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 trea...

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Autores principales:	Guimin Qin, Longting Du, Yuying Ma, Yu Yin, Liming Wang
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Glioma Single-cell gene expression profile Cell type Tumor gene biomarkers Internal medicine RC31-1245 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/a0ffd114be374eaa813f6e766b708cfa
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spelling	oai:doaj.org-article:a0ffd114be374eaa813f6e766b708cfa2021-12-05T12:05:25ZGene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network10.1186/s12920-021-01115-61755-8794https://doaj.org/article/a0ffd114be374eaa813f6e766b708cfa2021-12-01T00:00:00Zhttps://doi.org/10.1186/s12920-021-01115-6https://doaj.org/toc/1755-8794Abstract 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.Guimin QinLongting DuYuying MaYu YinLiming WangBMCarticleGliomaSingle-cell gene expression profileCell typeTumor gene biomarkersInternal medicineRC31-1245GeneticsQH426-470ENBMC Medical Genomics, Vol 14, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Glioma Single-cell gene expression profile Cell type Tumor gene biomarkers Internal medicine RC31-1245 Genetics QH426-470
spellingShingle	Glioma Single-cell gene expression profile Cell type Tumor gene biomarkers Internal medicine RC31-1245 Genetics QH426-470 Guimin Qin Longting Du Yuying Ma Yu Yin Liming Wang Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network
description	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.
format	article
author	Guimin Qin Longting Du Yuying Ma Yu Yin Liming Wang
author_facet	Guimin Qin Longting Du Yuying Ma Yu Yin Liming Wang
author_sort	Guimin Qin
title	Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network
title_short	Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network
title_full	Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network
title_fullStr	Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network
title_full_unstemmed	Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network
title_sort	gene biomarker prediction in glioma by integrating scrna-seq data and gene regulatory network
publisher	BMC
publishDate	2021
url	https://doaj.org/article/a0ffd114be374eaa813f6e766b708cfa
work_keys_str_mv	AT guiminqin genebiomarkerpredictioningliomabyintegratingscrnaseqdataandgeneregulatorynetwork AT longtingdu genebiomarkerpredictioningliomabyintegratingscrnaseqdataandgeneregulatorynetwork AT yuyingma genebiomarkerpredictioningliomabyintegratingscrnaseqdataandgeneregulatorynetwork AT yuyin genebiomarkerpredictioningliomabyintegratingscrnaseqdataandgeneregulatorynetwork AT limingwang genebiomarkerpredictioningliomabyintegratingscrnaseqdataandgeneregulatorynetwork
_version_	1718372274769231872

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

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