Quantitative radiomics and qualitative LI-RADS imaging descriptors for non-invasive assessment of ß-catenin mutation status in hepatocellular carcinoma.

PURPOSE: Gain-of-function mutations in CTNNB1, gene encoding for ß-catenin, are observed in 25-30% of hepatocellular carcinomas (HCCs). Recent studies have shown ß-catenin activation to have distinct roles in HCC susceptibility to mTOR inhibitors and resistance to immunotherapy. Our goal was to develop and test a computational imaging-based model to non-invasively assess ß-catenin activation in HCC, since liver biopsies are often not done due to risk of complications. METHODS: This IRB-approved retrospective study included 134 subjects with pathologically proven HCC and available ß-catenin activation status, who also had either CT or MR imaging of the liver performed within 1 year of histological assessment. For qualitative descriptors, experienced radiologists assessed the presence of imaging features listed in LI-RADS v2018. For quantitative analysis, a single biopsy proven tumor underwent a 3D segmentation and radiomics features were extracted. We developed prediction models to assess the ß-catenin activation in HCC using both qualitative and quantitative descriptors. RESULTS: There were 41 cases (31%) with ß-catenin mutation and 93 cases (69%) without. The model's AUC was 0.70 (95% CI 0.60, 0.79) using radiomics features and 0.64 (0.52, 0.74; p = 0.468) using qualitative descriptors. However, when combined, the AUC increased to 0.88 (0.80, 0.92; p = 0.009). Among the LI-RADS descriptors, the presence of a nodule-in-nodule showed a significant association with ß-catenin mutations (p = 0.015). Additionally, 88 radiomics features exhibited a significant association (p < 0.05) with ß-catenin mutations. CONCLUSION: Combination of LI-RADS descriptors and CT/MRI-derived radiomics determine ß-catenin activation status in HCC with high confidence, making precision medicine a possibility.

Prognostic analysis and risk assessment based on RNA editing in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model's utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

A signature based on neutrophil extracellular trap-related genes for the assessment of prognosis, immunoinfiltration, mutation and therapeutic response in hepatocellular carcinoma.

BACKGROUND: Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC. METHODS: The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC. RESULTS: Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis. CONCLUSION: We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.

Assessment of TLL1 variant and risk of hepatocellular carcinoma in Latin Americans and Europeans.

INTRODUCTION AND OBJECTIVES: Tolloid like protein 1 (TLL1) rs17047200 has been reported to be associated with HCC development and liver fibrosis. However, to our knowledge, no studies have been performed on Latin Americans and comparative differences between TLL1 rs17047200 in HCC patients from Latin America and Europe are undefined. MATERIALS AND METHODS: Cross-sectional analysis was performed on Latin American and European individuals. We analyzed TLL1 rs17047200 on DNA from 1194 individuals, including 420 patients with HCC (86.0 % cirrhotics) and 774 without HCC (65.9 % cirrhotics). RESULTS: TLL1 rs17047200 genotype AT/TT was not associated with HCC development in Latin Americans (OR: 0.699, 95 %CI 0.456-1.072, p = 0.101) or Europeans (OR: 0.736, 95 %CI 0.447-1.211, p = 0.228). TLL1 AT/TT was not correlated with fibrosis stages among metabolic dysfunction-associated steatotic liver disease (MASLD) patients from Latin America (OR: 0.975, 95 %CI 0.496-1.918, p = 0.941). Among Europeans, alcohol-related HCC had lower TLL1 AT/TT frequencies than cirrhosis (18.3 % versus 42.3 %, OR: 0.273, 95 %CI 0.096-0.773, p = 0.015). CONCLUSIONS: We found no evidence that the TLL1 rs17047200 AT/TT genotype is a risk factor for HCC development in Latin Americans or Europeans. A larger study integrating ethnic and etiology backgrounds is needed to determine the importance of the TLL1 SNP in HCC development.

