{"title":"基于口腔鳞状细胞癌中顺铂耐药相关基因的预后模型","authors":"Rong Lu, Qian Yang, Siyu Liu, Lu Sun","doi":"10.3290/j.ohpd.b4836127","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To screen for the cisplatin resistance-related prognostic signature in oral squamous cell carcinoma (OSCC) and assess its correlation with the immune microenvironment.</p><p><strong>Materials and methods: </strong>The gene expression data associated with OSCC and cisplatin-resistance were downloaded from TCGA and GEO databases. Cisplatin-resistant genes were selected through taking the intersection of differentially expressed genes (DEGs) between tumor and control groups as well as between cisplatin-resistant samples and parental samples. Then, prognosis-related cisplatin-resistant genes were further selected by univariate Cox regression and LASSO regression analyses to construct a survival prognosis model. A GSEA (gene set enrichment analysis) between two risk groups was conducted with the MSigDB v7.1 database. Finally, the immune landscape of the sample was studied using CIBERSORT. The IC50 values of 57 drugs were predicted using pRRophetic 0.5.</p><p><strong>Results: </strong>A total 230 candidate genes were obtained. Then 7 drug-resistant genes were selected for prognostic risk-score (RS) signature construction using LASSO regression analysis, including STC2, TBC1D2, ADM, NDRG1, OLR1, PDGFA and ANO1. RS was an independent prognostic factor. Additionally, a nomogram model was established to predict the 1-, 2-, and 3-year survival rates of samples. The GSEA identified several differential pathways between two risk groups, such as EMT, hypoxia, and oxidative phosphorylation. Fifteen immune cells were statistically significantly different in infiltration level between the two groups, such as macrophages M2, and resting NK cells. A total of 57 drugs had statistically significantly different IC50 values between two risk groups.</p><p><strong>Conclusion: </strong>These model genes and immune cells may play a role in predicting the prognosis and chemoresistance in OSCC.</p>","PeriodicalId":19696,"journal":{"name":"Oral health & preventive dentistry","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Prognostic Model Based on Cisplatin-Resistance Related Genes in Oral Squamous Cell Carcinoma.\",\"authors\":\"Rong Lu, Qian Yang, Siyu Liu, Lu Sun\",\"doi\":\"10.3290/j.ohpd.b4836127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To screen for the cisplatin resistance-related prognostic signature in oral squamous cell carcinoma (OSCC) and assess its correlation with the immune microenvironment.</p><p><strong>Materials and methods: </strong>The gene expression data associated with OSCC and cisplatin-resistance were downloaded from TCGA and GEO databases. Cisplatin-resistant genes were selected through taking the intersection of differentially expressed genes (DEGs) between tumor and control groups as well as between cisplatin-resistant samples and parental samples. Then, prognosis-related cisplatin-resistant genes were further selected by univariate Cox regression and LASSO regression analyses to construct a survival prognosis model. A GSEA (gene set enrichment analysis) between two risk groups was conducted with the MSigDB v7.1 database. Finally, the immune landscape of the sample was studied using CIBERSORT. The IC50 values of 57 drugs were predicted using pRRophetic 0.5.</p><p><strong>Results: </strong>A total 230 candidate genes were obtained. Then 7 drug-resistant genes were selected for prognostic risk-score (RS) signature construction using LASSO regression analysis, including STC2, TBC1D2, ADM, NDRG1, OLR1, PDGFA and ANO1. RS was an independent prognostic factor. Additionally, a nomogram model was established to predict the 1-, 2-, and 3-year survival rates of samples. The GSEA identified several differential pathways between two risk groups, such as EMT, hypoxia, and oxidative phosphorylation. Fifteen immune cells were statistically significantly different in infiltration level between the two groups, such as macrophages M2, and resting NK cells. A total of 57 drugs had statistically significantly different IC50 values between two risk groups.</p><p><strong>Conclusion: </strong>These model genes and immune cells may play a role in predicting the prognosis and chemoresistance in OSCC.</p>\",\"PeriodicalId\":19696,\"journal\":{\"name\":\"Oral health & preventive dentistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oral health & preventive dentistry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3290/j.ohpd.b4836127\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oral health & preventive dentistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3290/j.ohpd.b4836127","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
A Prognostic Model Based on Cisplatin-Resistance Related Genes in Oral Squamous Cell Carcinoma.
Purpose: To screen for the cisplatin resistance-related prognostic signature in oral squamous cell carcinoma (OSCC) and assess its correlation with the immune microenvironment.
Materials and methods: The gene expression data associated with OSCC and cisplatin-resistance were downloaded from TCGA and GEO databases. Cisplatin-resistant genes were selected through taking the intersection of differentially expressed genes (DEGs) between tumor and control groups as well as between cisplatin-resistant samples and parental samples. Then, prognosis-related cisplatin-resistant genes were further selected by univariate Cox regression and LASSO regression analyses to construct a survival prognosis model. A GSEA (gene set enrichment analysis) between two risk groups was conducted with the MSigDB v7.1 database. Finally, the immune landscape of the sample was studied using CIBERSORT. The IC50 values of 57 drugs were predicted using pRRophetic 0.5.
Results: A total 230 candidate genes were obtained. Then 7 drug-resistant genes were selected for prognostic risk-score (RS) signature construction using LASSO regression analysis, including STC2, TBC1D2, ADM, NDRG1, OLR1, PDGFA and ANO1. RS was an independent prognostic factor. Additionally, a nomogram model was established to predict the 1-, 2-, and 3-year survival rates of samples. The GSEA identified several differential pathways between two risk groups, such as EMT, hypoxia, and oxidative phosphorylation. Fifteen immune cells were statistically significantly different in infiltration level between the two groups, such as macrophages M2, and resting NK cells. A total of 57 drugs had statistically significantly different IC50 values between two risk groups.
Conclusion: These model genes and immune cells may play a role in predicting the prognosis and chemoresistance in OSCC.
期刊介绍：
Clinicians, general practitioners, teachers, researchers, and public health administrators will find this journal an indispensable source of essential, timely information about scientific progress in the fields of oral health and the prevention of caries, periodontal diseases, oral mucosal diseases, and dental trauma. Central topics, including oral hygiene, oral epidemiology, oral health promotion, and public health issues, are covered in peer-reviewed articles such as clinical and basic science research reports; reviews; invited focus articles, commentaries, and guest editorials; and symposium, workshop, and conference proceedings.
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