{"title":"通过整合铁蛋白沉积和免疫相关基因,开发用于预测前列腺癌预后和免疫治疗反应的新型风险特征。","authors":"Yang Song, Qiang Zhang","doi":"10.1007/s12033-024-01293-5","DOIUrl":null,"url":null,"abstract":"<p><p>Ferroptosis and immune response correlation studies have not been reported in prostate cancer (PCa), and the main goal of this paper is to identify biomarkers that can be used for early diagnosis of prostate cancer. Data on PCa were retrieved from the TCGA and MSKCC2010 databases. Thereafter, the differentially expressed ferroptosis-related genes (DE-FRGs: ACSF2) and immune-related genes (DE-IRGs: ANGPT1, NPPC, and PTGDS) were identified using the \"limma\" package. Additionally, we used univariate and multivariate Cox regression analyses to obtain biochemical relapse (BCR)-free survival-related genes and construct a risk signature. Patients with high-risk scores were characterized by poor BCR-free survival, relatively low immune cell abundance, and comparably weak expression of immune checkpoint molecules. Moreover, gene set variation analysis (GSVA) was performed to explore the biological pathways related to the risk signature. Single sample gene set enrichment analysis (ssGESA) was applied to evaluate the status of immune cells in patients with PCa, which demonstrated that the risk score was intimately affiliated with immune response and cancer pathways. Ultimately, the connection between the risk score and response of PCa patients to immunotherapy was appraised using the TIDE algorithm. The TIDE algorithm implied that the high-risk score PCa population might benefit more from immunotherapy regimens. Finally, qRT-PCR were used to evaluate the expression of DE-FRGs and DE-IRGs in PCa cell and normal prostate epithelial cells. The result of qRT-PCR showed that the mRNA expression levels of ACSF2, ANGPT1, NPPC, and PTGDS in normal prostate epithelial cell were higher than that in PCa cells. Therefore, a risk score model was generated based on one DE-FRG and three DE-IRGs, which could predict the BCR-free survival and response of immunotherapy for patients with PCa.</p>","PeriodicalId":18865,"journal":{"name":"Molecular Biotechnology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a Novel Risk Signature for Predicting the Prognosis and Immunotherapeutic Response of Prostate Cancer by Integrating Ferroptosis and Immune-Related Genes.\",\"authors\":\"Yang Song, Qiang Zhang\",\"doi\":\"10.1007/s12033-024-01293-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Ferroptosis and immune response correlation studies have not been reported in prostate cancer (PCa), and the main goal of this paper is to identify biomarkers that can be used for early diagnosis of prostate cancer. Data on PCa were retrieved from the TCGA and MSKCC2010 databases. Thereafter, the differentially expressed ferroptosis-related genes (DE-FRGs: ACSF2) and immune-related genes (DE-IRGs: ANGPT1, NPPC, and PTGDS) were identified using the \\\"limma\\\" package. Additionally, we used univariate and multivariate Cox regression analyses to obtain biochemical relapse (BCR)-free survival-related genes and construct a risk signature. Patients with high-risk scores were characterized by poor BCR-free survival, relatively low immune cell abundance, and comparably weak expression of immune checkpoint molecules. Moreover, gene set variation analysis (GSVA) was performed to explore the biological pathways related to the risk signature. Single sample gene set enrichment analysis (ssGESA) was applied to evaluate the status of immune cells in patients with PCa, which demonstrated that the risk score was intimately affiliated with immune response and cancer pathways. Ultimately, the connection between the risk score and response of PCa patients to immunotherapy was appraised using the TIDE algorithm. The TIDE algorithm implied that the high-risk score PCa population might benefit more from immunotherapy regimens. Finally, qRT-PCR were used to evaluate the expression of DE-FRGs and DE-IRGs in PCa cell and normal prostate epithelial cells. The result of qRT-PCR showed that the mRNA expression levels of ACSF2, ANGPT1, NPPC, and PTGDS in normal prostate epithelial cell were higher than that in PCa cells. Therefore, a risk score model was generated based on one DE-FRG and three DE-IRGs, which could predict the BCR-free survival and response of immunotherapy for patients with PCa.</p>\",\"PeriodicalId\":18865,\"journal\":{\"name\":\"Molecular Biotechnology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Biotechnology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12033-024-01293-5\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Biotechnology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12033-024-01293-5","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Development of a Novel Risk Signature for Predicting the Prognosis and Immunotherapeutic Response of Prostate Cancer by Integrating Ferroptosis and Immune-Related Genes.
Ferroptosis and immune response correlation studies have not been reported in prostate cancer (PCa), and the main goal of this paper is to identify biomarkers that can be used for early diagnosis of prostate cancer. Data on PCa were retrieved from the TCGA and MSKCC2010 databases. Thereafter, the differentially expressed ferroptosis-related genes (DE-FRGs: ACSF2) and immune-related genes (DE-IRGs: ANGPT1, NPPC, and PTGDS) were identified using the "limma" package. Additionally, we used univariate and multivariate Cox regression analyses to obtain biochemical relapse (BCR)-free survival-related genes and construct a risk signature. Patients with high-risk scores were characterized by poor BCR-free survival, relatively low immune cell abundance, and comparably weak expression of immune checkpoint molecules. Moreover, gene set variation analysis (GSVA) was performed to explore the biological pathways related to the risk signature. Single sample gene set enrichment analysis (ssGESA) was applied to evaluate the status of immune cells in patients with PCa, which demonstrated that the risk score was intimately affiliated with immune response and cancer pathways. Ultimately, the connection between the risk score and response of PCa patients to immunotherapy was appraised using the TIDE algorithm. The TIDE algorithm implied that the high-risk score PCa population might benefit more from immunotherapy regimens. Finally, qRT-PCR were used to evaluate the expression of DE-FRGs and DE-IRGs in PCa cell and normal prostate epithelial cells. The result of qRT-PCR showed that the mRNA expression levels of ACSF2, ANGPT1, NPPC, and PTGDS in normal prostate epithelial cell were higher than that in PCa cells. Therefore, a risk score model was generated based on one DE-FRG and three DE-IRGs, which could predict the BCR-free survival and response of immunotherapy for patients with PCa.
期刊介绍:
Molecular Biotechnology publishes original research papers on the application of molecular biology to both basic and applied research in the field of biotechnology. Particular areas of interest include the following: stability and expression of cloned gene products, cell transformation, gene cloning systems and the production of recombinant proteins, protein purification and analysis, transgenic species, developmental biology, mutation analysis, the applications of DNA fingerprinting, RNA interference, and PCR technology, microarray technology, proteomics, mass spectrometry, bioinformatics, plant molecular biology, microbial genetics, gene probes and the diagnosis of disease, pharmaceutical and health care products, therapeutic agents, vaccines, gene targeting, gene therapy, stem cell technology and tissue engineering, antisense technology, protein engineering and enzyme technology, monoclonal antibodies, glycobiology and glycomics, and agricultural biotechnology.