Degradation of FAK-targeting by proteolytic targeting chimera technology to inhibit the metastasis of hepatocellular carcinoma.

Liver cancer is a prevalent malignant cancer, ranking third in terms of mortality rate. Metastasis and recurrence primarily contribute to the high mortality rate of liver cancer. Hepatocellular carcinoma (HCC) has low expression of focal adhesion kinase (FAK), which increases the risk of metastasis and recurrence. Nevertheless, the efficacy of FAK phosphorylation inhibitors is currently limited. Thus, investigating the mechanisms by which FAK affects HCC metastasis to develop targeted therapies for FAK may present a novel strategy to inhibit HCC metastasis. This study examined the correlation between FAK expression and the prognosis of HCC. Additionally, we explored the impact of FAK degradation on HCC metastasis through wound healing experiments, transwell invasion experiments, and a xenograft tumor model. The expression of proteins related to epithelial-mesenchymal transition (EMT) was measured to elucidate the underlying mechanisms. The results showed that FAK PROTAC can degrade FAK, inhibit the migration and invasion of HCC cells in vitro, and notably decrease the lung metastasis of HCC in vivo. Increased expression of E-cadherin and decreased expression of vimentin indicated that EMT was inhibited. Consequently, degradation of FAK through FAK PROTAC effectively suppressed liver cancer metastasis, holding significant clinical implications for treating liver cancer and developing innovative anti-neoplastic drugs.

An optimized short-term steroid therapy for chronic drug-induced liver injury: A prospective randomized clinical trial.

BACKGROUND AND AIMS: The use of corticosteroids in chronic drug-induced liver injury (DILI) is an important issue. Our previous randomized controlled trial showed that patients with chronic DILI benefited from a 48-week steroid stepwise reduction (SSR) regimen. However, it remains unclear whether a shorter course of therapy can achieve similar efficacy. In this study, we aimed to assess whether a 36-week SSR can achieve efficacy similar to that of 48-week SSR. METHODS: A randomized open-label trial was performed. Eligible patients were randomly assigned to the 36- or 48-week (1:1) SSR group. Liver biopsies were performed at baseline and at the end of treatment. The primary outcome was the proportion of patients with relapse rate (RR). The secondary outcomes were improvement in liver histology and safety. RESULTS: Of the 90 participants enrolled, 84 (87.5%) completed the trial, and 62 patients (68.9%) were women. Hepatocellular damage was observed in 53.4% of the cohort. The RR was 7.1% in the 36-week SSR group but 4.8% in the 48-week SSR group, as determined by per-protocol set analysis (p = 1.000). Significant histological improvements in histological activity (93.1% vs. 92.9%, p = 1.000) and fibrosis (41.4% vs. 46.4%, p = .701) were observed in both the groups. Biochemical normalization time did not differ between the two groups. No severe adverse events were observed. CONCLUSIONS: Both the 36- and 48-week SSR regimens demonstrated similar biochemical response and histological improvements with good safety, supporting 36-week SSR as a preferable therapeutic choice (ClinicalTrials.gov, NCT03266146).

NLRP6 deficiency suppresses colorectal cancer liver metastasis growth by modulating M-MDSC-induced immunosuppressive microenvironment.

Colorectal cancer liver metastasis (CRLM) a profound influence on the prognosis of patients with colorectal cancer (CRC), prompting a comprehensive inquiry into its underlying mechanisms. Amidst the multifaceted tumor microenvironment, myeloid-derived suppressor cells (MDSCs) have emerged as pivotal orchestrators of immune modulation. However, their specific contributions to the CRLM have not been explored. The role of NLRP6, a member of the NOD-like receptor family, is of interest. Employing a liver metastasis model, our investigation revealed a heightened accumulation of monocytic MDSCs (M-MDSCs) within metastatic sites, culminating in an immunosuppressive milieu characterized by depleted CD8+ T cell populations. Remarkably, the absence of NLRP6 disrupts this intricate immunosuppressive network, highlighting its nuanced role in sculpting the trajectory of CRLM. This study elucidates the interplay between NLRP6 and MDSCs, potentially guiding novel therapeutic strategies to recalibrate the immune microenvironment in CRLM and enhance patient outcomes.

