Identification and Validation of an Immune-Associated RNA-Binding Proteins Signature to Predict Clinical Outcomes and Therapeutic Responses in Glioma Patients.
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ABSTRACT:
SUBMITTER: Tian R
PROVIDER: S-EPMC8038676 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe immune infiltration of patients with colon cancer (CC) is closely associated with RNA-binding proteins (RBPs). However, immune-associated RBPs (IARBPs) in CC remain unexplored.MethodsThe data were downloaded from The Cancer Genome Atlas (TCGA) and the patients were divided into four immune subgroups by single sample gene set enrichment analysis (ssGSEA), in which weighted gene correlation network analysis (WGCNA) identified modules of co-expressed genes correlated with immune infiltration. Univariate (UCR) and multivariate Cox regression (MCR) analyses were applied to screen survival-associated IARBPs. Then, a prognostic signature was performed on TCGA dataset. Risk model was constructed based on the TCGA dataset. Based on the median risk score, CC patients were subdivided into low- and high-risk groups. Furthermore, the accuracy and prognostic value of this signature were validated by using Kaplan-Meier (K-M) curve, receiver operating characteristic (ROC). We further validated the findings in Gene Expression Omnibus (GEO) database. Finally, we evaluated the association between gene expression level and drug sensitivity.ResultsBased on the infiltration of immune cells, the TCGA patients were divided into four subgroups. In total, we identified 25 IARBPs, after differential expression and WGCNA analysis. Subsequently, two IARBP signatures (FBXO17 and PPARGC1A) were identified to be significantly associated with the overall survival (OS) of CC patients. K-M survival analysis revealed that the low-risk group correlated with prolonged OS. The prognostic signature was an independent prognostic factor and reflects the immune status of CC patients. Finally, FBXO17 was related with drug sensitivity of bleomycin, gemcitabine, and lenvatinib. PPARGC1A was related to drug sensitivity of dabrafenib, vemurafenib, and trametinib.ConclusionA novel two immune-associated RBPs that was established that may be useful in predicting survival and individualized treatment.
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Project description:BackgroundThe present study aims to identify immune-related RBPs signature to predict prognosis and therapy response in prostate cancer.MethodsDifferentially expressed RBPs were compared and visualized using R packages. Immune-related RBPs were selected by Pearson correlation analysis. The prognostic immune-related RBPs were identified using the Kaplan-Meier method and LASSO regression. A multivariable Cox regression model was used to construct immune-related RBPs signature.ResultsWe constructed a prognostic predictive risk model of prostate cancer containing ten immune-related RBP genes. We found that high-risk prostate cancer patients presented poorer prognosis, higher tumor immune cell infiltration, higher rates of genomic alterations, and were more sensitive to targeted and immunotherapy than the low-risk group.ConclusionsThe immune-related RBPs' signature is an independent prognostic marker that could help screen patients with advanced prostate cancer who are better suited for targeted and immunotherapy.
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Project description:Glioblastoma (GBM) patients exhibit high mortality and recurrence rates despite multimodal therapy. Small nucleolar RNA host genes (SNHGs) are a group of long noncoding RNAs that perform a wide range of biological functions. We aimed to reveal the role of SNHGs in GBM subtypes, cell infiltration into the tumor microenvironment (TME), and stemness characteristics. SNHG interaction patterns were determined based on 25 SNHGs and systematically correlated with GBM subtypes, TME and stemness characteristics. The SNHG interaction score (SNHGscore) model was generated to quantify SNHG interaction patterns. The high SNHGscore group was characterized by a poor prognosis, the mesenchymal (MES) subtype, the infiltration of suppressive immune cells and a differentiated phenotype. Further analysis indicated that high SNHGscore was associated with a weaker response to anti-PD-1/L1 immunotherapy. Tumor cells with high SNHG scores were more sensitive to drugs targeting the EGFR and ERK-MAPK signaling pathways. Finally, we assessed SNHG interaction patterns in multiple cancers to verify their universality. This is a novel and comprehensive study that provides targeted therapeutic strategies based on SNHG interactions. Our work highlights the crosstalk and potential clinical utility of SNHG interactions in cancer therapy.
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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.
