Early Diagnosis Of Hepatocellular Carcinoma Research Articles

Near-Infrared II Fluorescence Imaging Highlights Tumor Angiogenesis in Hepatocellular Carcinoma with a VEGFR-Targeted Probe.

Hepatocellular carcinoma (HCC) is typically characterized by rich vascularity, with angiogenesis playing a crucial role in its growth and invasion. Molecular imaging of specific receptors in blood vessels is crucial in HCC diagnosis. In particular, in vivo imaging utilizing the second near-infrared (NIR-II) window offers improved tissue penetration, reduced light scattering, and lower autofluorescence. Despite the great potential of the NIR-II window, developing safe and effective probes to provide better imaging performance for HCC is urgently needed. In this study, NIR-II imaging integrated with a vascular endothelial growth factor receptor (VEGFR)-targeted probe generated by combining a VEGFR-targeted peptide with indocyanine green (ICG) is used to characterize HCC-related angiogenesis at a resolution of 56.0µm. For the first time, liver metabolic curves and parameters of liver function reserve (LFR) are obtained by fitting NIR-II fluorescence signals with high spatiotemporal resolution, showing significant differences between HCC mice and controls. Moreover, unlike ICG, the targeting probe has a targeted effect on blood vessels in vivo. The tumor-to-normal (T/N) ratio in NIR-II imaging reaches up to 3.30 after post-injection of the targeting probe. The results indicate that the VEGFR-targeted probe is a powerful tool for NIR-II fluorescence imaging to enhance early diagnosis of HCC.

Altered Metabolites in Hepatocellular Carcinoma (HCC) Paving the Road for Metabolomics Signature and Biomarkers for Early Diagnosis of HCC

Altered Metabolites in Hepatocellular Carcinoma (HCC) Paving the Road for Metabolomics Signature and Biomarkers for Early Diagnosis of HCC

Novel Biomarkers for Early Detection of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality globally. Most patients present with late diagnosis, leading to poor prognosis. This narrative review explores novel biomarkers for early HCC detection. We conducted a comprehensive literature review analyzing protein, circulating nucleic acid, metabolite, and quantitative proteomics-based biomarkers, evaluating the advantages and limitations of each approach. While established markers like alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, and AFP-L3 remain relevant, promising candidates include circulating tumor DNA, microRNAs, long noncoding RNAs, extracellular vesicle, and metabolomic biomarkers. Multi-biomarker panels like the GALAD score, Oncoguard, and Helio liver test show promise for improved diagnostic accuracy. Non-invasive approaches like urine and gut microbiome analysis are also emerging possibilities. Integrating these novel biomarkers with current screening protocols holds significant potential for earlier HCC detection and improved patient outcomes. Future research should explore multi-biomarker panels, omics technologies, and artificial intelligence to further enhance early HCC diagnosis and management.

EXPRESS: Study of Role of Melanoma-Associated Antigen D1 (MAGE-D1) in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) ranks as one of the most prevalent malignant tumors globally, being the 5th most common neoplasm and the 3rd leading cause of cancer-related deaths. Despite efforts, current serum biomarkers for HCC surveillance and early diagnosis exhibit low sensitivity and varying specificity, even with different cut-off points and longitudinal assessments. AFP, the most commonly used marker, lacks satisfactory sensitivity and specificity, highlighting an urgent need for more effective markers with higher accuracy for early HCC detection. Exploring novel diagnostic biomarkers holds promise in improving HCC prognosis. There is evidence suggesting that downregulation of MAGE-D1 transcription plays a crucial role in apoptosis and inhibits cancer cell proliferation when expressed ectopically. Moreover, reduced MAGE-D1 expression correlates with improved prognosis in many cancers, underscoring its relevance in cancer development and progression. Therefore, this study aims to evaluate the diagnostic potential of MAGE-D1 in HCC, proposing it as a novel biomarker for early diagnosis and monitoring of tumor progression.

A Novel Prognostic Model of Hepatocellular Carcinoma per Two NAD+ Metabolic Synthesis-Associated Genes.

Hepatocellular carcinoma (HCC) is a formidable challenge to global human health, while recent years have witnessed the important role of NAD+ in tumorigenesis and progression. However, the expression pattern and prognostic value of NAD+ in HCC still remain elusive. Gene expression files and corresponding clinical pathological files associated with HCC were obtained from the Cancer Genome Atlas (TCGA) database, and genes associated with NAD+ were retrieved from the GSEA and differentially analyzed in tumor and normal tissues. A consensus clustering analysis was conducted by breaking down TCGA patients into four distinct groups, while Kaplan-Meier curves were generated to investigate the disparity in clinical pathology and endurance between clusters. A prognostic model based on NAD+-associated genes was established and assessed by combining LASSO-Cox regression, uni- and multi-variate Cox regression, and ROC curve analyses. Investigations were conducted to determine the expression of distinct mRNAs and proteins in both HCC and non-tumor tissues. A novel two-gene signature including poly (ADP-Ribose) polymerase 2 (PARP2) and sirtuin 6 (SIRT6) was obtained through LASSO-Cox regression and was identified to have favorable prognostic performance in HCC patients from TCGA. Analyses of both single and multiple variables showed that the prognostic model was a distinct prognostic factor in the endurance of liver cancer patients in both the training and trial groups. The nomogram also exhibited clinical significance in the prognosis of HCC patients. Immunohistochemistry, qRT-PCR, and Western blotting revealed that HCC samples exhibited higher PARP2 and SIRT6 expression levels than those of normal controls. This study identified a robust prognostic model comprising two NAD+-associated genes using bioinformatic methods, which is accurate in predicting the survival outcome of HCC patients. This model might benefit the early diagnosis of HCC and further facilitate the management of individualized medical service and clinical decision-making.

Mass spectrometry-based multi-omics analysis reveals distinct molecular features in early and advanced stages of hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) is a serious primary solid tumor that is prevalent worldwide. Due to its high mortality rate, it is crucial to explore both early diagnosis and advanced treatment for HCC. In recent years, multi-omics approaches have emerged as promising tools to identify biomarkers and investigate molecular mechanisms of biological processes and diseases. In this study, we performed proteomics, phosphoproteomics, metabolomics, and lipidomics to reveal the molecular features of early- and advanced-stage HCC. The data obtained from these omics were analyzed separately and then integrated to provide a comprehensive understanding of the disease. The multi-omics results unveiled intricate biological pathways and interaction networks underlying the initiation and progression of HCC. Moreover, we proposed specific potential biomarker panels for both early- and advanced-stage HCC by overlapping our data with CPTAC database for HCC diagnosis, and deduced novel insights and mechanisms related to HCC origination and development, such as glucose depletion during tumor progression, ROCK1 deactivation and GSK3A activation.

Surface-enhanced Raman scatting microfluidic chip based on the identification competition strategy were used for rapid and simultaneous detection of liver cancer related proteins

Biomarker testing plays a crucial role in the early detection of liver cancer. Herein, we developed a dual-signal amplification approach utilizing magnetic aggregation and a recognition competition strategy for the simultaneous detection of alpha-fetoprotein (AFP) and manganese superoxide dismutase (MnSOD) in serum. 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 were synthesized by functionalizing Raman signaling molecules and aptamer complementary chains onto the surface of gold nanoparticles (AuNPs). The detection complex Raman signal molecule@AuNPs@H-Fe3O4@cDNA was assembled by conjugating 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 with two nucleic acid aptamers (cDNA1 and cDNA2) modified with Fe3O4 through partial base complementary pairing. The target protein exhibited specific binding with the aptamer, leading to the competitive displacement of 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 from the Fe3O4 array surface, consequently resulting in a reduction of the Surface-Enhanced Raman Spectroscopy (SERS) signal. Through this approach, the limit of detection (LOD) for AFP and MnSOD in serum was achieved at remarkably low levels of 5.89 pg/mL and 6.23 pg/mL, respectively, with a reaction incubation period of only 5 min. Finally, the platform was utilized for the quantification of AFP and MnSOD in the serum of a nude mouse hepatocellular carcinoma model. The results obtained through SERS were consistent with those from enzyme-linked immunosorbent assay (ELISA), validating its accuracy. This methodology presents a novel approach for the swift and concurrent detection of proteins, holding significant clinical promise for the early diagnosis of hepatocellular carcinoma.

