensifies with increasing stage. In addition, previous research has shown that the potential of lipid metabolism-related genes (LMRGs) to influence prognosis in numerous types of cancer, including ovarian carcinomas [ 7 ] . Therefore, screening for lipid metabolic genes associated with EC progress and selecting a targeted therapy for EC patients is vital for the prognosis of these patients. The tumor immune microenvironment (TIME) depicts the immunological state of the tumor microenvironment and is essential for tumor growth and occurrence. [ 8 ] . Immune cells participate in the reprogramming of tumor cells by changing the surrounding microenvironment through secreting different types of biological factors, so that surrounding cells can control the survival and progression of tumors. [ 9 ] . Therefore, TIME is recognized as a crucial component in the formation and growth of cancers. Evidence demonstrates that TIME is intimately connected with the development of endometrial cancer [ 10 ] . Observing the TIME of endometrial cancer can assist determine the immunological state of tumor cells, aid in the development of immunotherapy, and enhance the outcome for patients with endometrial cancer. In this research, a complete analysis of LMRGs was performed to explore the influence of lipid metabolism on TIME and survival in endometrial cancer patients. To examine the predictive usefulness of LMRGs in endometrial cancer, we also built a risk score model based on LMRGs. We hope that our work may give fresh insight into the underlying molecular causes of endometrial cancer, throw new light on the development of targeted therapeutics for endometrial cancer, and provide a new guide for the individualized management of EC patients. TABLE 1 | Comparative clinicopathological features of training and internal validation cohorts. Characteristics TCGA training cohort (n = 359) TCGA internal validation cohort (n = 180) P-value Age (years) >=65 167 83 0.9288 BMI Histological type Stage Grade Menopause status Peritoneal cytology Survival status =30 <30 NA Endometrioid Serous Mixed Stage I Stage II Stage III Stage IV G1 G2 G3 Post Pre NA negative positive NA Alive Dead 192 200 135 24 269 77 13 226 38 75 20 65 76 218 294 47 18 231 39 89 298 61 97 102 71 7 134 37 9 110 13 48 0 33 43 104 148 22 10 118 18 44 154 26 0.4157 0.7381 0.3445 0.7486 0.9323 0.9429 0.4484 Materials and methods Data acquisition and procession The TCGA-UCEC dataset includes RNA-seq data and associated clinical features of 548 cancer samples and 36 normal tissue samples from the TCGA database ( https://portal.gdc.cancer.gov/ ). We preprocess the raw data according to the following criteria. (1) Exclude the gene if the FPKM value (fragments per kilobase) is zero in more than 50% of the samples; (2) exclude genes with missing expression values in more than 50% of the samples; (3) ) to exclude samples without relevant clinical information; (4) to exclude normal tissue samples. The TCGA-UCEC dataset was randomly divided 2:1 into two cohorts: training set (n = 359) and internal validation set (n = 180). Table 1 lists the demographics and clinical characteristics of the populations in the training and validation sets. As the data are public, no ethics approval from the committee is required. Metabolism-related genes were obtained from the Molecular Signature Database (MSigDB) ( http://software.broadinstitute.org/gsea/index.jsp ) Table S1 lists 743 genes involved in lipid metabolism. Molecular Subgroup Identification and TIME Evaluation Initially, univariate Cox regression analysis revealed a relationship between 173 lipid metabolism genes and the prognosis of EC. The "ConsensusClusterPlus" R program was utilized to conduct consensus clustering on the expression matrix of these 173 genes. The Estimation of Stromal and Immune Cells in Malignant Tissue Using Expression Data (ESTATE) method was utilized to quantify stromal scores, immune scores, and tumor purity [ 11 ] . CIBERSORT is a method for determining the composition of a cell based on expression profiles. This deconvolution technique was utilized to determine the percentage of 22 types of immune cells for each EC patient [ 12 ] . The total of the proportions of each sample's 22 immune cell types was 1. Microenvironmental cell population counting (MCP-counter), which permits accurate assessment of the absolute abundance of eight immune cell types and two stromal cell populations in diverse tissues based on transcriptome data. [ 13 ] Using GSVA [ 14 ] , we used the R package single-sample gene set enrichment analysis (ssGSEA) to analyze the expression levels of 28 previously reported gene sets from immune cells to determine the extent to which these cells infiltrated the tissue. [ 15 ] . DEG Identification and Bioinformatics Analysis The "Limma" R package was used to determine DEGs of different subtypes (FDR0.05 and |log2FC|>1.5). DEGs were used to conduct a functional enrichment study using the GO and KEGG Risk Model Construction and Validation The TCGA-UCEC patients have been put into training and testing groups at random. We used the "glmnet" R package, the absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis to find out which LMRGs were important for predicting the future. We used the following formula to figure out the risk score for predicting the outcome of EC patients: risk score = multi-variate Cox regression coefficient ratio of each mRNA multiplied by its expression level. Using the median risk score, we split the training group into subgroups with high and low risk. Both subgroups were able to view each patient's gene expression profile and survival status by using the "pheatmap" and "survival" R packages. Also, the Kaplan-Meier curve analysis was done, and receiver operating characteristic (ROC) curves were made to figure out how sensitive and specific the prognostic signature was. Statistical Analysis In this research, the mean and standard deviation (SD) or median were used to describe continuous variables, while frequency (n) and percentage (%) were used to describe categorical variables. To see if the variables were different, ANOVA, chi-square, and Student t-tests were used. The log-rank test was used to compare the OS rates for high- and low-risk groups. We did both a univariate and a multivariate logistic COX regression to figure out the hazard ratio (HR) and its 95% confidence interval (CI). Statistical analysis was done with R software (4.1.3 version) and GraphPad Prism (version 8.0.1). All statistical tests for all of the analyses were two-sided, and a p-value threshold of 0.05 was used to figure out if changes were statistically significant. Results LMRG-Based Identification of Two Molecular Subtypes A flow chart of the research and development process of our study can be viewed in Fig. 1 . We applied consensus clustering to stratify patients based on 173 prognostic genes utilizing univariable Cox analysis in the training set. (Table S2 ). (Figs. 2 A–C and Figure S1 ) indicate that K = 2 is ideal for clustering stability. The C1 subtype accounted for 297 patients and the C2 subtype accounted for 242 patients. The expression levels of LMRGs in the two subtypes were shown using a heatmap (Fig. 2 D), indicating that a difference in expression level was seen between the two subtypes. In addition, individuals with the C1 subtype had a higher overall survival rate compared to those with the C2 subtype (P = 2.278e-08; Fig. 2 E). These findings suggest that the LMRGs differentiate individuals with EC into two discrete molecular subgroups with markedly differing overall survival. Patients of the Two Molecular Subtypes Displayed Distinct TIME and Immune Status. In addition, we assessed the difference in immune response between the two molecular subtypes by performing immunological analyses. As demonstrated in Figures(3A-B), EC patients with the C1 subtype had substantially higher immune scores (P < 0.001), stromal scores (P < 0.01), and ESTIMATE scores (P < 0.001), but lower tumor purity (P < 0.001) than those with the C2 subtype. In addition, the MCP-counter data demonstrated that tumor immune infiltration of the C1 subtype was more pronounced than that of the C2 subtype, including CD8 T cells, Cytotoxic lymphocytes, NK cells, Myeloid dendritic cells, Neutrophils, Endothelial cells, and Fibroblasts (Fig. 3 C). Moreover, CIBERSORT results indicated that there were significantly more M1 macrophages, M2 macrophages, and Dendritic cells activated in the C2 subtype than the C1 subtype (Fig. 3 D). Based on the ssGSEA algorithm results, the immune landscape between subtype C1 and subtype C2 differed significantly, with a relatively low immune status in subtype C2. As a result of statistical analysis, most of the 24 cell types were significantly more prevalent in C1 subtype than in C2 subtype (Fig. 3 E). The findings reveal a substantial difference between the two molecular subtypes in terms of TIME and immunological status. According to three distinct methodologies, the distribution of tumor-infiltrating immune cells (TIICs) across the two subtypes was almost identical. As a result, we hypothesized that the C1 subtype could be more susceptible to immunotherapy. Figure 3 F depicts the distribution of TIIC for the two subtypes. DEG Identification and Bioinformatics Analysis Using the aforementioned criterion, all DEGs among C1 and C2 were uncovered (Figure S2 ). There were 1494 DEGs identified (Table S3 ). Then, GO and KEGG enrichment analysis was conducted. It was discovered that DEGs are concentrated in a variety of processes including organelle fission, nuclear division, microtubule-based motility, mitotic nuclear division, and chromosome segregation (Fig. 4 C), and pathways that are related to cell cycle, human papillomavirus infection, Hippo signaling pathway, sulfur metabolism, and mucin type O-glycan biosynthesis (Fig. 4 D). The enrichment results are listed in Tables S4 and S5. GSEA Identification of Immunology-Related Pathways A GSEA analysis was performed to determine the functional distinctions between the two clusters. In the enrichment of MSigDB Collection (c5.cp.v7.0.symbols.gmt), several significant pathways related to immunity and metabolism were identified, including cell activation involved in immune response, immune effector processes, leukocyte-mediated immunity, myeloid leukocyte-mediated immunity, negative regulation of protein metabolism, and regulation of cell death (Fig. 4 B). The results of the enrichment are presented in Table S6 . Development of a Risk Model for the Training Cohort Based on LMRG Table 1 illustrates the clinical features of the TCGA training cohort (n = 359) and the internal validation cohort (n = 