Distinguish Hepatocellular Carcinoma Patients Research Articles

Assessment of combined serum sST2 and AFP levels in the diagnosis of hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is a common malignant tumor with high morbidity and mortality. Alpha-fetoprotein (AFP) is the most widely used diagnostic serum biomarker, but it still has limited accuracy in detecting HCC, suggesting the necessity of seeking more ideal biomarkers with high sensitivity and specificity. Soluble growth stimulation gene 2 (sST2) form of growth stimulating expression gene 2 (ST2), is expressed in various organs and can bind competitively to interleukin 33 (IL-33). Whether sST2 can serve as a serum biomarker for HCC is largely unknown. Objective To investigate the value of sST2 as a serum diagnostic marker for HCC. Methods This study included 93 newly diagnosed HCC patients (HCC group), 90 chronic hepatitis B patients (CHB group), and 90 healthy individuals (HCs group). Spearman correlation analysis was used to explore the relationships between sST2 and the experimental indicators in HCC group. The receiver operating characteristic (ROC) curve evaluated the efficacy of sST2 alone or in combination with AFP in the diagnosis of HCC. Result The median level of sST2 was significantly higher in HCC group (24.00 [15.20-49.90] ng/mL) compared to CHB group (19.55 [15.23-24.95] ng/mL) and HCs group (7.65 [5.20-10.53] ng/mL). No significant correlations were found between sST2 and other clinical indicators in HCC group. The Area Under Curve (AUC) of ROC curve to distinguish HCC patients from healthy controls and CHB group was 0.861 (sensitivity 82.80%, specificity 72.10%) and 0.709 (sensitivity 80.60%, specificity 52.50%), respectively. When combined with AFP, the AUC increased to 0.963 (sensitivity 82.90%, specificity 94.20%), and 0.895 (sensitivity 72.0%, specificity 100%), respectively. Conclusions The serum level of sST2 increased in HCC and its diagnostic performance is comparable to that of AFP, supporting its potential as a promising biomarker for detection of HCC. The combined use of sST2 and AFP enhances diagnostic efficacy for HCC.

Cuproptosis-related molecular classification and gene signature of hepatocellular carcinoma and experimental verification.

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor overall prognosis. Cuproptosis, a recently proposed mode of copper-dependent cell death, plays a critical role in the malignant progression of various tumors; however, the expression and prognostic value of cuproptosis-related regulatory genes in HCC remain unclear. Genomic, genetic, and expression profiles of ten key cuproptosis-related regulatory genes were analyzed using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset and protein expression data from the Human Protein Atlas (HPA) database. Unsupervised clustering of HCC patients based on these ten key cuproptosis-related regulatory genes was used to identify different HCC subtypes and analyze the differences in clinical and immune characteristics among subtypes. Subsequently, univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox analyses were used to establish a cuproptosis-related prognostic signature, and the accuracy of prognostic signature prediction was internally validated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic curve in TCGA training and testing cohorts. The prognostic signature was externally validated using TCGA-LIHC entire cohort and International Cancer Genome Consortium Liver Cancer (ICGC-LIRI) cohorts. Finally, the expression landscape of cuproptosis-related regulatory genes in prognostic signature was explored by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting and immunohistochemistry (IHC) experiments. Ten cuproptosis-related genes were differentially expressed in normal and HCC tissues. Unsupervised clustering identified two subtypes and HCC patients with these two subtypes had different clinical prognoses and immune characteristics, as well as different degrees of response to immunotherapy. Lipoyltransferase 1 (LIPT1), dihydrolipoamide s-acetyltransferase (DLAT), and cyclin dependent kinase inhibitor 2A (CDKN2A) were selected to construct a prognostic signature, which significantly distinguished HCC patients with different survival periods in the TCGA training and testing cohorts and was well validated in both the TCGA-LIHC entire cohort and ICGC-LIRI cohort. The risk score of the prognostic signature was confirmed to be an independent prognostic factor, and nomograms were generated to effectively predict the probability of HCC patient survival. The qRT-PCR, western blotting and IHC results also revealed a significant imbalance in the expression of these cuproptosis-related genes in HCC. The classification and prognostic signature based on cuproptosis-related regulatory genes helps to explain the heterogeneity of HCC, which may contribute to the individualized treatment of patients with the disease.

