Background: Long non-coding RNAs (lncRNAs) play an important role in cellular activities and functions, but our understanding of their involvement in cancer is limited. Methods: TCGA data on RNA expression and DNA methylation were analyzed for lncRNAs’ association with breast cancer survival, using the Cox proportional hazard regression model. Fresh tumor samples and clinical information from 361 breast cancer patients in our study were used to confirm the TCGA finding on ZNF582-AS1. A RT-qPCR method was developed to measure ZNF582-AS1 expression. Survival associations with ZNF582-AS1 were verified with a meta-analysis. In silico predictions of molecular targets and cellular functions of ZNF582-AS1 were performed based on its molecular signatures and nucleotide sequences. Results:ZNF582-AS1 expression was lower in breast tumors than adjacent normal tissues. Low ZNF582-AS1 was associated with high-grade or ER-negative tumors. Patients with high ZNF582-AS1 had a lower risk of relapse and death. These survival associations were confirmed in a meta-analysis and remained significant after adjustment for tumor grade, disease stage, patient age, and hormone receptor status. Correlation analysis indicated the possible suppression of ZNF582-AS1 expression by promoter methylation. Bioinformatics interrogation of molecular signatures suggested that ZNF582-AS1 could suppress tumor cell proliferation via downregulating the HER2-mediated signaling pathway. Analysis of online data also suggested that HIF-1-related transcription factors could suppress ZNF582-AS1 expression, and the lncRNA might bind to hsa-miR-940, a known oncogenic miRNA in breast cancer. Conclusions: ZNF582-AS1 may play a role in suppressing breast cancer progression. Elucidating the lncRNA’s function and regulation may improve our understanding of the disease.

Ferroptosis plays an important role in various cancer processes and is regulated by microRNAs. This study aimed to establish a prognostic model based on ferroptosis-related microRNAs (FermiRNAs) to predict the prognosis of endometrial cancer (EC). Tumor transcriptomes and corresponding clinical data of 544 EC patients were downloaded from the cancer genome atlas database, and the ferroptosis database (FerrDb) was used to identify ferroptosis-related genes (mRNAs). FermiRNAs in EC were selected based on their correlation with ferroptosis-related genes. Univariate and multivariate Cox regression analyses were conducted to construct a prognostic model based on the miRNA signature. EC patients were grouped into high- and low-risk categories based on the risk score of the prognostic model. Kaplan–Meier survival analysis and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the prognostic value of risk scores. A predictive nomogram was then established. Finally, we compared the proportion of infiltrating immune cells and the expression of potential immune checkpoints between the 2 groups to understand the tumor immune microenvironment associated with signature FermiRNAs. A prognostic model based on the 2 FermiRNAs (miR-4635 and miR-3131) was developed. Kaplan–Meier survival analysis indicated that patients with high-risk scores had worse overall survival ( P < .001). ROC curves showed that the area under curve values of the prognostic model were 0.621, 0.712, and 0.696 for 1, 3, and 5 years, respectively, indicating good predictive ability. ROC curves also indicated that the prognostic model had a better capability to predict the prognosis of patients with EC than the other clinical factors. A predictive nomogram suggested that the risk model could offer independent prognostic evaluation with high accuracy. The tumor immune microenvironment, including infiltrating immune cells and immune checkpoints, showed several differences between patients with high- and low-risk scores. In an external validation cohort (213 EC patients from the clinical proteomic tumor analysis consortium dataset), 2 FermiRNAs were confirmed to be associated with EC prognosis in the same manner. A novel ferroptosis-related miRNA prognostic model is useful for predicting the prognosis of patients with EC.

Background: As a newly recognized type of cell death implicated in cancer, ferroptosis plays multiple roles in tumor biology. Here, we sought to construct a prognostic framework for EC on the basis of ferroptosis-related long non-coding RNAs (FerlncRNAs), microRNAs (FermiRNAs), and mRNAs (FRGs) for endometrial cancer (EC). Methods: Transcriptomic profiles of tumors and matched clinical data for 544 EC patients were retrieved from TCGA-UCEC. A prognostic framework was generated through Cox regression, integrating ferroptosis-linked lncRNAs, miRNAs, and mRNAs. EC cases were stratified into groups with high or low predicted risk based on ferroptosis-related gene expression. The model’s prognostic utility was examined through Kaplan–Meier (K–M) analysis and receiver operating characteristic curves. Results: A prognostic model based on 16 RNAs, including 10 FerlncRNAs, 2 FermiRNAs, and 4 FRGs, was developed. Analysis using K–M plots showed that high-risk patients experienced shorter overall survival than their low-risk counterparts (p < 0.001). The model’s area under curve (AUC) values were 0.731, 0.749, and 0.768 at 1-, 3-, and 5-year time points, surpassing those of standard clinical parameters. Furthermore, in an external validation cohort, these signature RNAs were associated with EC prognosis. Conclusions: The novel ferroptosis-related lncRNA–miRNA–mRNA prognostic model provides a basis to assess clinical prognosis in EC patients.

Background: Endometrial cancer (EC) is the most common gynecological cancer. Ferroptosis is a novel type of programmed cell death that is dependent on iron, and mounting evidence suggests that ferroptosis plays an important role in cancer. Long non-coding RNAs (lncRNAs) are known to regulate ferroptosis; however, little is known about the involvement of ferroptosis-related lncRNAs (FerlncRNAs) in EC. This study aimed to determine a FerlncRNA-based prognostic signature associated with the overall survival (OS) and clinicopathological characteristics of patients with EC. Methods: Tumor transcriptomes and corresponding clinical data from patients with EC were downloaded from The Cancer Genome Atlas (TCGA) database, and the ferroptosis database, FerrDb, was used to identify ferroptosis-related genes (FRGs) (mRNAs). FerlncRNAs in EC were selected based on their correlations with FRGs. Univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were conducted to construct a prognostic model based on the FerlncRNAs signature. The EC patients were grouped into high- and low-risk categories based on the prognostic model risk score. Kaplan-Meier (K-M) survival analysis and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the prognostic value of the risk scores. A predictive nomogram was then established. Gene set enrichment analysis (GSEA) was performed to explore the enriched pathways in the two risk groups. Finally, we compared the proportion of infiltrating immune cells and the expression of potential immune checkpoints between the two groups to understand the tumor immunological microenvironment associated with signature FerlncRNAs. Results: We constructed a FerlncRNAs model to predict the prognosis of patients with EC. K-M analysis demonstrated that patients in the high-risk group had a worse OS. According to the ROC curves, our prognostic model had a better ability to predict the prognosis of patients with EC than other clinical factors. Moreover, the predictive nomogram suggested that our model could offer an independent prognostic evaluation with high accuracy. GSEA identified several enriched pathways in both groups. Finally, the immune microenvironment, including the infiltrating immune cells and immune checkpoints, showed several differences between the two groups. Conclusions: This study revealed that a prognostic model based on 10 ferroptosis-related lncRNAs is useful for predicting the prognosis of patients with EC. Our findings provide novel directions for prognostic assessments, immunotherapies, and targeted treatments of EC.