Yuxuan Zhang, Qibing Huang

Department of Emergency Neurosurgical Intensive Care Unit, Qilu Hospital of Shandong University, Jinan, 250012, People’s Republic of China

Correspondence: Qibing Huang, Department of Emergency Neurosurgical Intensive Care Unit, Qilu Hospital of Shandong University, Jinan, 250012, People’s Republic of China, Tel +86 18560083135, Email huangqibing01@163.com

Purpose: Cancer-associated fibroblasts (CAFs) could promote the progression and migration of tumors. However, the roles of CAFs infiltration related genes in glioblastoma (GBM) were still unclear.
Methods: GBM-related transcriptome data and clinical information were downloaded from the UCSC Xena and CGGA databases. In this study, the abundance of fibroblasts were calculated by “MCPcounter”, and the CAFs infiltration related genes were identified by “WGCNA”. Then, the biomarkers of GBM were screened out, and based on it, the survival risk model (risk score) and the prognostic model (nomogram) were constructed to clinical predict the survival of GBM. Moreover, the function and mutation analyses were performed to further study the mechanisms of GBM. Furthermore, the competitive endogenous RNAs (ceRNA) regulatory network were used to reveal the potential regulation of biomarkers.
Results: Totals of 46 CAFs infiltration related genes were associated with focal adhesion. Four biomarkers, including STC1, BDKRB2, SOCS3 and FURIN were identified, and all of them were negative factors. Nomogram constructed based on risk scores and clinical indicators had good predictive performance (AUC > 0.68). Noticeable, COL5A1 might be the key gene, which were extremely positively associated with all these four biomarkers. Besides, the genes in high-risk groups were associated with the functions of epithelial mesenchymal transition (EMT) and angiogenesis. In addition, hsa-miR-107 could regulate STC1 through the TGF-β signaling pathway and further regulating the migration and invasion of tumour.
Conclusion: This study identified four CAFs infiltration related biomarkers associated with the prognosis of GBM. This finding might help to deepen the understanding the roles of CAFs in development of GBM.

Keywords: glioblastoma, cancer-associated fibroblasts, biomarkers, survival risk model, prognosis