Risk of hepatocellular carcinoma in individuals without traditional risk factors: development and validation of a novel risk score

Hepatocellular Carcinoma in Metabolic Dysfunction-Associated Steatotic Liver Disease

Importance

In the US, hepatocellular carcinoma (HCC) has been the most rapidly increasing cancer since 1980, and metabolic dysfunction-associated steatotic liver disease (MASLD) is expected to soon become the leading cause of HCC.

Objective

To develop a prediction model for HCC incidence in a cohort of patients with MASLD.

Design, Setting, and Participants

This prognostic study was conducted among patients aged at least 18 years with MASLD, identified using diagnosis of MASLD using International Classification of Diseases, Ninth Revision (ICD-9) or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes; natural language processing of radiology imaging report text, which identified patients who had imaging evidence of MASLD but had not been formally diagnosed; or the Dallas Steatosis Index, a risk equation that identifies individuals likely to have MASLD with good precision. Patients were enrolled from Kaiser Permanente Northern California, an integrated health delivery system with more than 4.6 million members, with study entry between January 2009 and December 2018, and follow-up until HCC development, death, or study termination on September 30, 2021. Statistical analysis was performed during February 2023 and January 2024.

Exposure

Data were extracted from the electronic health record and included 18 routinely measured factors associated with MASLD.

Main Outcome and Measures

The cohort was split (70:30) into derivation and internal validation sets; extreme gradient boosting was used to model HCC incidence. HCC risk was divided into 3 categories, with the cumulative estimated probability of HCC 0.05% or less classified as low risk; 0.05% to 0.09%, medium risk; and 0.1% or greater, high risk.

Results

A total of 1 811 461 patients (median age [IQR] at baseline, 52 [41-63] years; 982 300 [54.2%] female) participated in the study. During a median (range) follow-up of 9.3 (5.8-12.4) years, 946 patients developed HCC, for an incidence rate of 0.065 per 1000 person-years. The model achieved an area under the curve of 0.899 (95% CI, 0.882-0.916) in the validation set. At the medium-risk threshold, the model had a sensitivity of 87.5%, specificity of 81.4%, and a number needed to screen of 406. At the high-risk threshold, the model had a sensitivity of 78.4%, a specificity of 90.1%, and a number needed to screen of 241.

Conclusions and Relevance

This prognostic study of more than 1.8 million patients with MASLD used electronic health record data to develop a prediction model to discriminate between individuals with and without incident HCC with good precision. This model could serve as a starting point to identify patients with MASLD who may need intervention and/or HCC surveillance.

Scoring System for Predicting the Risk of Liver Cancer among Diabetes Patients: A Random Survival Forest-Guided Approach

BACKGROUND

Most liver cancer scoring systems focus on patients with preexisting liver diseases such as chronic viral hepatitis or liver cirrhosis. Patients with diabetes are at higher risk of developing liver cancer than the general population. However, liver cancer scoring systems for patients in the absence of liver diseases or those with diabetes remain rare. This study aims to develop a risk scoring system for liver cancer prediction among diabetes patients and a sub-model among diabetes patients without cirrhosis/chronic viral hepatitis.

METHODS

A retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. The outcome was diagnosis of liver cancer during follow-up. A risk scoring system was developed by applying random survival forest in variable selection, and Cox regression in weight assignment.

RESULTS

The liver cancer incidence was 0.92 per 1000 person-years. Patients who developed liver cancer (n = 1995) and those who remained free of cancer (n = 1969) during follow-up (median: 6.2 years) were selected for model building. In the final time-to-event scoring system, presence of chronic hepatitis B/C, alanine aminotransferase, age, presence of cirrhosis, and sex were included as predictors. The concordance index was 0.706 (95%CI: 0.676-0.741). In the sub-model for patients without cirrhosis/chronic viral hepatitis, alanine aminotransferase, age, triglycerides, and sex were selected as predictors.

CONCLUSIONS

The proposed scoring system may provide a parsimonious score for liver cancer risk prediction among diabetes patients.

