Loading…
Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis
To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology. Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as...
Saved in:
Published in: | Frontiers in immunology 2022-12, Vol.13, p.839197 |
---|---|
Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c465t-70acd29b831fc8435e201d5a0ef106507128530fdd1fb5d03b300857db944e493 |
---|---|
cites | cdi_FETCH-LOGICAL-c465t-70acd29b831fc8435e201d5a0ef106507128530fdd1fb5d03b300857db944e493 |
container_end_page | |
container_issue | |
container_start_page | 839197 |
container_title | Frontiers in immunology |
container_volume | 13 |
creator | Wang, Lin Yang, Zhihua Yu, Hangxing Lin, Wei Wu, Ruoxi Yang, Hongtao Yang, Kang |
description | To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.
Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.
Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.
C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future. |
doi_str_mv | 10.3389/fimmu.2022.839197 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_50606871a07847fa8ba3886ff61ad62e</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_50606871a07847fa8ba3886ff61ad62e</doaj_id><sourcerecordid>2755804332</sourcerecordid><originalsourceid>FETCH-LOGICAL-c465t-70acd29b831fc8435e201d5a0ef106507128530fdd1fb5d03b300857db944e493</originalsourceid><addsrcrecordid>eNpVkUtv1TAQhSMEaqvSH9ANypLNvfgdZ4OEKgqVKsEC1pZjj3NdJXawHR7_vr5NqVpvPPJ8czxHp2kuMdpTKvsPzs_zuieIkL2kPe67V80ZFoLtKCHs9bP6tLnI-Q7Vw3pKKT9pTqnglCAsz5r8PYH1pvgwttbrMcRcvGlHCNDC3yVBzj6GdknR-Qlyq3OOxusCtv3jy6E9LlFRH2q7JF2OsK98rSCUvEHTuqy5DbAcki8-v23eOD1luHi8z5uf159_XH3d3X77cnP16XZnmOBl1yFtLOkHSbEzklEOdWXLNQKHkeCow0Ryipy12A3cIjpQhCTv7NAzBtXreXOz6dqo79SS_KzTPxW1Vw8PMY1Kp-p2AsWRQEJ2WKNOss5pOWgqpXBOYG0Fgar1cdNa1mEGa6q5pKcXoi87wR_UGH-rvuOcI14F3j8KpPhrhVzU7LOBadIB4poVqZxEjFJSUbyhJsWcE7inbzBSx-zVQ_bqmL3asq8z757v9zTxP2l6Dw4SrmY</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2755804332</pqid></control><display><type>article</type><title>Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis</title><source>PubMed Central Free</source><creator>Wang, Lin ; Yang, Zhihua ; Yu, Hangxing ; Lin, Wei ; Wu, Ruoxi ; Yang, Hongtao ; Yang, Kang</creator><creatorcontrib>Wang, Lin ; Yang, Zhihua ; Yu, Hangxing ; Lin, Wei ; Wu, Ruoxi ; Yang, Hongtao ; Yang, Kang</creatorcontrib><description>To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.
Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.
Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.
C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.</description><identifier>ISSN: 1664-3224</identifier><identifier>EISSN: 1664-3224</identifier><identifier>DOI: 10.3389/fimmu.2022.839197</identifier><identifier>PMID: 36532018</identifier><language>eng</language><publisher>Switzerland: Frontiers Media S.A</publisher><subject>biomarkers ; CIBERSORT ; comprehensive bioinformatics ; Computational Biology ; Humans ; immune infiltration ; Immunology ; Lupus Erythematosus, Systemic ; lupus nephritis ; Lupus Nephritis - diagnosis ; Lupus Nephritis - genetics ; Machine Learning ; Transcriptome</subject><ispartof>Frontiers in immunology, 2022-12, Vol.13, p.839197</ispartof><rights>Copyright © 2022 Wang, Yang, Yu, Lin, Wu, Yang and Yang.</rights><rights>Copyright © 2022 Wang, Yang, Yu, Lin, Wu, Yang and Yang 2022 Wang, Yang, Yu, Lin, Wu, Yang and Yang</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c465t-70acd29b831fc8435e201d5a0ef106507128530fdd1fb5d03b300857db944e493</citedby><cites>FETCH-LOGICAL-c465t-70acd29b831fc8435e201d5a0ef106507128530fdd1fb5d03b300857db944e493</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755505/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755505/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36532018$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Lin</creatorcontrib><creatorcontrib>Yang, Zhihua</creatorcontrib><creatorcontrib>Yu, Hangxing</creatorcontrib><creatorcontrib>Lin, Wei</creatorcontrib><creatorcontrib>Wu, Ruoxi</creatorcontrib><creatorcontrib>Yang, Hongtao</creatorcontrib><creatorcontrib>Yang, Kang</creatorcontrib><title>Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis</title><title>Frontiers in immunology</title><addtitle>Front Immunol</addtitle><description>To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.
Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.
Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.
