Decompensated liver cirrhosis (dLC) is associated with intestinal dysbiosis, however, underlying reasons and clinical consequences remain largely unexplored. We investigated bacterial and fungal microbiota, their relation with gut barrier integrity, inflammation, and cirrhosis-specific complications in dLC-patients. Competing-risk analyses were performed to investigate clinical outcomes within 90 days. Samples were prospectively collected from 95 dLC-patients between 2017 and 2022. Quantitative metagenomic analyses clustered patients into three groups (G1-G3) showing distinct microbial patterns. G1 (n = 39) displayed lowest diversity and highest Enterococcus abundance, G2 (n = 24) was dominated by Bifidobacteria, G3 (n = 29) was most diverse and clustered most closely with healthy controls (HC). Of note, bacterial concentrations were significantly lower in cirrhosis compared with HC, especially for G1 that also showed the lowest capacity to produce short chain fatty acids and secondary bile acids. Consequently, fungal overgrowth, dominated by Candida spp. (51.63%), was observed in G1. Moreover, G1-patients most frequently received antibiotics (n = 33; 86.8%), had highest plasma-levels of Zonulin (p = 0.044) and a proinflammatory cytokine profile along with numerically higher incidences of subsequent infections (p = 0.09). In conclusion, distinct bacterial clusters were observed at qualitative and quantitative levels and correlated with fungal abundances. Antibiotic treatment significantly contributed to dysbiosis, which translated into intestinal barrier impairment and systemic inflammation.

Spectroscopic data often contain artifacts or noise related to the sample characteristics, instrumental variations, or experimental design flaws. Therefore, classifying the raw data is not recommended and might lead to biased results. Nevertheless, most issues may be addressed through appropriate data pre-processing. Effective pre-processing is particularly crucial in critical applications like liquid biopsy for disease detection, where even minor performance improvements may impact patient outcomes. Unfortunately, there is no consensus regarding optimal pre-processing, complicating cross-study comparisons. This study presents a comprehensive evaluation of various pre-processing methods and their combinations to assess their influence on classification results. The goal was to identify whether some pre-processing methods are associated with higher classification outcomes and find an optimal strategy for the given data. Data from Raman optical activity and infrared and Raman spectroscopy were processed, applying tens of thousands of possible pre-processing pipelines. The resulting data were classified using three algorithms to distinguish between subjects with liver cirrhosis and those who had developed hepatocellular carcinoma. Results highlighted that some specific pre-processing methods often ranked among the best classification results, such as the Rolling Ball for correcting the baseline of Raman spectra or the Doubly Reweighted Penalized Least Squares and Mixture model in the case of Raman optical activity. On the other hand, the selection of filtering and/or normalization approach usually did not have a significant impact. Nonetheless, the pre-processing of top-scoring pipelines also depended on the classifier utilized. The best pipelines yielded an AUROC of 0.775-0.823, varying with the evaluated spectroscopic data and classifier.

To retrospectively evaluate the role of stereotactic body radiation therapy (SBRT) played for the outcomes of intrahepatic recurrent small hepatocellular carcinoma (HCC).

We collected 51 intrahepatic recurrent ≤5 cm small HCC patients between January 2016 and December 2021. SBRT was given as 4-5 fractions with 32.5-50Gy. The baseline data of the patients and the radiotherapy strategy data were collected and survival analyses were performed among these factors. The outcomes comprised overall survival (OS), freedom from local progression (FFLP), and progression-free survival (PFS), with the 95% confidence interval (95%CI). The follow-up time was calculated from the date of the SBRT to the date of the last follow-up communication, hospitalization, or death. Survival analysis was conducted by the Kaplan-Meier methods and log-rank test.

The median follow-up time was 48 months (range: 4.8-90). The 1-year, 3-year, and 5-year OS rates of the overall cohort were 95.9% (95%CI: 0.905-1.000), 84.9% (95%CI: 0.751-0.959) and 69.1% (95%CI: 0.553-0.862), respectively. The 1-year, 3-year, and 5-year FFLP rates of the overall cohort were 97.5% (95%CI: 0.928-1.000), 82.0% (95%CI: 0.697-0.965), and 72.8% (95%CI: 0.578-0.918), respectively. The 1-year, 3-year, and 5-year PFS rates of the overall cohort were 85.7% (95%CI: 0.758-0.970), 43.4% (95%CI: 0.296-0.635), and 27.3% (95%CI: 0.149-0.498), respectively. The 5-year FFLP rate of lesions less than 2 cm [72.5% (95%CI: 0.52-1)] and those 2-5 cm [71.9% (95%CI: 0.514-0.976)] were similar. We suggested that the lesions received 45Gy/50Gy with 5 fractions were associated with a higher 5-year FFLP rate [74.6% (95%CI: 0.57-0.976)] than 40Gy/5F [40.0% (95%CI: 0.137-1)].

We found SBRT was effective in patients with lesion size of 2-5 cm, with similar results in those with tumor size of 0-2 cm. We suggested that the lesions received over 85.5Gy of biological effective dose with α/β = 10Gy were associated with a higher FFLP.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality globally. Increasing evidence suggests that long non-coding RNAs play a critical role in cancer development, with the small nucleolar RNA host gene family being a key participant in multiple types of carcinogenesis, including HCC. Small nucleolar RNA host gene 1 (SNHG1) is a significant member of the SNHG family. SNHG1 expression consistently increases in various HCC-associated processes, such as cell proliferation, apoptosis, angiogenesis, migration, invasion, and treatment resistance. Higher SNHG1 expression levels predict worse prognosis by positively correlating with clinicopathological features, including larger tumour size, poor differentiation, and advanced stages in patients with HCC. Nevertheless, the precise role of SNHG1 in the initiation and progression of HCC remains unclear. Therefore, this review aims to summarise the current investigations on the pathogenesis of SNHG1 in HCC, highlighting its potential as a molecular marker for early prediction and prognostic assessment. As a multifunctional modulator, SNHG1 is extensively involved in molecular signalling pathways in HCC progression and is valuable for therapeutic targeting.

Since its introduction in 1977, transarterial chemoembolization (TACE) has widely been accepted treatment for unresectable intermediate stage hepatocellular carcinoma (HCC). Conventional TACE (c-TACE) uses an emulsion of chemotherapeutic agent and ethiodized oil with subsequent embolization of the feeding artery using gelatin sponge. Drug eluting beads (DEB) were introduced in clinical practice in the 2000s and have since been used as an alternative to c-TACE with better outcomes, especially in larger tumors. Considering the widespread use of TACE in HCC, it is important to revisit the current knowledge and the advances that have developed for better safety and efficacy. This article aims to emphasize on the current knowledge and importance of TACE, touch upon the technical aspects including post-TACE care, response assessment, and discontinuation strategies and highlight the recent advances in the technology, catheters, and embolization particles. Thus, despite a rapid change in treatment algorithms and availability of newer drugs for HCC, TACE will remain an integral part of HCC treatment alone or in combination with other therapies.

PANoptosis is a newly identified form of programmed cell death that integrates elements of pyroptosis, apoptosis, and necroptosis. It plays a pivotal role in shaping the tumor immune microenvironment. Despite its significance, the specific functions and mechanisms of PANoptosis within the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) remain unclear. This study aims to investigate these mechanisms using single-cell RNA sequencing data.

Single-cell RNA sequencing data from HCC patients were obtained from the GEO database. The AUCell algorithm was used to quantify PANoptosis activity across various cell types in the TME. Cell populations with high PANoptosis scores were further analyzed using CytoTRACE and scMetabolism to assess their differentiation states and metabolic profiles. Associations between these high-score cell subsets and patient prognosis, tumor stage, and response to immunotherapy were examined. Cell-cell communication analysis was performed to explore how PANoptosis-related APO+ endothelial cells (ECs) may influence HCC progression. Immunofluorescence staining was used to assess the spatial distribution of APO+ ECs in tumor and adjacent tissues. Finally, a CCK8 assay was conducted to evaluate the effect of APOH+ HUVECs on HCC cell proliferation.

A total of 16 HCC patient samples with single-cell RNA sequencing data were included in the study. By calculating the PANoptosis scores of different cell types, we found that ECs, macrophages, hepatocytes, and fibroblasts exhibited higher PANoptosis scores. The PANoptosis scores, differentiation trajectories, intercellular communication, and metabolic characteristics of these four cell subpopulations with high PANoptosis scores were visualized. Among all subpopulations, APO+ ECs demonstrated the most significant clinical relevance, showing a positive correlation with better clinical staging, prognosis, and response to immunotherapy in HCC patients. Cellular communication analysis further revealed that APO+ ECs might regulate the expression of HLA molecules, thereby influencing T cell proliferation and differentiation, potentially contributing to improved prognosis in HCC patients. Immunofluorescence staining results indicated that APO+ ECs were primarily located in the adjacent tissues of HCC patients, with lower expression in tumor tissues. The results of cellular experiments showed that APOH+ HUVECs significantly inhibited the proliferation of HCC cells.

This study systematically mapped the cellular landscape of the TME in HCC patients and explored the differences in differentiation trajectories, metabolic pathways, and other aspects of subpopulations with high PANoptosis scores. Additionally, the study elucidated the potential molecular mechanisms through which APO+ ECs inhibit HCC cell proliferation and improve prognosis and immunotherapeutic efficacy in HCC patients. This research provides new insights for clinical prognosis evaluation and immunotherapy strategies in HCC.

Steatotic hepatocellular carcinoma (HCC) has emerged as a significant subtype of HCC. Understanding the complex tumor microenvironment in HCC is particularly important for stratifying patients and improving treatment response. In this study, we performed proteomic analysis on clinical samples of steatotic HCC and identified human-specific gene alcohol dehydrogenase 1C (ADH1C) as a key factor. ADH1C is a favorable prognostic factor in both steatotic and non-steatotic HCC. ADH1C promotes fatty acid degradation through a novel non-enzymatic function, inhibiting the development of hepatocellular carcinoma. Specifically, in vitro experiments revealed that ADH1C interacts with splicing factor retinitis pigmentosa 9 (RP9) to enhance the splicing of key transcription factor peroxisome proliferator activated receptor alpha (PPARa) pre-mRNA, which is crucial for fatty acid degradation. The regulation of the ADH1C/RP9/PPARa axis was supported by in vivo experiments and clinical relevance. This leads to a reduction in the critical metabolite palmitic acid, subsequently decreasing the palmitoylation levels of oncogenic protein TEA domain transcription factor 1 (TEAD1), thereby regulating the hippo pathway and subsequent cell proliferation inhibition. Additionally, we found that ADH1C and PPARa can serve as combined biomarkers to distinguish between patients with steatotic and non-steatotic HCC. Combination therapy targeting ADH1C and anti-programmed cell death protein 1 (PD1) enhances the response of steatotic HCC to anti- PD1 immunotherapy. Our study revealed a central role of ADH1C/PPARa in lipid metabolism and HCC suppression. Targeting lipid metabolism via ADH1C/PPARa may provide new therapeutic strategies for the treatment of liver cancer.

The introduction of several new systemic therapies in recent years has significantly altered the treatment landscape for advanced hepatocellular carcinoma. However, while the approval of the combination of atezolizumab and bevacizumab as the preferred first-line therapy over sorafenib represents progress, it has also raised uncertainties regarding optimal treatment sequencing for advanced disease.

This study evaluates the sequential treatment of hepatocellular carcinoma following therapy with atezolizumab and bevacizumab, providing evidence from a prospective real-world cohort.

Data were derived from the ongoing IMMUreal cohort, which investigates immunotherapy in hepatocellular carcinoma across two tertiary centres in Bavaria. A total of 124 patients treated with atezolizumab and bevacizumab as first-line therapy between June 2020 and December 2023 were analysed. Feasibility, treatment patterns, and outcomes of sequential therapy were assessed, with a focus on defined prognostic subgroups.

The median overall survival under real-world conditions was 19.8 months. Less than half of the patients (41.2%) proceeded to second-line therapy, and only 19.2% were eligible for third-line treatment. This decline in treatment eligibility corresponded to a marked reduction in therapy duration and progressive deterioration in liver function, as indicated by Albumin-Bilirubin and Child-Pugh scores. While patients with worse baseline liver function, such as patients with Child-Pugh B or ALBI > 1, had a significantly lower probability of transitioning to 2nd line therapy, no significant association was found between the number of treatment lines and factors such as liver cirrhosis, poor physical condition, extrahepatic disease, or macrovascular invasion.

Sequential therapy following atezolizumab and bevacizumab is feasible only for selected patients. However, preserving liver function seems crucial to optimising multi-line therapy and improving outcomes in advanced hepatocellular carcinoma.

To identify proliferative hepatocellular carcinoma (HCC) preoperatively using quantitative measurements combined with the updated standard 2021 LI-RADS universal lexicon-based qualitative features on multiphase dynamic CT (MDCT).

We retrospectively analyzed 273 patients (102 proliferative HCCs) who underwent preoperative MDCT with surgically confirmed HCC in two medical centers. Imaging features were evaluated according to the updated 2021 LI-RADS universal lexicon, and quantitative measurements were analyzed. All MDCT findings and clinical factors were compared. Four predictive models (clinical, CT quantitative-clinical, CT qualitative-clinical, and combinational models) were developed and validated in an external cohort for identifying proliferative HCC. ROC analysis was used to assess model performances. All models were tested in a subgroup of patients with a single lesion ≤ 5 cm (n = 124).

Both the CT quantitative-clinical and CT qualitative-clinical models effectively identified proliferative HCC in the training and external validation cohorts (all AUCs > 0.79). The combinational model, integrating one clinical (AFP ≥ 200 ng/mL), three qualitative (rim arterial phase hyperenhancement (APHE), non-smooth tumor margin, and incomplete or absent capsule), and one quantitative feature (standardized tumor-to-aorta density ratio in portal venous phase ≤ (- 0.13), showed significant improvement in the training cohort (AUC 0.871) and comparable performance in the validation cohort (AUC 0.870). Additionally, AFP ≥ 200 ng/mL and Rim APHE were significantly associated with HCC recurrence (p < 0.05).

The combinational model, integrating clinical, CT quantitative, and qualitative features, shows potential for the noninvasively preoperative prediction of proliferative HCC. Further validation is needed to establish its broader clinical utility.

Question Preoperative identification of proliferative HCC could influence patient treatment and prognosis, yet there is no CT-based universally applicable model to identify this subtype. Findings The updated standard 2021 LI-RADS universal lexicon-based features, in combination with quantitative MDCT measurements, could aid in the noninvasive detection of proliferative HCC. Clinical relevance The updated standard 2021 LI-RADS universal lexicon-based CT qualitative features and quantitative measurements may aid in identifying proliferative HCC and tumor recurrence, offering potential guidance for personalized treatment. Further studies are required to assess their generalizability to different clinical scenarios.

Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.

An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software. The derivation cohort (n = 5,585) was randomly divided into the training and internal validation sets at a 3:1 ratio. The external validation cohort included 2,883 patients. Six imaging biomarkers (i.e. abdominal visceral fat-total fat volume ratio, total fat-trunk volume ratio, spleen volume, liver volume, liver-spleen Hounsfield unit ratio, and muscle Hounsfield unit) and eight clinical variables were selected as the main variables of our model, PLAN-B-DF.

In the internal validation set (median follow-up duration = 7.4 years), PLAN-B-DF demonstrated an excellent predictive performance with a c-index of 0.91 and good calibration function (p = 0.78 by the Hosmer-Lemeshow test). In the external validation cohort (median follow-up duration = 4.6 years), PLAN-B-DF showed a significantly better discrimination function compared to previous models, including PLAN-B, PAGE-B, modified PAGE-B, and CU-HCC (c-index, 0.89 vs. 0.65-0.78; all p <0.001), and maintained a good calibration function (p = 0.42 by the Hosmer-Lemeshow test). When patients were classified into four groups according to the risk probability calculated by PLAN-B-DF, the 10-year cumulative HCC incidence was 0.0%, 0.4%, 16.0%, and 46.2% in the minimal-, low-, intermediate-, and high-risk groups, respectively.

This AI prediction model, integrating deep learning-based auto-segmentation of CT images, offers improved performance in predicting HCC risk among patients with CHB compared to previous models.

The novel predictive model PLAN-B-DF, employing an automated computed tomography segmentation algorithm, significantly improves predictive accuracy and risk stratification for hepatocellular carcinoma in patients with chronic hepatitis B (CHB). Using a gradient-boosting algorithm and computed tomography metrics, such as visceral fat volume and myosteatosis, PLAN-B-DF outperforms previous models based solely on clinical and demographic data. This model not only shows a higher c-index compared to previous models, but also effectively classifies patients with CHB into different risk groups. This model uses machine learning to analyze the complex relationships among various risk factors contributing to hepatocellular carcinoma occurrence, thereby enabling more personalized surveillance for patients with CHB.

To predict microvascular invasion (MVI) status and tumor grading of hepatocellular carcinoma (HCC) by evaluating preoperative non-radiomics ultrasound and contrast-enhanced ultrasound (US-CEUS) features and determine the influences of MVI/tumor grading on the category of CEUS LI-RADS for HCC.

A total of 506 HCC patients who underwent preoperative US-CEUS examinations from 8 hospitals between July 2020 and June 2023 were enrolled. According to the MVI status, all the patients were classified, and HCC differentiation was assessed by using Edmondson-Steiner (ES) grading: MVI-negative (M0) and low-grade ES (GI/II) (MN-L, n = 297) and MVI-positive (M1/M2) and/or high-grade ES (GIII/IV) (MP-H, n = 209). Stratified analysis was performed based on fibrosis stage and tumor size.

The results proved that MN-L HCC was more frequently classified into the LR-5 category (p = 0.034), while MP-H HCC was more frequently classified into the LR-TIV (p = 0.010). The heterogeneously arterial phase hyperenhancement (APHE) is significantly correlated with MVI(+)/high grade-ES (p = 0.003). Compared with MN-L HCC, the onset of washout was earlier, washout rate was higher, and tumor-invasion border was larger (all p < 0.01) in MP-H HCC. In addition, fibrosis stage and tumor size significantly influenced the onset of washout and washout rate of HCC (all p < 0.01). The tumor-invasion border was only positively correlated with tumor size (p < 0.001) rather than fibrosis stage (p > 0.05).

MVI status and tumor grading influence the classification of LR-5 and LR-TIV. Heterogeneous APHE, higher washout rate, earlier onset of washout (≤65 s), larger tumor-invasion border (≥3 mm) and higher alpha fetoprotein level indicate the presence of MVI and/or high-grade ES.

The association between preoperative imaging and postoperative pathological tumor size disparity, and cancer-specific survival (CSS) among patients undergoing hepatectomy for hepatocellular carcinoma (HCC) remains unclear. We sought to evaluate this association and identify predictors of size disparity.

Patients undergoing curative-intent hepatectomy for HCC (2000-2023) were identified from an international, multi-institutional database. Size ratio was defined as the ratio of pathological to imaging tumor size. Patients with a size ratio of 0.5-1.5 were classified as "without size disparity," while patients outside this range were considered "with size disparity." Multivariable Cox regression was used to identify predictors of CSS, while logistic regression was utilized to determine factors associated with size disparity. For variables identified as significant in multivariable analyses, further evaluation including cutoff determination, were performed using receiver operating characteristic (ROC) analysis.

Among 833 patients, median size ratio was 1.02, with a strong correlation between imaging and pathological tumor sizes (r = 0.87). Size disparity was present in 106 patients (12.7 %); in general, patients had smaller median imaging sizes (2.85 vs. 4.80 cm; p < 0.001) while size on pathology was noted to be larger(both 4.50 cm; p = 0.370). Patients with size disparity had worse 5-year CSS (60.1% vs. 79.0 %; p < 0.001). Multivariable Cox regression identified higher ALBI score (HR:2.56 [1.50-4.37]; p < 0.001), larger pathological tumor size (HR:1.09 [1.03-1.15]; p = 0.001), and size disparity (HR:2.53 [1.37-4.66]; p = 0.002) as independent predictors of CSS. Logistic regression demonstrated that cirrhosis (OR: 2.68 [1.43-5.02]; p = 0.002) and log alpha-fetoprotein (AFP) (OR:1.11 [1.01-1.22]; p = 0.030) were associated with an increased likelihood of size disparity. Cirrhosis and log AFP could be used to stratify patients relative to probability of a size disparity (low-risk:9.9 %, medium-risk:12.2 %, high-risk:17.7 %). The optimal AFP cutoff value was 3928 ng/mL for non-cirrhotic (AUC:0.90) versus 28.9 ng/mL for cirrhotic (AUC:0.74) patients.

Tumor size disparity was associated with worse CSS among patients with HCC undergoing hepatectomy. Size disparity could be predicted preoperatively using cirrhosis status and AFP level, which may help identify high-risk patients who may benefit from more detailed imaging assessments.

The principal aim of the present study was to develop and validate a nomogram predicting overall survival (OS) in patients with α-fetoprotein (AFP)-negative hepatocellular carcinoma (AFP-NHCC) who experience dynamic changes in AFP level after hepatectomy. A cohort of 870 patients were enrolled and randomly assigned into a training cohort (n=600) and a validation cohort (n=270) at a 7:3 ratio. The key variables contributing to the nomogram were determined through random survival forest analysis and multivariate Cox regression. The discriminative ability of the nomogram was evaluated using time-dependent receiver operating characteristic curves and the area under the curves. Furthermore, the nomogram was comprehensively assessed using the concordance index (C-index), calibration curves and clinical decision curve analysis (DCA). Kaplan-Meier (KM) curves analysis was employed to discern survival rates across diverse risk strata of patients. Ultimately, the nomogram incorporated critical factors including sex, tumor size, globulin levels, gamma-glutamyl transferase and fibrinogen levels. In the training and validation cohorts, the C-indexes were 0.72 [95% confidence interval (CI): 0.685-0.755) and 0.664 (95% CI: 0.611-0.717], respectively, attesting to its predictive validity. The nomogram demonstrated excellent calibration and DCA further confirmed its clinical usefulness. Additionally, KM curve analysis unveiled statistically significant differences in OS among three distinct risk groups. In conclusion, the present study successfully formulated a nomogram predicting 3-, 5- and 8-year OS in patients with AFP-NHCC with dynamic changes in AFP level post-local resection. This model serves as a valuable tool for clinicians to promptly identify high-risk patients, thereby facilitating timely interventions and potentially enhancing patient survival outcomes.

To develop and validate a deep learning model using multimodal PET/CT imaging for detecting and classifying focal liver lesions (FLL).

This study included 185 patients who underwent 18F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging. Liver lesions were segmented on PET and CT, serving as the "reference standard". Deep learning models were trained using PET and CT images to generate predicted segmentations and classify lesion nature. Model performance was evaluated by comparing the predicted segmentations with the reference segmentations, using metrics such as Dice, Precision, Recall, F1-score, ROC, and AUC, and compared it with physician diagnoses.

This study finally included 150 patients, comprising 46 patients with benign liver nodules, 51 patients with malignant liver nodules, and 53 patients with no FLLs. Significant differences were observed among groups for age, AST, ALP, GGT, AFP, CA19-9and CEA. On the validation set, the Dice coefficient of the model was 0.740. For the normal group, the recall was 0.918, precision was 0.904, F1-score was 0.909, and AUC was 0.976. For the benign group, the recall was 0.869, precision was 0.862, F1-score was 0.863, and AUC was 0.928. For the malignant group, the recall was 0.858, precision was 0.914, F1-score was 0.883, and AUC was 0.979. The model's overall diagnostic performance was between that of junior and senior physician.

This deep learning model demonstrated high sensitivity in detecting FLLs and effectively differentiated between benign and malignant lesions.

A 45-year-old male with chronic hepatitis B presented with an advanced hepatocellular carcinoma (HCC) occupying the entire left liver and invading the left portal vein. Despite multiple poor prognostic imaging features, including vascular invasion, corona enhancement, an incomplete capsule, intratumoral necrosis, intratumoral arteries, and irregular tumor borders, the patient elected to undergo a left hepatectomy. Although Barcelona Clinic Liver Cancer (BCLC) staging classified the case as stage C, a resection was successfully performed. Remarkably, 6 years postsurgery, the patient remains recurrence-free. This report highlights a rare, fortunate outcome in a high-risk HCC case and underscores the potential of surgical intervention even in advanced HCC.

Treatment decisions for hepatocellular carcinoma (HCC) involve considering tumour stage, liver function and performance status, including comorbidities, although rarely analysed specifically. This study examines the patterns and prognostic impact of comorbidities in HCC patients.

We included patients diagnosed with HCC before undergoing transplantation, resection or ablation, registered in the Swedish Registry for Cancers in the Liver and Bile ducts (SweLiv) 2008-2016. Data were cross-linked with the Swedish National Patient Registry (NPR) to capture International Classification of Diseases (ICD) codes reflecting comorbidities within 10 years before the HCC treatment decision. The Charlson Comorbidity Index (CCI), excluding the liver disease category (CCI-P), was used to estimate accumulated comorbidity.

We identified 980 HCC patients with transplantation (225), resection (425) or ablation (330). The comorbidity burden, assessed using the CCI-P, was highest in ablation patients and lowest in the transplanted group (p < 0.001). The CCI-P category distribution varied across treatment groups. After adjusting for age and tumour burden, several CCI-P categories were associated with 5-year mortality, including heart failure, cerebrovascular disease, pulmonary disease, ulcers, and renal disease. ICD diagnoses not included in the CCI, such as trauma, infection, psychiatric disease, anaemia, and obesity, were also linked to 5-year mortality.

Comorbidity burden and patterns differed between HCC treatment groups, with CCI-P significantly associated with mortality. Preoperative attention to cardiovascular disease is important, but other comorbid conditions may require vigilance. Given the higher prevalence of comorbidities in ablation and resection patients, efforts to optimize comorbidity in these groups may be warranted.

Disease modeling is vital for our understanding of disease mechanisms and for developing new therapeutic strategies. Accurately modeling the intact tumor microenvironment (TME) is increasingly recognized as essential for gaining insights into cancer biology and therapeutic response. Preclinical mouse models have provided utility for studying the evolving TME, but these models are costly and can lead to animal suffering and the discontinuation of drug investigations. To address these limitations, particularly in hepatocellular carcinoma (HCC), we have developed an ex vivo model using tumor precision-cut slices (TPCS) derived from orthotopic liver tumors.

Murine HCC tumors were generated via intrahepatic injection of Hep-53.4 cells, providing a source of tumor tissue for TPCS generation. Subsequent scaling to a 96-well format and modification to include a secreted luciferase enabled longitudinal ex vivo screening of 26 drugs applied at 2 doses over an 8-day period, using just 5 tumors. One drug identified in the screen, salinomycin, was then validated in vivo via intraperitoneal injection of mice with orthotopic liver tumors.

Histological characterization determined that TPCS maintain the architecture, cellular complexity, and drug responsiveness of the original HCC-TME under simplified culture conditions that preserve viability and metabolic activity. In addition to typical HCC therapies, sorafenib and anti-PD1 immunotherapy, the screen identified 2 drugs as potent anticancer agents capable of impacting the viability of TPCS: salinomycin and rottlerin. Salinomycin was further validated in vivo, significantly reducing tumor burden without evidence of toxicity.

We present a 3Rs (Reduction, Refinement, Replacement) approach for studying HCC biology and performing 96-well-scale drug screening within an intact, metabolically active TME, offering a more ethical and effective platform for drug discovery.

Cancer immunotherapy stimulates and enhances antitumor immune responses to eliminate cancer cells. Neoantigens, which originate from specific mutations within tumor cells, are key targets in cancer immunotherapy. Neoantigens manifest as abnormal peptide fragments or protein segments that are uniquely expressed in tumor cells, making them highly immunogenic. As a result, they activate the immune system, particularly T cell‑mediated immune responses, effectively identifying and eliminating tumor cells. Certain tumor‑associated antigens that are abnormally expressed in normal host proteins in cancer cells are promising targets for immunotherapy. Neoantigens derived from mutated proteins in cancer cells offer true cancer specificity and are often highly immunogenic. Furthermore, most neoantigens are unique to each patient, highlighting the need for personalized treatment strategies. The precise identification and screening of neoantigens are key for improving treatment efficacy and developing individualized therapeutic plans. The neoantigen prediction process involves somatic mutation identification, human leukocyte antigen (HLA) typing, peptide processing and peptide‑HLA binding prediction. The present review summarizes the major current methods used for neoantigen screening, available computational tools and the advantages and limitations of various techniques. Additionally, the present review aimed to summarize experimental strategies for validating the immunogenicity of the predicted neoantigens, which will determine whether these neoantigens can effectively trigger immune responses, as well as challenges encountered during neoantigen screening, providing relevant recommendations for the optimization of neoantigen‑based immunotherapy.

In atezolizumab/bevacizumab (atezo/bev) therapy for unresectable hepatocellular carcinoma (HCC), the tumor immune environment is regulated through vascular endothelial growth factor A (VEGFA) inhibition to maximize immune checkpoint blockade; however, evidence for VEGFA as a biomarker is limited. This study aimed to investigate serum VEGFA and associated microRNAs (miRNAs) as rapid biomarkers and their regulatory mechanisms in the microenvironment of HCC.

Fifty-four patients with unresectable HCC who were treated with atezo/bev therapy were enrolled and assigned into objective response (OR) and non-OR groups according to the best therapeutic response in 12 weeks using the modified response evaluation criteria in solid tumors. Serum VEGFA levels and associated miRNA expression were compared. Furthermore, the effect of cell transfection with specific miRNA was investigated.

There was no significant difference in the pre-treatment serum VEGFA levels between the groups; however, the 3-week/pre-treatment ratio of serum VEGFA levels was significantly lower in the OR group than in the non-OR group. The pre-treatment serum levels of 10 miRNAs, especially miR-485-3p involved in VEGFA expression, were higher in the OR group than in the non-OR group and were further elevated after 1-7 days and 3 weeks. MiR-485-3p suppressed HuH-7 migration, enhanced the expression of protein inhibitor of activated (PIA) signal transducer and activator of transcription 3 (STAT3) (PIAS3), and suppressed the expression of phosphorylated STAT3/VEGFA.

Circulating miR-485-3p is a more sensitive biomarker than VEGFA for the early prediction of therapeutic response in atezo/bev therapy for HCC.

The introduction of immune checkpoint inhibitor (ICI) based therapies has significantly improved the prognosis of patients with unresectable hepatocellular carcinoma (HCC). However, the variable treatment response and the uncertain benefit in patients with advanced liver cirrhosis emphasise the urgent need for prognostic and predictive biomarkers guiding patient selection. The soluble urokinase plasminogen activator receptor (suPAR) is strongly associated with inflammation, liver cirrhosis and various types of cancer. In this study, we investigated suPAR as a potential novel biomarker in patients with unresectable HCC.

This multicenter retrospective study, conducted at three German tertiary care centers, included 90 patients with unresectable HCC and suPAR measurements prior to and during atezolizumab/bevacizumab therapy. Patients with liver cirrhosis without HCC (n = 235) and non-cirrhotic patients with other gastrointestinal tumours (n = 155) were selected as control cohorts.

Median suPAR levels were significantly higher in patients with liver cirrhosis compared to non-cirrhotic cancer patients. A strong association with parameters of liver function, but not with HCC characteristics, was observed. In patients with HCC receiving atezolizumab/bevacizumab, suPAR was the most accurate independent predictor of hepatic decompensation and overall survival (OS). In addition, suPAR was able to stratify the risk of hepatic decompensation within the different Child-Pugh classes.

SuPAR represents a promising novel biomarker in patients with HCC treated with ICI-based therapies and bears the potential to guide the selection of antitumoral systemic therapies in patients with advanced liver cirrhosis.

To develop and externally validate an MRI-based diagnostic model for microvascular invasion (MVI) or Edmondson-Steiner G3/4 (i.e., high-risk histopathology) in solitary BCLC 0/A hepatocellular carcinoma (HCC) ≤ 5 cm and to assess its performance in predicting adjuvant therapy benefits.

This multicenter retrospective cohort study included 577 consecutive adult patients who underwent contrast-enhanced MRI and subsequent curative resection or ablation for solitary BCLC 0/A HCC ≤ 5 cm (December 2011 to January 2024) from four hospitals. For resection-treated patients, a diagnostic model integrating clinical and 50 semantic MRI features was developed against pathology with logistic regression analyses on the training set (center 1) and externally validated on the testing dataset (centers 2-4), with its utilities in predicting posttreatment recurrence-free survival (RFS) and adjuvant therapy benefit evaluated by Cox regression analyses.

Serum α-fetoprotein > 100 ng/mL (odds ratio (OR), 1.94; p = 0.006), non-simple nodular growth subtype (OR, 1.69; p = 0.03), and the VICT2 trait (OR, 4.49; p < 0.001) were included in the MVI or high-grade (MHG) trait, with testing set AUC, sensitivity, and specificity of 0.832, 74.0%, and 82.5%, respectively. In the multivariable Cox analysis, the MHG-positive status was associated with worse RFS (resection testing set HR, 3.55, p = 0.02; ablation HR, 3.45, p < 0.001), and adjuvant therapy was associated with improved RFS only for the MHG-positive patients (resection HR, 0.39, p < 0.001; ablation HR, 0.30, p = 0.005).

