Establishment of the Prognosis Predicting Signature for Endometrial Cancer Patient

Epigenetic Signatures and Prognostic Biomarkers Analysis of Methylation-Driven Genes in Uterine Endometrial Carcinosarcoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecological malignancies, and understanding the molecular mechanisms underlying its development is essential for improving diagnosis and treatment. However, the role of DNA methylation, a key epigenetic modification, in UCEC prognosis prediction and clinical treatment strategies has rarely been studied. This study utilized publicly available datasets from The Cancer Genome Atlas (TCGA) and online bioinformatics tools to analyze the differential methylation and expression of six selected genes: TP53, PTEN, PTX3, TNK1, PPP2R1A, and KLRG2. These genes were chosen based on their known roles in cancer-related pathways, previous associations with oncogenic processes, and preliminary data showing significant changes in methylation and expression in UCEC compared with normal tissues. We integrated mRNA expression and DNA methylation data with the MethylMix method to identify genes with methylation-driven expression changes. Our analysis revealed that these genes exhibit distinct differential expression and methylation patterns in UCEC, suggesting potential regulatory mechanisms. The expression patterns across the six genes were observed, and TP53, TNK1, PPP2R1A, and KLRG2 were upregulated in tumors, and PTX3 was downregulated in tumors. At the same time, there was no significant change in the expression of PTEN gene. The differential expression correlates with changes in methylation, providing insights into the gene regulation occurring in UCEC. Additionally, Kaplan-Meier survival analysis revealed that the expression levels of specific genes, particularly PTX3, TNK1, and KLRG1, are significantly associated with overall survival in UCEC patients. Higher expression of these genes correlated with poorer survival outcomes, suggesting their potential as prognostic markers. In contrast, the expression of TP53, PTEN, and PPP2R1A did not show a significant impact on patient survival. The functional importance of these genes was investigated utilizing pathway enrichment and protein-protein interaction networks. Additionally, pathway enrichment analysis indicated these genes are involved in critical cancer pathways. The findings highlight the importance of integrating epigenetic and transcriptomic data to understand UCEC pathogenesis and suggest that the identified genes could serve as potential biomarkers for early diagnosis and treatment strategies.

Identification of methylation-driven genes prognosis signature and immune microenvironment in uterus corpus endometrial cancer

Background

Uterus corpus endometrial cancer (UCEC) is the main malignant tumor in gynecology, with a high degree of heterogeneity, especially in terms of prognosis and immunotherapy efficacy. DNA methylation is one of the most important epigenetic modifications. Studying DNA methylation can help predict the prognosis of cancer patients and provide help for clinical treatment. Our research aims to discover whether abnormal DNA methylation can predict the prognosis of UCEC and reflect the patient's tumor immune microenvironment.

Patients and methods

The clinical data, DNA methylation data, gene expression data and somatic mutation data of UCEC patients were all downloaded from the TCGA database. The MethylMix algorithm was used to integrate DNA methylation data and mRNA expression data. Univariate Cox regression analysis, Multivariate Cox regression analysis, and Lasso Cox regression analysis were used to determine prognostic DNA methylation-driven genes and to construct an independent prognostic index (MDS). ROC curve analysis and Kaplan–Meier survival curve analysis were used to evaluate the predictive ability of MDS. GSEA analysis was used to explore possible mechanisms that contribute to the heterogeneity of the prognosis of UCEC patients.

Results

3 differential methylation-driven genes (DMDGs) (PARVG, SYNE4 and CDO1) were considered as predictors of poor prognosis in UCEC. An independent prognostic index was finally established based on 3 DMDGs. From the results of ROC curve analysis and survival curve analysis, MDS showed excellent prognostic ability in TCGA-UCEC. A new nomogram based on MDS and other prognostic clinical indicators has also been successfully established. The C-index of the nomogram for OS prediction was 0.764 (95% CI = 0.702–0.826). GSEA analysis suggests that there were differences in immune-related pathways among patients with different prognosis. The abundance of M2 macrophages and M0 macrophages were significantly enhanced in the high-risk group while T cells CD8, Eosinophils and Neutrophils were markedly elevated in the low-risk group. Meanwhile, patients in the low-risk group had higher levels of immunosuppressant expression, higher tumor mutational burden and immunophenoscore (IPS) scores. Joint survival analysis revealed that 7 methylation-driven genes could be independent prognostic factors for overall survival for UCEC.

Conclusion

We have successfully established a risk model based on 3 DMDGs, which could accurately predict the prognosis of patients with UCEC and reflect the tumor immune microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02038-z.