The chemokine CCL20 can assist AFP in serological diagnosis of hepatocellular carcinoma

The chemokine 20 (CCL20) is a member of the CC chemokine family and plays a role in tumor immunity and autoimmune disease. This work investigated the value of CCL20 as a serum diagnostic marker for primary hepatocellular carcinoma (HCC). Based on the data of hepatocellular carcinoma patients in the TCGA database, the up-regulated genes encoding secretory proteins were analyzed in each pathological stage, and the candidate marker CCL20 gene was selected. Serum concentrations of CCL20 in patients with primary HCC, benign liver disease, and healthy subjects were analyzed by enzyme-linked immunosorbent assay (ELISA). The ROC curve evaluated the efficacy of CCL20 alone or in combination with AFP in the diagnosis of HCC. It was found the expression of CCL20 in HCC patients was significantly higher than that in the benign liver disease group and healthy controls (P < 0.05); The AUC of ROC curve to distinguish HCC patients from healthy controls was 0.859, the sensitivity was 73.42%, and the specificity was 86.84%. After combination with AFP, the AUC increased to 0.968, the sensitivity was 88.16%, and the specificity was 97.37%. Although CCL20 was increased in the serum of patients with benign liver diseases, combined with AFP, the AUC to distinguish HCC patients from non-HCC cohorts (benign liver disease group and healthy control group) was 0.902, with a sensitivity of 91.67% and a specificity of 75.26%. Collectively, serum CCL20 is closely related to the occurrence of HCC, and detection of serum CCL20 can assist AFP in improving the diagnostic sensitivity of HCC.

Long non-coding RNA CASC7 is a promising serum biomarker for hepatocellular carcinoma

BackgroundAt present, a large number of studies have found that long non-coding RNAs (lncRNAs) can be used as biomarkers for diagnosis and monitoring prognosis of hepatocellular carcinoma (HCC). The expression of lncRNA cancer susceptibility candidate 7 (CASC7) in HCC has rarely been studied. The purpose of this study was to explore the expression of CASC7 and its correlation with clinical features, and to further analyze its diagnostic value in HCC.MethodsSerum samples were collected from 80 patients with HCC, 80 patients with chronic hepatitis B (CHB), and 80 healthy people. The expression level of serum CASC7 was detected by droplet digital PCR. Appropriate parametric and nonparametric tests were used for data analysis.ResultsThe results showed that the expression of CASC7 in serum of patients with HCC was significantly higher than that of patients with CHB (median: 8.8 versus 2.2 copies/µl, p < 0.001) and healthy controls (median: 8.8 versus 3.8 copies/µl, p < 0.001). High expression of serum CASC7 was significantly correlated with tumor number (p = 0.005), intrahepatic metastasis (IM) (p < 0.001), tumor size (p = 0.007) and tumor-node-metastasis (TNM) stage (p = 0.008). The area under the curve (AUC) of CASC7 to distinguish HCC patients from CHB patients and healthy controls was 0.808 (95% CI: 0.742–0.874) at the cut-off value of 7.24 copies/µl with 63.8% sensitivity and 95.2% specificity.ConclusionsThis study suggested that CASC7 was significantly up-regulated in serum of patients with HCC and closely related to tumor number, IM, tumor size and TNM stage, which may serve as a promising diagnostic biomarker.

Hepatocellular carcinoma detection via targeted enzymatic methyl sequencing of plasma cell-free DNA

BackgroundEpigenetic variants carried by circulating tumor DNA can be used as biomarkers for early detection of hepatocellular carcinoma (HCC) by noninvasive liquid biopsy. However, traditional methylation analysis method, bisulfite sequencing, with disadvantages of severe DNA damage, is limited in application of low-amount cfDNA analysis.ResultsThrough mild enzyme-mediated conversion, enzymatic methyl sequencing (EM-seq) is ideal for precise determination of cell-free DNA methylation and provides an opportunity for HCC early detection. EM-seq of methylation control DNA showed that enzymatic conversion of unmethylated C to U was more efficient than bisulfite conversion. Moreover, a relatively large proportion of incomplete converted EM-seq reads contains more than 3 unconverted CH site (CH = CC, CT or CA), which can be removed by filtering to improve accuracy of methylation detection by EM-seq. A cohort of 241 HCC, 76 liver disease, and 279 normal plasma samples were analyzed for methylation value on 1595 CpGs using EM-seq and targeted capture. Model training identified 283 CpGs with significant differences in methylation levels between HCC and non-HCC samples. A HCC screening model based on these markers can efficiently distinguish HCC sample from non-HCC samples, with area under the curve of 0.957 (sensitivity = 90%, specificity = 97%) in the test set, performing well in different stages as well as in serum α-fetoprotein/protein induced by vitamin K absence-II negative samples.ConclusionFiltering of reads with ≥ 3 CHs derived from incomplete conversion can significantly reduce the noise of EM-seq detection. Based on targeted EM-seq analysis of plasma cell-free DNA, our HCC screening model can efficiently distinguish HCC patients from non-HCC individuals with high sensitivity and specificity.

