Abstract
Background/Objectives: Gene expression-guided drug repurposing has emerged as a strategy to identify new therapy opportunities by associating disease transcriptional signatures with drug-induced gene expression profiles. This is relevant for prostate and bladder cancers, which have high molecular heterogeneity and therapy resistance limits for their standard treatment regimens. Antiretrovirals have been of great interest as repurposed candidates for these cancers due to their various effects on cancer cell pathways. The objective of this review is to assess the principles, applications, and challenges of this approach, with emphasis on antiretrovirals. Methods: This review summarizes published literature on gene expression-based drug repurposing methodologies, including signature reversion, pathway level analysis, and validation studies. Studies applying these concepts to prostate and bladder cancer were analyzed, and evidence of antiretroviral repurposing for cancer therapy was assessed based on transcriptomic alterations, pathway perturbation, and preclinical outcomes. Results: Transcriptomic-driven studies identified several drug candidates capable of modulating gene expression associated with therapy resistance, tumor progression, and cell stress responses. The anticancer effects of antiretrovirals were shown to be related to cell cycle arrest, apoptosis, metabolic alterations, and proteostasis. Nonetheless, transcriptomic responses are highly context-dependent and can be influenced by tumor subtype and experiment and treatment conditions. Off-target effects can also complicate mechanism interpretation. Conclusions: Gene expression-guided drug repurposing enables the systematic prioritization of clinically actionable candidates by matching disease and drug transcriptional signatures, but successful translation will require the integration of other omics results, careful model selection, and the development of clinically relevant biomarkers to support mechanism-informed repurposing. Translation will depend on subtype-aware signature matching, integration with complementary omics, and biomarker-backed validation to support precision deployment.