Low-density lipoprotein cholesterol and risk of hepatocellular carcinoma: a Mendelian randomization and mediation analysis

BACKGROUND

A previous study demonstrated that low-density lipoprotein cholesterol (LDL-C) is associated with hepatocellular carcinoma (HCC); however, the causality between them has not been proven due to conflicting research results and the interference of confounders. This study utilized Mendelian randomization (MR) to investigate the causal relationship between LDL-C and HCC and identify the mediating factors.

METHODS

LDL-C, HCC, and coronary artery disease (CAD) genome-wide association study (GWAS) data were obtained from a public database. To investigate causality, inverse variance weighting (IVW) was the main analysis approach. MR‒Egger, simple mode, weighted median (WM), and weighted mode were employed as supplementary analytic methods. In addition, horizontal pleiotropy and heterogeneity were tested. To evaluate the stability of the MR results, a "leave-one-out" approach was used. Multivariate MR (MVMR) was utilized to correct the confounders that might affect causality, and mediation analysis was used to investigate the potential mediating effects. Finally, we used HCC risk to infer the reverse causality with LDL-C level.

RESULTS

Random effects IVW results were (LDL-C-HCC: odds ratio (OR) = 0.703, 95% confidence interval (CI) = [0.508, 0.973], P = 0.034; CAD-HCC: OR = 0.722, 95% CI = [0.645, 0.808], P = 1.50 × 10^-8; LDL-C-CAD: OR = 2.103, 95% CI = [1.862, 2.376], P = 5.65 × 10^-33), demonstrating a causal link between LDL-C levels and a lower risk of HCC. Through MVMR, after mutual correction, the causal effect of LDL-C and CAD on HCC remained significant (P < 0.05). Through mediation analysis, it was proven that CAD mediated the causative connection between LDL-C and HCC, and the proportion of mediating effect on HCC was 58.52%. Reverse MR showed that HCC could affect LDL-C levels with a negative correlation (OR_IVW = 0.979, 95% CI = [0.961, 0.997], P = 0.025).

CONCLUSION

This MR study confirmed the causal effect between LDL-C levels and HCC risk, with CAD playing a mediating role. It may provide a new view on HCC occurrence and development mechanisms, as well as new metabolic intervention targets for treatment.

Serum resistin and the risk for hepatocellular carcinoma in diabetic patients

Hepatocellular carcinoma (HCC), the predominant type of liver cancer, is a major contributor to cancer-related fatalities across the globe. Diabetes has been identified as a significant risk factor for HCC, with recent research indicating that the hormone resistin could be involved in the onset and advancement of HCC in diabetic individuals. Resistin is a hormone that is known to be involved in inflammation and insulin resistance. Patients with HCC have been observed to exhibit increased resistin levels, which could be correlated with more severe disease stages and unfavourable prognoses. Nevertheless, the exact processes through which resistin influences the development and progression of HCC in diabetic patients remain unclear. This article aims to examine the existing literature on the possible use of resistin levels as a biomarker for HCC development and monitoring. Furthermore, it reviews the possible pathways of HCC initiation due to elevated resistin and offers new perspectives on comprehending the fundamental mechanisms of HCC in diabetic patients. Gaining a better understanding of these processes may yield valuable insights into HCC’s development and progression, as well as identify possible avenues for prevention and therapy.

Risk stratification and early detection biomarkers for precision HCC screening

Hepatocellular carcinoma (HCC) mortality remains high primarily due to late diagnosis as a consequence of failed early detection. Professional societies recommend semi-annual HCC screening in at-risk patients with chronic liver disease to increase the likelihood of curative treatment receipt and improve survival. However, recent dynamic shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population for the screening, whereas annual incidence rate has become substantially lower. Thus, with the contemporary HCC etiologies, the traditional screening approach might not be practical and cost-effective. HCC screening consists of (i) definition of rational at-risk population, and subsequent (ii) repeated application of early detection tests to the population at regular intervals. The suboptimal performance of the currently available HCC screening tests highlights an urgent need for new modalities and strategies to improve early HCC detection. In this review, we overview recent developments of clinical, molecular, and imaging-based tools to address the current challenge, and discuss conceptual framework and approaches of their clinical translation and implementation. These encouraging progresses are expected to transform the current "one-size-fits-all" HCC screening into individualized precision approaches to early HCC detection and ultimately improve the poor HCC prognosis in the foreseeable future.