Construction and evaluation of a liver cancer risk prediction model based on machine learning.

Liver cancer is one of the most prevalent malignant tumors worldwide, and its early detection and treatment are crucial for enhancing patient survival rates and quality of life. However, the early symptoms of liver cancer are often not obvious, resulting in a late-stage diagnosis in many patients, which significantly reduces the effectiveness of treatment. Developing a highly targeted, widely applicable, and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals. To develop a liver cancer risk prediction model by employing machine learning techniques, and subsequently assess its performance. In this study, a total of 550 patients were enrolled, with 190 hepatocellular carcinoma (HCC) and 195 cirrhosis patients serving as the training cohort, and 83 HCC and 82 cirrhosis patients forming the validation cohort. Logistic regression (LR), support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) regression models were developed in the training cohort. Model performance was assessed in the validation cohort. Additionally, this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) to determine the optimal predictive model for assessing liver cancer risk. Six variables including age, white blood cell, red blood cell, platelet counts, alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR, SVM, RF, and LASSO regression models. The RF model exhibited superior discrimination, and the area under curve of the training and validation sets was 0.969 and 0.858, respectively. These values significantly surpassed those of the LR (0.850 and 0.827), SVM (0.860 and 0.803), LASSO regression (0.845 and 0.831), and ASAP (0.866 and 0.813) models. Furthermore, calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity. The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.

Causal Effect of Immunocytes, Plasma Metabolites, and Hepatocellular Carcinoma: A Bidirectional Two-Sample Mendelian Randomization Study and Mediation Analysis in East Asian Populations.

Background: Hepatocellular carcinoma (HCC) is a primary malignant liver tumor characterized by a low survival rate and high mortality. This study aimed to investigate the causal effect of immune cell phenotypes, plasma metabolites, and HCC in East Asian populations. Methods: The summary results for 731 immunocytes, 1400 plasma metabolites, and HCCs were acquired from publicly available genome-wide association studies (GWASs). This study utilized two-sample Mendelian randomization (MR) analysis to establish causal relationships, which was achieved by employing various statistical methods including inverse variance-weighted, simple mode, MR-Egger, weighted median, and weighted mode. Multiple sensitivity analyses were conducted to confirm the reliability of the MR data. Ultimately, mediation analysis was employed to ascertain the path that leads from immunocytes to plasma metabolites. Results: Among the 20 immune cells and HCC for East Asians, causal links were found, with one showing an inverse correlation. In addition, 36 metabolites were significantly associated with HCC for East Asians. Through analysis of established causative metabolites, we identified a strong correlation between the glycerophospholipid metabolic pathway and HCC for East Asians. By employing a two-step MR analysis, we identified 11 immunocytes that are causally linked to HCC for East Asians through the mediation of 14 plasma metabolites, with Linolenate [α or γ; (18:3n3 or 6)] levels showing the highest mediation proportion (19.3%). Conclusions: Our findings affirm the causal links among immunocytes, plasma metabolites, and HCC in eastern Asia populations by calculating the percentage of the impact that is influenced by plasma metabolites. This study offers innovative perspectives on the early detection, diagnosis, and therapy of HCC.

Rational Design of Mono-Substituted Gd-DOTA as Highly Stable and Efficient MRI Contrast Agents for Hepatobiliary and Inflammation Imaging.

