| Literature DB >> 34211843 |
Rodolfo Bortolozo Serafim1,2,3, Patrick da Silva1, Cibele Cardoso2,3, Luis Fernando Macedo Di Cristofaro1, Renato Petitto Netto1, Rodrigo de Almeida1, Geovana Navegante1, Camila Baldin Storti2,3, Juliana Ferreira de Sousa4, Felipe Canto de Souza2,3, Rodrigo Panepucci2,3, Cristiano Gallina Moreira1, Larissa Siqueira Penna1, Wilson Araujo Silva2,3, Valeria Valente1,2,3.
Abstract
Glioblastoma (GBM) is the most lethal and frequent type of brain tumor, leading patients to death in approximately 14 months after diagnosis. GBM treatment consists in surgical removal followed by radio and chemotherapy. However, tumors commonly relapse and the treatment promotes only a slight increase in patient survival. Thus, uncovering the cellular mechanisms involved in GBM resistance is of utmost interest, and the use of cell lines has been shown to be an extremely important tool. In this work, the exploration of RNAseq data from different GBM cell lines revealed different expression signatures, distinctly correlated with the behavior of GBM cell lines regarding proliferation indexes and radio-resistance. U87MG and U138MG cells, which presented expressively reduced proliferation and increased radio-resistance, showed a particular expression signature encompassing enrichment in many extracellular matrix (ECM) and receptor genes. Contrasting, U251MG and T98G cells, that presented higher proliferation and sensibility to radiation, exhibited distinct signatures revealing consistent enrichments for DNA repair processes and although several genes from the ECM-receptor pathway showed up-regulation, enrichments for this pathway were not detected. The ECM-receptor is a master regulatory pathway that is known to impact several cellular processes including: survival, proliferation, migration, invasion, and DNA damage signaling and repair, corroborating the associations we found. Furthermore, searches to The Cancer Genome Atlas (TCGA) repository revealed prognostic correlations with glioma patients for the majority of genes highlighted in the signatures and led to the identification of 31 ECM-receptor genes individually correlated with radiation responsiveness. Interestingly, we observed an association between the number of upregulated genes and survivability greater than 5 years after diagnosis, where almost all the patients that presented 21 or more upregulated genes were deceased before 5 years. Altogether our findings suggest the clinical relevance of ECM-receptor genes signature found here for radiotherapy decision and as biomarkers of glioma prognosis.Entities:
Keywords: ECM-receptors; GBM cell lines; expression profiling; extracellular matrix; glioblastoma; radioresistance
Year: 2021 PMID: 34211843 PMCID: PMC8240593 DOI: 10.3389/fonc.2021.668090
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Figure 1Proliferation and ionizing radiation resistance analysis in GBM cell lines. (A) Proliferation assay of T98G, U343MG, U251MG, U87MG, and U138MG. (B) Cytotoxicity assay of GBM cells after radiation treatment. (C) Quantification of apoptosis and cell death of GBM cells after irradiation are shown in the upper graphics and representative images of flow cytometry experiments are presented in the lower panel. Error bars represent the SEM of 3 independent experiments. Graphs were plotted with GraphPad Prism 4.0 software. *p < 0.05, **p < 0.001, and ***p < 0.0001.
Figure 2The enriched KEGG pathways in treatment-resistant cells in comparison to sensitive cells. All genes with padj ≤ 0,0001 and log fold change > 2 in U87MG and/or U138MG cells compared to T98G and/or U251MG cells were subjected to pathway analysis by KEGG. The pathways related to cell-extracellular matrix interaction were highlighted in red. A False Discovery Rate (FDR) ≤ 0.05 was used as a threshold to select significant pathways. Graphs were plotted with GraphPad Prism 4.0 software.
Figure 3Expression profile of the ECM-receptor interaction genes in GBM cell lines. (A) Heatmap of the relative expression of genes enriched in the ECM-receptor interaction pathway, estimated expression in each GBM cell line relative to non-tumor astrocytes (ACBRI-371) are shown. (B) Western blot validating ITGA5 and ITGB1 expression in U87MG, U138MG, T98G, and U251MG cell lines. (C) Protein samples were collected 0, 6, 12, 18, 24, 30, 36, 42, and 48h after irradiation treatment with 10 Gy and the ITGA5 and ITGB1 protein levels assessed by western blot. The two bands detected for these integrins correspond to the precursor (upper band) and mature (lower band) forms of the protein, and variations are commonly observed depending on the cell line analyzed.
Figure 4Homologous Recombination and Mismatch Repair pathways are enriched in proliferative GBM cell lines. Heatmaps representative of the expression of genes from the Homologous Recombination and Mismatch Repair pathways were generated with the data from each GBM cell line relative to non-tumor astrocytes (ACBRI-371).
Figure 5High levels of 31 ECM-receptor interaction transcripts are correlated with poor survival prognosis of radiation-treated patients. Kaplan Meier survival curves for LGG patients according to expression levels of the ECM-receptor interaction genes in the tumors. Patients were divided into four groups: i) untreated patients whose tumors expressed low levels of the analyzed gene; ii) untreated patients whose tumors expressed high levels of the analyzed gene; iii) irradiated patients whose tumors expressed low levels of the analyzed gene; iv) irradiated patients whose tumors expressed high levels of the analyzed gene. *p < 0.05, **p < 0.001, and ***p < 0.0001. The P-values were obtained from a log-rank test. Graphs were plotted with GraphPad Prism 4.0 software.
Figure 6The progressive accumulation of up-regulated ECM-receptor interaction genes impacts the resistance of tumors to irradiation. (A) The number of genes with expression above the threshold defined by the ROC curve was evaluated in each patient submitted to IR treatment and six categories, with an increasing number of altered genes, were defined. The percentage of alive or deceased patients included in each category is indicated. (B) Kaplan Meier survival curves for LGG patients according to the categories described in (A). Graphs were plotted with GraphPad Prism 4.0 software.