Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer

Author:

Serretiello Enrica1ORCID,Smimmo Annafrancesca1ORCID,Ballini Andrea2ORCID,Parmeggiani Domenico3,Agresti Massimo3,Bassi Paola3,Moccia Giancarlo3,Sciarra Antonella3,De Angelis Alessandra1,Della Monica Paola3,Marino Maria Michela1ORCID,Di Domenico Marina1

Affiliation:

1. Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy

2. Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy

3. Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, 80013 Naples, Italy

Abstract

Breast cancer (BC) caused 685,000 deaths globally in 2020, earning the title of the most common type of tumor among females. With a multifactorial genesis, BC is influenced by several factors such as age, genetic and epigenetic predisposition, and an individual’s exposome, and its classification is based on morphological/histological, invasiveness, and molecular futures. Extracellular vesicles (EVs) are cell-derived lipid-bilayer-delimited nanoparticles, which are distinguishable by size, genesis, and the markers expressed in exosomes (40 to 150 nm), microvesicles (40 to 10,000 nm), and apoptotic bodies (100–5000 nm). Produced in physiological and pathological cellular contexts, EVs are shuttles of biological material and are implicated in cell-to-cell communications, thus attracting significant interest in diagnostic and drug delivery research. We report and discuss the latest evidence regarding the important role of EVs in BC, deepening their implication in tumorigenesis and metastatic mechanisms. On the other hand, the use of BC-derived EVs as prognostic biomarkers and therapeutic approaches is undergoing investigation. Hence, EVs have become new weapons in precision medicine; however, only with the support of advanced algorithms such as artificial intelligence (AI) can we develop a wide range of information. Looking ahead, it is possible to see the application of AI in the prognosis and diagnosis of different pathologies.

Funder

European Union—NextGenerationEU

Publisher

MDPI AG

Reference178 articles.

1. (2023, November 07). Available online: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death.

2. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries;Sung;CA Cancer J. Clin.,2021

3. Global trends and forecasts of breast cancer incidence and deaths;Xu;Sci. Data,2023

4. (2023, November 13). Available online: https://www.who.int/news-room/fact-sheets/detail/breast-cancer.

5. (2023, November 30). Available online: https://www.aiom.it/wp-content/uploads/2022/12/2022_AIOM_NDC-web.pdf.

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