Hepatobiliary-specific magnetic resonance imaging contrast agents (MRI CAs) play a crucial role in the early diagnosis of hepatocellular carcinoma (HCC). However, only two acyclic CAs, Gd-BOPTA and Gd-EOB-DTPA, exhibit unfavorable kinetic inertness. Our study focused on the development of superior stable innovative macrocyclic CAs. By introducing a lipophilic benzyloxy group (OBn) into the H4DOTA ring (Gd-L1), we achieved significant enhancement in kinetic inertness. In vivo experiments in mice demonstrated that 40% of the dosage was distributed to the liver at 5 min, providing sustained hepatic enhancement for over 35 min. We also developed an MPO-responsive MRI CA (Gd-L3), which can participate in the "peroxidase cycle" as the substrate, generating oligomers with a 3.8-fold increase in relaxivity, and selectively enhance the lesion in an acute gout mouse model. Overall, our work represents a significant advancement in the field of hepatic and inflammatory MRI, offering promising avenues for early diagnosis and improved imaging outcomes.

Analysis of Serum Exosome Metabolites Identifies Potential Biomarkers for Human Hepatocellular Carcinoma.

Currently, the clinical cure rate for primary liver cancer remains low. Effective screening and early diagnosis of hepatocellular carcinoma (HCC) remain clinical challenges. Exosomes are intimately associated with tumor development and their contents have the potential to serve as highly sensitive tumor-specific markers. A comprehensive untargeted metabolomics study was conducted using exosome samples extracted from the serum of 48 subjects (36 HCC patients and 12 healthy controls) via a commercial kit. An ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) strategy was used to identify the metabolic compounds. A total of 18 differential metabolites were identified using the non-targeted metabolomics approach of UPLC-QTOF-MS/MS. Pathway analysis revealed significant alterations in the arachidonic acid metabolism, linoleic acid metabolism, and unsaturated fatty acid metabolism pathways. ROC analysis indicated that three metabolites with AUC values exceeding 0.900 were selected as potential biomarkers: caprylic acid and linoleic acid were upregulated in the HCC group, whereas pentadecanoic acid was downregulated. Linoleic acid, caprylic acid, and pentadecanoic acid are potential biomarkers for diagnosing HCC. The significant alterations in these three metabolic pathways offer new insights into the mechanisms underlying HCC formation and progression.

Cascade signal amplifying strategy for ultrasensitive detection of tumor biomarker by DNAzyme cleaving mediated HCR

Tumor biomarkers are a kind of important biomolecules for aiding cancer diagnosis and assessing tumor efficacy, among which alpha-fetoprotein (AFP) is a crucial tumor biomarker for the early diagnosis of hepatocellular carcinoma. In this study, a novel cascade signal amplifying strategy was designed for the ultrasensitive detection of AFP by DNAzyme cleaving-mediated hybridization chain reaction (HCR). The primary antibody was immobilized on the Au nanoparticles modified glassy carbon electrode, which subsequently captured AFP and the secondary antibody. Then, streptavidin was combined with the biotin-modified secondary antibody and Cu2+-specific DNAzyme. When Cu2+ was present, the substrate strand of the DNAzyme would cleave and produce an ssDNA end. Finally, HCR was triggered to form long double-stranded DNA on the electrode, which would greatly inhibit the charge transfer at the electrode/solution interface. Therefore, the DNAzyme cleaving-mediated HCR strategy can be applied as cascade signal amplification for the ultrasensitive electrochemical determination of tumor biomarker AFP. Under optimal conditions, the immunosensor exhibits a broad linear range of 0.001−100 ng·mL−1 (R2 = 0.990) and a low detection limit of 15.8 fg·mL−1. The proposed sensor displays high sensitivity and selectivity for AFP detection, which can be employed as a universal sensing platform in clinical diagnostics.

