Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis
Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are assoc...
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| creator | Ma, Shaohan Li, Ruyue Li, Guangqi Wei, Meng Li, Bowei Li, Yongmei Ha, Chunfang |
| description | Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.
We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan–Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.
Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.
Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.
•GPCRRGs were hypothesized to correlate to OV prognosis and serve as markers.•A prognostic model was established using LASSO regression and validated.•CXCR4, GPR34, LGR6, LPAR3, and RGS2 effectively predicted OV prognosis.•Prognostic utility of CXCR4 was significantly higher than that of other genes.•Our GPCRRG-based model provides insights into targets for, and prognosis in, OV. |
| doi_str_mv | 10.1016/j.compbiomed.2024.108747 |
| format | Article |
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We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan–Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.
Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.
Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.
•GPCRRGs were hypothesized to correlate to OV prognosis and serve as markers.•A prognostic model was established using LASSO regression and validated.•CXCR4, GPR34, LGR6, LPAR3, and RGS2 effectively predicted OV prognosis.•Prognostic utility of CXCR4 was significantly higher than that of other genes.•Our GPCRRG-based model provides insights into targets for, and prognosis in, OV.</description><identifier>ISSN: 0010-4825</identifier><identifier>ISSN: 1879-0534</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2024.108747</identifier><identifier>PMID: 38897150</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Apoptosis ; Bioinformatics ; Brain cancer ; Cancer ; Cancer therapies ; Cell surface ; Cell surface receptors ; Chemotherapy ; CXCR4 protein ; Datasets ; Diagnosis ; epithelial ; G protein-coupled receptor ; G protein-coupled receptors ; Gene expression ; Gene set enrichment analysis ; Genomes ; Immune infiltration ; Immune response ; Immune system ; Immunology ; Infiltration rate ; Medical prognosis ; Metastases ; Molecular marker ; Ovarian cancer ; Patients ; Physiology ; Polymerase chain reaction ; Prognosis ; Prognostic model ; Proteins ; Receptors ; Regression analysis ; Regression models ; Reproductive system ; Risk analysis ; Risk groups ; Survival ; Womens health</subject><ispartof>Computers in biology and medicine, 2024-08, Vol.178, p.108747, Article 108747</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.</rights><rights>2024. The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2427-f41331d8e63d75f874f1ae79a631e00635e894d360c813dcaf0530246c6c618d3</cites><orcidid>0000-0003-3648-8864 ; 0009-0003-1222-137X ; 0009-0006-8890-2966</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.compbiomed.2024.108747$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38897150$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ma, Shaohan</creatorcontrib><creatorcontrib>Li, Ruyue</creatorcontrib><creatorcontrib>Li, Guangqi</creatorcontrib><creatorcontrib>Wei, Meng</creatorcontrib><creatorcontrib>Li, Bowei</creatorcontrib><creatorcontrib>Li, Yongmei</creatorcontrib><creatorcontrib>Ha, Chunfang</creatorcontrib><title>Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.
We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan–Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.
Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.
Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.
•GPCRRGs were hypothesized to correlate to OV prognosis and serve as markers.•A prognostic model was established using LASSO regression and validated.•CXCR4, GPR34, LGR6, LPAR3, and RGS2 effectively predicted OV prognosis.•Prognostic utility of CXCR4 was significantly higher than that of other genes.•Our GPCRRG-based model provides insights into targets for, and prognosis in, OV.</description><subject>Apoptosis</subject><subject>Bioinformatics</subject><subject>Brain cancer</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Cell surface</subject><subject>Cell surface receptors</subject><subject>Chemotherapy</subject><subject>CXCR4 protein</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>epithelial</subject><subject>G protein-coupled receptor</subject><subject>G protein-coupled receptors</subject><subject>Gene expression</subject><subject>Gene set enrichment analysis</subject><subject>Genomes</subject><subject>Immune infiltration</subject><subject>Immune response</subject><subject>Immune system</subject><subject>Immunology</subject><subject>Infiltration rate</subject><subject>Medical prognosis</subject><subject>Metastases</subject><subject>Molecular marker</subject><subject>Ovarian cancer</subject><subject>Patients</subject><subject>Physiology</subject><subject>Polymerase chain reaction</subject><subject>Prognosis</subject><subject>Prognostic model</subject><subject>Proteins</subject><subject>Receptors</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Reproductive