Dissecting the Invasion-Associated Long Non-coding RNAs Using Single-Cell RNA-Seq Data of Glioblastoma

Abstract

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.

Knockdown of lncRNA CCAT1 Inhibits the Progression of Colorectal Cancer via hsa-miR-4679 Mediating the Downregulation of GNG10

“…Long noncoding RNAs, which are polyadenylated, could be captured by poly(dT) oligos and detected by 10x genomics single-cell RNA sequencing. Recently, long noncoding RNAs involved in the development and progression of glioblastoma were identified [42,43]. To further identify more candidates involved in CRC, transcriptome analyses using patient tissues in single-cell resolution will be useful to identify abnormally expressed noncoding and coding RNAs in a certain cell type.…”

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals

“…For each gene, we mapped it as seed node into the co-expression protein interaction network, and the dysregulated information derived from the seed node was diffused to genes according to the topological structure of the co-expression protein interaction network. The dysregulated information could also restart from the seed nodes with probability r. The formula for the RWR principle was calculated as follows [17,18]:…”

Systematic characterization and biological functions of non‐coding RNAs in glioblastoma

“…Moreover, by exploiting invasion‐related lncRNAs from single‐cell RNA sequencing data, it was found that patients with GBM exhibiting high NEAT1 expression had poor OS and DFS, and could promote the occurrence and progression of the malignant phenotype in patients with GBM. 89 …”

“…Moreover, by exploiting invasion-related lncRNAs from single-cell RNA sequencing data, it was found that patients with GBM exhibiting high NEAT1 expression had poor OS and DFS, and could promote the occurrence and progression of the malignant phenotype in patients with GBM. 89 Previous studies have used microarray analyses to construct a ceRNA network and to explore whether lncRNAs play roles of ceRNA in GBM as well as the specific molecular mechanism. It has been reported that lncRNA OXCT1-AS1 competitively binds to miR-195 and negatively regulates CDC25A to facilitate the proliferation, migration and invasion of GBM cells, while the number of cells in G 0 /G 1 phase decreases and the number of cells in G 2 /M phase increases, which promotes the malignant progression of GBM.…”

“…Yang et al 88 constructed EMT‐related lncRNA prognostic signatures for patients with GBM, and confirmed that EMT and metastasis‐related pathways are risk indicators for patients with GBM. Moreover, by exploiting invasion‐related lncRNAs from single‐cell RNA sequencing data, it was found that patients with GBM exhibiting high NEAT1 expression had poor OS and DFS, and could promote the occurrence and progression of the malignant phenotype in patients with GBM 89 …”