AI Article Synopsis

  • Prostate cancer is the most prevalent cancer among men globally, posing significant treatment challenges due to its complex development and high mortality rate.
  • Current clinical concerns include dilemmas around overtreatment, recognizing dormant tumors, and the need for personalized drug approaches, alongside understanding genetic mutations linked to the disease.
  • The review highlights the necessity for integrating multidimensional data and advanced technologies like AI and machine learning to enhance the identification of cancer subtypes and improve personalized treatment strategies.

Article Abstract

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10728428PMC
http://dx.doi.org/10.1007/s13755-023-00264-5DOI Listing

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