180). Included were age, body mass index (BMI), type of histology, stage, grade, menopausal status, peritoneal cytology, and survival status. There were no statistically significant differences (P > 0.05) between the training cohort and the internal validation cohort, suggesting their comparability. Subsequently, an evaluation of the prognostic predictive potential of LMRGs for endometrial cancer was conducted. To estimate the risk score model, the number of genes was reduced using LASSO-Cox regression analysis. In addition, a 10-fold cross-validation was used to determine the optimum model. The model with a lambda of 0.0646860064274793 and nine genes was picked as the final model (Fig. 5). The following was the model formula:riskscore = 0.00942640931904423*ACOT11 + 0.0504325830028766*CYP1A2 + 0.0389403528420956*GDPD5 + 0.0152717405790258*MOGAT3 + 0.108665321885266*OLAH-0.383128628508224*PIAS4-0.116079225223333*PIP5K1C-0.00125543696645166*PLPP2 + 0.43678448595434*SRD5A1 High expression levels of ACOT11, CYP1A2, GDPD5, MOGAT3, OLAH, and SRD5A1 as prognostic risk variables were related with a poor prognosis, as shown by the above formula. In contrast, high expression of PIAS4, PIP5K1C, and PLPP2 was related with a low risk as a prognostic factor. After calculating the risk score for each patient in the training cohort, the risk score distribution was calculated, as indicated in (Fig. 6 ). Risk models accurately separated EC patients into high- and low-risk categories (Fig. 6 A-B). Nine genes differed significantly between the high-risk and low-risk groups. As demonstrated in (Fig. 6 C). Derive the KM curve from the median risk score. A statistically significant difference in survival probability was discovered between the two groups (P < 0.001), as demonstrated in (Fig. 6 D). Consequently, patients with high-risk scores had considerably worse OS, suggesting that the high-risk score was a negative prognostic factor. In addition, the forecasting accuracy of the formula was examined for 1, 3, and 5 years, as seen in (Fig. 6 E). The model has a significantly large area under the curve (AUC). Model of constructed risk independence In addition, we studied the association between the risk score and clinical characteristics and performed subgroup and regression studies to determine the model's independence. Figures 8 A- 8 C; p < 0.001 indicate that the risk score effectively distinguished between the high-risk group and the low risk group based on age, grade, and cluster. In addition, even when patients are categorized according to their age (Figs. 8 D- 8 E), stage (8F-8G), or grade (Figs. 8 H- 8 I), the risk model continues to provide strong predictive accuracy, and patients with a lower risk score have a better prognosis. Moreover, multivariate and univariate Cox regression studies demonstrated that the established risk model was an independent predictor of EC patients' prognosis (Tables 2 , 3 ). As a result, our risk score successfully predicted prognosis based on a vast array of clinical data. Moreover, our risk model was extremely independent in predicting the prognosis of EC. Internal Validation to Assess the Reliability of the Risk Model Subsequently, the constructed prognostic risk score model was further verified in the verification cohort. EC patients in the verification cohort were sorted into high-risk and low-risk subgroups using the above-mentioned method (Fig. 7A-B). The expression of nine potential genes was shown using a heatmap (Fig. 7C). The study of survival indicated that high-risk individuals had a worse outcome (P = 0.003; Fig. 7D). In addition, ROC curves were produced to evaluate the accuracy of prognostic prediction for 1, 3, and 5 years in the cohort used for internal validation, as indicated (Fig. 7E). Additionally, the AUC of the model in the validation cohort is rather high. Table 2 Analysis of risk scores and attributes in the training cohort using a univariate approach. Characteristics HR HR95%CI (lower) HR95%CI (upper) P-value Age 1.028 1.003 1.053 0.02533 BMI Histological type Stage Grade Menopause status Risk score 1.004 3.179 3.753 7.993 0.9469 10.14 0.9851 1.92 2.26 1.95 0.4481 5.522 1.022 5.262 6.23 32.76 2.001 18.62 0.7086 6.889e-06 3.161e-07 0.003875 0.8863 7.997e-14 Table 3 Analysis of risk scores and cohort characteristics using a multivariate approach. Characteristics HR HR95%CI (lower) HR95%CI (upper) P-value Age 1.042 1.01 1.076 1.095e-02 BMI Histological type Stage Grade Menopause status Risk score 1.037 1.001 3.31 4.644 0.5441 5.23 1.008 0.5201 1.828 1.083 0.1981 2.398 1.066 1.928 5.994 19.92 1.494 11.41 1.158e-02 9.965e-01 7.764e-05 3.873e-02 2.377e-01 3.201e-05 Nomogram building and validation For screening prognostic risk variables, the TCGA training cohort was employed. Risk score, age, tumor grade, tumor FIGO stage, and histological type were identified as OS risk variables by univariate Cox regression analysis (Table 2 ). We next conducted a multivariable Cox regression analysis utilizing the aforementioned variables and find that risk score, age, BMI, tumor grade, and tumor stage are independent risk factors for OS (Table 3 ). The forest plot was utilized to demonstrate the clinical characteristics and risk score as indicated in (Fig. 9A). P < 0.001 indicated that the HR value of the risk score was the highest. As seen in (Fig. 9B), we then developed a nomogram model with a C-index value of 0.775% (95% CI = 0.712–0.838) that includes all of the clinical variables and risk ratings. The calibration curve demonstrated that the nomogram projected survival rates for each of the 1-, 3-, and 5-year survivals were similar to their actual values (Fig. 9C). To further validate the accuracy of the nomogram, ROC curves were utilized to evaluate the nomogram's prediction accuracy for each patient. According to statistical analysis, the 1-, 3-, and 5-year AUCs of the nomogram model were 0.772, 0.787, and 0.825, respectively (Fig. 9D). Immune and Stromal Scores and ImmuneCell Infiltration Analysis ESTIMATE, MCPcounter, CIBERSORT, and ssGSEA were done to better comprehend the variations in immune function. In the ESTIMATE analysis, the low-risk group had higher scores for stromal, immune, and ESTIMATE, and lower tumor purity than the high-risk group (Figs. 10A–B). In addition, the MCP-counter results demonstrated that the tumor immune infiltration of the low-risk group was more prominent than that of the high-risk group, including CD8 T cells, cytotoxic lymphocytes, neutrophils, endothelial cells, and fibroblasts. (Figs. 10C) In addition, CIBERSORT analysis revealed that the low-risk group had a higher proportion of CD8 T cells, regulatory T cells, gamma delta T cells, plasma cells, and dendritic cells (Fig. 10D). Twelve immune cell subtypes, including activated B cells, active B cells, activated CD8 T cells, CD56dim natural killer cells, eosinophils, MDSC, monocytes, and type 17 T helper cells, were strongly expressed in the Low risk group, as determined by ssGSEA. (Fig. 10E). In terms of CD8 T cells, the results suggested that the immunological infiltration of the low-risk group was generally greater than that of the high-risk group. Discussion In the previous two decades, the EC mortality rate has doubled. Although the 5-year survival rate for early EC patients is more than 85%, roughly 13 to 25% of them (considered originally to have a favorable prognosis) have recurrence and metastasis [ 2 ] . Consequently, reliable prognostic indications are required to aid clinicians in conducting more precise clinical examinations. Increasingly, database-based bioinformatics techniques are employed to find biological compounds having diagnostic potential. However, earlier research focused primarily on either genetic variables or clinical factors [ 16 , 17 ] , both of which had a significant influence in the carcinogenesis and prognosis of EC. This study presents an LMRG-related risk signature for EC obtained from large cancer datasets, followed by the development of a nomogram for OS prediction based on the risk signature and clinical and pathological data that accurately predicts the outcomes of EC patients. Our findings may assist the development of EC-targeted therapies and enable physicians to make more informed treatment choices. It is of the utmost significance to develop effective ways for classifying patients based on their risk scores and to provide suitable tailored and targeted therapies. A bioinformatic study based on the sequencing of RNA data was shown to be a suitable method for risk classification and identification of targeted genes. Previous research has shown that the expression of lipid metabolism genes is associated with the immunological microenvironment of osteosarcoma patients, which may be utilized to correctly predict osteosarcoma prognosis [ 18 ] . In recent years, some molecular markers have changed with tumor progression, and the accuracy of a group of molecular markers in reflecting tumor prognosis has been significantly improved compared with a single marker [ 19 ] . Although previous studies have established risk models based on the tumor immune microenvironment and EC energy metabolism [ 10 , 20 ] . Compared to earlier investigations, our research exhibited distinct advantages. Based on their lipid metabolism, we identified two molecular subgroups with markedly different prognosis and immunological state in EC patients using consensus clustering. In addition, based on the clustering data, we investigated biological factors and partially elucidated the underlying mechanism. In addition, the effect of lipid metabolism on outcome and TIME was elucidated. In the present investigation, the public gene expression data from the TCGA database were used to classify EC patients into two molecular subgroups based on gene expression data linked to lipid metabolism and prognosis. It was discovered that the prognosis and immunological state of the two subtypes varied significantly. Furthermore, 1494 DEGs were discovered between the two classes. According to GO and KEGG analysis, these DEGs were functionally relevant to cancer and development, and GSEA enrichment analysis indicated numerous critical immune and metabolism-related pathways. To investigate the underlying biological processes, functional comparisons of the two groupings were done. Based on the discovered DEGs, GO analysis and KEGG analysis revealed that dysregulation of immunity and chromosomal segregation may mediate the effect of lipid metabolism on the carcinogenesis and development of EC. However, the precise link between lipid metabolism, abnormal immunity, and chromosomal segregation remains unknown. GSEA analysis was a standard approach for integrating gene expression data that immediately revealed the expression trend of gene sets in distinct groups. To comprehend the functional differences between the two clusters, GSEA was performed. In the enrichment of MSigDB Collection (c5.cp.v7.0.symbols.gmt), we identified numerous significant immunity-related pathways, including cell activation involved in immune response, immune effector process, leukocyte-mediated immunity, negative regulation of protein metabolic process, and regulation of cell death. These findings reveal anomalies in lipid metabolism and a link between inadequate immune control and endometrial cancer prognosis. In recent years, tumor lipid metabolic anomalies have attracted more attention [ 21 ] . Targeting abnormal lipid metabolic pathways is a viable anticancer treatment approach. For instance, anticancer drugs based on hydroxylated lipid are widely employed for clinical tumor therapy [ 22 ] . In addition, a prognostic model based on the selection of nine genes utilizing univariate Cox and LASSO-Cox regression was developed. In both training and validation cohorts, the risk model comprised of ACOT11, CYP1A2, GDPD5, MOGAT3, OLAH, PIAS4, PIP5K2C, PLPP2, and SRD5A1 accurately predicted the prognosis of EC patients. Additionally, the improved risk model differentiates effectively between the clinicopathological features of EC patients. In addition, we constructed a nomogram with an accurate survival prognostic. With the greatest HR value in the nomogram, the risk score is among the most significant OS risk variables. Noteworthy is the fact that some of these genes have been implicated in past cancer studies, Researchers have demonstrated that patients with lung squamous carcinoma (LUSC) who have high expression of ACOT11 have a significantly poorer prognosis. Knocking down ACOT11 inhibits cell proliferation, migration, and invasion in vitro and in vivo [ 23 ] . Experiments with transgenic knockout mice revealed that ACOT11 decreased thermogenesis after cold exposure by inhibiting endogenous fatty acid oxidation in brown adipose tissue [ 24 ] . In addition to biotransforming many important endogenous and exogenous substances, CYP1A2 is one of the most important cytochrome P450 isoforms. Although CYP1A2 is expressed predominantly in hepatocytes, little is known about its expression in extrahepatic tissues. CYP1A2 knockout mice displayed large increases in blood cholesterol and free testosterone, followed by minor liver damage and fat deposition, as demonstrated by Sun et al [ 25 ] . GDPD5, which gene is located on chromosome 11q13.5, was discovered as a member of the glycerophosphodiesterase (GDE) family, which is essential for glycerol metabolism, in humans. Serum responsive element (SRE), a nuclear regulator of the MAP kinase cascade and transcription factor, is inhibited by GDPD5 [ 26 ] . As a positive regulator of oncogenesis, GDPD5 is also related with breast cancer [ 27 ] . It has been identified that there are three isoforms of MGAT enzyme to date: MGAT1, MGAT2, and MGAT3. Accordingly, the MOGAT gene family consists of three members (MOGAT1, MOGAT2, MOGAT3) in humans and three members (Mogat1, Mogat2, Mogat3) in rodents [ 28 ] . In contrast, MGAT1 is predominantly expressed in the gastrointestinal tract, kidneys, and adipose tissue, while MGAT2 and MGAT3 are highly expressed in the gastrointestinal tract [ 29 ] . Molecular targets like Mogat3 may be relevant in the treatment of obesity and its associated disorders, such as Type 2 Diabetes [ 28 ] . Protein inhibitors of activated STAT, such as PIAS4, are members of the PIAS4 family of proteins. In vertebrates, the PIAS gene families include PIAS1, PIAS2, PIAS3, and PIAS4. [ 30 ] Research has shown that the main functions of PIAS4 involve regulating protein SUMOylation and participating in the repair of DNA damage. [ 31 ] PIAS4 was overexpressed abnormally in ovarian cancer cells and promoted hypoxia-induced Sirtuin1 transcriptional repression and epithelial-to-mesenchymal transition. [ 32 ] Lack of PIP5K1c in adipocytes markedly slowed HFD-induced fat storage, decreased adipose tissue mass, enhanced insulin sensitivity, and decreased ectopic fatty acid accumulation in the liver, indicating that PIP5K1c is a driver of diet-induced obesity and metabolic syndrome [ 33 ] SRD5A1 is widespread throughout the body, including in the skin, liver, kidney, immunological, and neural tissues. SRD5A1 plays a critical role in the catabolism of testosterone and DHT, and SRD5A1 deficiency may result in aberrant local or systemic androgen levels, which can eventually cause illness. [ 34 ] In a research using rats fed a high-fat diet, the intensity of SRD5A1 expression was positively associated with their body weight. [ 35 ] As a consequence, our work demonstrates that the nine discovered genes may serve as promising indicators for EC prognosis. However, neither individual genes nor correlations between these genes can express the predictive activities of these genes. Immune cells have the ability to stimulate metabolic processes. To supply energy for the operations of immune cells, the immune system generates a variety of metabolites [ 36 ] . In addition, a substantial proportion of immune cells are active in metabolic pathways [ 37 , 38 ] . Specifically, our data reveal that the immune, stromal, and total scores are inversely connected with the lipid-metabolism-related risk of EC patients, demonstrating a correlation between an abundance of immune cells at TIME and a favorable outcome in patients at low risk. It has been proven that metastatic foci with the lowest immune cell infiltration constitute the poorest immunological milieu, which is most favourable to immune evasion [ 39 ] . Fan et al [ 10 ] . found in a prior study that low immunological, stromal, and total scores were related with a poor prognosis in EC. Similar to the data stated previously, the low-risk group exhibited a much lower immune cell infiltration density. High levels of CD8 + T-lymphocytes are an independent, favorable predictor of OS in patients with EC [ 40 ] . In addition, our findings demonstrated that patients with high immune ratings had prolonged OS, indicating that the TIME composition effects the final clinical outcomes of EC patients. However, further study is required to elucidate the processes causing these immunological microenvironments. This investigation uncovered two distinct molecular subtypes: C1 and C2. Patients with a poor prognosis in the C1 subtype also had a TIME abnormality with a low immune score and a high tumor purity, and low immunity was connected with limited lipid metabolism. According to the risk model of LMRGs, the prognosis of EC could be predicted with a high degree of accuracy, despite the fact that patients in high-risk groups with poor outcomes had low immune scores and high tumor purity. The outcomes of these studies demonstrated that the landscape of lipid metabolism was connected with TIME and should be considered an essential factor in establishing the treatment strategy for EC patients, as it may be a potential target for individualized treatment. We found a prognostic profile comprised of nine gene signatures that predicted survival at 1, 3, and 5 years with reasonably high AUCs in both the training and validation populations. When generalizing our findings, it is important to take into account the limitations of our study. Our results were derived through bioinformatics research and were not empirically confirmed. The data for this study were obtained from public databases and not from our cohort. Further prospective studies are required to validate the predictive utility of LMRGs in EC in light of the poor level of evidence in retrospective investigations. Conclusion The present investigation revealed two molecular subgroups based on consensus clustering of LMRGs in EC. Immunological and functional investigations demonstrated that disruption of lipid metabolism would compromise the immune system, resulting in a dismal prognosis. Our research might give new insight on the creation of new targeted medications. As part of the study, we created a prognostic predictor based on nine lipid metabolism genes and an integrated nomogram that could correctly and effectively assess the chance of OS, serve as a clinical prognostic tool, and guide EC patients' tailored anticancer therapy. Declarations Ethics approval and consent to participate This manuscripts do not involve human participants, human data or human tissue. Consent for publication Regarding the publishing of this paper, the authors state that they have no conflicting interests. Consent to participate All the authors are agreed to submit and publish this manuscription. Consent for publishion All the authors are agreed to submit and publish this manuscription. Data Availability Statement Publicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/). Metabolism-related genes were obtained from the Molecular Signature Database (MSigDB) (http://software.broadinstitute.org/gsea/index.jsp) Funding This work was funded by Guangzhou Science and Technology Projects (201904010363); Guangzhou Health Technology Project (20221A011118); Science and Technology Planning Project of Panyu in Guangzhou (2020-Z04-014). Conflict of Interest Regarding the publishing of this paper, the authors state that they have no conflicting interests. Authors’ Contributions All authors participated in the design and interpretation of the study, data analysis, and review of the manuscript. Huang Chen, Ye Chen, conceived and designed the project and wrote the manuscript. Huang Chen, Xiaoli Liu, and Ling Weng, analyzed and visualized the data. Huang Chen, Yongping Zeng, and Yanying Wang interpreted the data and participated in discussions. Huang Chen, and Lijuan Zhao have revised the final version of the manuscript. All authors have contributed to the manuscript and approved the submitted version. Acknowledgments This work was funded by Guangzhou Science and Technology Projects (201904010363); Guangzhou Health Technology Project (20221A011118); Science and Technology Planning Project of Panyu in Guangzhou (2020-Z04-014). References Li X, Yang X, Fan Y, Cheng Y, Dong Y, Zhou J, Wang Z, Li X, Wang J. 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Supplementary Files FigureS1.pdf FigureS2.pdf TableS1.txt TableS2.txt TableS3.txt TableS4.txt TableS5.txt TableS6.txt SuppLegends.