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Hepatocellular carcinoma (HCC) is an aggressive and fatal disease caused by a subset of cancer stem cells (CSCs). It is estimated that there are approximately 100 000 long noncoding RNAs (lncRNAs) in humans. However, the mechanisms by which lncRNAs affect tumor stemness remain poorly understood. In the present study, it is found that DIO3OS is a conserved lncRNA that is generally downregulated in multiple cancers, including HCC, and its low expression correlates with poor clinical outcomes in HCC. In in vitro cancer cell lines and an in vivo spontaneous HCC mouse model, DIO3OS markedly represses tumor development via its suppressive role in CSCs through downregulation of zinc finger E‐box binding homeobox 1 (ZEB1). Interestingly, DIO3OS represses ZEB1 post‐transcriptionally without affecting its mRNA levels. Subsequent experiments show that DIO3OS interacts with the NONO protein and restricts NONO‐mediated nuclear export of ZEB1 mRNA. Overall, these findings demonstrate that the DIO3OS‐NONO‐ZEB1 axis restricts HCC development and offers a valuable candidate for CSC‐targeted therapeutics for HCC. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is the third most commonly diagnosed cancer in the world, and surgical resection is the commonly used curative management of early-stage disease. However, the recurrence rate is high after resection, and liver fibrosis has been thought to increase the risk of recurrence. Conventional histological staging of fibrosis is highly subjective to observer variations. To overcome this limitation, we used a fully quantitative fibrosis assessment tool, qFibrosis (utilizing second harmonic generation and two-photon excitation fluorescence microscopy), with multi-dimensional artificial intelligence analysis to establish a fully-quantitative, accurate fibrotic score called a "combined index", which can predict early recurrence of HCC after curative intent resection. Therefore, we can pay more attention on the patients with high risk of early recurrence. Background: Liver fibrosis is thought to be associated with early recurrence of hepatocellular carcinoma (HCC) after resection. To recognize HCC patients with higher risk of early recurrence, we used a second harmonic generation and two-photon excitation fluorescence (SHG/TPEF) microscopy to create a fully quantitative fibrosis score which is able to predict early recurrence. Methods: The study included 81 HCC patients receiving curative intent hepatectomy. Detailed fibrotic features of resected hepatic tissues were obtained by SHG/TPEF microscopy, and we used multi-dimensional artificial intelligence analysis to create a recurrence prediction model "combined index" according to the morphological collagen features of each patient's non-tumor hepatic tissues. Results: Our results showed that the "combined index" can better predict early recurrence (area under the curve = 0.917, sensitivity = 81.8%, specificity = 90.5%), compared to alpha fetoprotein level (area under the curve = 0.595, sensitivity = 68.2%, specificity = 47.6%). Using a Cox proportional hazards analysis, a higher "combined index" is also a poor prognostic factor of disease-free survival and overall survival. Conclusions: By integrating multi-dimensional artificial intelligence and SHG/TPEF microscopy, we may locate patients with a higher risk of recurrence, follow these patients more carefully, and conduct further management if needed. [ABSTRACT FROM AUTHOR]