Identification and development of TP53 mutation-associated Long non-coding RNAs signature for optimized prognosis assessment and treatment selection in hepatocellular carcinoma.

BACKGROUND: The TP53 gene is estimated to be mutated in over 50% of tumors, with the majority of tumors exhibiting abnormal TP53 signaling pathways. However, the exploration of TP53 mutation-related LncRNAs in Hepatocellular carcinoma (HCC) remains incomplete. This study aims to identify such LncRNAs and enhance the prognostic accuracy for Hepatoma patients. MATERIAL AND METHODS: Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database. RESULTS: In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability. CONCLUSION: The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.

The Non-Invasive Assessment of Circulating D-Loop and mt-ccf Levels Opens an Intriguing Spyhole into Novel Approaches for the Tricky Diagnosis of NASH.

Nonalcoholic fatty liver disease (NAFLD) is the commonest liver disease worldwide affecting both adults and children. Nowadays, no therapeutic strategies have been approved for NAFLD management, and hepatic biopsy remains the gold standard procedure for its diagnosis. NAFLD is a multifactorial disease whose pathogenesis is affected by environmental and genetic factors, and it covers a spectrum of conditions ranging from simple steatosis up to nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Several studies underlined the urgent need to develop an NAFLD risk prediction model based on genetics, biochemical indicators, and metabolic disorders. The loss of mitochondrial dynamics represents a typical feature of progressive NAFLD. The imbalance of mitochondrial lifecycle together with the impairment of mitochondrial biomass and function trigger oxidative stress, which in turn damages mitochondrial DNA (mtDNA). We recently demonstrated that the main genetic predictors of NAFLD led to mitochondrial dysfunction. Moreover, emerging evidence shows that variations in the displacement loop (D-loop) region impair mtDNA replication, and they have been associated with advanced NAFLD. Finally, lower levels of mitophagy foster the overload of damaged mitochondria, resulting in the release of cell-free circulating mitochondrial DNA (mt-ccf) that exacerbates liver injury. Thus, in this review we summarized what is known about D-loop region alterations and mt-ccf content during NAFLD to propose them as novel non-invasive biomarkers.

Critical Assessment of Whole Genome and Viral Enrichment Shotgun Metagenome on the Characterization of Stool Total Virome in Hepatocellular Carcinoma Patients.

Viruses are the most abundant form of life on earth and play important roles in a broad range of ecosystems. Currently, two methods, whole genome shotgun metagenome (WGSM) and viral-like particle enriched metagenome (VLPM) sequencing, are widely applied to compare viruses in various environments. However, there is no critical assessment of their performance in recovering viruses and biological interpretation in comparative viral metagenomic studies. To fill this gap, we applied the two methods to investigate the stool virome in hepatocellular carcinoma (HCC) patients and healthy controls. Both WGSM and VLPM methods can capture the major diversity patterns of alpha and beta diversities and identify the altered viral profiles in the HCC stool samples compared with healthy controls. Viral signatures identified by both methods showed reductions of Faecalibacterium virus Taranis in HCC patients' stool. Ultra-deep sequencing recovered more viruses in both methods, however, generally, 3 or 5 Gb were sufficient to capture the non-fragmented long viral contigs. More lytic viruses were detected than lysogenetic viruses in both methods, and the VLPM can detect the RNA viruses. Using both methods would identify shared and specific viral signatures and would capture different parts of the total virome.

Cytochrome P450-2D6: A novel biomarker in liver cancer health disparity.

