Glioblastomas and the Special Role of Adhesion Molecules in Their Invasion

Moura‐Neto, Vivaldo; Campanati, Loraine; Matias, Diana; Pereira, Cláudia Maria; Freitas, Catarina; Coelho-Aguiar, Juliana M.; Spohr, Tânia Cristina Leite de Sampaio e; Tavares-Gomes, Ana Lúcia; Pinheiro-Aguiar, Diego; Kahn, Suzana Assad; Silva-Balça, Joana; Pontes, Bruno; Porto-Carreiro, Isabel; Faria, Jane; Martins, Rodrigo A. P.; Lima-Costa, Silvia; Dias-Costa, Maria de Fátima; Lopes, María Celeste; Lima, Flávia Farias

doi:10.1007/978-3-7091-1431-5_11

Cited by 2 publications

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References 156 publications

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“…As aforementioned, the yellow and red modules had similar gene expression patterns. Moreover, the two modules were functionally related to biological adhesion and immune response, which were critical for GBM pathogenesis 25,26. Therefore, the 19 lncRNAs in the yellow and red modules were selected for further analysis.…”

Section: Resultsmentioning

confidence: 99%

Analysis of long non-coding RNAs in glioblastoma for prognosis prediction using weighted gene co-expression network analysis, Cox regression, and L1-LASSO penalization

Liang¹,

Zhi

Guo³

et al. 2018

OTT

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PurposeThis study focused on identification of long non-coding RNAs (lncRNAs) for prognosis prediction of glioblastoma (GBM) through weighted gene co-expression network analysis (WGCNA) and L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model.Materials and methodsWGCNA was performed based on RNA expression profiles of GBM from Chinese Glioma Genome Atlas (CGGA), National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), and the European Bioinformatics Institute ArrayExpress for the identification of GBM-related modules. Subsequently, prognostic lncRNAs were determined using LASSO Cox PH model, followed by constructing a risk scoring model based on these lncRNAs. The risk score was used to divide patients into high- and low-risk groups. Difference in survival between groups was analyzed using Kaplan–Meier survival analysis. IncRNA-mRNA networks were built for the prognostic lncRNAs, followed by pathway enrichment analysis for these networks.ResultsThis study identified eight preserved GBM-related modules, including 188 lncRNAs. Consequently, C20orf166-AS1, LINC00645, LBX2-AS1, LINC00565, LINC00641, and PRRT3-AS1 were identified by LASSO Cox PH model. A risk scoring model based on the lncRNAs was constructed that could divide patients into different risk groups with significantly different survival rates. Prognostic value of this six-lncRNA signature was validated in two independent sets. C20orf166-AS1 was associated with antigen processing and presentation and cell adhesion molecule pathways, involving nine common genes. LBX2-AS1, LINC00641, PRRT3-AS1, and LINC00565 were related to focal adhesion, extracellular matrix receptor interaction, and mitogen-activated protein kinase signaling pathways, which shared 12 common genes.ConclusionThis prognostic six-lncRNA signature may improve prognosis prediction of GBM. This study reveals many pathways and genes involved in the mechanisms behind these lncRNAs.

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Section: Resultsmentioning

confidence: 99%