Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of an immune checkpoint gene signature that accurately predicts prognosis and immunotherapy response in endometrial carcinoma.

ABSTRACT: In this study, we performed a bioinformatics analysis to identify immune checkpoint genes (ICGs) associated with prognosis and the immunotherapeutic response in endometrial carcinoma (EC) patients. We classified 47 ICGs into high, medium, and low expression groups by performing RNA-sequencing data analysis of EC patient samples from The Cancer Genome Atlas (n = 521) and GSE77688 (n = 88) datasets. Univariate Cox regression analysis showed that seven ICGs (VTCN1, TNFRSF18, TNFRSF14, TNFRSF4, CD40LG, TMIGD2, and BTLA) were associated with prognosis in EC patients. Spearman correlation analysis showed that prognosis-related ICGs correlated positively with immunotherapy response factors, including tumor mutation burden (TMB), mismatch repair gene mutations, neoantigens, clinical stages, and adaptive immune resistance pathway genes. We identified a prognostic gene signature of four ICGs (IDO1, CD274, CTLA4, and TNFRSF14) that accurately predicted survival outcomes of EC patients. TIMER database and Kaplan-Meier survival analysis showed that OS among EC patients with low TNFRSF14 expression was significantly shorter than among those with high TNFRSF14 expression. In vitro experiments showed that TNFRSF14 silencing increased the migration and invasiveness of EC cells by promoting epithelial-mesenchymal transition (EMT). Collectively, these findings reveal an immune checkpoint gene signature that accurately predicts survival outcomes and immunotherapeutic responses in EC patients.

SUBMITTER: Li S

PROVIDER: S-EPMC8266314 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Identification of a Novel Immune Landscape Signature for Predicting Prognosis and Response of Endometrial Carcinoma to Immunotherapy and Chemotherapy.

Project description:Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecological cancer. Here, we have investigated the significance of immune-related genes in predicting the prognosis and response of UCEC patients to immunotherapy and chemotherapy. Based on the Cancer Genome Atlas (TCGA) database, the single-sample gene-set enrichment analysis (ssGSEA) scores was utilized to obtain enrichment of 29 immune signatures. Univariate, multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to generate an immune-related prognostic signature (IRPS). The biological functions of IRPS-associated genes were evaluated using GSEA, Tumor Immune Estimation Resource (TIMER) Database analysis, Mutation analysis, Immunophenoscore (IPS) analysis, Gene Expression Profiling Interactive Analysis (GEPIA), Genomics of Drug Sensitivity in Cancer (GDSC) and Immune Cell Abundance Identifier (ImmuCellAI). Potential small molecule drugs for UCEC were predicted using the connectivity map (Cmap). The mRNA and protein expression levels of IRPS-associated genes were tested via quantitative real-time PCR (qPCR) and immunohistology. Two immune-related genes (CCL13 and KLRC1) were identified to construct the IRPS. Both genes were related to the prognosis of UCEC patients (P < 0.05). The IRPS could distinguish patients with different prognosis and was closely associated with the infiltration of several types of immune cells. Our findings showed that patients with low IRPS benefited more from immunotherapy and developed stronger response to several chemotherapies, which was also confirmed by the results of ImmuCellAI. Finally, we identified three small molecular drugs that might improve the prognosis of patients with high IRPS. IRPS can be utilized to predict the prognosis of UCEC patients and provide valuable information about their therapeutic response to immunotherapy and chemotherapy.

| S-EPMC8343236 | biostudies-literature

Project description:BACKGROUND:There is no good prognostic model that could predict the prognosis of bladder cancer (BCa) and the benefit of immunotherapy. METHODS:Through the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a 13-mRNA immune signature from the TCGA cohort (n = 406). We validated its prognostic value and predictive value for the benefit of immunotherapy with four independent validation cohort (GSE13507 [n = 256], GSE31684 [n = 93], GSE32894 [n = 308], and IMvigor210 cohort [n = 298]). RESULTS:Our results indicating that high-risk group with higher inhibitory immune cell infiltration (regulatory T cells [Tregs] and macrophage, etc), higher expression of immune checkpoints, and more T cell suppressive pathways (transforming growth factor ? [TGF-?], epithelial-mesenchymal transition [EMT], etc) were activated. Besides, the immune signature showed a good predictive value for the benefit of immunotherapy in a cohort of urothelial carcinoma patients treated with PD-L1. CONCLUSIONS:The immune signature constructed is convenient to classify the immunotherapeutic susceptibility of patients with BCa, so as to achieve precision immunotherapy for BCa.

| S-EPMC7571842 | biostudies-literature

A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients.