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Project description:Objective: Glioma is the most common and fatal primary brain tumor that has a high risk of recurrence in adults. Identification of predictive biomarkers is necessary to optimize therapeutic strategies. This study investigated the predictive efficacy of a previously identified radiosensitivity signature as well as Exportin 1 (XPO1) expression levels. Methods: A total of 1,552 patients diagnosed with glioma were analyzed using the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. The radiosensitive and radioresistant groups were identified based on a radiosensitivity signature. Patients were also stratified into XPO1-high and XPO1-low groups based on XPO1 mRNA expression levels. Overall survival rates were compared across patient groups. Differential gene expression was detected and analyzed through pathway enrichment and Gene Set Enrichment Analysis (GSEA). To predict 1-, 3-, and 5-years survival rates for glioma patients, a nomogram was established combining the radiosensitivity gene signature, XPO1 status, and clinical characteristics. An artificial intelligence clustering system and a survival prediction system of glioma were developed to predict individual risk. Results: This proposed classification based on a radiosensitivity gene signature and XPO1 expression levels provides an independent prognostic factor for glioma. The RR-XPO1-high group shows a poor prognosis and may benefit most from radiotherapy-combined anti-XPO1 treatment. The nomogram based on the radiosensitivity gene signature, XPO1 expression, and clinical characteristics performs more optimally compared to the WHO classification and IDH status in predicting survival rates for glioma patients. The online clustering and prediction systems make it accessible to predict risk and optimize treatment for a special patient. The cell cycle, p53, and focal adhesion pathways are associated with more invasive glioma cases. Conclusion: Combining the radiosensitivity signature and XPO1 expression is a favorable approach to predict outcomes as well as determine optimal therapeutic strategies for glioma patients.
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Project description:Background: The inflammatory response in the tumor immune microenvironment has implications for the progression and prognosis in glioma. However, few inflammatory response-related biomarkers for lower-grade glioma (LGG) prognosis and immune infiltration have been identified. We aimed to construct and identify the prognostic value of an inflammatory response-related signature, immune infiltration, and drug targets for LGG. Methods: The transcriptomic and clinical data of LGG samples and 200 inflammatory response genes were obtained from public databases. The LGG samples were separated into two inflammatory response-related subtypes based on differentially expressed inflammatory response genes between LGG and normal brain tissue. Next, inflammatory response-related genes (IRRGs) were determined through a difference analysis between the aforementioned two subtypes. An inflammatory response-related prognostic model was constructed using IRRGs by using univariate Cox regression and Lasso regression analyses and validated in an external database (CGGA database). ssGSEA and ESTIMATE algorithms were conducted to evaluate immune infiltration. Additionally, we performed integrated analyses to investigate the correlation between the prognostic signature and N 6-methyladenosine mRNA status, stemness index, and drug sensitivity. We finally selected MSR1 from the prognostic signature for further experimental validation. Results: A total of nine IRRGs were identified to construct the prognostic signature for LGG. LGG patients in the high-risk group presented significantly reduced overall survival than those in the low-risk group. An ROC analysis confirmed the predictive power of the prognostic model. Multivariate analyses identified the risk score as an independent predictor for the overall survival. ssGSEA revealed that the immune status was definitely disparate between two risk subgroups, and immune checkpoints such as PD-1, PD-L1, and CTLA4 were significantly expressed higher in the high-risk group. The risk score was strongly correlated with tumor stemness and m6A. The expression levels of the genes in the signature were significantly associated with the sensitivity of tumor cells to anti-tumor drugs. Finally, the knockdown of MSR1 suppressed LGG cell migration, invasion, epithelial-mesenchymal transition, and proliferation. Conclusion: The study constructed a novel signature composed of nine IRRGs to predict the prognosis, potential drug targets, and impact immune infiltration status in LGG, which hold promise for screening prognostic biomarkers and guiding immunotherapy for LGG.
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Project description:BackgroundThis study aimed to construct a risk signature with predictive power based on immune-related RNA-binding proteins (RBPs) for lung adenocarcinoma (LUAD) patients.MethodsThe Cancer Genome Atlas (TCGA) database was used as the data source. Immune genes (IGs) were obtained from the Immunology Database and Analysis Portal (immPort) database. Differentially expressed RBPs and IGs between tumor and normal tissues were screened. For external validation, an independent cohort from the Gene-Expression Omnibus (GEO) database was used. The accuracy of the risk signature prediction was evaluated using Cox regression analysis and the receiver operating characteristic (ROC) curve.ResultsThe risk signature was constructed from four immune-related and prognostic RBPs (OAS3, PCF11, TLR7, and EXO1). The patients were divided into the low- and high-risk groups, with the low-risk group having a higher survival rate than the high-risk group. The risk signature outperformed other clinical parameters, with a multivariable hazard ratio of 1.862 (95% confidence interval: 1.292-2.683). The tumor immune microenvironment, stemness index, immune checkpoint, immune infiltration, and proportion of immune cells were significantly different between the low- and high-risk groups (all P<0.05).ConclusionsThe risk signature of immune-related RBPs can provide the basis for clinical decisions regarding diagnosis, prognosis, and immunotherapy in LUAD patients.