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3885090","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272396307,"identity":"f7b6c026-3159-4548-bc7e-ebccca500b19","order_by":0,"name":"Huang Chen","email":"","orcid":"","institution":"Wuhan Eighth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Chen","suffix":""},{"id":272396308,"identity":"24bde129-bfd1-4a8f-b450-8f85c303deb1","order_by":1,"name":"Ye Chen","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Chen","suffix":""},{"id":272396309,"identity":"db0c9fd1-8723-4d6d-a994-67a144bb7435","order_by":2,"name":"Xiaoli Liu","email":"","orcid":"","institution":"Wuhan Eighth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Liu","suffix":""},{"id":272396310,"identity":"3b52b7cc-1cfc-40a9-a1d5-7f6996bcf19d","order_by":3,"name":"Ling Weng","email":"","orcid":"","institution":"Wuhan Eighth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Weng","suffix":""},{"id":272396311,"identity":"048c0355-ae03-4bde-a7ba-758dc221bc10","order_by":4,"name":"Yongping Zeng","email":"","orcid":"","institution":"Wuhan Eighth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yongping","middleName":"","lastName":"Zeng","suffix":""},{"id":272396312,"identity":"bdb8ab31-08e2-4cf4-841c-8383ac2e14d1","order_by":5,"name":"Yanying Wang","email":"","orcid":"","institution":"Wuhan Eighth Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanying","middleName":"","lastName":"Wang","suffix":""},{"id":272396313,"identity":"2dc95b2c-3ac6-45c8-95d0-a3c7549508b0","order_by":6,"name":"Lijuan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACNmb2gw8+/pHg4WdvPkCcFj72nmTDmQ02MpI9xxKI0yLHc8BMmLchzcbgho8BkQ6TSEhj4N1xmEdyBs/HG28Y7OR0GwhqSTz2QPLMYR5+6d7NlnMYko3NDhC2Jd3AgA1oy5yz26R5GA4kbiNCi5lEAlCLwY2cZ0RqAXpf4mBbGkgLG5FaQIHccMaGBxjIxpZzDIjwi3wz+8HHfyok7IFR+fDGmwo7OYJaUIAED5FRg6yFVB2jYBSMglEwIgAApZ1BzCjT+qgAAAAASUVORK5CYII=","orcid":"","institution":"Wuhan Eighth Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-01-21 15:31:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3885090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3885090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51138534,"identity":"4b198059-f73b-4a91-938c-7511edb8bbfe","added_by":"auto","created_at":"2024-02-14 19:00:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171181,"visible":true,"origin":"","legend":"\u003cp\u003eThe analytical method flowchart for discovering a metabolism-related prognostic signature\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/7837294225c4093e6b307fb1.png"},{"id":51138533,"identity":"46b69594-1b16-4402-a135-9531c496406f","added_by":"auto","created_at":"2024-02-14 19:00:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":336263,"visible":true,"origin":"","legend":"\u003cp\u003eAgreement cluster. (A–C) K = 2 was shown to be the best value for consensus clustering, (D) a heatmap that visualized the expression of genes involved in lipid metabolism in the two subgroups, and (E) a survival curve for the patients in each of the subgroups.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/3c7a1b4cf55619de8357bbf5.png"},{"id":51137968,"identity":"1d2abc4f-c1be-4e7e-b7ba-5bb5dac81603","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256776,"visible":true,"origin":"","legend":"\u003cp\u003eComparing the immunological properties of two cultures. Score for ESTIMATE (A), Tumor purity (B), and MCP-counter immune score (C) (C), Immune cell proportion (D), immune cell expression (E) between two clusters, (F) Heat map comparing three immunological ratings across molecular subtypes. The P values are shown with asterisks (ns, no statistical significance, *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/a4b85bb765054593a4984f59.png"},{"id":51138793,"identity":"74f7da9f-257f-4cf5-a01e-1e6e1dd36292","added_by":"auto","created_at":"2024-02-14 19:08:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228443,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differentially expressed genes and functional assessments. (A) A volcano plot illustrating the DEGs between the two subgroups, and (B) GSEA plots illustrating the outcome of the GSEA analysis. 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Each dot represents a lambda value, accompanied by error bars that provide a confidence interval for the cross-validated error rate.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/80bf9be89e13a3a7d2cbfa98.png"},{"id":51137979,"identity":"a4034ce4-7ec0-4ae9-ace9-4bf315d08fc4","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":171596,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the risk model's performance in the training cohort. (A-C) Distribution of risk score, overall survival, survival status (red dots indicate dead, blue dots indicate alive), and expression heat map for nine genes in the training cohort; (D) The OS Kaplan–Meier (KM) curves for the training cohort. Risk model training cohort ROC curves and area under the curve (AUC) for 1-, 3-, and 5-year survival.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/a702a50ec7127567628bc588.png"},{"id":51137983,"identity":"203dfebc-881f-45e7-b19d-4b1a5d3cbdbd","added_by":"auto","created_at":"2024-02-14 18:52:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":150295,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the risk model internally. 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Regrouped survival curves of patients by age (D, E), FIGO stage (F, G), and grade (H, I).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/f5ee804677b61215ae483736.png"},{"id":51137975,"identity":"caf28bd4-7834-486d-9577-1d2162a998c0","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":262280,"visible":true,"origin":"","legend":"\u003cp\u003eEstablish survival prediction nomogram (A) multivariate COX regression analysis of the connection between clinical and pathological parameters (including risk score); (B) OS predictive nomogram of EC patients that includes risk score. (C) ROC curve and area under curve (AUC) of normal survival times of 1, 3, and 5 years. (d) Calibration curves for 1-, 3-, and 5-year survival rates in nomograms and ideal models.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/37de35eaa9e75f9f5eb65f82.png"},{"id":51138535,"identity":"1e665143-bef3-4ac8-acd9-a4cfaa277217","added_by":"auto","created_at":"2024-02-14 19:00:38","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":142535,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of the immunological properties of the two groups. ESTIMATE Score (A), Tumor purity (B), MCP-counter immune score (C), Immune cell proportion (D), and immune cell expression (E) between the two groups. The P values are shown with asterisks (ns, no statistical significance, *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/8116195cbda19ac40e4ccbc2.png"},{"id":55264479,"identity":"32f2d893-c314-4636-866f-fe45560dbe4b","added_by":"auto","created_at":"2024-04-25 01:44:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3669614,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/24fe9f90-6690-4a1d-b6f3-993d8ee599bd.pdf"},{"id":51137965,"identity":"3606731f-13ac-457b-9ec3-c51de8c075b2","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20760,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/d348a3cd7bb9c1ee7c1f2557.pdf"},{"id":51137969,"identity":"32cf0142-477d-4262-80ce-ea63f8c5b199","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1211830,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/e23ecc6f0a692ae4639b22a4.pdf"},{"id":51137963,"identity":"adcf6803-694a-48e2-8ecd-a1276f18f858","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5451,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.txt","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/ae9417c0cf507b8329ce384b.txt"},{"id":51137978,"identity":"30707832-9421-4334-92c1-d14dabd868bd","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1295,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.txt","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/64cee1a319e9389d4beb4a7d.txt"},{"id":51137977,"identity":"6c93e7b3-c39a-4aa8-b733-d7e4bbc9a629","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"txt","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":110984,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.txt","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/fd385350454b019d060307b7.txt"},{"id":51137982,"identity":"28a44b12-2d8f-43ad-94ba-0ea18fbde458","added_by":"auto","created_at":"2024-02-14 18:52:39","extension":"txt","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":63790,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.txt","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/d67976c73442064e45bd35cf.txt"},{"id":51137970,"identity":"ac736757-4392-4d43-a80e-5003beebcf53","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"txt","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":41147,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.txt","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/43c817e6158299660e6ba2dc.txt"},{"id":51137973,"identity":"0d25b02c-aa94-4c05-8efb-1e1b9adfea11","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"txt","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":93846,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.txt","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/c727f7e22e94974a98eb2695.txt"},{"id":51137976,"identity":"b7cc5189-36f4-4625-a9a0-27000cad30f4","added_by":"auto","created_at":"2024-02-14 18:52:38","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":12720,"visible":true,"origin":"","legend":"","description":"","filename":"SuppLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-3885090/v1/009fdf2cb08b07d606f63bc1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression of Lipid Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Endometrial Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer is a prevalent genital malignancy among women in China and worldwide. \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Symptoms of endometrial cancer usually manifest early in life and include excessive bleeding after menopause or abnormally heavy bleeding prior to menopause. Approximately 85% of women survive for 5 years after diagnosis. However, 13\u0026ndash;25% of patients with advanced EC experience recurrence and metastasis. \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Thus, identifying biological ways to improve prognosis or exploring significant molecular functions in EC is necessary. Understanding its pathogenesis and better treating endometrial cancer might be possible. The development and prognosis of EC are affected by various factors including genomic and clinical factors. Unfortunately, the current classification system cannot accurately predict EC patient survival outcomes\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Consequently, it is imperative to investigate prognostic factors further and develop prognostic models combining gene expression profiles with traditional clinical features.