Diagnostic Biomarker For Hepatocellular Carcinoma Research Articles

Evaluating the diagnostic accuracy of heat shock proteins and their combination with Alpha-Fetoprotein in the detection of hepatocellular carcinoma: a meta-analysis

BackgroundA growing body of research suggests that heat shock proteins (HSPs) may serve as diagnostic biomarkers for hepatocellular carcinoma (HCC), but their results are still controversial. This meta-analysis endeavors to evaluate the diagnostic accuracy of HSPs both independently and in conjunction with alpha-fetoprotein (AFP) as novel biomarkers for HCC detection.MethodsPooled statistical indices, including sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) with 95% confidence intervals (CI), were computed to assess the diagnostic accuracy of HSPs, AFP, and their combinations. Additionally, the area under the summary receiver operating characteristic (SROC) curve (AUC) was determined.ResultsA total of 2013 HCC patients and 1031 control subjects from nine studies were included in this meta-analysis. The summary estimates for HSPs and AFP are as follows: sensitivity of 0.78 (95% CI: 0.69–0.85) compared to 0.73 (95% CI: 0.65–0.80); specificity of 0.89 (95% CI: 0.81–0.95) compared to 0.86 (95% CI: 0.77–0.91); PLR of 7.4 (95% CI: 3.7–14.9) compared to 5.1 (95% CI: 3.3–8.1); NLR of 0.24 (95% CI: 0.16–0.37) compared to 0.31 (95% CI: 0.24–0.41); DOR of 30.19 (95% CI: 10.68–85.37) compared to 16.34 (95% CI: 9.69–27.56); and AUC of 0.90 (95% CI: 0.87–0.92) compared to 0.85 (95% CI: 0.82–0.88). The pooled sensitivity, specificity, PLR, NLR, DOR and AUC were 0.90 (95% CI: 0.82–0.95), 0.94 (95% CI: 0.82–0.98), 14.5 (95% CI: 4.6–45.4), 0.11 (95% CI: 0.06–0.20), 133.34 (95% CI: 29.65–599.61), and 0.96 (95% CI: 0.94–0.98) for the combination of HSPs and AFP.ConclusionOur analysis suggests that HSPs have potential as a biomarker for clinical use in the diagnosis of HCC, and the concurrent utilization of HSPs and AFP shows notable diagnostic effectiveness for HCC.

TRIM5 as a promising diagnostic biomarker of hepatocellular carcinoma: integrated analysis and experimental validation.

The TRIM family is associated with the membrane, and its involvement in the progression, growth, and development of various cancer types has been researched extensively. However, the role played by the TRIM5 gene within this family has yet to be explored to a great extent in terms of hepatocellular carcinoma (HCC). The data of patients relating to mRNA expression and the survival rate of individuals diagnosed with HCC were extracted from The Cancer Genome Atlas (TCGA) database. UALCAN was employed to examine the potential link between TRIM5 expression and clinicopathological characteristics. In addition, enrichment analysis of differentially expressed genes (DEGs) was conducted as a means of deciphering the function and mechanism of TRIM5 in HCC. The data in the TCGA and TIMER2.0 databases was utilized to explore the correlation between TRIM5 and immune infiltration in HCC. WGCNA was performed as a means of assessing TRIM5-related co-expressed genes. The "OncoPredict" R package was also used for investigating the association between TRIM5 and drug sensitivity. Finally, qRT-PCR, Western blotting (WB) and immunohistochemistry (IHC) were employed for exploring the differential expression of TRIM5 and its clinical relevance in HCC. According to the results that were obtained from the vitro experiments, mRNA and protein levels of TRIM5 demonstrated a significant upregulation in HCC tissues. It is notable that TRIM5 expression levels were found to have a strong association with the infiltration of diverse immune cells and displayed a positive correlation with several immune checkpoint inhibitors. The TRIM5 expression also displayed promising clinical prognostic value for HCC patients.

Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning

BackgroundTo exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology. MethodThree HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the prognostic value of the markers and the sensitivity of the drugs were analyzed. ResultWGCNA and differential expression analysis were used to screen 396 distinct gene signatures in HCC tissues. They were mostly engaged in cytoplasmic fusion and the cell division cycle, according to gene enrichment analyses. Five genes were shown to have a high diagnostic value for use as diagnostic biomarkers for HCC, including EFHD1 (AUC = 0.77), KIF4A (AUC = 0.97), UBE2C (AUC = 0.96), SMYD3 (AUC = 0.91), and MCM7 (AUC = 0.93). T cells, NK cells, macrophages, and dendritic cells were found to be related to diagnostic markers in HCC tissues by immune cell infiltration analysis, indicating that these cells are intimately linked to the onset and spread of HCC. Concurrently, these five genes and their constructed models have considerable prognostic value. ConclusionThese five genes (EFHD1, KIF4A, UBE2C, SMYD3, and MCM7) may serve as new candidate molecular markers for HCC, providing new insights for future diagnosis, prognosis, and molecular therapy of HCC.

Circulating microRNAs as promising diagnostic biomarkers for hepatocellular carcinoma: a systematic review and meta-analysis.

Introduction: Hepatocellular carcinoma (HCC), the most common type of liver cancer, is a major global health problem, ranking as the third leading cause of cancer-related death worldwide. Early identification and diagnosis of HCC requires the discovery of reliable biomarkers. Therefore, the study aimed to assess the diagnostic accuracy of miRNAs for HCC. The protocol was registered on PROSPERO website with the registration number CRD42023417494. Method: A literature search was conducted in PubMed, Scopus, Embase, Wiley Online Library, and Science Direct databases to identify pertinent articles published between 2018 and 30 July 2023. Stata 17.0 software was employed to determine the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR), and area under the curve (AUC) for evaluating the accuracy of miRNAs in diagnosing HCC. The assessment of heterogeneity among studies involved the use of the Cochran-Q test and I2 statistic tests. Due to the observed significant heterogeneity, the random-effect model was chosen. Subgroup analysis and meta-regression analysis were also undertaken to explore potential sources contributing to heterogeneity. Deeks' funnel plot was used to assess publication bias. In addition, Fagan's nomogram and likelihood ratio scattergram were utilized to assess the clinical validity of miRNAs for HCC. Result: Twenty-four articles were included, involving 1,668 individuals diagnosed with HCC and 1,236 healthy individuals. The findings revealed pooled sensitivity of 0.84 (95% CI: 0.80-0.88), specificity of 0.81 (95% CI: 0.77-0.84), PLR of 4.36 (95% CI: 3.59-5.30), NLR of 0.19 (95% CI: 0.15-0.25), DOR of 22.47 (95% CI: 14.47-32.64), and an AUC of 0.89 (95% CI: 0.86-0.91) for the diagnosis of HCC using miRNAs. Furthermore, results from the subgroup analysis demonstrated that superior diagnostic performance was observed when utilizing plasma miRNAs, a large sample size (≥100), and miRNA panels. Conclusion: Hence, circulating miRNAs demonstrate substantial diagnostic utility for HCC and can serve as effective non-invasive biomarkers for the condition. Additionally, miRNA panels, miRNAs derived from plasma, and miRNAs evaluated in larger sample sizes (≥100) demonstrate enhanced diagnostic efficacy for HCC diagnosis. Nevertheless, a large pool of prospective studies and multi-center research will be required to confirm our findings in the near future.

LncRNA TCTN2 Promotes the Malignant Development of Hepatocellular Carcinoma via Regulating mIR-1285-3p/ARF6 Axis.

