A robust signature associated with patient prognosis and tumor immune microenvironment based on immune-related genes in lung squamous cell carcinoma
•An immune-related prognostic model was constructed to predict the overall survival.•High risk score was independently related to worse overall survival.•The risk score was correlated with several tumor-infiltrating immune cells. Lung squamous cell carcinoma (LUSC) is one common type of lung cancer....
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| Veröffentlicht in: | International immunopharmacology 2020-11, Vol.88, p.106856-106856, Article 106856 |
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| container_title | International immunopharmacology |
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| creator | Zhou, Hao Zhang, Haijian Shi, Muqi Wang, Jinjie Huang, Zhanghao Shi, Jiahai |
| description | •An immune-related prognostic model was constructed to predict the overall survival.•High risk score was independently related to worse overall survival.•The risk score was correlated with several tumor-infiltrating immune cells.
Lung squamous cell carcinoma (LUSC) is one common type of lung cancer. Immune-related genes (IRGs) are closely associated with cancer prognosis. This study aims to screen the key genes associated with LUSC and establish an immune-related prognostic model.
Based on the Cancer Genome Atlas (TCGA) database, we screened the differentially expressed genes (DEGs) between LUSC and normal samples. Intersecting the DEGs with the immune-related genes (IRGs), we obtained the differentially expressed IRGs (DEIRGs). Univariate as well as multivariate Cox regression analyses were performed to identify the survival-associated IRGs and establish an immune-related prognostic model. The relationship between the prognostic model and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT.
A total of 229 DEIRGs were screened, and 14 IRGs associated with survival were identified using univariate Cox analysis. Among the 14 IRGs, six genes were selected out using Lasso and multivariate Cox analyses, and they were used to build the prognostic model. Further analysis indicated that overall survival (OS) of high-risk groups was lower than that of low-risk groups. High risk score was independently related to worse OS. Moreover, the risk score was positively correlated with several immune infiltration cells. Finally, the efficacy of the prognostic model was validated by another independent cohort GSE73403.
The DEIRGs described in the study may have the potential to be the prognostic molecular markers for LUSC. In addition, the risk score model could predict the OS and provides more information for the immunotherapy of patients with LUSC. |
| doi_str_mv | 10.1016/j.intimp.2020.106856 |
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Lung squamous cell carcinoma (LUSC) is one common type of lung cancer. Immune-related genes (IRGs) are closely associated with cancer prognosis. This study aims to screen the key genes associated with LUSC and establish an immune-related prognostic model.
Based on the Cancer Genome Atlas (TCGA) database, we screened the differentially expressed genes (DEGs) between LUSC and normal samples. Intersecting the DEGs with the immune-related genes (IRGs), we obtained the differentially expressed IRGs (DEIRGs). Univariate as well as multivariate Cox regression analyses were performed to identify the survival-associated IRGs and establish an immune-related prognostic model. The relationship between the prognostic model and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT.
A total of 229 DEIRGs were screened, and 14 IRGs associated with survival were identified using univariate Cox analysis. Among the 14 IRGs, six genes were selected out using Lasso and multivariate Cox analyses, and they were used to build the prognostic model. Further analysis indicated that overall survival (OS) of high-risk groups was lower than that of low-risk groups. High risk score was independently related to worse OS. Moreover, the risk score was positively correlated with several immune infiltration cells. Finally, the efficacy of the prognostic model was validated by another independent cohort GSE73403.
The DEIRGs described in the study may have the potential to be the prognostic molecular markers for LUSC. In addition, the risk score model could predict the OS and provides more information for the immunotherapy of patients with LUSC.</description><identifier>ISSN: 1567-5769</identifier><identifier>EISSN: 1878-1705</identifier><identifier>DOI: 10.1016/j.intimp.2020.106856</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Cancer ; Correlation analysis ; Differentially expressed genes ; Genes ; Genomes ; Immune infiltration ; Immune system ; Immunotherapy ; Lung cancer ; Lung carcinoma ; Lung squamous cell carcinoma ; Medical prognosis ; Metastases ; Microenvironments ; Multivariate analysis ; Prognosis ; Prognostic model ; Regression analysis ; Risk ; Risk groups ; Squamous cell carcinoma ; Survival ; TCGA ; Tumor-infiltrating lymphocytes ; Tumors</subject><ispartof>International immunopharmacology, 2020-11, Vol.88, p.106856-106856, Article 106856</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright Elsevier BV Nov 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-ea45dbea09ff92bdeaf5530c7e6a13e3461b4a4bdb4a8f36f8ceac6e27b4ad6a3</citedby><cites>FETCH-LOGICAL-c367t-ea45dbea09ff92bdeaf5530c7e6a13e3461b4a4bdb4a8f36f8ceac6e27b4ad6a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.intimp.2020.106856$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Zhou, Hao</creatorcontrib><creatorcontrib>Zhang, Haijian</creatorcontrib><creatorcontrib>Shi, Muqi</creatorcontrib><creatorcontrib>Wang, Jinjie</creatorcontrib><creatorcontrib>Huang, Zhanghao</creatorcontrib><creatorcontrib>Shi, Jiahai</creatorcontrib><title>A robust signature associated with patient prognosis and tumor immune microenvironment based on immune-related genes in lung squamous cell carcinoma</title><title>International immunopharmacology</title><description>•An immune-related prognostic model was constructed to predict the overall survival.•High risk score was independently related to worse overall survival.•The risk score was correlated with several tumor-infiltrating immune cells.
