Dataset Information

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma.

ABSTRACT: Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes' functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

SUBMITTER: Chen JQ

PROVIDER: S-EPMC8766324 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma.

Chen Jia-Qi JQ Zhang Nuo N Su Zhi-Lin ZL Qiu Hui-Guo HG Zhuang Xin-Guo XG Tao Zhi-Hua ZH

Frontiers in integrative neuroscience 20220105

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Wei ...[more]

PMID: 35069135

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Project description:BackgroundGliomas are the most common intracranial tumors and are classified as I-IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients.Materials and methodsTo identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis.ResultsAfter differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk groups. Increased expression of SLC12A5, CCL2, IGFBP2, and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these genes could be used as independent predictors of survival in patients with glioblastoma.ConclusionOur analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.

Project description:BackgroundGlioblastoma (GBM) is a frequent malignant tumor in neurosurgery characterized by a high degree of heterogeneity and genetic instability. DNA double-strand breaks generated by homologous recombination deficiency (HRD) are a well-known contributor to genomic instability, which can encourage tumor development. It is unknown, however, whether the molecular characteristics linked with HRD have a predictive role in GBM. The study aims to assess the extent of genomic instability in GBM using HRD score and investigate the prognostic significance of HRD-related molecular features in GBM.MethodsThe discovery cohort comprised 567 GBM patients from The Cancer Genome Atlas (TCGA) database. We established HRD scores using the single nucleotide polymorphism (SNP) array data and analyzed transcriptomic data from patients with different HRD scores to identify biomarkers associated with HRD. A prognostic model was built by using HRD-related differentially expressed genes (DEGs) and validated in a distinct cohort from the Chinese Glioma Genome Atlas (CGGA) database.ResultsBased on the SNP array data, the gene expression profile data, and the clinical characteristics of GBM patients, we found that patients with a high HRD score had a better prognosis than those with a low HRD score. The DNA damage repair (DDR) signaling pathways were notably enriched in the HRD-positive subgroup. The prognostic model was developed by including HRD-related DEGs that could evaluate the clinical prognosis of patients more efficiently than the HRD score. In addition, patients with a low-risk score had a considerably augmented signature of γδT cells. Finally, through univariate and multivariate Cox regression analyses, it was demonstrated that the prognostic model was superior to other prognostic markers.ConclusionsIn conclusion, our research has not only demonstrated that a high HRD score is a valid prognostic biomarker in GBM patients but also built a stable prognosis model [odds ratio (OR) 0.18, 95% confidence interval (CI): 0.11-0.23, P<0.001] that is more accurate than conventional prognostic markers such as O6-methylguanine-DNA methyltransferase (MGMT) methylation (OR 0.55, 95% CI: 0.33-0.91, P=0.02).

Project description:BackgroundCervical cancer (CC) is a common gynecological malignancy in women worldwide. Evidence suggests that long non-coding RNAs (lncRNAs) can be used as biomarkers in patients with CC. However, prognostic biomarkers for CC are still lacking. The aim of our study was to find lncRNA biomarkers which are able to predict prognosis in CC based on the data from The Cancer Genome Atlas (TCGA).MethodsThe patients were divided into three groups according to FIGO stage. Differentially expressed lncRNAs were identified in CC tissue compared to adjacent normal tissues based on a fold change >2 and <0.5 at P < 0.05 for up- and downregulated lncRNA, respectively. The relationship between survival outcome and lncRNA expression was assessed with univariate and multivariate Cox proportional hazards regression analysis. We constructed a risk score as a method to evaluate prognosis. We used receiver operating characteristic (ROC) curve and the area under curve (AUC) analyses to assess the diagnostic value of a two-lncRNA signature. We detected the expression levels of the two lncRNAs in 31 pairs of newly diagnosed CC specimens and paired adjacent non-cancerous tissue specimens, and also in CC cell lines. Finally, the results were statistically compared using t-tests.ResultsIn total, 289 RNA sequencing profiles and accompanying clinical data were obtained. We identified 49 differentially expressed lncRNAs, of which two related to overall survival (OS) in CC patients. These two lncRNAs (ILF3-AS1 and RASA4CP) were found together as a single prognostic signature. Meanwhile, the prognosis of patients with low-risk CC was better and positively correlated with OS (P < 0.001). Further analysis showed that the combined two-lncRNA expression signature could be used as an independent biomarker to evaluate the prognosis in CC. qRT-PCR results were consistent with TCGA, confirming downregulated expression of both lncRNAs. Furthermore, upon ROC curve analysis, the AUC of the combined lncRNAs was greater than that of the single lncRNAs alone (0.723 vs 0.704 and 0.685), respectively; P < 0.05.ConclusionsOur study showed that the two-lncRNA signature of ILF3-AS1 and RASA4CP can be used as an independent biomarker for the prognosis of CC, based on bioinformatic analysis.

Dataset Information

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma.

Publications

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma.

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