Liver cancer morbidity and mortality rates differ among ethnic groups. In the United States, the burden of liver cancer in Asian Americans (AS) is higher compared to Caucasian Americans (CA). Research on liver cancer health disparities has mainly focused on environmental and socioeconomic factors yet has ignored the genotypic differences among various racial/ethnic groups. This lack of molecular level understanding has hindered the development of personalized medical approaches for liver cancer treatment. To understand the genetic heterogeneity of liver cancer between AS and CA, we performed a systematic analysis of RNA-seq data of AS and CA patients from The Cancer Genome Atlas (TCGA). We used four differential gene expression analysis packages; DESeq2, limma, edgeR, and Superdelta2, to identify the differentially expressed genes. Our analysis identified cytochrome P450-2D6 enzyme (CYP2D6) as the gene with the greatest differential expression with higher levels in AS compared to CA. To scrutinize the underlying mechanism of CYP2D6, Ingenuity Pathway Analysis (IPA) and Cytoscape were conducted and found hepatocyte nuclear factor-4α (HNF4A) and interleukin-6 (IL6) in direct association with CYP2D6. IL6 is downregulated in AS compared to CA, while HNF4A is not significantly different. Herein, we report that CYP2D6 may serve as a putative biomarker in liver cancer health disparities. Its negative association with IL6 proclaims an intricate relationship between CYP2D6 and inflammation in the ethnic differences seen in AS and CA liver cancer patients. The goal of the present study was to understand how genetic factors may contribute to the interethnic variability of liver cancer prevalence and outcomes in AS and CA patients. Identifying ethnic-specific genes may help ameliorate detection, diagnosis, surveillance, and treatments of liver cancer, as well as reduce disease-related incidence and mortality rates in the vulnerable population.

Standardization of diethylnitrosamine-induced hepatocellular carcinoma rat model with time based molecular assessment.

This study was intended (1) to develop a robust animal model for hepatocellular carcinoma (HCC) research, in which HCC tumors develop in a background of fibrosis or cirrhosis; and (2) to explore time-dependent regulatory changes in key molecular markers during disease advancement and HCC development. With the aim of establishing such HCC model, male Sprague-Dawley rats were injected with diethylnitrosamine (DEN) at a dose of 30 mg/kg twice a week for 10 weeks then once a week from 12th to 16th weeks. The rats were kept under observation until 18th week. At defined time intervals (2nd, 4th, 12th, and 18th week), serum biomarkers and microscopic components of tissue samples were used to investigate the chronic progression of liver disease, while gene and protein analysis was used to monitor expression patterns during HCC development. DEN-intoxicated rats manifested inflammation at week 4, fibrosis at week 12 and cirrhosis with early HCC tumors at week 18. Molecular analysis revealed that key markers of inflammation (Il-1ß, Il-6, and Tnf-α), fibrosis (Tgf-ß1, Col1α1, Col3α1, and Timp-1), and angiogenesis (Hif1-α and Vegf) were promptly (P ≤ 0.001) up-regulated at week 4, week 12 and week 18, respectively. Oxidative stress (iNos, Cyp2e1, and Sod1) and pro-apoptotic (Bax) markers showed significant upregulation from week 4 to week 12. However, Sod1 and Bax expressions dropped after week 12 and reached a minimum at 18th week. Strikingly, expressions of anti-apoptotic (Bcl-2) and cell proliferation (Pcna, Hgf, and Afp) markers were abruptly increased at week 18. Collectively, we describe an 18-week HCC model in DEN-intoxicated rats that exhibit chronic inflammation, oxidative imbalance, advance fibrosis/cirrhosis, halted apoptosis, and angiogenic sprouting, progressively.

Morin Hydrate Sensitizes Hepatoma Cells and Xenograft Tumor towards Cisplatin by Downregulating PARP-1-HMGB1 Mediated Autophagy.