Circulating microRNAs as biomarkers for stratifying different phases of liver cancer progression and response to therapy

Hepatocellular carcinoma (HCC) is the most common liver cancer and is among the leading causes of cancer-related death worldwide. There is no reliable biomarker for the early diagnosis of HCC. Circulating microRNAs (miRNAs) have attracted attention as potential biomarkers of disease. By small-RNA next-generation sequencing, the analysis of serum miRNAs led to the identification of molecular signatures able to discriminate advanced HCC from early HCC (n = 246); advanced HCC from CIRRHOSIS (n = 299); advanced HCC from HEALTHY (n = 320); HEALTHY from early HCC (n = 343); and HEALTHY from CIRRHOSIS (n = 414). Cirrhotic patients and early HCC patients exhibited similar serum miRNA profiles, yet a small number of miRNAs (n = 57) were able to distinguish these two classes of patients. A second objective of the study was to identify serum miRNAs capable of predicting the response to therapy in patients with advanced HCC. All patients were treated with sorafenib as first-line therapy: 24 were nonresponsive and 24 responsive. Analysis of circulating miRNAs revealed a 54 miRNAs signature able to separate the two subgroups. This study suggested that circulating miRNAs could be useful biomarkers for monitoring patients with liver diseases ranging from cirrhosis to advanced HCC and possibly predicting susceptibility to first-line treatment based on sorafenib.

Qualitative and quantitative differentiation efficiency of dual-tracer PET/CT with 18F-fluorodeoxyglucose and 11C-acetate for primary hepatocellular carcinoma: a systematic review and meta-analysis.

Primary hepatocellular carcinoma (HCC) represents a substantial global health challenge. Early diagnosis of HCC is crucial for improved patient outcomes. The aim of this study was to assess qualitative and quantitative diagnostic performance of PET/CT using 11C-acetate and [18F]-fluorodeoxyglucose (FDG) in detection of primary HCC and to determine if 11C-acetate added to [18F]-FDG alleviates the low sensitivity rate mentioned in guidelines. Protocol was pre-registered at https://osf.io/2vcb9 . We searched PubMed, Web of Science, Embase, and the Cochrane Library for included studies. Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess the risk of bias. Possible sources of statistical heterogeneity were explored. Additionally, mentioned three PET/CT tests were evaluated for their diagnostic performance in differentiating HCC from its differential diagnoses. Grades of Recommendation, Assessment, Development, and Evaluation was used to assess quality of generated evidence. Twenty-four studies were analyzed. Qualitative dual-tracer PET/CT demonstrated 92.0% per-lesion sensitivity, and a significantly higher direct sensitivity difference of 30% to conventional CT, 44.7% to [18F]-FDG, and 12.0% to 11C-acetate. Regarding differentiation rate, [18F]-FDG was superior to 11C-acetate in poorly differentiated lesions while 11C-acetate was superior in well-differentiated lesions. Regarding size, dual tracer combination solved the high missing rate of HCC lesions in 1-2cm and 2-5cm groups but could not help in size < 1cm. Dual-tracer PET/CT utilizing 11C-acetate and [18F]-FDG represents a sensitive method for detecting primary HCC. By concurrently quantifying or qualifying the uptake of 11C-acetate and [18F]-FDG, this multimodal approach enables precise localization of intrahepatic lesions.

Transcriptomic analysis of Paraoxonase 1 expression in hepatocellular carcinoma and its potential impact on tumor immunity.