Hepatocellular carcinoma (HCC) is one of the most life-threatening malignant diseases. TCTN2 protein participates in tumorigenesis and development. However, whether lncRNA TCTN2 is associated with HCC pathogenesis remains unclear. The expression of lncRNA, TCTN2, miR-1285-3p, and ARF6 in HCC tissues and cells was detected by a quantitative Real-Time PCR (qRT-PCR) assay. lncRNA TCTN2-specific shRNA was transfected into HCC cells, and a functional investigation was performed. The direct interactions between lncRNA TCTN2 and miR-1285-3p and ARF6 were verified by dualluciferase reporter gene assay. A rescue experiment was performed to confirm the role of miR- 1285-3p/ARF6 in association with lncRNA TCTN2. LncRNA TCTN2 exhibited a high expression in HCC tumor tissues and cell lines. Knockdown of lncRNA TCTN2 suppressed cell proliferation and induced cell cycle arrest and apoptosis through regulating Cyclin D1/p21 and Bax/Bcl-2 signals. Meanwhile, the knockdown of lncRNA TCTN2 inhibited HCC cell migration and invasion through upregulating MMP2/MMP9. Mechanistic investigation revealed that lncRNA TCTN2 upregulated the expression of ARF6 via sponging miR-1285-3p. Rescue experiments indicated that miR-1285-3p inhibitor reversed the antitumor effects of lncRNA TCTN2 and ARF6 knockdown inhibited the progression of HCC. Our results suggested that the knockdown of lncRNA TCTN2 inhibited HCC development by regulating the miR-1285-3p/ARF6 axis, implying that the lncRNA TCTN2 is upregulated in HCC and may serve as a diagnostic biomarker in HCC. Furthermore, it may demonstrate an important value for the clinical treatment of patients with HCC.

Systematic review: Glycomics as diagnostic markers for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer with one of the highest cancer-related mortality rates worldwide. Early diagnosis is crucial for improving the therapeutic options and reducing the disease-related mortality. To investigate serum N-glycomics as diagnostic markers for HCC. We performed a comprehensive search in PubMed, EMBASE, Web of Science and Scopus through August 17, 2023. Eligible studies assessed the potential use of serum N-glycomics as diagnostic biomarkers for HCC. Study selection, data extraction and quality assessment were performed by two independent reviewers. Of the 48 articles included, 11 evaluated the utility of N-glycomics for the diagnosis of HCC in whole serum while the remaining articles focused on specific protein glycoforms or protein levels. Of these specific proteins, haptoglobin, alpha-fetoprotein (AFP), kininogen (Kin), α-1-antitrypsin and Golgi protein 73 (GP73) were the most frequently studied. Increased levels of fucosylation and branching presented as the most prevalent post-translational modifications of glycoproteins in patients with HCC compared to controls. Notably, glycomics-based biomarkers may provide a clinical benefit for the diagnosis of early HCC, as several algorithms achieved AUCs between 0.92-0.97. However, these were based on single studies with limited sample sizes and should therefore be validated. Alterations in serum N-glycomics, characterised by increased levels of fucosylation and branching, have potential as diagnostic biomarkers for HCC. Optimisation of study design, patient selection and analysing techniques are needed before clinical implementation will be possible.

Extracellular Vesicles in Hepatocellular Carcinoma: Progress and Challenges in the Translation from the Laboratory to Clinic.

Extracellular vesicles (EVs) play critical roles in intercellular communication by transporting bioactive cargo to recipient cells. EVs have been implicated in a range of physiological and pathological processes, including tumor progression, metastasis, immune modulation, and drug resistance. The objective of this review is to present a thorough overview of recent studies focusing on EVs in hepatocellular carcinoma (HCC), with an emphasis on their potential utility as diagnostic biomarkers as well as therapeutic agents. Initially, we explore the utility of EVs as diagnostic biomarkers for HCC, followed by a discussion of their potential as carriers of therapeutic payloads. Additionally, we delve into the emerging field of therapeutic EVs for modulating tumor immune responses. Through this review, our ultimate aim is to provide a comprehensive understanding of the opportunities and challenges in the clinical translation of EV research in the domain of HCC.