Project description:BackgroundThe prognosis of renal cell carcinoma (RCC) varies greatly among different risk groups, and the traditional indicators have limited effect in the identification of risk grade in patients with RCC. The purpose of our study is to explore a glycolysis-based long non-coding RNAs (lncRNAs) signature and verify its potential clinical significance in prognostic prediction of RCC patients.MethodsIn this study, RNA data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regression displayed six significantly related lncRNAs (AC124854.1, AC078778.1, EMX2OS, DLGAP1-AS2, AC084876.1, and AC026401.3) which were utilized in construction of risk score by a formula. The accuracy of risk score was verified by a series of statistical methods such as receiver operating characteristic (ROC) curves, nomogram and Kaplan-Meier curves. Its potential clinical significance was excavated by gene enrichment analysis.ResultsKaplan-Meier curves and ROC curves showed reliability of the risk score to predict the prognosis of RCC patients. Stratification analysis indicated that the risk score was independent predictor compare to other traditional clinical parameters. The clinical nomogram showed highly rigorous with index of 0.73 and precisely predicted 1-, 3-, and 5-year survival time of RCC patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) depicted the top ten correlated pathways in both high-risk group and low-risk group. There are 6 lncRNAs and 25 related mRNAs including 36 lncRNA-mRNA links in lncRNA-mRNA co-expression network.ConclusionThis research demonstrated that glycolysis-based lncRNAs possessed an important value in survival prediction of RCC patients, which would be a potential target for future treatment.

| S-EPMC8047215 | biostudies-literature

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

Project description:BackgroundUterine 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.MethodsWe 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.ResultsThe 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.ConclusionsIn summary, the ten-gene prognostic signature may guide the selection of the immunotherapy for UCEC.

| S-EPMC7650210 | biostudies-literature

The role of an immune signature for prognosis and immunotherapy response in endometrial cancer.

Project description:Immunotherapy is a practical and promising treatment for advanced and recurrent endometrial cancer (EC). In this study, we identified an immune-related gene (IRG) signature to predict the overall survival (OS) and response to immune checkpoints inhibitors (ICIs) in patients with EC. The RNA expression profiles of EC were obtained from The Cancer Genome Atlas database and then were filtered for IRGs based on the Immport database. Using the conjoint Cox regression model, an immune signature consisting of seven risk IRGs (CBLC, PLA2G2A, TNF, NR3C1, APOD, TNFRSF18, and LTB) was developed. The immune signature was independent of other clinical factors and was superior to the traditional staging method for OS prediction in EC. Immunohistochemistry staining from the Human Protein Atlas database and quantitative real-time PCR analysis of EC samples were also performed to validate the expression levels of risk IRGs. By further analyzing the tumor microenvironment in EC, patients in the low-risk subgroup showed a higher immune cell infiltration status, which was associated with a better prognosis. Moreover, the tumor mutational burden and immunophenoscore analysis demonstrated that the low-risk subgroup was more sensitive to ICI-based immunotherapy. These findings might shed light on the development of targeted treatment and novel biomarkers for patients with EC.

| S-EPMC7868845 | biostudies-literature

An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF.

Project description:The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention.

| S-EPMC4726345 | biostudies-literature

Myeloid signature reveals immune contexture and predicts the prognosis of hepatocellular carcinoma.

Project description:BACKGROUNDDespite an increasing appreciation of the roles that myeloid cells play in tumor progression and therapy, challenges remain in interpreting the tumor-associated myeloid response balance and its translational value. We aimed to construct a simple and reliable myeloid signature for hepatocellular carcinoma (HCC).METHODSUsing in situ immunohistochemistry, we assessed the distribution of major myeloid subtypes in both peri- and intratumoral regions of HCC. A 2-feature-based, myeloid-specific prognostic signature, named the myeloid response score (MRS), was constructed using an L1-penalized Cox regression model based on data from a training subset (n = 244), a test subset (n = 244), and an independent internal (n = 341) and 2 external (n = 94; n = 254) cohorts.RESULTSThe MRS and the MRS-based nomograms displayed remarkable discriminatory power, accuracy, and clinical usefulness for predicting recurrence and patient survival, superior to current staging algorithms. Moreover, an increase in MRS was associated with a shift in the myeloid response balance from antitumor to protumor activities, accompanied by enhanced CD8+ T cell exhaustion patterns. Additionally, we provide evidence that the MRS was associated with the efficacy of sorafenib treatment for recurrent HCC.CONCLUSIONWe identified and validated a simple myeloid signature for HCC that showed remarkable prognostic potential and may serve as a basis for the stratification of HCC immune subtypes.FUNDINGThis work was supported by the National Science and Technology Major Project of China, the National Natural Science Foundation of China, the Science and Information Technology of Guangzhou, the Fundamental Research Funds for the Central Universities, the Guangdong Basic and Applied Basic Research Foundation, and the China Postdoctoral Science Foundation.

| S-EPMC7456226 | biostudies-literature

High TGF-β signature predicts immunotherapy resistance in gynecologic cancer patients treated with immune checkpoint inhibition.

