A Five-microRNA Signature as Risk Stratification System in Uterine Corpus Endometrial Carcinoma

LncRNAs in necroptosis: Deciphering their role in cancer pathogenesis and therapy

Necroptosis, a controlled type of cell death that is different from apoptosis, has become a key figure in the aetiology of cancer and offers a possible target for treatment. A growing number of biological activities, including necroptosis, have been linked to long noncoding RNAs (lncRNAs), a varied family of RNA molecules with limited capacity to code for proteins. The complex interactions between LncRNAs and important molecular effectors of necroptosis, including mixed lineage kinase domain-like pseudokinase (MLKL) and receptor-interacting protein kinase 3 (RIPK3), will be investigated. We will explore the many methods that LncRNAs use to affect necroptosis, including protein-protein interactions, transcriptional control, and post-transcriptional modification. Additionally, the deregulation of certain LncRNAs in different forms of cancer will be discussed, highlighting their dual function in influencing necroptotic processes as tumour suppressors and oncogenes. The goal of this study is to thoroughly examine the complex role that LncRNAs play in controlling necroptotic pathways and how that regulation affects the onset and spread of cancer. In the necroptosis for cancer treatment, this review will also provide insight into the possible therapeutic uses of targeting LncRNAs. Techniques utilising LncRNA-based medicines show promise in controlling necroptotic pathways to prevent cancer from spreading and improve the effectiveness of treatment.

The Role of miRNAs in the Development, Proliferation, and Progression of Endometrial Cancer

Endometrial cancer is one of the most common cancers in developing and developed countries. Although the detection of this cancer is high at the early stages, there is still a lack of markers to monitor the disease, its recurrence, and metastasis. MiRNAs are in charge of the post-transcriptional regulation of genes responsible for the most important biological processes, which is why they are increasingly used as biomarkers in many types of cancer. Many studies have demonstrated the influence of miRNAs on the processes related to carcinogenesis. The characteristics of miRNA expression profiles in endometrial cancer will allow their use as diagnostic and prognostic biomarkers. This paper focuses on the discussion of selected miRNAs based on the literature and their role in the development of endometrial cancer.

Identification of m7G Methylation-Related miRNA Signature Associated with Survival and Immune Microenvironment Regulation in Uterine Corpus Endometrial Carcinoma

Background. N7-methylguanosine (m7G) has been implicated in the development of cancer. The role of m7G-related miRNAs in the survival prediction of UCEC patients has not been investigated. Current research was the first to construct an m7G-related miRNA model to accurately predict the survival of patients with uterine corpus endometrial carcinoma (UCEC) and to explore immune cell infiltration and immune activity in the tumor microenvironment. Methods. RNA-seq data and clinical information of UCEC patients were derived from The Cancer Genome Atlas (TCGA) database. Using the TargetScan online database, we predicted miRNAs linked to the m7G-related genes and identified miRNAs which were significantly associated with the survival in UCEC patients and constructed a risk scoring model. The TCGA-UCEC cases were scored according to the risk model, and the high- and low-risk groups were divided by the median risk value. Gene enrichment analysis and immune cell infiltration and immune function analysis were performed using “clusterProfiler” and “GSVA” packages in R. Results. The survival prediction model consisted of 9 miRNAs, namely, hsa-miR-1301, hsa-miR-940, hsa-miR-592, hsa-miR-3170, hsa-miR-876, hsa-miR-215, hsa-miR-934, hsa-miR-3920, and hsa-miR-216b. Survival of UCEC patients in the high-risk group was worse than that in the low-risk group ( $p<0.001$ ). The receiver operating characteristic (ROC) curve showed that the model had good predictive performance, and the area under the curve was 0.800, 0.690, and 0.705 for 1-, 3-, and 5-year survival predictions, respectively. There were differences in the degree of immune cell infiltration and immune activity between the low-risk and high-risk groups. The expression levels of the identified differentially expressed genes correlated with the susceptibility to multiple anticancer drugs. Conclusions. The survival prediction model constructed based on 9 m7G-related miRNAs had good predictive performance.