The Prognostic Value of a lncRNA Risk Model Consists of 9 m6A Regulator-Related lncRNAs in Hepatocellular Carcinoma (HCC).

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver. Although the RNA modification N6-methyladenine (m6A) has been reported to be involved in HCC carcinogenesis, early diagnostic markers and promising personalized therapeutic targets are still lacking. In this study, we identified that 19 m6A regulators and 34 co-expressed lncRNAs were significantly upregulated in HCC samples; based on these factors, we established a prognostic signal of HCC associated with 9 lncRNAs and 19 m6A regulators using LASSO Cox regression analysis. Kaplan-Meier survival estimate revealed correlations between the risk scores and patients' OS in the training and validation dataset. The ROC curve demonstrated that the risk score-based curve has satisfactory prediction efficiency for both training and validation datasets. Multivariate Cox's proportional hazard regression analysis indicated that the risk score was an independent risk factor within the training and validation dataset. In addition, the risk score could distinguish HCC patients from normal non-cancerous samples and HCC samples of different pathological grades. Eventually, 232 mRNAs were co-expressed with these 9 lncRNAs according to GSE101685 and GSE112790; these mRNAs were enriched in cell cycle and cell metabolic activities, drug metabolism, liver disease-related pathways, and some important cancer related pathways such as p53, MAPK, Wnt, RAS and so forth. The expression of the 9 lncRNAs was significantly higher in HCC samples than that in the neighboring non-cancerous samples. Altogether, by using the Consensus Clustering, PCA, ESTIMATE algorithm, LASSO regression model, Kaplan-Meier survival assessment, ROC curve analysis, and multivariate Cox's proportional hazard regression model analysis, we established a prognostic marker consisting of 9 m6A regulator-related lncRNAs that markers may have prognostic and diagnostic potential for HCC.

Protein Kinase C Delta Is a Novel Biomarker for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common cancer with a poor prognosis. Identification of an alternative biomarker that can detect early-stage and conventional tumor marker-negative HCC is urgently needed. We found that protein kinase C delta (PKCδ) is specifically secreted from HCC cell lines into extracellular space and contributes to tumor development and that its serum levels were elevated in HCC patients. This study aimed to assess the practical usefulness of serum PKCδ for detecting HCC in chronic liver disease (CLD) patients. Serum PKCδ levels in 313 CLD patients with and without HCC (n= 187 and 126, respectively) were measured using a sandwich enzyme-linked immunosorbent assay. The diagnostic performance of PKCδ for HCC was evaluated using the receiver operating characteristic curve analysis and was compared with that of conventional markers, α-fetoprotein (AFP), and des-γ-carboxy prothrombin (DCP). Serum PKCδ levels in HCC patients were significantly higher than those in CLD patients without HCC. PKCδ distinguished HCC patients from CLD patients without HCC, with high sensitivity and specificity. Subgroup analyses revealed that the diagnostic performance of PKCδ for HCC was comparable to that of AFP and DCP, and that approximately 40% of AFP/DCP double-negative HCC patients were positive for PKCδ. PKCδ yielded better diagnostic performance for detecting solitary small-sized (ie, very early stage) HCC than AFP and DCP. There was no significant correlation between serum PKCδ and AFP/DCP levels. Serum PKCδ is a novel HCC biomarker, which is independent of and complementary to conventional markers. Specifically, PKCδ may be useful for detecting very early-stage or AFP/DCP double-negative HCC.

Proteome Multimarker Panel for the Early Detection of Hepatocellular Carcinoma: Multicenter Derivation, Validation, and Comparison.

Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case–control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring–mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both P < 0.05), test (0.894 and 0.893 vs 0.593, both P < 0.05), and confirmation sets (0.961 and 0.937 vs 0.806, both P < 0.05) in detecting small single HCC. The combination and standalone MRM-MS panels had significantly higher AUROC values than the GALAD score (0.945 and 0.931 vs 0.829, both P < 0.05). Our proteome 17-protein multimarker panel distinguished HCC patients from high-risk controls and had high accuracy in the early detection of HCC.

Predictive Models for HCC Prognosis, Recurrence Risk, and Immune Infiltration Based on Two Exosomal Genes: MYL6B and THOC2.

IntroductionHepatocellular carcinoma (HCC) is a heterogeneous molecular disease with complex molecular pathogenesis that influences the efficacy of therapies. Exosomes play a crucial role in tumorigenesis and poor disease outcomes in HCC.ObjectiveThe aim of this study was to identify the optimal gene set derived from exosomes in HCC with substantial predictive value to construct models for determining prognosis, recurrence risk and diagnosis and to identify candidates suitable for immunotherapy and chemotherapy, thereby providing new ideas for the individualized treatment of patients and for improving prognosis.MethodsWeighted correlation network analysis (WGCNA) and univariate and multivariate Cox PH regression analyses were applied to identify exosome-related signatures in the TCGA and exoRbase databases associated with clinical relevance, immunogenic features and tumor progression in HCC. Cell experiments were performed to further confirm the oncogenic effect of MYL6B and THOC2.ResultsThe models for prognosis and recurrence risk prediction were built based on two exosomal genes (MYL6B and THOC2) and were confirmed to be independent predictive factors with superior predictive performance. Patients with high prognostic risk had poorer prognosis than patients with low prognostic risk in all HCC datasets, namely, the TCGA cohort (HR=2.5, P<0.001), the ICGC cohort (HR=3.15, P<0.001) and the GSE14520 cohort (HR=1.85, P=0.004). A higher recurrence probability was found in HCC patients with high recurrence risk than in HCC patients with low recurrence risk in the TCGA cohort (HR=2.44, P<0.001) and the GSE14520 cohort (HR=1.54, P=0.025). High prognostic risk patients had higher expression of immune checkpoint genes, such as PD1, B7H3, B7H5, CTLA4 and TIM3 (P<0.05). Diagnostic models based on the same two genes were able to accurately distinguish HCC patients from normal individuals and HCC from dysplastic nodules.ConclusionOur findings lay the foundation for identifying molecular markers to increase the early detection rate of HCC, improve disease outcomes, and determine more effective individualized treatment options for patients.

An immunogenomic signature for molecular classification in hepatocellular carcinoma

Immunity plays an important role in tumor development. In this study, we aimed to investigate molecular classification and its prognostic value in hepatocellular carcinoma (HCC) based on immune signature. Gene set enrichment analysis (GSEA) was used to calculate scores of immune pathways for HCC and hierarchical clustering in two databases (The Cancer Genome Atlas [TCGA], Liver Cancer-RIKEN, JP [LIRI_JP]). The scores of the immune microenvironment and the proportions of 22 immune cells were also calculated. Single-sample GSEA (ssGSEA) was used to screen survival prognosis-related immune pathways and calculate the hazard radio of differentially expressed immune-related genes (IRGs), which were validated in clinical samples and multiple datasets. Based on the immune characteristics, we identified three HCC subtypes, namely immunity high (Immunity_H), immunity medium (Immunity_M), and immunity low (Immunity_L), and confirmed that the classification was reliable and predictable. Immunity_H with a higher immune and stromal score indicated better survival rate. Cox regression analysis showed that IL18RAP and IL7R were the protective genes. Immune risk score was the independent risk factor of overall survival in HCC patients. These results indicated that immunogenomic classification could distinguish HCC patients with different immune status, which could impact the prognosis of the patients with HCC.

Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellular carcinoma: A SQUIRE-compliant study.

