Our previous study has demonstrated that hepatocyte nuclear factor 1α (HNF1α) exerts potent therapeutic effects on hepatocellular carcinoma (HCC). However, the molecular mechanisms by which HNF1α reverses HCC malignancy need to be further elucidated. lncRNA microarray was performed to identify the long noncoding RNAs (lncRNAs) regulated by HNF1α. Chromatin immunoprecipitation and luciferase reporter assays were applied to clarify the mechanism of the transcriptional regulation of HNF1α to HNF1A antisense RNA 1 (HNF1A-AS1). The effect of HNF1A-AS1 on HCC malignancy was evaluated in vitro and in vivo. RNA pulldown, RNA-binding protein immunoprecipitation and the Bio-Layer Interferometry assay were used to validate the interaction of HNF1A-AS1 and Src homology region 2 domain-containing phosphatase 1 (SHP-1). HNF1α regulated the expression of a subset of lncRNAs in HCC cells. Among these lncRNAs, the expression levels of HNF1A-AS1 were notably correlated with HNF1α levels in HCC cells and human HCC tissues. HNF1α activated the transcription of HNF1A-AS1 by directly binding to its promoter region. HNF1A-AS1 inhibited the growth and the metastasis of HCC cells in vitro and in vivo. Moreover, knockdown of HNF1A-AS1 reversed the suppressive effects of HNF1α on the migration and invasion of HCC cells. Importantly, HNF1A-AS1 directly bound to the C-terminal of SHP-1 with a high binding affinity (KD = 59.57 ± 14.29 nM) and increased the phosphatase activity of SHP-1. Inhibition of SHP-1 enzymatic activity substantially reversed the HNF1α- or HNF1A-AS1-induced reduction on the metastatic property of HCC cells. Our data revealed that HNF1A-AS1 is a direct transactivation target of HNF1α in HCC cells and involved in the anti-HCC effect of HNF1α. HNF1A-AS1 functions as phosphatase activator through the direct interaction with SHP-1. These findings suggest that regulation of the HNF1α/HNF1A-AS1/SHP-1 axis may have beneficial effects in the treatment of HCC.

Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF. Patients with MSAP and SAP who were admitted from July 2014 to June 2017 were included. Firstly, parameters with significant differences between patients with MOF and without MOF were screened out by univariate analysis. Then, support vector machine (SVM), logistic regression analysis (LRA) and artificial neural networks (ANN) models were constructed based on these factors, and five-fold cross-validation was used to train each model. A total of 263 patients were enrolled. Univariate analysis screened out sixteen parameters referring to blood volume, inflammatory, coagulation and renal function to construct machine-learning models. The predictive efficiency of the optimal combinations of features by SVM, LRA, and ANN was almost equal (AUC = 0.840, 0.832, and 0.834, respectively), as well as the Acute Physiology and Chronic Health Evaluation II score (AUC = 0.814, P > 0.05). The common important predictive factors were HCT, K-time, IL-6 and creatinine in three models. Three machine-learning models can be efficient prognostic tools for predicting MOF in MSAP and SAP. ANN is recommended, which only needs four common parameters.

Protein kinase C δ (PKCδ) plays an important role in nonalcoholic fatty liver disease (NAFLD), however, the mechanism remains unknown. The present study explored the role of PKCδ in NAFLD development and investigated the relationships between PKCδ, calcium homeostasis, and endoplasmic reticulum (ER) stress (ERS). Hepatic steatosis cell model was induced by palmitic acid (PA) in L02 cells. Lipid accretion was evaluated using Oil Red O staining and a triglyceride (TG) detection kit. PKCδ was down-regulated by siRNA. RT-PCR and Western blotting were used to detect the expression of ERS markers. The fluorescence of Ca²⁺ influx was recorded using confocal microscopy. Sarco-ER Ca²⁺-ATPase (SERCA) activity was measured by ultramicro-ATP enzyme test kit. PA treatment induced lipid accretion in L02 cells, destroyed the ER structure, and increased PKCδ activation in a time-dependent manner. Further, PA treatment significantly increased the expression of ERS markers, Ig heavy chain binding protein (Bip), and homologous proteins of CCAAT-enhancer binding proteins (CHOP). PKCδ silencing down-regulated Bip and CHOP expression, indicating a successful alleviation of ERS. The increased calcium storage induced by PA stimulation was significantly decreased in L02 cells treated with PKCδ siRNA compared with the negative control. Moreover, diminished SERCA activity caused by PA was recovered in PKCδ siRNA transfected cells. To the best of our knowledge, this is the first report demonstrating that the inhibition of PKCδ alleviates ERS by enhancing SERCA activity and stabilizing calcium homeostasis.