Comparison of hepatitis B virus reactivation in hepatocellular carcinoma patients who received tyrosine kinase inhibitor alone or together with programmed cell death protein-1 inhibitors.

BACKGROUND AND AIMS: Programmed cell death protein-1 (PD-1) inhibitors plus tyrosine kinase inhibitor (TKI) have dramatically improved survival of patients with advanced hepatocellular carcinoma (HCC). However, the risk of hepatitis B virus (HBV) reactivation from these antitumor medications remains unclear. METHODS: Patients receiving TKI monotherapy (TKI group) or TKI combined with PD-1 inhibitors (combination group) were included. The primary endpoint was HBV reactivation as defined by an increase in HBV DNA titer by at least 1 log (tenfold) from baseline. The secondary endpoints included tumor progression and overall survival. RESULTS: Four hundred and ninety-nine patients met the inclusion criteria, including 296 patients in the TKI group and 203 patients in the combination group. The 3-, 6- and 12-month cumulative incidence rates of HBV reactivation in the TKI group vs. combination group were 7.8%, 12.8% and 21.3% vs. 9.9%, 19.2% and 30.0%, respectively (p = 0.02). The Cox proportional hazard model indicated that combination therapy (HR 1.41, 95% CI 1.00-1.99, p = 0.05), ALT > 40 U/ml (HR 1.50, 95% CI 1.05-2.16, p = 0.03), and tumor size > 5 cm (HR 1.58, 95% CI 1.10-2.28, p = 0.01) were independent risk factors for HBV reactivation. Compared with the HBV reactivation group, the progression-free survival and overall survival of patients in the HBV non-reactivation group were significantly prolonged (p < 0.001 and p = 0.001). CONCLUSIONS: Patients who received TKI combined with PD-1 inhibitors had a greater risk for HBV reactivation, and those with HBV reactivation had a higher rate of tumor progression and shorter survival time, than those receiving TKI alone.

Development and Validation of a nomogram for forecasting survival of alcohol related hepatocellular carcinoma patients.

Background: With the increasing incidence and prevalence of alcoholic liver disease, alcohol-related hepatocellular carcinoma has become a serious public health problem worthy of attention in China. However, there is currently no prognostic prediction model for alcohol-related hepatocellular carcinoma. Methods: The retrospective analysis research of alcohol related hepatocellular carcinoma patients was conducted from January 2010 to December 2014. Independent prognostic factors of alcohol related hepatocellular carcinoma were identified by Lasso regression and multivariate COX proportional model analysis, and the nomogram model was constructed. The reliability and accuracy of the model were assessed using the concordance index(C-Index), receiver operating characteristic (ROC) curve and calibration curve. Evaluate the clinical benefit and application value of the model through clinical decision curve analysis (DCA). The prognosis was assessed by the Kaplan-Meier (KM) survival curve. Results: In sum, 383 patients were included in our study. Patients were stochastically assigned to training cohort (n=271) and validation cohort (n=112) according to 7:3 ratio. The predictors included in the nomogram were splenectomy, platelet count (PLT), creatinine (CRE), Prealbumin (PA), mean erythrocyte hemoglobin concentration (MCHC), red blood cell distribution width (RDW) and TNM. Our nomogram demonstrated excellent discriminatory power (C-index) and good calibration at 1-year, 3-year and 5- year overall survival (OS). Compared to TNM and Child-Pugh model, the nomogram had better discriminative ability and higher accuracy. DCA showed high clinical benefit and application value of the model. Conclusion: The nomogram model we established can precisely forcasting the prognosis of alcohol related hepatocellular carcinoma patients, which would be helpful for the early warning of alcohol related hepatocellular carcinoma and predict prognosis in patients with alcoholic hepatocellular carcinoma.