Radiomics contributes to the extraction of undetectable features with the naked eye from high-throughput quantitative images. In this study, 2 predictive models were constructed, which allowed recognition of poorly differentiated hepatocellular carcinoma (HCC). In addition, the effectiveness of the as-constructed signature was investigated in HCC patients.A retrospective study involving 188 patients (age, 29–85 years) enrolled from November 2010 to April 2018 was carried out. All patients were divided randomly into 2 cohorts, namely, the training cohort (n = 141) and the validation cohort (n = 47). The MRI images (DICOM) were collected from PACS before ablation; in addition, the radiomics features were extracted from the 3D tumor area on T1-weighted imaging (T1WI) scans, T2-weighted imaging (T2WI) scans, arterial images, portal images and delayed phase images. In total, 200 radiomics features were extracted. t test and Mann–Whitney U test were performed to exclude some radiomics signatures. Afterwards, a radiomics signature model was built through LASSO regression by RStudio Software. We constructed 2 support vector machine (SVM)-based models: 1 with a radiomics signature only (model 1) and 1 that integrated clinical and radiomics signatures (model 2). Then, the diagnostic performance of the radiomics signature was evaluated through receiver operating characteristic (ROC) analysis.The classification accuracy in the training and validation cohorts was 80.9% and 72.3%, respectively, for model 1. In the training cohort, the area under the ROC curve (AUC) was 0.623, while it was 0.576 in the validation cohort. The classification accuracy in the training and validation cohorts were 79.4% and 74.5%, respectively, for model 2. In the training cohort, the AUC was 0.721, while it was 0.681 in the validation cohort.The MRI-based radiomics signature and clinical model can distinguish HCC patients that belong in a low differentiation group from other patients, which helps in the performance of personal medical protocols.

Clinical significance of serum miR-487b in HBV-related hepatocellular carcinoma and its potential mechanism

Background Hepatocellular carcinoma (HCC) is a malignant tumour with high mortality. In recent years, microRNA (miRNA) has been recognized in the diagnosis and prognosis of cancer. miR-487b has been found to play a role in a variety of cancers. The purpose of this study is to explore the role of miR-487b in the diagnosis and prognosis of hepatitis B virus (HBV)-related HCC, and its influence on the biological behaviour of HCC cells. Methods Quantitative real-time polymerase chain reaction (qRT-PCR) method was used to detect the expression level of miR-487b in the serum of HCC patients, HBV patients, and healthy people. The ROC curve was used to evaluate the role of miR-487b in the diagnosis of HCC. The prognostic significance of miR-487b in HCC was analyzed by the Kaplan-Meier survival curve and Cox regression model. The effects of miR-487b on cell proliferation, migration, and invasion were explored through MTT assay and transwell assays. Results The expression level of miR-487b in the serum of HBV-related HCC patients was significantly higher than that of CHB patients and normal healthy people. The expression level of miR-487b can distinguish HCC patients from CHB patients or normal healthy people. High expression of miR-487b was associated with poor prognosis, which could be used as an independent prognostic factor for HCC. The upregulation of miR-487b promoted cell proliferation, migration, and invasion. Conclusion miR-487b can be used as a biomarker for the diagnosis and prognosis of HCC.

Liquid biopsy by combining 5-hydroxymethylcytosine signatures of plasma cell-free DNA and protein biomarkers for diagnosis and prognosis of hepatocellular carcinoma

BackgroundLiquid biopsy based on 5-hydroxymethylcytosine (5hmC) signatures of plasma cell-free DNA (cfDNA) originating from tumor cells provides a novel approach for early diagnosis in hepatocellular carcinoma (HCC). Here, we sought to develop a reliable model using cfDNA 5hmC signatures and protein biomarkers for diagnosis and prognosis of HCC.Patients and methodsWe carried out genome-wide 5hmC sequencing of cfDNA samples collected from 165 healthy volunteers, 62 liver cirrhosis (LC) patients and 135 HCC patients. A sensitive 5hmC diagnostic model was developed based on 5hmC signatures selected by sparse Partial Least Squares Discriminant Analysis and cross-validation to define the weighted diagnostic score (wd-score). Then, we combined protein biomarkers with the wd-score to build a more robust score (HCC score) by logistic regression.ResultsThe distribution pattern of differential 5hmC regions could clearly distinguish HCC patients, LC patients and healthy volunteers. The wd-score based on 64 5hmC signatures in cfDNA achieves 93.24% of area under the curve (AUC) to distinguish HCC patients from non-HCC patients, and the HCC score by combing protein biomarkers achieves 92.75% of AUC to distinguish HCC patients from LC patients. Meanwhile, the HCC score showed high capacity for screening high recurrence risk patients after receiving surgical resection, and appeared to be an independent indicator for both relapse-free survival (P = 0.00865) and overall survival (P = 0.000739). Furthermore, the values of the HCC score in patients' longitudinal plasma samples were positively associated with tumor burden dynamics during follow-up.ConclusionWe have developed and validated a novel non-invasive liquid biopsy strategy for HCC diagnosis, prognosis and surveillance during HCC progression.

Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.

Diagnostic Value of Basic Fibroblast Growth Factor and Platelet-Derived Growth Factor-AB Circulating Levels in Hepatitis C Virus-Associated Hepatocellular Carcinoma

Abstract Background: As a hypervascular tumor, Hepatocellular Carcinoma (HCC) is characterized by neovascularization which plays an important role in its growth and progression. Basic Fibroblast Growth Factor (bFGF) is a potent endothelial cell mitogen and angiogenic factor that was found to be elevated in different cancers. Platelet-Derived Growth Factor-AB (PDGF-AB) is another factor implicated in enhanced proliferation and migration of pericytes and is a potent stim-ulator of angiogenesis in many tumors. Aim of Study: This study aimed to evaluate the circulating levels of bFGF and PDGF-AB and to examine their diagnostic significance in Hepatitis C Virus (HCV)-associated HCC. Methods: This study included one hundred subjects divided into healthy controls (n=25), HCV patients (n=25), and HCV-associated HCC patients (n=50). The levels of bFGF, PDGF-AB, and Alpha-Fetoprotein (AFP) in addition to the activities of alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase as well as total bilirubin and albumin concentrations were determined in sera of the enrolled subjects. Results: Levels of bFGF and PDGF-AB were higher in HCC patients compared to controls and HCV patients and were found to be associated with increased susceptibility to HCV-associated HCC. Additionally, the diagnostic values of bFGF and PDGF-AB to distinguish HCC patients from non-cancerous patients were good, although they are still inferior to that of AFP. However, the combination of bFGF and PDGF-AB with AFP enhanced the efficacy of the latter. Conclusion: Serum bFGF and PDGF-AB may contribute to the pathogenesis of HCV-associated HCC, and they seem to be good diagnostic biomarkers of HCC along with AFP.

Tumor educated platelets, a promising source for early detection of hepatocellular carcinoma: Liquid biopsy an alternative approach to tissue biopsy

Liver cancer is considered to be the sixth deadliest cancer worldwide. Hepatocellular carcinoma (HCC) is known to be the most prevalent type of liver cancer. The number of deaths due to HCC reported per year is on a constant rise especially in lesser developed countries. There are several contributing factors to this rise in number. Among other contributing factors is the late diagnosis of HCC. Patients are usually diagnosed when the disease reaches its advance stage. The present study was conducted with total 30 samples. It was designed for investigating the potential of TGF-β, NF-κβ, VEGF, AKT and PI3K as RNA based biomarkers in tumor educated platelets for early detection of HCC. The results obtained from the transcriptional analysis revealed a significant high expression of TGF-β, NF-κβ, VEGF by 2.48, 2.35 and 2.78 folds respectively in comparison to the control. On the other hand, a decrease in expression by 0.6 and 0.65 folds was observed in AKT and PI3K respectively in comparison to controls. Although all selected RNA biomarkers showed promising potential to detect HCC however, AKT and PI3K were better able to detect early stage HCC. The results obtained clearly indicate the increased expression of TGF-β, NF-κβ, VEGF in HCC patients. All these biomarkers are previously known for cancer initiation, progression and metastasis. The significant decrease in expression of AKT and PI3K in HCC patients needs further investigation. All the selected RNA biomarkers can be used for detection of HCC as they were able to distinguish HCC patients from controls successfully with AKT and PI3K showing better potential to detect early stage HCC. However, translational analysis for all these RNA biomarkers should be performed to gain further evidence for the ability of these biomarkers to be used for early HCC detection.

LncRNA HAND2‑AS1 mediates the downregulation of ROCK2 in hepatocellular carcinoma and inhibits cancer cell proliferation, migration and invasion.