\u003c/p\u003e \u003cp\u003eCurrent information from clinical and academic research shows that lipid metabolism problems play a crucial role in carcinogenesis, tumor development, and therapy \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Su et al.\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e revealed that increased lipid metabolism is required for the initiation and development of tumor-associated macrophages. According to Niemi et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e showed that abnormal lipid metabolism intensifies with increasing stage. In addition, previous research has shown that the potential of lipid metabolism-related genes (LMRGs) to influence prognosis in numerous types of cancer, including ovarian carcinomas \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, screening for lipid metabolic genes associated with EC progress and selecting a targeted therapy for EC patients is vital for the prognosis of these patients.\u003c/p\u003e \u003cp\u003eThe tumor immune microenvironment (TIME) depicts the immunological state of the tumor microenvironment and is essential for tumor growth and occurrence. \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Immune cells participate in the reprogramming of tumor cells by changing the surrounding microenvironment through secreting different types of biological factors, so that surrounding cells can control the survival and progression of tumors. \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Therefore, TIME is recognized as a crucial component in the formation and growth of cancers. Evidence demonstrates that TIME is intimately connected with the development of endometrial cancer \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Observing the TIME of endometrial cancer can assist determine the immunological state of tumor cells, aid in the development of immunotherapy, and enhance the outcome for patients with endometrial cancer.\u003c/p\u003e \u003cp\u003eIn this research, a complete analysis of LMRGs was performed to explore the influence of lipid metabolism on TIME and survival in endometrial cancer patients. To examine the predictive usefulness of LMRGs in endometrial cancer, we also built a risk score model based on LMRGs. We hope that our work may give fresh insight into the underlying molecular causes of endometrial cancer, throw new light on the development of targeted therapeutics for endometrial cancer, and provide a new guide for the individualized management of EC patients.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTABLE 1 |\u0026nbsp;\u003c/strong\u003eComparative clinicopathological features of training and internal validation cohorts.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eTCGA training\u003c/p\u003e\n \u003cp\u003ecohort\u003c/p\u003e\n \u003cp\u003e(n = 359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eTCGA internal\u003c/p\u003e\n \u003cp\u003evalidation cohort\u003c/p\u003e\n \u003cp\u003e(n = 180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;=65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.9288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMenopause status\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ePeritoneal cytology\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSurvival status\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;65\u003c/p\u003e\n \u003cp\u003e\u0026gt;=30\u003c/p\u003e\n \u003cp\u003e\u0026lt;30\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eEndometrioid\u003c/p\u003e\n \u003cp\u003eSerous\u003c/p\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4157\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.7381\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.3445\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.7486\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.9323\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.9429\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4484\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition and procession\u003c/h2\u003e \u003cp\u003eThe TCGA-UCEC dataset includes RNA-seq data and associated clinical features of 548 cancer samples and 36 normal tissue samples from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We preprocess the raw data according to the following criteria. (1) Exclude the gene if the FPKM value (fragments per kilobase) is zero in more than 50% of the samples; (2) exclude genes with missing expression values in more than 50% of the samples; (3) ) to exclude samples without relevant clinical information; (4) to exclude normal tissue samples. The TCGA-UCEC dataset was randomly divided 2:1 into two cohorts: training set (n\u0026thinsp;=\u0026thinsp;359) and internal validation set (n\u0026thinsp;=\u0026thinsp;180). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the demographics and clinical characteristics of the populations in the training and validation sets. As the data are public, no ethics approval from the committee is required. Metabolism-related genes were obtained from the Molecular Signature Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"http://software.broadinstitute.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e lists 743 genes involved in lipid metabolism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Subgroup Identification and TIME Evaluation\u003c/h2\u003e \u003cp\u003eInitially, univariate Cox regression analysis revealed a relationship between 173 lipid metabolism genes and the prognosis of EC. The \"ConsensusClusterPlus\" R program was utilized to conduct consensus clustering on the expression matrix of these 173 genes. The Estimation of Stromal and Immune Cells in Malignant Tissue Using Expression Data (ESTATE) method was utilized to quantify stromal scores, immune scores, and tumor purity \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. CIBERSORT is a method for determining the composition of a cell based on expression profiles. This deconvolution technique was utilized to determine the percentage of 22 types of immune cells for each EC patient \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The total of the proportions of each sample's 22 immune cell types was 1. Microenvironmental cell population counting (MCP-counter), which permits accurate assessment of the absolute abundance of eight immune cell types and two stromal cell populations in diverse tissues based on transcriptome data. \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e Using GSVA\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, we used the R package single-sample gene set enrichment analysis (ssGSEA) to analyze the expression levels of 28 previously reported gene sets from immune cells to determine the extent to which these cells infiltrated the tissue. \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDEG Identification and Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eThe \"Limma\" R package was used to determine DEGs of different subtypes (FDR0.05 and |log2FC|\u0026gt;1.5). DEGs were used to conduct a functional enrichment study using the GO and KEGG\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRisk Model Construction and Validation\u003c/h2\u003e \u003cp\u003eThe TCGA-UCEC patients have been put into training and testing groups at random. We used the \"glmnet\" R package, the absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis to find out which LMRGs were important for predicting the future. We used the following formula to figure out the risk score for predicting the outcome of EC patients: risk score\u0026thinsp;=\u0026thinsp;multi-variate Cox regression coefficient ratio of each mRNA multiplied by its expression level. Using the median risk score, we split the training group into subgroups with high and low risk. Both subgroups were able to view each patient's gene expression profile and survival status by using the \"pheatmap\" and \"survival\" R packages. Also, the Kaplan-Meier curve analysis was done, and receiver operating characteristic (ROC) curves were made to figure out how sensitive and specific the prognostic signature was.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn this research, the mean and standard deviation (SD) or median were used to describe continuous variables, while frequency (n) and percentage (%) were used to describe categorical variables. To see if the variables were different, ANOVA, chi-square, and Student t-tests were used. The log-rank test was used to compare the OS rates for high- and low-risk groups. We did both a univariate and a multivariate logistic COX regression to figure out the hazard ratio (HR) and its 95% confidence interval (CI). Statistical analysis was done with R software (4.1.3 version) and GraphPad Prism (version 8.0.1). All statistical tests for all of the analyses were two-sided, and a p-value threshold of 0.05 was used to figure out if changes were statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLMRG-Based Identification of Two Molecular Subtypes\u003c/h2\u003e \u003cp\u003eA flow chart of the research and development process of our study can be viewed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We applied consensus clustering to stratify patients based on 173 prognostic genes utilizing univariable Cox analysis in the training set. (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) indicate that K\u0026thinsp;=\u0026thinsp;2 is ideal for clustering stability. The C1 subtype accounted for 297 patients and the C2 subtype accounted for 242 patients. The expression levels of LMRGs in the two subtypes were shown using a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), indicating that a difference in expression level was seen between the two subtypes. In addition, individuals with the C1 subtype had a higher overall survival rate compared to those with the C2 subtype (P\u0026thinsp;=\u0026thinsp;2.278e-08; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These findings suggest that the LMRGs differentiate individuals with EC into two discrete molecular subgroups with markedly differing overall survival.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePatients of the Two Molecular Subtypes Displayed Distinct TIME and Immune Status.