{"title":"基于参与花生四烯酸代谢的七个基因的肝细胞癌预测预后模型","authors":"Xinyu Gu, Jing Wang, Jun Guan, Guojun Li, Xiao Ma, Yanli Ren, Shanshan Wu, Chao Chen, Haihong Zhu","doi":"10.1002/cam4.70284","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The occult onset and rapid progression of hepatocellular carcinoma (HCC) lead to an unsatisfactory overall survival (OS) rate. Established prognostic predictive models based on tumor-node-metastasis staging and predictive factors do not report satisfactory predictive efficacy. Arachidonic acid plays pivotal roles in biological processes including inflammation, regeneration, immune modulation, and tumorigenesis. We, therefore, constructed a prognostic predictive model based on seven genes linked to arachidonic acid metabolism, using samples of HCC patients from databases to analyze the genomic profiles. We also assessed the predictive stability of the constructed model.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Sample data of 365 patients diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA, training set) and HCCDB18, GSE14520, and GSE76427 databases (validation sets). Patient samples were clustered using ConsensusClusterPlus analysis based on the expression levels of 12 genes involved in arachidonic acid metabolism that were significantly associated with HCC prognosis. Differentially expressed genes (DEGs) within different clusters were distinguished and compared using WebGestaltR. Immunohistochemistry (IHC) analysis was performed using a human HCC tissue microarray (TMA). Tumor immune microenvironment assessment was performed using ESTIMATE, ssGSEA, and TIDE.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Samples of patients with HCC were classified into three clusters, with significant differences in OS. Cluster 2 showed the best prognosis, whereas cluster 1 presented the worst. The three clusters showed significant differences in immune infiltration. We then performed Cox and LASSO regression analyses, which revealed CYP2C9, G6PD, CDC20, SPP1, PON1, TRNP1, and ADH4 as prognosis-related hub genes, making it a simplified prognostic model. TMA analysis for the seven target genes showed similar results of regression analyses. The high-risk group showed a significantly worse prognosis and reduced immunotherapy efficacy. Our model showed stable prognostic predictive efficacy.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>This seven-gene–based model showed stable outcomes in predicting HCC prognosis as well as responses to immunotherapy.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"13 22","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561968/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predictive Prognostic Model for Hepatocellular Carcinoma Based on Seven Genes Participating in Arachidonic Acid Metabolism\",\"authors\":\"Xinyu Gu, Jing Wang, Jun Guan, Guojun Li, Xiao Ma, Yanli Ren, Shanshan Wu, Chao Chen, Haihong Zhu\",\"doi\":\"10.1002/cam4.70284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>The occult onset and rapid progression of hepatocellular carcinoma (HCC) lead to an unsatisfactory overall survival (OS) rate. Established prognostic predictive models based on tumor-node-metastasis staging and predictive factors do not report satisfactory predictive efficacy. Arachidonic acid plays pivotal roles in biological processes including inflammation, regeneration, immune modulation, and tumorigenesis. We, therefore, constructed a prognostic predictive model based on seven genes linked to arachidonic acid metabolism, using samples of HCC patients from databases to analyze the genomic profiles. We also assessed the predictive stability of the constructed model.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Sample data of 365 patients diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA, training set) and HCCDB18, GSE14520, and GSE76427 databases (validation sets). Patient samples were clustered using ConsensusClusterPlus analysis based on the expression levels of 12 genes involved in arachidonic acid metabolism that were significantly associated with HCC prognosis. Differentially expressed genes (DEGs) within different clusters were distinguished and compared using WebGestaltR. Immunohistochemistry (IHC) analysis was performed using a human HCC tissue microarray (TMA). Tumor immune microenvironment assessment was performed using ESTIMATE, ssGSEA, and TIDE.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Samples of patients with HCC were classified into three clusters, with significant differences in OS. Cluster 2 showed the best prognosis, whereas cluster 1 presented the worst. The three clusters showed significant differences in immune infiltration. We then performed Cox and LASSO regression analyses, which revealed CYP2C9, G6PD, CDC20, SPP1, PON1, TRNP1, and ADH4 as prognosis-related hub genes, making it a simplified prognostic model. TMA analysis for the seven target genes showed similar results of regression analyses. The high-risk group showed a significantly worse prognosis and reduced immunotherapy efficacy. Our model showed stable prognostic predictive efficacy.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>This seven-gene–based model showed stable outcomes in predicting HCC prognosis as well as responses to immunotherapy.</p>\\n </section>\\n </div>\",\"PeriodicalId\":139,\"journal\":{\"name\":\"Cancer Medicine\",\"volume\":\"13 22\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561968/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cam4.70284\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cam4.70284","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Predictive Prognostic Model for Hepatocellular Carcinoma Based on Seven Genes Participating in Arachidonic Acid Metabolism
Background
The occult onset and rapid progression of hepatocellular carcinoma (HCC) lead to an unsatisfactory overall survival (OS) rate. Established prognostic predictive models based on tumor-node-metastasis staging and predictive factors do not report satisfactory predictive efficacy. Arachidonic acid plays pivotal roles in biological processes including inflammation, regeneration, immune modulation, and tumorigenesis. We, therefore, constructed a prognostic predictive model based on seven genes linked to arachidonic acid metabolism, using samples of HCC patients from databases to analyze the genomic profiles. We also assessed the predictive stability of the constructed model.
Methods
Sample data of 365 patients diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA, training set) and HCCDB18, GSE14520, and GSE76427 databases (validation sets). Patient samples were clustered using ConsensusClusterPlus analysis based on the expression levels of 12 genes involved in arachidonic acid metabolism that were significantly associated with HCC prognosis. Differentially expressed genes (DEGs) within different clusters were distinguished and compared using WebGestaltR. Immunohistochemistry (IHC) analysis was performed using a human HCC tissue microarray (TMA). Tumor immune microenvironment assessment was performed using ESTIMATE, ssGSEA, and TIDE.
Results
Samples of patients with HCC were classified into three clusters, with significant differences in OS. Cluster 2 showed the best prognosis, whereas cluster 1 presented the worst. The three clusters showed significant differences in immune infiltration. We then performed Cox and LASSO regression analyses, which revealed CYP2C9, G6PD, CDC20, SPP1, PON1, TRNP1, and ADH4 as prognosis-related hub genes, making it a simplified prognostic model. TMA analysis for the seven target genes showed similar results of regression analyses. The high-risk group showed a significantly worse prognosis and reduced immunotherapy efficacy. Our model showed stable prognostic predictive efficacy.
Conclusions
This seven-gene–based model showed stable outcomes in predicting HCC prognosis as well as responses to immunotherapy.
期刊介绍：
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
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