N-7 methylguanine (m7G) is one of the most common RNA base modifications in post-transcriptional regulation, which participates in multiple processes such as transcription, mRNA splicing and translation during the mRNA life cycle. However, its expression and prognostic value in uterine corpus endometrial carcinoma (UCEC) have not been systematically studied. In this paper, the data such as gene expression profiles, clinical data of UCEC patients, somatic mutations and copy number variants (CNVs) are obtained from the cancer genome atlas (TCGA) and UCSC Xena. By analyzing the expression differences of m7G-related mRNA in UCEC and plotting the correlation network maps, a risk score model composed of four m7G-related mRNAs (NSUN2, NUDT3, LARP1 and NCBP3) is constructed using least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression in order to identify prognosis and immune response. The correlation of clinical prognosis is analyzed between the m7G-related mRNA and UCEC via Kaplan–Meier method, receiver operating characteristic (ROC) curve, principal component analysis (PCA), t-SNE, decision curve analysis (DCA) curve and nomogram etc. It is concluded that the high risk is significantly correlated with (P < 0.001) the poorer overall survival (OS) in patients with UCEC. It is one of the independent risk factors affecting the OS. Differentially expressed genes are identified by R software in the high and low risk groups. The functional analysis and pathway enrichment analysis have been performed. Single sample gene set enrichment analysis (ssGSEA), immune checkpoints, m6A-related genes, tumor mutation burden (TMB), stem cell correlation, tumor immune dysfunction and rejection (TIDE) scores and drug sensitivity are also used to study the risk model. In addition, we have obtained 3 genotypes based on consensus clustering, which are significantly related to (P < 0.001) the OS and progression-free survival (PFS). The deconvolution algorithm (CIBERSORT) is applied to calculate the proportion of 22 tumor infiltrating immune cells (TIC) in UCEC patients and the estimation algorithm (ESTIMATE) is applied to work out the number of immune and matrix components. In summary, m7G-related mRNA may become a potential biomarker for UCEC prognosis, which may promote UCEC occurrence and development by regulating cell cycles and immune cell infiltration. It is expected to become a potential therapeutic target of UECE.

Necroptosis, a programmed form of necrotic cell death, plays critical regulatory roles in the progression and metastatic spread of cancers such as cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). However, there are few articles systematically analyzing the necroptosis-related long non-coding RNAs (NRlncRNAs) correlated with CESC patients. Both RNA-sequencing and clinical data of CESC patients are downloaded from TCGA database in this study. Pearson correlation analysis, least absolute shrinkage, operator algorithm selection and Cox regression model are employed to screen and create a risk score model of eleven-NRlncRNAs (MIR100HG, LINC00996, SNHG30, LINC02688, HCG15, TUBA3FP, MIAT, DBH-AS1, ERICH6-AS1SCAT1, LINC01702) prognostic. Thereafter, a series of tests are carried out in sequence to evaluate the model for independent prognostic value. Gene set enrichment analytic paper, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analytic paper make it clear that immune-related signaling pathways are very rich in the high-risk subgroup. Additionally, the prognostic risk score model is correlated to immune cell infiltration, potential immune checkpoint, immune function, immune micro-environmental and m6A-related gene. Mutation frequency in mutated genes and survival probability trend are higher in the low-risk subgroup in most of test cases when compared to the high-risk subgroup. This study constructs a renewed prognostic model of eleven-NRlncRNAs, which may make some contribution to accurately predicting the prognosis and the immune response from CESC patients, and improve the recognition of CESC patients and optimize customized treatment regimens to some extent.