Long noncoding (lnc)RNA HAND2‑AS1 inhibits the development of several human malignancies. The role of HAND2‑AS1 was investigated in hepatocellular carcinoma (HCC). It was found that levels of HAND2‑AS1 in serum were significantly lower, while serum levels of Rho‑associated protein kinase 2 (ROCK2) in HCC patients were significantly increased compared with hepatitis B (HB) patients and healthy controls. Decreased HAND2‑AS1 levels distinguished HCC patients but not HB patients from healthy controls. A significant negative correlation between HAND2‑AS1 and ROCK2 was found in HCC patients but not in HB patients or healthy controls. HAND2‑AS1 overexpression inhibited, while ROCK2 overexpression promoted HCC cell migration, proliferation and invasion. HAND2‑AS1 overexpression led to downregulated ROCK2 expression. ROCK2 overexpression did not significantly affect ROCK2 expression but attenuated the inhibitory effects of HAND2‑AS1 overexpression. It was therefore concluded that HAND2‑AS1 might mediate the downregulation of ROCK2 in HCC to inhibit cancer cell migration, proliferation and invasion.

Serum milk fat globule-EGF factor 8 (MFG-E8) as a diagnostic and prognostic biomarker in patients with hepatocellular carcinoma

Current serum hepatocellular carcinoma (HCC) biomarkers are insufficient for early diagnosis. We aimed to clarify whether serum MFG-E8 can serve as a diagnostic or prognostic biomarker of HCC. Serum MFG-E8 levels of 282 HCC patients, who underwent primary hepatectomy, were examined by ELISA. We also quantified serum MFG-E8 levels in patients with chronic hepatitis (CH), liver cirrhosis (LC), as well as in healthy volunteers (HVs). Serum MFG-E8 levels were significantly lower in HCC patients than in HVs regardless of the etiology of liver disease (3.6 ± 0.1 vs 5.8 ± 0.2 ng/mL, p < 0.0001), and recovered after treatment of HCC. Serum MFG-E8 levels in CH and LC patients were comparable to those in HVs. Serum MFG-E8 could detect HCCs, even α-fetoprotein (AFP)-negative or des-γ-carboxy prothrombin (DCP)-negative HCCs, in CH and LC patients. Our new HCC prediction model using MFG-E8 and DCP (Logit(p) = 2.619 − 0.809 × serum MFG-E8 + 0.0226 × serum DCP) distinguished HCC patients from CH and LC patients with an area under the curve of 0.923, a sensitivity of 81.1%, and a specificity of 89.8%. Futhermore, low preoperative serum MFG-E8 was an independent predictor of poor overall survival. Thus, serum MFG-E8 could serve as a feasible diagnostic and prognostic biomarker for HCC.

LINC00978 promotes the progression of hepatocellular carcinoma by regulating EZH2-mediated silencing of p21 and E-cadherin expression

Long non-coding RNAs (lncRNAs) have been suggested as important regulators of cancer development and progression in hepatocellular carcinoma (HCC). Nevertheless, the clinical value and biological roles of LINC00978 in HCC remain unclear. In this study, we detected the expression of LINC00978 in tumor tissues and serum of HCC patients, examined the roles of LINC00978 in HCC progression and elucidated the underlying molecular mechanisms. We found that LINC00978 expression was upregulated in tumor tissues and serum of HCC patients. Higher serum levels of LINC00978 could distinguish HCC patients from hepatitis and liver cirrhosis patients and healthy controls. LINC00978 knockdown inhibited HCC cell proliferation, migration and invasion while promoted cell cycle arrest and apoptosis. Overexpression of LINC00978 led to the opposite effects. LINC00978 knockdown also inhibited HCC growth and metastasis in mouse tumor models. Mechanistically, LINC00978 bound to EZH2 and mediated its accumulation at the promoter region of p21 and E-cadherin genes, leading to the trimethylation of H27K3 and the inhibition of p21 and E-cadherin expression. Moreover, the simultaneous depletion of p21 and E-cadherin expression reversed the inhibitory effects of LINC00978 knockdown on HCC cell proliferation, migration, and invasion. Taken together, these findings suggest that LINC00978 promotes HCC progression by inhibiting p21 and E-cadherin expression via EZH2-mediated epigenetic silencing. LINC00978 may represent a novel biomarker for HCC diagnosis, prognosis, and therapy.

Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection.

Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic andarecurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively.