Dataset Information

Integrated analysis of immune-related genes in endometrial carcinoma.

ABSTRACT: Background:Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis. Methods:Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related lncRNAs. The accuracy of model was evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) analysis, and the independent prognostic indicator was identified with Cox analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to detect the accuracy of our results. Results:A network of 29 transcription factors and 17 immune-related genes was constructed. Furthermore, four immune-prognosis-related lncRNAs were screened out. Kaplan-Meier survival analysis and time-dependent ROC analysis revealed a satisfactory predictive potential of the 4-lncRNA model. Consistency was achieved among the results from the training set, testing set and entire cohort. The distributed patterns between the high- and low-risk groups could be distinguished in principal component analysis. Comparisons of the risk score and clinical factors confirmed the four-lncRNA-based signature as an independent prognostic indicator. Last, the reliability of the results was verified by qRT-PCR in 29 cases of endometrial carcinoma and in cells. Conclusions:Overall, our study constructed a network of transcription factors and immune-related genes and explored a four immune-related lncRNA signature that could serve as a novel potential biomarker of EC.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7531161 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrated analysis of immune-related genes in endometrial carcinoma.

Wang Yiru Y Liu Yunduo Y Guan Yue Y Li Hao H Liu Yuan Y Zhang Mengjun M Cui Ping P Kong Dan D Chen Xiuwei X Yin Hang H

Cancer cell international 20201002

<h4>Background</h4>Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis.<h4>Methods</h4>Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least ...[more]

PMID: 33024415

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Project description:Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.

Project description:Background Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC. Methods The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients’ overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells. Results We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature. Conclusions A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies.

Project description:Background Immunotherapy has made great strides in cancer treatment. Endometrial carcinoma (EC) has been 1 of the most common tumors among women. This study aimed to screen immune-related prognosis biomarkers for EC. Methods The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) public database, and differentially expressed genes (DEGs) were obtained through the limma package in R software. An immune-related genes (IRGs) list was collected from the ImmPort database. We constructed a free-scale gene co-expression network via weighted gene co-expression network analysis (WGCNA). Then, the intersection genes of the module genes which significantly related to EC, along with IRGs and DEGs were screened as the candidate genes for further analysis. We identified the hub gene via Venn analysis of the protein-protein interaction (PPI) network genes and the prognostic genes, and verified expression of the hub gene through Human Protein Atlas (HPA) and Gene Expression Omnibus (GEO) databases which provided the GSE17025 dataset. Furthermore, we used the CIBERSORT deconvolution algorithm to explore tumor immune cells infiltration in EC, and investigated correlations between the hub gene and immune cells. Results The differential expression analysis demonstrated that there were 900 up-regulated genes and 1,008 down-regulated genes in TCGA-UCEC (Uterine Corpus Endometrial Carcinoma) cohort. There were 74 candidate intersection genes in blue module genes, IRGs, and DEGs. Finally, angiopoietin 1 (ANGPT1) was identified as the hub gene in EC. Low expression of ANGPT1 was associated with better overall survival (OS) in EC patients. The expression of ANGPT1 was negatively correlated with regulatory T cells (Tregs), but positively correlated with resting memory cluster of differentiation 4 (CD4) T cells, activated dendritic cells (DCs), activated natural killer (NK) cells, and activated memory CD4 T cells (P<0.05, Spearman). A high-infiltrating regulatory T cell would improve the prognosis for EC patients. Conclusions The gene ANGPT1 can increase the infiltration of T cells and improve the prognosis of EC patients.

Dataset Information

Integrated analysis of immune-related genes in endometrial carcinoma.

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Integrated analysis of immune-related genes in endometrial carcinoma.

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