A Novel Defined Endoplasmic Reticulum Stress-Related lncRNA Signature for Prognosis Prediction and Immune Therapy in Glioma
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ABSTRACT:
SUBMITTER: Zheng Y
PROVIDER: S-EPMC9282894 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
| S-EPMC8051731 | biostudies-literature
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.
| S-EPMC9131635 | biostudies-literature
Project description:BackgroundThe tumor immune microenvironment is closely related to the malignant progression and treatment resistance of glioma. Long non-coding RNA (lncRNA) plays a regulatory role in this process. We investigated the pathological mechanisms within the glioma microenvironment and potential immunotherapy resistance related to lncRNAs.MethodWe downloaded datasets derived from glioma patients and analyzed them by hierarchical clustering. Next, we analyzed the immune microenvironment of glioma, related gene expression, and patient survival. Coexpressed lncRNAs were analyzed to generate a model of lncRNAs and immune-related genes. We analyzed the model using survival and Cox regression. Then, univariate, multivariate, receiver operating characteristic (ROC), and principle component analysis (PCA) methods were used to verify the accuracy of the model. Finally, GSEA was used to evaluate which functions and pathways were associated with the differential genes.ResultsNormal brain tissue maintains a low-medium immune state, and gliomas are clearly divided into three groups (low to high immunity). The stromal, immune, and estimate scores increased along with immunity, while tumor purity decreased. Further, human leukocyte antigen (HLA), programmed cell death-1 (PDL1), T cell immunoglobulin and mucin domain 3 (TIM-3), B7-H3, and cytotoxic T lymphocyte-associated antigen-4 (CTLA4) expression increases concomitantly with immune state, and the patient prognosis worsens. Five immune gene-related lncRNAs (AP001007.1, LBX-AS1, MIR155HG, MAPT-AS1, and LINC00515) were screened to construct risk models. We found that risk scores are related to patient prognosis and clinical characteristics, and are positively correlated with PDL1, TIM-3, and B7-H3 expression. These lncRNAs may regulate the tumor immune microenvironment through cytokine-cytokine receptor interactions, complement, and coagulation cascades, and may promote CD8 + T cell, regulatory T cell, M1 macrophage, and infiltrating neutrophils activity in the high-immunity group. In vitro, the abnormal expression of immune-related lncRNAs and the relationship between risk scores and immune-related indicators (PDL1, CTLA4, CD3, CD8, iNOS) were verified by q-PCR and immunohistochemistry (IHC).ConclusionFor the first time, we constructed immune gene-related lncRNA risk models. The risk score may be a new biomarker for tumor immune subtypes and provide molecular targets for glioma immunotherapy.
| S-EPMC7772413 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Therefore, the identification of a novel prediction signature for predicting the prognosis risk and survival outcomes is urgently demanded.MethodsWe integrated a machine-learning frame by combing the Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify the LUAD-related long non-coding RNA (lncRNA) survival biomarkers. Subsequently, the Spearman correlation test was employed to interrogate the relationships between lncRNA signature and tumor immunity and constructed the competing endogenous RNA (ceRNA) network.ResultsHerein, we identified an eight-lncRNA signature (PR-lncRNA signature, NPSR1-AS1, SATB2-AS1, LINC01090, FGF12-AS2, AC005256.1, MAFA-AS1, BFSP2-AS1, and CPC5-AS1), which contributes to predicting LUAD patient's prognosis risk and survival outcomes. The PR-lncRNA signature has also been confirmed as the robust signature in independent datasets. Further parsing of the LUAD tumor immune infiltration showed the PR-lncRNAs were closely associated with the abundance of multiple immune cells infiltration and the expression of MHC molecules. Furthermore, by constructing the PR-lncRNA-related ceRNA network, we interrogated more potential anti-cancer therapy targets.ConclusionlncRNAs, as emerging cancer biomarkers, play an important role in a variety of cancer processes. Identification of PR-lncRNA signatures allows us to better predict patient's survival outcomes and disease risk. Finally, the PR-lncRNA signatures could help us to develop novel LUAD anti-cancer therapeutic strategies.
| S-EPMC8605161 | biostudies-literature
Project description:Background: Immune checkpoint inhibitors have been successfully used in a variety of tumors, however, the efficacy of immune checkpoint blockade therapy for patients with glioma is limited. In this study, we tried to clarify gene expression signatures related to the prognosis of gliomas and construct a signature to predict the survival of patients with gliomas. Methods: Calcium-related differential expressed genes (DEGs) between gliomas and normal brain tissues were comprehensively analyzed in two independent databases. Univariate, multivariate Cox regression analysis and proportional hazards model were used to identify the prognostic of calcium-related risk score signature. The CIBERSORT algorithm and association analysis were carried out to evaluate the relationship between calcium-related signature and characteristic clinical features, tumor-infiltrating immune cell signatures as well as immune checkpoint molecules in glioma. A nomogram model was developed for predicting the overall survival for patients with gliomas. Results: We found the intersection of 415 DEGs between gliomas and normal brain tissues, and identified that an eighteen calcium-related gene panel was significantly enriched in these DEGs. A calcium-related signature derived risk score was developed to divide patients into high- and low-risk groups. Low levels of calcium-related gene expression in high-risk score cases were accompanied with worse outcomes of patients. Calcium-related risk scores were significantly associated with characteristic clinical features, immune infiltrating signatures of tumor microenvironment, and exhausted T cell markers including programmed cell death 1 (PD-1), lymphocyte activating 3 (LAG3), and T cell membrane protein 3 (TIM-3), which contribute to an adverse therapeutic effect of immunotherapy. Calcium-related signature risk score was considered as an independent prognostic parameter to predict the of overall survival of patients with gliomas in nomogram model. Conclusion: Our study demonstrated that calcium signaling pathway is highly associated with immunosuppression of gliomas and overall survival of patients. Targeting the calcium signaling pathway might be a new strategy to reverse the immunosuppressive microenvironment of gliomas and improve the efficacy of glioma immunotherapy.
| S-EPMC8505737 | biostudies-literature
Project description:Glioma is one of the most common intracranial malignancies that plagues people around the world. Despite current improvements in treatment, the prognosis of glioma is often unsatisfactory. Necroptosis is a form of programmed cell death. As research progresses, the role of necroptosis in tumors has gradually attracted the attention of researchers. And lncRNA is regarded as a critical role in the development of cancer. Therefore, this study is aimed at establishing a prognostic model based on necroptosis-associated lncRNAs to accurately assess the prognosis and immune response of patients with glioma. The RNA sequences of glioma patients and normal brain samples were downloaded from The Cancer Genome Atlas (TCGA) and GTEx databases, respectively. The coexpression analysis was performed to identify the necroptosis-related lncRNAs. Then, we utilized LASSO analysis following univariate Cox analysis to construct a prognostic model. Subsequently, we applied the Kaplan-Meier curve, time-dependent receiver operating characteristics (ROC), and univariate and multivariate Cox regression analyses to assess the effectiveness of this model. And the functional enrichment analyses and immune-related analyses were employed to investigate the potential biological functions. A validation set was obtained from the Chinese Glioma Genome Atlas (CGGA) database. And qRT-PCR was employed to further validate the expression levels of selected necroptosis-associated lncRNAs. Seven necroptosis-related lncRNAs (FAM13A-AS1, JMJD1C-AS1, LBX2-AS1, ZBTB20-AS4, HAR1A, SNHG14, and LINC00900) were determined to construct a prognostic model. The area under the ROC curve (AUC) was 0.871, 0.901, and 0.911 at 1, 2, and 3 years, respectively. The risk score was shown to be an important independent predictor in both univariate and multivariate Cox regression analyses. Through functional enrichment analyses, we found that the differentially expressed genes (DEGs) were mainly enriched in protein binding and signaling-related biological functions and immune-associated pathways. In conclusion, we established and validated a novel necroptosis-related lncRNA signature, which could accurately predict the overall survival of glioma patients and serve as potential therapeutic targets.
| S-EPMC9226973 | biostudies-literature
Project description:BackgroundThe prognosis of lower-grade glioma (LGG) is highly variable, and more accurate predictors are still needed. The aim of our study was to explore the prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in LGG and to develop a novel risk signature for predicting survival with LGG.MethodsWe first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. A least absolute shrinkage and selection operator (LASSO) analysis was then utilized to develop a risk signature for prognostic prediction. Based on the results of multivariate Cox analysis, a prognostic nomogram model for LGG was constructed. Finally, functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), immunity, and m6A correlation analyses were conducted to explore the possible mechanisms by which these ferroptosis-related lncRNAs affect survival with LGG.ResultsA total of 11 ferroptosis-related lncRNAs related to the prognosis of LGG were identified. Based on prognostic lncRNAs, a risk signature consisting of 8 lncRNAs was constructed and demonstrated good predictive performance in both the training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed by combining the risk signature and clinical parameters proved to be more accurate in predicting the prognosis of LGG. In addition, there were differences in the levels of immune cell infiltration, immune-related functions, immune checkpoints, and m6A-related gene expression between the high- and low-risk groups.ConclusionIn summary, our ferroptosis-related lncRNA signature exhibits good performance in predicting the prognosis of LGG. This study may provide useful insight into the treatment of LGG.