The cross-talk between apoptosis and autophagy influences anticancer drug sensitivity and cellular death in various cancer cell lines. However, the fundamental mechanisms behind this phenomenon are still unidentified. We demonstrated anti-cancerous role of cisplatin (CP) and morin hydrate (Mh) as an individual and/or in combination (CP-Mh) in hepatoma cells and tumor model. Exposure of CP resulted in the production of intracellular reactive oxygen species (ROS)-mediated cellular vacuolization, expansion of mitochondria membrane and activation of endoplasmic reticulum (ER)-stress. Consequently, Cyt c translocation led to the increase of Bax/Bcl-2 ratio, which simultaneously triggered caspase-mediated cellular apoptosis. In addition, CP-induced PARP-1 activation led to ADP-ribosylation of HMGB1, which consequently developed autophagy as evident by the LC3I/II ratio. Chemically-induced inhibition of autophagy marked by increased cell death signified a protective role of autophagy against CP treatment. CP-Mh abrogates the PARP-1 expression and significantly reduced HMGB1-cytoplasmic translocation with subsequent inhibition of the HMGB1-Beclin1 complex formation. In the absence of PARP-1, a reduced HMGB1 mediated autophagy was observed followed by induced caspase-dependent apoptosis. To confirm the role of PARP-1-HMGB1 signaling in autophagy, we used the PARP-1 inhibitor, 4-amino-1,8-naphthalimide (ANI), HMGB1 inhibitor, ethyl pyruvate (EP), autophagy inhibitors, 3-methyl adenine (3-MA) and bafilomycin (baf) and small interfering RNAs (siRNA) to target Atg5 in combination of CP and Mh. Exposure to these inhibitors enhanced the sensitivity of HepG2 cells to CP. Collectively, our findings indicate that CP-Mh in combination served as a prominent regulator of autophagy and significant inducer of apoptosis that maintains a homeostatic balance towards HepG2 cells and the subcutaneous tumor model.

New insights on sorafenib resistance in liver cancer with correlation of individualized therapy.

Liver cancer is highly malignant and insensitive to cytotoxic chemotherapy and is associated with very poor patient prognosis. In 2007, the small-molecule targeted drug sorafenib was approved for the treatment of advanced liver cancer. In the subsequent ten years, sorafenib has been the only first-line therapeutic targeted drug for advanced hepatocellular carcinoma (HCC). However, a number of clinical studies show that a considerable percentage of patients with liver cancer are insensitive to sorafenib. The number of patients who actually benefit significantly from sorafenib treatment is very limited, and the overall efficacy of sorafenib is far from satisfactory, which has attracted the attention of researchers. Based on previous studies and reports, this article reviews the potential mechanisms of sorafenib resistance (SR) and summarizes the biomarkers and clinicopathological indicators that might be used for predicting sorafenib response and developing personalized therapy.

Assessment of miR-212 and Other Biomarkers in the Diagnosis and Treatment of HBV-infection-related Liver Diseases.

OBJECTIVES: Our study aims to detect the sensitivity of the new biomarker miR-212 existing in serum exosomes along with other hepatocellular carcinoma biomarkers such as AFP (alpha-fetoprotein), CA125 (carbohydrate antigen-ca125), and Hbx protein in the diagnosis of HBV-related liver diseases. We also aim to study the roles of these biomarkers in the progression of chronic hepatitis B and provide scientific data to show the clinical value of these biomarkers. METHODS: We selected 200 patients with HBV-infection (58 cases of chronic hepatitis B, 47 cases of hepatocellular carcinoma, 30 cases of compensatory phase cirrhosis, and 65 cases of decompensatory phase cirrhosis), 31 patients with primary liver cancer without HBV infection, and 70 healthy individuals as the control group. The expression level of serum AFP and CA125 was detected with electrochemiluminescence immunoassay. The expression level of the Hbx protein was detected with ELISA. Meanwhile, the expression level of miR-212 in serum was analyzed with RT-qPCR. We collected patients' clinical information following the Child-Pugh classification and MELD score criterion, and statistical analysis was made between the expression level of miR-212 and the collected clinical indexes. Lastly, we predicted the target genes of the miR-212 and its functions using bioinformatics methods such as cluster analysis and survival prediction. RESULTS: Compared to the control group, the expression level of miR-212 in HBV infected patients was remarkably increased (P<0.05), especially between the HBV-infection Hepatocellular carcinoma group and the non-HBVinfection liver cancer group (P<0.05). The expression of miR-212 was increased in patients' Child-Pugh classification, MELD score, and TNM staging. Moreover, the sensitivity and specificity of miR-212 were superior to AFP, CA125, and HBx protein. CONCLUSION: There is a linear relationship between disease progression and expression level of miR-212 in the serum of HBV infected patients. This demonstrates that miR-212 plays a significant role in liver diseases. miR-212 is expected to be a new biomarker used for the diagnosis and assessment of patients with HBV-infection-related liver diseases.

