Abstract
BackgroundAlthough great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear. Single-cell sequencing technology provides a new perspective for researchers to explore the pathogens of tumors to further help make treatment and prognosis decisions for patients with tumors.MethodsIn this study, we proposed an algorithm framework to explore the molecular mechanisms of glioma by integrating single-cell gene expression profiles and gene regulatory relations. First, since there were great differences among malignant cells from different glioma samples, we analyzed the expression status of malignant cells for each sample, and then tumor consensus genes were identified by constructing and analyzing cell-specific networks. Second, to comprehensively analyze the characteristics of glioma, we integrated transcriptional regulatory relationships and consensus genes to construct a tumor-specific regulatory network. Third, we performed a hybrid clustering analysis to identify glioma cell types. Finally, candidate tumor gene biomarkers were identified based on cell types and known glioma-related genes.ResultsWe got six identified cell types using the method we proposed and for these cell types, we performed functional and biological pathway enrichment analyses. The candidate tumor gene biomarkers were analyzed through survival analysis and verified using literature from PubMed.ConclusionsThe results showed that these candidate tumor gene biomarkers were closely related to glioma and could provide clues for the diagnosis and prognosis of patients with glioma. In addition, we found that four of the candidate tumor gene biomarkers (NDUFS5, NDUFA1, NDUFA13, and NDUFB8) belong to the NADH ubiquinone oxidoreductase subunit gene family, so we inferred that this gene family may be strongly related to glioma.
Highlights
Great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear
Overview of the computational framework We proposed a computational framework, which consisted of four steps (Fig. 1), to gain insight into the molecular mechanisms of glioma
For each single-sample single-cell gene expression data, we explored the gene expression patterns of all malignant cells through principal component analysis (PCA), cellspecific network construction, and differential gene identification
Summary
Great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear. Single-cell sequencing technology provides a new perspective for researchers to explore the pathogens of tumors to further help make treatment and prognosis decisions for patients with tumors. Malignant tumors have a very large impact on human health due to their high mortality rate and high recurrence rate. Glioma can be classified into 4 grades from WHO grade I to IV, with the higher WHO grade is, the more malignant the glioma is. The WHO grades of astrocytoma and oligodendroglioma are II or III. The WHO grade of glioblastoma is IV [2]. It is of great importance to explore the molecular mechanisms of glioma, as these may help researchers develop glioma treatment strategies and drugs
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