Intra-tumor heterogeneity for endometrial cancer and its clinical significance

Glutamine and amino acid metabolism as a prognostic signature and therapeutic target in endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is the most common female reproductive system cancer in developed countries with growing incidence and associated mortality, which may be due to the growing prevalence of obesity. Metabolism reprogramming including glucose, amino acid, and lipid remodeling is a hallmark of tumors. Glutamine metabolism has been reported to participate in tumor proliferation and development. This study aimed to develop a glutamine metabolism-related prognostic model for EC and explore potential targets for cancer treatment.

METHOD

Transcriptomic data and survival outcome of EC were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed genes related to glutamine metabolism were recognized and utilized to build a prognostic model by univariate and multivariate Cox regressions. The model was confirmed in the training, testing, and the entire cohort. A nomogram combing prognostic model and clinicopathologic features was established and tested. Moreover, we explored the effect of a key metabolic enzyme, PHGDH, on the biological behavior of EC cell lines and xenograft model.

RESULTS

Five glutamine metabolism-related genes, including PHGDH, OTC, ASRGL1, ASNS, and NR1H4, were involved in prognostic model construction. Kaplan-Meier curve suggested that patients recognized as high risk underwent inferior outcomes. The receiver operating characteristic (ROC) curve showed the model was sufficient to predict survival. Enrichment analysis recognized DNA replication and repair dysfunction in high-risk patients whereas immune relevance analysis revealed low immune scores in the high-risk group. Finally, a nomogram integrating the prognostic model and clinical factors was created and verified. Further, knockdown of PHGDH showed cell growth inhibition, increasing apoptosis, and reduced migration. Promisingly, NCT-503, a PHGDH inhibitor, significantly repressed tumor growth in vivo (p = 0.0002).

CONCLUSION

Our work established and validated a glutamine metabolism-related prognostic model that favorably evaluates the prognosis of EC patients. DNA replication and repair may be the crucial point that linked glutamine metabolism, amino acid metabolism, and EC progression. High-risk patients stratified by the model may not be sufficient for immune therapy. PHGDH might be a crucial target that links serine metabolism, glutamine metabolism as well as EC progression.

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.

Excavation of Molecular Subtypes of Endometrial Cancer Based on DNA Methylation

Tumor heterogeneity makes the diagnosis and treatment of endometrial cancer difficult. As an important modulator of gene expression, DNA methylation can affect tumor heterogeneity and, therefore, provide effective information for clinical treatment. In this study, we explored specific prognostic clusters based on 482 examples of endometrial cancer methylation data in the TCGA database. By analyzing 4870 CpG clusters, we distinguished three clusters with different prognostics. Differences in DNA methylation levels are associated with differences in age, grade, clinical pathological staging, and prognosis. Subsequently, we screened out 264 specific hypermethylation and hypomethylation sites and constructed a prognostic model for Bayesian network classification, which corresponded to the classification of the test set to the classification results of the train set. Since the tumor microenvironment plays a key role in determining immunotherapy responses, we conducted relevant analyses based on clusters separated from DNA methylation data to determine the immune function of each cluster. We also predicted their sensitivity to chemotherapy drugs. Specific classifications of DNA methylation may help to address the heterogeneity of previously existing molecular clusters of endometrial cancer, as well as to develop more effective, individualized treatments.

Biomarkers discovery for endometrial cancer: A graph convolutional sample network method

BACKGROUND

Endometrial carcinoma is the sixth most common cancer in women worldwide. Importantly, endometrial cancer is among the few types of cancers with patient mortality that is still increasing, which indicates that the improvement in its diagnosis and treatment is still urgent. Moreover, biomarker discovery is essential for precise classification and prognostic prediction of endometrial cancer.

METHODS

A novel graph convolutional sample network method was used to identify and validate biomarkers for the classification of endometrial cancer. The sample networks were first constructed for each sample, and the gene pairs with high frequencies were identified to construct a subtype-specific network. Putative biomarkers were then screened using the highest degrees in the subtype-specific network. Finally, simplified sample networks are constructed using the biomarkers for the graph convolutional network (GCN) training and prediction.

RESULTS

Putative biomarkers (23) were identified using the novel bioinformatics model. These biomarkers were then rationalised with functional analyses and were found to be correlated to disease survival with network entropy characterisation. These biomarkers will be helpful in future investigations of the molecular mechanisms and therapeutic targets of endometrial cancers.

CONCLUSIONS

A novel bioinformatics model combining sample network construction with GCN modelling is proposed and validated for biomarker discovery in endometrial cancer. The model can be generalized and applied to biomarker discovery in other complex diseases.