Ensemble of decision tree reveals potential miRNA-disease associations.

In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model's reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature.

A simple and low-cost screen printed electrode for hepatocellular carcinoma methylation detection.

There is a great demand for robust diagnostic and prognostic approaches for Hepatocellular Carcinoma (HCC). DNA methylation, a common epigenetic modification, has been found in many promoter regions of tumor suppressor genes. Hypermethylation of these gene promoters will repress the gene transcription and lead to the occurrence of cancers. The abnormal methyation level of the p16 gene promoter could be a promising marker for the detection of HCC. The adsorption affinities between different DNA bases and AuNPs are not the same. After bisulfite treatment and asymmetric PCR, methylation and unmethylation sequences can be changed into guanine-enriched and adenine-enriched sequences, respectively. A home-made gold nanoparticle modified screen printed carbon electrode (AuNP-SPCE) was employed to distinguish the adsorption affinities between guanine-enriched and adenine-enriched sequences, which could be used to analyze the level of DNA methylation. Several key experimental factors were investigated and optimized. The results had shown that the optimal AuNP electrodeposition time was 100 s and 15 min of adsorption could distinguish guanine-enriched and adenine-enriched sequences with a concentration of 100 nM at 25 °C. The detection limit of our AuNP-SPCE was 1.1 ng, and the assay had a good sensitivity of 10% methylation change and was able to distinguish only one methylated CpG site. What's more, the RSD over three assays with a disposable AuNP-SPCE was ≤7.2%. The assay was applied to real samples including cell lines and clinical tissues. Compared with normal hepatic cell lines and normal tissues, lower signals of HCC cell lines and cancer tissues were observed, respectively. It had shown a good discrimination of the abnormal methylation level of the p16 gene promoter.

MicroRNA-1277 Inhibits Proliferation and Migration of Hepatocellular Carcinoma HepG2 Cells by Targeting and Suppressing BMP4 Expression and Reflects the Significant Indicative Role in Hepatocellular Carcinoma Pathology and Diagnosis After Magnetic Resonance Imaging Assessment.

Our study aimed to investigate the roles and possible regulatory mechanism of miR-1277 in the development of hepatocellular carcinoma (HCC). HCC patients were identified from patients who were diagnosed with focal liver lesions using magnetic resonance imaging (MRI). The expression levels of miR-1277 in the serum of HCC patients and HepG2 cells were measured. Then miR-1277 mimic, miR-1277 inhibitor, or scramble RNA was transfected into HepG2 cells. The effects of miR-1277 overexpression and suppression on HepG2 cell proliferation, migration, and invasion were then investigated. Additionally, the expression levels of epithelial-mesenchymal transition (EMT)-related markers, including E-cadherin, ß-catenin, and vimentin, were detected. Target prediction and luciferase reporter assay were performed to explore the potential target of miR-1277. miR-1277 was significantly downregulated in the serum of HCC patients and HepG2 cells. Suppression of miR-1277 promoted HepG2 cell proliferation, migration, and invasion, whereas overexpression of miR-1277 had opposite effects. In addition, after miR-1277 was suppressed, the expressions of E-cadherin and ß-catenin were significantly increased, while the expressions of vimentin were markedly decreased. Bone morphogenetic protein 4 (BMP4) was identified as the direct target of miR-1277. Knockdown of BMP4 reversed the effects of miR-1277 suppression on HepG2 cell migration and invasion, as well as the expressions of E-cadherin, ß-catenin, and vimentin. Our results indicate that downregulation of miR-1277 may promote the migration and invasion of HepG2 cells by targeting BMP4 to induce EMT. Combination of MRI and miR-1277 level will facilitate the diagnosis and treatment of HCC.

