Integration analysis of single-cell and spatial transcriptomics reveal the cellular heterogeneity landscape in glioblastoma and establish a polygenic risk model.

Abstract

Glioblastoma (GBM) is adults' most common and fatally malignant brain tumor. The heterogeneity is the leading cause of treatment failure. However, the relationship between cellular heterogeneity, tumor microenvironment, and GBM progression is still elusive. Integrated analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing (stRNA-seq) of GBM were conducted to analyze the spatial tumor microenvironment. We investigated the subpopulation heterogeneity of malignant cells through gene set enrichment analyses, cell communications analyses, and pseudotime analyses. Significantly changed genes of the pseudotime analysis were screened to create a tumor progress-related gene risk score (TPRGRS) using Cox regression algorithms in the bulkRNA-sequencing(bulkRNA-seq) dataset. We combined the TPRGRS and clinical characteristics to predict the prognosis of patients with GBM. Furthermore, functional analysis was applied to uncover the underlying mechanisms of the TPRGRS. GBM cells were accurately charted to their spatial locations and uncovered their spatial colocalization. The malignant cells were divided into five clusters with transcriptional and functional heterogeneity, including unclassified malignant cells and astrocyte-like, mesenchymal-like, oligodendrocytes-progenitor-like, and neural-progenitor-like malignant cells. Cell-cell communications analysis in scRNA-seq and stRNA-seq identified ligand-receptor pairs of the CXCL, EGF, FGF, and MIF signaling pathways as bridges implying that tumor microenvironment may cause malignant cells' transcriptomic adaptability and disease progression. Pseudotime analysis showed the differentiation trajectory of GBM cells from proneural to mesenchymal transition and identified genes or pathways that affect cell differentiation. TPRGRS could successfully divide patients with GBM in three datasets into high- and low-risk groups, which was proved to be a prognostic factor independent of routine clinicopathological characteristics. Functional analysis revealed the TPRGRS associated with growth factor binding, cytokine activity, signaling receptor activator activity functions, and oncogenic pathways. Further analysis revealed the association of the TPRGRS with gene mutations and immunity in GBM. Finally, the external datasets and qRT-PCR verified high expressions of the TPRGRS mRNAs in GBM cells. Our study provides novel insights into heterogeneity in GBM based on scRNA-seq and stRNA-seq data. Moreover, our study proposed a malignant cell transition-based TPRGRS through integrated analysis of bulkRNA-seq and scRNA-seq data, combined with the routine clinicopathological evaluation of tumors, which may provide more personalized drug regimens for GBM patients.