Objective Predicting the therapeutic response before initiation of hepatic artery infusion chemotherapy (HAIC) with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) remains challenging for patients with unresectable hepatocellular carcinoma (HCC). Herein, we investigated the potential of a contrast-enhanced CT-based habitat radiomics model as a novel approach for predicting the early therapeutic response to HAIC-FOLFOX in patients with unresectable HCC. Methods A total of 148 patients with unresectable HCC who received HAIC-FOLFOX combined with targeted therapy or immunotherapy at three tertiary care medical centers were enrolled retrospectively. Tumor habitat features were extracted from subregion radiomics based on CECT at different phases using k-means clustering. Logistic regression was used to construct the model. This CECT-based habitat radiomics model was verified by bootstrapping and compared with a model based on clinical variables. Model performance was evaluated using the area under the curve (AUC) and a calibration curve. Results Three intratumoral habitats with high, moderate, and low enhancement were identified to construct a habitat radiomics model for therapeutic response prediction. Patients with a greater proportion of high-enhancement intratumoral habitat showed better therapeutic responses. The AUC of the habitat radiomics model was 0.857 (95% CI: 0.798–0.916), and the bootstrap-corrected concordance index was 0.842 (95% CI: 0.785–0.907), resulting in a better predictive value than the clinical variable-based model, which had an AUC of 0.757 (95% CI: 0.679–0.834). Conclusion The CECT-based habitat radiomics model is an effective, visualized, and noninvasive tool for predicting the early therapeutic response of patients with unresectable HCC to HAIC-FOLFOX treatment and could guide clinical management and decision-making.

Effective treatment for metastasis, a leading cause of cancer‐associated death, is still lacking. To seed on a distal organ, disseminated cancer cells (DCCs) must adapt to the local tissue microenvironment. However, it remains elusive how DCCs respond the pro‐metastatic niche signals. Here, systemic motif‐enrichment identified myocyte enhancer factor 2D (MEF2D) as a critical sensor of niche signals to regulate DCCs adhesion and colonization, leading to intrahepatic metastasis and recurrence of liver cancer. In this context, MEF2D transactivates Itgb1 (coding β1‐integrin) and Itgb4 (coding β4‐integrin) to execute temporally unique functions, where ITGB1 recognizes extracellular matrix for early seeding, and ITGB4 acts as a novel sensor of neutrophil extracellular traps‐DNA (NETs‐DNA) for subsequent chemotaxis and colonization. In turn, an integrin‐FAK circuit promotes a phosphorylation‐dependent USP14‐orchastrated deubiquitination switch to stabilize MEF2D via circumventing degradation by the E3‐ubiquitin‐ligase MDM2. Clinically, the USP14(pS432)‐MEF2D‐ITGB1/4 feedback loop is often hyper‐active and indicative of inferior outcomes in human malignancies, while its blockade abrogated intrahepatic metastasis of DCCs. Together, DCCs exploit a deubiquitination‐dependent switch on MEF2D to integrate niche signals in the liver mesenchyme, thereby amplifying the pro‐metastatic integrin‐FAK signaling. Disruption of this feedback loop is clinically applicable with fast‐track potential to block microenvironmental cues driving metastasis.

Tissue invasion is an initiating step of the cancer metastatic cascade. Unraveling the mechanisms underlying intracellular signaling pathway rewiring that activates downstream transcriptional machinery to drive invasiveness could help identify improved strategies to prevent and treat metastasis. Through an unbiased genome-wide CRISPR screen in a mouse model of gastric adenocarcinoma (GAC), an E3 ubiquitin ligase, tripartite motif–containing protein 49 (TRIM49), was identified as a potent suppressor of cancer invasiveness. In two thirds of GAC, TRIM49 expression was downregulated in invading cancer cells, in which TRIM49 deficiency correlated with deeper tumor infiltration and lymph node metastasis and was indicative of shorter overall patient survival. In multiple orthotopic GAC mouse models, TRIM49-deficient cancer cells were highly infiltrative, leading to multiorgan metastasis. Mechanistically, galectin-3, a putative regulator of cancer invasion, was stabilized in TRIM49-deficient cancer, largely because of the failure to undergo TRIM49-mediated polyubiquitination and proteasomal degradation. Consequently, galectin-3 assembled a complex with EGR1, thereby regulating transcriptional activities of a proinvasive gene module. As the galectin-3/EGR1 complex acted as a key node relaying proinvasive signaling, its disruption using GB1107, an oral galectin-3 inhibitor, suppressed tissue infiltration and metastasis of patient-derived xenografts. Taken together, a proinvasive galectin-3/EGR1 transcriptional complex was exploited by TRIM49-deficient GAC to fuel tissue invasion, representing an Achilles’ heel that is potentially targetable to prevent metastasis. Significance: A proinvasion galectin-3/EGR1 transcriptional complex is a therapeutic vulnerability in the highly invasive TRIM49-deficient gastric adenocarcinoma, which can be disrupted by the oral galectin-3 inhibitor GB1107 to prevent cancer spreading.