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

Establishment and verification of a prognostic signature associated with fatty acid metabolism in endometrial cancer

Endometrial carcinoma (EC) is one of the leading causes of mortality in women. Metabolic disorders, such as abnormal fatty acid metabolism (FAM), are considered to be indicators of tumorigenesis. However, to the best of our knowledge, the relationship between EC and FAM remains unclear. The process of FAM is associated with the function of immune cells, thus samples from The Cancer Genome Atlas were grouped according to immune infiltration levels. Subsequently, prognostic gene signatures were constructed based on selected FAM‑associated genes. The signature effect was validated, and enrichment analyses were conducted based on sample classification. Nomograms were used to predict survival, merging clinical data and the gene signature. Samples were divided into high‑ and low‑risk groups based on the gene signature. The survival status, clinical characteristics, enrichment analysis and immune infiltration were significantly different between high‑ and low‑risk groups. According to the nomogram, low microsatellite instability‑high as well as a high tumor mutation burden can be observed in the low‑nomo‑score group. Immune checkpoint inhibitor‑associated genes were differentially expressed between groups and 35 sensitive compounds were identified. Comprehensive bioinformatics analysis in EC revealed potential roles of FAM in tumorigenesis, the tumor microenvironment and prognosis, suggesting that FAM‑associated signatures are promising biomarkers for EC. These findings may improve the understanding of FAM in EC and pave the way for a more accurate assessment of prognosis and immunotherapy outcomes.

Unveiling DNA methylation: early diagnosis, risk assessment, and therapy for endometrial cancer

Endometrial cancer (EC), one of the most common gynecologic malignancies worldwide, poses a significant burden particularly among young women, with poor treatment outcomes and prognosis for advanced and recurrent patients. Epigenetic changes, encompassing DNA methylation, are involved in the occurrence and progression of tumors and hold promise as effective tools for screening, early diagnosis, treatment strategy, efficacy evaluation, and prognosis analysis. This review provides a comprehensive summary of DNA methylation-based early diagnostic biomarkers in EC, with a focus on recent valuable research findings published in the past two years. The discussion is organized according to sample sources, including cervical scraping, vaginal fluid, urine, blood, and tissue. Additionally, we outline the role of DNA methylation in EC risk assessment, such as carcinogenesis risk, feasibility of fertility preservation approaches, and overall prognosis, aiming to provide personalized treatment decisions for patients. Finally, we review researches on DNA methylation in resistance to first-line treatment of EC and the development of new drugs, and envision the future applications of DNA methylation in EC.

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.

Screening of m6A-associated ferroptosis-related genes in atherosclerosis based on WGCNA

Background

N6-methyladenosine (m6A) has been shown to mediate ferroptosis but its role in atherosclerosis (AS) is unclear.

Methods

Differentially expressed m6A-associated ferroptosis-related genes (DE-m6A-Ferr-RGs) were obtained using differential expression analysis and Pearson correlation analysis. Weighted gene co-expression network analysis (WGCNA) was also performed. The intersection of the module genes and the DE-m6A-Ferr-RGs were recorded as candidate m6A-Ferr-related signature genes. Finally, the m6A-Ferr-related signature genes were screened using least absolute shrinkage and selection operator (LASSO) analysis. Expression validation, receiver operating characteristic ( mapping, and immune correlation analysis were also performed based on the m6A-Ferr-related signature genes. The expression of m6A-Ferr-related signature genes was further validated using a real-time polymerase chain reaction (RT-qPCR).

Results

In total, 6,167 differentially expressed genes were intersected with 24 m6A- and 259 ferroptosis-related genes, respectively, resulting in 113 DE-m6A-Ferr-RGs obtained using Pearson's correlation analysis. The module genes obtained from the WGCNA and the 113 DE-m6A-Ferr-RGs were intersected to obtain 48 candidate m6A-Ferr-related signature genes. LASSO analysis was performed and six m6A-Ferr-related signature genes were screened. In addition, the area under the curve values of all six m6A-Ferr-related signature genes were greater than 0.7, indicating that they had potential diagnostic value. Furthermore, the RT-qPCR results revealed that the expression of SLC3A2, NOX4, and CDO1 was consistent with the transcriptome level. Moreover, there was a significant difference in two types of immune cells between the AS and control groups. Naive B cells, CD8+ T cells, regulatory T cells, and activated natural killer cells were positively correlated with CDO1 and NOX4 but negatively correlated with ATG7, CYBB, and SLC3A2.

Conclusion

In total, three m6A-Ferr-related signature genes (NOX4, CDO1, and SLC3A2) were obtained through a series of bioinformatics analyses and an RT-qPCR.

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach

Background

DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.

Method

Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.

Results

A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.

