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The incidence of diabetes mellitus and hepatocellular carcinoma (HCC) has been increasing worldwide during the last few decades, in the context of an increasing prevalence of obesity and non-alcoholic fatty liver disease (NAFLD). Epidemiologic studies have revealed that patients with diabetes have a 2- to 3-fold increased risk of developing HCC, independent of the severity and cause of the underlying liver disease. A bidirectional relationship exists between diabetes and liver disease: advanced liver disease promotes the onset of diabetes, and HCC is an important cause of death in patients with diabetes; conversely, diabetes is a risk factor for liver fibrosis progression and HCC development, and may worsen the long-term prognosis of patients with HCC. The existence of close interconnections among diabetes, obesity, and NAFLD causes insulin resistance-related hyperinsulinemia, increased oxidative stress, and chronic inflammation, which are assumed to be the underlying causes of hepatocarcinogenesis in patients with diabetes. No appropriate surveillance methods for HCC development in patients with diabetes have been established, and liver diseases, including HCC, are often overlooked as complications of diabetes. Although some antidiabetic drugs are expected to prevent HCC development, further research on the optimal use of antidiabetic drugs aimed at hepatoprotection is warranted. Given the increasing medical and socioeconomic impact of diabetes on HCC development, diabetologists and hepatologists need to work together to develop strategies to address this emerging health issue. This article reviews the current knowledge on the impact of diabetes on the development and progression of HCC.

Because of its multifactorial nature, predicting the presence of cancer using a single biomarker is difficult. We aimed to establish a novel machine-learning model for predicting hepatocellular carcinoma (HCC) using real-world data obtained during clinical practice. To establish a predictive model, we developed a machine-learning framework which developed optimized classifiers and their respective hyperparameter, depending on the nature of the data, using a grid-search method. We applied the current framework to 539 and 1043 patients with and without HCC to develop a predictive model for the diagnosis of HCC. Using the optimal hyperparameter, gradient boosting provided the highest predictive accuracy for the presence of HCC (87.34%) and produced an area under the curve (AUC) of 0.940. Using cut-offs of 200 ng/mL for AFP, 40 mAu/mL for DCP, and 15% for AFP-L3, the accuracies of AFP, DCP, and AFP-L3 for predicting HCC were 70.67% (AUC, 0.766), 74.91% (AUC, 0.644), and 71.05% (AUC, 0.683), respectively. A novel predictive model using a machine-learning approach reduced the misclassification rate by about half compared with a single tumor marker. The framework used in the current study can be applied to various kinds of data, thus potentially become a translational mechanism between academic research and clinical practice.

BackgroundUltrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the diagnosis of liver tumors in US.MethodsWe constructed three AI models based on still B-mode images: model-1 using 24,675 images, model-2 using 57,145 images, and model-3 using 70,950 images. A convolutional neural network was used to train the US images. The four-class liver tumor discrimination by AI, namely, cysts, hemangiomas, hepatocellular carcinoma, and metastatic tumors, was examined. The accuracy of the AI diagnosis was evaluated using tenfold cross-validation. The diagnostic performances of the AI models and human experts were also compared using an independent test cohort of video images.ResultsThe diagnostic accuracies of model-1, model-2, and model-3 in the four tumor types are 86.8%, 91.0%, and 91.1%, whereas those for malignant tumor are 91.3%, 94.3%, and 94.3%, respectively. In the independent comparison of the AIs and physicians, the percentages of correct diagnoses (accuracies) by the AIs are 80.0%, 81.8%, and 89.1% in model-1, model-2, and model-3, respectively. Meanwhile, the median percentages of correct diagnoses are 67.3% (range 63.6%–69.1%) and 47.3% (45.5%–47.3%) by human experts and non-experts, respectively.Conclusion The performance of the AI models surpassed that of human experts in the four-class discrimination and benign and malignant discrimination of liver tumors. Thus, the AI models can help prevent human errors in US diagnosis.

BackgroundWe previously reported that the incidence of hepatocellular carcinoma (HCC) with non-viral etiologies increased rapidly between 1991 and 2010 in Japan.MethodsTo update this investigation, we enrolled patients who were initially diagnosed as having non-B, non-C HCC at participating hospitals between 2011 and 2015. In addition to the patient characteristics investigated in the previous report, we also investigated the duration of alcohol consumption. The overall survival rate was analyzed using the Kaplan–Meier method, and the hazard function against the body mass index (BMI) was plotted using cubic splines.ResultsA total of 2087 patients were enrolled. The proportion of patients with non-viral etiologies has continued to increase from 10.0% in 1991 to 32.5% in 2015. Patients were also older (median ages, 70–73 years) and more obese (median BMIs, 23.9–24.2 kg/m2), and the proportions of patients with diabetes mellitus (46.1% to 51.6%), hypertension (42.7% to 58.6%), dyslipidemia (14.6% to 22.9%), and fatty liver (24.0% to 28.8%) had all increased significantly. There was a significant inverse relationship between the duration and the amount of daily alcohol consumption. The improvement in the overall survival was relatively small, with a decreased proportion of patients under surveillance (41.3% to 31.6%). A hazard function plot showed a curve similar to that in our previous report, with a lowest hazard of ~ 26 kg/m2.ConclusionsThe proportion of HCC patients with non-viral etiologies continues to increase in Japan. Lifetime total amount of alcohol consumption may be a risk factor.

BackgroundA phase 3, multinational, randomized, non-inferiority trial (REFLECT) compared the efficacy and safety of lenvatinib (LEN) and sorafenib (SOR) in patients with unresectable hepatocellular carcinoma (uHCC). LEN had an effect on overall survival (OS) compared to SOR, statistically confirmed by non-inferiority [OS: median = 13.6 months vs. 12.3 months; hazard ratio (HR) 0.92, 95% confidence interval (CI) 0.79–1.06], and demonstrated statistically significant improvements in progression-free survival (PFS) and the objective response rate (ORR) in the overall population. The results of a subset analysis that evaluated the efficacy and safety of LEN and SOR in the Japanese population are reported.MethodsThe intent-to-treat population enrolled in Japan was analyzed.ResultsOf 954 patients in the overall population, 168 Japanese patients were assigned to the LEN arm (N = 81) or the SOR arm (N = 87). Median OS was 17.6 months for LEN vs. 17.8 months for SOR (HR 0.90; 95% CI 0.62–1.29). LEN showed statistically significant improvements over SOR in PFS (7.2 months vs. 4.6 months) and ORR (29.6% vs. 6.9%). The relative dose intensity of LEN and SOR in the Japanese population was lower than in the overall population. Frequently observed, related adverse events included palmar-plantar erythrodysaesthesia syndrome (PPES), hypertension, decreased appetite, and proteinuria in the LEN arm, and PPES, hypertension, diarrhea, and alopecia in the SOR arm.ConclusionsThe efficacy and safety of LEN in the Japanese population were similar to those in the overall population of REFLECT. With manageable adverse events, LEN is a new treatment option for Japanese patients with uHCC.Trial registration IDClinicalTrials.gov. No. NCT01761266.

Proteoglycans, which consist of a protein core and glycosaminoglycan chains, are major components of the extracellular matrix and play physiological roles in maintaining tissue homeostasis. In the carcinogenic tissue microenvironment, proteoglycan expression changes dramatically. Altered proteoglycan expression on tumor and stromal cells affects cancer cell signaling pathways, which alters growth, migration, and angiogenesis and could facilitate tumorigenesis. This dysregulation of proteoglycans has been implicated in the pathogenesis of diseases such as hepatocellular carcinoma (HCC) and the underlying mechanism has been studied extensively. This review summarizes the current knowledge of the roles of proteoglycans in the genesis and progression of HCC. It focuses on well-investigated proteoglycans such as serglycin, syndecan-1, glypican 3, agrin, collagen XVIII/endostatin, versican, and decorin, with particular emphasis on the potential of these factors as biomarkers and therapeutic targets in HCC regarding the future perspective of precision medicine toward the “cure of HCC”.

Enhanced de novo lipogenesis mediated by sterol regulatory element-binding proteins (SREBPs) is thought to be involved in nonalcoholic steatohepatitis (NASH) pathogenesis. In this study, we assessed the impact of SREBP inhibition on NASH and liver cancer development in murine models. Unexpectedly, SREBP inhibition via deletion of the SREBP cleavage-activating protein (SCAP) in the liver exacerbated liver injury, fibrosis, and carcinogenesis despite markedly reduced hepatic steatosis. These phenotypes were ameliorated by restoring SREBP function. Transcriptome and lipidome analyses revealed that SCAP/SREBP pathway inhibition altered the fatty acid (FA) composition of phosphatidylcholines due to both impaired FA synthesis and disorganized FA incorporation into phosphatidylcholine via lysophosphatidylcholine acyltransferase 3 (LPCAT3) downregulation, which led to endoplasmic reticulum (ER) stress and hepatocyte injury. Supplementation with phosphatidylcholines significantly improved liver injury and ER stress induced by SCAP deletion. The activity of the SCAP/SREBP/LPCAT3 axis was found to be inversely associated with liver fibrosis severity in human NASH. SREBP inhibition also cooperated with impaired autophagy to trigger liver injury. Thus, excessively strong and broad lipogenesis inhibition was counterproductive for NASH therapy; this will have important clinical implications in NASH treatment.

One of most challenging issues in tumor immunology is a better understanding of the dynamics in the accumulation of myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment (TIME), as this would lead to the development of new cancer therapeutics. Here, we show that translationally controlled tumor protein (TCTP) released by dying tumor cells is an immunomodulator crucial to full-blown MDSC accumulation in the TIME. We provide evidence that extracellular TCTP mediates recruitment of the polymorphonuclear MDSC (PMN-MDSC) population in the TIME via activation of Toll-like receptor-2. As further proof of principle, we show that inhibition of TCTP suppresses PMN-MDSC accumulation and tumor growth. In human cancers, we find an elevation of TCTP and an inverse correlation of TCTP gene dosage with antitumor immune signatures and clinical prognosis. This study reveals the hitherto poorly understood mechanism of the MDSC dynamics in the TIME, offering a new rationale for cancer immunotherapy. Cell death in the context of cancer therapies is often associated with immunogenicity. Taniguchi and colleagues instead find that release of the cellular protein TCTP following cell death triggers an immunosuppressive pathway in the tumor microenvironment.

ObjectiveMetabolic reprogramming of tumour cells that allows for adaptation to their local environment is a hallmark of cancer. Interestingly, obesity-driven and non-alcoholic steatohepatitis (NASH)-driven hepatocellular carcinoma (HCC) mouse models commonly exhibit strong steatosis in tumour cells as seen in human steatohepatitic HCC (SH-HCC), which may reflect a characteristic metabolic alteration.DesignNon-tumour and HCC tissues obtained from diethylnitrosamine-injected mice fed either a normal or a high-fat diet (HFD) were subjected to comprehensive metabolome analysis, and the significance of obesity-mediated metabolic alteration in hepatocarcinogenesis was evaluated.ResultsThe extensive accumulation of acylcarnitine species was seen in HCC tissues and in the serum of HFD-fed mice. A similar increase was found in the serum of patients with NASH-HCC. The accumulation of acylcarnitine could be attributed to the downregulation of carnitine palmitoyltransferase 2 (CPT2), which was also seen in human SH-HCC. CPT2 downregulation induced the suppression of fatty acid β-oxidation, which would account for the steatotic changes in HCC. CPT2 knockdown in HCC cells resulted in their resistance to lipotoxicity by inhibiting the Src-mediated JNK activation. Additionally, oleoylcarnitine enhanced sphere formation by HCC cells via STAT3 activation, suggesting that acylcarnitine accumulation was a surrogate marker of CPT2 downregulation and directly contributed to hepatocarcinogenesis. HFD feeding and carnitine supplementation synergistically enhanced HCC development accompanied by acylcarnitine accumulation in vivo.ConclusionIn obesity-driven and NASH-driven HCC, metabolic reprogramming mediated by the downregulation of CPT2 enables HCC cells to escape lipotoxicity and promotes hepatocarcinogenesis.

Background The number of hepatocellular carcinoma (HCC) patients with non-viral etiologies is increasing in Japan. We conducted a nation-wide survey to examine the characteristics of those patients. Methods After we assessed the trend of patients who were first diagnosed with HCC at 53 tertiary care centers in Japan from 1991 to 2010, we collected detailed data of 5326 patients with non-viral etiology. The etiologies were categorized as autoimmune hepatitis, primary biliary cirrhosis, alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), unclassified, and other. Baseline characteristics at initial diagnosis, the modality of the initial treatment, and survival status were collected via a website. Survival of the patients was assessed by the Kaplan–Meier method and Cox proportional hazard regression. Results The proportion of patients with non-viral etiologies increased from 10.0 % in 1991 to 24.1 % in 2010. Of the patients, 92 % were categorized as ALD, NAFLD, or unclassified. Body mass index (BMI) was ≥ 25 kg/m 2 in 39 %. Diabetes was most prevalent in NAFLD (63 %), followed by unclassified etiology (46 %) and ALD (45 %). Approximately 80 % of patients underwent radical therapy, including resection, ablation, or transarterial chemoembolization. Survival rates at 3, 5, 10, 15, and 20 years were 58.2, 42.6, 21.5, 15.2, and 15.2 %, respectively. Multivariate analysis revealed that patients with BMI > 22 and ≤ 25 kg/m 2 showed the best prognosis versus other BMI categories, after adjusting by age, gender, tumor-related factors, and Child-Pugh score. Conclusions Most cases of non-B, non-C HCC are related to lifestyle factors, including obesity and diabetes. Slightly overweight patients showed the best prognosis.