Single-Cell RNA-Seq Data Analysis Reveals Functionally Important Cell-to-Cell Interactions Inside Glioma

Glioma is the most commonly diagnosed malignant and aggressive brain cancer in adults.Traditional researches mainly explored the expression profile of glioma at cellpopulation level,but ignored the heterogeneity and interactions of among glioma cells.Here,we systematically analyzed the single-cell RNA-seq(scRNA-seq)data of 6341 glioma cells and identified neoplastic and healthy cells infiltrating in tumor microenvironment.A total of 16 significantly correlated autorine ligand-receptor signal pairs inside neoplastic cells were identified based on the scRNA-seq and TCGA data of glioma.Furthermore,we explored the intercellular communications between cancer stemlike cells(CSCs)and macrophages,and identified 66 ligand-receptor pairs,some of which could significantly affect prognostic outcomes.An efficient machine learning model was constructed to accurateely predict the prognosis of glioma patients based on the ligand-receptor interactions.Collectively,our study not only reveals functionally important cell-to-cell interactions inside glioma,but also provides potential markers for the prognosis of glioma patients.