Role of Micro RNA-148a (miR-148a) as an Early Diagnostic Biomarker for Hepatocellular Carcinoma in Egyptian Patients

Abstract Background Hepatocellular carcinoma (HCC) is a difficult disease to diagnose in its early stages. It has a high mortality rate due to its dismal prognosis. MiRNAs are dysregulated in HCC, rendering them a promising marker of the disease's progress and outcome. Objective The purpose of this study was to look at the potential use of miR-148a as a non-invasive biomarker detection and prognosis of HCC. Subjects and methods This case-control study was applied on (64) subjects, divided into three groups: the first group (HCC patients “n = 34”); the second group (HCV patients “n = 20”) and the third group (control group “n = 10”). miR-148a expression was estimated by quantitative real time PCR. miR-148a expression was recorded and studied with the clinical parameters of each group. Results There was a highly statistically significant difference between three groups in terms of AFP, AST, ALT, Hb, and, platelets (p < 0.001); 82.4 percent of HCC patients tested positive for HCV and 50% tested positive for vascular invasion. T2 and T4 tumors were found in less than half of patients; TNM stage III was found in 50% of HCC patients and the majority of HCC patients (76.5%) had a tumor size of ≤ 5 cm. There was a significant statistical difference between groups in terms of miR148a expression (P < 0.05) with the lowest value in the HCC patients. In the HCC group, there was a negative correlation between serum miR-148a and AFP (p = 0.031), tumor size (p = 0.018), primary tumor (T) (p = 0.022), and TNM staging (p = 0.009). Receiver operator characteristics (ROC) curves showed that the best cut off value for early prediction of HCC using miR-148a was ≤ 7.331, with sensitivity of 82.4 percent and specificity of 80 percent in HCC. In addition, there was statistically significant difference between the HCC and HCV & control group, according to cut-off of miR148a (P < 0.001). Conclusion miR-148a was found to be down regulated in HCC patients; our findings suggested that it could be utilized as a non-invasive biological marker for HCC detection and prediction of the prognosis.

Evaluation of the Role of Serum Vimentin as a Diagnostic Biomarker for Hepatocellular Carcinoma

Background Hepatocellular carcinoma (HCC) is a major unresolved medical issue, which is considered as the seventh most common cancer globally and the fourth leading cause of cancer deaths, with approximately 700,000 deaths per year. Aim of the Work The aim of this study; to evaluate the role of serum vimentin as a diagnostic biomarker in patients with HCC. Patients and Methods This case control study enrolled 60 patients; divided into two groups: Group (I): included 20 patients with liver cirrhosis without HCC as a control group and Group (II): 20 patients divided into 2 subgroups: (IIa): includes 20 patients with HCC and serum AFP is high and (IIb): includes 20 patients with HCC and serum AFP is normal. Results The present study found statistical significant decrease in Hb level in group IIb compared to group I, there was high statistical significant decrease in platelet count in patients of group IIa and IIb compared to control group. a statistical significant elevation of total bilirubin and INR with decrease of albumin in group II compared to control group.There was high statistical significant elevation in AFP in group IIa and IIb compared to control group and in group IIa compared to group IIb, a high statistical significant elevation in Vimentin level in group IIa and IIb compared to control group while there was no statistical significant difference between group IIa and group IIb. Conclusion Vimentin can be considered as an important independent and discriminative serological biomarker for detecting early stage HCC in patients with normal serum AFP.

A wide scan of plasma proteins demonstrates thioredoxin reductase 1 as a potential new diagnostic biomarker for hepatocellular carcinoma

Background Patients with liver cirrhosis are recommended ultrasonography screening for early detection of hepatocellular carcinoma to increase the chances of curative treatment. However, ultrasonography alone lacks in sensitivity. Adding plasma biomarkers may increase the detection rate. We performed a broad exploratory analysis to find new plasma proteins with potential applicability for HCC screening in patients with cirrhosis. Methods In a protein discovery cohort of 172 patients with cirrhosis or HCC, we screened for 481 proteins with suspension bead array or proximity extension assay. From these, 24 proteins were selected for further analysis in a protein verification cohort (n = 160), using ELISA, Luminex or an electrochemiluminescence platform. A cut-off model and a stepwise logistic regression model were used to find combinations of proteins with the best discriminatory performance between HCC and cirrhosis. Results Stepwise logistic regression revealed alpha-fetoprotein (AFP), decarboxy-prothrombin (DCP), thioredoxin reductase 1 (TXNRD1), and fibroblast growth factor 21 (FGF21) as the proteins with the best discriminatory performance between HCC and cirrhosis. Adding TXNRD1 to DCP and AFP increased the AUC from 0.844 to 0.878, and combining AFP, DCP and TXNRD1 with age and sex resulted in an AUC of 0.920. FGF21, however, did not further increase the performance when including age and sex. Conclusion In the present study, TXNRD1 improves the sensitivity and specificity of AFP and DCP as HCC screening tools in patients with cirrhosis. We suggest that TXNRD1 should be validated in prospective settings as a new complementary HCC biomarker together with AFP and DCP.