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn addition, we assessed the difference in immune response between the two molecular subtypes by performing immunological analyses. As demonstrated in Figures(3A-B), EC patients with the C1 subtype had substantially higher immune scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stromal scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and ESTIMATE scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but lower tumor purity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those with the C2 subtype. In addition, the MCP-counter data demonstrated that tumor immune infiltration of the C1 subtype was more pronounced than that of the C2 subtype, including CD8 T cells, Cytotoxic lymphocytes, NK cells, Myeloid dendritic cells, Neutrophils, Endothelial cells, and Fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Moreover, CIBERSORT results indicated that there were significantly more M1 macrophages, M2 macrophages, and Dendritic cells activated in the C2 subtype than the C1 subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Based on the ssGSEA algorithm results, the immune landscape between subtype C1 and subtype C2 differed significantly, with a relatively low immune status in subtype C2. As a result of statistical analysis, most of the 24 cell types were significantly more prevalent in C1 subtype than in C2 subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The findings reveal a substantial difference between the two molecular subtypes in terms of TIME and immunological status. According to three distinct methodologies, the distribution of tumor-infiltrating immune cells (TIICs) across the two subtypes was almost identical. As a result, we hypothesized that the C1 subtype could be more susceptible to immunotherapy. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF depicts the distribution of TIIC for the two subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDEG Identification and Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eUsing the aforementioned criterion, all DEGs among C1 and C2 were uncovered (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). There were 1494 DEGs identified (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Then, GO and KEGG enrichment analysis was conducted. It was discovered that DEGs are concentrated in a variety of processes including organelle fission, nuclear division, microtubule-based motility, mitotic nuclear division, and chromosome segregation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), and pathways that are related to cell cycle, human papillomavirus infection, Hippo signaling pathway, sulfur metabolism, and mucin type O-glycan biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The enrichment results are listed in Tables S4 and S5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGSEA Identification of Immunology-Related Pathways\u003c/h2\u003e \u003cp\u003eA GSEA analysis was performed to determine the functional distinctions between the two clusters. In the enrichment of MSigDB Collection (c5.cp.v7.0.symbols.gmt), several significant pathways related to immunity and metabolism were identified, including cell activation involved in immune response, immune effector processes, leukocyte-mediated immunity, myeloid leukocyte-mediated immunity, negative regulation of protein metabolism, and regulation of cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The results of the enrichment are presented in Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of a Risk Model for the Training Cohort Based on LMRG\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the clinical features of the TCGA training cohort (n\u0026thinsp;=\u0026thinsp;359) and the internal validation cohort (n\u0026thinsp;=\u0026thinsp;180). Included were age, body mass index (BMI), type of histology, stage, grade, menopausal status, peritoneal cytology, and survival status. There were no statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the training cohort and the internal validation cohort, suggesting their comparability. Subsequently, an evaluation of the prognostic predictive potential of LMRGs for endometrial cancer was conducted. To estimate the risk score model, the number of genes was reduced using LASSO-Cox regression analysis. In addition, a 10-fold cross-validation was used to determine the optimum model. The model with a lambda of 0.0646860064274793 and nine genes was picked as the final model (Fig.\u0026nbsp;5). The following was the model formula:riskscore\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cp\u003e=\u0026thinsp;0.00942640931904423*ACOT11\u0026thinsp;+\u0026thinsp;0.0504325830028766*CYP1A2\u0026thinsp;+\u0026thinsp;0.0389403528420956*GDPD5\u0026thinsp;+\u0026thinsp;0.0152717405790258*MOGAT3\u0026thinsp;+\u0026thinsp;0.108665321885266*OLAH-0.383128628508224*PIAS4-0.116079225223333*PIP5K1C-0.00125543696645166*PLPP2\u0026thinsp;+\u0026thinsp;0.43678448595434*SRD5A1\u003c/p\u003e \u003cp\u003eHigh expression levels of ACOT11, CYP1A2, GDPD5, MOGAT3, OLAH, and SRD5A1 as prognostic risk variables were related with a poor prognosis, as shown by the above formula. In contrast, high expression of PIAS4, PIP5K1C, and PLPP2 was related with a low risk as a prognostic factor.\u003c/p\u003e \u003cp\u003eAfter calculating the risk score for each patient in the training cohort, the risk score distribution was calculated, as indicated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Risk models accurately separated EC patients into high- and low-risk categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Nine genes differed significantly between the high-risk and low-risk groups. As demonstrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Derive the KM curve from the median risk score. A statistically significant difference in survival probability was discovered between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as demonstrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Consequently, patients with high-risk scores had considerably worse OS, suggesting that the high-risk score was a negative prognostic factor. In addition, the forecasting accuracy of the formula was examined for 1, 3, and 5 years, as seen in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). The model has a significantly large area under the curve (AUC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel of constructed risk independence\u003c/h2\u003e \u003cp\u003eIn addition, we studied the association between the risk score and clinical characteristics and performed subgroup and regression studies to determine the model's independence. Figures\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eC; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicate that the risk score effectively distinguished between the high-risk group and the low risk group based on age, grade, and cluster. In addition, even when patients are categorized according to their age (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eE), stage (8F-8G), or grade (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eH-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003eI), the risk model continues to provide strong predictive accuracy, and patients with a lower risk score have a better prognosis. Moreover, multivariate and univariate Cox regression studies demonstrated that the established risk model was an independent predictor of EC patients' prognosis (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As a result, our risk score successfully predicted prognosis based on a vast array of clinical data. Moreover, our risk model was extremely independent in predicting the prognosis of EC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInternal Validation to Assess the Reliability of the Risk Model\u003c/h2\u003e \u003cp\u003eSubsequently, the constructed prognostic risk score model was further verified in the verification cohort. EC patients in the verification cohort were sorted into high-risk and low-risk subgroups using the above-mentioned method (Fig.\u0026nbsp;7A-B). The expression of nine potential genes was shown using a heatmap (Fig.\u0026nbsp;7C). The study of survival indicated that high-risk individuals had a worse outcome (P\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;7D). In addition, ROC curves were produced to evaluate the accuracy of prognostic prediction for 1, 3, and 5 years in the cohort used for internal validation, as indicated (Fig.\u0026nbsp;7E). Additionally, the AUC of the model in the validation cohort is rather high.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of risk scores and attributes in the training cohort using a univariate approach.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR95%CI (lower)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR95%CI (upper)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003cp\u003eStage\u003c/p\u003e \u003cp\u003eGrade\u003c/p\u003e \u003cp\u003eMenopause status\u003c/p\u003e \u003cp\u003eRisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003cp\u003e3.179\u003c/p\u003e \u003cp\u003e3.753\u003c/p\u003e \u003cp\u003e7.993\u003c/p\u003e \u003cp\u003e0.9469\u003c/p\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9851\u003c/p\u003e \u003cp\u003e1.92\u003c/p\u003e \u003cp\u003e2.26\u003c/p\u003e \u003cp\u003e1.95\u003c/p\u003e \u003cp\u003e0.4481\u003c/p\u003e \u003cp\u003e5.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003cp\u003e5.262\u003c/p\u003e \u003cp\u003e6.23\u003c/p\u003e \u003cp\u003e32.76\u003c/p\u003e \u003cp\u003e2.001\u003c/p\u003e \u003cp\u003e18.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7086\u003c/p\u003e \u003cp\u003e6.889e-06\u003c/p\u003e \u003cp\u003e3.161e-07\u003c/p\u003e \u003cp\u003e0.003875\u003c/p\u003e \u003cp\u003e0.8863\u003c/p\u003e \u003cp\u003e7.997e-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of risk scores and cohort characteristics using a multivariate approach.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR95%CI (lower)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR95%CI (upper)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.095e-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003eHistological type\u003c/p\u003e \u003cp\u003eStage\u003c/p\u003e \u003cp\u003eGrade\u003c/p\u003e \u003cp\u003eMenopause status\u003c/p\u003e \u003cp\u003eRisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e3.31\u003c/p\u003e \u003cp\u003e4.644\u003c/p\u003e \u003cp\u003e0.5441\u003c/p\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003cp\u003e0.5201\u003c/p\u003e \u003cp\u003e1.828\u003c/p\u003e \u003cp\u003e1.083\u003c/p\u003e \u003cp\u003e0.1981\u003c/p\u003e \u003cp\u003e2.