Endometrial cancer (EC) is one of the most common gynecologic malignancies. To identify potential prognostic biomarkers for EC, we analyzed the relationship between the EC tumor microenvironment and gene expression profiles. Using the ESTIMATE R tool, we found that immune and stromal scores correlated with clinical data and the prognosis of EC patients. Based on the immune and stromal scores, 387 intersection differentially expressed genes were identified. Eight immune-related genes were then identified using two machine learning algorithms. Functional enrichment analysis revealed that these genes were mainly associated with T cell activation and response. Kaplan-Meier survival analysis showed that expression of TMEM150B, CACNA2D2, TRPM5, NOL4, CTSW, and SIGLEC1 significantly correlated with overall survival times of EC patients. In addition, using the TIMER algorithm, we found that expression of TMEM150B, SIGLEC1, and CTSW correlated positively with the tumor infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, and dendritic cells. These findings indicate that the composition of the tumor microenvironment affects the clinical outcomes of EC patients, and suggests that it may provide a basis for development of novel prognostic biomarkers and immunotherapies for EC patients.

Ovarian cancer (OvCa) is an intractable gynecological malignancy due to the high recurrence rate. Several molecular biomarkers have been previously screened for early identifying patients with a high recurrence risk and poor prognosis. However, all the known studies focused on a single type of RNAs, not integrating various types. This study was to construct a new multi‐RNA‐based model to predict the recurrence and prognosis for OvCa patients by using the messenger RNA (mRNA, including long noncoding RNA (lncRNA)) and microRNA (miRNA) sequencing data of The Cancer Genome Atlas database. After univariate Cox regression and least absolute shrinkage and selection operator analyses, a multi‐RNA‐based signature (2 miRNAs: hsa‐miR‐508, hsa‐miR‐506; 1 lncRNA: TM4SF1‐AS1; 11 mRNAs: MAGI3, SLAMF7, GLI2, PDK1, ARID3A, PLEKHG4B, TNFAIP8L3, C1QTNF3, NDUFAF1, CH25H, TMEM129) was generated and used to establish a risk score model. The high‐ and low‐risk patients classified by the median risk score exhibited significantly different recurrence risks (89% versus 61%, p < 0.001) and survival time (the area under the receiver operating characteristic curve (AUC) = 0.901 for 5‐year disease‐free survival (DFS)). This risk model was independent of other clinical features and superior to pathologic staging for DFS prediction (AUC, 0.906 versus 0.524; C‐index, 0.633 versus 0.510). Furthermore, some new interaction axes were revealed to explain the possible functions of these RNAs (competing endogenous RNA: TM4SF1‐AS1‐miR‐186‐STEAP2, LINC00536‐miR‐508‐STEAP2, LINC00475‐miR‐506‐TMEM129; coexpression: LINC00598‐PLEKHG4B). In conclusion, this multi‐RNA‐based risk model may be clinically useful to stratify OvCa patients with different recurrence risks and survival outcomes and included RNAs may be potential therapeutic targets.

(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients’ overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment.

Background: Alternative splicing (AS) is one of the critical post-transcriptional regulatory mechanisms of various cancers and also plays a crucial role in the development of cancers, including endometrial cancer (EC). Methods: The splicing data and gene expression profiles of EC were obtained from The Cancer Genome Atlas. The corresponding clinical data were extracted from TCGA-CDR. With univariate Cox regression analysis, least absolute shrinkage and selection operator model, and multivariate Cox regression analysis, the survival-related AS events were selected. Functional enrichment analysis was also performed to investigate the functions of these AS events. Splicing factors and AS regulation network were constructed to understand the correlation among these AS events. Result: A total of 1826 AS events were identified as survival-related events. Functional enrichment analysis showed that these AS events were associated with several immune system-related processes. Then, the prognostic signatures were developed based on these survival-related events and acted as an independent prognostic factor for EC. Splicing factors and AS regulation network were also constructed to understand the regulatory mechanisms of AS events in EC. Conclusion: This study systematically analyzed the role of AS events in EC and developed the prognostic model for EC.

Background Endometrial carcinoma (EC) remain a malignancy with incompletely understood risk factors. To address this knowledge gap, we employed mendelian randomization study to investigate potential protective and risk elements associated with endometrial cancer. Methods We conducted a two-sample Mendelian randomization (MR) study using genetic association data for overall EC and its subtypes from a large-scale genome-wide association study (GWAS). This GWAS encompassed 12,906 EC patients and 108,979 healthy controls. The EC cases were further categorized into 8758 endometrioid and 1230 non-endometrioid subtypes. To serve as instrumental variables, we identified independent genetic variants strongly associated with 5 lifestyle factors and 14 metabolic factors from relevant GWASs. Subsequently, we conducted univariable Mendelian randomization (MR) analyses. Results Our study revealed the relationship among EC with lifetime smoking index (OR: 1.43; 95% CI 1.05–1.96), frequency of alcohol consumption (OR:1.23; 95% CI 1.04–1.45), body mass index (BMI) (OR:1.82; 95% CI 1.64–2.01), type 2 diabetes mellitus (T2DM) (OR:1.06; 95% CI 1.00–1.12), and fasting insulin (OR:1.97; 95% CI 1.30–2.98). Conversely, inverse associations with EC were observed for education level (OR:0.72, 95% CI 0.62–0.83), moderate-level physical exercise (OR 0.35, 95% CI 0.15–0.84), and low-density lipoproteins (LDL) (OR 0.91, 95% CI 0.84–0.99). Conclusions Our findings underscore a causal association between genetically predicted lifetime smoking index, alcohol intake frequency, BMI, T2DM, and fasting insulin with EC risk. Furthermore, our study highlights the potential protective effects of a high education level, moderate-intensity physical exercise, and LDL reduction against EC risk. This MR analysis provided valuable insights into underlying EC risk mechanisms and paved new ways for EC prevention strategies.