Identification of the mitophagy-related diagnostic biomarkers in hepatocellular carcinoma based on machine learning algorithm and construction of prognostic model.

As a result of increasing numbers of studies most recently, mitophagy plays a vital function in the genesis of cancer. However, research on the predictive potential and clinical importance of mitophagy-related genes (MRGs) in hepatocellular carcinoma (HCC) is currently lacking. This study aimed to uncover and analyze the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance. In our research, by using Least absolute shrinkage and selection operator (LASSO) regression and support vector machine- (SVM-) recursive feature elimination (RFE) algorithm, six mitophagy genes (ATG12, CSNK2B, MTERF3, TOMM20, TOMM22, and TOMM40) were identified from twenty-nine mitophagy genes, next, the algorithm of non-negative matrix factorization (NMF) was used to separate the HCC patients into cluster A and B based on the six mitophagy genes. And there was evidence from multi-analysis that cluster A and B were associated with tumor immune microenvironment (TIME), clinicopathological features, and prognosis. After then, based on the DEGs (differentially expressed genes) between cluster A and cluster B, the prognostic model (riskScore) of mitophagy was constructed, including ten mitophagy-related genes (G6PD, KIF20A, SLC1A5, TPX2, ANXA10, TRNP1, ADH4, CYP2C9, CFHR3, and SPP1). This study uncovered and analyzed the mitophagy-related diagnostic biomarkers in HCC using machine learning (ML), as well as to investigate its biological role, immune infiltration, and clinical significance. Based on the mitophagy-related diagnostic biomarkers, we constructed a prognostic model(riskScore). Furthermore, we discovered that the riskScore was associated with somatic mutation, TIME, chemotherapy efficacy, TACE and immunotherapy effectiveness in HCC patients. Mitophagy may play an important role in the development of HCC, and further research on this issue is necessary. Furthermore, the riskScore performed well as a standalone prognostic marker in terms of accuracy and stability. It can provide some guidance for the diagnosis and treatment of HCC patients.

Identification of a novel plasma metabolite panel as diagnostic biomarker for hepatocellular carcinoma

Background and aimsMetabolic reprogramming is one of the hallmarks of cancer. Hepatocellular carcinoma (HCC) is one of the most lethal malignancy camcer, but the early diagnosis of HCC remains difficult. In this study, we searched for potential plasma metabolite biomarkers of HCC. MethodsA total of plasma samples of 104 HCC, 76 cirrhosis and 10 healthy subjects were assessed and validated through Gas chromatography-Mass spectrometry. Receiver-operating characteristic curves (ROC) combined with multivariate statistical analyses were used to assess the diagnostic performance of metabolites and combinations. Results10 metabolites in screening cohort were significantly changed in the plasma of HCC patients. Multivariate logistic regression analysis of candidate metabolites in validation cohort showed that N-formylglycine, oxoglutaric acid, citrulline and heptaethylene glycol could distinguish HCC from cirrhosis. The combination of these four metabolites showed a better performance than AFP with the Area Under the Curve (AUC), sensitivity, specificity as 0.940, 84.00%, 97.56%, respectively. In further, the panel of N-formylglycine, heptaethylene glycol and citrulline can more effectively discriminate early stage HCC from cirrhosis than AFP (AUC: 0.835 vs. 0.634). Finally, heptaethylene glycol could significantly inhibit the proliferation, migration and invasion of HCC cells in vitro. ConclusionThe combination of plasma N-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol can be an efficient novel diagnostic biomarker for HCC.

