Diagnosis Of Hepatocellular Carcinoma Research Articles

Integrative transcriptome analysis identifies a crotonylation gene signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) stands as the most prevalent and treatment-resistant malignant tumour, characterized by a dismal prognosis. Croton acylation (CA) has recently gained attention as a critical factor in cancer pathogenesis. This study sought to rapidly identify prognostic features of HCC linked to CA. Differential analysis was conducted between tumour tissues and adjacent non-tumour tissues in the TCGA-LIHC and GSE76427 datasets to uncover differentially expressed genes (DEG1 and DEG2). The intersection of DEG1 and DEG2 highlighted DEGs with consistent expression patterns. Single-sample gene set enrichment analysis scores were calculated for 18 lysine crotonylation-related genes (LCRGs) identified in prior research, showing significant differences between tumour and normal groups. Subsequently, weighted gene co-expression network analysis was employed to identify key module genes correlated with the LCRG score. Candidate genes were identified by overlapping consistently expressed DEGs with key module genes. Prognostic features were identified, and risk scores were determined via regression analysis. Patients were categorized into risk groups based on the optimal cutoff value. Gene set enrichment analysis (GSEA) and immunoassays were also performed. The prognostic features were further validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 88 candidate genes were identified from 1179 consistently expressed DEGs and 4200 key module genes. Seven prognostic features were subsequently identified: TMCO3, RAP2A, ITGAV, ZFYVE26, CHST9, HMGN4, and KLHL21. GSEA revealed that DEGs between risk groups were primarily associated with chylomicron metabolism, among other pathways. Additionally, activated CD4+ T cells demonstrated the strongest positive correlation with risk scores, and most immune checkpoints showed significant differences between risk groups, with ASXL1 exhibiting the strongest correlation with risk scores. The Tumour Immune Dysfunction and Exclusion score was notably higher in the high-risk group. Moreover, in both the TCGA-LIHC and ICGC-LIRI-JP datasets, the expression of other prognostic features was elevated in tumour tissues, with the exception of CHST9. RT-qPCR confirmed the increased expression of TMCO3, RAP2A, ITGAV, ZFYVE26, and HMGN4. This study establishes a risk model for HCC based on seven crotonylation-associated prognostic features, offering a theoretical framework for the diagnosis and treatment of HCC.

A combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound in the Kupffer phase for the diagnosis of well-differentiated hepatocellular carcinoma and atypical focal liver lesions: a prospective, multicenter study.

The objective of this study was to develop a combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound (CEUS) during the Kupffer phase and to evaluate its value in differentiating well-differentiated hepatocellular carcinoma (w-HCC) from atypical benign focal liver lesions (FLLs). A total of 116 patients with preoperatively Sonazoid-CEUS confirmed w-HCC or benign FLL were selected from a prospective multiple study on the clinical application of Sonazoid in FLLs conducted from August 2020 to March 2021. According to the randomization principle, the patients were divided into a training cohort and a test cohort in a 7:3 ratio. Seventy-nine patients were used for establishing and training the radiomics model and combined model. In comparison, 37 patients were used for validating and comparing the performance of the models. The diagnostic efficacy of the models for w-HCC and atypical benign FLLs was evaluated using ROCs curves and decision curves. A combined model nomogram was created to assess its value in reducing unnecessary biopsies. Among the patients, there were 55 cases of w-HCC and 61 cases of atypical benign FLLs, including 28 cases of early liver abscess, 16 cases of atypical hepatic hemangioma, 8 cases of hepatocellular dysplastic nodules (DN), and 9 cases of focal nodular hyperplasia (FNH). The radiomics model and combined model we established had AUCs of 0.905 and 0.951, respectively, in the training cohort, and the AUCs of the two models in the test cohort were 0.826 and 0.912, respectively. The combined model outperformed the radiomics feature model significantly. Decision curve analysis demonstrated that the combined model achieved a higher net benefit within a specific threshold probability range (0.25 to 1.00). A nomogram of the combined model was developed. The combined model based on the radiomics features of Sonazoid-CEUS in the Kupffer phase showed satisfactory performance in diagnosing w-HCC and atypical benign FLLs. It can assist clinicians in timely detecting malignant FLLs and reducing unnecessary biopsies for benign diseases.

