Dataset Information

Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma.

ABSTRACT:

Background

Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC.

Methods

We analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We constructed a transcription factor regulatory network based on Cistrome databases, and also performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using the Cox regression analysis. We then constructed and verified a prognostic signature. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content.

Results

The immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic signature revealed a ten-gene prognostic signature, comprising of PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC. This signature showed a strong prognostic ability in both the training and testing sets and thus can be used as an independent tool to predict the prognosis of UCEC. In addition, levels of B cells and neutrophils were significantly correlated with the patient's risk score, while the expression of ten genes was associated with immune cell infiltrates.

Conclusions

In summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC.

SUBMITTER: Zhou C

PROVIDER: S-EPMC7650210 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma.

Zhou Cankun C Li Chaomei C Yan Fangli F Zheng Yuhua Y

Cancer cell international 20201109 1

<h4>Background</h4>Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis particularly at an advanced stage. Herein, this study aims to construct prognostic markers of UCEC based on immune-related genes to predict the prognosis of UCEC.<h4>Methods</h4>We analyzed expression data of 575 UCEC patients from The Cancer Genome Atlas database and immune genes from the ImmPort database, which were used for generation and validation of the signature. We ...[more]

PMID: 33292199

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Project description:BackgroundsUterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.MethodsConsensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.ResultsAll UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.ConclusionWe successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

Project description:Background: Matrix metalloproteinases (MMPs) are involved in many processes of tumour progression and invasion. However, few studies have analysed the effects of MMP expression patterns on endometrial cancer (EC) development from the perspective of the tumour microenvironment (TME). we quantified MMP expression in individual by constructing an MMP score and found MMP score effectively predict the prognosis of EC patients. Methods: MMPs expression profiles were determined based on the differential expression of 12 MMP-related regulators. Principal component analysis (PCA) was used to construct an MMP scoring system which can quantify the MMPs expression patterns individually of EC patients. Kaplan-Meier analysis, the log-rank test, and time-dependent receiver operating characteristic (ROC) curve analysis were used to evaluate the value of MMPs expression in predicting prognosis. Single-cell RNA sequencing (scRNA-seq) dataset was used to verify correlation between MMPs and progression of EC. Gene Ontology (GO) analysis was used to investigate the pathways and functions underlying MMPs expression. Tumour immune dysfunction, exclusion prediction, and pharmacotherapy response analyses were performed to assess the potential response to pharmacotherapy based on MMPs patterns. Results: We downloaded the MMPs expression data, somatic mutation data and corresponding clinical information of EC patients from the TCGA website and ICGC portal. Based on the MMP-related differentially expressed genes (DEGs), the MMP score was constructed, and EC patients were divided into high and low MMP score groups. There was a positive correlation between MMP score and prognosis of EC patients. Patients with high MMP scores had better prognosis, more abundant immune cell infiltration and stronger antitumoor immunity. Although prognosis is worse with the lower group than the high, patients with low MMP score had better response to immunotherapy, which means they could prolong the survival time through Immunological checkpoint blockade (ICB) therapy. scRNA-seq analysis identified significant heterogeneity between MMP score and classical pathways in EC. Conclusion: Our work indicates that the MMP score could be a potential tool to evaluate MMP expression patterns, immune cell infiltration, response to pharmacotherapy, clinicopathological features, and survival outcomes in EC. This will provide the more effective guide to select immunotherapeutic strategies of EC in the future.

Project description:Uterine corpus endometrial carcinoma (UCEC) is a common malignant tumor of the female reproductive system with poor prognosis in advanced, recurrent, and metastatic cases. Identification of reliable molecular markers will help in the development of clinical strategies for early detection, diagnosis, and intervention. Gamma-glutamyl hydrolase (GGH) is a key enzyme in folate metabolism pathway. High expression of GGH is associated with severe clinicopathological features and poor prognosis of several cancers. High GGH expression is also related to cell resistance to antifolate drugs such as methotrexate. In this study we focused on the prognostic value of immunohistochemical GGH expression level in UCEC tissue and RNA-seq data from The Cancer Genome Atlas to establish associations with clinical features and outcomes. Further, we conducted comprehensive bioinformatics analyses to identify and functionally annotate differentially expressed genes (DEGs) associated with UCEC upregulation and assessed the effects of upregulation on immune infiltration. Both GGH mRNA and protein expression levels were elevated in tumor tissues, and higher expression was significantly associated with advanced clinicopathological features and poor prognosis by univariate analysis. Further multivariate analysis identified elevated GGH expression as an independent risk factor for poor outcome. Nomograms including GGH expression yielded a c-index for disease-specific survival prediction of 0.884 (95% confidence interval: 0.861-0.907). A total of 520 DEGs (111 upregulated and 409 downregulated) were identified between high and low GGH expression groups. Analysis using Gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway, Gene set enrichment analysis, and protein‒protein interaction indicated significant associations of altered GGH expression with cell proliferation, immune response, and the occurrence and development of UCEC tumors. Finally, GGH expression level was associated with high Th2 cell and low natural killer CD56bright cell infiltration. Collectively, these findings indicate that GGH drives UCEC progression and could be a useful biomarker for survival prediction as well as a therapeutic target.

Project description:BackgroundUterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.MethodsTo identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.ResultsThere were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.ConclusionsBased on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Project description:Uterine corpus endometrial carcinoma (UCEC) is the most common type of gynecologic malignancy worldwide. Despite advances in the treatments of UCEC, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer pathogenesis and progression. Thus, we aimed to identify the landscape of m6A regulators and build a prognostic gene signature in UCEC. In this study, we first analyzed copy number variations (CNVs), single nucleotide variations (SNVs) and gene expression profiles as well as matched clinical information of UCEC patients from The Cancer Genome Atlas (TCGA) database. Next, we determined that CNVs in m6A regulatory genes had a significant negative impact on patient survival. The mRNA expression levels of a total of 16 m6A regulators were significantly correlated with different CNV patterns. Using univariate Cox regression analysis, IGF2BP1, KIAA1429, IGF2BP3, YTHDF3, and IGF2BP2 were found to be closely associated with UCEC patient survival outcomes. Based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression models, we built a 3-gene (IGF2BP3, KIAA1429 and IGF2BP1) signature of m6A regulators with prognostic value in UCEC that could effectively predict patient prognosis (log-rank test p-value < 0.0001). In addition, risk scores were significantly different between patients stratified by tumor stage, SNV, and CNV. Multivariate Cox regression analysis suggested that risk score might be an independent prognostic indicator for the overall survival of patients with UCEC (p-value < 0.05). Gene enrichment analysis indicated that high IGF2BP1 gene expression is associated with cytoplasmic stress granules. KIAA1429 gene expression is associated with cellular nucleic acid metabolism. The expression of the IGF2BP3 gene is associated with RNA binding processes. In conclusion, we determined that genetic alterations in m6A regulatory genes could be effective and reliable biomarkers for UCEC prognosis prediction.

Dataset Information

Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma.

Background

Methods

Results

Conclusions

Publications

Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma.

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