Abstract

Abstract Glioblastoma (GBM) is one of the most lethal human tumors with a highly infiltrative phenotype. Although invasiveness is related to poor prognosis, no therapeutic intervention targeting invasion is available for treatment of GBM patient, partially due to the diversity and redundancy of invasion machinery genes. In this regard, additional identification of deterministic and causative targets for invasion is required. Invasiveness of GBM patients and matched tumorspheres (TSs) was quantified using MR images and collagen-based 3D invasion assays, respectively. Transcriptome of GBM samples were obtained using microarrays. The knockdown effects of invasion-deterministic transcription factors (TFs) were evaluated using western blot and mouse orthotopic xenograft model. This study is aimed to identify novel transcriptional regulatory networks, which can collectively modulate invasion-involved genes in GBM. After classification of 23 GBM patient-derived TSs into low and high invasion groups, we applied single sample gene set enrichment analysis using TF target gene sets. According to enrichment scores, TFs responsible for low (PCBP1) and high (STAT3 and SRF) invasiveness were identified. Consistently with computational prediction, knockdown of PCBP1 significantly increased invasion area, whereas knockdown of STAT3 or SRF significantly suppressed invasive properties in all tested TSs. Notably, MR images showed coherent patterns with invasion of originated TS, and high invasiveness was associated with poor prognosis. In addition, mouse orthotopic xenograft model using TSs with down-regulated STAT3 or SRF showed significantly prolonged survival time compared to control. We identified invasion-deterministic TFs in glioblastoma using integrative transcriptome analysis. Owing to relationship among these transcriptional regulatory networks, invasive phenotype, and prognosis, we suggest that these TFs as novel drug targets for GBM.

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