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Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation

OBJECTIVES: Lumbar disc herniation (LDH) is a musculoskeletal disease that contributes to low back pain, sciatica, and movement disorder. Existing studies have suggested that the immune environment factors are the primary contributions to LDH. However, its etiology remains unknown. We sought to iden...

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Autores principales: Li, Kai, Li, Shijue, Zhang, Haojie, Lei, Di, Lo, Wai Leung Ambrose, Ding, Minghui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069112/
https://www.ncbi.nlm.nih.gov/pubmed/35531067
http://dx.doi.org/10.3389/fnmol.2022.846554
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author Li, Kai
Li, Shijue
Zhang, Haojie
Lei, Di
Lo, Wai Leung Ambrose
Ding, Minghui
author_facet Li, Kai
Li, Shijue
Zhang, Haojie
Lei, Di
Lo, Wai Leung Ambrose
Ding, Minghui
author_sort Li, Kai
collection PubMed
description OBJECTIVES: Lumbar disc herniation (LDH) is a musculoskeletal disease that contributes to low back pain, sciatica, and movement disorder. Existing studies have suggested that the immune environment factors are the primary contributions to LDH. However, its etiology remains unknown. We sought to identify the potential diagnostic biomarkers and analyze the immune infiltration pattern in LDH. METHODS: The whole-blood gene expression level profiles of GSE124272 and GSE150408 were downloaded from the Gene Expression Omnibus (GEO) database, including that of 25 patients with LDH and 25 healthy volunteers. After merging the two microarray datasets, Differentially Expressed Genes (DEGs) were screened, and a functional correlation analysis was performed. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithm and support vector machine recursive feature elimination (SVM-RFE) were applied to identify diagnostic biomarkers by a cross-validation method. Then, the GSE42611 dataset was used as a validation dataset to detect the expression level of these diagnostic biomarkers in the nucleus pulposus and evaluate their accuracy. The hub genes in the network were identified by the CIBERSORT tool and the Weighted Gene Coexpression Network Analysis (WGCNA). A Spearman correlation analysis between diagnostic markers and infiltrating immune cells was conducted to further illustrate the molecular immune mechanism of LDH. RESULTS: The azurophil granule and the systemic lupus erythematosus pathway were significantly different between the healthy group and the LDH group after gene enrichment analysis. The XLOC_l2_012836, lnc-FGD3-1, and scavenger receptor class A member 5 were correlated with the immune cell infiltration in various degrees. In addition, five hub genes that correlated with LDH were identified, including AQP9, SIRPB2, SLC16A3, LILRB3, and HSPA6. CONCLUSION: The XLOC_l2_012836, lnc-FGD3-1, and SCARA5 might be adopted for the early diagnosis of LDH. The five identified hub genes might have similar pathological mechanisms that contribute to the degeneration of the lumbar disc. The identified hub genes and immune infiltrating pattern extend the knowledge on the potential functioning mechanisms, which offer guidance for the development of therapeutic targets of LDH.
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spelling pubmed-90691122022-05-05 Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation Li, Kai Li, Shijue Zhang, Haojie Lei, Di Lo, Wai Leung Ambrose Ding, Minghui Front Mol Neurosci Neuroscience OBJECTIVES: Lumbar disc herniation (LDH) is a musculoskeletal disease that contributes to low back pain, sciatica, and movement disorder. Existing studies have suggested that the immune environment factors are the primary contributions to LDH. However, its etiology remains unknown. We sought to identify the potential diagnostic biomarkers and analyze the immune infiltration pattern in LDH. METHODS: The whole-blood gene expression level profiles of GSE124272 and GSE150408 were downloaded from the Gene Expression Omnibus (GEO) database, including that of 25 patients with LDH and 25 healthy volunteers. After merging the two microarray datasets, Differentially Expressed Genes (DEGs) were screened, and a functional correlation analysis was performed. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithm and support vector machine recursive feature elimination (SVM-RFE) were applied to identify diagnostic biomarkers by a cross-validation method. Then, the GSE42611 dataset was used as a validation dataset to detect the expression level of these diagnostic biomarkers in the nucleus pulposus and evaluate their accuracy. The hub genes in the network were identified by the CIBERSORT tool and the Weighted Gene Coexpression Network Analysis (WGCNA). A Spearman correlation analysis between diagnostic markers and infiltrating immune cells was conducted to further illustrate the molecular immune mechanism of LDH. RESULTS: The azurophil granule and the systemic lupus erythematosus pathway were significantly different between the healthy group and the LDH group after gene enrichment analysis. The XLOC_l2_012836, lnc-FGD3-1, and scavenger receptor class A member 5 were correlated with the immune cell infiltration in various degrees. In addition, five hub genes that correlated with LDH were identified, including AQP9, SIRPB2, SLC16A3, LILRB3, and HSPA6. CONCLUSION: The XLOC_l2_012836, lnc-FGD3-1, and SCARA5 might be adopted for the early diagnosis of LDH. The five identified hub genes might have similar pathological mechanisms that contribute to the degeneration of the lumbar disc. The identified hub genes and immune infiltrating pattern extend the knowledge on the potential functioning mechanisms, which offer guidance for the development of therapeutic targets of LDH. Frontiers Media S.A. 2022-04-21 /pmc/articles/PMC9069112/ /pubmed/35531067 http://dx.doi.org/10.3389/fnmol.2022.846554 Text en Copyright © 2022 Li, Li, Zhang, Lei, Lo and Ding. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Kai
Li, Shijue
Zhang, Haojie
Lei, Di
Lo, Wai Leung Ambrose
Ding, Minghui
Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation
title Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation
title_full Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation
title_fullStr Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation
title_full_unstemmed Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation
title_short Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation
title_sort computational analysis of the immune infiltration pattern and candidate diagnostic biomarkers in lumbar disc herniation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069112/
https://www.ncbi.nlm.nih.gov/pubmed/35531067
http://dx.doi.org/10.3389/fnmol.2022.846554
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