Integrated single-cell and bulk RNA sequencing analysis establishes a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in neuroblastoma.

BACKGROUND: Cancer-associated fibroblasts (CAFs) greatly contribute to the growth, invasion, metastasis and drug resistance of neuroblastoma (NB). This study aimed to construct a CAF-related prognostic model and identify the immune status of patients with NB via single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq). METHODS: ScRNA-seq data of NB acquired from the Gene Expression Omnibus (GEO) database were used to identify cellular subpopulations. Bulk gene expression data were downloaded from GEO, The Cancer Genome Atlas (TCGA) and ArrayExpress databases and the prognostic model was constructed using univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in immune infiltration, therapeutic responses and signaling pathways between the high- and low-risk groups were investigated. Finally, immunohistochemistry was performed to evaluate the protein expressions and a nomogram based on the risk signature and clinical characteristics was constructed. RESULTS: ScRNA-seq data of eight NB samples were integrated to identify 253 marker genes for CAF. An eight-gene prognostic CAF-related signature was established based on the GEO data. The CAF model was strongly associated with immune infiltration, drug response and active signaling pathways in tumors. Univariate and multivariate Cox regression analyses verified that the CAF model was as an independent prognostic indicator, and a nomogram integrating the clinical signature and CAF-related risk signature was constructed for clinical prediction. CONCLUSIONS: The CAF-related signature can effectively predict the prognosis of NB and provide new genomic evidence for anti-CAF immunotherapeutic strategies.