Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

Glioblastoma (GBM) is characterized by rapid and lethal infiltration of brain tissue, which is the primary cause of treatment failure and deaths for GBM. Therefore, understanding the molecular mechanisms of tumor cell invasion is crucial for the treatment of GBM. In this study, we dissected the single-cell RNA-seq data of 3345 cells from four patients and identified dysregulated genes including long non-coding RNAs (lncRNAs), which were involved in the development and progression of GBM. Based on co-expression network analysis, we identified a module (M1) that significantly overlapped with the largest number of dysregulated genes and was confirmed to be associated with GBM invasion by integrating EMT signature, experiment-validated invasive marker and pseudotime trajectory analysis. Further, we denoted invasion-associated lncRNAs which showed significant correlations with M1 and revealed their gradually increased expression levels along the tumor cell invasion trajectory, such as VIM-AS1, WWTR1-AS1, and NEAT1. We also observed the contribution of higher expression of these lncRNAs to poorer survival of GBM patients. These results were mostly recaptured in another validation data of 7930 single cells from 28 GBM patients. Our findings identified lncRNAs that played critical roles in regulating or controlling cell invasion and migration of GBM and provided new insights into the molecular mechanisms underlying GBM invasion as well as potential targets for the treatment of GBM.