Machine learning model based on dynamic contrast-enhanced ultrasound assisting LI-RADS diagnosis of HCC: A multicenter diagnostic study

BackgroundTo enhance the accuracy of hepatocellular carcinoma (HCC) diagnosis using contrast-enhanced (CE) US, the American College of Radiology developed the CEUS Liver Imaging Reporting and Data System (LI-RADS). However, the system still exhibits limitations in distinguishing between HCC and non-HCC lesions. PurposeTo investigate the viability of employing machine learning methods based on quantitative parameters of contrast-enhanced ultrasound for distinguishing HCC within LR-M nodules. Materials and methodsThis retrospective analysis was conducted on pre-treatment CEUS data from liver nodule patients across multiple centers between January 2013 and June 2022. Quantitative analysis was performed using CEUS images, and the machine learning diagnostic models based on quantitative parameters were utilized for the classification diagnosis of LR-M nodules. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) and compared with the performance of four radiologists. ResultsThe training and internal testing datasets comprised 168 patients (median age, 53 years [IQR, 18 years]), while the external testing datasets from two other centers included 110 patients (median age, 54 years [IQR, 16 years]). In the internal independent test set, the top-performing Random Forest model achieved an AUC of 0.796 (95%CI: 0.729–0.853) for diagnosing HCC. This model exhibited a sensitivity of 0.752 (95%CI: 0.750–0.755) and a specificity of 0.761 (95%CI: 0.758–0.764), outperforming junior radiologists who achieved an AUC of 0.619 (95%CI: 0.543–0.691, p < .01) with sensitivity and specificity of 0.716 (95%CI: 0.713–0.718) and 0.522 (95%CI: 0.519–0.526), respectively. ConclusionSignificant differences in contrast-enhanced ultrasound quantitative parameters are observed between HCC and non-HCC lesions. Machine learning models leveraging these parameters effectively distinguish HCC categorized as LR-M, offering a valuable adjunct for the accurate classification of liver nodules within the CEUS LI-RADS framework.

Impact of screening on mortality for patients diagnosed with hepatocellular carcinoma at a safety-net hospital: An opportunity for addressing disparities.

140 Background: Hepatocellular carcinoma (HCC) is a common and deadly cancer with a rising incidence in the US. HCC almost always develops on a background of chronic liver disease, which disproportionately affects individuals of lower socioeconomic status (SES), perpetuating existing disparities. Compounding these disparities, individuals of lower SES are less likely to participate in recommended cancer screening exams. Here, we describe the outcomes of patients diagnosed with HCC at a large urban safety-net hospital primarily serving the underserved. Methods: We identified patients diagnosed with HCC at John Peter Smith Hospital in Fort Worth, Texas from January 1, 2018, to March 31, 2021 to determine the impact of screening on survival. Patients were allocated to the screened cohort if they had undergone liver imaging within one year prior to HCC diagnosis. Kaplan-Meier methods and log-rank test were used to illustrate and compare 3-year survival curves from an index date of diagnosis until death. Cox proportional hazards model were used to calculate unadjusted and adjusted hazards ratios (HR) and 95% confidence intervals (CI). Lead time bias was adjusted in a sensitivity analysis using the Duffy adjustment and a sojourn time of 70 days and 140 days. Results: A total of 158 patients were included (n=53 screened, n=105 unscreened). The mean age was 62 years; 144 (91%) had cirrhosis, 78% were male, 34% were non-Hispanic white, and 8.2% had private insurance. Those in the unscreened cohort had more severe liver dysfunction (as measured by the ALBI grade) and produced higher levels of AFP, but were less likely to have cirrhosis than the screened cohort. The median overall survival (OS) for the screened cohort was 19.0 months (95% CI: 9.9-NA) and 5.4 months (95% CI: 3.7-8.5) in the unscreened cohort (HR death (unscreened v screened) = 2.4, 95% CI: 1.6-3.6; log rank P &lt;0.0001). After adjusting for ALBI grade, AFP, hepatitis C, insurance status and initial treatment, there was a statistical difference in hazard of death in the three years (HR: 2.3; 95% CI: 1.5-3.6), favoring the screened cohort. The benefit for screening remained after adjusting for lead-time bias. With a mean sojourn time of 70 days, the HR death was 2.19 (95% CI 1.4-3.3, P =0.0002) and was 2.09 (1.4-3.2, P =0.0005) with a 140-day sojourn time. Conclusions: In an urban safety-net population, screening for HCC was associated with improved outcomes compared to patients with incidentally-diagnosed HCC. Altogether, this implies that earlier diagnosis and institution of treatment can positively improve survival for patients with newly diagnosed HCC in a population of patients evaluated at a safety-net hospital. Development of screening programs in accordance with accepted guidelines with outreach to high-risk individuals represents an area of high value care for safety-net hospitals.

