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Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma.

ABSTRACT: Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12-15 months, and the 5 years survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival-related genes were found among these DEGs (p < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Moreover, with the assistance of GEPIA http://gepia.cancer-pku.cn/, all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.

SUBMITTER: Yin W

PROVIDER: S-EPMC6509566 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma.

Yin Wen W Tang Guihua G Zhou Quanwei Q Cao Yudong Y Li Haixia H Fu Xianyong X Wu Zhaoping Z Jiang Xingjun X

Frontiers in genetics 20190503

Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12-15 months, and the 5 years survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data ...[more]

PMID: 31130992

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Project description:BackgroundLong noncoding RNAs (lncRNAs) are involved in different pathogenesis pathways including cancer pathogenesis. The adenoma-carcinoma pathway in colorectal cancer may involve the aberrant and variable gene expression of regulatory RNAs. This study was conducted to analyse the expression and prognosis prediction ability of two natural antisense transcripts, protein kinase C theta antisense RNA 1 (PRKCQ-AS1), and special AT-rich sequence binding protein 1 antisense RNA 1 (SATB1-AS1) in colorectal low-grade adenoma, advanced adenoma, and adenocarcinomas.MethodsIn this study, from two RNA-seq analyses of CCAT1-ko cells and colorectal carcinoma biopsies having diminished and increased levels of CCAT1 transcription, respectively, we nominated two antisense lncRNAs of PRKCQ-AS1 and SATB1-AS1. Samples from colorectal low-grade adenomas, advanced adenomas, adenocarcinomas, and adjacent tissue were subjected to RT-qPCR to determine the expression of PRKCQ-AS1, SATB1-AS1 along with colon cancer-associated transcript 1 (CCAT1) and cMYC. In addition, we used different bioinformatics analyses and webservers (including GEPIA 2, TCGA, and CancerMine) to elucidate the prognosis prediction value, the expression correlation of sense-antisense pair of genes, and the expression profile of these antisense transcripts at the presence or absence of mutations in the driver genes, or the corresponding sense genes.ResultsPRKCQ-AS1 showed a wide range of expression levels in colorectal adenoma, advanced adenoma, and adenocarcinoma. Upregulation of PRKCQ-AS1 was related to a significant decrease in survival of colorectal cancer (CRC) patients. The expression levels of PRKCQ-AS1 and PRKCQ were strong and significantly concordant in normal and cancerous colorectal tissues. While SATB1-AS1 showed a wide range of expression in colorectal adenoma, advanced adenoma, and adenocarcinoma as well, its expression was not related to a decrease in survival of CRC patients. The expression levels of SATB1-AS1 and SATB1 (the sense gene) were not strong in normal colorectal tissues. In addition, where SATB1 gene was mutated, the expression of SATB1-AS1 was significantly downregulated.ConclusionsWe found the expression of PRKCQ-AS1 and SATB1-AS1 at a given stage of CRC very variable, and not all biopsy samples showed the increased expression of these antisense transcripts. PRKCQ-AS1 in contrast to SATB1-AS1 showed a significant prognostic value. Since a significantly concordant expression was observed for SATB1-AS1 and SATB1 in only cancerous, and for PRKCQ-AS1 and PRKCQ in both normal and cancerous colorectal tissues, it can be concluded that common mechanisms may regulate the expression of these sense and antisense genes.

Project description:AbstractRecent studies suggested that RNA binding proteins (RBPs) were related to the tumorigenesis and progression of glioma. This study was conducted to identify prognostic RBPs of glioblastoma (GBM) and construct an RBP signature to predict the prognosis of GBM.Univariate Cox regression analysis was carried out to identify the RBPs associated with overall survival of GBM in the The Cancer Genome Atlas (TCGA), GSE16011, and Repository for Molecular Brain Neoplasia data (Rembrandt) datasets, respectively. Overlapping RBPs from the TCGA, GSE16011, and Rembrandt datasets were selected. The biological role of prognostic RBPs was assessed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction analyses. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to construct an RBP-related risk signature. The prognostic value of RBP signature was measured by Kaplan-Meier method and time-dependent receiver operating characteristic curve. A nomogram based on independent prognostic factors was established to predict survival for GBM. The CGGA cohort was used as the validation cohort for external validation.This study identified 27 RBPs associated with the prognosis of GBM and constructed a 6-RPBs signature. Kaplan-Meier curves suggested that high-risk score was associated with a poor prognosis. Area under the curve of 1-, 3-, and 5-year overall survival was 0.618, 0.728, and 0.833 for TCGA cohort, 0.655, 0.909, and 0.911 for GSE16011 cohort, and 0.665, 0.792, and 0.781 for Rembrandt cohort, respectively. A nomogram with 4 parameters (age, chemotherapy, O6-methylguanine-DNA methyltransferase promoter status, and risk score) was constructed. The calibration curve showed that the nomogram prediction was in good agreement with the actual observation.The 6-RBPs signature could effectively predict the prognosis of GBM, and our findings supplemented the prognostic index of GBM to a certain extent.