Should we undertake surveillance for HCC in patients with MAFLD?

Over the last decade, metabolic-associated fatty liver disease (MAFLD) has become an important public health issue worldwide. In many countries, MAFLD has become the most common cause of chronic liver disease. On the contrary, hepatocellular carcinoma (HCC) mortality is rising. Liver tumors have become the third cause of cancer mortality worldwide. HCC is the most frequent liver tumor. While the burden of HCC related to viral hepatitis is declining, the prevalence of MAFLD-related HCC is rising rapidly. Classical screening criteria for HCC consider cirrhotic, advanced fibrosis, and viral hepatitis patients. Metabolic syndrome with liver involvement or MAFLD is associated with a higher risk of HCC development, even in the absence of cirrhosis. The question about the cost effectiveness of surveillance for HCC in MAFLD is yet not fully answered. There are no guidelines that address the question of when to start or how to define the population who can benefit of surveillance for HCC in MAFLD patients. This review aims to revise the evidence of HCC development in MAFLD. It hopes to be a step closer to defining screening criteria for HCC in MAFLD.

NAFLD-related hepatocellular carcinoma: The growing challenge

Hepatocellular carcinoma (HCC) is a common cause of cancer-related mortality and morbidity worldwide. With the obesity pandemic, NAFLD-related HCC is contributing to the burden of disease exponentially. Genetic predisposition and clinical risk factors for NAFLD-related HCC have been identified. Cirrhosis is a well-known and major risk factor for NAFLD-related HCC. However, the occurrence of NAFLD-related HCC in patients without cirrhosis is increasingly recognized and poses a significant challenge regarding cancer surveillance. It is of paramount importance to develop optimal risk stratification scores and models to identify subsets of the population at high risk so they can be enrolled in surveillance programs. In this review, we will discuss the risks and prediction models for NAFLD-related HCC.

Antitumor Effects and Mechanisms of Metabolic Syndrome Medications on Hepatocellular Carcinoma

Liver cancer has a high incidence and mortality rate worldwide, with hepatocellular carcinoma (HCC) being the most common histological type. With the decrease in the number of newly infected patients and the spread of antiviral therapy, hepatitis virus-negative chronic liver diseases including steatohepatitis are increasingly accounting for a large proportion of HCC, and an important clinical characteristic is the high prevalence of metabolic syndrome including hypertension, type 2 diabetes (T2D), dyslipidemia, and obesity. Since patients with steatohepatitis are less likely to undergo surveillance for early detection of HCC, they may be diagnosed at an advanced stage and have worse prognosis. Therefore, treatment strategies for patients with HCC caused by steatohepatitis, especially in advanced stages, become increasingly important. Further, hypertension, T2D, and dyslipidemia may occur as side effects during systemic treatment, and there will be increasing opportunities to prescribe metabolic syndrome medications, not only for originally comorbid diseases, but also for adverse events during HCC treatment. Interestingly, epidemiological studies have shown that patients taking some metabolic syndrome medications are less likely to develop various types of cancers, including HCC. Basic studies have also shown that these drugs have direct antitumor effects on HCC. In particular, angiotensin II receptor blockers (a drug group for treating hypertension), biguanides (a drug group for treating T2D), and statins (a drug group for treating dyslipidemia) have shown to elucidate antitumor effects against HCC. In this review, we focus on the antitumor effects of metabolic syndrome medications on HCC and their mechanisms based on recent literature. New therapeutic agents are also increasingly being reported. Analysis of the antitumor effects of metabolic syndrome medications on HCC and their mechanisms will be doubly beneficial for HCC patients with metabolic syndrome, and the use of these medications may be a potential strategy against HCC.