| S-EPMC9271629 | biostudies-literature
Project description:Glioma is the most common type of primary malignant tumor in the central nervous system. Tumor recurrence and progression are common in lower-grade glioma (LGG). Immune checkpoint blockade (ICB), as an emerging immunotherapy, is expected to improve the prognosis of patients undergoing conventional treatment, but it currently performs poorly in glioma. We divided patients into genome-stable and -unstable groups according to the somatic mutation count and then found that the expression of CDC20 was positively correlated with genomic instability. We compared the differences in long non-coding RNA (lncRNA) expression and immune infiltration between the two groups. Five lncRNAs and three immune cell types were identified to construct risk models and a nomogram combing clinical features. Through internal and external validation, the models exhibited sufficient ability to predict the prognosis and the possible response to ICB therapy of patients. This study provided a potential predictive approach for the precise application of ICB and support for improving the prognosis of LGG patients.
| S-EPMC8430277 | biostudies-literature
Project description:BackgroundsThe extended duration of endoplasmic reticulum stress (ERS) can impact the progression of hepatocellular carcinoma (HCC) and the efficacy of immunotherapies by interacting with immune cells that have infiltrated the tumor microenvironment (TME).Methods and resultsThe study utilized a training cohort of 364 HCC patients with complete information from The Cancer Genome Atlas Program (TCGA) database, and a validation cohort of 231 HCC patients from the International Cancer Genome Consortium (ICGC) database. The genes related to ERS exhibiting a strong correlation with overall survival (OS) were identified using univariate Cox regression analysis. A 13-gene predictive signature was then produced through the least absolute shrinkage and selection operator (LASSO) regression approach. The data revealed that the ERS-associated gene signature effectively stratified patients into high- or low-risk groups regarding OS in both the training and validation cohorts (P < 0.0001 and P = 0.00029, respectively). Using the multivariate method, it is still an independent prognostic factor in both the training and validation cohorts (P < 0.001 and P = 0.008, respectively). Moreover, several metabolic pathways were identified to be enriched among the 13 genes in the predictive signature. When the ERS-associated gene signature was combined with the tumor-node-metastasis (TNM) stage, the ERS nomogram performed better than either the gene signature or the TNM stage alone (C-index values: 0.731, 0.729, and 0.573, respectively). Further analysis revealed that patients in the high-risk group exhibited increased infiltration of immune cells. Additionally, GP6 was downregulated in HCC tissues among these signature genes (P < 0.05), which was related to poor OS.ConclusionsThe data suggest that this novel ERS-associated gene signature could contribute to personalized cancer management for HCC. Moreover, targeting GP6 inhibition might be a potential method for HCC therapy.
| S-EPMC10748430 | biostudies-literature
Project description:BackgroundThe endoplasmic reticulum (ER) is involved in Ca(2+) signaling and protein processing. Accumulation of unfolded proteins following ER Ca(2+) depletion triggers the ER stress response (ERSR), which facilitates protein folding and removal of damaged proteins and can induce cell death. Unfolded proteins bind to chaperones, such as the glucose-regulated protein (GRP)78 and cause the release of GRP78-repressed proteins executing ERSR.MethodsSeveral glioma cell lines and primary astrocytes were used to analyze ERSR using standard western blots, reverse transcription-PCR, viability assays, and single cell Ca(2+) imaging.ResultsERSR induction with thapsigargin results in a more intense ERSR associated with a larger loss of ER Ca(2+), activation of ER-associated caspases (4/12) and caspase 3, and a higher rate of malignant glioma cell death than in normal glial cells. Malignant glioma cells have higher levels of protein synthesis and expression of the translocon (a component of the ribosomal complex, guiding protein entry in the ER), the activity of which is associated with the loss of ER Ca(2+). Our experiments confirm increased expression of the translocon in malignant glioma cells. In addition, blockade of the ribosome-translocon complex with agents differently affecting translocon Ca(2+) permeability causes opposite effects on ERSR deployment and death of malignant glioma cells.ConclusionsExcessive ER Ca(2+) loss due to translocon activity appears to be responsible for the enhancement of ERSR, leading to the death of glioma cells. The results reveal a characteristic of malignant glioma cells that could be exploited to develop new therapeutic strategies to treat incurable glial malignancies.
| S-EPMC4096176 | biostudies-literature