Project description:The present study aimed to explore possible prognostic marker genes in glioblastoma (GBM). Differentially expressed genes (DEGs) were screened by comparing microarray data of tumor and normal tissue samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset GSE22866. Subsequently, the prognosis?associated DEGs were screened via Cox regression analysis, followed by construction of gene/protein/pathway interaction networks of these DEGs by calculating the correlation coefficient between the DEGs. Next, a prognostic prediction system was constructed using Bayes discriminant analysis, which was validated by the microarray data of samples from patients with good and bad prognosis from the TCGA and Chinese Glioma Genome Atlas (CGGA), as well as the GEO dataset. Finally, a co?expression network of the signature genes in the prediction system was constructed in combination with the significant pathways. A total of 288 overlapping DEGs (false discovery rate <0.5 and |log2 of fold change|>1) were screened, 123 of which were identified to be associated with the prognosis of GBM patients. The co?expression network of these prognosis?associated DEGs included 1405 interactions and 112 DEGs, and 6 functional modules were identified in the network. The prognostic prediction system was comprised of 63 signature genes with a specificity value of 0.929 and a sensitivity value of 0.948. GBM samples with good and bad prognosis in the TCGA, CGGA and GEO datasets were distinguishable by these signature genes (P=1.33x10?6, 1.63x10?4 and 0.00534, respectively). The co?expression network of signature genes with significant pathways was comprised of 56 genes and 361 interactions. Protein kinase C? (PRKCG), protein kinase C? (PRKCB) and calcium/calmodulin?dependent protein kinase II? (CAMK2A) were important genes in the network, and based on the expression of these genes, it was possible to distinguish between samples with significantly different survival risks. In the present study, an effective prognostic prediction system for GBM patients was constructed and validated. PRKCG, PRKCB and CAMK2A may be potential prognostic factors for GBM.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.Materials and methodsProtein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.ResultsFrom the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.ConclusionWe constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

Project description:BackgroundProstate cancer (PCa) is the most common malignancy and the leading cause of cancer death in men. Recent studies suggest the molecular signature was more effective than the clinical indicators for the prognostic prediction, but all of the known studies focused on a single RNA type. The present study was to develop a new prognostic signature by integrating long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) and evaluate its prognostic performance.MethodsThe RNA expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) or Gene Expression Omnibus database (GSE17951, GSE7076, and GSE16560). The PCa-driven modules were identified by constructing a weighted gene coexpression network, the corresponding genes of which were overlapped with differentially expressed RNAs (DERs) screened by the MetaDE package. The optimal prognostic signature was screened using the least absolute shrinkage and selection operator analysis. The prognostic performance and functions of the combined prognostic signature was then assessed.ResultsTwelve PCa-driven modules were identified using TCGA dataset and validated in the GSE17951 and GSE7076 datasets, and six of them were considered to be preserved. A total of 217 genes in these 6 modules were overlapped with 699 DERs, from which a nine-gene prognostic signature was identified (including 3 lncRNAs and 6 mRNAs), and the risk score of each patient was calculated. The overall survival was significantly shortened in patients having the risk score higher than the cut-off, which was demonstrated in TCGA (p = 5.063E - 03) dataset and validated in the GSE16560 (p = 3.268E - 02) dataset. The prediction accuracy of this risk score was higher than that of clinical indicators (the Gleason score and prostate-specific antigen) or the single RNA type, with the area under the receiver operator characteristic curve of 0.945. Besides, some new therapeutic targets and mechanisms (MAGI2-AS3-SPARC/GJA1/CYSLTR1, DLG5-AS1-DEFB1, and RHPN1-AS1-CDC45/ORC) were also revealed.ConclusionThe risk score system established in this study may provide a novel reliable method to identify PCa patients at a high risk of death.

Dataset Information

Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma.

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Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma.

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