Bo Tang, Xia Zhao, Hongbing Liu, Qingfeng Zhang, Kui Liu, Xiaoyan Yang, Yun Huang
{"title":"肺腺癌STK11突变及免疫相关预后预测模型的构建","authors":"Bo Tang, Xia Zhao, Hongbing Liu, Qingfeng Zhang, Kui Liu, Xiaoyan Yang, Yun Huang","doi":"10.30498/ijb.2022.307202.3168","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong><i>STK11</i> mutation in LUAD affects immune cell infiltration in tumor tissue, and is associated with tumor prognosis.</p><p><strong>Objective: </strong>This study aimed to construct a <i>STK11</i> mutation and immune-related LUAD prognostic model.</p><p><strong>Materials and methods: </strong>The mutation frequency of <i>STK11</i> in LUAD was queried via cBioPortal in TCGA and PanCancer Atlas databases. The degree of immune infiltration was analyzed by CIBERSORT analysis. DEGs in <i>STK11</i>mut and <i>STK11</i>wt samples were analyzed. Metascape, GO and KEGG methods were adopted for functional and signaling pathway enrichment analysis of DEGs. Genes related to immune were overlapped with DEGs to acquire immune-related DEGs, whose Cox regression and LASSO analyses were employed to construct prognostic model. Univariate and multivariate Cox regression analyses verified the independence of riskscore and clinical features. A nomogram was established to predict the OS of patients. Additionally, TIMER was introduced to analyze relationship between infiltration abundance of 6 immune cells and expression of feature genes in LUAD.</p><p><strong>Results: </strong>The mutation frequency of <i>STK11</i> in LUAD was 16%, and the degrees of immune cell infiltration were different between the wild-type and mutant <i>STK11</i>. DEGs of <i>STK11</i> mutated and unmutated LUAD samples were mainly enriched in immune-related biological functions and signaling pathways. Finally, 6 feature genes were obtained, and a prognostic model was established. Riskscore was an independent immuno-related prognostic factor for LUAD. The nomogram diagram was reliable.</p><p><strong>Conclusion: </strong>Collectively, genes related to <i>STK11</i> mutation and immunity were mined from the public database, and a 6-gene prognostic prediction signature was generated.</p>","PeriodicalId":14492,"journal":{"name":"Iranian Journal of Biotechnology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/23/0b/IJB-21-e3168.PMC10203181.pdf","citationCount":"0","resultStr":"{\"title\":\"Construction of an <i>STK11</i> Mutation and Immune-Related Prognostic Prediction Model in Lung Adenocarcinoma.\",\"authors\":\"Bo Tang, Xia Zhao, Hongbing Liu, Qingfeng Zhang, Kui Liu, Xiaoyan Yang, Yun Huang\",\"doi\":\"10.30498/ijb.2022.307202.3168\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong><i>STK11</i> mutation in LUAD affects immune cell infiltration in tumor tissue, and is associated with tumor prognosis.</p><p><strong>Objective: </strong>This study aimed to construct a <i>STK11</i> mutation and immune-related LUAD prognostic model.</p><p><strong>Materials and methods: </strong>The mutation frequency of <i>STK11</i> in LUAD was queried via cBioPortal in TCGA and PanCancer Atlas databases. The degree of immune infiltration was analyzed by CIBERSORT analysis. DEGs in <i>STK11</i>mut and <i>STK11</i>wt samples were analyzed. Metascape, GO and KEGG methods were adopted for functional and signaling pathway enrichment analysis of DEGs. Genes related to immune were overlapped with DEGs to acquire immune-related DEGs, whose Cox regression and LASSO analyses were employed to construct prognostic model. Univariate and multivariate Cox regression analyses verified the independence of riskscore and clinical features. A nomogram was established to predict the OS of patients. Additionally, TIMER was introduced to analyze relationship between infiltration abundance of 6 immune cells and expression of feature genes in LUAD.</p><p><strong>Results: </strong>The mutation frequency of <i>STK11</i> in LUAD was 16%, and the degrees of immune cell infiltration were different between the wild-type and mutant <i>STK11</i>. DEGs of <i>STK11</i> mutated and unmutated LUAD samples were mainly enriched in immune-related biological functions and signaling pathways. Finally, 6 feature genes were obtained, and a prognostic model was established. Riskscore was an independent immuno-related prognostic factor for LUAD. The nomogram diagram was reliable.</p><p><strong>Conclusion: </strong>Collectively, genes related to <i>STK11</i> mutation and immunity were mined from the public database, and a 6-gene prognostic prediction signature was generated.</p>\",\"PeriodicalId\":14492,\"journal\":{\"name\":\"Iranian Journal of Biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/23/0b/IJB-21-e3168.PMC10203181.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iranian Journal of Biotechnology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.30498/ijb.2022.307202.3168\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iranian Journal of Biotechnology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.30498/ijb.2022.307202.3168","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
Construction of an STK11 Mutation and Immune-Related Prognostic Prediction Model in Lung Adenocarcinoma.
Background: STK11 mutation in LUAD affects immune cell infiltration in tumor tissue, and is associated with tumor prognosis.
Objective: This study aimed to construct a STK11 mutation and immune-related LUAD prognostic model.
Materials and methods: The mutation frequency of STK11 in LUAD was queried via cBioPortal in TCGA and PanCancer Atlas databases. The degree of immune infiltration was analyzed by CIBERSORT analysis. DEGs in STK11mut and STK11wt samples were analyzed. Metascape, GO and KEGG methods were adopted for functional and signaling pathway enrichment analysis of DEGs. Genes related to immune were overlapped with DEGs to acquire immune-related DEGs, whose Cox regression and LASSO analyses were employed to construct prognostic model. Univariate and multivariate Cox regression analyses verified the independence of riskscore and clinical features. A nomogram was established to predict the OS of patients. Additionally, TIMER was introduced to analyze relationship between infiltration abundance of 6 immune cells and expression of feature genes in LUAD.
Results: The mutation frequency of STK11 in LUAD was 16%, and the degrees of immune cell infiltration were different between the wild-type and mutant STK11. DEGs of STK11 mutated and unmutated LUAD samples were mainly enriched in immune-related biological functions and signaling pathways. Finally, 6 feature genes were obtained, and a prognostic model was established. Riskscore was an independent immuno-related prognostic factor for LUAD. The nomogram diagram was reliable.
Conclusion: Collectively, genes related to STK11 mutation and immunity were mined from the public database, and a 6-gene prognostic prediction signature was generated.
期刊介绍:
Iranian Journal of Biotechnology (IJB) is published quarterly by the National Institute of Genetic Engineering and Biotechnology. IJB publishes original scientific research papers in the broad area of Biotechnology such as, Agriculture, Animal and Marine Sciences, Basic Sciences, Bioinformatics, Biosafety and Bioethics, Environment, Industry and Mining and Medical Sciences.