Long noncoding RNA MyD88 functions as a promising diagnostic biomarker in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most frequent malignancies. Alpha-fetoprotein (AFP) has some limitations in diagnosing early HCC. Recently, long noncoding RNAs (lncRNAs) showed great potential as tumor diagnostic biomarkers, and lnc-MyD88 was previously identified as a carcinogen in HCC. Here, we explored its diagnostic value as a plasma biomarker. Quantitative real-time PCR was adopted to detect lnc-MyD88 expression in plasma samples of 98 HCC patients, 52 liver cirrhosis (LC) patients, and 105 healthy people. The correlation between lnc-MyD88 and clinicopathological factors was analyzed through chi-square test. The receiver operating characteristic (ROC) curve was used to analyze the sensitivity, specificity, Youden index, and area under the curve (AUC) of lnc-MyD88 and AFP alone and in combination for the diagnosis of HCC. The relationship between MyD88 and immune infiltration was analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm. Lnc-MyD88 was highly expressed in plasma samples of HCC and hepatitis B virus (HBV)-associated HCC patients. Lnc-MyD88 had better diagnostic value than AFP in HCC patients using healthy people or LC patients as control (healthy people, AUC: 0.776 vs. 0.725; LC patients, AUC: 0.753 vs. 0.727). The multivariate analysis showed that lnc-MyD88 had great diagnostic value for distinguishing HCC from LC and healthy people. Lnc-MyD88 had no correlation with AFP. Lnc-MyD88 and AFP were independent diagnostic factors for HBV-associated HCC. The AUC, sensitivity, and Youden index of the combined diagnosis of lnc-MyD88 and AFP combined were higher than those of lnc-MyD88 and AFP alone. The ROC curve of lnc-MyD88 for the diagnosis of AFP-negative HCC was plotted with a sensitivity of 80.95%, a specificity of 79.59%, and an AUC value of 0.812 using healthy people as control. The ROC curve also presented its great diagnostic value using LC patients as control (sensitivity: 76.19%, specificity: 69.05%, AUC value: 0.769). Lnc-MyD88 expression was correlated with microvascular invasion in HBV-associated HCC patients. MyD88 was positively correlated with infiltrating immune cells and immune-related genes. The high expression of plasma lnc-MyD88 in HCC is distinct and could be utilized as a promising diagnostic biomarker. Lnc-MyD88 had great diagnostic value for HBV-associated HCC and AFP-negative HCC, and it had higher efficacy in combination with AFP.

Classification of the mitochondrial ribosomal protein-associated molecular subtypes and identified a serological diagnostic biomarker in hepatocellular carcinoma.

The objective of this study was to sort out innovative molecular subtypes associated with mitochondrial ribosomal proteins (MRPs) to predict clinical therapy response and determine the presence of circulating markers in hepatocellular carcinoma (HCC) patients. Using an unsupervised clustering method, we categorized the relative molecular subtypes of MRPs in HCC patients. The prognosis, biological properties, immune checkpoint inhibitor and chemotherapy response of the patients were clarified. A signature and nomogram were developed to evaluate the prognosis. Enzyme-linked immunosorbent assay (ELISA) measured serum mitochondrial ribosomal protein L9 (MRPL9) levels in liver disease patients and normal individuals. Receiver operating characteristic (ROC) curves were conducted to calculate the diagnostic effect. The Cell Counting Kit 8 was carried out to examine cell proliferation, and flow cytometry was used to investigate the cell cycle. Transwell assay was applied to investigate the potential of cell migration and invasion. Western blot detected corresponding changes of biological markers. Participants were classified into two subtypes according to MRPs expression levels, which were characterized by different prognoses, biological features, and marked differences in response to chemotherapy and immune checkpoint inhibitors. Serum MRPL9 was significantly higher in HCC patients than in normal individuals and the benign liver disease group. ROC curve analysis showed that MRPL9 was superior to AFP and Ferritin in differentiating HCC from healthy and benign patients, or alone. Overexpressed MRPL9 could enhance aggressiveness and facilitate the G1/S progression in HCC cells. We constructed novel molecular subtypes based on MRPs expression in HCC patients, which provided valuable strategies for the prediction of prognosis and clinical personalized treatment. MRPL9 might act as a reliable circulating diagnostic biomarker and therapeutic target for HCC patients.

