Background: Glioma stem cells (GSCs) are glioma cells with stemness and are responsible for a variety of malignant behaviors of glioma. Evidence has shown that signals from tumor microenvironment (TME) enhance stemness of glioma cells. However, identification of the signaling molecules and underlying mechanisms has not been completely elucidated. Methods: Human samples and glioma cell lines were cultured in vitro to determine the effects of adenovirus (ADV) infection by sphere formation, RT-qPCR, western blotting, FACS and immunofluorescence. For in vivo analysis, mouse intracranial tumor model was applied. Bioinformatics analysis, gene knockdown by siRNA, RT-qPCR and western blotting were applied for further mechanistic studies. Results: Infection of patient-derived glioma cells with ADV increases the formation of tumor spheres. ADV infection upregulated stem cell markers and in turn promoted the capacities of self-renewal and multi-lineage differentiation of the infected tumor spheres. These ADV infected tumor spheres had stronger potential to form xenograft tumors in immune-compromised mice. GSCs formation could be promoted by ADV infection via TLR9, because TLR9 was upregulated after ADV infection, and knockdown of TLR9 reduced ADV-induced GSCs. Consistently, MYD88, as well as total STAT3 and phosphorylated (p-)STAT3, were also upregulated in ADV-induced GSCs. Knockdown of MYD88 or pharmaceutical inhibition of STAT3 attenuated stemness of ADV-induced GSCs. Moreover, we found that ADV infection upregulated lncRNA NEAT1. Knockdown of NEAT1 impaired stemness of ADV-induced GSCs. Lastly, HMGB1, a damage associated molecular pattern (DAMP) that triggers TLR signaling, also upregulated stemness markers in glioma cells. Conclusion: ADV, which has been developed as vectors for gene therapy and oncolytic virus, promotes the formation of GSCs via TLR9/NEAT1/STAT3 signaling.

Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.