C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.</description><subject>biomarkers</subject><subject>CIBERSORT</subject><subject>comprehensive bioinformatics</subject><subject>Computational Biology</subject><subject>Humans</subject><subject>immune infiltration</subject><subject>Immunology</subject><subject>Lupus Erythematosus, Systemic</subject><subject>lupus nephritis</subject><subject>Lupus Nephritis - diagnosis</subject><subject>Lupus Nephritis - genetics</subject><subject>Machine Learning</subject><subject>Transcriptome</subject><issn>1664-3224</issn><issn>1664-3224</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVkUtv1TAQhSMEaqvSH9ANypLNvfgdZ4OEKgqVKsEC1pZjj3NdJXawHR7_vr5NqVpvPPJ8czxHp2kuMdpTKvsPzs_zuieIkL2kPe67V80ZFoLtKCHs9bP6tLnI-Q7Vw3pKKT9pTqnglCAsz5r8PYH1pvgwttbrMcRcvGlHCNDC3yVBzj6GdknR-Qlyq3OOxusCtv3jy6E9LlFRH2q7JF2OsK98rSCUvEHTuqy5DbAcki8-v23eOD1luHi8z5uf159_XH3d3X77cnP16XZnmOBl1yFtLOkHSbEzklEOdWXLNQKHkeCow0Ryipy12A3cIjpQhCTv7NAzBtXreXOz6dqo79SS_KzTPxW1Vw8PMY1Kp-p2AsWRQEJ2WKNOss5pOWgqpXBOYG0Fgar1cdNa1mEGa6q5pKcXoi87wR_UGH-rvuOcI14F3j8KpPhrhVzU7LOBadIB4poVqZxEjFJSUbyhJsWcE7inbzBSx-zVQ_bqmL3asq8z757v9zTxP2l6Dw4SrmY</recordid><startdate>20221202</startdate><enddate>20221202</enddate><creator>Wang, Lin</creator><creator>Yang, Zhihua</creator><creator>Yu, Hangxing</creator><creator>Lin, Wei</creator><creator>Wu, Ruoxi</creator><creator>Yang, Hongtao</creator><creator>Yang, Kang</creator><general>Frontiers Media S.A</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20221202</creationdate><title>Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis</title><author>Wang, Lin ; Yang, Zhihua ; Yu, Hangxing ; Lin, Wei ; Wu, Ruoxi ; Yang, Hongtao ; Yang, Kang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c465t-70acd29b831fc8435e201d5a0ef106507128530fdd1fb5d03b300857db944e493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>biomarkers</topic><topic>CIBERSORT</topic><topic>comprehensive bioinformatics</topic><topic>Computational Biology</topic><topic>Humans</topic><topic>immune infiltration</topic><topic>Immunology</topic><topic>Lupus Erythematosus, Systemic</topic><topic>lupus nephritis</topic><topic>Lupus Nephritis - diagnosis</topic><topic>Lupus Nephritis - genetics</topic><topic>Machine Learning</topic><topic>Transcriptome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Lin</creatorcontrib><creatorcontrib>Yang, Zhihua</creatorcontrib><creatorcontrib>Yu, Hangxing</creatorcontrib><creatorcontrib>Lin, Wei</creatorcontrib><creatorcontrib>Wu, Ruoxi</creatorcontrib><creatorcontrib>Yang, Hongtao</creatorcontrib><creatorcontrib>Yang, Kang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Frontiers in immunology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Lin</au><au>Yang, Zhihua</au><au>Yu, Hangxing</au><au>Lin, Wei</au><au>Wu, Ruoxi</au><au>Yang, Hongtao</au><au>Yang, Kang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis</atitle><jtitle>Frontiers in immunology</jtitle><addtitle>Front Immunol</addtitle><date>2022-12-02</date><risdate>2022</risdate><volume>13</volume><spage>839197</spage><pages>839197-</pages><issn>1664-3224</issn><eissn>1664-3224</eissn><abstract>To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.
Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.
Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.
C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.</abstract><cop>Switzerland</cop><pub>Frontiers Media S.A</pub><pmid>36532018</pmid><doi>10.3389/fimmu.2022.839197</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1664-3224 |
ispartof | Frontiers in immunology, 2022-12, Vol.13, p.839197 |
issn | 1664-3224 1664-3224 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_50606871a07847fa8ba3886ff61ad62e |
source | PubMed Central Free |
subjects | biomarkers CIBERSORT comprehensive bioinformatics Computational Biology Humans immune infiltration Immunology Lupus Erythematosus, Systemic lupus nephritis Lupus Nephritis - diagnosis Lupus Nephritis - genetics Machine Learning Transcriptome |
title | Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T21%3A47%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20diagnostic%20gene%20expression%20profiles%20associated%20with%20immune%20infiltration%20in%20patients%20with%20lupus%20nephritis&rft.jtitle=Frontiers%20in%20immunology&rft.au=Wang,%20Lin&rft.date=2022-12-02&rft.volume=13&rft.spage=839197&rft.pages=839197-&rft.issn=1664-3224&rft.eissn=1664-3224&rft_id=info:doi/10.3389/fimmu.2022.839197&rft_dat=%3Cproquest_doaj_%3E2755804332%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c465t-70acd29b831fc8435e201d5a0ef106507128530fdd1fb5d03b300857db944e493%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2755804332&rft_id=info:pmid/36532018&rfr_iscdi=true |