A Comprehensive Analysis of Interferon Regulatory Factor Expression: Correlation with Immune Cell Infiltration and Patient Prognosis in Endometrial Carcinoma

As a family of transcription factors, the correlations between expression pattern of nine interferon regulatory factor (IRF) family members, the immune invasion pattern, and the associated patient survival rate in endometrial carcinoma (EC) remain to be elucidated. Based on The Cancer Genome Atlas (TCGA), the expression profiles of the high and low IRF mRNA expression groups were analyzed using R (3.6.3) statistical software. Gene annotation and pathway analyses were performed using Metascape. GSEA was performed using the R package clusterProfiler (3.6.3). The single-sample gene set enrichment analysis (ssGSEA) was used to quantify the relative tumor infiltration levels of immune cell types. Immunohistochemistry data provided by HPA database was used to study the expression of the IRF proteins. Using the GEPIA dataset, the correlation between the expression of IRFs and the tumor stage of EC was analyzed. The correlations between the different IRFs were analyzed using cBioPortal. The expression of IRF2, IRF3, IRF5, IRF6, IRF7, IRF8, and IRF9 was different when comparing EC and normal endometrial samples. IRF2, IRF6, IRF7, and IRF8 were indicated to be potential diagnostic markers for EC. In combination with receiver operating characteristic analysis results, IRF2, IRF6, IRF7, and IRF8 were indicated to be potential diagnostic markers for EC. Levels of individual IRFs were associated with alternate outcomes, with the expression of IRF3 being correlated with the stage of EC and high expression of IRF4 being positively correlated with overall survival (OS); conversely, high expression of IRF5 was negatively correlated with OS. Additionally, high expression levels of both IRF2 and IRF4 were positively correlated with the disease-specific survival rate, and high expression of IRF4 was positively correlated with the progression-free interval. These data suggest a role for IRF2, IRF4, and IRF5 in the prognosis of EC. The expression of IRFs is associated with immune infiltration.

The IFN-γ-related long non-coding RNA signature predicts prognosis and indicates immune microenvironment infiltration in uterine corpus endometrial carcinoma

Background

One of the most common diseases that have a negative impact on women’s health is endometrial carcinoma (EC). Advanced endometrial cancer has a dismal prognosis and lacks solid prognostic indicators. IFN-γ is a key cytokine in the inflammatory response, and it has also been suggested that it has a role in the tumor microenvironment. The significance of IFN-γ-related genes and long non-coding RNAs in endometrial cancer, however, is unknown.

Methods

The Cancer Genome Atlas (TCGA) database was used to download RNA-seq data from endometrial cancer tissues and normal controls. Genes associated with IFN-γ were retrieved from the gene set enrichment analysis (GSEA) website. Co-expression analysis was performed to find lncRNAs linked to IFN-γ gene. The researchers employed weighted co-expression network analysis (WGCNA) to find lncRNAs that were strongly linked to survival. The prognostic signature was created using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The training cohort, validation cohort, and entire cohort of endometrial cancer patients were then split into high-risk and low-risk categories. To investigate variations across different risk groups, we used survival analysis, enrichment analysis, and immune microenvironment analysis. The platform for analysis is R software (version X64 3.6.1).

Results

Based on the transcript expression of IFN-γ-related lncRNAs, two distinct subgroups of EC from TCGA cohort were formed, each with different outcomes. Ten IFN-γ-related lncRNAs were used to build a predictive signature using Cox regression analysis and the LASSO regression, including CFAP58, LINC02014, UNQ6494, AC006369.1, NRAV, BMPR1B-DT, AC068134.2, AP002840.2, GS1-594A7.3, and OLMALINC. The high-risk group had a considerably worse outcome (p < 0.05). In the immunological microenvironment, there were also substantial disparities across different risk categories.

Conclusion

Our findings give a reference for endometrial cancer prognostic type and immunological status assessment, as well as prospective molecular markers for the disease.

Grading of endometrial cancer using 1H HR-MAS NMR-based metabolomics

The tissue metabolomic characteristics associated with endometrial cancer (EC) at different grades were studied using high resolution (400 MHz) magic angle spinning (HR-MAS) proton spectroscopy. The metabolic profiles were obtained from 64 patients (14 with grade 1 (G1), 33 with grade 2 (G2) and 17 with grade 3 (G3) tumors) and compared with the profile acquired from 10 patients with the benign disorders. OPLS-DA revealed increased valine, isoleucine, leucine, hypotaurine, serine, lysine, ethanolamine, choline and decreased creatine, creatinine, glutathione, ascorbate, glutamate, phosphoethanolamine and scyllo-inositol in all EC grades in reference to the non-transformed tissue. The increased levels of taurine was additionally detected in the G1 and G2 tumors in comparison to the control tissue, while the elevated glycine, N-acetyl compound and lactate—in the G1 and G3 tumors. The metabolic features typical for the G1 tumors are the increased dimethyl sulfone, phosphocholine, and decreased glycerophosphocholine and glutamine levels, while the decreased myo-inositol level is characteristic for the G2 and G3 tumors. The elevated 3-hydroxybutyrate, alanine and betaine levels were observed in the G3 tumors. The differences between the grade G1 and G3 malignances were mainly related to the perturbations of phosphoethanolamine and phosphocholine biosynthesis, inositol, betaine, serine and glycine metabolism. The statistical significance of the OPLS-DA modeling was also verified by an univariate analysis. HR-MAS NMR based metabolomics provides an useful insight into the metabolic reprogramming in endometrial cancer.