Hepatocellular carcinoma (HCC) is characterized by a complex pathogenesis that confers aggressive malignancy, leading to a lack of dependable biomarkers for predicting invasion and metastasis, which results in poor prognoses in patients with HCC. Glycogen storage disease (GSD) is an uncommon metabolic disorder marked by hepatomegaly and liver fibrosis. Notably, hepatic adenomas in GSD patients present a heightened risk of malignancy compared to those in individuals without the disorder. In this investigation, PON1 emerged as a potential pivotal gene for HCC through bioinformatics analysis. Transcriptomic profiling data of liver cancer were collected and integrated from TCGA and GEO databases. Bioinformatics analysis was conducted to identify mutated mRNAs associated with GSD, and the PON1 gene was selected as a key gene. Patients were grouped based on the expression levels of PON1, and differences in clinical characteristics, biological pathways, immune infiltration, and expression of immune checkpoints were compared. The expression levels of the PON1 gene showed significant differences between the high-expression group and the low-expression group in HCC patients. Further analysis indicated that the PON1 gene at different expression levels might influence the clinical manifestations, biological processes, immune infiltration, and expression of immune checkpoints in HCC. Additionally, immunohistochemistry (IHC) results revealed high expression of PON1 in normal tissues and low expression in HCC tissues. These findings provide important clues and future research directions for the early diagnosis, prognosis, immunotherapy, and potential molecular interactions of HCC. Our investigation underscores the noteworthy prognostic significance of PON1 in HCC, suggesting its potential pivotal role in modulating tumor progression and immune cell infiltration. These findings establish PON1 as a novel tumor biomarker with significant implications for the prognosis, targeted therapy, and immunotherapy of patients with HCC.

Circulating tumor DNA mutation analysis: advances in its application for early diagnosis of hepatocellular carcinoma and therapeutic efficacy monitoring.

In recent years, the detection and analysis of circulating tumor DNA (ctDNA) have emerged as a new focus in the field of cancer research, particularly in the early diagnosis of hepatocellular carcinoma (HCC) and monitoring of therapeutic efficacy. ctDNA, which refers to cell-free DNA fragments released into the bloodstream from tumor cells upon cell death or shedding, carries tumor-specific genetic and epigenetic alterations, thereby providing a non-invasive approach for cancer diagnosis and prognosis. The concentration of ctDNA in the blood is higher compared to that in healthy individuals or other liquid biopsies from early-stage cancers, which is closely associated with the early diagnosis and comprehensive sequencing studies of HCC. Recent studies have indicated that sequential ctDNA analysis in patients receiving primary or adjuvant therapy for HCC can detect treatment resistance and recurrence before visible morphological changes in the tumor, making it a valuable basis for rapid adjustment of treatment strategies. However, this technology is continuously being optimized and improved. Challenges such as enhancing the accuracy of ctDNA sequencing tests, reducing the burden of high-throughput sequencing on a large number of samples, and controlling variables in the assessment of the relationship between ctDNA concentration and tumor burden, need to be addressed. Overall, despite the existing challenges, the examination and analysis of ctDNA have opened up new avenues for early diagnosis and therapeutic efficacy monitoring in hepatocellular carcinoma, expanding the horizons of this field.

Recent Advances in Biosensor Technology for Early-Stage Detection of Hepatocellular Carcinoma-Specific Biomarkers: An Overview.

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.

The Value of CEUS LI-RADS combined with AFP in early diagnosis of hepatocellular carcinoma in low- and high-risk patients.

