Targeting mitochondria in Cancer therapy: Machine learning analysis of hyaluronic acid-based drug delivery systems

Background: Mitochondrial alterations play a crucial role in the development and progression of cancer. Dysfunctional mitochondria contribute to the acquisition of key hallmarks of cancer, including sustained proliferative signaling, evasion of growth suppressors, and resistance to cell death. Consequently, targeting mitochondrial dysfunction has emerged as a promising therapeutic strategy. Hyaluronic acid (HA), a naturally occurring glycosaminoglycan, has garnered significant attention due to its multifaceted roles in cancer biology. Methods: We employed a Systematic Literature Review (SLR) approach to examine a collection of 90 scientific publications using a text mining technique leveraging the Latent Dirichlet Allocation (LDA) algorithm. Results: The result of this activity, performed through the MySLR digital platform, allowed us to identify a set of two distinct topics representing the research domain. Specifically, Topic 1 comprised 41 papers, while Topic 2 comprised 49 papers. Conclusions: The computational analysis highlighted that the integration of HA into drug delivery systems represents a promising approach to enhance the effectiveness and safety of cancer therapies. The discussed clinical trials provided compelling evidence of the potential of HA-based treatments in targeting cancer cells while minimizing adverse effects on healthy tissues.