Risk factors and diagnostic biomarkers for nonalcoholic fatty liver disease-associated hepatocellular carcinoma: Current evidence and future perspectives

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records

OBJECTIVE

The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease.

METHODS

We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration.

RESULTS

We developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training.

CONCLUSIONS

The strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

Exploring Opportunities to Enhance the Screening and Surveillance of Hepatocellular Carcinoma in Non-Alcoholic Fatty Liver Disease (NAFLD) through Risk Stratification Algorithms Incorporating Ultrasound Elastography

Non-alcoholic fatty liver disease (NAFLD), with its progressive form, non-alcoholic steatohepatitis (NASH), has emerged as a significant public health concern, affecting over 30% of the global population. Hepatocellular carcinoma (HCC), a complication associated with both cirrhotic and non-cirrhotic NAFLD, has shown a significant increase in incidence. A substantial proportion of NAFLD-related HCC occurs in non-cirrhotic livers, highlighting the need for improved risk stratification and surveillance strategies. This comprehensive review explores the potential role of liver ultrasound elastography as a risk assessment tool for HCC development in NAFLD and highlights the importance of effective screening tools for early, cost-effective detection and improved management of NAFLD-related HCC. The integration of non-invasive tools and algorithms into risk stratification strategies could have the capacity to enhance NAFLD-related HCC screening and surveillance effectiveness. Alongside exploring the potential advancement of non-invasive tools and algorithms for effectively stratifying HCC risk in NAFLD, we offer essential perspectives that could enable readers to improve the personalized assessment of NAFLD-related HCC risk through a more methodical screening approach.

Performance of GALAD score and serum biomarkers for detecting NAFLD-related HCC: a systematic review and network meta-analysis

INTRODUCTION

The incidence of nonalcoholic fatty liver disease (NAFLD)-related hepatocellular carcinoma (HCC) is increasing globally. We aimed to assess the performance of alpha-fetoprotein (AFP), AFP-L3, des-gamma-carboxy prothrombin (DCP), and GALAD score in detecting NAFLD-related HCC.

METHODS

We searched the relevant literature in PubMed, Embase and Cochrane. Conventional and network meta-analyses were performed for sensitivity, specificity, Youden index (YI), and the area under the summary receiver operator characteristic curve (AUC).

RESULTS

Fifteen studies involving 2031 NAFLD participants were included in this meta-analysis. When detecting early-stage NAFLD-related HCC, GALAD score and DCP process excellent performance. The sensitivity and AUC of DCP (0.60, 0.74, respectively) were higher than AFP (0.34, 0.59, respectively). The network meta-analysis showed that DCP and GALAD score had similar performance. In detecting all-stage NAFLD-related HCC, GALAD score (sensitivity = 0.87; YI = 0.77) performed better than AFP (sensitivity = 0.56; YI = 0.50), AFP-L3 (sensitivity = 0.39; YI = 0.36) and DCP (sensitivity = 0.73; YI = 0.62). Network meta-analysis obtained consistent results with conventional meta-analysis.

CONCLUSIONS

Due to the lower cost-effectiveness, DCP was more suitable for detecting early NAFLD-related HCC. AFP could be used in detecting all-stage NAFLD-related HCC.

The contradictory roles of macrophages in non-alcoholic fatty liver disease and primary liver cancer-Challenges and opportunities

Chronic liver diseases from varying etiologies generally lead to liver fibrosis and cirrhosis. Among them, non-alcoholic fatty liver disease (NAFLD) affects roughly one-quarter of the world population, thus representing a major and increasing public health burden. Chronic hepatocyte injury, inflammation (non-alcoholic steatohepatitis, NASH) and liver fibrosis are recognized soils for primary liver cancer, particularly hepatocellular carcinoma (HCC), being the third most common cause for cancer-related deaths worldwide. Despite recent advances in liver disease understanding, therapeutic options on pre-malignant and malignant stages remain limited. Thus, there is an urgent need to identify targetable liver disease-driving mechanisms for the development of novel therapeutics. Monocytes and macrophages comprise a central, yet versatile component of the inflammatory response, fueling chronic liver disease initiation and progression. Recent proteomic and transcriptomic studies performed at singular cell levels revealed a previously overlooked diversity of macrophage subpopulations and functions. Indeed, liver macrophages that encompass liver resident macrophages (also named Kupffer cells) and monocyte-derived macrophages, can acquire a variety of phenotypes depending on microenvironmental cues, and thus exert manifold and sometimes contradictory functions. Those functions range from modulating and exacerbating tissue inflammation to promoting and exaggerating tissue repair mechanisms (i.e., parenchymal regeneration, cancer cell proliferation, angiogenesis, fibrosis). Due to these central functions, liver macrophages represent an attractive target for the treatment of liver diseases. In this review, we discuss the multifaceted and contrary roles of macrophages in chronic liver diseases, with a particular focus on NAFLD/NASH and HCC. Moreover, we discuss potential therapeutic approaches targeting liver macrophages.

Immunotherapy and the Combination with Targeted Therapies for Advanced Hepatocellular Carcinoma

One of the most important abilities of a tumor is to establish a state of immunosuppression inside the tumor microenvironment. This is made possible through numerous mechanisms of tumor immune escape that have been identified in experimental studies during the last decades. In addition, the hepatic microenvironment is commonly oriented towards a state of immune tolerance because the liver receives blood from the hepatic arteries and portal veins containing a variety of endogenous antigens. Therefore, the hepatic microenvironment establishes an autoimmune tolerance, preventing an autoimmune reaction in the liver. On this basis, hepatic tumor cells may escape the immune system, avoiding being recognized and destroyed by immune cells. Moreover, since the etiology of Hepatocellular Carcinoma (HCC) is often related to cirrhosis, and hepatitis B or C, this tumor develops in the context of chronic inflammation. Thus, the HCC microenvironment is characterized by important immune cell infiltration. Given these data and the poor prognosis of advanced HCC, different immunotherapeutic strategies have been developed and evaluated for these patients. In this review, we describe all the clinical applications of immunotherapy for advanced HCC, from the drugs that have already been approved to the ongoing clinical trials.

Upregulated TUBG1 expression is correlated with poor prognosis in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) development is a complex pathological process. Tubulin gamma 1 (TUBG1) plays an oncogenic role in several human cancers; however, its functional role in HCC tumorigenesis remains unknown.

Methods

Herein we first evaluated the gene expression levels of TUBG1 in HCC using data from The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases. We then elucidated the association between TUBG1 gene expression levels and survival rates of patients with HCC. Cell cycle, proliferation, transwell migration, and matrigel invasion assays were used to study the effects of TUBG1 on the malignant phenotypes of HCC cells.

Results

Based on the data obtained from the aforementioned databases and our in vitro experiments, TUBG1 was found to be overexpressed in HCC and patients with high TUBG1 expression levels showed a remarkably poor overall survival rate. In addition, the expression of TUBG1 significantly promoted the malignant phenotypes of HCC cells in vitro. Gene ontology term enrichment analysis revealed that co-regulated genes were enriched in biological processes mainly involved in chromosome segregation, chromosomal region, and chromatin binding; moreover, Kyoto Encyclopedia of Genes and Genome pathway analysis showed that they were mainly involved in cell cycle, oocyte meiosis, platinum drug resistance, and the p53 signaling pathway.

Conclusions

We report that TUBG1 is an important oncogene in HCC. It promotes HCC progression and may serve as a potential prognostic biomarker for HCC. Future studies are warranted to unveil molecular biological mechanisms underlying TUBG1 carcinogenesis.