Identification of an Immune Gene Expression Signature for Predicting Lung Squamous Cell Carcinoma Prognosis
Growing evidence indicates that immune-related biomarkers play an important role in tumor processes. This study investigates immune-related gene expression and its prognostic value in lung squamous cell carcinoma (LUSC). A cohort of 493 samples of patients with LUSC was collected and analyzed from d...
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| description | Growing evidence indicates that immune-related biomarkers play an important role in tumor processes. This study investigates immune-related gene expression and its prognostic value in lung squamous cell carcinoma (LUSC). A cohort of 493 samples of patients with LUSC was collected and analyzed from data generated by the TCGA Research Network and ImmPort database. The R coxph package was employed to mine significant immune-related genes using univariate analysis. Lasso and stepwise regression analyses were used to construct the LUSC prognosis prediction model, and clusterProfiler was used for gene functional annotation and enrichment analysis. The Kaplan-Meier analysis and ROC were used to evaluate the model efficiency in predicting and classifying LUSC case prognoses. We identified 14 immune-related genes to incorporate into our prognosis model. The patients were divided into two subgroups (Risk-H and Risk-L) according to their risk score values. Compared to Risk-L patients, Risk-H patients showed significantly improved overall survival (OS) in both training and testing sets. Functional annotation indicated that the 14 identified genes were mainly enriched in several immune-related pathways. Our results also revealed that a risk score value was correlated with various signaling pathways, such as the JAK-STA signaling pathway. Establishment of a nomogram for clinical application demonstrated that our immune-related model exhibited good predictive prognostic performance. Our predictive prognosis model based on immune signatures has potential clinical implications for assessing the overall survival and precise treatment for patients with LUSC. |
| doi_str_mv | 10.1155/2020/5024942 |
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This study investigates immune-related gene expression and its prognostic value in lung squamous cell carcinoma (LUSC). A cohort of 493 samples of patients with LUSC was collected and analyzed from data generated by the TCGA Research Network and ImmPort database. The R coxph package was employed to mine significant immune-related genes using univariate analysis. Lasso and stepwise regression analyses were used to construct the LUSC prognosis prediction model, and clusterProfiler was used for gene functional annotation and enrichment analysis. The Kaplan-Meier analysis and ROC were used to evaluate the model efficiency in predicting and classifying LUSC case prognoses. We identified 14 immune-related genes to incorporate into our prognosis model. The patients were divided into two subgroups (Risk-H and Risk-L) according to their risk score values. Compared to Risk-L patients, Risk-H patients showed significantly improved overall survival (OS) in both training and testing sets. Functional annotation indicated that the 14 identified genes were mainly enriched in several immune-related pathways. Our results also revealed that a risk score value was correlated with various signaling pathways, such as the JAK-STA signaling pathway. Establishment of a nomogram for clinical application demonstrated that our immune-related model exhibited good predictive prognostic performance. Our predictive prognosis model based on immune signatures has potential clinical implications for assessing the overall survival and precise treatment for patients with LUSC.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2020/5024942</identifier><identifier>PMID: 32802850</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Adolescent ; Adult ; Aged ; Aged, 80 and over ; Annotations ; Biomarkers ; Cancer therapies ; Carcinoma, Squamous Cell - genetics ; Carcinoma, Squamous Cell - metabolism ; Carcinoma, Squamous Cell - mortality ; Child ; Child, Preschool ; Classification ; Correlation analysis ; Databases, Nucleic Acid ; Disease-Free Survival ; Female ; Gene expression ; Gene Expression Regulation, Neoplastic ; Genes ; Genetic aspects ; Health aspects ; Humans ; Infant ; Infant, Newborn ; Lung cancer ; Lung carcinoma ; Lung Neoplasms - genetics ; Lung Neoplasms - metabolism ; Lung Neoplasms - mortality ; Lungs ; Male ; Medical prognosis ; Middle Aged ; Models, Biological ; Nomograms ; Patients ; Performance prediction ; Prediction models ; Predictive Value of Tests ; Prognosis ; Regression analysis ; Risk ; Risk Factors ; Signal transduction ; Signaling ; Squamous cell carcinoma ; Subgroups ; Survival ; Survival Rate ; Transcriptome</subject><ispartof>BioMed research international, 2020, Vol.2020 (2020), p.1-12</ispartof><rights>Copyright © 2020 Yubo Yan et al.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>Copyright © 2020 Yubo Yan et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2020 Yubo Yan et al. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-87bc64fff9a6c37a21427ac75b5ee36855e6070f9052efada7abb0c037f1b6b53</citedby><cites>FETCH-LOGICAL-c466t-87bc64fff9a6c37a21427ac75b5ee36855e6070f9052efada7abb0c037f1b6b53</cites><orcidid>0000-0001-8209-4677</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/PMC7338973/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338973/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4023,27922,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32802850$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Thai, Khac-Minh</contributor><contributor>Khac-Minh Thai</contributor><creatorcontrib>Xu, Shidong</creatorcontrib><creatorcontrib>Xu, Shanqi</creatorcontrib><creatorcontrib>Zhang, Minghui</creatorcontrib><creatorcontrib>Yan, Yubo</creatorcontrib><title>Identification of an Immune Gene Expression Signature for Predicting Lung Squamous Cell Carcinoma Prognosis</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Growing evidence indicates that immune-related biomarkers play an important role in tumor processes. This study investigates immune-related gene expression and its prognostic value in lung squamous cell carcinoma (LUSC). A cohort of 493 samples of patients with LUSC was collected and analyzed from data generated by the TCGA Research Network and ImmPort database. The R coxph package was employed to mine significant immune-related genes using univariate analysis. Lasso and stepwise regression analyses were used to construct the LUSC prognosis prediction model, and clusterProfiler was used for gene functional annotation and enrichment analysis. The Kaplan-Meier analysis and ROC were used to evaluate the model efficiency in predicting and classifying LUSC case prognoses. We identified 14 immune-related genes to incorporate into our prognosis model. The patients were divided into two subgroups (Risk-H and Risk-L) according to their risk score values. Compared to Risk-L patients, Risk-H patients showed significantly improved overall survival (OS) in both training and testing sets. Functional annotation indicated that the 14 identified genes were mainly enriched in several immune-related pathways. Our results also revealed that a risk score value was correlated with various signaling pathways, such as the JAK-STA signaling pathway. Establishment of a nomogram for clinical application demonstrated that our immune-related model exhibited good predictive prognostic performance. Our predictive prognosis model based on immune signatures has potential clinical implications for assessing the overall survival and precise treatment for patients with LUSC.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Annotations</subject><subject>Biomarkers</subject><subject>Cancer therapies</subject><subject>Carcinoma, Squamous Cell - genetics</subject><subject>Carcinoma, Squamous Cell - metabolism</subject><subject>Carcinoma, Squamous Cell - mortality</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Classification</subject><subject>Correlation analysis</subject><subject>Databases, Nucleic Acid</subject><subject>Disease-Free Survival</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Lung cancer</subject><subject>Lung carcinoma</subject><subject>Lung Neoplasms - 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This study investigates immune-related gene expression and its prognostic value in lung squamous cell carcinoma (LUSC). A cohort of 493 samples of patients with LUSC was collected and analyzed from data generated by the TCGA Research Network and ImmPort database. The R coxph package was employed to mine significant immune-related genes using univariate analysis. Lasso and stepwise regression analyses were used to construct the LUSC prognosis prediction model, and clusterProfiler was used for gene functional annotation and enrichment analysis. The Kaplan-Meier analysis and ROC were used to evaluate the model efficiency in predicting and classifying LUSC case prognoses. We identified 14 immune-related genes to incorporate into our prognosis model. The patients were divided into two subgroups (Risk-H and Risk-L) according to their risk score values. Compared to Risk-L patients, Risk-H patients showed significantly improved overall survival (OS) in both training and testing sets. Functional annotation indicated that the 14 identified genes were mainly enriched in several immune-related pathways. Our results also revealed that a risk score value was correlated with various signaling pathways, such as the JAK-STA signaling pathway. Establishment of a nomogram for clinical application demonstrated that our immune-related model exhibited good predictive prognostic performance. Our predictive prognosis model based on immune signatures has potential clinical implications for assessing the overall survival and precise treatment for patients with LUSC.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>32802850</pmid><doi>10.1155/2020/5024942</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-8209-4677</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adolescent Adult Aged Aged, 80 and over Annotations Biomarkers Cancer therapies Carcinoma, Squamous Cell - genetics Carcinoma, Squamous Cell - metabolism Carcinoma, Squamous Cell - mortality Child Child, Preschool Classification Correlation analysis Databases, Nucleic Acid Disease-Free Survival Female Gene expression Gene Expression Regulation, Neoplastic Genes Genetic aspects Health aspects Humans Infant Infant, Newborn Lung cancer Lung carcinoma Lung Neoplasms - genetics Lung Neoplasms - metabolism Lung Neoplasms - mortality Lungs Male Medical prognosis Middle Aged Models, Biological Nomograms Patients Performance prediction Prediction models Predictive Value of Tests Prognosis Regression analysis Risk Risk Factors Signal transduction Signaling Squamous cell carcinoma Subgroups Survival Survival Rate Transcriptome |
| title | Identification of an Immune Gene Expression Signature for Predicting Lung Squamous Cell Carcinoma Prognosis |
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