Lung squamous cell carcinoma (LUSC) is one common type of lung cancer. Immune-related genes (IRGs) are closely associated with cancer prognosis. This study aims to screen the key genes associated with LUSC and establish an immune-related prognostic model.
Based on the Cancer Genome Atlas (TCGA) database, we screened the differentially expressed genes (DEGs) between LUSC and normal samples. Intersecting the DEGs with the immune-related genes (IRGs), we obtained the differentially expressed IRGs (DEIRGs). Univariate as well as multivariate Cox regression analyses were performed to identify the survival-associated IRGs and establish an immune-related prognostic model. The relationship between the prognostic model and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT.
A total of 229 DEIRGs were screened, and 14 IRGs associated with survival were identified using univariate Cox analysis. Among the 14 IRGs, six genes were selected out using Lasso and multivariate Cox analyses, and they were used to build the prognostic model. Further analysis indicated that overall survival (OS) of high-risk groups was lower than that of low-risk groups. High risk score was independently related to worse OS. Moreover, the risk score was positively correlated with several immune infiltration cells. Finally, the efficacy of the prognostic model was validated by another independent cohort GSE73403.
The DEIRGs described in the study may have the potential to be the prognostic molecular markers for LUSC. In addition, the risk score model could predict the OS and provides more information for the immunotherapy of patients with LUSC.</description><subject>Cancer</subject><subject>Correlation analysis</subject><subject>Differentially expressed genes</subject><subject>Genes</subject><subject>Genomes</subject><subject>Immune infiltration</subject><subject>Immune system</subject><subject>Immunotherapy</subject><subject>Lung cancer</subject><subject>Lung carcinoma</subject><subject>Lung squamous cell carcinoma</subject><subject>Medical prognosis</subject><subject>Metastases</subject><subject>Microenvironments</subject><subject>Multivariate analysis</subject><subject>Prognosis</subject><subject>Prognostic model</subject><subject>Regression analysis</subject><subject>Risk</subject><subject>Risk groups</subject><subject>Squamous cell carcinoma</subject><subject>Survival</subject><subject>TCGA</subject><subject>Tumor-infiltrating lymphocytes</subject><subject>Tumors</subject><issn>1567-5769</issn><issn>1878-1705</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kb2O1TAQhSMEEsvCG1BYoqHJxY4TO7dBWq34k1aigdqaOJOLr2L7rsdexHvwwDhkKwoajzX6ztHMnKZ5LfhBcKHenQ8uZOcvh453W0uNg3rSXIlRj63QfHha_4PS7aDV8XnzgujMee334qr5fcNSnAplRu4UIJeEDIiidZBxZj9d_sEukB2GzC4pnkIkRwzCzHLxMTHnfQnIvLMpYnhwKQa_sRNQlcfwCLQJ17-GJwxIzAW2lnBidF_Ax0LM4royC8m6ED28bJ4tsBK-eqzXzfePH77dfm7vvn76cntz11qpdG4R-mGeEPhxWY7dNCMswyC51ahASJS9ElMP_TTXd1ykWkaLYBV2ujZmBfK6ebv71s3uC1I23tE2CgSsU5mul9046KPuK_rmH_QcSwp1ukrpcew6KWSl-p2q1yBKuJhLch7SLyO42aIyZ7NHZbaozB5Vlb3fZViXfXCYDNl6cYuzS2izmaP7v8EfKfqj9g</recordid><startdate>202011</startdate><enddate>202011</enddate><creator>Zhou, Hao</creator><creator>Zhang, Haijian</creator><creator>Shi, Muqi</creator><creator>Wang, Jinjie</creator><creator>Huang, Zhanghao</creator><creator>Shi, Jiahai</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T5</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>202011</creationdate><title>A robust signature associated with patient prognosis and tumor immune microenvironment based on immune-related genes in lung squamous cell carcinoma</title><author>Zhou, Hao ; Zhang, Haijian ; Shi, Muqi ; Wang, Jinjie ; Huang, Zhanghao ; Shi, Jiahai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-ea45dbea09ff92bdeaf5530c7e6a13e3461b4a4bdb4a8f36f8ceac6e27b4ad6a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Cancer</topic><topic>Correlation analysis</topic><topic>Differentially expressed genes</topic><topic>Genes</topic><topic>Genomes</topic><topic>Immune infiltration</topic><topic>Immune system</topic><topic>Immunotherapy</topic><topic>Lung cancer</topic><topic>Lung