Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients

Gao, Weizhen; Guo, Liemei; Xu, Tianqi; Yin, Yu-Hua; Feng, Jia

doi:10.1186/s12967-018-1744-8

Cited by 49 publications

(40 citation statements)

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“…Other studies also showed that these lncRNAs were associated with cancers. As shown in the literature, FLG-AS1 may be involved in the pathogenesis of oral cancer [46]; SNAI3-AS1 can promote the growth and metastasis of hepatocellular carcinoma by inducing tumor epithelial to epithelial-mesenchymal transition [47]; LINC02015 is a protective factor for glioblastoma multiforme and is significantly upregulated in metastatic esophageal squamous cell carcinoma [48,49]. In addition, ZNF295-AS1 is involved [50] and lung cancer [51] and can predict survival in patients with gastric cancer [52].…”

Section: Discussionmentioning

confidence: 90%

The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity

Ding

Wang

et al. 2020

Journal of Diabetes Research

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Background. The prevalence of obesity and type 2 diabetes mellitus (T2DM) has become the most serious global public health issue. In recent years, there has been increasing attention to the role of long noncoding RNAs (lncRNAs) in the occurrence and development of obesity and T2DM. The aim of this work was to find new lncRNAs as potential predictive biomarkers or therapeutic targets for obesity and T2DM. Methods. In this study, we identified significant differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) between adipose tissue of individuals with obesity and T2DM and normal adipose tissue (absolute log 2 FC ≥ 1 and FDR < 0:05). Then, the lncRNA-miRNA interactions predicted by miRcode were further screened with a threshold of MIC > 0:2. Simultaneously, the mRNA-miRNA interactions were explored by miRWalk 2.0. Finally, a ceRNA network consisting of lncRNAs, miRNAs, and mRNAs was established by integrating lncRNA-miRNA interactions and mRNA-miRNA interactions. Results. Upon comparing adipose tissue from individuals with obesity and T2DM and normal adipose tissues, 364 significant DEmRNAs, including 140 upregulated and 224 downregulated mRNAs, were identified in GSE104674; in addition, 231 significant DEmRNAs, including 146 upregulated and 85 downregulated mRNAs, were identified in GSE133099. GO and KEGG analyses have shown that downregulated DEmRNAs in GSE104674 and GSE133099 were associated with obesity-and T2DM-related biological pathways, such as lipid metabolism, AMPK signaling, and insulin resistance. Furthermore, 28 significant DElncRNAs, including 14 upregulated and 14 downregulated lncRNAs, were found. Based on the predicted lncRNA-miRNA and mRNA-miRNA relationships, we constructed a competitive endogenous RNA (ceRNA) network, including five lncRNAs, ten miRNAs, and 15 mRNAs. KEGG-GSEA analysis revealed that four lncRNAs (FLG-AS1, SNAI3-AS1, AC008147.0, and LINC02015) in the ceRNA network were related to the biological pathways of metabolic diseases. Conclusions. Through ceRNA network analysis, our study identified four new lncRNAs that may be used as potential biomarkers and therapeutic targets of obesity and T2DM, thus laying a foundation for future clinical studies.

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Section: Discussionmentioning

confidence: 90%

The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity

Ding

Wang

et al. 2020

Journal of Diabetes Research

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“…And we used the R NbClust package for determining the number of clusters 13–15 . The model was estimated as follows 16,17 :…”

Section: Methodsmentioning

confidence: 99%

A robust two‐gene signature for glioblastoma survival prediction

Pan

Zhang

Guo

et al. 2020

J of Cellular Biochemistry

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Glioblastoma multiforme (GBM) is a highly malignant brain tumor. We explored the prognostic gene signature in 443 GBM samples by systematic bioinformatics analysis, using GSE16011 with microarray expression and corresponding clinical data from Gene Expression Omnibus as the training set. Meanwhile, patients from The Chinese Glioma Genome Atlas database (CGGA) were used as the test set and The Cancer Genome Atlas database (TCGA) as the validation set. Through Cox regression analysis, Kaplan-Meier analysis, t-distributed Stochastic Neighbor Embedding algorithm, clustering, and receiver operating characteristic analysis, a two-gene signature (GRIA2 and RYR3) associated with survival was selected in the GSE16011 dataset. The GRIA2-RYR3 signature divided patients into two risk groups with significantly different survival in the GSE16011 dataset (median: 0.72, 95% confidence interval [CI]: 0.64-0.98, vs median: 0.98, 95% CI: 0.65-1.61 years, logrank test P < .001), the CGGA dataset (median: 0.84, 95% CI: 0.70-1.18, vs median: 1.21, 95% CI: 0.95-2.94 years, logrank test P = .0017), and the TCGA dataset (median:1.03, 95% CI: 0.86-1.24, vs median: 1.23, 95% CI: 1.04-1.85 years, logrank test P = .0064), validating the predictive value of the signature. And the survival predictive potency of the signature was independent from clinicopathological prognostic features in multivariable Cox analysis. We found that after transfection of U87 cells with small interfering RNA, GRIA2 and RYR3 influenced the biological behaviors of proliferation, migration, and invasion of glioblastoma cells. In conclusion, the two-gene signature was a robust prognostic model to predict GBM survival. K E Y W O R D Sglioblastoma multiforme, prognostic, protein-coding genes, signature

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“…Glioblastoma is defined as a grade IV tumor characterized by high heterogeneity and a dismal prognosis, according to the World Health Organization guidelines . The current first‐line treatment for newly diagnosed GBM is maximal safe resection followed by chemoradiation . Despite aggressive interventions, the disease almost inevitably turns into recurrent GBM, for which no standard and effective treatment approach has been shown to prolong patient survival significantly .…”

Section: Introductionmentioning

confidence: 99%

“…1 The current first-line treatment for newly diagnosed GBM is maximal safe resection followed by chemoradiation. [2][3][4][5][6] Despite aggressive interventions, the disease almost inevitably turns into recurrent GBM, for which no standard and effective treatment approach has been shown to prolong patient survival significantly. [7][8][9][10][11][12] The overall survival of recurrent GBM is generally no more than half a year.…”

Section: Introductionmentioning

confidence: 99%

Role of alternative splicing signatures in the prognosis of glioblastoma

Xie

Dang

et al. 2019

Cancer Medicine

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Background Increasing evidence has validated the crucial role of alternative splicing (AS) in tumors. However, comprehensive investigations on the entirety of AS and their clinical value in glioblastoma (GBM) are lacking. Methods The AS profiles and clinical survival data related to GBM were obtained from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses were performed to identify survival‐associated AS events. A risk score was calculated, and prognostic signatures were constructed using seven different types of independent prognostic AS events, respectively. The Kaplan‐Meier estimator was used to display the survival of GBM patients. The receiver operating characteristic curve was applied to compare the predictive efficacy of each prognostic signature. Enrichment analysis and protein interactive networks were conducted using the gene symbols of the AS events to investigate important processes in GBM. A splicing network between splicing factors and AS events was constructed to display the potential regulatory mechanism in GBM. Results A total of 2355 survival‐associated AS events were identified. The splicing prognostic model revealed that patients in the high‐risk group have worse survival rates than those in the low‐risk group. The predictive efficacy of each prognostic model showed satisfactory performance; among these, the Alternate Terminator (AT) model showed the best performance at an area under the curve (AUC) of 0.906. Enrichment analysis uncovered that autophagy was the most enriched process of prognostic AS gene symbols in GBM. The protein network revealed that UBC, VHL, KCTD7, FBXL19, RNF7, and UBE2N were the core genes in GBM. The splicing network showed complex regulatory correlations, among which ELAVL2 and SYNE1_AT_78181 were the most correlated (r = −.506). Conclusions Applying the prognostic signatures constructed by independent AS events shows promise for predicting the survival of GBM patients. A splicing regulatory network might be the potential splicing mechanism in GBM.

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Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients

Cited by 49 publications

References 50 publications

The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity

The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity

A robust two‐gene signature for glioblastoma survival prediction

Role of alternative splicing signatures in the prognosis of glioblastoma

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