Normal Glypican-3 Serum Levels Do Not Eliminate the Risk of Hepatocellular Carcinoma

Introduction. Hepatocellular carcinoma (HCC) is a malignancy that primarily affects patients with liver cirrhosis. Early diagnosis is crucial for improving patient outcomes. The study aimed to investigate the potential role of glypican-3 (GPC3) serum levels, both alone and in combination with alpha-fetoprotein (AFP) and gamma-glutamyl transpeptidase (GGT) serum levels, as diagnostic biomarkers for HCC. Methods. The study included 53 patients with liver cirrhosis and confirmed HCC, who were admitted to the Department of Infectious Diseases, Tropical Diseases and Hepatology at the Medical University of Warsaw, Warsaw, Poland, underwent physical examination and laboratory testing and were compared with 29 healthy volunteers. GPC3, along with demographic data, Child-Pugh and Model for End-Stage Liver Disease (MELD) scores, AFP, and GGT levels, was analyzed in relation to HCC diagnosis. The sensitivity and specificity of GPC3 alone and in combination with AFP and GGT were evaluated for diagnosing HCC. Results. The difference in serum GPC3 levels between patients with HCC and the control group was statistically insignificant (p=0.765). However, a significant correlation was found between GPC3 and AFP serum levels in patients with HCC (p=0.009). Additionally, a correlation was observed between higher GPC3 serum levels within the normal range and the occurrence of HCC. The sensitivity for GPC3 levels greater than 75 ng/ml for detecting HCC was 93.1%, while the specificity was 49.1%. The utility of GPC3, when combined with AFP and GGT, was inconsistent across different thresholds in detecting HCC. Conclusions. The serum levels of GPC3 do not provide satisfactory effectiveness in detecting HCC, and the disease can occur in patients with GPC3 serum concentrations within the normal range. In patients with liver cirrhosis, serum GPC3 levels above 75.00 ng/ml may suggest an increased risk of HCC development. Additionally, the combination of AFP, GPC3, and GGT demonstrates some utility in detecting HCC; however, it shows no superiority over GPC3 alone.

Is concurrent LR-5 associated with a higher rate of hepatocellular carcinoma in LR-3 or LR-4 observations? An individual participant data meta-analysis.

The Liver Imaging Reporting and Data System (LI-RADS) does not consider factors extrinsic to the observation of interest, such as concurrent LR-5 observations. To evaluate whether the presence of a concurrent LR-5 observation is associated with a difference in the probability that LR-3 or LR-4 observations represent hepatocellular carcinoma (HCC) through an individual participant data (IPD) meta-analysis. Multiple databases were searched from 1/2014 to 2/2023 for studies evaluating the diagnostic accuracy of CT/MRI for HCC using LI-RADS v2014/2017/2018. The search strategy, study selection, and data collection process can be found at https://osf.io/rpg8x . Using a generalized linear mixed model (GLMM), IPD were pooled across studies and modeled simultaneously with a one-stage meta-analysis approach to estimate positive predictive value (PPV) of LR-3 and LR-4 observations without and with concurrent LR-5 for the diagnosis of HCC. Risk of bias was assessed using a composite reference standard and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Twenty-nine studies comprising 2591 observations in 1456 patients (mean age 59years, 1083 [74%] male) were included. 587/1960 (29.9%) LR-3 observations in 1009 patients had concurrent LR-5. The PPV for LR-3 observations with concurrent LR-5 was not significantly different from the PPV without LR-5 (45.4% vs 37.1%, p = 0.63). 264/631 (41.8%) LR-4 observations in 447 patients had concurrent LR-5. The PPV for LR-4 observations with concurrent LR-5 was not significantly different from LR-4 observations without concurrent LR-5 (88.6% vs 69.5%, p = 0.08). A sensitivity analysis for low-risk of bias studies (n = 9) did not differ from the primary analysis. The presence of concurrent LR-5 was not significantly associated with differences in PPV for HCC in LR-3 or LR-4 observations, supporting the current LI-RADS paradigm, wherein the presence of synchronous LR-5 may not alter the categorization of LR-3 and LR-4 observations.

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.