Conclusions

This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

The endometrial cancer detection using non-invasive hypermethylation of CDO1 and CELF4 genes in women with postmenopausal bleeding in Northwest China

Objective

The objective of this study was to verify the clinical predictive performance of methylated cysteine dioxygenase type 1 (CDO1m) and CUGBP Elav-like family member 4 (CELF4m) in endometrial cancer (EC) women with postmenopausal bleeding (PMB).

Material and Methods

A single-center, prospective, and case-control study was conducted in the Gansu Provincial Maternity and Child-care Hospital with 138 female postmenopausal patients enrolled in 2022. All patients underwent body mass index (BMI) detection, transvaginal ultrasonography (TVUS) detection, carbohydrate antigen 125 detection, and the cervical exfoliated cell CDO1/CELF4 gene methylation detection to analyze the sensitivity, specificity, and accuracy of different screening tests statistically with the biopsy and/or dilation and curettage (D&C) pathological diagnosis under hysteroscopy as the gold standard.

Results

There was no significant difference in age between the EC group and the non-EC group, P = 0.492. Using quantitative polymerase chain reaction (qPCR) technology, we validated the CDO1 and CELF4 methylation detection with 87.5% sensitivity and 95.9% specificity as a useful strategy for the triage of women with PMB for the detection of EC. In addition, 100% of type II EC (n = 6) were positively detected by the CDO1 or CELF4 methylation test.

Conclusion

The CDO1 and CELF4 methylation test with high specificity as an auxiliary diagnostic tool or alternative method provides physicians with a reference to distinguish between benign and malignant tumors in women with postmenopausal bleeding, to justify the necessity of using invasive methods to confirm diagnosis.

Hypermethylated CDO1 and CELF4 in cytological specimens as triage strategy biomarkers in endometrial malignant lesions

Objective

Developing a non-invasive and reliable triage test for endometrial malignant lesions is an important goal, as it could help to reduce the number of invasive diagnostic procedures required and improve patient survival. We aimed to estimate the diagnostic value of DNA methylation levels in cervical cytological samples of endometrial cancer (EC) and endometrial atypical hyperplasia (AH).

Methods

A total of 607 women who had indications for endometrial biopsy in the Department of Obstetrics and Gynecology of Cangzhou Central Hospital from October 2022 to April 2023 were enrolled in this study. The cervical exfoliated cells were collected for gene methylation before endometrial biopsy. Clinical information, tumor biomarkers, and endometrial thickness (ET) of transvaginal ultrasonography (TVS) were also collected. With endometrial histopathology as the gold standard, multivariate unconditional logistic regression was applied to analyze the risk factors of endometrial malignant lesions. The role of cysteine dioxygenase type 1 (CDO1) and CUGBP Elav-like family member 4 (CELF4) gene methylation as a triage strategy biomarker in endometrial malignant lesions was specifically explored.

Results

Multivariate logistic regression analysis showed that premenopausal ET ≥ 11 mm or postmenopausal ET ≥ 5 mm, CDO1 ΔCt ≤ 8.4, or CELF4 ΔCt ≤ 8.8 were the risk factors for AH and EC, with odds ratios (ORs) (95%CI) of 5.03 (1.83-13.82) and 6.92 (1.10-43.44), respectively (p-values < 0.05). The sensitivity and specificity of CDO1/CELF4 dual-gene methylation assay for AH and EC reached 84.9% (95%CI: 75.3%-94.5%) and 86.6% (95%CI: 83.8%-89.5%), respectively. ET combined with DNA methylation detection further improved the specificity to (94.9%, 95%CI: 93.1%-96.8%).

Conclusion

The accuracy of cervical cytology DNA methylation is superior to that of other clinical indicators in the non-invasive examination of endometrial malignant lesions. DNA methylation combined with TVS can further improve the specificity and is a promising biomarker triage strategy in women with suspected endometrial lesions.

Mechanism of action of icaritin on uterine corpus endometrial carcinoma based on network pharmacology and experimental evaluation

Background

Uterine corpus endometrial carcinoma (UCEC) belongs to a group of epithelial malignant tumors. Icaritin is the main active compound of Epimedii Folium. Icaritin has been utilized to induce UCEC cells to death.

Methods

We wished to identify potential targets for icaritin in the treatment of UCEC, as well as to provide a groundwork for future studies into its pharmacologic mechanism of action. Network pharmacology was employed to conduct investigations on icaritin. Target proteins were chosen from the components of icaritin for UCEC treatment. A protein-protein interaction (PPI) network was established using overlapping genes. Analyses of enrichment of function and signaling pathways were undertaken using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively, to select "hub genes". Finally, experiments were carried out to ascertain the effect of icaritin on endometrial cancer (HEC-1-A) cells.

