Comprehensive Prognostic Assessment by Integrating Single-Cell and Bulk RNA-seq Signatures in Glioblastoma

Background Glioblastoma (GBM) is one of the most challenging malignancies in all cancers. The immune response in the tumor microenvironment has an important impact on the prognosis of GBM patients. Therefore, it becomes critical to correlate tumors with the immune response in their microenvironment and to screen for genes of potential prognostic value associated with the immune microenvironment. Methods We first evaluated the tumor microenvironment on bulk RNA-seq data using the Xcell and ESTIMATE algorithms, followed by an integrated analysis of single-cell and bulk RNA-seq data from the GEO database, with a special focus on GBM-related datasets. From this analysis, we identified a set of differentially expressed genes (DEGs) that were consistently observed in scRNA-seq and bulk RNA-seq datasets. We then performed random forest analysis on these DEGs to identify core genes for our prognostic model. Findings regarding the function of IFI44 in the glioma cell line were validated by siRNA knockdown, overexpression, and transwell experiments. Result We ultimately identified 235 DEGs that were consistently observed in both single-cell and bulk RNA-seq datasets. Through Cox regression and random forest analysis, we further identified nine genes, namely AK5, ATP2B1, CNTN2, GABARAPL1, HK2, IFI44, PLP2, S100A11 and ST18, which exhibited a strong association with glioblastoma multiforme (GBM) prognosis. Notably, these genes were predominantly expressed in macrophages, DC14 cells, and T cells within the single-cell dataset. Patients classified as low-risk demonstrated significantly better prognoses compared to those classified as high-risk. Importantly, these findings were robustly reproduced in the test dataset. The IFI44 could promote both glioma cells proliferation and migration in vitro. Higher levels of IFI44 expression are associated with poorer survival rates. Conclusions We identified nine genes as prognostic biomarkers in GBM. These results may provide valuable insights into the molecular mechanisms underlying GBM progression.