Loading…

Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma

Lower grade gliomas (LGGs) exhibit invasiveness and heterogeneity as distinguishing features. The outcome of patients with LGG differs greatly. Recently, more and more studies have suggested that infiltrating inflammation cells and inflammation-related genes (IRGs) play an essential role in tumorige...

Full description

Saved in:
Bibliographic Details
Published in:Heliyon 2023-12, Vol.9 (12), p.e22429, Article e22429
Main Authors: Long, Cheng, Song, Ya, Pan, Yimin, Wu, Changwu
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c482t-afdf4fdb9776565705cf80491e983ed387e1ce3153723b4ea1826ac9e15d1893
container_end_page
container_issue 12
container_start_page e22429
container_title Heliyon
container_volume 9
creator Long, Cheng
Song, Ya
Pan, Yimin
Wu, Changwu
description Lower grade gliomas (LGGs) exhibit invasiveness and heterogeneity as distinguishing features. The outcome of patients with LGG differs greatly. Recently, more and more studies have suggested that infiltrating inflammation cells and inflammation-related genes (IRGs) play an essential role in tumorigenesis, prognosis, and treatment responses. Nevertheless, the role of IRGs in LGG remains unclear. In The Cancer Genome Atlas (TCGA) cohort, we conducted a thorough examination of the predictive significance of IRGs and identified 245 IRGs that correlated with the clinical prognosis of individuals diagnosed with LGG. Based on unsupervised cluster analysis, we identified two inflammation-associated molecular clusters, which presented different tumor immune microenvironments, tumorigenesis scores, and tumor stemness indices. Furthermore, a prognostic risk model including ten prognostic IRGs (ADRB2, CD274, CXCL12, IL12B, NFE2L2, PRF1, SFTPC, TBX21, TNFRSF11B, and TTR) was constructed. The survival analysis indicated that the IRGs risk model independently predicted the prognosis of patients with LGG, which was validated in an independent LGG cohort. Moreover, the risk model significantly correlated with the infiltrative level of immune cells, tumor mutation burden, expression of HLA and immune checkpoint genes, tumorigenesis scores, and tumor stemness indices in LGG. Additionally, we found that our risk model could predict the chemotherapy response of some drugs in patients with LGG. This study may enhance the advancement of personalized therapy and improve outcomes of LGG.
doi_str_mv 10.1016/j.heliyon.2023.e22429
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_b0e919c5242e4522969ddd27b333a844</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S2405844023096378</els_id><doaj_id>oai_doaj_org_article_b0e919c5242e4522969ddd27b333a844</doaj_id><sourcerecordid>2897485422</sourcerecordid><originalsourceid>FETCH-LOGICAL-c482t-afdf4fdb9776565705cf80491e983ed387e1ce3153723b4ea1826ac9e15d1893</originalsourceid><addsrcrecordid>eNqFUk1v1DAQjRCIVqU_AeQjlyz-jn1CqOJjpUpcercce7LrxYkXO2m16p_H7S6lPSEfbM3MezPz_JrmPcErgon8tFttIYZDmlYUU7YCSjnVr5pzyrFoFef49bP3WXNZyg5jTISSumNvmzOmMJdEyPPmfu1hmsMQnJ1DmlAa0JgiuCXajMrSz4c9FGQnjyzKofyqWQ8R9baAR7U-TEO04_gIbjNEO9f4BqYKChPa13ilL-guzFsU0x3aZOsBbWJIo33XvBlsLHB5ui-am29fb65-tNc_v6-vvly3jis6t3bwAx98r7tOCik6LNxQ59cEtGLgmeqAOGBEsI6ynoMlikrrNBDhidLsolkfaX2yO7PPYbT5YJIN5jGQ8sbYPAcXwfQYNNFOVDmBC0q11N572vWMMVu1rFyfj1z7pR_Bu7pctvEF6cvMFLZmk24NwVLVIyvDxxNDTr8XKLMZQ3EQo50gLcVQpTuuBKe0lopjqcuplAzDUx-CzYMPzM6cfGAefGCOPqi4D8-HfEL9_fV_W0BV_TZANsXVf3LgQwY3V13Cf1r8AckOyRg</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2897485422</pqid></control><display><type>article</type><title>Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma</title><source>ScienceDirect®</source><source>PubMed Central</source><creator>Long, Cheng ; Song, Ya ; Pan, Yimin ; Wu, Changwu</creator><creatorcontrib>Long, Cheng ; Song, Ya ; Pan, Yimin ; Wu, Changwu</creatorcontrib><description>Lower grade gliomas (LGGs) exhibit invasiveness and heterogeneity as distinguishing features. The outcome of patients with LGG differs greatly. Recently, more and more studies have suggested that infiltrating inflammation cells and inflammation-related genes (IRGs) play an essential role in tumorigenesis, prognosis, and treatment responses. Nevertheless, the role of IRGs in LGG remains unclear. In The Cancer Genome Atlas (TCGA) cohort, we conducted a thorough examination of the predictive significance of IRGs and identified 245 IRGs that correlated with the clinical prognosis of individuals diagnosed with LGG. Based on unsupervised cluster analysis, we identified two