We found that the occurrence of hepatocellular carcinoma (HCC) has increased significantly in non-cirrhotic individuals, with HCC being frequently overlooked or misdiagnosed. Contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) is known to have a high diagnostic quality in high-risk HCC patients. Therefore, we aimed to compare the detection accuracy of CEUS LI-RADS for HCC between low- and high-risk individuals, to confirm its value in low-risk patients at increased risk of HCC, but not yet included in the high-risk groups of LI-RADS. In addition, since CEUS LR-4 and LR-M categories contain a relatively high proportion of HCC, and serum alpha-fetoprotein (AFP) is the most commonly used biomarker for HCC, and the clinically valid, we attempted to further improve the early diagnostic capability of CEUS LI-RADS for HCC in the low-risk and high-risk patients by combining CEUS LR-4 and LR-M categories with AFP. We defined high-risk groups (HR)-included in the high-risk patients of LI-RADS, low-risk groups (LR)-not included in the high-risk patients of LI-RADS and enrolled 189 HCC patients with LR and HR settings in a retrospective study. All lesions were confirmed histopathologically. The CEUS LI-RADS accuracy for detecting HCC in these two patients was compared. In addition, the diagnostic algorithm in our study was proposed (for CEUS LR-4 and LR-M patients with AFP>20 ng/ml). we analyzed the ability of CEUS LI-RADS as a valid method of establishing the early diagnosis of HCC in LR and HR patients by combining LR-4 and LR-M categories with AFP. Through comparative analysis, the specificity of the CEUS LR-5 category for HCC in the HR group was 78.4%, whereas in the LR group, it was 94.2%. Meanwhile, the sensitivity (63.2% vs. 63.0%) and positive predictive value (PPV) (75.0% vs. 88.7%) did not differ between the LR and HR groups ( P = 0.990, P = 0.299). It is noteworthy that there were the high proportion of HCC in CEUS LR-4 and LR-M categories in our cases and when we combined CEUS LR-4 and LR-M categories with AFP significantly improved the sensitivity by 21.0% (84.2%) in the LR group, and by 16.0% (79.0%) in the HR group, with statistically difference in sensitivity after combination in the HR group ( P = 0.014). The CEUS LR-5 category has real meaningful utility in the diagnosis of HCC in both LR and HR patients. The early detection power of the CEUS LI-RADS category for HCC patients was further increased when the CEUS LR-4 and LR-M categories were combined with elevated AFP.

Engineering Tb(III)-functionalized HOF fluorescence sensing system for intelligent visual detection of two biomarkers of hepatocellular carcinoma

Abnormal levels of bilirubin and human serum albumin are precursors to hepatocellular carcinoma and many fatal diseases. Establishing a portable, highly sensitive and intuitive dual biomarker detection method for bilirubin and human serum albumin is important to protect human health. Combined with the excellent photoluminescence properties of lanthanide functionalized hydrogen bond organic framework, a novel ratiometric fluorescence intelligent sensing system was engineered with the outstanding features of large Stokes shifts, high luminous stability and the self-calibration effect of dual emission centers in this paper. With the advantage of high sensitivity, strong stability and strong anti-interference, the simultaneous detection of dual biomarkers was realized successfully. The developed fluorescence sensing system had a wide detection range of bilirubin and human serum albumin (13.5 nM ∼ 30.0 μM and 448 nM ∼ 15.0 μM), low detection limits (7.20 nM and 15.6 nM). Furthermore, the developed sensing system realized the smartphone-assisted fluorescence visual detection of bilirubin and human serum albumin in serum and urine successfully with the detection limit of 85 nM and 163 nM, providing a promising detection strategy for the early diagnosis of hepatocellular carcinoma.

Prediction of drug targets related to HCC metastasis from the perspective of programmed cell death based on transformer

Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent cancer globally and the third leading cause of cancer-related deaths. The early diagnosis of HCC is challenging, and its propensity for metastasis results in generally poor prognosis. Existing studies have demonstrated a close association between HCC metastasis and programmed cell death (PCD). However, due to the high-dimensional, high-order nonlinear interactions and complex regulatory mechanisms of genes, establishing the biological process from genes to PCD to HCC metastasis through biological experiments is difficult. These genes represent crucial drug targets for blocking HCC progression. Deep learning technologies, particularly Transformer models, have shown great potential in analyzing clinical and genetic data in oncology. In this study, we identified 147 key genes related to PCD that are closely associated with HCC metastasis and developed a Transformer-based predictive model for HCC metastasis. To elucidate the potential of these genes as drug targets, we first investigated the impact of various types of PCD on the pathophysiology of HCC. Subsequently, we validated the drug responses of different patients through immunoassays and drug sensitivity tests. Finally, survival analysis indicated that these genes significantly affect patient survival rates. In summary, we have identified numerous drug targets influencing HCC metastasis through PCD.