Effect of Necrosis on the miRNA-mRNA Regulatory Network in CRT-MG Human Astroglioma Cells

Abstract

Purpose: Glioblastoma multiforme (GBM) is the most common adult primary intracranial tumor. The remarkable features of GBM include central necrosis. MicroRNAs (miRNAs) have been considered as diagnostic/prognostic biomarkers for many cancers, including glioblastoma. However, the effect of necrosis on the miRNA expression profile and predicted miRNA-mRNA regulatory information remain unclear. The purpose of this study is to examine the effect of necrotic cells on the modulation of miRNA and mRNA expression profiles and miRNA-mRNA network in CRT-MG cells.

Materials and methods: We used human astroglioma cells, CRT-MG, treated with necrotic CRT-MG cells to examine the effect of necrosis on the modulation of miRNA and mRNA by next-generation sequencing. For preparation of necrotic cells, CRT-MGcellswere frozen and thawed through cycle of liquid nitrogen-water bath. The putative miRNA-mRNA regulatory relationshipwas inferred through target information, using miRDB.

Results: The necrotic cells induced dysregulation of 106 miRNAs and 887 mRNAs. Among them, 11 miRNAs that had a negative correlation value of p < 0.05 by the hypergeometric test were screened, and their target mRNAs were analyzed by Gene Ontology enrichment analysis. Using the Kyoto Encyclopedia of Genes and Genomes database, we also found several necrotic cell treatment-activated pathways that were modulated by relevant gene targets of differentially expressed miRNAs.

Conclusion: Our result demonstrated that dysregulation of miRNA and mRNA expression profiles occurs when GBM cells are exposed to necrotic cells, suggesting that several miRNAs may have the potential to be used as biomarkers for predicting GBM progression and pathogenesis.

Keywords: Glioblastoma; MicroRNA; Necrosis.

Fig. 1.

Graphical representation of the 106 miRNAs differentially expressed between necrotic cell-treated and untreated CRT-MG cells. (A) Clustering of the 106 most differentially upregulated and downregulated miRNAs for classification between the necrotic cell–treated (NC) and untreated CRT-MG cells (control). Complete linkage hierarchical clustering was performed with the Euclidian distance measure. The NC and control cells clustered separately. The colors in the heat map represent the normalized expression values, with lower expression values being colored in shades of green and higher expression values in shades of red. (B) Counts of mature miRNAs upregulated or downregulated in NC cells. (C) Scatter plot showing the normalized miRNA volume of all conditions analyzed (y-axis) and changes in miRNAs expression between the NC and control cells. The top five most significantly dysregulated miRNAs are marked. (D) Quantitative real-time polymerase chain reaction analysis was performed glioblastoma multiforme tissues from the near-necrosis tumor area (Nec) and from non-necrosis tumor area (Tm). Data are presented as fold induction compared with non-necrosis tumor area. ^*p < 0.05, ^**p < 0.01 vs. Tm.

Fig. 2.

Graphical representation of the 887 mRNAs differentially expressed between necrotic cell–treated (NC) and untreated CRT-MG cells. (A) Clustering of the 887 most differentially upregulated and downregulated mRNAs for classification between the NC and untreated CRT-MG cells (control). Complete linkage hierarchical clustering was performed with the Euclidian distance measure. The NC and control cells clustered separately. The colors in the heatmap represent the normalized expression values, with lower expression values being colored in shades of green and higher expression values in shades of red. (B) Counts of mRNAs upregulated or downregulated in NC cells. (C) Scatter plot showing the normalized mRNA volume of all conditions analyzed (y-axis) and changes in mRNA expression between the NC and control cells. The top five most significantly dysregulated genes are marked. (D, E) Validation of mRNA analyzed reverse transcription–polymerase chain reaction (RT-PCR) and quantitative reverse transcription–polymerase chain reaction (qRT-PCR). (D) The expression level of six upregulated mRNA (CXCL3, CCL2, IL1A, TNIP1 [transcript variant 6 and 10], and ALOX5AP) and three downregulated mRNA (CYBRD1, COL2A1, and CNR1) were validated by RT-PCR. GAPDH gene was evaluated as external control. (E) Two upregulated mRNA (ICAM1 and LCN2) and three downregulated mRNA (POSTN, PCP4, and LYPD1) were determined by qRT-PCR. Data are presented as fold induction compared with untreated necrotic cells. ^**p < 0.01, ^***p < 0.001 vs. control.

Fig. 3.

Pie chart representing the distribution of negatively correlated miRNA-mRNA pairs. Pie chart depicting the distribution of negatively correlated miRNA-mRNA pairs within the three color-coded groups. Eighty-eight miRNAs were found to have at least one negatively regulated miRNA-mRNA pair for significant mRNAs, and 11 miRNAs a negative correlation with p < 0.05 by the hypergeometric test. ^a)Upregulated miRNAs–downregulated mRNAs in necrotic cell–treated CRT-MG with p < 0.05 by the hypergeometric test, ^b)Downregulated miRNAs–upregulated mRNAs in necrotic cell–treated CRT-MG with p < 0.05 by the hypergeometric test, ^c)Negatively regulated miRNA-mRNA pairs with p ≥ 0.05 by the hypergeometric test.

Fig. 4.

Gene Ontology (GO) enrichment analysis for negatively correlated miRNA-mRNA. The 188 genes that were upregulated and downregulated in necrotic cell–treated CRT-MG cells, with p < 0.05 by the hypergeometric test, were subjected to GO functional enrichment analysis. To study the regulatory effects of the significant negatively correlated miRNA-mRNA pairs, and to further explore the function of the predicted target genes. These mRNAs were included in the biological process, cell component, and molecular function classifications.

Fig. 5.

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for negatively correlated miRNA-mRNA pairs. The 188 genes that were upregulated and downregulated in necrotic cell–treated CRT-MG cells, with p < 0.05 by the hypergeometric test, were subjected to KEGG database analysis. These mRNAs were related to various biological pathways of metabolism (red), genetic information (green), environmental information (orange), cellular processes (gray), organismal system (purple), and human diseases (pink). The colors in the enrichment map represent the significant p-values of the geneset enrichment, with high significant values (p ≤ 0.001) being colored in blue shade. PI3K, phosphoinositide 3-kinase; ECM, extracellular matrix; AMPK, AMP-activated protein kinase.