Utilization of Microfluidic Droplet-Based Methods in Diagnosis and Treatment Methods of Hepatocellular Carcinoma: A Review.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is associated with high morbidity and mortality. One of the main challenges in the management of HCC is late clinical presentation and thus diagnosis of the disease, which results in poor survival. The pathogenesis of HCC is complex and involves chronic liver injury and genetic alterations. Diagnosis of HCC can be made either by biopsy or imaging; however, conventional tissue-based biopsy methods and serological biomarkers such as AFP have limited clinical applications. While hepatocellular carcinoma is associated with a range of molecular alterations, including the activation of oncogenic signaling pathways, such as Wnt-TGFβ, PI3K-AKT-mTOR, RAS-MAPK, MET, IGF, and Wnt-β-catenin and TP53 and TERT promoter mutations, microfluidic applications have been limited. Early diagnosis is crucial for advancing treatments that would address the heterogeneity of HCC. In this context, microfluidic droplet-based methods are crucial, as they enable comprehensive analysis of the genome and transcriptome of individual cells. Single-cell RNA sequencing (scRNA-seq) allows the examination of individual cell transcriptomes, identifying their heterogeneity and cellular evolutionary relationships. Other microfluidic methods, such as Drop-seq, InDrop, and ATAC-seq, are also employed for single-cell analysis. Here, we examine and compare these microfluidic droplet-based methods, exploring their advantages and limitations in liver cancer research. These technologies provide new opportunities to understand liver cancer biology, diagnosis, treatment, and prognosis, contributing to scientific efforts in combating this challenging disease.

PPM1G and its diagnostic, prognostic and therapeutic potential in HCC (Review).

Global statistics indicate that hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer‑related death. Protein phosphatase Mg2+/Mn2+ dependent 1G (PPM1G, also termed PP2Cγ) is one of the 17 members of the PPM family. The enzymatic activity of PPM1G is highly reliant on Mg2+ or Mn2+ and serves as a dephosphorylation regulator for numerous key proteins. PPM1G, functioning as a phosphatase, is involved in a number of significant biological processes such as the regulation of eukaryotic gene expression, DNA damage response, cell cycle and apoptosis, cell migration ability, cell survival and embryonic nervous system development. Additionally, PPM1G serves a role in regulating various signaling pathways. In recent years, further research has increasingly highlighted PPM1G as an oncogene in HCC. A high expression level of PPM1G is closely associated with the occurrence, progression and poor prognosis of HCC, offering notable diagnostic and therapeutic value for this patient population. In the present review, the regulatory role of PPM1G in diverse biological processes and signaling pathway activation in eukaryotes is evaluated. Furthermore, its potential application as a biomarker in the diagnosis and prognosis evaluation of HCC is assessed, and future prospects for HCC treatment strategies centered on PPM1G are discussed.

A highly sensitive sandwich-type electrochemical sensor for detection glypican-3 based on H-rGO-CMC@Pt NPs and aptamers

Glypican-3 (GPC3), which is a surface heparan sulfate proteoglycan, has been widely known the ideal biomarker for diagnosis and treatment of hepatocellular carcinoma (HCC). Accurate and sensitive detection of serum GPC3 level is an important and challenging task. In this paper, a sandwich electrochemical aptasensor was designed to specifically detect serum GPC3 level through aptamer-target-aptamer recognition. A GPC3 aptamer (GPC3Apt) immobilized on the surface of gold nanoparticles@reduced graphene oxide modified screen-printed electrode (Au NPs@rGO/SPCE) was used as the capture probe. Hemin-reduced graphene oxide-carboxymethyl chitosan@platinum nanoparticles (H-rGO-CMC@Pt NPs), with good mimicking peroxidases activity, were labeled with amination GPC3 aptamer, which was employed as the signal probe. Once the target GPC3 was anchored on the surface of GPC3Apt/Au NPs@rGO/SPCE, the H-rGO-CMC@Pt NPs-GPC3Apt was further specifically bound to the GPC3, forming the aptamer-target-aptamer sandwich structure, which can catalyze the oxidation of hydroquinone (HQ) to benzoquinone (BQ) and lead the oxidation current of HQ recorded by DPV to changing. The linear relationship between the current signal of oxidation of HQ and GPC3 concentration range of 0.0001–3.0 µg/mL and 3.0–60.0 µg/mL, with a low detection limit (LOD) of 0.0685 ng/mL. Additionally, the electrochemical aptasensor was successfully applied for the detection of GPC3 in human serum samples with the recovery of 99.95–104.06 % and relative standard deviation (RSD) of 1.31–5.22 %. Thus, the sandwich aptasensor could provide a new strategy for detection of serum GPC3 level and improve the early diagnosis rate of HCC.