Impact of diabetes, obesity, and dyslipidemia on the risk of hepatocellular carcinoma in patients with chronic liver diseases

Despite the increasing prevalence of metabolic disorders, the potential effects of metabolic factors on hepatocellular carcinoma (HCC) development in individuals with chronic liver diseases (CLDs) are not well understood. For a metabolic factor to be identified as a risk factor for HCC in patients with CLDs, such as hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, there should be a strong synergistic interaction between the carcinogenic mechanisms of the metabolic factor and the CLD itself. This review aims to comprehensively summarize the published data on the relationship between metabolic factors such as diabetes mellitus (DM), obesity, and blood lipids and the risk of HCC in patients with CLDs. DM consistently increases the risk of HCC in patients with CLD. When associated with DM, the risk of HCC seems to be highest in HCV and non-alcoholic fatty liver disease (NAFLD), followed by alcoholic liver disease (ALD) and HBV. Obesity may increase the risk of HCC. Among CLDs, the evidence is relatively consistent and clear for ALD, while clear evidence is limited in other CLDs including HBV, HCV, and NAFLD. Total cholesterol, potentially low-density lipoprotein cholesterol and triglyceride, seems to have strong inverse associations with HCC in individuals with CLDs. Despite evidence from observational studies, statins had no effect in preventing HCC in randomized controlled trials. Whether statins have a preventive effect against HCC is unclear. A better understanding and management of metabolic factors may be beneficial to reduce the risk of HCC in patients with CLDs.

Prognostic stratification based on m5C regulators acts as a novel biomarker for immunotherapy in hepatocellular carcinoma

Background

Immunotherapy is a promising anti-cancer strategy in hepatocellular carcinoma (HCC). However, a limited number of patients can benefit from it. There are currently no reliable biomarkers available to find the potential beneficiaries. Methylcytosine (m⁵C) is crucial in HCC, but its role in forecasting clinical responses to immunotherapy has not been fully clarified.

Methods

In this study, we analyzed 371 HCC patients from The Cancer Genome Atlas (TCGA) database and investigated the expression of 18 m⁵C regulators. We selected 6 differentially expressed genes (DEGs) to construct a prognostic risk model as well as 2 m⁵C-related diagnostic models.

Results

The 1-, 3-, and 5-year area under the curve (AUC) of m⁵C scores for the overall survival (OS) was 0.781/0.762/0.711, indicating the m⁵C score system had an ideal distinction of prognostic prediction for HCC. The survival analysis showed that patients with high-risk scores present a worse prognosis than the patients with low-risk scores (p< 0.0001). We got consistent results in 6 public cohorts and validated them in Xiangya real-world cohort by quantitative real-time PCR and immunohistochemical (IHC) assays. The high-m⁵C score group was predicted to be in an immune evasion state and showed low sensitivity to immunotherapy, but high sensitivity to chemotherapy and potential targeted drugs and agents, such as sepantronium bromide (YM-155), axitinib, vinblastine and docetaxel. Meanwhile, we also constructed two diagnostic models to distinguish HCC tumors from normal liver tissues or liver cirrhosis.

Conclusion

In conclusion, our study helps to early screen HCC patients and select patients who can benefit from immunotherapy. Step forwardly, for the less likely beneficiaries, this study provides them with new potential targeted drugs and agents for choice to improve their prognosis.

Liver cancer risk-predictive molecular biomarkers specific to clinico-epidemiological contexts

Hepatocellular carcinoma (HCC) risk prediction is increasingly important because of the low annual HCC incidence in patients with the rapidly emerging non-alcoholic fatty liver disease or cured HCV infection. To date, numerous clinical HCC risk biomarkers and scores have been reported in literature. However, heterogeneity in clinico-epidemiological context, e.g., liver disease etiology, patient race/ethnicity, regional environmental exposure, and lifestyle-related factors, obscure their real clinical utility and applicability. Proper characterization of these factors will help refine HCC risk prediction according to certain clinical context/scenarios and contribute to improved early HCC detection. Molecular factors underlying the clinical heterogeneity encompass various features in host genetics, hepatic and systemic molecular dysregulations, and cross-organ interactions, which may serve as clinical-context-specific biomarkers and/or therapeutic targets. Toward the goal to enable individual-risk-based HCC screening by incorporating the HCC risk biomarkers/scores, their assessment in patient with well-defined clinical context/scenario is critical to gauge their real value and to maximize benefit of the tailored patient management for substantial improvement of the poor HCC prognosis.