Project description:Various immune signatures predictive of resistance to immune checkpoint inhibitors (ICI) have been described in multiple solid cancers, but still under-investigated in gynecological (GYN) cancer. For 49 GYN cancer patients included in our study, without transcriptome signature, immune-related toxicity was the only clinical predictor of ICI treatment response (p = 0.008). The objective clinical response was the only predictor of progression-free survival (ICI-PFS, p = 0.0008) and overall survival (ICI-OS, p = 0.01). Commonly used ICI marker PD-L1 expression negatively correlated with progression-free survival (ICI-PFS) (p = 0.0019). We performed transcriptome and signaling pathway enrichment analyses based on ICI treatment responses and the survival outcome, and further estimated immune cell abundance using 547 gene markers. Our data revealed that TGF-β regulated signaling pathway was noted to play an important role in immunotherapy failure. Using our 6-genes TGF-β score, we observed longer ICI-PFS associated with lower TGF-β score (8.1 vs. 2.8 months, p = 0.046), which was especially more prominent in ovarian cancer (ICI-PFS 16.6 vs. 2.65 months, p = 0.0012). Further, abundant immunosuppressive cells like T-regulatory cells, eosinophils, and M2 macrophages were associated with shorter ICI-OS and correlated positively with CD274 and CTLA4 expressions. This study provides insight on the potential role of TGF-β in mediating immunotherapy resistance and cross-talking to immunosuppressive environment in GYN cancer. The TGF-β score, if validated in a larger cohort, can identify patients who likely to fail ICI and benefit from targeting this pathway to enhance the response to ICI.

| S-EPMC8683510 | biostudies-literature

A Cholesterol Homeostasis-Related Gene Signature Predicts Prognosis of Endometrial Cancer and Correlates With Immune Infiltration.

Project description:Background: Endometrial cancer (EC) is one of the most common gynecological malignancies in women. Cholesterol metabolism has been confirmed to be closely related to tumor proliferation, invasion and metastasis. However, the correlation between cholesterol homeostasis-related genes and prognosis of EC remains unclear. Methods: EC patients from the Cancer Genome Atlas (TCGA) database were randomly divided into training cohort and test cohort. Transcriptome analysis, univariate survival analysis and LASSO Cox regression analysis were adopted to construct a cholesterol homeostasis-related gene signature from the training cohort. Subsequently, Kaplan-Meier (KM) plot, receiver operating characteristic (ROC) curve and principal component analysis (PCA) were utilized to verify the predictive performance of the gene signature in two cohorts. Additionally, enrichment analysis and immune infiltration analysis were performed on differentially expressed genes (DEGs) between two risk groups. Results: Seven cholesterol homeostasis-related genes were selected to establish a gene signature. KM plot, ROC curve and PCA in two cohorts demonstrated that the gene signature was an efficient independent prognostic indicator. The enrichment analysis and immune infiltration analysis indicated that the high-risk group generally had lower immune infiltrating cells and immune function. Conclusion: We constructed and validated a cholesterol homeostasis-related gene signature to predict the prognosis of EC, which correlated to immune infiltration and expected to help the diagnosis and precision treatment of EC.

| S-EPMC8591263 | biostudies-literature

Disulfidptosis-related signature predicts prognosis and characterizes the immune microenvironment in hepatocellular carcinoma.

Project description:BackgroundDisulfidptosis is a type of programmed cell death caused by excessive cysteine-induced disulfide bond denaturation leading to actin collapse. Liver cancer has a poor prognosis and requires more effective intervention strategies. Currently, the prognostic and therapeutic value of disulfidptosis in liver cancer is not clear.MethodsWe investigated the features of 16 disulfidptosis-related genes (DRGs) of HCC patients in the TCGA and classified the patients into two disulfidptosis pattern clusters by consensus clustering analysis. Then, we constructed a prognostic model using LASSO Cox regression. Next, the microenvironment and drug sensitivity were evaluated. Finally, we used qPCR and functional analysis to verify the reliability of hub DRGs.ResultsMost of the DRGs showed significantly higher expression in cancer tissues than in adjacent tissues. Our prognostic model, the DRG score, can well predict the survival of HCC patients. There were significant differences in survival, features of the microenvironment, effects of immunotherapy, and drug sensitivity between the high- and low-DRG score groups. Ultimately, we demonstrated that a few hub DRGs have differential mRNA expression between liver cancer cells and normal cells and that the protective gene LCAT can inhibit liver cancer metastasis in vitro.ConclusionWe established a novel risk model based on DRG scores to predict HCC patient prognosis, drug sensitivity and immunotherapy efficacy, which provides new insight into the relationship between disulfidptosis and HCC and provides valuable assistance for the personalized treatment of HCC.

| S-EPMC10775580 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data