RESUMO
BACKGROUND: Our previous bioinformatics-based study found that midkine (MDK) was associated with poor prognosis of glioblastoma (GBM). However, the mechanism of MDK in GBM remains elusive. METHODS: A public GBM-related dataset and GBM tissues from our center were used validate the aberrant expression of MDK in GBM at the RNA and protein levels. The relationship between MDK expression and survival of GBM patients was also explored through survival analysis. Subsequently, we identified MDK-related GBM-specific genes using differential expression analysis. Functional enrichment analyses were performed to reveal their potential biological functions. CCK-8, 5-ethynyl-2'-deoxyuridine, and Matrigel-transwell assays were performed in GBM cell lines in which MDK was knocked out or overexpressed in order assess the effects of MDK on proliferation, migration, and invasion of GBM cells. Western blotting was performed to detect candidate proteins. RESULTS: Our study showed MDK is a promising diagnostic and prognostic biomarker for GBM because it is highly expressed in the disease and it is associated with poor prognosis. MDK is involved in various cancer-related pathways, such as PI3K-Akt signaling, the cell cycle, and VEGF signaling. A comprehensive transcriptional regulatory network was constructed to show the potential pathways through which MDK may be involved in GBM. In vitro, Overexpression of MDK augmented proliferation, migration, and invasion of GBM cell lines, whereas suppression of MDK led to the opposite effects. Furthermore, our study confirmed that MDK promotes the progression of GBM by activating the PI3K-Akt signaling pathway. CONCLUSIONS: Our present study proposes that MDK promotes GBM by activating the PI3K-Akt signaling pathway, and it describes a potential regulatory network involved.
RESUMO
Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Diagnostic and therapeutic challenges have been raised because of poor prognosis. Gene expression profiles of GBM and normal brain tissue samples from GSE68848, GSE16011, GSE7696, and The Cancer Genome Atlas (TCGA) were downloaded. We identified differentially expressed genes (DEGs) by differential expression analysis and obtained 3,800 intersected DEGs from all datasets. Enrichment analysis revealed that the intersected DEGs were involved in the MAPK and cAMP signaling pathways. We identified seven different modules and 2,856 module genes based on the co-expression analysis. Module genes were used to perform Cox and Kaplan-Meier analysis in TCGA to obtain 91 prognosis-related genes. Subsequently, we constructed a random survival forest model and a multivariate Cox model to identify seven hub genes (KDELR2, DLEU1, PTPRN, SRBD1, CRNDE, HPCAL1, and POLR1E). The seven hub genes were subjected to the risk score and survival analyses. Among these, CRNDE may be a key gene in GBM. A network of prognosis-related genes and the top three differentially expressed microRNAs with the largest fold-change was constructed. Moreover, we found a high infiltration of plasmacytoid dendritic cells and T helper 17 cells in GBM. In conclusion, the seven hub genes were speculated to be potential prognostic biomarkers for guiding immunotherapy and may have significant implications for the diagnosis and treatment of GBM.