Advances in RNA Epigenetic Modifications in Hepatocellular Carcinoma and Potential Targeted Intervention Strategies

The current interventions for hepatocellular carcinoma (HCC) are not satisfactory, and more precise targets and promising strategies need to be explored. Recent research has demonstrated the non-negligible roles of RNA epigenetic modifications such as N6-methyladenosine (m6A) and 5-methylcytosine (m5C) in various cancers, including HCC. However, the specific targeting mechanisms are not well elucidated. In this review, we focus on the occurrence and detailed physiopathological roles of multiple RNA modifications on diverse RNAs closely related to the HCC process. In particular, we highlight fresh insights into the impact mechanisms of these posttranscriptional modifications on the whole progression of HCC. Furthermore, we analyzed the possibilities and significance of these modifications and regulators as potential therapeutic targets in HCC treatment, which provides the foundation for exploring targeted intervention strategies. This review will propel the identification of promising therapeutic targets and novel strategies that can be translated into clinical applications for HCC treatment.

Prediction of the risk of developing hepatocellular carcinoma in health screening examinees: a Korean cohort study

Background

Almost all Koreans are covered by mandatory national health insurance and are required to undergo health screening at least once every 2 years. We aimed to develop a machine learning model to predict the risk of developing hepatocellular carcinoma (HCC) based on the screening results and insurance claim data.

Methods

The National Health Insurance Service-National Health Screening database was used for this study (NHIS-2020-2-146). Our study cohort consisted of 417,346 health screening examinees between 2004 and 2007 without cancer history, which was split into training and test cohorts by the examination date, before or after 2005. Robust predictors were selected using Cox proportional hazard regression with 1000 different bootstrapped datasets. Random forest and extreme gradient boosting algorithms were used to develop a prediction model for the 9-year risk of HCC development after screening. After optimizing a prediction model via cross validation in the training cohort, the model was validated in the test cohort.

Results

Of the total examinees, 0.5% (1799/331,694) and 0.4% (390/85,652) in the training cohort and the test cohort were diagnosed with HCC, respectively. Of the selected predictors, older age, male sex, obesity, abnormal liver function tests, the family history of chronic liver disease, and underlying chronic liver disease, chronic hepatitis virus or human immunodeficiency virus infection, and diabetes mellitus were associated with increased risk, whereas higher income, elevated total cholesterol, and underlying dyslipidemia or schizophrenic/delusional disorders were associated with decreased risk of HCC development (p < 0.001). In the test, our model showed good discrimination and calibration. The C-index, AUC, and Brier skill score were 0.857, 0.873, and 0.078, respectively.

Conclusions

Machine learning-based model could be used to predict the risk of HCC development based on the health screening examination results and claim data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08498-w.

Survival benefit of induction chemotherapy for locally advanced nasopharyngeal carcinoma: prognosis based on a new risk estimation model

BACKGROUND

Although the National Comprehensive Cancer Network (NCCN) Guidelines recommend CCRT+AC and IC + CCRT as level 2A evidence for treatment of the locoregionally advanced NPC (II-IVa), IC + CCRT+AC could also be an alternative but it is seldom used because of the low completion rates. This article aimed to compare the effectiveness of the three radiotherapy regimens using a large-scale retrospective study.

METHODS

This retrospective single center analysis enrolled 1812 diagnosed NPC patients at Nanfang Hospital from January 2005 to December 2015 and only 729 patients met the inclusion criteria and were analyzed. Patients without distant metastasis, age of 18-70 years, Karnofsky scores of at least 70,stage III-IVb, and adequate adequate bone marrow, liver and renal function. Were enrolled. Adverse events and other categorical variables were compared by Pearson chi-square test or Fishier exact test. Time-to-event data were described with the Kaplan-Meier curves, time-to-event intervals compared with the log-rank test. We did multivariable analyses with the Cox proportional hazards model to test the independent signifi cance of diff erent factors. Cox proportional hazards model was used to estimate the β regression coeffi cient, p value, and hazard ratio and its 95% CI for each of the selected risk predictors.

