Cargando…

Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set

BACKGROUND: Mesenchymal stem cells (MSCs) play a crucial role in osteosarcoma (OS) growth and progression. This study conducted a bioinformatics analysis of a single-cell ribonucleic acid sequencing data set and explored the MSC-specific differentially expressed genes (DEGs) in advanced OS. METHODS:...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Haoli, Du, Haoyuan, Liu, Yingnan, Tian, Xiao, Xia, Jinquan, Yang, Shucai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641133/
https://www.ncbi.nlm.nih.gov/pubmed/36388032
http://dx.doi.org/10.21037/tcr-22-2370
_version_ 1784826026954588160
author Jiang, Haoli
Du, Haoyuan
Liu, Yingnan
Tian, Xiao
Xia, Jinquan
Yang, Shucai
author_facet Jiang, Haoli
Du, Haoyuan
Liu, Yingnan
Tian, Xiao
Xia, Jinquan
Yang, Shucai
author_sort Jiang, Haoli
collection PubMed
description BACKGROUND: Mesenchymal stem cells (MSCs) play a crucial role in osteosarcoma (OS) growth and progression. This study conducted a bioinformatics analysis of a single-cell ribonucleic acid sequencing data set and explored the MSC-specific differentially expressed genes (DEGs) in advanced OS. METHODS: MSC-specific DEGs from GSE152048 was extracted using Seurat R package. These DEGs were then subjected to the functional analysis, and several key genes were further identified and underwent a prognosis analysis. RESULTS: A total of 234 upregulated and 280 downregulated DEGs were identified between the MSCs and other cells, and a total of 188 upregulated and 158 downregulated DEGs were identified between the MSCs and osteoblastic cells. The Gene Ontology (GO) functional analysis showed that the specific DEGs between the MSCs and osteoblastic cells were enriched in GO terms such as “collagen catabolic process”, “positive regulation of pathway-restricted SMAD protein phosphorylation”, “osteoblast differentiation”, “regulation of release of cytochrome c from mitochondria” and “interleukin-1 production”. The specific DEGs between the MSCs and osteoblastic cells were subjected to a protein-protein interaction network analysis. Further, a survival analysis of 20 genes with combined scores >0.94 revealed that the low expression of ANXA1 (annexin A1) and TPM1 (tropomyosin 1) was associated with the shorter overall survival of OS patients, while the high expression of FDPS (farnesyl pyrophosphate synthase), IFITM5 (interferon-induced transmembrane protein 5), FKBP11 (FKBP prolyl isomerase 11), SP7, and SQLE (squalene epoxidase) was associated with the shorter overall survival of OS patients. In a further analysis, we compared the expression of ANXA1, FDPS, IFITM5, FKBP11, SP7, SQLE, and TPM1 between the MSCs and high-grade OS cells. Further validation studies using the GSE42352 data set revealed that ANXA1, FKBP11, SP7, and TPM1 were more upregulated in the MSCs than the high-grade OS cells, while FDPS, IFITM5, and SQLE were more downregulated in the MSCs than the high-grade OS cells. CONCLUSIONS: Our bioinformatics analysis revealed 7 hub genes derived from the specific DEGs between the MSCs and osteoblastic cells. The 7 hub genes may serve as potential prognostic biomarkers for patients with OS.
format Online
Article
Text
id pubmed-9641133
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-96411332022-11-15 Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set Jiang, Haoli Du, Haoyuan Liu, Yingnan Tian, Xiao Xia, Jinquan Yang, Shucai Transl Cancer Res Original Article BACKGROUND: Mesenchymal stem cells (MSCs) play a crucial role in osteosarcoma (OS) growth and progression. This study conducted a bioinformatics analysis of a single-cell ribonucleic acid sequencing data set and explored the MSC-specific differentially expressed genes (DEGs) in advanced OS. METHODS: MSC-specific DEGs from GSE152048 was extracted using Seurat R package. These DEGs were then subjected to the functional analysis, and several key genes were further identified and underwent a prognosis analysis. RESULTS: A total of 234 upregulated and 280 downregulated DEGs were identified between the MSCs and other cells, and a total of 188 upregulated and 158 downregulated DEGs were identified between the MSCs and osteoblastic cells. The Gene Ontology (GO) functional analysis showed that the specific DEGs between the MSCs and osteoblastic cells were enriched in GO terms such as “collagen catabolic process”, “positive regulation of pathway-restricted SMAD protein phosphorylation”, “osteoblast differentiation”, “regulation of release of cytochrome c from mitochondria” and “interleukin-1 production”. The specific DEGs between the MSCs and osteoblastic cells were subjected to a protein-protein interaction network analysis. Further, a survival analysis of 20 genes with combined scores >0.94 revealed that the low expression of ANXA1 (annexin A1) and TPM1 (tropomyosin 1) was associated with the shorter overall survival of OS patients, while the high expression of FDPS (farnesyl pyrophosphate synthase), IFITM5 (interferon-induced transmembrane protein 5), FKBP11 (FKBP prolyl isomerase 11), SP7, and SQLE (squalene epoxidase) was associated with the shorter overall survival of OS patients. In a further analysis, we compared the expression of ANXA1, FDPS, IFITM5, FKBP11, SP7, SQLE, and TPM1 between the MSCs and high-grade OS cells. Further validation studies using the GSE42352 data set revealed that ANXA1, FKBP11, SP7, and TPM1 were more upregulated in the MSCs than the high-grade OS cells, while FDPS, IFITM5, and SQLE were more downregulated in the MSCs than the high-grade OS cells. CONCLUSIONS: Our bioinformatics analysis revealed 7 hub genes derived from the specific DEGs between the MSCs and osteoblastic cells. The 7 hub genes may serve as potential prognostic biomarkers for patients with OS. AME Publishing Company 2022-10 /pmc/articles/PMC9641133/ /pubmed/36388032 http://dx.doi.org/10.21037/tcr-22-2370 Text en 2022 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Jiang, Haoli
Du, Haoyuan
Liu, Yingnan
Tian, Xiao
Xia, Jinquan
Yang, Shucai
Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set
title Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set
title_full Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set
title_fullStr Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set
title_full_unstemmed Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set
title_short Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set
title_sort identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9641133/
https://www.ncbi.nlm.nih.gov/pubmed/36388032
http://dx.doi.org/10.21037/tcr-22-2370
work_keys_str_mv AT jianghaoli identificationofnovelprognosticbiomarkersforosteosarcomaabioinformaticsanalysisofdifferentiallyexpressedgenesinthemesenchymalstemcellsfromsinglecellsequencingdataset
AT duhaoyuan identificationofnovelprognosticbiomarkersforosteosarcomaabioinformaticsanalysisofdifferentiallyexpressedgenesinthemesenchymalstemcellsfromsinglecellsequencingdataset
AT liuyingnan identificationofnovelprognosticbiomarkersforosteosarcomaabioinformaticsanalysisofdifferentiallyexpressedgenesinthemesenchymalstemcellsfromsinglecellsequencingdataset
AT tianxiao identificationofnovelprognosticbiomarkersforosteosarcomaabioinformaticsanalysisofdifferentiallyexpressedgenesinthemesenchymalstemcellsfromsinglecellsequencingdataset
AT xiajinquan identificationofnovelprognosticbiomarkersforosteosarcomaabioinformaticsanalysisofdifferentiallyexpressedgenesinthemesenchymalstemcellsfromsinglecellsequencingdataset
AT yangshucai identificationofnovelprognosticbiomarkersforosteosarcomaabioinformaticsanalysisofdifferentiallyexpressedgenesinthemesenchymalstemcellsfromsinglecellsequencingdataset