Dataset Information

Characterization of an endoplasmic reticulum stress-related signature to evaluate immune features and predict prognosis in glioma.

ABSTRACT: Endoplasmic reticulum (ER) stress has considerable impact on cell growth, proliferation, metastasis, invasion, angiogenesis and chemoradiotherapy resistance in various cancers. However, the effect of ER stress on the outcomes of glioma patients remains unclear. In this study, we established an ER stress risk model based on The Cancer Genome Atlas (TCGA) glioma data set to reflect immune characteristics and predict the prognosis of glioma patients. Survival analysis indicated that there were significant differences in the overall survival (OS) of glioma patients with different ER stress-related risk scores. Moreover, the ER stress-related risk signature, which was markedly associated with the clinicopathological properties of glioma patients, could serve as an independent prognostic indicator. Functional enrichment analysis revealed that the risk model correlated with immune and inflammation responses, as well as biosynthesis and degradation. In addition, the ER stress-related risk model indicated an immunosuppressive microenvironment. In conclusion, we present an ER stress risk model that is an independent prognostic factor and indicates the general immune characteristics in the glioma microenvironment.

SUBMITTER: Zhang Q

PROVIDER: S-EPMC8051731 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Characterization of an endoplasmic reticulum stress-related signature to evaluate immune features and predict prognosis in glioma.

Zhang Qing Q Guan Gefei G Cheng Peng P Cheng Wen W Yang Lianhe L Wu Anhua A

Journal of cellular and molecular medicine 20210221 8

Endoplasmic reticulum (ER) stress has considerable impact on cell growth, proliferation, metastasis, invasion, angiogenesis and chemoradiotherapy resistance in various cancers. However, the effect of ER stress on the outcomes of glioma patients remains unclear. In this study, we established an ER stress risk model based on The Cancer Genome Atlas (TCGA) glioma data set to reflect immune characteristics and predict the prognosis of glioma patients. Survival analysis indicated that there were sign ...[more]

PMID: 33611848

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Project description:Liver cancer is the sixth most frequently diagnosed primary malignancy and ranks as the third leading cause of cancer-related death worldwide in 2020. ER stress also plays a vital role in the pathogenesis of malignancies. In the current study, we aimed to construct an endoplasmic reticulum stress-related genes (ERGs) signature to predict the overall survival (OS) of patients with HCC. Differentially expressed ERGs (DE-ERGs) were analyzed using The Cancer Genome Atlas (TCGA-LIHC cohort) and International Cancer Genome Consortium (ICGC-LIRI-JP cohort) databases. The prognostic gene signature was identified by the univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the model was evaluated by utilizing Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the underlying biological processes and signaling pathways. CIBERPORT and single-sample Gene Set Enrichment Analysis (ssGSEA) were implemented to estimate the immune status between the different risk groups. A total of 113 DE-ERGs were identified between 50 normal samples and 365 HCC samples in the TCGA-LIHC cohort, and 48 DE-ERGs were associated with OS through the univariate Cox regression. A six DE-ERGs (PPARGC1A, SQSTM1, SGK1, PON1, CDK1, and G6PD) signature was constructed and classified patients into high-risk and low-risk groups. The risk score was an independent prognostic indicator for OS (HR > 1, p < 0.001). The function enrichment analysis indicated that cell cycle, RNA degradation, protein localization, and cell division were the main biological processes. The high-risk group had higher immune cell infiltration levels than those of the low-risk group. We predicted the response to targeted therapy in high- and low-risk patients with HCC and found that the high-risk patients were more sensitive to pazopanib. At last, we verified the expression of the six gene patterns in HCC tissues by qRT-PCR and immunohistochemistry. This signature may be a potential tool to provide a choice for prognosis prediction and personal management of patients with HCC.

Project description:BackgroundGlioma is the most common brain tumor in adults and is characterized by a short survival time and high resistance to chemotherapy. It is imperative to determine the prognosis and therapy-related targets for glioma. Endoplasmic reticulum stress (ERS), as an adaptive protective mechanism, indicates the unfolded protein response (UPR) to determine cell survival and affects chemotherapy sensitivity, which is related to the prognosis of glioma.MethodsOur research used the TCGA database as the training group and the CGGA database as the testing group. Lasso regression and Cox analysis were performed to construct an ERS signature-based risk score model in glioma. Three methods (time-dependent receiver operating characteristic analysis and multivariate and univariate Cox regression analysis) were applied to assess the independent prognostic effect of texture parameters. Consensus clustering was used to classify the two clusters. In addition, functional and immune analyses were performed to assess the malignant process and immune microenvironment. Immunotherapy and anticancer drug response prediction were adopted to evaluate immune checkpoint and chemotherapy sensitivity.ResultsThe results revealed that the 7-gene signature strongly predicts glioma prognosis. The two clusters have markedly distinct molecular and prognostic features. The validation group result revealed that the signature has exceptional repeatability and certainty. Functional analysis showed that the ERS-related gene signature was closely associated with the malignant process and prognosis of tumors. Immune analysis indicated that the ERS-related gene signature is strongly related to immune infiltration. Immunotherapy and anticancer drug response prediction indicated that the ERS-related gene signature is positively correlated with immune checkpoint and chemotherapy sensitivity.ConclusionsCollectively, the ERS-related risk model can provide a novel signature to predict glioma prognosis and treatment.