RESULTS

The median follow-up time was 47 months. Kaplan-Meier analyses revealed no significant differences among three groups in 3-year failure-free survival (FFS, P = 0.225), 3-year overall survival (OS, P = 0.992), 3-year locoregional failure-free survival (LFFS, P = 0.549), and 3-year distant failure-free survival (DFFS, P = 0.174). Stratified survival analysis based on the risk scoring model revealed no differences in FFS, OS, LFFS, and DFFS between IC + CCRT and CCRT+AC groups for low-risk patients, however, the 3-year OS (88.3% vs. 77.6%, P = 0.049) and 3-year DFFS (84.0% vs.66.8%, P = 0.032) were respectively significantly better in IC + CCRT group compared with CCRT+AC group for high-risk patients.

CONCLUSIONS

Compared with CCRT+AC, IC + CCRT lowers distant metastasis rate and improves OS among patients with locally advanced NPC in high risk group.

Tannins in Terminalia bellirica inhibit hepatocellular carcinoma growth by regulating EGFR-signaling and tumor immunity

The fruits of Terminalia bellirica (Gaertn.) Roxb. (TB) are used as a multi-use therapeutic herbal product in the Tibetan medicinal system and are prescribed as a general health tonic in the traditional Ayurvedic medicinal system. It has been demonstrated that these fruits have a variety of pharmacological activities, including anti-tumor, anti-oxidative, anti-inflammatory, hepatoprotective and immunoregulatory effects, etc. However, the therapeutic effects of tannins in TB on HCC and the underlying mechanisms remain uncharacterized. In the current study, we aimed to identify the anti-tumor effect of tannins in TB by employing a H22 xenograft mouse model and by performing cell-based in vitro studies with the assistance of the network pharmacology analysis. The crude extract of TB was purified to yield total tannin fraction (TB-TF), and our results found that TB-TF significantly inhibited the tumor growth of H22 xenografts in mice by inducing apoptosis and reducing angiogenesis. A total of 90 compounds were then identified in TB-TF by UPLC-MS/MS, and 27 were found in serum after oral administration of TB-TF in mice. The network pharmacology analysis based on these absorbed components was performed and, along with experimental evidence, it revealed that the ERBB, PI3K-Akt, and MAPK signaling pathways may be involved in the anti-tumor effect of TB-TF on HCC. Furthermore, we suggested that TB-TF effectively modulated the immunosuppressive tumor microenvironment in H22 xenograft mice. In summary, our study demonstrated that TB-TF could be developed as a functional food, which is not only a promising anti-cancer reagent but also a potential candidate with bright prospects for the emerging trends of immunotherapy for HCC.

Clinical and Molecular Prediction of Hepatocellular Carcinoma Risk

Prediction of hepatocellular carcinoma (HCC) risk becomes increasingly important with recently emerging HCC-predisposing conditions, namely non-alcoholic fatty liver disease and cured hepatitis C virus infection. These etiologies are accompanied with a relatively low HCC incidence rate (~1% per year or less), while affecting a large patient population. Hepatitis B virus infection remains a major HCC risk factor, but a majority of the patients are now on antiviral therapy, which substantially lowers, but does not eliminate, HCC risk. Thus, it is critically important to identify a small subset of patients who have elevated likelihood of developing HCC, to optimize the allocation of limited HCC screening resources to those who need it most and enable cost-effective early HCC diagnosis to prolong patient survival. To date, numerous clinical-variable-based HCC risk scores have been developed for specific clinical contexts defined by liver disease etiology, severity, and other factors. In parallel, various molecular features have been reported as potential HCC risk biomarkers, utilizing both tissue and body-fluid specimens. Deep-learning-based risk modeling is an emerging strategy. Although none of them has been widely incorporated in clinical care of liver disease patients yet, some have been undergoing the process of validation and clinical development. In this review, these risk scores and biomarker candidates are overviewed, and strategic issues in their validation and clinical translation are discussed.