MicroRNA as Epigenetic Modifiers in Endometrial Cancer: A Systematic Review

Simple Summary

Endometrial cancer (EC) is the 2nd most common gynecologic cancer worldwide. MicroRNAs (miRNAs) are small noncoding RNAs that contribute to epigenetic regulation. The objective of this systematic review is to summarize our current knowledge on the role of miRNAs in the epigenetic deregulation of tumor-related genes in EC. It includes all miRNAs reported to be involved in EC including their roles in DNA methylation and RNA-associated silencing. This systematic review should be useful for development of novel strategies to improve diagnosis and risk assessment as well as for new treatments aimed at miRNAs, their target genes or DNA methylation.

Abstract

The objective of this systematic review is to summarize our current knowledge on the influence of miRNAs in the epigenetic deregulation of tumor-related genes in endometrial cancer (EC). We conducted a literature search on the role of miRNAs in the epigenetic regulation of EC applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following terms were used: microRNA, miRNA, miR, endometrial cancer, endometrium, epigenetic, epimutation, hypermethylation, lynch, deacetylase, DICER, novel biomarker, histone, chromatin. The miRNAs were classified and are presented according to their function (tumor suppressor or onco-miRNA), their targets (when known), their expression levels in EC tissue vs the normal surrounding tissue, and the degree of DNA methylation in miRNA loci and CpG sites. Data were collected from 201 articles, including 190 original articles, published between November 1, 2008 and September 30, 2020 identifying 313 different miRNAs implicated in epigenetic regulation of EC. Overall, we identified a total of 148 miRNAs with decreased expression in EC, 140 miRNAs with increased expression in EC, and 22 miRNAs with discordant expression levels. The literature implicated different epigenetic phenomena including altered miRNA expression levels (miR-182, -230), changes in the methylation of miRNA loci (miR-34b, -129-2, -130a/b, -152, -200b, -625) and increased/decreased methylation of target genes (miR-30d,-191). This work provides an overview of all miRNAs reported to be involved in epigenetic regulation in EC including DNA methylation and RNA-associated silencing. These findings may contribute to novel strategies in diagnosis, risk assessment, and treatments aimed at miRNAs, their target genes or DNA methylation.

Low Expression of miR-375 and miR-190b Differentiates Grade 3 Patients with Endometrial Cancer

Endometrial cancer (EC) is treated according to the stage and prognostic risk factors. Most EC patients are in the early stages and they are treated surgically. However some of them, including those with high grade (grade 3) are in the intermediate and high intermediate prognostic risk groups and may require adjuvant therapy. The goal of the study was to find differences between grades based on an miRNA gene expression profile. Tumor samples from 24 patients with grade 1 (n = 10), 2 (n = 7), and 3 (n = 7) EC were subjected to miRNA profiling using next generation sequencing. The results obtained were validated using the miRNA profile of 407 EC tumors from the external Cancer Genome Atlas (TCGA) cohort. We obtained sets of differentially expressed (DE) miRNAs with the largest amount between G2 to G1 (50 transcripts) and G3 to G1 (40 transcripts) patients. Validation of our results with external data (TCGA) gave us a reasonable gene overlap of which we selected two miRNAs (miR-375 and miR190b) that distinguish the high grade best from the low grade EC. Unsupervised clustering showed a high degree of heterogeneity within grade 2 samples. MiR-375 as well as 190b might be useful to create grading verification test for high grade EC. One of the possible mechanisms that is responsible for the high grade is modulation by virus of host morphology or physiology.

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.

The optimal cut-off value of immunohistochemical parameter P53 for predicting recurrence of endometrial cancer

OBJECTIVE

To explore the optimal cut-off value of immunohistochemical parameter P53 for predicting the recurrence of Stage I-III endometrial cancer.

METHODS

A total of 473 patients who were treated between October 2013 and May 2018 were retrospectively studied. Receiver operating characteristic (ROC) curves and the Youden index were used to calculate the optimal cut-off value of P53. Cox regression analysis was used to detect the association between the threshold of P53 and recurrence of endometrial cancer. Recurrence-free survival (RFS) and overall survival (OS) were exhibited by Kaplan-Meier curve.

RESULTS

The study showed that 67% was the optimal cut-off value of P53 to predict the recurrence of endometrial cancer. P53 above 67% was an independent predictor for relapse of endometrial cancer (p < 0.001). The 3-year RFS was 89.7% in the low-value group and 66.6% in the high-value group (p < 0.001), while the 3-year OS was 93.9% and 76.4%, respectively (p < 0.001). Furthermore, the 3-year RFS of patients who did not receive adjuvant chemotherapy or radiotherapy was 95.7% and 78.2% between the two groups (p < 0.001).

CONCLUSION

The optimal cut-off value of immunohistochemical parameter P53 for predicting recurrence was confirmed as 67% and a P53 index above 67% was an independent prognostic factor.