Dataset Information

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme.

ABSTRACT: Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223, GSE4058, GSE4290, GSE13276, GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA-seq data from TCGA-GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up-regulated and 51 down-regulated genes). Subsequently, through the PPI (protein-protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer-related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.

SUBMITTER: Jiang L

PROVIDER: S-EPMC7520335 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme.

Jiang Lan L Zhong Min M Chen Tianbing T Zhu Xiaolong X Yang Hui H Lv Kun K

Journal of cellular and molecular medicine 20200721 17

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223, GSE4058, GSE4290, GSE13276, GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA-seq data from TCGA-GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up-regulated and 51 down-regulated genes). Subse ...[more]

PMID: 32696617

Similar Datasets

Project description:Tumor growth is a spatiotemporal birth-and-death process with loss of heterotypic contact-inhibition of locomotion (CIL) of tumor cells promoting invasion and metastasis. Therefore, representing tumor cells as two-dimensional points, we can expect the tumor tissues in histology slides to reflect realizations of spatial birth-and-death process which can be mathematically modeled to reveal molecular mechanisms of CIL, provided the mathematics models the inhibitory interactions. Gibbs process as an inhibitory point process is a natural choice since it is an equilibrium process of the spatial birth-and-death process. That is if the tumor cells maintain homotypic contact inhibition, the spatial distributions of tumor cells will result in Gibbs hard core process over long time scales. In order to verify if this is the case, we applied the Gibbs process to 411 TCGA Glioblastoma multiforme patient images. Our imaging dataset included all cases for which diagnostic slide images were available. The model revealed two groups of patients, one of which - the "Gibbs group," showed the convergence of the Gibbs process with significant survival difference. Further smoothing the discretized (and noisy) inhibition metric, for both increasing and randomized survival time, we found a significant association of the patients in the Gibbs group with increasing survival time. The mean inhibition metric also revealed the point at which the homotypic CIL establishes in tumor cells. Besides, RNAseq analysis between patients with loss of heterotypic CIL and intact homotypic CIL in the Gibbs group unveiled cell movement gene signatures and differences in Actin cytoskeleton and RhoA signaling pathways as key molecular alterations. These genes and pathways have established roles in CIL. Taken together, our integrated analysis of patient images and RNAseq data provides for the first time a mathematical basis for CIL in tumors, explains survival as well as uncovers the underlying molecular landscape for this key tumor invasion and metastatic phenomenon.

Project description:ObjectivesThis study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM).MethodsIn this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan-Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability.ResultsThe concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high- and low-risk groups based on the median prognostic index (PI). Kaplan-Meier curve was used for survival analysis (p = < 2e-16 in the training set, p = 3e-04 in the validation set, and p = 0.007 in the test set), which showed that the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve of the training set were 0.843, 0.871, 0.903, and 0.941; those of the validation set were 0.687, 0.895, 1.000, and 0.967; and those of the test set were 0.757, 0.852, 0.683, and 0.898.ConclusionThe survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of patients with GBM.

Dataset Information

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme.

Publications

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets