This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (US) images using artificial neural networks. The HCC-Net integrates the discrete wavelet transform (DWT) to decompose US images into four sub-band images, a lesion detector for hierarchical lesion localization, and a pattern-augmented classifier for generating pattern-enhanced lesion images and subsequent classification. The lesion detection uses a hierarchical coarse-to-fine approach to minimize missed lesions. CoarseNet performs initial lesion localization, while FineNet identifies any lesions that were missed. In the classification phase, the wavelet components of detected lesions are synthesized to create pattern-augmented images that enhance feature distinction, resulting in highly accurate classifications. These augmented images are classified into 'Normal,' 'Benign,' or 'Malignant' categories according to their morphologic features on sonography. The experimental results demonstrate the significant effectiveness of the proposed coarse-to-fine detection framework and pattern-augmented classifier in lesion detection and classification. We achieved an accuracy of 96.2 %, a sensitivity of 97.6 %, and a specificity of 98.1 % on the Samsung Medical Center dataset, indicating HCC-Net's potential as a reliable tool for liver cancer screening.

Alpha-fetoprotein (AFP) is a glycoprotein primarily expressed during embryogenesis, with declining levels postnatally. Elevated AFP levels correlate with pathological conditions such as liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Recent investigations underscore AFP's intracellular role in HCC progression, wherein it forms complexes with proteins like Phosphatase and tensin homolog (PTEN), Caspase 3 (CASP3), and Retinoic acid receptors and Retinoid X receptors (RAR/RXR). RAR and RXR regulate gene expression linked to cell death and tumorigenesis in normal physiology. AFP impedes RAR/RXR dimerization, nuclear translocation, and function, promoting gene expression favoring cancer progression in HCC that provoked us to target AFP as a drug candidate. Despite extensive studies, inhibitors targeting AFP to disrupt complex formation and activities remain scarce. In this study, employing protein-protein docking, amino acid residues involved in AFP-RARβ interaction were identified, guiding the definition of AFP's active site for potential inhibitor screening. Currently, kinase inhibitors play a significant role in cancer treatment and, the present study explores the potential of repurposing FDA-approved protein kinase inhibitors to target AFP. Molecular docking with kinase inhibitors revealed Lapatinib as a candidate drug of the AFP-RARβ complex. Molecular dynamics simulations and binding energy calculations, employing Mechanic/Poisson-Boltzmann Surface Area (MM-PBSA), confirmed Lapatinib's stability with AFP. The study suggests Lapatinib's potential in disrupting the AFP-RARβ complex, providing a promising avenue for treating molecularly stratified AFP-positive HCC or its early stages.

Given the resistance to conventional treatments and limitations of immune checkpoint blockade therapy in hepatocellular carcinoma (HCC), it is imperative to explore novel prognostic models and biomarkers. The dependence of cancer cell on exogenous methionine, known as Hoffman effect, is a hallmark of HCC, with numerous studies reporting a strong correlation between methionine metabolism and tumor development. Betaine-homocysteine S-methyltransferase (BHMT), a critical component of methionine metabolism pathway, has polymorphisms linking to poor prognosis in multiple cancers. Nevertheless, there is little literature regarding the relationship between methionine metabolism and incidence, mortality of HCC, as well as the function of BHMT in HCC progression. In this study, by analyzing multiple datasets, we constructed a methionine metabolism-related prognostic model and thoroughly investigated the influence of BHMT on the prognosis of HCC. Bioinformatics analysis revealed a marked decrease in BHMT expression in HCC, which was linked to adverse clinical outcomes. CIBERSORT results suggest that BHMT promotes infiltration of M1 macrophages. Our results suggest its potential as an ideal prognostic biomarker for anti PD-L1 immunotherapy. In summary, this study innovatively provides first methionine metabolism-related prognostic model and unveils the tumor suppressive function of BHMT in HCC, providing potential mechanism by which BHMT exert its function.

Hepatocellular carcinoma (HCC) is the most common form of hepatic malignancy, with high mortality rates recorded globally. Early detection through clinical biomarkers, medical imaging and histological assessment followed by rapid intervention are integral for positive patient outcomes. Although contrast-enhanced computed tomography scans and magnetic resonance imaging are recognised as the reference standard for the diagnosis and staging of HCC in international guidelines, ultrasound (US) examination is recommended as a screening tool for patients at risk. Contrast-enhanced US (CEUS) elevates the standard of an US examination using US contrast agents (UCAs), capable of diagnosing focal liver lesions with high efficacy. Most UCAs are purely intravascular, offering clinicians a dynamic representation of a lesions' arterial phase vascular kinetics, which is seldom seen in such detail during computed tomography or magnetic resonance imaging assessments. Despite its benefits, there is incongruity between international societies on the role of CEUS in the HCC clinical pathway. The transient nature of pure blood-pool agents is suggested to be insufficient to justify CEUS as a primary modality due to the inability to consistently perform whole liver imaging, alongside disputes regarding its capabilities to differentiate HCC from intrahepatic cholangiocarcinoma. A sinusoidal phase UCA affords clinicians the opportunity to perform whole liver imaging through Kupffer cell uptake in addition to visualising lesion vascular kinetics in the arterial and portal venous phases. Therefore, the purpose of this review was to examine the role of CEUS in the HCC clinical pathway in its current practice and observe how a Kupffer cell sinusoidal phase UCA may supplement contemporary diagnostic techniques through a multi-modality, multi-agent approach.

Hepatocellular carcinoma (HCC) is a common cause of cancer‑related mortality and morbidity worldwide. While iodine‑125 (125I) particle brachytherapy has been extensively used in the clinical treatment of various types of cancer, the precise mechanism underlying its effectiveness in treating HCC remains unclear. In the present study, MHCC‑97H cells were treated with 125I, after which, cell viability and proliferation were assessed using Cell Counting Kit‑8, 5‑ethynyl‑2'‑deoxyuridine and colony formation assays, cell invasion and migration were evaluated using wound healing and Transwell assays, and cell apoptosis was determined using flow cytometry. Omics data were analyzed using Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and STRING analyses to observe the key genes that exhibited significant changes at the transcriptional and protein levels in MHCC‑97H cells treated with 125I particles. Finally, the expression levels of key genes (GPNMB, C4BPA, CTH, H1‑0 and MT2A) were verified through reverse transcription quantitative PCR. Following treatment with 125I, the proliferation, invasion and migration of MHCC‑97H cells were inhibited, and apoptosis was enhanced. The results of omics data analysis indicated that the biological behavior of MHCC‑97H cells treated with 125I was related to the expression levels of CTH and MT2A genes. These findings indicated that intervention with 125I radiation particles may induce changes in gene expression, potentially influencing alterations in biological characteristics. In conclusion, these insights may shed light on the underlying mechanisms of 125I radiation particle therapy in HCC and offer novel targets for HCC treatment.

Background/Objectives: Ultrasound (US) imaging plays a crucial role in the early detection and treatment of hepatocellular carcinoma (HCC). However, challenges such as speckle noise, low contrast, and diverse lesion morphology hinder its diagnostic accuracy. Methods: To address these issues, we propose CSM-FusionNet, a novel framework that integrates clustering, SoftMax-weighted Box Fusion (SM-WBF), and padding. Using raw US images from a leading hospital, Samsung Medical Center (SMC), we applied intensity adjustment, adaptive histogram equalization, low-pass, and high-pass filters to reduce noise and enhance resolution. Data augmentation generated ten images per one raw US image, allowing the training of 10 YOLOv8 networks. The mAP@0.5 of each network was used as SoftMax-derived weights in SM-WBF. Threshold-lowered bounding boxes were clustered using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and outliers were managed within clusters. SM-WBF reduced redundant boxes, and padding enriched features, improving classification accuracy. Results: The accuracy improved from 82.48% to 97.58% with sensitivity reaching 100%. The framework increased lesion detection accuracy from 56.11% to 95.56% after clustering and SM-WBF. Conclusions: CSM-FusionNet demonstrates the potential to significantly improve diagnostic reliability in US-based lesion detection, aiding precise clinical decision-making.

Abnormal expression of Fanconi anaemia complementation group I (FANCI) has been implicated in carcinogenesis. However, the precise role of FANCI in the development of hepatocellular carcinoma (HCC) remains unclear.

We conducted a comprehensive bioinformatics analysis of FANCI's role based on HCC patient sequencing data in the TCGA and GEO databases. Then, we performed qPCR, Western blotting (WB), and immunohistochemistry (IHC) assays. SiRNA-mediated knockdown of FANCI was conducted, followed by CCK-8, EdU staining, and colony formation experiments to evaluate the impact of FANCI knockdown on HCC cell behaviour. Flow cytometry was employed to explore alterations in the cell cycle after FANCI knockdown in HCC cell lines. Furthermore, RNA sequencing was performed to investigate potential mechanisms following FANCI knockdown, and WB analysis was used to validate the corresponding pathway.

Our bioinformatics analysis revealed elevated expression of FANCI in HCC, which was subsequently validated through qPCR, WB, and IHC assays. High expression of FANCI was significantly associated with a poor prognosis in HCC patients. Univariate and multivariate Cox regression analyses identified FANCI as an independent prognostic risk factor for HCC patients. Additionally, the coexpressed genes of FANCI were found to be associated with multiple cancer pathways. Knockdown of FANCI expression significantly inhibited HCC cell proliferation and colony formation by inducing cell cycle arrest. Further WB analysis revealed that FANCI knockdown suppressed the expression of Cyclin D1 and p-AKT while increasing the expression of GSK-3β in HCC cells. However, no significant differences were observed in the expression levels of AKT and PI3K.

Overall, our research provides substantial proof of FANCI's crucial function as an oncogene in HCC. It could serve as a potential prognostic marker, therapeutic target, and tumorigenic factor in HCC.

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

Hepatocellular carcinoma (HCC) is one of the five most common cancers and the second leading cause of cancer-related death worldwide. In this study, three monoclonal antibodies were developed for the early detection of HCC. The enzyme-linked immunosorbent assay (ELISA) method is used to detect antigens causing HCC. The final working dilutions of the coated antigen, monoclonal antibody, and enzyme-labeled secondary antibody were determined to be 1:5, 1:100, and 1:15,000, respectively. The optimal dilution of blocking buffer was 1.5% BSA phosphate buffer. The cutoff values were determined to be 0.1989, 0.2539, and 0.3059 for the Des-gamma carboxyprothrombin (DCP), Alpha-fetoprotein (AFP) and Thioredoxin (TXN) antigens, respectively. There is no cross-reaction between antigens and antibodies of different types. The coincidence rates between the indirect ELISA and commercial kits for detecting DCP, AFP, and TXN antigens were 95.24%, 95.24%, and 96.83%, respectively. In addition, a procedure to detect genes encoding TXN, DCP, and AFP via PCR has been developed. The results of the indirect ELISA and PCR methods are similar. In summary, we successfully constructed an indirect ELISA method to detect HCC-causing antigens via three monoclonal antibodies and designed primers to amplify HCC-causing gene fragments, which can be used for diagnosis and screening in clinical medicine.

We compared the clinical performance of the novel GAAD (gender [biological sex], age, alpha-fetoprotein [AFP], des-gamma carboxyprothrombin [DCP]) and GALAD (gender [biological sex], age, AFP, Lens culinaris agglutinin-reactive AFP [AFP-L3], DCP) algorithms to deduce the clinical utility of AFP-L3 for detecting early-stage hepatocellular carcinoma (HCC) from chronic liver disease (CLD).

An algorithm development study (STOP-HCC-ARP) and clinical validation study (STOP-HCC-MCE) were conducted, recruiting adult participants with HCC (confirmed by radiology or pathology) or CLD in an international, multicenter, case-control design. Serum biomarkers were measured using Elecsys assays (GAAD and GALAD [Cobas]) or μTASWAKO assays (GALAD [μTASWAKO]) while blinded to case/control status.

In STOP-HCC-ARP (algorithm development cohort), 1,006 patients {297 HCC (41.4% early-stage [Barcelona Clinic Liver Cancer {BCLC} 0/A) and 709 CLD} were included. Area under the curve (AUCs) for discriminating between early-stage HCC vs. CLD were 91.4%, 91.4%, and 90.8% for GAAD (Cobas), GALAD (Cobas), and GALAD (μTASWAKO), respectively. The clinical validation cohort of STOP-HCC-MCE comprised 1,142 patients, (366 HCC cases [48% early-stage], 468 specificity samples and 302 CLD); AUCs for GAAD (Cobas), GALAD (Cobas), and GALAD (μTASWAKO) for discriminating between early-stage HCC vs. CLD were 91.4%, 91.5%, and 91.0%, respectively; AUCs were 94.7-95.0% for all-stage HCC. The GAAD and GALAD algorithms demonstrated similar good performance regardless of disease etiology, presence of cirrhosis, geographical region, and within pan-tumor specificity panels (p <0.001).

GAAD (Cobas) demonstrated good clinical performance, similar to GALAD (Cobas and μTASWAKO) algorithms, in differentiating HCC and CLD controls, across all disease stages, etiologies, and regions; therefore, AFP-L3 may have a negligible role in GALAD for HCC surveillance.

To improve the detection of early-stage hepatocellular carcinoma (HCC) from benign chronic liver disease (CLD), algorithms combining demographic characteristics and serum biomarkers, such as GAAD and GALAD, have been developed. GAAD combines gender (biological sex), age, alpha-fetoprotein (AFP), des-gamma carboxy-prothrombin (DCP); GALAD combines the same characteristics and biomarkers as GAAD with the addition of Lens culinaris agglutinin-reactive AFP (AFP-L3). Changing disease etiologies and treatment paradigms have raised questions regarding the utility of AFP-L3 in HCC surveillance. Our work demonstrates that the GAAD (Cobas) algorithm demonstrated good clinical performance and was as sensitive and specific as the GALAD (Cobas) and GALAD (μTASWAKO) algorithms in differentiating HCC and CLD controls, across all disease stages, etiologies, and geographical regions; therefore, AFP-L3 may have a negligible role in HCC detection. Our study provides supporting evidence that in participants with CLD undergoing guideline-directed HCC surveillance, the GAAD (Cobas) algorithm may be used as an effective method for the detection of HCC, potentially resulting in improved patient outcomes.

Hepatocellular carcinoma (HCC) is a highly fatal malignant worldwide. As different expression levels of specific genes can lead to different HCC outcomes, we aimed to develop a gene signature capable of predicting HCC prognosis.

In this study, transcriptomic sequencing and relevant clinical data were extracted from public platforms. The guanine monophosphate synthase (GMPS)|receptor activity-modifying protein 3 (RAMP3) gene pair was developed based on the relative values of gene expression levels. Nomograms were developed using R software. Immune status was assessed through single-sample gene set enrichment analysis. GMPS knockdown was achieved through siRNA transfection. Quantitative reverse transcription PCR, apoptosis assays, and cell proliferation were performed to verify the function of GMPS|RAMP3 in HCC cells.

Here, a gene pair containing GMPS and RAMP3 was successfully constructed. We demonstrated that the GMPS|RAMP3 gene pair was an independent predictor with strong prognostic prediction power, based on which a nomogram was established. Functional analysis revealed that the enrichment of cell cycle-related pathways and immune status differed considerably between the two groups, with cell cycle-related genes highly expressed in the high GMPS|RAMP3 value group. Finally, cell experiments indicated that GMPS knockdown significantly repressed proliferation, promoted apoptosis, and enhanced the sensitivity of HCC cells to gemcitabine.

The gene pair GMPS|RAMP3 is a novel prognostic predictor of HCC, providing a promising approach to the treatment and assessment of immune heterogeneity in HCC.

Recent research has highlighted lactate's crucial role in epigenetic regulation, particularly by influencing histone modifications that drive the initiation and progression of hepatocellular carcinoma (HCC). While mitochondria are known to regulate tumor behavior, the interaction between lactate metabolism and mitochondrial function in cancer tissues remains underexplored. Understanding this relationship may provide deeper insights into tumor metabolic reprogramming and reveal novel therapeutic targets for HCC and other malignancies.

We conducted a comprehensive screening of lactylation- and mitochondria-associated genes (LMRGs) in HCC patients, followed by clustering based on these genes. Prognostic outcomes and pathway enrichment were analyzed across the identified clusters. Additionally, we developed a prognostic model based on LMRGs, examining its implications for survival, immune response, and drug sensitivity. In vitro experiments were performed to validate the expression patterns and functional role of MRPL3 in HCC.

We developed a prognostic model, named the LMRG model, incorporating three key genes: ACACA, MRPL3, and MRPS23. This model revealed significant differences in survival outcomes, immune responses, and drug sensitivity between patients with high and low LMRG scores. MRPL3 was found to be overexpressed in HCC, playing a critical role in tumor growth and metastasis. These results were further validated through in vitro experiments, confirming MRPL3's role in HCC cell proliferation and invasion.

We created a predictive model, LMRG, and identified MRPL3 as a key biomarker. Our findings suggest that MRPL3 has significant potential as a reliable predictive biomarker for clinical applications in HCC diagnosis and treatment.

To develop and validate a modality-invariant Swin U-Net Transformer (UNETR) deep learning model for liver and spleen segmentation on abdominal T1-weighted (T1w) or T2-weighted (T2w) MR images from multiple institutions for pediatric and adult patients with known or suspected chronic liver diseases. In this IRB-approved retrospective study, clinical abdominal axial T1w and T2w MR images from pediatric and adult patients were retrieved from four study sites, including Cincinnati Children's Hospital Medical Center (CCHMC), New York University (NYU), University of Wisconsin (UW) and University of Michigan / Michigan Medicine (UM). The whole liver and spleen were manually delineated as the ground truth masks. We developed a modality-invariant 3D Swin UNETR using a modality-invariant training strategy, in which each patient's T1w and T2w MR images were treated as separate training samples. We conducted both internal and external validation experiments. A total of 241 T1w and 339 T2w MR sequences from 304 patients (age [mean ± standard deviation], 31.8 ± 20.3 years; 132 [43%] female) were included for model development. The Swin UNETR achieved a Dice similarity coefficient (DSC) of 0.95 ± 0.02 on T1w images and 0.93 ± 0.05 on T2w images for liver segmentation. This is significantly better than the U-Net model (0.90 ± 0.05, p < 0.001 and 0.90 ± 0.13, p < 0.001, respectively). The Swin UNETR achieved a DSC of 0.88 ± 0.12 on T1w images and 0.93 ± 0.10 on T2w images for spleen segmentation, and it significantly outperformed a modality-invariant U-Net model (0.80 ± 0.18, p = 0.001 and 0.88 ± 0.12, p = 0.002, respectively). Our study demonstrated that a modality-invariant Swin UNETR model can segment the liver and spleen on routinely collected clinical bi-parametric abdominal MR images from children and adult patients.