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.

Risk prediction model of hepatitis B associated hepatocellular carcinoma

Hepatocellular carcinoma is one of the most common malignant tumors in the world, which is a serious threat to human health. HBV infection is one of the most common causes of hepatocellular carcinoma.The diagnosis of most hepatocellular carcinoma has progressed to the middle and late stage, and the prognosis is poor. Early detection, diagnosis and treatment are important supports to improve the clinical outcome of hepatocellular carcinoma. In recent years, scholars at home and abroad have established various hepatocellular carcinoma risk prediction models, which are conducive to improving the early diagnosis rate of hepatocellular carcinoma and reducing the mortality rate. This article reviews the risk factors and risk prediction models of chronic hepatitis B associated hepatocellular carcinoma, in order to provide reference for HBV-associated liver cancer risk monitoring and management decision.

Circulating tumor DNA (ctDNA) as a biomarker of response to therapy in advanced Hepatocellular carcinoma treated with Nivolumab.

Circulating tumor DNA (ctDNA) is a promising non-invasive marker for detection, diagnosis, treatment selection, and prognosis of hepatocellular carcinoma (HCC). This study aimed to examine the utility of ctDNA as a prognostic and predictive tool in HCC patients treated with nivolumab. We analyzed pre-treatment ctDNA from 44 HCC patients using comprehensive genomic testing on a commercially available platform. We utilized log rank test and univariate Cox models to correlate overall survival (OS) and progression-free survival (PFS) with ctDNA expressions. Of 44patients, 77.3% were men with median age of 67years. All but 3patients had at least one alteration identified, and TP53 was the most commonly altered gene (52.3%). Median OS was 17.5months (95% CI: 12.7, NA). Mutations involving PIK3CA, BRCA1, and CCND1 amplification were associated with shorter OS (P 0.0001, 0.0001 and 0.01, respectively). Median PFS time was 4.01months (95% CI: 3.06, 9.33). Mutations involving KIT and PIK3CA were associated with shorter PFS (P 0.0001 and 0.0004, respectively), while mutation involving CTNNB1 were associated with longer PFS (p= 0.04). ctDNA profiling may provide a benefit for prediction of survival and progression of HCC patients treated with nivolumab. Future studies are needed for confirmation.

The expression and clinical significance of syncytin-1 in serum exosomes of hepatocellular carcinoma patients.

This study aimed to investigate the expression and clinical significance of syncytin-1 in the serum exosomes of hepatocellular carcinoma (HCC) patients. Serum samples were collected from 61 patients with newly diagnosed HCC and 61 healthy individuals. Exosomes were extracted from serum samples and identified using transmission electron microscopy and Western blot. The relative expression levels of syncytin-1 in exosomes were determined by real-time quantitative PCR. The protein expression levels of alpha-fetoprotein and syncytin-1 in HCC patients were detected using enzyme-linked immunosorbent assay (ELISA). Statistical analysis was performed to evaluate the sensitivity and specificity of serum exosomal syncytin-1 in diagnosing HCC. The relationships between syncytin-1 expression and clinical pathological features were analyzed using receiver operating characteristic curve analysis. The results showed that the expression level of syncytin-1 in the serum of patients with newly diagnosed HCC was significantly higher than that in the normal control group (P < 0.0001). Using pathological diagnosis as the gold standard, the sensitivity and specificity of syncytin-1 for the auxiliary diagnosis of HCC were 91.3% and 75.5%, respectively, which were significantly higher than those of alpha-fetoprotein (P < 0.0001). The relative expression level of serum exosomal syncytin-1 was significantly associated with lymph node metastasis, degree of differentiation, and CNLC staging of HCC patients (P < 0.05). In conclusion, syncytin-1 in serum exosomes has high sensitivity and specificity for diagnosing HCC and can serve as a novel tumor marker for early screening, detection, and staging of HCC.

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.

68Ga]Ga-RAYZ-8009: A Glypican-3-Targeted Diagnostic Radiopharmaceutical for Hepatocellular Carcinoma Molecular Imaging-A First-in-Human Case Series.

