Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood

Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood.Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) databa...

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Autores principales:	Xin Jin, Jun Wang, Lina Ge, Qing Hu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	sciatica immunity peripheral blood biomarker consensus clustering Genetics QH426-470
Acceso en línea:	https://doaj.org/article/88bfe9e647bf4794b421968bd3c6a32d
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id	oai:doaj.org-article:88bfe9e647bf4794b421968bd3c6a32d
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spelling	oai:doaj.org-article:88bfe9e647bf4794b421968bd3c6a32d2021-12-02T11:39:01ZIdentification of Immune-Related Biomarkers for Sciatica in Peripheral Blood1664-802110.3389/fgene.2021.781945https://doaj.org/article/88bfe9e647bf4794b421968bd3c6a32d2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.781945/fullhttps://doaj.org/toc/1664-8021Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood.Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset and extracted immune-related genes for further analysis. Differentially expressed immune-related genes (DEIRGs) between healthy controls and patients with sciatica were selected using the “limma” package and verified in clinical specimens by quantitative reverse transcription PCR (RT-qPCR). A diagnostic immune-related gene signature was established using the training model and random forest (RF), generalized linear model (GLM), and support vector machine (SVM) models. Sciatica patient subtypes were identified using the consensus clustering method.Results: Thirteen significant DEIRGs were acquired, of which five (CRP, EREG, FAM19A4, RLN1, and WFIKKN1) were selected to establish a diagnostic immune-related gene signature according to the most appropriate training model, namely, the RF model. A clinical application nomogram model was established based on the expression level of the five DEIRGs. The sciatica patients were divided into two subtypes (C1 and C2) according to the consensus clustering method.Conclusions: Our research established a diagnostic five immune-related gene signature to discriminate sciatica and identified two sciatica subtypes, which may be beneficial to the clinical diagnosis and treatment of sciatica.Xin JinJun WangLina GeQing HuFrontiers Media S.A.articlesciaticaimmunityperipheral bloodbiomarkerconsensus clusteringGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sciatica immunity peripheral blood biomarker consensus clustering Genetics QH426-470
spellingShingle	sciatica immunity peripheral blood biomarker consensus clustering Genetics QH426-470 Xin Jin Jun Wang Lina Ge Qing Hu Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood
description	Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood.Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset and extracted immune-related genes for further analysis. Differentially expressed immune-related genes (DEIRGs) between healthy controls and patients with sciatica were selected using the “limma” package and verified in clinical specimens by quantitative reverse transcription PCR (RT-qPCR). A diagnostic immune-related gene signature was established using the training model and random forest (RF), generalized linear model (GLM), and support vector machine (SVM) models. Sciatica patient subtypes were identified using the consensus clustering method.Results: Thirteen significant DEIRGs were acquired, of which five (CRP, EREG, FAM19A4, RLN1, and WFIKKN1) were selected to establish a diagnostic immune-related gene signature according to the most appropriate training model, namely, the RF model. A clinical application nomogram model was established based on the expression level of the five DEIRGs. The sciatica patients were divided into two subtypes (C1 and C2) according to the consensus clustering method.Conclusions: Our research established a diagnostic five immune-related gene signature to discriminate sciatica and identified two sciatica subtypes, which may be beneficial to the clinical diagnosis and treatment of sciatica.
format	article
author	Xin Jin Jun Wang Lina Ge Qing Hu
author_facet	Xin Jin Jun Wang Lina Ge Qing Hu
author_sort	Xin Jin
title	Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood
title_short	Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood
title_full	Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood
title_fullStr	Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood
title_full_unstemmed	Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood
title_sort	identification of immune-related biomarkers for sciatica in peripheral blood
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/88bfe9e647bf4794b421968bd3c6a32d
work_keys_str_mv	AT xinjin identificationofimmunerelatedbiomarkersforsciaticainperipheralblood AT junwang identificationofimmunerelatedbiomarkersforsciaticainperipheralblood AT linage identificationofimmunerelatedbiomarkersforsciaticainperipheralblood AT qinghu identificationofimmunerelatedbiomarkersforsciaticainperipheralblood
_version_	1718395771849539584

Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood

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