The MHG trait effectively predicted high-risk histopathology, RFS and adjuvant therapy benefit among patients receiving curative resection or ablation for solitary BCLC 0/A HCC ≤ 5 cm.

Question Despite being associated with increased recurrence and potential benefit from adjuvancy in HCC, microvascular invasion or Edmondson-Steiner grade 3/4 are hardly assessable noninvasively. Findings We developed and externally validated an MRI-based model for predicting high-risk histopathology, post-resection/ablation recurrence-free survival, and adjuvant therapy benefit in solitary HCC ≤ 5 cm. Clinical relevance Among patients receiving curative-intent resection or ablation for solitary HCC ≤ 5 cm, noninvasive identification of high-risk histopathology (MVI or high-grade) using our proposed MRI model may help improve individualized prognostication and patient selection for adjuvant therapies.

Bulevirtide (BLV) 2 mg/day is EMA approved for the treatment of compensated chronic HDV infection; however, real-world data in large cohorts of patients with cirrhosis are lacking.

Consecutive HDV-infected patients with cirrhosis starting BLV 2 mg/day from September 2019 were included in a European retrospective multicenter real-world study (SAVE-D). Patient characteristics before and during BLV treatment were collected. Virological, biochemical, combined responses, adverse events and liver-related events (hepatocellular carcinoma [HCC], decompensation, liver transplant) were assessed.

A total of 244 patients with HDV-related cirrhosis receiving BLV monotherapy for a median of 92 (IQR 71-96) weeks were included: at BLV start, median (IQR) age was 49 (40-58) years and 61% were men; median ALT, LSM and platelet count were 80 (55-130) U/L, 18.3 (13.0-26.3) kPa, and 94 (67-145) x103/mm3, respectively; 54% had esophageal varices, 95% Child-Pugh A cirrhosis, and 10% HIV coinfection; 92% were on nucleos(t)ide analogues; median HDV RNA and HBsAg were 5.4 (4.1-6.5) log10 IU/ml and 3.8 (3.4-4.1) log10 IU/ml, respectively. At weeks 48 and 96, virological, biochemical and combined responses were observed in 65% and 79%, 61% and 64%, 44% and 54% of patients, respectively. AST, GGT, albumin, IgG and LSM values significantly improved throughout treatment. Serum bile acid levels increased in most patients, but only 10% reported mild and transient pruritus, which was independent of bile acid levels. The week 96 cumulative risks of de novo HCC and decompensation were 3.0% (95% CI 2-6%) and 2.8% (95% CI 1-5%), respectively. Thirteen (5%) patients underwent liver transplantation (n = 11 for HCC, n = 2 for decompensation).

BLV 2 mg/day monotherapy for up to 96 weeks was safe and effective in patients with HDV-related cirrhosis. Virological and clinical responses increased over time, while the incidence of liver-related complications was low.

Bulevirtide 2 mg/day is EMA approved for the treatment of compensated chronic hepatitis delta; however, real-world data in large cohorts of patients with cirrhosis are lacking. Bulevirtide 2 mg/day monotherapy for up to 96 weeks was safe and effective (week 96: 79% virological, 64% biochemical and 54% combined response) in a large real-world cohort of patients with HDV-related cirrhosis, including patients with clinically significant portal hypertension. Liver function tests and liver stiffness improved, suggesting a potential clinical benefit in patients with advanced liver disease, while the incidence of de novo liver-related events (hepatocellular carcinoma and decompensation) was low during the 96-week study period.

Liver Hepatocellular Carcinoma (LIHC) and Kidney Renal Clear Cell Carcinoma (KIRC) are leading causes of cancer death worldwide, but their early detections remain hindered by a lack of genetic markers. Our study aims to find prospective biomarkers that could serve as prognostic indicators for efficient drug candidates for KIRC and LIHC treatment.

To detect differentially expressed genes (DEGs), four datasets were used: GSE66271 and GSE213324 for KIRC, and GSE135631 and GSE202853 for LIHC. Visualization of DEGs was done using heatmaps, volcano plots, and Venn diagrams. Hub genes were identified via PPI analysis and the cytoHubba plugin in Cytoscape. Their expression was evaluated using box plots, stage plots, and survival plots for prognostic assessment via GEPIA2. Molecular docking with PyRx's AutoDock Vina identified optimal binding interactions between compounds and proteins. Pharmacokinetic and toxicity analyses reinforced the drug-likeness and safety of these compounds.

In this study, 47 DEGs were identified, with the top 10 hub genes being TOP2A, BUB1, PTTG1, CCNB2, NUSAP1, KIF20A, BIRC5, RRM2, NDC80 and CDC45, chosen for their high MCC scores. Data mining revealed a correlation between TOP2A expression and clinical survival outcomes in KIRC and LIHC patients. Docking studies of the TOP2A structure identified a promising compound from Andrographis paniculata with high binding energy and interactions with TOP2A. Pharmacokinetic and toxicity assessments support its potential as a drug candidate.

Our study emphasizes TOP2A's prognostic significance in KIRC and LIHC and recognizes Andrographis paniculata compound as potential therapeutics, offering prospective for enhanced treatment and patient outcomes in these cancers.

Changes in protein and lipid metabolism could provide additional prognostic information for hepatocellular carcinoma (HCC).This study aimed to explore whether 3D automatic assessment of skeletal muscle and adipose tissue can contribute to the precise prognosis for HCC.

The data of 458 HCC patients from 6 hospitals were divided into training and external validation datasets. Preoperative CT Images were used for this study. First, we tested the stability of the 2D factors. Second, we tested whether standardization for volume assessment was necessary. Third, we compared the clinical (ModelC), skeletal muscle and adipose tissue (ModelNSA), and combined (ModelC-NSA) models by discrimination and calibration to identify the optimal model. Subgroup analysis was performed for the optimal model.

For the 16 2D factors, 13 factors were statistically different among the three 2D slices. Standardization of the volume factors was necessary. Among the three models, ModelC-NSA had a higher area under the curve [AUC] than ModelC and ModelNSA, both in the training dataset (0.809 vs. 0.649 vs. 0.797) and the validation dataset (0.770 vs. 0.718 vs. 0.719). For calibration, the performance of ModelC-NSA was similar to those of ModelC and ModelNSA. The performance of ModelC-NSA was not influenced by age (P = 0.753), sex (P = 0.781), treatments (P = 0.504), Barcelona Clinic Liver Cancer stage (P = 0.913), or Child-Pugh class (P = 0.580).

Compared to 2D evaluation, 3D assessment is more stable. 3D automatic assessment of skeletal muscle and adipose tissue can accurately predict progression in patients with HCC.

Severe postoperative complications still occur following hepatectomy among patients with hepatocellular carcinoma (HCC). There is a need to identify high-risk patients for severe complications to enhance patient safety. We sought to evaluate the combined impact of pre- and postoperative albumin-bilirubin (ALBI) score and Fibrosis-4 (FIB-4) index trends to predict severe complications after HCC resection.

Patients with HCC undergoing curative-intent hepatectomy (2000-2023) were identified from an international, multi-institutional database. The cohort was divided into training (n = 439) and testing (n = 651) sets. ALBI score and FIB-4 index trends from preoperative to postoperative days 1, 3, and 5 were used for K-means clustering (K = 3). A logistic regression model was developed using the training set, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC) in both cohorts.

Severe complications (Clavien-Dindo Grade ≥ IIIa) occurred in 118 patients (10.8 %); 43 (9.8 %) in training and 75 (11.5 %) in testing set (p = 0.42). K-means clustering identified three groups: Cluster1 (low), Cluster2 (intermediate), and Cluster3 (high), which was associated with a progressively increasing risk of complications (p < 0.01). On multivariable logistic regression, patients in ALBI Cluster1 had 76 % decreased odds (odds ratio[OR] 0.24, 95 % CI 0.07-0.83, p = 0.02) of postoperative complications relative to Cluster3 patients. Individuals categorized into FIB-4 Cluster1 had 85 % decreased odds (OR 0.15, 95 % CI 0.02-1.24, p = 0.07) versus patients in FIB-4 Cluster3. A new prediction model incorporating ALBI and FIB-4 index clusters achieved an AUC of 0.71, outperforming models based on preoperative data. A tool was made available at https://nm49jf-miho-akabane.shinyapps.io/HCC_ALBI/.

A dynamic ALBI score and FIB-4 index trend tool improved risk stratification of patients undergoing resection of HCC relative to severe complications.

Ultrasound is the primary imaging modality used for surveillance of patients at risk for HCC. In 2017, the American College of Radiology Liver Imaging Reporting and Data Systems (ACR LI-RADS) introduced US LI-RADS to standardize the performance, interpretation, and reporting of US for HCC surveillance, with the algorithm recently updated as LI-RADS US Surveillance v2024. The American Association for the Study of Liver Diseases (AASLD) recommends reporting both the examination-level LI-RADS US Category as well as the US Visualization Score. The US Category conveys the overall findings of the exam and primarily determines follow up recommendations. The US Visualization Score conveys the expected sensitivity of the test and stratifies patients into appropriate surveillance pathways. One of the goals of routine surveillance is the detection of HCC at an early, potentially curable stage. Therefore, optimizing US technique is of critical importance. Increasing North American and worldwide utilization of LI-RADS US Surveillance, which includes technical recommendations, through education and outreach will undoubtedly benefit patients undergoing US HCC surveillance.

To compare concordance of radiologic and pathologic response of hepatocellular carcinoma (HCC) to transarterial chemoembolization (TACE), percutaneous ablation, and multimodal treatment using radiologic-pathologic correlation.

This single-center retrospective study analyzed 56 treatment-naive patients (male, 75%; Barcelona Clinic Liver Cancer Stage A, 63%) with 81 HCC tumors (mean diameter, 2.1 cm [SD ± 0.9]) who underwent locoregional therapy (LRT) (TACE, n = 44; ablation, n = 10; TACE + ablation, n = 27) prior to liver transplantation (LT) between 2010 and 2019. Immediate pre-LT cross-sectional imaging was used to assess modified Response Evaluation Criteria in Solid Tumours (mRECIST) response. Explant liver pathology was reviewed for percent pathologic necrosis (PN). Associations between imaging and pathologic observations were statistically characterized using the chi-square and Kruskal-Wallis tests.

Median time from imaging to LT was 37 days (range, 2-191 days). Across all LRT types, 68% (55/81), 19% (15/81), and 13% (11/83) of tumors displayed mRECIST complete response (CR), partial response (PR), and stable disease. The mean percent PN (%PN) in CR tumors (89% [SD ± 21]) was significantly higher than those in PR (68% [SD ± 34], P = .005) and stable disease (67% [SD ± 36], P = .009) tumors. Sixty percent (33/55) of CR tumors showed 100% complete PN (CPN), whereas only 20% (3/15) of PR tumors and 18% (2/11) of stable disease tumors showed CPN (P = .002). There was no association between %PN and CPN across different LRT modalities and radiologic response categories, indicating consistent performance between treatments. Sensitivity and specificity for radiologic CR to predict 100% PN were 87% and 49%, respectively.

Herein, radiologic-pathologic outcomes suggest that radiologic response criteria are associated with PN, with no differences across treatment modalities. However, the imperfect predictive capacity of imaging for PN supports surveillance of treated tumors before LT.