Necroptosis is a kind of programmed necrosis mode that plays a double-edged role in tumor progression. However, the role of necroptosis-related Messenger RNA (mRNA) in predicting the prognosis and immune response of cervical squamous carcinoma and adenocarcinoma (CESC) has not been fully studied. Firstly, the incidence of somatic mutation rate and copy number variation for 74 necroptosis-related mRNAs (NRmRNAs) were analyzed. Secondly, CESC patients were divided into four stable clusters based on the consensus clustering results and analyzed for correlations with a series of clinical factors. Subsequently, a total of 291 The Cancer Genome Atlas samples were randomly divided into either training or validation cohorts. A Cox proportional hazard model consisting of three NRmRNAs (CXCL8, CLEC9A, and TAB2) was constructed by univariate, least absolute shrinkage and selection operator and multivariate COX regression analysis to identify the prognosis and immune response. Its performance and stability were further validated in another testing dataset (GSE44001) from Gene Expression Omnibus database. The results of the receiver operating characteristic curve, principal component analysis, t-SNE, and nomogram indicated that the prognostic model we constructed can serve as an independent prognostic factor. The combination of the prognostic model and the classic TNM staging system could improve the performance in predicting the survival of CESC patients. In addition, differentially expressed genes from high and low-risk patients are screened by R software for functional analysis and pathway enrichment analysis. Besides, single-sample gene set enrichment analysis revealed that tumor-killing immune cells were reduced in the high-risk group. Moreover, patients in the low-risk group are more likely to benefit from immune checkpoint inhibitors. The analysis of tumor immune dysfunction and exclusion scores, M6A-related genes, stem cell correlation and Tumor mutational burden data with clinical information has quantified the expression levels of NRmRNAs between the two risk subgroups. According to tumor immune microenvironment scores, Spearman’s correlation analysis, and drug sensitivity, immunotherapy may have a higher response rate and better efficacy in patients of the low-risk subgroup. In conclusion, we have reported the clinical significance of NRmRNAs for the prognosis and immune response in CESC patients for the first time. Screening of accurate and effective prognostic markers is important for designing a multi-combined targeted therapeutic strategy and the development of individualized precision medicine.

Endometrial carcinoma is one of the two cancers with rising mortality and morbidity in recent years. In the light of many controversies about its treatment, it is urgent to construct a new prognostic model and to find out new therapeutic directions. As a small drug molecule widely used in clinical treatment and experimental research in China, puerarin has recently been proven to have obvious anti-cancer effects in multiple cancer cells. In this study, bioinformatics analysis and experimental validation were used to explore the potential mechanism of puerarin for endometrial carcinoma and construct a prognostic model. A total of 22 drug-related differential genes were found by constructing a database of drug targets and disease genes. The protein–protein interaction network was constructed for GO and KEGG enrichment analysis to initially explore the potential mechanism of its therapeutic effects. To construct the prognostic model, validation was performed by risk regression analysis and LASSO analysis. Finally, two prognostic genes—PIM1 and BIRC5 were determined to establish high and low risk groups. Kaplan–Meier analysis displayed a higher survival rate in the low-risk group than in the high-risk group. ROC curves indicated the stable and good effect in prediction (one-year AUC is 0.626; two-year AUC is 0.620; three-year AUC is 0.623). The interrelationship between immunity and its disease was explored by immune infiltration analysis. Finally, the potential effect of puerarin on endometrial carcinoma cells was further verified by experiments.

Necroptosis is one of the common modes of apoptosis, and it has an intrinsic association with cancer prognosis. However, the role of the necroptosis-related long non-coding RNA LncRNA (NRLncRNAs) in uterine corpora endometrial cancer (UCEC) has not yet been fully elucidated at present. Therefore, the present study is designed to investigate the potential prognostic value of necroptosis-related LncRNAs in UCEC. In the present study, the expression profiles and clinical data of UCEC patients were downloaded from TCGA database to identify the differentially expressed NRLncRNAs associated with overall survival. A LncRNA risk model was constructed via Cox regression analysis, and its prognostic value was evaluated. We have also further evaluated the relationships between the LncRNA features and the related cellular function, related pathways, immune status, and immune checkpoints m6A-related genes. Seven signatures, including PCAT19, CDKN2B-AS1, LINC01936, LINC02178, BMPR1B-DT, LINC00237, and TRPM2-AS, were established to assess the overall survival (OS) of the UCEC in the present study. Survival analysis and ROC curves indicated that the correlated signature has good predictable performance. The normogram could accurately predict the overall survival of the patients with an excellent clinical practical value. Enrichment analysis of gene sets indicated that risk signals were enriched in several immune-related pathways. In addition, the risk characteristics were significantly correlated with immune cells, immune function, immune cell infiltration, immune checkpoints, and some m6A-related genes. This study has identified seven necroptosis-related LncRNA signatures for the first time, providing a valuable basis for a more accurate prognostic prediction of UCEC.