LncRNA ZFAS1 in hepatocellular carcinoma: A systematic review of molecular mechanisms and clinical translation.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, with its high recurrence rate and therapeutic resistance underscoring the urgent need for breakthrough molecular targets. The long non-coding RNA ZFAS1 has emerged as a critical regulatory hub in HCC pathogenesis through its multidimensional mechanisms. Clinical investigations reveal significant ZFAS1 overexpression in HCC tissues, which is strongly associated with microvascular invasion, lymph node metastasis, and unfavorable clinical outcomes. Meta-analytical data further corroborate its independent prognostic value in survival prediction. Mechanistically, ZFAS1 functions as a competitive endogenous RNA (ceRNA) that sequesters tumor-suppressive miRNAs including miR-150 and miR-193a-3p, thereby de-repressing downstream oncogenic targets such as ZEB1/MMP14 and RALY/HGF/c-Met. This molecular interplay drives epithelial-mesenchymal transition (EMT) and metastatic progression, while ZFAS1-encoded micropeptides concurrently inhibit ferroptosis through mitochondrial ROS modulation and the miR-150/AIFM2 axis, thereby synergistically enhancing tumor proliferation and apoptotic resistance. Within the tumor microenvironment (TME), exosome-derived ZFAS1 remodels intercellular communication networks, promoting angiogenesis via STAT3/VEGFA signaling, though its immunometabolic regulatory mechanisms warrant further elucidation. Clinically, plasma ZFAS1 demonstrates enhanced diagnostic utility when combined with alpha-fetoprotein (AUC = 0.891), while therapeutic targeting of ZFAS1-mediated PI3K-AKT and PERK/ATF4 pathways shows promise in overcoming sorafenib/donafenib resistance. Current translational challenges include ZFAS1 isoform heterogeneity, suboptimal liquid biopsy sensitivity, and dynamic TME interactions. Future directions should employ multi-omics integration (spatial transcriptomics/single-cell sequencing) coupled with AI-driven network modeling to systematically decode ZFAS1's regulatory architecture, ultimately enabling precision theranostic strategies for HCC management.