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003cp\u003e1.928\u003c/p\u003e \u003cp\u003e5.994\u003c/p\u003e \u003cp\u003e19.92\u003c/p\u003e \u003cp\u003e1.494\u003c/p\u003e \u003cp\u003e11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.158e-02\u003c/p\u003e \u003cp\u003e9.965e-01\u003c/p\u003e \u003cp\u003e7.764e-05\u003c/p\u003e \u003cp\u003e3.873e-02\u003c/p\u003e \u003cp\u003e2.377e-01\u003c/p\u003e \u003cp\u003e3.201e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNomogram building and validation\u003c/h2\u003e \u003cp\u003eFor screening prognostic risk variables, the TCGA training cohort was employed. Risk score, age, tumor grade, tumor FIGO stage, and histological type were identified as OS risk variables by univariate Cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We next conducted a multivariable Cox regression analysis utilizing the aforementioned variables and find that risk score, age, BMI, tumor grade, and tumor stage are independent risk factors for OS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The forest plot was utilized to demonstrate the clinical characteristics and risk score as indicated in (Fig.\u0026nbsp;9A). P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 indicated that the HR value of the risk score was the highest. As seen in (Fig.\u0026nbsp;9B), we then developed a nomogram model with a C-index value of 0.775% (95% CI\u0026thinsp;=\u0026thinsp;0.712\u0026ndash;0.838) that includes all of the clinical variables and risk ratings. The calibration curve demonstrated that the nomogram projected survival rates for each of the 1-, 3-, and 5-year survivals were similar to their actual values (Fig.\u0026nbsp;9C). To further validate the accuracy of the nomogram, ROC curves were utilized to evaluate the nomogram's prediction accuracy for each patient. According to statistical analysis, the 1-, 3-, and 5-year AUCs of the nomogram model were 0.772, 0.787, and 0.825, respectively (Fig.\u0026nbsp;9D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImmune and Stromal Scores and ImmuneCell Infiltration Analysis\u003c/h2\u003e \u003cp\u003eESTIMATE, MCPcounter, CIBERSORT, and ssGSEA were done to better comprehend the variations in immune function. In the ESTIMATE analysis, the low-risk group had higher scores for stromal, immune, and ESTIMATE, and lower tumor purity than the high-risk group (Figs.\u0026nbsp;10A\u0026ndash;B). In addition, the MCP-counter results demonstrated that the tumor immune infiltration of the low-risk group was more prominent than that of the high-risk group, including CD8 T cells, cytotoxic lymphocytes, neutrophils, endothelial cells, and fibroblasts. (Figs.\u0026nbsp;10C) In addition, CIBERSORT analysis revealed that the low-risk group had a higher proportion of CD8 T cells, regulatory T cells, gamma delta T cells, plasma cells, and dendritic cells (Fig.\u0026nbsp;10D). Twelve immune cell subtypes, including activated B cells, active B cells, activated CD8 T cells, CD56dim natural killer cells, eosinophils, MDSC, monocytes, and type 17 T helper cells, were strongly expressed in the Low risk group, as determined by ssGSEA. (Fig.\u0026nbsp;10E). In terms of CD8 T cells, the results suggested that the immunological infiltration of the low-risk group was generally greater than that of the high-risk group.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the previous two decades, the EC mortality rate has doubled. Although the 5-year survival rate for early EC patients is more than 85%, roughly 13 to 25% of them (considered originally to have a favorable prognosis) have recurrence and metastasis \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Consequently, reliable prognostic indications are required to aid clinicians in conducting more precise clinical examinations. Increasingly, database-based bioinformatics techniques are employed to find biological compounds having diagnostic potential. However, earlier research focused primarily on either genetic variables or clinical factors \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, both of which had a significant influence in the carcinogenesis and prognosis of EC. This study presents an LMRG-related risk signature for EC obtained from large cancer datasets, followed by the development of a nomogram for OS prediction based on the risk signature and clinical and pathological data that accurately predicts the outcomes of EC patients. Our findings may assist the development of EC-targeted therapies and enable physicians to make more informed treatment choices.\u003c/p\u003e \u003cp\u003eIt is of the utmost significance to develop effective ways for classifying patients based on their risk scores and to provide suitable tailored and targeted therapies. A bioinformatic study based on the sequencing of RNA data was shown to be a suitable method for risk classification and identification of targeted genes. Previous research has shown that the expression of lipid metabolism genes is associated with the immunological microenvironment of osteosarcoma patients, which may be utilized to correctly predict osteosarcoma prognosis\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In recent years, some molecular markers have changed with tumor progression, and the accuracy of a group of molecular markers in reflecting tumor prognosis has been significantly improved compared with a single marker\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Although previous studies have established risk models based on the tumor immune microenvironment and EC energy metabolism \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Compared to earlier investigations, our research exhibited distinct advantages. Based on their lipid metabolism, we identified two molecular subgroups with markedly different prognosis and immunological state in EC patients using consensus clustering. In addition, based on the clustering data, we investigated biological factors and partially elucidated the underlying mechanism. In addition, the effect of lipid metabolism on outcome and TIME was elucidated.\u003c/p\u003e \u003cp\u003eIn the present investigation, the public gene expression data from the TCGA database were used to classify EC patients into two molecular subgroups based on gene expression data linked to lipid metabolism and prognosis. It was discovered that the prognosis and immunological state of the two subtypes varied significantly. Furthermore, 1494 DEGs were discovered between the two classes. According to GO and KEGG analysis, these DEGs were functionally relevant to cancer and development, and GSEA enrichment analysis indicated numerous critical immune and metabolism-related pathways. To investigate the underlying biological processes, functional comparisons of the two groupings were done. Based on the discovered DEGs, GO analysis and KEGG analysis revealed that dysregulation of immunity and chromosomal segregation may mediate the effect of lipid metabolism on the carcinogenesis and development of EC. However, the precise link between lipid metabolism, abnormal immunity, and chromosomal segregation remains unknown. GSEA analysis was a standard approach for integrating gene expression data that immediately revealed the expression trend of gene sets in distinct groups. To comprehend the functional differences between the two clusters, GSEA was performed. In the enrichment of MSigDB Collection (c5.cp.v7.0.symbols.gmt), we identified numerous significant immunity-related pathways, including cell activation involved in immune response, immune effector process, leukocyte-mediated immunity, negative regulation of protein metabolic process, and regulation of cell death. These findings reveal anomalies in lipid metabolism and a link between inadequate immune control and endometrial cancer prognosis. In recent years, tumor lipid metabolic anomalies have attracted more attention \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Targeting abnormal lipid metabolic pathways is a viable anticancer treatment approach. For instance, anticancer drugs based on hydroxylated lipid are widely employed for clinical tumor therapy \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, a prognostic model based on the selection of nine genes utilizing univariate Cox and LASSO-Cox regression was developed. In both training and validation cohorts, the risk model comprised of ACOT11, CYP1A2, GDPD5, MOGAT3, OLAH, PIAS4, PIP5K2C, PLPP2, and SRD5A1 accurately predicted the prognosis of EC patients. Additionally, the improved risk model differentiates effectively between the clinicopathological features of EC patients. In addition, we constructed a nomogram with an accurate survival prognostic. With the greatest HR value in the nomogram, the risk score is among the most significant OS risk variables.\u003c/p\u003e \u003cp\u003eNoteworthy is the fact that some of these genes have been implicated in past cancer studies, Researchers have demonstrated that patients with lung squamous carcinoma (LUSC) who have high expression of ACOT11 have a significantly poorer prognosis. Knocking down ACOT11 inhibits cell proliferation, migration, and invasion in vitro and in vivo \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Experiments with transgenic knockout mice revealed that ACOT11 decreased thermogenesis after cold exposure by inhibiting endogenous fatty acid oxidation in brown adipose tissue \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In addition to biotransforming many important endogenous and exogenous substances, CYP1A2 is one of the most important cytochrome P450 isoforms. Although CYP1A2 is expressed predominantly in hepatocytes, little is known about its expression in extrahepatic tissues. CYP1A2 knockout mice displayed large increases in blood cholesterol and free testosterone, followed by minor liver damage and fat deposition, as demonstrated by Sun et al \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. GDPD5, which gene is located on chromosome 11q13.5, was discovered as a member of the glycerophosphodiesterase (GDE) family, which is essential for glycerol metabolism, in humans. Serum responsive element (SRE), a nuclear regulator of the MAP kinase cascade and transcription factor, is inhibited by GDPD5 \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. As a positive regulator of oncogenesis, GDPD5 is also related with breast cancer \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. It has been identified that there are three isoforms of MGAT enzyme to date: MGAT1, MGAT2, and MGAT3. Accordingly, the MOGAT gene family consists of three members (MOGAT1, MOGAT2, MOGAT3) in humans and three members (Mogat1, Mogat2, Mogat3) in rodents \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In contrast, MGAT1 is predominantly expressed in the gastrointestinal tract, kidneys, and adipose tissue, while MGAT2 and MGAT3 are highly expressed in the gastrointestinal tract\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Molecular targets like Mogat3 may be relevant in the treatment of obesity and its associated