Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network

Abstract

Background Although 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. Methods In 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. Results We 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. Conclusions The 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.

WITHDRAWN: LonP1 Drives Proneural Mesenchymal Transition in IDH1-R132H Diffuse Glioma

“…The counts data from each dataset was used to reassign cell types using scSorter 41 with the following markers used to identify Neoplastic (e.g. NDUFS5, NDUFA1, NDUFA13, NDUFB8 42 , CEND1, DCHS1, TPP1, GATD1, RNH1, SMCR8, SMPD1, CD151 43 , "PTPRZ1", "OLIG2", "PDGFRA", "DLL3", "AQP4", "CLU" 44 ), Proliferating Tumor Cells ("MKI67" 44 ), Oligodendrocytes ("MBP", "TF", "PLP1", "MAG", "MOG", "CLDN11" 45 , "PLP1", "MOG", "SOX10", "MBP" 36,46 ), Astrocytes ('S100B', 'GFAP', "SLC1A3", "GLAST", "MLC1" 45 , "GFAP", "ALDH1L1", "SOX9", "AQP4" 36,46 ), Macrophages ("CD14", "AIF1", "FCER1G", "FCGR3A", "TYROBP", "CSF1R" 45 , "C1QA", "CX3CR1", "CCL3", "TNF" 36,46 , Endothelial (), Neuron ("VGLUT1", "STMN2", "SYT1", "SYN1" 36,46 ), Neural progenitor cells (e.g. "SOX4", "SOX11", "DCX" 45 ,), T-Cillium ("IGFBPL1", "HYDIN" 44 ), T-Cells ("CD2", "CD3D", "CD3E", "CD3G" 45 ,), and Non-identified Immune Cells ("PTPRC", "CD3E", "P2RY12", "CD163", "CXCL1", "FCGR3B", "FCN1" 44 ), and Endothelial Cells ("CLDN5", "ELTD1", "ITM2A", "ESAM" 36,46 ).…”

A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma

“…Schaum et al [24] performed the CIBERSORTx deconvolution algorithm on annotated scRNA-seq to quantify the abundance of immune cells in 17 organs at ten ages based on their massive bulk RNA seq data which confirmed her findings with scRNA-seq. Recent studies reported applying scRNA-seq and bulk RNA seq data to analyze the tumor heterogeneity and immune cells in ovarian cancer [25], glioma [26], and esophageal squamous cell carcinoma [27]. Jerby-Arnon et al [28] identified a cancer cell-related T resistance program to predict the immunotherapy response in melanoma patients.…”

Integrated Analysis of Cortex Single-Cell Transcriptome and Serum Proteome Reveals the Novel Biomarkers in Alzheimer’s Disease

“…Single-cell RNA sequencing (scRNA-seq) is the amplification and sequencing of the transcriptome at the single-cell level [ 9 ], which provides an effective method for studying the cellular heterogeneity of the brain and illuminates the complex mechanisms of the normal physiological or pathological development process [ 10 ]. At present, scRNA-seq is widely used in the research of neurological diseases, such as AD [ 11 ], brain aging [ 12 ], and glioma [ 13 ]. At the same time, the cellular and biochemical components of blood play a central role in human physiology, and their dynamic levels are thought to correlate with the individual’s health and disease states [ 14 ].…”