system</subject><subject>Risk analysis</subject><subject>Risk groups</subject><subject>Survival</subject><subject>Womens health</subject><issn>0010-4825</issn><issn>1879-0534</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqFkcFu1DAQhi0EotvCKyBLXLhkGcdO4j1CBaVSJS5wtlx7svWS2MF2KvVdeFhmta2QekE-WBp_M__4_xnjArYCRP_xsHVpXm5DmtFvW2gVlfWghhdsI_Swa6CT6iXbAAholG67M3ZeygEAFEh4zc6k1rtBdLBhf649xhrG4GwNKfI0csuvmiWniiFyl9ZlQs8zOlxqyk3GyVYq7DEiL2EfbV0z8nqX07q_47RSiGPKM01zhdtop4cSCq-JRsVS8-oqCeRQfvE5eZw4wTzd2xwsqdnoMHMS38dEbW_Yq9FOBd8-3hfs59cvPy6_NTffr64vP900rlXt0IxKSCm8xl76oRvJiFFYHHa2lwIBetmh3ikve3BaSO_sSP6Qab2jI7SXF-zDaS4p_16xVDOH4nCabMS0FiNhAN2K3SAJff8MPaQ10zePlBYDKKE0UfpEuZxKyTiaJYfZ5gcjwBwTNAfzL0FzTNCcEqTWd48C6-3x7anxKTICPp8AJEfuA2ZTXEAyzgdKqRqfwv9V_gKQibQl</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Ma, Shaohan</creator><creator>Li, Ruyue</creator><creator>Li, Guangqi</creator><creator>Wei, Meng</creator><creator>Li, Bowei</creator><creator>Li, Yongmei</creator><creator>Ha, Chunfang</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>M7Z</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-3648-8864</orcidid><orcidid>https://orcid.org/0009-0003-1222-137X</orcidid><orcidid>https://orcid.org/0009-0006-8890-2966</orcidid></search><sort><creationdate>20240801</creationdate><title>Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis</title><author>Ma, Shaohan ; Li, Ruyue ; Li, Guangqi ; Wei, Meng ; Li, Bowei ; Li, Yongmei ; Ha, Chunfang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2427-f41331d8e63d75f874f1ae79a631e00635e894d360c813dcaf0530246c6c618d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Apoptosis</topic><topic>Bioinformatics</topic><topic>Brain cancer</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Cell surface</topic><topic>Cell surface receptors</topic><topic>Chemotherapy</topic><topic>CXCR4 protein</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>epithelial</topic><topic>G protein-coupled receptor</topic><topic>G protein-coupled receptors</topic><topic>Gene expression</topic><topic>Gene set enrichment analysis</topic><topic>Genomes</topic><topic>Immune infiltration</topic><topic>Immune response</topic><topic>Immune system</topic><topic>Immunology</topic><topic>Infiltration rate</topic><topic>Medical prognosis</topic><topic>Metastases</topic><topic>Molecular marker</topic><topic>Ovarian cancer</topic><topic>Patients</topic><topic>Physiology</topic><topic>Polymerase chain reaction</topic><topic>Prognosis</topic><topic>Prognostic model</topic><topic>Proteins</topic><topic>Receptors</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Reproductive system</topic><topic>Risk analysis</topic><topic>Risk groups</topic><topic>Survival</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Shaohan</creatorcontrib><creatorcontrib>Li, Ruyue</creatorcontrib><creatorcontrib>Li, Guangqi</creatorcontrib><creatorcontrib>Wei, Meng</creatorcontrib><creatorcontrib>Li, Bowei</creatorcontrib><creatorcontrib>Li, Yongmei</creatorcontrib><creatorcontrib>Ha, Chunfang</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biochemistry Abstracts 1</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ma, Shaohan</au><au>Li, Ruyue</au><au>Li, Guangqi</au><au>Wei, Meng</au><au>Li, Bowei</au><au>Li, Yongmei</au><au>Ha, Chunfang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2024-08-01</date><risdate>2024</risdate><volume>178</volume><spage>108747</spage><pages>108747-</pages><artnum>108747</artnum><issn>0010-4825</issn><issn>1879-0534</issn><eissn>1879-0534</eissn><abstract>Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.
We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan–Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.
Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.
Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.
•GPCRRGs were hypothesized to correlate to OV prognosis and serve as markers.•A prognostic model was established using LASSO regression and validated.•CXCR4, GPR34, LGR6, LPAR3, and RGS2 effectively predicted OV prognosis.•Prognostic utility of CXCR4 was significantly higher than that of other genes.•Our GPCRRG-based model provides insights into targets for, and prognosis in, OV.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>38897150</pmid><doi>10.1016/j.compbiomed.2024.108747</doi><orcidid>https://orcid.org/0000-0003-3648-8864</orcidid><orcidid>https://orcid.org/0009-0003-1222-137X</orcidid><orcidid>https://orcid.org/0009-0006-8890-2966</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Apoptosis Bioinformatics Brain cancer Cancer Cancer therapies Cell surface Cell surface receptors Chemotherapy CXCR4 protein Datasets Diagnosis epithelial G protein-coupled receptor G protein-coupled receptors Gene expression Gene set enrichment analysis Genomes Immune infiltration Immune response Immune system Immunology Infiltration rate Medical prognosis Metastases Molecular marker Ovarian cancer Patients Physiology Polymerase chain reaction Prognosis Prognostic model Proteins Receptors Regression analysis Regression models Reproductive system Risk analysis Risk groups Survival Womens health |
| title | Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis |
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