Results

We demonstrated that icaritin has bioactive components and putative targets that are therapeutically important. Icaritin treatment induced sustained activation of the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt pathway) and inhibited growth of HEC-1-A cells.

Conclusion

Our data provide a rationale for preclinical and clinical evaluations of icaritin for UCEC therapy.

Screening and validation of potential markers associated with uterine corpus endometrial carcinoma and polycystic ovary syndrome based on bioinformatics methods

Background: Endometrial cancer (UCEC) is a commonly occurring tumor in females, and polycystic ovary syndrome (PCOS) is closely related to UCEC, but the molecular mechanisms remain unclear. This article aims to explore potential molecular mechanisms in UCEC and PCOS, as well as identify prognostic genes for UCEC. Methods: Bioinformatics methods were employed to screen for DEGs in UCEC and PCOS. The shared DEGs were analyzed by constructing a protein-protein interaction (PPI) network using the String database and Cytoscape software. The enrichment analysis was performed using Metascape. The shared DEGs associated with the prognosis of UCEC were identified through univariate and lasso Cox regression methods. A multivariate Cox regression model was constructed and internally validated. The expression and test efficiency of the key prognostic genes were verified using external datasets for UCEC and PCOS. Furthermore, the Gepia database was utilized to analyze the expression of key prognostic genes and their correlation with the disease-free survival (RFS) of UCEC. Tumor mutation burden (TMB), immune infiltration, and the correlation of immune cells were assessed for the prognostic genes of UCEC. Results: There were 151 shared DEGs identified between UCEC and PCOS through bioinformatics screening. These shared DEGs were primarily enriched in leukocyte activation. Following model construction and verification, nine genes were determined to be prognostic for UCEC from the shared DEGs. Among them, TSPYL5, KCNJ15, RTN1, HMOX1, DCAF12L1, VNN2, and ANXA1 were confirmed as prognostic genes in UCEC through external validation. Additionally, RTN1 was identified as a key gene in both UCEC and PCOS. Gepia analysis revealed that higher expression of RTN1 was associated with RFS in UCEC. Immune infiltration analysis of the shared DEGs demonstrated significant differences in the expression of various immune cells between UCEC high and low TMB groups. The seven key prognostic genes in UCEC exhibited regulatory relationships with immune cells. Conclusion: This study identified TSPYL5, KCNJ15, RTN1, HMOX1, DCAF12L1, VNN2, and ANXA1 as the key prognostic DEGs of UCEC. These genes are associated with UCEC survival, TMB, immune cell infiltration, and immune cell regulation. Among them, RTN1 may serve as a potential biomarker for both UCEC and PCOS.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Identification of tumour antigens and immune subtypes in the development of an anti-cancer vaccine for endometrial carcinoma

Therapeutic application of vaccines to endometrial carcinoma (EC) remains uncertain. In this study, we aimed to identify potential tumour antigens for use in the development of an anti-tumour mRNA vaccine and clarify immune subtypes and their characteristics for immunotherapeutic application in heterogeneous EC by integrating multi-omics data. Importantly, four potential tumour antigen candidates-PGR, RBPJ, PARVG and MSX1-were identified and significantly correlated with better overall survival, disease-free survival and distinct antigen-presenting cell infiltration in EC. In addition, two different immune subtypes by consensus clustering analysis of the immune-related genes were identified. Patients with C2 immunophenotypes exhibited superior survival outcomes and 'hot' immunoreactivity and harboured higher microsatellite instability scores and tumoral mutation burden but lower copy-number variation burden. Furthermore, the distinct expression of immunogenic cell death modulators and differential microenvironmental characteristics of immune-cell infiltration were also revealed between C1 and C2 immune-subtype tumours. Enrichment analysis of the co-expression of immune-related genes showed enrichment in immune response, immune cell-mediated immunity and antigen processing pathways. These results indicated that these identified tumour antigens can be used for developing antitumour mRNA vaccines, and tumours with C2 immunophenotypic characteristics demonstrated sensitivity and susceptibility to immunotherapy in EC.

High MutS homolog 2 expression predicts poor prognosis and is related to immune infiltration in endometrial carcinoma

Several studies have shown that MutS homolog 2 (MSH2) is highly expressed in many cancer tissues. Transcriptome expression data were collected from the Cancer Genome Atlas (TCGA) database. We analyzed the expression of MSH2 in normal and tumor tissues, the relationship between MSH2 expression and various prognostic factors, and the relationship between MSH2 expression and overall survival, disease specific survival, and progression free interval. We also examined MSH2 promoter methylation between endometrial cancer and normal endometrial tissues, and identified the prognostic value of MSH2 methylation in endometrial cancer. MSH2 was highly expressed in endometrial cancer tumor tissues compared with normal tissues. High MSH2 expression might be an independent prognostic factor for OS, DSS, and PFI. Further, high MSH2 expression was correlated with age and histological type, but not with BMI, clinical stage, tumor invasion, or other clinical features. MSH2 promoter methylation in endometrial cancer was significantly lower than in normal tissues. Additionally, MSH2 levels, OS, DSS, and PFI were associated with BMI, age, tumor invasion, and histological type. ssGSEA showed that MSH2 expression was positively correlated with the infiltration of Th2 cells, Tcm cells, T helper cells, and Tgd cells, whereas it was negatively correlated with NK CD56 bright cells, pDC cells, iDC cells, cytotoxic cells, and neutrophils. Increased MSH2 expression and reduced MSH2 methylation in endometrial cancer predicts poor prognosis. MSH2 may be used as a biomarker for the diagnosis and prognosis of endometrial cancer and as an immunotherapy target.

A novel prognostic model based on six methylation-driven genes predicts overall survival for patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.

A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients

BACKGROUND

Abnormal DNA methylation is one of the most general epigenetic modifications in hepatocellular carcinoma (HCC). Recent research showed that DNA methylation was a prognostic indicator of all-cause HCC and nonviral HCC. However, whether DNA methylation-driver genes could be used for predicting survival, the probability of hepatitis-positive HCC remains unclear.

METHODS

In this study, DNA methylation-driver genes (MDGs) were screened by a joint analysis of methylome and transcriptome data of 142 hepatitis-positive HCC patients. Subsequently, a prognostic risk score and nomogram were constructed. Finally, correlation analyses between the risk score and signaling pathways and immunity were conducted by GSVA and CIBERSORT.

RESULTS

Through random forest screening and Cox progression analysis, 10 prognostic methylation-driver genes (AC008271.1, C11orf53, CASP8, F2RL2, GBP5, LUCAT1, RP11-114B7.6, RP11-149I23.3, RP11-383 J24.1, and SLC35G2) were screened out. As a result, a prognostic risk score signature was constructed. The independent value of the risk score for prognosis prediction were addressed in the TCGA-HCC and the China-HCC cohorts. Next, clinicopathological features were analyzed and HBV status and histological grade were screened to construct a nomogram together with the risk score. The prognostic efficiency of the nomogram was validated by the calibration curves and the concordance index (C index: 0.829, 95% confidence interval: 0.794-0.864), while its clinical application ability was confirmed by decision curve analysis (DCA). At last, the relationship between the risk score and signaling pathways, as well as the correlations between immune cells were elucidated preliminary.

CONCLUSIONS

Taken together, our study explored a novel DNA methylation-driver gene risk score signature and an efficient nomogram for long-term survival prediction of hepatitis-positive HCC patients.

Immune Infiltrates of m5C RNA Methylation-Related LncRNAs in Uterine Corpus Endometrial Carcinoma

Aberrant 5-methylcytidine (m5C) modification plays an essential role in the progression of different cancers. More and more researchers are focusing on developing a lncRNA-based risk model to assess the clinical prognosis of cancer patients. However, the impact of m5C-related lncRNAs on the prognosis of patients with uterine corpus endometrial carcinoma (UCEC), as well as the immune microenvironment of UCEC, remains unclear. Here, we comprehensively analyzed the predictive value of m5C-associated lncRNAs in UCEC and their association with the tumor immune microenvironment, according to the information extracted from the TCGA-UCEC dataset. We identified a total of 32 m5C-associated lncRNAs that were significantly correlated with the prognosis of UCEC patients. Two molecular subtypes were determined by consensus clustering analysis of these 32 m5C-associated prognostic lncRNAs. Further data showed that cluster 1 was associated with poor clinical prognosis, advanced tumor grade, higher PD-L1 expression levels, higher ESTIMATEScore, and higher immuneScore, as well as the immune cell infiltration. Then, 17 m5C-associated lncRNAs with prognostic values were obtained using LASSO regression analysis. And a risk model was constructed based on these 17 lncRNAs. It was revealed that the risk model could be used as an independent factor for UCEC prognosis. In addition, patients with UCEC in the high-risk group had higher tumor grades and immune scores. The risk model based on m5C-related lncRNAs was also closely associated with infiltrating immune cells. In conclusion, our study elucidated the crucial roles of the identified m5C-related lncRNAs in the UCEC patients' prognoses, as well as in the immune microenvironment in UCEC. The results suggest that the components of risk models based on the m5C-related lncRNAs may serve as important mediators of the immune microenvironment in UCEC.

Establishing a Prognostic Signature Based on Epithelial-Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

Backgrounds

Epithelial-mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).

Methods

EMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.

Results

A total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N ⁶-methyladenosine (m⁶A) RNA expression, microsatellite instability level, and stemness indices.

Conclusion

We constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.