To date, the imaging and diagnosis of hepatocellular carcinoma (HCC) rely on CT/MRI, which have well-known limitations. Glypican-3 (GPC3) is a cell surface receptor highly expressed by HCC but not by normal or cirrhotic liver tissue. Here we report initial clinical results of GPC3-targeted PET imaging with [68Ga]Ga-DOTA-RYZ-GPC3 (RAYZ-8009), a peptide-based GPC3 ligand in patients with known or suspected HCC. Methods: [68Ga]Ga-RAYZ-8009 was obtained after labeling the peptide precursor with 68Ga from a 68Ge/68Ga generator and heating at 90°C for 10 min followed by sterile filtration. After administration of [68Ga]Ga-RAYZ-8009, a dynamic or static PET/CT scan was acquired between 45 min and 4 h after administration. Radiotracer uptake was measured by SUVs for the following tissues: suspected or actual HCC or hepatoblastoma lesions, non-tumor-bearing liver, renal cortex, blood pool in the left ventricle, and gastric fundus. Additionally, tumor-to-healthy-liver ratios (TLRs) were calculated. Results: Twenty-four patients (5 patients in the dynamic protocol; 19 patients in the static protocol) were scanned. No adverse events occurred. Two patients had no lesion detected and did not have HCC during follow-up. In total, 50 lesions were detected and analyzed. The mean SUVmax of these lesions was 19.6 (range, 2.7-95.3), and the mean SUVmean was 10.1 (range, 1.0-49.2) at approximately 60 min after administration. Uptake in non-tumor-bearing liver and blood pool rapidly decreased over time and became negligible 45 min after administration (mean SUVmean, <1.6), with a continuous decline to 4 h after administration (mean SUVmean, 1.0). The opposite was observed for HCC lesions, for which SUVs and TLRs continuously increased for up to 4 h after administration. In individual lesion analysis, TLR was the highest between 60 and 120 min after administration. Uptake in the gastric fundus gradually increased for up to 45 min (to an SUVmax of 31.3) and decreased gradually afterward. Conclusion: [68Ga]Ga-RAYZ-8009 is safe and allows for high-contrast imaging of GPC3-positive HCC, with rapid clearance from most normal organs. Thereby, [68Ga]Ga-RAYZ-8009 is promising for HCC diagnosis and staging. Further research is warranted.

DNA Logical Computing on a Transistor for Cancer Molecular Diagnosis.

Given the high degree of variability and complexity of cancer, precise monitoring and logical analysis of different nucleic acid markers are crucial for improving diagnostic precision and patient survival rates. However, existing molecular diagnostic methods normally suffer from high cost, cumbersome procedures, dependence on specialized equipment and the requirement of in-depth expertise in data analysis, failing to analyze multiple cancer-associated nucleic acid markers and provide immediate results in a point-of-care manner. Herein, we demonstrate a transistor-based DNA molecular computing (TDMC) platform that enables simultaneous detection and logical analysis of multiple microRNA (miRNA) markers on a single transistor. TDMC can perform not only basic logical operations such as "AND" and "OR", but also complex cascading computing, opening up new dimensions for multi-index logical analysis. Owing to the high efficiency, sensing and computations of multi-analytes can be operated on a transistor at a concentration as low as 2×10-16 M, reaching the lowest concentration for DNA molecular computing. Thus, TDMC achieves an accuracy of 98.4 % in the diagnosis of hepatocellular carcinoma from 62 serum samples. As a convenient and accurate platform, TDMC holds promise for applications in "one-stop" personalized medicine.

DNA Logical Computing on a Transistor for Cancer Molecular Diagnosis

AbstractGiven the high degree of variability and complexity of cancer, precise monitoring and logical analysis of different nucleic acid markers are crucial for improving diagnostic precision and patient survival rates. However, existing molecular diagnostic methods normally suffer from high cost, cumbersome procedures, dependence on specialized equipment and the requirement of in‐depth expertise in data analysis, failing to analyze multiple cancer‐associated nucleic acid markers and provide immediate results in a point‐of‐care manner. Herein, we demonstrate a transistor‐based DNA molecular computing (TDMC) platform that enables simultaneous detection and logical analysis of multiple microRNA (miRNA) markers on a single transistor. TDMC can perform not only basic logical operations such as “AND” and “OR”, but also complex cascading computing, opening up new dimensions for multi‐index logical analysis. Owing to the high efficiency, sensing and computations of multi‐analytes can be operated on a transistor at a concentration as low as 2×10−16 M, reaching the lowest concentration for DNA molecular computing. Thus, TDMC achieves an accuracy of 98.4 % in the diagnosis of hepatocellular carcinoma from 62 serum samples. As a convenient and accurate platform, TDMC holds promise for applications in “one‐stop” personalized medicine.

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.