Chemotherapy is the primary palliative treatment for advanced hepatocellular carcinoma (HCC). However, the systemic delivery is associated with the drawbacks including a high risk of adverse effects and a low efficacy. Therefore, local injection therapy may be beneficial. Nevertheless, the existing local drug-carrying microspheres(DOBM)have the characteristics of low loading and abrupt release, can not simultaneously load two drugs, and may cause unnecessary toxicity. In this study, we created the dual-loaded bovine serum albumin (BSA) microspheres (also known as DOBM), which were hollow and contained both oxaliplatin (OXA) and Adriamycin hydrochloride (DOX). In addition, this pH-sensitive drug delivery method exhibited a high drug loading capacity and was promising in breaking through biological barriers, making it a viable option for the treatment of HCC through local implantation. Based on physiochemical evaluation of BSA microspheres, they had a porous structure which was close to the surface. Adriamycin and oxaliplatin were successfully added to the surface of BSA microspheres. According to in vitro experimental results, the growth of human HCC (HCC-LM3 and PLC/PRF/5) cell lines was significantly inhibited by DOBM. Furthermore, in the subcutaneous PLC/PRF/5 HCC model, DOBM played an essential role in tumor development and change in the tumor microenvironment.

The increasing availability of RNA sequencing data has opened up numerous opportunities to analyze various RNA interactions, including microRNA-target interactions (MTIs). In response to the necessity for a specialized tool to study MTIs in cancer and normal tissues, we developed AmiCa (https://amica.omics.si/), a web server designed for comprehensive analysis of mature microRNA (miRNA) and gene expression in 32 cancer types. Data from 9498 tumor samples and 626 normal samples from The Cancer Genome Atlas were obtained through the Genomic Data Commons and used to calculate differential expression and miRNA-target gene (MTI) correlations. AmiCa provides data on differential expression of miRNAs/genes for cancers for which normal tissue samples were available. In addition, the server calculates and presents correlations separately for tumor and normal samples for cancers for which normal samples are available. Furthermore, it enables the exploration of miRNA/gene expression in all cancer types with different miRNA/gene expression. In addition, AmiCa includes a ranking system for genes and miRNAs that can be used to identify those that are particularly highly expressed in certain cancers compared to other cancers, facilitating targeted and cancer-specific research. Finally, the functionality of AmiCa is illustrated by two case studies.

The hepatocarcinoma (HCC) is the most important liver tumor. It represents 90% of liver cancer cases. One of the main problems is the limited prompt cancer diagnosis and the advanced stages where the chances of treatment are limited. The main diagnostic methods for HCC are imaging techniques and liver biopsy. With advances in technology, proteins as significant diagnostic biomarkers have increased. The objective of this review is to describe the role of Alpha-fetoprotein (AFP), Glipican 3 (GPC-3), and Kininogen 1 (KNG-1) as biomarkers for the diagnosis of hepatocellular carcinoma. A systematic search of studies was carried out in the literature and the diagnostic values of these proteins were compared. The results showed that the combined use of biomarkers increases the diagnostic capacity for the detection of hepatocellular carcinoma.

The high recurrence rate of hepatocellular carcinoma (HCC) and poor prognosis after medical treatment reflects the necessity to improve the current chemotherapy protocols, particularly drug delivery methods. Development of targeted and efficient drug delivery systems (DDSs), in all active, passive and stimuli-responsive forms for selective delivery of therapeutic drugs to the tumour site has been extended to improve efficacy and reduce the severe side effects. Recent advances in nanotechnology offer promising breakthroughs in the diagnosis, treatment and monitoring of cancer cells. In this review, the specific design of DDSs based on the different nano-particles and their surface engineering is discussed. In addition, the innovative clinical studies in which nano-based DDS was used in the treatment of HCC were highlighted.

Hepatocellular carcinoma (HCC) is the third prime cause of malignancy-related mortality worldwide. Early and accurate identification of HCC is crucial for good prognosis, efficacy of therapy, and survival rates of the patients. We aimed to develop a machine-learning model incorporating differentially expressed RNA signatures with laboratory parameters to construct an RNA signature-based diagnostic model for HCC.

We have used five classifiers (KNN, RF, SVM, LGBM, and DNNs) to predict the liver disease (HCC). The classifiers were trained on 187 samples and then tested on 80 samples. The model included 22 features (age, sex, smoking, cirrhosis, non-cirrhosis, albumin, ALT, AST bilirubin (total and direct), INR, AFP, HBV Ag, HCV Abs, RQmiR-1298, RQmiR-1262, RQmiR-106b-3p, RQmRNARAB11A, and RQSTAT1, RQmRNAATG12, RQLnc-WRAP53, RQLncRNA- RP11-513I15.6).

LGBM achieved the highest accuracy of 98.75% in predicting HCC among all models surpassing Random Forest (96.25%), DNN (91.25%), SVC (88.75%), and KNN (87.50%).

Our machine-learning model incorporating the expression data of RAB11A/STAT1/ATG12/miR-1262/miR-1298/miR-106b-3p/lncRNA-RP11-513I15.6/lncRNA-WRAP53 signature and clinical data represents a potential novel diagnostic model for HCC.

Previous studies suggest a role for inflammation in hepatocarcinogenesis. However, no study has comprehensively evaluated associations between circulating inflammatory proteins and risk of hepatocellular carcinoma (HCC) among the general population. We conducted a nested case-control study in the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) with 56 pairs of incident HCC cases and controls. External validation was performed in the UK Biobank (34 HCC cases and 48,471 non-HCC controls). Inflammatory protein levels were measured in pre-diagnostic plasma using the Olink® Inflammation Panel. We used conditional logistic regression to calculate multivariable odds ratios (ORs) with 95% confidence intervals (CIs) for associations between a 1-standard deviation (SD) increase in biomarker levels and HCC risk, considering a statistically significant threshold of false discovery rate (FDR)-adjusted p < .05. In the NHS/HPFS, among 70 analyzed proteins with call rates >80%, 15 proteins had significant associations with HCC risk (pFDR < .05). Two proteins (stem cell factor, OR per SD = 0.31, 95% CI = 0.16-0.58; tumor necrosis factor superfamily member 12, OR per SD = 0.51, 95% CI = 0.31-0.85) were inversely associated whereas 13 proteins were positively associated with risk of HCC; positive ORs per SD ranged from 1.73 for interleukin (IL)-10 to 2.35 for C-C motif chemokine-19. A total of 11 proteins were further replicated in the UK Biobank. Seven of the eight selected positively associated proteins also showed positive associations with HCC risk by enzyme-linked immunosorbent assay, with ORs ranging from 1.56 for IL-10 to 2.72 for hepatocyte growth factor. More studies are warranted to further investigate the roles of these observed inflammatory proteins in HCC etiology, early detection, risk stratification, and disease treatment.

Hepatocellular carcinoma (HCC) is a leading liver cancer that significantly impacts global life expectancy and remains challenging to treat due to often late diagnoses. Despite advances in treatment, the prognosis is still poor, especially in advanced stages. Studies have pointed out that investigations into the molecular mechanisms underlying HCC, including mitochondrial dysfunction and epigenetic regulators, are potentially important targets for diagnosis and therapy. Mitoepigenetics, or the epigenetic modifications of mitochondrial DNA, have drawn wide attention for their role in HCC progression. Besides, molecular biomarkers such as mitochondrial DNA alterations and non-coding RNAs showed early diagnosis and prognosis potential. Additionally, natural compounds like alkaloids, resveratrol, curcumin, and flavonoids show promise in HCC show promise in modulating mitochondrial and epigenetic pathways involved in cancer-related processes. This review discusses how mitochondrial dysfunction and epigenetic modifications, especially mitoepigenetics, influence HCC and delves into the potential of natural products as new adjuvant treatments against HCC.

Hepatocellular carcinoma (HCC) is a common type of tumor with a high incidence. Alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II or des-gamma-carboxy prothrombin) are proven effective biomarkers for HCC. Combining them can enhance detection rates. However, when both AFP and PIVKA-II are negative, clinical diagnosis may be missed. This study aims to explore the risk factors for AFP and PIVKA-II negativity in HCC, thereby reducing missed diagnoses. A retrospective study enrolled 609 HCC patients at Shandong Public Health Clinical Center Affiliated with Shandong University from January 2010 to March 2022. Patients with negative AFP and PIVKA-II were the observed group, and others with at least 1 positive were controls. Epidemiological, clinical, laboratory, and radiological data were collected and analyzed to identify the frequency and factors influencing AFP and PIVKA-II negativity. Receiver operating characteristic (ROC) curves were used to assess the prediction model's ability to detect negative AFP and PIVKA-II in HCC. Gender (P = .045, 95% confidence interval [95%CI] = 1.013-3.277), diabetes mellitus (P = .018, 95%CI = 1.151-4.422), tumor size (P = .000, 95%CI = 0.677-0.841), glutamate transpeptidase (P = .003, 95%CI = 0.239-0.737), total bilirubin (P = .001, 95%CI = 0.235-0.705), and hepatitis B virus-associated infections (P = .007, 95%CI = 0.077-0.661) were significantly associated with AFP and PIVKA-II negativity in HCC. The prediction model had an area under curve of 0.832 (P < .001, 95%CI = 0.786-0.877), with a sensitivity of 81.2% and specificity of 75.5% in all HCC patients. Female diabetic patients with levels closer to normal for glutamate transpeptidase and total bilirubin are more likely to develop AFP and PIVKA-II-negative HCC. Imaging is crucial for screening liver cancer in these patients.

Since differentiating malignant ascites from benign ascites has always been a clinical difficult, recognition of novel biomarkers in malignant ascites of hepatocellular carcinoma (HCC) patients could be helpful for establishing a diagnosis for HCC patients with ascitic fluids.

Thirty-five HCC patients with malignant ascites and chronic liver diseases patients with benign ascites were enrolled. Serum and ascites specimens were collected to determine TAN subpopulations and NETs concentration. Then, the correlation between ascitic NETs levels and clinical features were analyzed, and ROC curves were generated to evaluate the diagnostic value of NETs. For in vitro study, fresh neutrophils were employed to explore the underlying mechanism of TAN polarization and NETs formation using RNAseq analysis.

Significantly increased pro-tumor PD-L1+ TANs and higher lactate levels were measured in HCC ascites. RNAseq data showed that lactate regulated genes expression involving PD-L1 expression and NETs formation, suggesting that ascitic lactate might be responsible for tumor progression in TME. Then, NETs-related markers including calprotectin, dsDNA, CitH3, MPO and MPO-DNA were found dramatically elevated in malignant ascites. Next, correlation analysis revealed that ascitic NETs levels positively correlated with LDH, a classic ascitic biochemical indicator. Furthermore, we identified the diagnostic values of NETs in discriminating malignant ascites from benign ascites.

Our findings highlighted that elevated ascitic NETs served as a biomarker in HCC patients with malignant ascites, which provided useful insights for both clinical and basic research for malignant ascites diagnosis and management.

Identifying diagnostic biomarkers for cancer is crucial in the field of personalized medicine. The available transcriptome and interactome provide unprecedented opportunities and challenges for biomarker screening. From a systematic perspective, network-based medicine methods provide alternative approaches to organizing the available high-throughput omics data for deciphering molecular interactions and their associations with phenotypic states. In this work, we propose a bioinformatics strategy named TopMarker for discovering diagnostic biomarkers by comparing the network topology differences in control and disease samples. Specifically, we build up gene-gene interaction networks in the two states of control and disease respectively. The network rewiring status across the two networks results in differential network topologies reflecting dynamics and changes in normal samples when compared with those in disease. Thus, we identify the potential biomarker genes with differential network topological parameters between the control and disease gene networks. For a proof-of-concept study, we introduce the computational pipeline of biomarker discovery in hepatocellular carcinoma (HCC). We prove the effectiveness of the proposed TopMarker method using these candidate biomarkers in classifying HCC samples and validate its signature capability across numerous independent datasets. We also compare the discriminant power of biomarker genes identified by TopMarker with those identified by other baseline methods. The higher classification performances and functional implications indicate the advantages of our proposed method for discovering biomarkers from differential network topology.