inflammation-associated molecular clusters, which presented different tumor immune microenvironments, tumorigenesis scores, and tumor stemness indices. Furthermore, a prognostic risk model including ten prognostic IRGs (ADRB2, CD274, CXCL12, IL12B, NFE2L2, PRF1, SFTPC, TBX21, TNFRSF11B, and TTR) was constructed. The survival analysis indicated that the IRGs risk model independently predicted the prognosis of patients with LGG, which was validated in an independent LGG cohort. Moreover, the risk model significantly correlated with the infiltrative level of immune cells, tumor mutation burden, expression of HLA and immune checkpoint genes, tumorigenesis scores, and tumor stemness indices in LGG. Additionally, we found that our risk model could predict the chemotherapy response of some drugs in patients with LGG. This study may enhance the advancement of personalized therapy and improve outcomes of LGG.</description><identifier>ISSN: 2405-8440</identifier><identifier>EISSN: 2405-8440</identifier><identifier>DOI: 10.1016/j.heliyon.2023.e22429</identifier><identifier>PMID: 38046156</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Immune infiltration ; Inflammation ; Low grade glioma ; Molecular subtyping ; Prognosis ; Risk model</subject><ispartof>Heliyon, 2023-12, Vol.9 (12), p.e22429, Article e22429</ispartof><rights>2023 The Authors</rights><rights>2023 The Authors.</rights><rights>2023 The Authors 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c482t-afdf4fdb9776565705cf80491e983ed387e1ce3153723b4ea1826ac9e15d1893</cites><orcidid>0000-0002-9062-5930</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10686866/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S2405844023096378$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,3549,27924,27925,45780,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38046156$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Long, Cheng</creatorcontrib><creatorcontrib>Song, Ya</creatorcontrib><creatorcontrib>Pan, Yimin</creatorcontrib><creatorcontrib>Wu, Changwu</creatorcontrib><title>Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma</title><title>Heliyon</title><addtitle>Heliyon</addtitle><description>Lower grade gliomas (LGGs) exhibit invasiveness and heterogeneity as distinguishing features. The outcome of patients with LGG differs greatly. Recently, more and more studies have suggested that infiltrating inflammation cells and inflammation-related genes (IRGs) play an essential role in tumorigenesis, prognosis, and treatment responses. Nevertheless, the role of IRGs in LGG remains unclear. In The Cancer Genome Atlas (TCGA) cohort, we conducted a thorough examination of the predictive significance of IRGs and identified 245 IRGs that correlated with the clinical prognosis of individuals diagnosed with LGG. Based on unsupervised cluster analysis, we identified two inflammation-associated molecular clusters, which presented different tumor immune microenvironments, tumorigenesis scores, and tumor stemness indices. Furthermore, a prognostic risk model including ten prognostic IRGs (ADRB2, CD274, CXCL12, IL12B, NFE2L2, PRF1, SFTPC, TBX21, TNFRSF11B, and TTR) was constructed. The survival analysis indicated that the IRGs risk model independently predicted the prognosis of patients with LGG, which was validated in an independent LGG cohort. Moreover, the risk model significantly correlated with the infiltrative level of immune cells, tumor mutation burden, expression of HLA and immune checkpoint genes, tumorigenesis scores, and tumor stemness indices in LGG. Additionally, we found that our risk model could predict the chemotherapy response of some drugs in patients with LGG. This study may enhance the advancement of personalized therapy and improve outcomes of LGG.</description><subject>Immune infiltration</subject><subject>Inflammation</subject><subject>Low grade glioma</subject><subject>Molecular subtyping</subject><subject>Prognosis</subject><subject>Risk model</subject><issn>2405-8440</issn><issn>2405-8440</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqFUk1v1DAQjRCIVqU_AeQjlyz-jn1CqOJjpUpcercce7LrxYkXO2m16p_H7S6lPSEfbM3MezPz_JrmPcErgon8tFttIYZDmlYUU7YCSjnVr5pzyrFoFef49bP3WXNZyg5jTISSumNvmzOmMJdEyPPmfu1hmsMQnJ1DmlAa0JgiuCXajMrSz4c9FGQnjyzKofyqWQ8R9baAR7U-TEO04_gIbjNEO9f4BqYKChPa13ilL-guzFsU0x3aZOsBbWJIo33XvBlsLHB5ui-am29fb65-tNc_v6-vvly3jis6t3bwAx98r7tOCik6LNxQ59cEtGLgmeqAOGBEsI6ynoMlikrrNBDhidLsolkfaX2yO7PPYbT5YJIN5jGQ8sbYPAcXwfQYNNFOVDmBC0q11N572vWMMVu1rFyfj1z7pR_Bu7pctvEF6cvMFLZmk24NwVLVIyvDxxNDTr8XKLMZQ3EQo50gLcVQpTuuBKe0lopjqcuplAzDUx-CzYMPzM6cfGAefGCOPqi4D8-HfEL9_fV_W0BV_TZANsXVf3LgQwY3V13Cf1r8AckOyRg</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Long, Cheng</creator><creator>Song, Ya</creator><creator>Pan, Yimin</creator><creator>Wu, Changwu</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-9062-5930</orcidid></search><sort><creationdate>20231201</creationdate><title>Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma</title><author>Long, Cheng ; Song, Ya ; Pan, Yimin ; Wu, Changwu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c482t-afdf4fdb9776565705cf80491e983ed387e1ce3153723b4ea1826ac9e15d1893</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Immune infiltration</topic><topic>Inflammation</topic><topic>Low grade glioma</topic><topic>Molecular subtyping</topic><topic>Prognosis</topic><topic>Risk model</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Long, Cheng</creatorcontrib><creatorcontrib>Song, Ya</creatorcontrib><creatorcontrib>Pan, Yimin</creatorcontrib><creatorcontrib>Wu, Changwu</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Heliyon</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Long, Cheng</au><au>Song, Ya</au><au>Pan, Yimin</au><au>Wu, Changwu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma</atitle><jtitle>Heliyon</jtitle><addtitle>Heliyon</addtitle><date>2023-12-01</date><risdate>2023</risdate><volume>9</volume><issue>12</issue><spage>e22429</spage><pages>e22429-</pages><artnum>e22429</artnum><issn>2405-8440</issn><eissn>2405-8440</eissn><abstract>Lower grade gliomas (LGGs) exhibit invasiveness and heterogeneity as distinguishing features. The outcome of patients with LGG differs greatly. Recently, more and more studies have suggested that infiltrating inflammation cells and inflammation-related genes (IRGs) play an essential role in tumorigenesis, prognosis, and treatment responses. Nevertheless, the role of IRGs in LGG remains unclear. In The Cancer Genome Atlas (TCGA) cohort, we conducted a thorough examination of the predictive significance of IRGs and identified 245 IRGs that correlated with the clinical prognosis of individuals diagnosed with LGG. Based on unsupervised cluster analysis, we identified two inflammation-associated molecular clusters, which presented different tumor immune microenvironments, tumorigenesis scores, and tumor stemness indices. Furthermore, a prognostic risk model including ten prognostic IRGs (ADRB2, CD274, CXCL12, IL12B, NFE2L2, PRF1, SFTPC, TBX21, TNFRSF11B, and TTR) was constructed. The survival analysis indicated that the IRGs risk model independently predicted the prognosis of patients with LGG, which was validated in an independent LGG cohort. Moreover, the risk model significantly correlated with the infiltrative level of immune cells, tumor mutation burden, expression of HLA and immune checkpoint genes, tumorigenesis scores, and tumor stemness indices in LGG. Additionally, we found that our risk model could predict the chemotherapy response of some drugs in patients with LGG. This study may enhance the advancement of personalized therapy and improve outcomes of LGG.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38046156</pmid><doi>10.1016/j.heliyon.2023.e22429</doi><orcidid>https://orcid.org/0000-0002-9062-5930</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2405-8440
ispartof Heliyon, 2023-12, Vol.9 (12), p.e22429, Article e22429
issn 2405-8440
2405-8440
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_b0e919c5242e4522969ddd27b333a844
source ScienceDirect®; PubMed Central
subjects Immune infiltration
Inflammation
Low grade glioma
Molecular subtyping
Prognosis
Risk model
title Identification of molecular subtypes and a risk model based on inflammation-related genes in patients with low grade glioma
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T12%3A45%3A21IST&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=Identification%20of%20molecular%20subtypes%20and%20a%20risk%20model%20based%20on%20inflammation-related%20genes%20in%20patients%20with%20low%20grade%20glioma&rft.jtitle=Heliyon&rft.au=Long,%20Cheng&rft.date=2023-12-01&rft.volume=9&rft.issue=12&rft.spage=e22429&rft.pages=e22429-&rft.artnum=e22429&rft.issn=2405-8440&rft.eissn=2405-8440&rft_id=info:doi/10.1016/j.heliyon.2023.e22429&rft_dat=%3Cproquest_doaj_%3E2897485422%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c482t-afdf4fdb9776565705cf80491e983ed387e1ce3153723b4ea1826ac9e15d1893%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2897485422&rft_id=info:pmid/38046156&rfr_iscdi=true