An OX40L mRNA vaccine inhibits the growth of hepatocellular carcinoma.

mRNA cancer vaccines show therapeutic potential for malignant tumors, including hepatocellular carcinoma (HCC). We optimized and synthesized stable mRNA encoding costimulator Oxford 40 ligand (OX40L). For systemic delivery, OX40L mRNAs were loaded into lipid nanoparticles (LNPs). The expression and costimulatory effects of OX40L were investigated in vitro. OX40L was expressed on the cell surface and costimulated T cells. In vivo, intratumoral injection of LNPs encapsulating OX40L mRNAs significantly reduced tumor growth and increased the survival of mice bearing H22 tumors. Importantly, CD4+ and CD8+ T cells were significantly increased in the OX40L mRNA group in vivo. Taken together, our findings provide a promising clinical strategy for immunotherapy for HCC using mRNA vaccines.

Intestinal Candida albicans Promotes Hepatocarcinogenesis by Up-Regulating NLRP6.

Hepatocellular carcinoma (HCC), a primary liver cancer, is closely associated with the gut microbiota. However, the role of gut fungi in the development of HCC remains unclear. The aim of this study was to explore the influence of intestinal Candida albicans on HCC. Here, We found that patients with HCC showed significantly decreased diversity of the gut mycobiome and increased abundance of C. albicans, compared to the patients with liver cirrhosis. The gavage of C. albicans in the WT models increased the tumor size and weight and influenced the plasma metabolome, which was indicated by alterations in 117 metabolites, such as L-carnitine and L-acetylcarnitine, and several KEGG enriched pathways, such as phenylalanine metabolism and citrate cycle. Moreover, the expression of nucleotide oligomerization domain-like receptor family pyrin domain containing 6 (NLRP6) in the intestinal tissues and primary intestinal epithelial cells of the WT mice interacted with C. albicans increased. Notably, the colonization of C. albicans had no effect on tumor growth in Nlrp6 -/- mice. In conclusion, the abnormal colonization of C. albicans reprogrammed HCC metabolism and contributed to the progression of HCC dependent on NLRP6, which provided new targets for the treatment of HCC.

Immune-mediated hepatitis induced by immune checkpoint inhibitors: Current updates and future perspectives.

In recent years, cancer immunotherapy has made remarkable achievements. Immune checkpoint inhibitors (ICIs) have been used successfully in several types of cancer in the past decade. However, expanded indication and increased use of Immune checkpoint inhibitors have resulted in increased reports of toxicity called immune-related adverse events (irAEs). Due to the unique immunological characteristics of the liver, a hepatic immune-related adverse events has also been reported, which is usually termed Immune-mediated hepatitis (IMH). So far, it is generally considered that the mechanism of IMH induced by Immune checkpoint inhibitors is mainly the overactivation of T cells. It has been reported that the incidence of IMH ranges from 1% to 15%. Because of the lack of specific markers, a diagnosis of exclusion of IMH is critical. Although most IMH is mild and recoverable, several death cases have been reported, which has been increasingly concerned. This review summarizes the current understanding of the pathophysiology, epidemiology, diagnosis, management and prognosis of IMH caused by Immune checkpoint inhibitors. It also discusses the controversial issues in IMH, such as the role of liver biopsy, grading criteria, risk factors, rational treatment strategies with steroids, and the timing of Immune checkpoint inhibitors rechallenging, which may provide helpful information for IMH in future clinical practice.

An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study.

BACKGROUND: Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. OBJECTIVE: This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. METHODS: An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. RESULTS: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. CONCLUSIONS: Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients' age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review.

BACKGROUND: Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. OBJECTIVE: This review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run. METHODS: We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. RESULTS: A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. CONCLUSIONS: Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians' experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.