Background/Objectives: Dysregulation of purinergic signaling, particularly CD73 overexpression, influences tumor progression, immune evasion, and chemoresistance in hepatocellular carcinoma (HCC). We aimed to characterize the transcriptional landscape of this system, identify prognostic markers, and investigate how the tumor microenvironment modulates pharmacological response to combined sorafenib and doxazosin in 3D spheroid models. Methods: We integrated RNA-seq data from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma (TCGA-LIHC) to identify differentially expressed genes, pathway enrichment, gene co-expression networks, prognostic associations, and machine learning-based biomarker selection. Modulation of key targets was assessed in HepG2 and HepG2/LX-2 spheroids treated with sorafenib and doxazosin using qPCR and flow cytometry. Results: Transcriptomics revealed dysregulation and network fragmentation. Specifically, analysis of the TCGA cohort indicated that high expression of ADA, NT5E, and ADORA1 correlated with poor overall survival. Given the critical role of CD73 in therapy resistance, we evaluated these findings in 3D models. Co-treatment significantly downregulated NT5E and ADORA1 mRNA expression, while ADORA2A was specifically reduced in the co-culture setting. For the ADA, effect-size analysis revealed a large magnitude of inhibition in HepG2 spheroids. Although flow cytometry showed that high CD73 protein expression remained stable across treatments in co-culture, the combination therapy overcame stromal protection, significantly increasing apoptosis (active caspase-3) in both mono- and co-culture spheroids compared with vehicle and monotherapy. Conclusions: We identified a purinergic prognostic signature in HCC and demonstrated that the combination therapy of sorafenib and doxazosin targets the adenosine pathway and specific receptors. We show that the stromal microenvironment sustains CD73 protein expression even under transcriptional inhibition, highlighting the critical role of 3D co-culture models in deciphering therapeutic resistance mechanisms.
Sousa et al. (Wed,) studied this question.