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Integrated profiling identifies caveolae-associated protein 1 as a prognostic biomarker of malignancy in glioblastoma patients.

ABSTRACT: AIMS:Glioblastoma (GBM) is a lethal disease of the central nervous system with high mortality, and novel therapeutic targets and strategies for GBM are urgently needed. Caveolae-associated protein 1 (CAVIN1) is an essential caveolar component-encoding gene and has been poorly studied in glioma. To this end, in this study, we evaluated CAVIN1 expression in glioma tissue as well as the correlation between CAVIN1 expression and prognosis in glioma patients using the data collected from clinical samples or from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Rembrandt, and Gene Expression Omnibus (GEO) data sets. METHODS:Survival analysis was performed with the Kaplan-Meier curve and log-rank test. The predictive role of CAVIN1 in progressive malignancy in glioma was evaluated by using a receiver operator characteristic (ROC) curve. Gene ontology (GO), Gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) methods were used to interpret the functions of CAVIN1 in GBM. RESULTS:CAVIN1 expression was elevated in GBM compared with that in low-grade glioma and nontumor brain samples and was correlated with unfavorable outcomes in glioma patients. Additionally, CAVIN1 could serve as an independent predictive factor for progressive malignancy in GBM. Furthermore, CAVIN1 was associated with disrupted angiogenesis and immune response in the tumor microenvironment of GBM. CONCLUSIONS:We identified CAVIN1 as a prognostic biomarker and potential target for developing novel therapeutic strategies against GBM.

SUBMITTER: Guo Q

PROVIDER: S-EPMC6488874 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Integrated profiling identifies caveolae-associated protein 1 as a prognostic biomarker of malignancy in glioblastoma patients.

Guo Qing Q Guan Ge-Fei GF Cheng Wen W Zou Cun-Yi CY Zhu Chen C Cheng Peng P Wu An-Hua AH

CNS neuroscience & therapeutics 20181011 3

<h4>Aims</h4>Glioblastoma (GBM) is a lethal disease of the central nervous system with high mortality, and novel therapeutic targets and strategies for GBM are urgently needed. Caveolae-associated protein 1 (CAVIN1) is an essential caveolar component-encoding gene and has been poorly studied in glioma. To this end, in this study, we evaluated CAVIN1 expression in glioma tissue as well as the correlation between CAVIN1 expression and prognosis in glioma patients using the data collected from clin ...[more]

PMID: 30311408

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Project description:Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Project description:BackgroundGastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction.MethodsThe lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs.ResultsDifferentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells.ConclusionsThe authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

Project description:Tumor microenvironment (TME) is emerging as an essential part of cervical cancer (CC) tumorigenesis and development, becoming a hotspot of research these years. However, comprehending the specific composition of TME is still facing enormous challenges, especially the immune and stromal components. In this study, we downloaded the RNA-seq profiles and somatic mutation data of 309 CC cases from The Cancer Genome Atlas (TCGA) database, which were analyzed by integrative bioinformatical methods. Initially, ESTIMATE computational method was employed to calculate the amount of immune and stromal components. Then, based on the high- and low-immunity cohorts, we recognized the differentially expressed genes (DEGs) as well as the differentially mutated genes (DMGs). Additionally, we conducted an intersection analysis of DEGs and DMGs, ultimately determining an immune-related prognostic signature, GTPase, IMAP Family Member 4 (GIMAP4). Moreover, sequential analyses demonstrated that GIMAP4 was a protective factor in CC, positively correlated with the overall survival (OS) and negatively with distant metastasis. Besides, we utilized the Gene Set Enrichment Analysis (GSEA) to explore the enrichment-pathways in high and low-expression cohorts of GIMAP4. The results indicated that the genes of the high-expression cohort had a high enrichment in immune-related biological processes and metabolic activities in the low one. Furthermore, CIBERSORT analysis was applied to evaluate the proportion of tumor-infiltrating immune cells (TICs), illustrating that several activated TICs were strongly associated with GIMAP4 expression, which suggested that GIMAP4 had the potential to be an indicator for the immune state in TME of CC. Hence, GIMAP4 contributed to predicting the CC patients' clinical outcomes, such as survival rate, distant metastasis and immunotherapy response. Moreover, GIMAP4 could serve as a promising biomarker for TME remodeling, suggesting the possible underlying mechanisms of tumorigenesis and CC progression, which may provide different therapeutic perceptions of CC, and therefore improve treatment.

Dataset Information

Integrated profiling identifies caveolae-associated protein 1 as a prognostic biomarker of malignancy in glioblastoma patients.

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Integrated profiling identifies caveolae-associated protein 1 as a prognostic biomarker of malignancy in glioblastoma patients.

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