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Project description:BackgroundGlioma is the most common malignant brain tumor in adults, with its tumor-promoting immune microenvironment always being intricate to handle with. Amounts of evidence has accumulated to suggest that alternative splicing (AS) is related to tumor immune microenvironment. However, comprehensive analysis of immune-related AS events and their clinical significance are still lacking in glioma.MethodsAS events and transcriptome data of 653 glioma patients were downloaded online. ssGSEA was performed on transcriptome data of 653 patients to divided them into low, medium and high immune cell infiltration groups. Immune-related AS events were filtrated based on this grouping. Then lasso Cox regression analysis and multivariate Cox regression analysis were done to achieve an immune-related AS events prognostic signature for glioma. Kaplan-Meier analysis, ROC analyses, univariate Cox regression and multivariate Cox regression were performed to reveal the independent prognostic role of this signature. Meanwhile, a nomogram was constructed to achieved better prognostic value for glioma patients. Besides, functional enrichment analyses and correlation analyses with immune cells infiltration were used to validated the immune-related characteristic of this signature.Results36 immune-related AS events were achieved based on the grouping mentioned above. A nine-immune-related alternative splicing event signature was built for glioma patients. This signature showed an independent prognostic value and a nomogram containing gender, age, Karnofsky performance score, grade, IDH status, MGMT promoter status and risk score derived from the signature was constructed with a higher predictive ability for overall survival. Association with the infiltration of immune cell subtypes was validated and functional enrichment analysis found that the signature was mainly enriched in immune-related and pro-tumor functions.ConclusionOur research presented all immune-related AS events in glioma, identified an immune-related prognostic AS events risk model and a nomogram was constructed to predict the prognosis individually and more precisely. Tight connection was verified between this signature and clinical characteristics. Also, immune cells infiltration and immune checkpoints expression level were proved to link to risk scores, which enhanced the understanding of relationship between AS events and glioma immune microenvironment, firstly revealing the potential role of AS in immunotherapy of glioma.
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Project description:BackgroundGlioma is one of the commonest malignant tumors of the brain. However, glioma present with a poor clinical prognosis. Therefore, specific detection markers and therapeutic targets need to be explored as a way to promote the survival rate of BC patients. Therefore, we need to search for quality immune checkpoints to support the efficacy of immunotherapy for glioma.MethodsWe first recognized differentially expressed telomere-related genes (TRGs) and accordingly developed a risk model by univariate and multivariate Cox analysis. The accuracy of the model is then verified. We evaluated the variations in immune function and looked at the expression levels of immune checkpoint genes. Finally, to assess the anti-tumor medications often used in the clinical treatment of glioma, we computed the half inhibitory concentration of pharmaceuticals.ResultsWe finally identified nine TRGs and built a risk model. Through the validation of the model, we found good agreement between the predicted and observed values. Then, we found 633 differentially expressed genes between various risk groups to identify the various molecular pathways between different groups. The enrichment of CD4+ T cells, CD8+ T cells, fibroblasts, endothelial cells, macrophages M0, M1, and M2, mast cells, myeloid dendritic cells, and neutrophils was favorably correlated with the risk score, but the enrichment of B cells and NK cells was negatively correlated with the risk score. The expression of several immune checkpoint-related genes differed significantly across the risk groups. Finally, in order to create individualized treatment plans for diverse individuals, we searched for numerous chemotherapeutic medications for patients in various groups.ConclusionThe findings of this research provide evidence that TRGs may predict a patient's prognosis for glioma, assist in identifying efficient targets for glioma immunotherapy, and provide a foundation for an efficient, customized approach to treating glioma patients.
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Project description:Background Low-grade glioma (LGG) is a common malignant tumor of the central nervous system. The clinical prognosis of different patients varies greatly, so exploring appropriate markers that affect the prognosis and treatment of LGG is important. The purpose of this study was to identify the potential effect of autophagy-related DNA methylation on the prognosis and immune microenvironment in LGG. Methods The methylation profile, transcription data and corresponding clinical information of 451 patients with LGG were obtained from The Cancer Genome Atlas (TCGA). Another methylation data and clinical information of 110 patients with LGG from Chinese Glioma Genome Atlas (CGGA) were used as the validation set. Through univariate and multivariate COX regression analysis, we identified the autophagy-related genes (ARGs) associated with methylation levels and prognosis, and established a risk assessment signature. The receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival curve were used to verify the model’s effectiveness in predicting prognosis. Patients were divided into low- and high-risk groups based on risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to explore the differences in biological functions between the two groups. ESTIMATE and CIBERSORT algorithms were used to explore differences in immune infiltration and immunotherapy sites. Pearson correlation analysis was used to analyze the relative relationship between methylated cg sites and corresponding genes. Results A total of 6 ARGs (ARSB, CFLAR, WIPI2, RB1, ERN1, RAB24) were selected that were associated with methylation levels and prognosis. The area under the curve (AUC) =0.96, and the KM survival curve P<0.0001, which proves that the risk assessment model has a good effect in predicting the prognosis of LGG. GO and KEGG enrichment analysis showed that the model mainly involved major histocompatibility complex (MHC) II receptors, antigen processing and presentation, and immune cell differentiation. In addition, we also found differences in immune infiltration and immune checkpoints between high- and low-risk groups. Conclusions The methylation levels of these 6 ARGs have a strong predictive potential for LGG, and the methylation regulation of ARGs has an important impact on the immune microenvironment of LGGs.
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