carcinoma</topic><topic>Lung squamous cell carcinoma</topic><topic>Medical prognosis</topic><topic>Metastases</topic><topic>Microenvironments</topic><topic>Multivariate analysis</topic><topic>Prognosis</topic><topic>Prognostic model</topic><topic>Regression analysis</topic><topic>Risk</topic><topic>Risk groups</topic><topic>Squamous cell carcinoma</topic><topic>Survival</topic><topic>TCGA</topic><topic>Tumor-infiltrating lymphocytes</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Hao</creatorcontrib><creatorcontrib>Zhang, Haijian</creatorcontrib><creatorcontrib>Shi, Muqi</creatorcontrib><creatorcontrib>Wang, Jinjie</creatorcontrib><creatorcontrib>Huang, Zhanghao</creatorcontrib><creatorcontrib>Shi, Jiahai</creatorcontrib><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>International immunopharmacology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Hao</au><au>Zhang, Haijian</au><au>Shi, Muqi</au><au>Wang, Jinjie</au><au>Huang, Zhanghao</au><au>Shi, Jiahai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A robust signature associated with patient prognosis and tumor immune microenvironment based on immune-related genes in lung squamous cell carcinoma</atitle><jtitle>International immunopharmacology</jtitle><date>2020-11</date><risdate>2020</risdate><volume>88</volume><spage>106856</spage><epage>106856</epage><pages>106856-106856</pages><artnum>106856</artnum><issn>1567-5769</issn><eissn>1878-1705</eissn><abstract>•An immune-related prognostic model was constructed to predict the overall survival.•High risk score was independently related to worse overall survival.•The risk score was correlated with several tumor-infiltrating immune cells.
Lung squamous cell carcinoma (LUSC) is one common type of lung cancer. Immune-related genes (IRGs) are closely associated with cancer prognosis. This study aims to screen the key genes associated with LUSC and establish an immune-related prognostic model.
Based on the Cancer Genome Atlas (TCGA) database, we screened the differentially expressed genes (DEGs) between LUSC and normal samples. Intersecting the DEGs with the immune-related genes (IRGs), we obtained the differentially expressed IRGs (DEIRGs). Univariate as well as multivariate Cox regression analyses were performed to identify the survival-associated IRGs and establish an immune-related prognostic model. The relationship between the prognostic model and tumor-infiltrating immune cells was analyzed by TIMER and CIBERSORT.
A total of 229 DEIRGs were screened, and 14 IRGs associated with survival were identified using univariate Cox analysis. Among the 14 IRGs, six genes were selected out using Lasso and multivariate Cox analyses, and they were used to build the prognostic model. Further analysis indicated that overall survival (OS) of high-risk groups was lower than that of low-risk groups. High risk score was independently related to worse OS. Moreover, the risk score was positively correlated with several immune infiltration cells. Finally, the efficacy of the prognostic model was validated by another independent cohort GSE73403.
The DEIRGs described in the study may have the potential to be the prognostic molecular markers for LUSC. In addition, the risk score model could predict the OS and provides more information for the immunotherapy of patients with LUSC.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.intimp.2020.106856</doi><tpages>1</tpages></addata></record> |
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| subjects | Cancer Correlation analysis Differentially expressed genes Genes Genomes Immune infiltration Immune system Immunotherapy Lung cancer Lung carcinoma Lung squamous cell carcinoma Medical prognosis Metastases Microenvironments Multivariate analysis Prognosis Prognostic model Regression analysis Risk Risk groups Squamous cell carcinoma Survival TCGA Tumor-infiltrating lymphocytes Tumors |
| title | A robust signature associated with patient prognosis and tumor immune microenvironment based on immune-related genes in lung squamous cell carcinoma |
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