Abstract
BackgroundGlioma is the most common malignant tumor of the brain. The existence of metastatic tumor cells is an important cause of recurrence even after radical glioma resection.MethodsSingle-cell sequencing data and high-throughput data were downloaded from GEO database and TCGA/CGGA database. By means of PCA and tSNE clustering methods, metastasis-associated genes in glioma were identified. GSEA explored possible biological functions that these metastasis-associated genes may participate in. Univariate and multivariate Cox regression were used to construct a prognostic model.ResultsGlioma metastatic cells and metastasis-associated genes were identified. The prognostic model based on metastasis-associated genes had good sensitivity and specificity for the prognosis of glioma. These genes may be involved in signal pathways such as cellular protein catabolic process, p53 signaling pathway, transcriptional misregulation in cancer and JAK-STAT signaling pathway.ConclusionThis study explored glioma metastasis-associated genes through single-cell sequencing data mining, and aimed to identify prognostic metastasis-associated signatures for glioma and may provide potential targets for further cancer research.
Highlights
Glioma is the most common malignant tumor of the brain
Acquisition and quality control of glioma expression sequencing data The glioma single-cell sequencing data were sourced from GSE84465 of the GEO database (Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/). “limma” package of R software was used for preliminary data correction
The results showed that the The area under ROC curves (AUC) for 1-year and 3-years Overall survival (OS) of the training cohort were 0.661 and 0.701, respectively; the AUC for the 3-year and 5-year OS of the validation cohort were 0.563 and 0.605, respectively
Summary
The existence of metastatic tumor cells is an important cause of recurrence even after radical glioma resection. Research on glioma especially on the molecular mechanisms has been increasing. In 2016, the World Health Organization first applied histology and molecular classification to define central nervous system tumors simultaneously [1]. The metastatic tumor cells can be located a few Traditional high-throughput sequencing data analysis is difficult to analyze the heterogeneity inside the tumor [7,8,9], while the single-cell sequencing technology developed in recent years allows researchers to view the expression profile of each cell and explore the expression heterogeneity of single cells within the tumor mass [10,11,12].
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