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From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

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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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Data availability

The data generated or analyzed during this study are available from the corresponding author upon reasonable request.

Abbreviations

PCa:

Prostate cancer

SNPs:

Single nucleotide polymorphisms

DHT:

Dihydrotestosterone

ADT:

Androgen deprivation therapy

CRPC:

Castration-resistant PCa

NEPC:

Neuroendocrine PCa

AVPC:

Aggressive variant PCa

AR:

Androgen receptor

AR-v:

Androgen receptor variant

CNVs:

Copy number variations

CNA:

Copy number alterations

DTC:

Disseminated tumor cells

BCR:

Biochemical recurrence

CAD:

Computer-aided diagnosis

IMRT:

Intensity-modulated radiotherapy

MRI:

Magnetic resonance imaging

AI:

Artificial intelligence

ML:

Machine learning

DL:

Deep learning

AUC:

Area under the curve

SVM:

Support vector machines

RF:

Random forests

ANN:

Artificial neural network

ST:

Spatial transcriptomics

IFPTML:

Perturbation-Theory Machine Learning Information Fusion

KG:

Knowledge graph

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Funding

The study was supported by the National Natural Science Foundation of China (Grant No. 32270690, Grant No. 32070671).

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Authors and Affiliations

  1. Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041, China

    Shumin Ren, Jiakun Li & Bairong Shen

  2. Department of Computer Science and Information Technology, University of A Coruña, 15071, A Coruña, Spain

    Shumin Ren, Julián Dorado, Alejandro Sierra, Humbert González-Díaz & Aliuska Duardo

  3. IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940, Leioa, Spain

    Alejandro Sierra, Humbert González-Díaz & Aliuska Duardo

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SR performed the literature search and data analysis, and wrote the manuscript. BS conceived and supervised the study. HG contributed to the review of the article and provided comments. AD, JD, AP and JL contributed to the review and modification of the article. All authors read and approved the final manuscript.

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Ren, S., Li, J., Dorado, J. et al. From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine. Health Inf Sci Syst 12, 6 (2024). https://doi.org/10.1007/s13755-023-00264-5

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