Essential amino acids as diagnostic biomarkers of hepatocellular carcinoma based on metabolic analysis.

Metabolomics, defined as the comprehensive identification of all small metabolites in a biological sample, has the power to shed light on phenotypic changes associated with various diseases, including cancer. To discover potential metabolomic biomarkers of hepatocellular carcinoma (HCC), we investigated the metabolomes of tumor and non-tumor tissue in 20 patients with primary HCC using capillary electrophoresis-time-of-flight mass spectrometry. We also analyzed blood samples taken immediately before and 14 days after hepatectomy to identify associated changes in the serum metabolome. Marked changes were detected in the different quantity of 61 metabolites that could discriminate between HCC tumor and paired non-tumor tissue and additionally between HCC primary tumors and colorectal liver metastases. Among the 30 metabolites significantly upregulated in HCC tumors compared with non-tumor tissues, 10 were amino acids, and 7 were essential amino acids (leucine, valine, tryptophan, isoleucine, methionine, lysine, and phenylalanine). Similarly, the serum metabolomes of HCC patients before hepatectomy revealed a significant increase in 16 metabolites, including leucine, valine, and tryptophan. Our results reveal striking differences in the metabolomes of HCC tumor tissue compared with non-tumor tissue, and identify the essential amino acids leucine, valine, and tryptophan as potential metabolic biomarkers for HCC.

Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm.

The purpose of this study was to identify the potential diagnostic biomarkers in hepatocellular carcinoma (HCC) by machine learning (ML) and to explore the significance of immune cell infiltration in HCC. From GEO datasets, the microarray datasets of HCC patients were obtained and downloaded. Differentially expressed genes (DEGs) were screened from five datasets of GSE57957, GSE84402, GSE112790, GSE113996, and GSE121248, totalling 125 normal liver tissues and 326 HCC tissues. In order to find the diagnostic indicators of HCC, the LASSO regression and the SVM-RFE algorithms were utilized. The prognostic value of VIPR1 was analyzed. Finally, the difference of immune cell infiltration between HCC tissues and normal liver tissues was evaluated by CIBERSORT algorithm. In this study, a total of 232 DEGs were identified in 125 normal liver tissues and 326 HCC tissues. 11 diagnostic markers were identified by LASSO regression and SVM-RFE algorithms. FCN2, ECM1, VIRP1, IGFALS, and ASPG genes with AUC>0.85 were regarded as candidate biomarkers with high diagnostic value, and the above results were verified in GSE36376. Survival analyses showed that VIPR1 and IGFALS were significantly correlated with the OS, while ASPG, ECM1, and FCN2 had no statistical significance with the OS. Multivariate assays indicated that VIPR1 gene could be used as an independent prognostic factor for HCC, while FCN2, ECM1, IGFALS, and ASPG could not be used as independent prognostic factors for HCC. Immune cell infiltration analyses showed that the expression of VIPR1 in HCC was positively correlated with the levels of several immune cells. Overall, VIPR1 gene can be used as a diagnostic feature marker of HCC and may be a potential target for the diagnosis and treatment of liver cancer in the future.

Circulating Non-Coding RNAs as Potential Diagnostic Biomarkers in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is currently the second leading cause of cancer-related deaths worldwide, with high morbidity and mortality. The clinical diagnosis of HCC mainly depends on imaging technology, such as ultrasound and computed tomography, and serum biomarkers, such as alpha-fetoprotein (AFP). However, HCC is still hard to diagnose at an early stage due to the low sensitivity of the above mentioned traditional methods. Typically, HCC is diagnosed at an advanced stage when limited treatment options are available. It is urgent to identify effective biomarkers for the early diagnosis of HCC. Increasing evidence uncovered ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), could be used in HCC diagnosis. The aim of this review is to summarize our understanding of circulating miRNAs, lncRNAs and circRNAs as fluid-based non-invasive biomarkers, and aiming at providing new insights into the diagnosis of HCC.

Vitamin B6 Metabolic Pathway is Involved in the Pathogenesis of Liver Diseases via Multi-Omics Analysis.