disorders, such as Type 2 Diabetes \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Protein inhibitors of activated STAT, such as PIAS4, are members of the PIAS4 family of proteins. In vertebrates, the PIAS gene families include PIAS1, PIAS2, PIAS3, and PIAS4. \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e Research has shown that the main functions of PIAS4 involve regulating protein SUMOylation and participating in the repair of DNA damage. \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e PIAS4 was overexpressed abnormally in ovarian cancer cells and promoted hypoxia-induced Sirtuin1 transcriptional repression and epithelial-to-mesenchymal transition. \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e Lack of PIP5K1c in adipocytes markedly slowed HFD-induced fat storage, decreased adipose tissue mass, enhanced insulin sensitivity, and decreased ectopic fatty acid accumulation in the liver, indicating that PIP5K1c is a driver of diet-induced obesity and metabolic syndrome \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e SRD5A1 is widespread throughout the body, including in the skin, liver, kidney, immunological, and neural tissues. SRD5A1 plays a critical role in the catabolism of testosterone and DHT, and SRD5A1 deficiency may result in aberrant local or systemic androgen levels, which can eventually cause illness. \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e In a research using rats fed a high-fat diet, the intensity of SRD5A1 expression was positively associated with their body weight. \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e As a consequence, our work demonstrates that the nine discovered genes may serve as promising indicators for EC prognosis. However, neither individual genes nor correlations between these genes can express the predictive activities of these genes.\u003c/p\u003e \u003cp\u003eImmune cells have the ability to stimulate metabolic processes. To supply energy for the operations of immune cells, the immune system generates a variety of metabolites \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In addition, a substantial proportion of immune cells are active in metabolic pathways \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Specifically, our data reveal that the immune, stromal, and total scores are inversely connected with the lipid-metabolism-related risk of EC patients, demonstrating a correlation between an abundance of immune cells at TIME and a favorable outcome in patients at low risk. It has been proven that metastatic foci with the lowest immune cell infiltration constitute the poorest immunological milieu, which is most favourable to immune evasion \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Fan et al \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. found in a prior study that low immunological, stromal, and total scores were related with a poor prognosis in EC. Similar to the data stated previously, the low-risk group exhibited a much lower immune cell infiltration density. High levels of CD8\u0026thinsp;+\u0026thinsp;T-lymphocytes are an independent, favorable predictor of OS in patients with EC \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. In addition, our findings demonstrated that patients with high immune ratings had prolonged OS, indicating that the TIME composition effects the final clinical outcomes of EC patients. However, further study is required to elucidate the processes causing these immunological microenvironments.\u003c/p\u003e \u003cp\u003eThis investigation uncovered two distinct molecular subtypes: C1 and C2. Patients with a poor prognosis in the C1 subtype also had a TIME abnormality with a low immune score and a high tumor purity, and low immunity was connected with limited lipid metabolism. According to the risk model of LMRGs, the prognosis of EC could be predicted with a high degree of accuracy, despite the fact that patients in high-risk groups with poor outcomes had low immune scores and high tumor purity. The outcomes of these studies demonstrated that the landscape of lipid metabolism was connected with TIME and should be considered an essential factor in establishing the treatment strategy for EC patients, as it may be a potential target for individualized treatment.\u003c/p\u003e \u003cp\u003eWe found a prognostic profile comprised of nine gene signatures that predicted survival at 1, 3, and 5 years with reasonably high AUCs in both the training and validation populations. When generalizing our findings, it is important to take into account the limitations of our study. Our results were derived through bioinformatics research and were not empirically confirmed. The data for this study were obtained from public databases and not from our cohort. Further prospective studies are required to validate the predictive utility of LMRGs in EC in light of the poor level of evidence in retrospective investigations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present investigation revealed two molecular subgroups based on consensus clustering of LMRGs in EC. Immunological and functional investigations demonstrated that disruption of lipid metabolism would compromise the immune system, resulting in a dismal prognosis. Our research might give new insight on the creation of new targeted medications. As part of the study, we created a prognostic predictor based on nine lipid metabolism genes and an integrated nomogram that could correctly and effectively assess the chance of OS, serve as a clinical prognostic tool, and guide EC patients' tailored anticancer therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscripts do not involve human participants, human data or human tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the publishing of this paper, the authors state that they have no conflicting interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors are agreed to submit and publish this manuscription.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publishion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors are agreed to submit and publish this manuscription.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: TCGA database (https://portal.gdc.cancer.gov/).\u003c/p\u003e\n\u003cp\u003eMetabolism-related genes were obtained from the Molecular Signature Database (MSigDB) (http://software.broadinstitute.org/gsea/index.jsp)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Guangzhou Science and Technology Projects (201904010363);\u0026nbsp;Guangzhou Health Technology Project (20221A011118);\u0026nbsp;Science and Technology Planning Project of Panyu in Guangzhou (2020-Z04-014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the publishing of this paper, the authors state that they have no conflicting interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in the design and interpretation of the study, data analysis, and review of the manuscript. Huang Chen, Ye Chen,\u0026nbsp;conceived and designed the project and wrote the manuscript. Huang Chen, Xiaoli Liu, and Ling Weng, analyzed and visualized the data. Huang Chen, Yongping Zeng, and Yanying Wang interpreted the data and participated in discussions. Huang Chen, and Lijuan Zhao have revised the final version of the manuscript. All authors have contributed to the manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Guangzhou Science and Technology Projects (201904010363); Guangzhou Health Technology Project (20221A011118); Science and Technology Planning Project of Panyu in Guangzhou (2020-Z04-014).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi X, Yang X, Fan Y, Cheng Y, Dong Y, Zhou J, Wang Z, Li X, Wang J. 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Gynecol Oncol. 2009;114(1):105\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\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":"endometrial cancer (EC), Lipid metabolism-related genes, nomogram, immune infiltration, risk model","lastPublishedDoi":"10.21203/rs.3.rs-3885090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3885090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eEndometrial carcinoma (EC) is one of the most prevalent types of gynecologic cancer. The purpose of this work was to identify the metabolic-related biological characteristics of endometrial cancer and to investigate the immune-related molecular pathways of carcinogenesis in endometrial cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from The Cancer Genome Atlas (TCGA) were utilized to identify lipid metabolism-related genes (LMRGs) with significant correlations to the prognosis of EC patients. Enrichment of functional pathways within the LMRGs was studied. LASSO and Cox regression analysis were conducted to identify LMRGs that were significantly associated with the prognosis of EC patients. We created a prognostic signature and proved its effectiveness in both training and validation groups. In addition, we constructed a complete nomogram consisting of risk models and clinical variables to estimate the survival probability of EC patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eACOT11, CYP1A2, GDPD5, MOGAT3, OLAH, PIASS4, PIP5K1C, PLPP2, and SRD5A1 were discovered to be strongly associated with the clinical outcomes of EC patients. On the basis of these nine LMRGs, we generated and validated our predictive signature using the training and validation cohorts. In addition to being independent of other clinical factors, the nine-LMRG signature distinguished between patients at high- and low-risk for EC and predict EC patient's probability of survival. Statistically, the nomogram exhibited a high correlation between survival forecasts and observations. In the high-risk group, immune/stromal scores were lower and there was a higher density of several kinds of immune cells.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe LMRG's prognostic model and comprehensive nomogram could guide therapeutic choices in clinical practice, and explore the underlying mechanisms involved in EC progression.\u003c/p\u003e","manuscriptTitle":"Expression of Lipid Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Endometrial Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-14 18:52:33","doi":"10.21203/rs.3.rs-3885090/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":"7342ff13-ec56-46ac-b529-9da3d7c52f1d","owner":[],"postedDate":"February 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-22T07:41:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-14 18:52:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3885090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3885090","identity":"rs-3885090","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}