Imaging tests, tumor marker (TM) screening, and biochemical tests provide a definitive diagnosis of hepatocellular carcinoma (HCC). However, some patients with HCC may present TM-negative results, warranting a need for developing more sensitive and accurate screening biomarkers. Various diseases exhibit increased blood levels of bile acids, biosynthesized from cholesterol in the liver, and they have been associated with HCC. Herein, we analyzed plasma bile acids using liquid chromatography/tandem mass spectrometry and integrated them with conventional biomarkers to develop a diagnostic screening model for HCC. Plasma samples were obtained from patients diagnosed with chronic hepatitis, hepatic cirrhosis (HC), and HCC. A QTRAP 6500 mass spectrometer and a Nexera liquid chromatograph with a YMC-Triart C18 analytical column were used. The mobile phase A was a 20 mmol/L ammonium formate solution, and mobile phase B was a methanol/acetonitrile mixture (1:1, v/v) with 20 mmol/L ammonium formate. After determining the concentrations of 32 bile acids, statistical analysis and diagnostic screening model development were performed. Plasma concentrations of bile acids differed between sample groups, with significant differences observed between patients with HC and HCC. By integrating bile acid results with conventional biochemical tests, a potential diagnostic screening model for HCC was successfully developed. Future studies should increase the sample size and analyze the data in detail to verify the diagnostic efficacy of the model.

Primary liver cancer (PLC) is one of the most common malignant gastrointestinal tumors worldwide. Limited by the shortage of liver transplantation donors and the heterogeneity of tumors, patients with liver cancer lack effective treatment options, which leads to rapid progression and metastasis. Currently, preclinical models of PLC fall short of clinical reality and are limited in their response to disease progression and the effectiveness of drug therapy. Organoids are in vitro three-dimensional cultured preclinical models with a high degree of heterogeneity that preserve the histomorphological and genomic features of primary tumors. Liver cancer organoids have been widely used for drug screening, new target discovery, and precision medicine; thus representing a promising tool to study PLC. Here, we summarize the progress of research on liver cancer organoids and their potential application as disease models. This review provides a comprehensive introduction to this emerging technology and offers new ideas for researchers to explore in the field of precision medicine.

NDRG1, the first member of the NDRG family, is a multifunctional protein associated with carcinogenesis. Its function in human cancer is currently poorly understood. The aim of this study was to explore the importance of NDRG1 in tumor immune cell infiltration and epithelial-mesenchymal transition (EMT) in hepatocellular carcinoma.

NDRG1 expression in various cancers was analyzed using TIMER 2.0, the Human Protein Atlas (HPA), UALCAN and PrognoScan. Wound healing, Transwell, MTT and colony formation assays were performed to confirm the effects of NDRG1 on the metastasis and proliferation of HCC cells. Western blotting was used to study the effect of NDRG1 on the expression of EMT-related proteins. Signaling networks were constructed using LinkedOmics and Metascape. TIMER2.0 and TISIDB were used for comprehensive analysis of tumor-infiltrating immune cells and tumor-infiltrating lymphocytes (TILs).

NDRG1 expression was higher in HCC tissue than in normal liver tissue at both the mRNA and protein levels. Overexpression of NDRG1 is associated with poor prognosis in HCC patients. Genomic analysis suggests that NDRG1 promoter hypermethylation leads to enhanced transcription, which may be one mechanism for NDRG1 upregulation in HCC. The overexpression of NDRG1 promotes the invasion, migration, and proliferation of HCC cells and induces the expression of EMT-related proteins. Immunoinfiltration analysis suggests that NDRG1 is involved in the recruitment of immune cells.

The present study showed that NDRG1 may induce metastasis and invasion through EMT and immune cell infiltration. NDRG1 could be used as a biomarker for the diagnosis and prognosis of HCC and could be a potential therapeutic target in HCC.

Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC.

Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm.

The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways.

In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

Circular RNAs (circRNAs) are cardinal players in numerous physiological and pathological processes. CircRNAs play dual roles as tumor suppressors and oncogenes in different oncological contexts, including hepatocellular carcinoma (HCC). Their roles significantly impact the disease at all stages, including initiation, development, progression, invasion, and metastasis, in addition to the response to treatment. In this review, we discuss the biogenesis and regulatory functional roles of circRNAs, as well as circRNA-protein-mRNA ternary complex formation, elucidating the intricate pathways tuned by circRNAs to modulate gene expression and cellular processes through a comprehensive literature search, in silico search, and bioinformatics analysis. With a particular focus on the interplay between circRNAs, epigenetics, and HCC pathology, the article sets the stage for further exploration of circRNAs as novel investigational theranostic agents in the dynamic realm of HCC.

Hepatocellular carcinoma is currently the most common malignancy of the liver. It typically occurs due to a series of oncogenic mutations that lead to aberrant cell replication. Most commonly, hepatocellular carcinoma (HCC) occurs as a result of pre-occurring liver diseases, such as hepatitis and cirrhosis. Given its aggressive nature and poor prognosis, the early screening and diagnosis of HCC are crucial. However, due to its plethora of underlying risk factors and pathophysiologies, patient presentation often varies in the early stages, with many patients presenting with few, if any, specific symptoms in the early stages. Conventionally, screening and diagnosis are performed through radiological examination, with diagnosis confirmed by biopsy. Imaging modalities tend to be limited by their requirement of large, expensive equipment; time-consuming operation; and a lack of accurate diagnosis, whereas a biopsy's invasive nature makes it unappealing for repetitive use. Recently, biosensors have gained attention for their potential to detect numerous conditions rapidly, cheaply, accurately, and without complex equipment and training. Through their sensing platforms, they aim to detect various biomarkers, such as nucleic acids, proteins, and even whole cells extracted by a liquid biopsy. Numerous biosensors have been developed that may detect HCC in its early stages. We discuss the recent updates in biosensing technology, highlighting its competitive potential compared to conventional methodology and its prospects as a tool for screening and diagnosis.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. As liver cancer often presents no noticeable symptoms in its early stages, most patients are diagnosed at an advanced stage, complicating treatment. Therefore, the identification of new biomarkers is crucial for the early detection and treatment of HCC. Research on exportin-5 (XPO5) could offer new avenues for early diagnosis and improve treatment strategies.

To explore the role of XPO5 in HCC progression and its potential as a prognostic biomarker.

This study assessed XPO5 mRNA expression in HCC using The Cancer Genome Atlas, TIMER, and International Cancer Genome Consortium databases, correlating it with clinical profiles and disease progression. We performed in vitro experiments to examine the effect of XPO5 on liver cell growth. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were used to elucidate the biological roles and signaling pathways. We also evaluated XPO5's impact on immune cell infiltration and validated its prognostic potential using machine learning.

XPO5 was significantly upregulated in HCC tissues, correlating with tumor grade, T-stage, and overall survival, indicating poor prognosis. Enrichment analyses linked high XPO5 expression with tumor immunity, particularly CD4 T cell memory activation and macrophage M0 infiltration. Drug sensitivity tests identified potential therapeutic agents such as MG-132, paclitaxel, and WH-4-023. Overexpression of XPO5 in HCC cells, compared to normal liver cells, was confirmed by western blotting and quantitative real-time polymerase chain reaction. The lentiviral transduction-mediated knockdown of XPO5 significantly reduced cell proliferation and metastasis. Among the various machine learning algorithms, the C5.0 decision tree algorithm achieved accuracy rates of 95.5% in the training set and 92.0% in the validation set.

Our analysis shows that XPO5 expression is a reliable prognostic indicator for patients with HCC and is significantly associated with immune cell infiltration.

The incidence of early-stage lung adenocarcinoma (ES-LUAD) is steadily increasing among non-smokers. Previous research has identified dysbiosis in the gut microbiota of patients with lung cancer. However, the local microbial profile of non-smokers with ES-LUAD remains largely unknown. In this study, we systematically characterized the local microbial community and its associated features to enable early intervention.

A prospective collection of ES-LUAD samples (46 cases) and their corresponding normal tissues adjacent to the tumor (41 cases), along with normal lung tissue samples adjacent to pulmonary bullae in patients with spontaneous pneumothorax (42 cases), were subjected to ultra-deep metagenomic sequencing, host transcriptomic sequencing, and proteomic sequencing. The obtained omics data were subjected to both individual and integrated analysis using Spearman correlation coefficients.

We concurrently detected the presence of bacteria, fungi, and viruses in the lung tissues. The microbial profile of ES-LUAD exhibited similarities to NAT but demonstrated significant differences from the healthy controls (HCs), characterized by an overall reduction in species diversity. Patients with ES-LUAD exhibited local microbial dysbiosis, suggesting the potential pathogenicity of certain microbial species. Through multi-omics correlations, intricate local crosstalk between the host and local microbial communities was observed. Additionally, we identified a significant positive correlation (rho > 0.6) between Methyloversatilis discipulorum and GOLM1 at both the transcriptional and protein levels using multi-omics data. This correlated axis may be associated with prognosis. Finally, a diagnostic model composed of six bacterial markers successfully achieved precise differentiation between patients with ES-LUAD and HCs.

Our study depicts the microbial spectrum in patients with ES-LUAD and provides evidence of alterations in lung microbiota and their interplay with the host, enhancing comprehension of the pathogenic mechanisms that underlie ES-LUAD. The specific model incorporating lung microbiota can serve as a potential diagnostic tool for distinguishing between ES-LUAD and HCs.

Hepatocellular carcinoma (HCC) arises from hepatocytes and accounts for 90% of primary liver cancer. Reasons for HCC prognosis remaining dismal are that HCC is asymptomatic in its early stages, leading to late diagnosis, and it is markedly resistant to conventional chemo- and radiotherapy. In this study, we investigated RNA interference (RNAi)-based treatment for HCC by targeting MDK.

The present study aimed to evaluate MDK serum levels as a diagnostic biomarker for HCC detection and the effect of MDK silencing by RNAi on HCC.

A total of 140 participants, including 120 patients diagnosed with HCC and 20 healthy volunteers were enrolled in this study, all patients who underwent liver resection were sampled for tumor and adjacent non-tumor liver tissues, in addition to 5 ml of blood sample. Midkine expression levels were evaluated by ELISA and by qRT-PCR. The in vitro transfection and gene knockdown efficiency of midkine by MDK-siRNA was detected by qRT-PCR and ELISA. Gene knockdown effect at the molecule level on the proliferation of HepG2 in vitro was determined by cell counting.

The results showed that the expression of MDK was significantly increased in the serum of HCC patients compared to control serum samples with P<0.001 and significant elevated expression levels of MDK in tumor tissues compared to non-tumor ones with P<0.001. It also showed that down-regulation of MDK using RNAi can significantly inhibit HepG2 cells.

Molecular targeting of MDK using RNAi interference decreases proliferation and could be a therapeutic target.

Alpha-fetoprotein (AFP), commonly employed for early diagnosis of liver cancer, serves as a biomarker for cancer screening and diagnosis. Combining the high sensitivity and specificity of fluorescence immunoassay (FIA), developing a low-cost and efficient immunoassay system for AFP detection holds significant importance in disease diagnosis. In this work, we developed a miniaturized oblique laser-induced fluorescence (LIF) immunoassay system, coupled with a microfluidic PMMA/paper hybrid chip, for rapid detection of AFP. The system employed an avalanche photodiode (APD) as the detector, and implemented multi-level filtering in the excitation light channel using the dichroic mirror and optical trap. At first, we employed the Savitzky-Golay filter and baseline off-set elimination methods to denoise and normalize the original data. Then the cutoff frequency of the low-pass filter and the reverse voltage of the APD were optimized to enhance the detection sensitivity of the system. Furthermore, the effect of laser power on the fluorescence excitation efficiency was investigated, and the sampling time during the scanning process was optimized. Finally, a four-parameter logistic (4PL) model was utilized to establish the concentration-response equation for AFP. The system was capable of detecting concentrations of AFP standard solution within the range of 1-500 ng/mL, with a detection limit of 0.8 ng/mL. The entire immunoassay process could be completed within 15 min. It has an excellent potential for applications in low-cost portable diagnostic instruments for the rapid detection of biomarkers.

Hepatocellular carcinoma (HCC) stands as one of the most malignant tumors characterized by poor prognosis and high mortality rates. Emerging evidence underscores the crucial role of the B7 protein family in various cancers, including HCC. However, the involvement of the human endogenous retrovirus H long-terminal repeat-associated protein 2 (HHLA2, or B7-H5) in HCC remains unclear. Immunohistochemistry was employed to assess the differential expression of HHLA2 between HCC and normal liver tissues. A battery of assays, including CCK8, EdU, tablet clone-forming, Transwell, and wound healing assays, were conducted to elucidate the function and potential mechanisms of HHLA2 in the malignant biological behaviors of HCC. Additionally, a xenograft mouse model was established to evaluate the tumorigenicity of hepatoma cell lines exhibiting different HHLA2 expression levels in vivo. Western blot analysis was used to analyze HHLA2, secretory phosphoprotein 1 (SPP1), and PI3K/AKT/mTOR levels. HHLA2 exhibited elevated expression in HCC tissues, correlating with poor tumor differentiation and shortened overall survival in HCC patients. In vitro experiments demonstrated that HHLA2 overexpression (OE) promoted the proliferation, migration, and invasion of hepatoma cells, while in vivo experiments revealed that HHLA2 OE enhanced HCC tumor growth. Conversely, inhibition of HHLA2 expression yielded the opposite effect. Downregulation of SPP1 inhibited the proliferation, migration, and invasion induced by HHLA2 OE, and this effect was linked to the PI3K/AKT/mTOR signaling pathway. Our findings indicate that HHLA2 promotes the proliferation, migration, and invasion of hepatoma cells via the SPP1/PI3K/AKT signaling pathway, establishing it as a potential therapeutic target for HCC.

Early diagnosis is paramount for enhancing survival rates and prognosis in the context of malignant diseases. Hepatocellular carcinoma (HCC), the second leading cause of cancer-related deaths worldwide, poses significant challenges for its early detection. In this study, we present an innovative approach which contributed to the early diagnosis of HCC. By lanthanide encoding signal amplification to map glycan-linkages at the single-cell level, the minute quantities of "soft" glycan-linkages on single cell surface were converted into "hard" elemental tags through the use of an MS2 signal amplifier. Harnessing the power of lanthanides encoded within MS2, we achieve nearly three orders of magnitude signal amplification. These encoded tags are subsequently quantified using single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS). Linear discriminant analysis (LDA) identifies seven specific glycan-linkages (α-2,3-Sia, α-Gal, α-1,2-Fuc, α-1,6-Fuc, α-2,6-Sia, α-GalNAc, and Gal-β-1,3-GalNAc) as biomarkers. Our methodology is initially validated at the cellular level with 100% accuracy in discriminating between hepatic carcinoma HepG2 cells and their normal HL7702 cells. We apply this approach to quantify and classify glycan-linkages on the surfaces of 55 clinical surgical HCC specimens. Leveraging these seven glycan-linkages as biomarkers, we achieve precise differentiation between 8 normal hepatic specimens, 40 early HCC specimens, and 7 colorectal metastasis HCC specimens. This pioneering work represents the first instance of employing single-cell glycan-linkages as biomarkers promising for the early diagnosis of HCC with a remarkable 100% predictive accuracy rate, which holds immense potential for enhancing the feasibility and precision of HCC diagnosis in clinical practice.

The prognosis of hepatocellular carcinoma (HCC) is universally poor. Early diagnosis plays a pivotal role in determining the outcome of HCC.

We employed a comparative proteomics approach to identify potential biomarkers and validated the application of retinol-binding protein 4 (RBP4) as a biomarker for HCC. RBP4 protein expression was examined in liver tissues from 80 HCC patients through immunohistochemical analysis. Serum RBP4 concentrations were measured by ELISA in a cohort comprising 290 HCC patients, matched 202 chronic hepatitis B patients and 269 healthy controls. Survival data were collected from HCC patients. The diagnostic and prognostic values of RBP4 were evaluated using receiver operating curve (ROC) analysis.

The validation results demonstrated a significant reduction in RBP4 levels in both liver tissues and serum samples from HCC patients. ROC analysis of the diagnostic value of RBP4 revealed an AUC of 0.879 (95 % CI: 0.854∼0.903) for HCC. When combined with AFP, the AUC increased to 0.919, with a sensitivity of 87.9 % and specificity of 80 %. Survival analysis revealed significantly reduced overall survival time in individuals with low-expression of RBP4 compared to those with high-expression. The joint prognostic model exhibited an AUC of 0.926 (95 % CI: 0.888∼0.964), which was significantly higher than that of AFP alone (AUC=0.809; P <0.0001).

RBP4 shows a great potential as a biomarker with appreciable diagnostic value, complementing the AFP in HCC diagnosis. Additionally, it holds promise as a prognostic biomarker that, when integrated into a combined prognostic model, could greatly improve HCC prognosis efficiency.

Hepatocellular carcinoma (HCC) is much rarer in children and adolescents in comparison to adults with an incidence of 0.7/1,000,000 per year. Hepatitis B virus, a known carcinogen increases the chances of HCC at a young age. Very few case reports of HCC developing in HBV-positive male children have been published.We present a case of a 14-year-old Hepatitis B-positive boy who presented with abdominal distension and jaundice. Contrast enhanced computerized tomography (CECT) whole abdomen suggested a diagnosis of multinodular HCC with no evidence of metastasis on FDG PET-CECT. Histopathology with immunohistochemistry confirmed the diagnosis of moderately differentiated HCC.Clinical presentation of HCC in children is similar to adults. Viral hepatitis, metabolic disorders, and male gender increase the risk of HCC. In our case, boy never had any prior history of jaundice, abdominal pain/distension, or any other illness suggestive of liver dysfunction. When the boy was found to be HBV positive, his mother was also screened and turned out to be Hepatitis B virus positive. Histopathology along with a panel of immunohistochemical markers clinched the final diagnosis.

As a hepatotropic virus, hepatitis B virus (HBV) can establish a persistent chronic infection in the liver, termed, chronic hepatitis B (CHB), which causes a series of liver-related complications, including fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). HCC with HBV infection has a significantly increased morbidity and mortality, whereas it could be preventable. The current goal of antiviral therapy for HBV infection is to decrease CHB-related morbidity and mortality, and achieve sustained suppression of virus replication, which is known as a functional or immunological cure. The natural history of chronic HBV infection includes four immune phases: the immune-tolerant phase, immune-active phase, inactive phase, and reactivation phase. However, many CHB patients do not fit into any of these defined phases and are regarded as indeterminate. A large proportion of indeterminate patients are only treated with dynamic monitoring rather than recommended antiviral therapy, mainly due to the lack of definite guidelines. However, many of these patients may gradually have significant liver histopathological changes during disease progression. Recent studies have focused on the prevalence, progression, and carcinogenicity of indeterminate CHB, and more attention has been given to the prevention, detection, and treatment for these patients. Herein, we discuss the latest understanding of the epidemiology, clinical characteristics, and therapeutic strategies of indeterminate CHB, to provide avenues for the management of these patients.