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GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data

BACKGROUND: Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes. MET...

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Autores principales: Zhang, ZhaoLiang, Huo, JianZhong, Ji, XingHua, Wei, LinDong, Zhang, Jinfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498557/
https://www.ncbi.nlm.nih.gov/pubmed/37700277
http://dx.doi.org/10.1186/s12891-023-06854-4
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author Zhang, ZhaoLiang
Huo, JianZhong
Ji, XingHua
Wei, LinDong
Zhang, Jinfeng
author_facet Zhang, ZhaoLiang
Huo, JianZhong
Ji, XingHua
Wei, LinDong
Zhang, Jinfeng
author_sort Zhang, ZhaoLiang
collection PubMed
description BACKGROUND: Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes. METHODS: Gene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded. RESULTS: By developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD. CONCLUSIONS: Our study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-023-06854-4.
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spelling pubmed-104985572023-09-14 GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data Zhang, ZhaoLiang Huo, JianZhong Ji, XingHua Wei, LinDong Zhang, Jinfeng BMC Musculoskelet Disord Research BACKGROUND: Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes. METHODS: Gene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded. RESULTS: By developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD. CONCLUSIONS: Our study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-023-06854-4. BioMed Central 2023-09-12 /pmc/articles/PMC10498557/ /pubmed/37700277 http://dx.doi.org/10.1186/s12891-023-06854-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, ZhaoLiang
Huo, JianZhong
Ji, XingHua
Wei, LinDong
Zhang, Jinfeng
GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data
title GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data
title_full GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data
title_fullStr GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data
title_full_unstemmed GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data
title_short GREM1, LRPPRC and SLC39A4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data
title_sort grem1, lrpprc and slc39a4 as potential biomarkers of intervertebral disc degeneration: a bioinformatics analysis based on multiple microarray and single-cell sequencing data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498557/
https://www.ncbi.nlm.nih.gov/pubmed/37700277
http://dx.doi.org/10.1186/s12891-023-06854-4
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