PurposeTo clarify the underlying regulatory mechanisms of progression from liver cirrhosis to hepatocellular carcinoma (HCC), we analyzed the microbiomics, metabolomics, and proteomics in plasma and tissues from patients with HCC or decompensated liver cirrhosis (DC).Patients and MethodsTissues and plasma from 44 HCC patients and 28 patients with DC were collected for metabolomic analysis. 16S rRNA sequencing was performed in nine HCC tissues (HCCT), four distal noncancerous tissues (HCCN), and 11 DC tissues (DCT). Five HCC tissues had liver cirrhosis (HCCT-LC). Five hepatocellular carcinoma tissues without liver cirrhosis (HCCT-NLC) and five DCT were selected for proteomic sequencing. After combining proteomic and metabolomic analysis, we constructed a mouse model of chronic liver injury using carbon tetrachloride (CCl4) and treated them with vitamin B6 (VB6).Results16s rRNA sequence results showed that HCC tissues had higher alpha diversity. The highest LDA scores were detected for Elizabethkingia in HCCT, Subsaxibacter in DCT, and Stenotrophomon in HCCN. Metabolomics results demonstrated some metabolites, including capric acid, L-threonate, choline, alpha-D-Glucose, D-ribose, betaine, 2E-eicosenoic acid, linoleic acid, L-palmitoylcarnitine, taurodeoxycholic acid, L-pyroglutamic acid, androsterone sulfate, and phthalic acid mono-2-ethylhexyl ester (MEHP), had better diagnostic efficacy than AFP (AUC: 0.852; 95% CI: 0.749, 0.954). In a combined analysis of metabolomics and proteomics, we found that HCCT-LC had more obvious disorders of VB6 metabolism and pentose and glucuronate interconversions than DCT, and kynurenine metabolism disorder was more significant in HCCT-LC than in HCCT-NLC. In the CCl4-induced chronic liver injury model, after VB6 supplementation, inflammatory cell infiltration, hepatocyte edema, and degeneration were significantly improved.ConclusionWe found significant differences in the flora distribution between HCCT and DC; MEHP was a new diagnostic biomarker of HCC, and VB6 ameliorated the inflammatory cell infiltration, hepatocyte edema, and degeneration in chronic liver injury.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma.

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Serum long non-coding RNA SCARNA10 serves as a potential diagnostic biomarker for hepatocellular carcinoma

BackgroundCirculating long non-coding RNAs (lncRNAs) have been demonstrated to serve as diagnostic or prognosis biomarkers for various disease. We aimed to elucidate the diagnostic efficacy of serum lncRNA SCARNA10 for the hepatocellular carcinoma (HCC).MethodsIn this study, a total of 182 patients with HCC, 105 patients with benign liver disease (BLD), and 149 healthy controls (HC) were enrolled. According to different classifications, the levels of serum SCARNA10 were assessed by quantitative real-time polymerase chain reaction (qPCR). The correlations between serum SCARNA10 and clinicopathological characteristics were further analyzed. The receiver operating characteristic (ROC) curve and area under curve (AUC) were utilized to estimate the diagnostic capacity of serum SCARNA10 and its combination with AFP for HCC.ResultsThe results demonstrated that the levels of serum SCARNA10 were significantly higher in HCC patients than in patients with BLD and healthy controls, and significantly increased in HCC patients with hepatitis B or C infection, or with liver cirrhosis. Furthermore, positive correlations were noted between serum SCARNA10 level and some clinicopathological characteristics, including tumor size, differentiation degrees, tumor stage, vascular invasion, tumor metastasis and complications. ROC analysis revealed that SCARNA10 had a significantly predictive value for HCC (Sensitivity = 0.70, Specificity = 0.77, and AUC = 0.82), the combination of SCARNA10 and AFP gained the higher sensitivity (AUCSCARNA10 + AFP = 0.92 vs AUCAFP = 0.83, p < 0.01). SCARNA10 retained significant diagnosis capabilities for AFP-negative HCC patients.ConclusionsIn summary, lncRNA SCARNA10 may serve as a novel and non-invasive biomarker with relatively high sensitivity and specificity for HCC diagnosis.