Project description:PurposeCuproptosis is a newly discovered type of cell death. Little is known about the roles that cuproptosis related genes (CRGs) play in colorectal cancer (CRC). The aim of this study is to evaluate the prognostic value of CRGs and their relationship with tumor immune microenvironment.MethodsTCGA-COAD dataset was used as the training cohort. Pearson correlation was employed to identify CRGs and paired tumor-normal samples were used to identify those CRGs with differential expression pattern. A risk score signature was constructed using LASSO regression and multivariate Cox stepwise regression methods. Two GEO datasets were used as validation cohorts for confirming predictive power and clinical significance of this model. Expression patterns of seven CRGs were evaluated in COAD tissues. In vitro experiments were conducted to validate the expression of the CRGs during cuproptosis.ResultsA total of 771 differentially expressed CRGs were identified in the training cohort. A predictive model termed riskScore was constructed consisting of 7 CRGs and two clinical parameters (age and stage). Survival analysis suggested that patients with higher riskScore showed shorter OS than those with lower (P<0.0001). ROC analysis revealed that AUC values of cases in the training cohort for 1-, 2-, and 3-year survival were 0.82, 0.80, 0.86 respectively, indicating its good predictive efficacy. Correlations with clinical features showed that higher riskScore was significantly associated with advanced TNM stages, which were further confirmed in two validation cohorts. Single sample gene set enrichment analysis (ssGSEA) showed that high-risk group presented with an immune-cold phenotype. Consistently, ESTIMATE algorithm analysis showed lower immune scores in riskScore-high group. Expressions of key molecules in riskScore model are strongly associated with TME infiltrating cells and immune checkpoint molecules. Patients with a lower riskScore exhibited a higher complete remission rate in CRCs. Finally, seven CRGs involved in riskScore were significantly altered between cancerous and paracancerous normal tissues. Elesclomol, a potent copper ionophore, significantly altered expressions of seven CRGs in CRCs, indicating their relationship with cuproptosis.ConclusionsThe cuproptosis-related gene signature could serve as a potential prognostic predictor for colorectal cancer patients and may offer novel insights into clinical cancer therapeutics.

Project description:BackgroundStomach adenocarcinoma (STAD) is a significant contributor to cancer-related mortality worldwide. Although previous research has identified endoplasmic reticulum stress (ERS) as a regulator of various tumor-promoting properties of cancer cells, the impact of ERS-related long non-coding RNAs (lncRNAs) on STAD prognosis has not yet been investigated. Therefore, our study aims to develop and validate an ERS-related lncRNA signature that can accurately predict the prognosis of STAD patients.MethodsWe collected RNA expression profiles and clinical data of STAD patients from The Cancer Genome Atlas (TCGA) and identified ERS-related genes from the Molecular Signature Database (MSigDB). Co-expression analysis enabled us to identify ERS-related lncRNAs, and we applied univariate Cox, least absolute shrinkage, and selection operator (LASSO), and multivariate Cox regression analyses to construct a predictive signature comprising of 9 ERS-related lncRNAs. We assessed the prognostic accuracy of our signature using Kaplan-Meier survival analysis, and validated our predictive signature in an independent gene expression omnibus (GEO) cohort. We also performed tumor mutational burden (TMB) and tumor immune microenvironment (TIME) analyses. Enrichment analysis was used to investigate the functions and biological processes of the signature, and we identified two distinct STAD patient subgroups through consensus clustering. Finally, we performed drug sensitivity analysis and immunologic efficacy analysis to explore further insights.ResultsThe 9 ERS related-lncRNAs signature demonstrated satisfactory predictive performance as an independent prognostic marker and was significantly associated with STAD clinicopathological characteristics. Furthermore, patients in the high-risk group displayed a worse STAD prognosis than those in the low-risk group. Notably, gene set enrichment analysis (GSEA) revealed significant enrichment of extracellular matrix pathways in the high-risk group, indicating their involvement in STAD progression. Additionally, the high-risk group exhibited significantly lower TMB expression levels than the low-risk group. Consensus clustering revealed two distinct STAD patient subgroups, with Cluster 1 exhibiting higher immune cell infiltration and more active immune functions. Drug sensitivity analysis suggested that the low-risk group was more responsive to oxaliplatin, epirubicinl, and other drugs.ConclusionOur study highlights the crucial regulatory roles of ERS-related lncRNAs in STAD, with significant clinical implications. The 9-lncRNA signature we have constructed represents a reliable prognostic indicator that has the potential to inform more personalized treatment decisions for STAD patients. These findings shed new light on the pathogenesis of STAD and its underlying molecular mechanisms, offering opportunities for novel therapeutic strategies to be developed for STAD patients.

Dataset Information

Characterization of an endoplasmic reticulum stress-related signature to evaluate immune features and predict prognosis in glioma.

Publications

Characterization of an endoplasmic reticulum stress-related signature to evaluate immune features and predict prognosis in glioma.

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