{"title":"局部高危前列腺癌新旧生物标志物的发展前景:最新进展、临床应用和精确肿瘤学的局限性。","authors":"Lilia Bardoscia, Angela Sardaro, Mariagrazia Quattrocchi, Paola Cocuzza, Elisa Ciurlia, Ilaria Furfaro, Maria Antonietta Gilio, Marcello Mignogna, Beatrice Detti, Gianluca Ingrosso","doi":"10.3390/jpm15080367","DOIUrl":null,"url":null,"abstract":"<p><p>High-risk prostate cancer (PC) accounts for 50-75% of 10-year relapse after primary treatment. Routine clinicopathological parameters for PC patient stratification have proven insufficient to inform clinical decisions in this setting. Tumor genomic profiling allowed overcoming the limits of diagnostic accuracy in the field of PC, integrated with radiomic features, automated platforms, evaluation of patient-related factors (age, performance status, comorbidity) and tumor-related factors (risk class, volume, T stage). In this scenario, the use of biomarkers to guide decision-making in localized, high-risk PC is evolving actively and rapidly. Additional tests for prostate-specific antigen have demonstrated superior sensitivity and specificity for detecting clinically significant PC, as well as commercially available genomic classifiers improving the risk prediction of disease recurrence/progression/metastasis, in combination with common clinical variables. This narrative review aimed to summarize the state of the art on the utility and evolution of old and emerging biomarkers in the diagnosis and prognosis of localized, high-risk PC, and the potential for their application in clinical practice. We focused on the theoretical molecular foundation of prostate carcinogenesis and explored the impact of genomic profiling, next-generation sequencing, and artificial intelligence in the extrapolation of customized features able to predict disease aggressiveness and possibly drive personalized therapeutic decisions.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 8","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387777/pdf/","citationCount":"0","resultStr":"{\"title\":\"The Evolving Landscape of Novel and Old Biomarkers in Localized High-Risk Prostate Cancer: State of the Art, Clinical Utility, and Limitations Toward Precision Oncology.\",\"authors\":\"Lilia Bardoscia, Angela Sardaro, Mariagrazia Quattrocchi, Paola Cocuzza, Elisa Ciurlia, Ilaria Furfaro, Maria Antonietta Gilio, Marcello Mignogna, Beatrice Detti, Gianluca Ingrosso\",\"doi\":\"10.3390/jpm15080367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>High-risk prostate cancer (PC) accounts for 50-75% of 10-year relapse after primary treatment. Routine clinicopathological parameters for PC patient stratification have proven insufficient to inform clinical decisions in this setting. Tumor genomic profiling allowed overcoming the limits of diagnostic accuracy in the field of PC, integrated with radiomic features, automated platforms, evaluation of patient-related factors (age, performance status, comorbidity) and tumor-related factors (risk class, volume, T stage). In this scenario, the use of biomarkers to guide decision-making in localized, high-risk PC is evolving actively and rapidly. Additional tests for prostate-specific antigen have demonstrated superior sensitivity and specificity for detecting clinically significant PC, as well as commercially available genomic classifiers improving the risk prediction of disease recurrence/progression/metastasis, in combination with common clinical variables. This narrative review aimed to summarize the state of the art on the utility and evolution of old and emerging biomarkers in the diagnosis and prognosis of localized, high-risk PC, and the potential for their application in clinical practice. We focused on the theoretical molecular foundation of prostate carcinogenesis and explored the impact of genomic profiling, next-generation sequencing, and artificial intelligence in the extrapolation of customized features able to predict disease aggressiveness and possibly drive personalized therapeutic decisions.</p>\",\"PeriodicalId\":16722,\"journal\":{\"name\":\"Journal of Personalized Medicine\",\"volume\":\"15 8\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12387777/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Personalized Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3390/jpm15080367\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Personalized Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/jpm15080367","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
The Evolving Landscape of Novel and Old Biomarkers in Localized High-Risk Prostate Cancer: State of the Art, Clinical Utility, and Limitations Toward Precision Oncology.
High-risk prostate cancer (PC) accounts for 50-75% of 10-year relapse after primary treatment. Routine clinicopathological parameters for PC patient stratification have proven insufficient to inform clinical decisions in this setting. Tumor genomic profiling allowed overcoming the limits of diagnostic accuracy in the field of PC, integrated with radiomic features, automated platforms, evaluation of patient-related factors (age, performance status, comorbidity) and tumor-related factors (risk class, volume, T stage). In this scenario, the use of biomarkers to guide decision-making in localized, high-risk PC is evolving actively and rapidly. Additional tests for prostate-specific antigen have demonstrated superior sensitivity and specificity for detecting clinically significant PC, as well as commercially available genomic classifiers improving the risk prediction of disease recurrence/progression/metastasis, in combination with common clinical variables. This narrative review aimed to summarize the state of the art on the utility and evolution of old and emerging biomarkers in the diagnosis and prognosis of localized, high-risk PC, and the potential for their application in clinical practice. We focused on the theoretical molecular foundation of prostate carcinogenesis and explored the impact of genomic profiling, next-generation sequencing, and artificial intelligence in the extrapolation of customized features able to predict disease aggressiveness and possibly drive personalized therapeutic decisions.
期刊介绍:
Journal of Personalized Medicine (JPM; ISSN 2075-4426) is an international, open access journal aimed at bringing all aspects of personalized medicine to one platform. JPM publishes cutting edge, innovative preclinical and translational scientific research and technologies related to personalized medicine (e.g., pharmacogenomics/proteomics, systems biology). JPM recognizes that personalized medicine—the assessment of genetic, environmental and host factors that cause variability of individuals—is a challenging, transdisciplinary topic that requires discussions from a range of experts. For a comprehensive perspective of personalized medicine, JPM aims to integrate expertise from the molecular and translational sciences, therapeutics and diagnostics, as well as discussions of regulatory, social, ethical and policy aspects. We provide a forum to bring together academic and clinical researchers, biotechnology, diagnostic and pharmaceutical companies, health professionals, regulatory and ethical experts, and government and regulatory authorities.