In vitro modeling of hepatocellular carcinoma molecular subtypes for anti-cancer drug assessment.

Tractable experimental model that accounts for inter-tumor molecular heterogeneity is a key element of anti-cancer drug development. Hepatocellular carcinoma is known to exhibit highly heterogeneous molecular aberrations across the tumors, including somatic genetic and epigenetic alterations. Previous studies showed that molecular tumor subtypes determined by transcriptome, as a comprehensive functional readout, are reproducibly observed across global patient populations irrespective of geographic and etiological variations. Here we demonstrate that transcriptomic hepatocellular carcinoma subtypes, S1 and S2, determined by our previous transcriptome meta-analysis of multiple clinical hepatocellular carcinoma cohorts, are presented in a panel of hepatoma cell lines widely used by the research community. Interestingly, cell line that resembles gene expression pattern of S3 subtype, representing less aggressive tumors, was not identified in the panel. MYC pathway-activated S2-like cell lines showed higher sensitivity to a small molecule BET bromodomain inhibitor, (+)-JQ1, which has anti-MYC activity. These results support the use of hepatoma cell lines as models to evaluate molecular subtype-specific drug response, which is expected to lead to development of tailored, precision care of the patients with hepatocellular carcinoma.

Activation of SRY accounts for male-specific hepatocarcinogenesis: Implication in gender disparity of hepatocellular carcinoma.

Sex affects the risk, treatment responses and outcome of many types of cancers. The mechanism of gender disparity in development of hepatocellular carcinoma (HCC) remains obscure. Sex-determining region on Y chromosome (SRY) was overexpressed in approximate 84% male patient HCC. Moreover, we are the first to generate a liver-specific transgenic (TG) murine model with overexpression of the male specific gene SRY. Subject to a single intraperitoneal injection N-nitrosodiethylamine (DEN) at day 14, TG and wildtype (WT) mice of both genders were sacrificed at different time points (6-13.5 months). Overexpression of SRY in male TG and ectopic expression of SRY in female TG livers promoted DEN-induced hepatocarcinogenesis compared to age- and sex-matched WT. This accelerated tumorigenesis in TG of both genders was a consequence of increased injury and inflammation, fibrosis, and compensatory enhancement in hepatocytes proliferation secondary to activation of downstream targets Sox9 and platelet-derived growth factor receptor α (PDGFRα)/phosphoinositide 3-kinase (PI3K)/Akt and c-myc/CyclinD1. In conclusion, activation of SRY and its downstream Sox9 and PDGFRα pathways are commonly involved in male hepatocarcinogenesis, which provides novel insights into gender disparity and sex-specific therapeutic strategies of HCC.

Network analysis of HBV­ and HCV­induced hepatocellular carcinoma based on Random Forest and Monte Carlo cross­validation.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer­associated mortality worldwide. Hepatitis B virus (HBV) and hepatitis C virus (HCV) are two common risk factors for HCC. The majority of patients with HCC present at an advanced stage and are refractory to therapy. It is important to identify a method for efficient diagnosis at early stage. In the present study gene expression profile data, generated from microarray data, were pretreated according to the annotation files. The genes were mapped to pathways of Ingenuity Pathways Analysis. Dysregulated pathways and dysregulated pathway pairs were identified and constructed into individual networks, and a main network was constructed from individual networks with several edges. Random Forest (RF) classification was introduced to calculate the area under the curve (AUC) value of this network. Subsequently, 50 runs of Monte Carlo cross­validation were used to screen the optimal main network. The results indicated that a total of 4,929 genes were identified in the pathways and gene expression profile. By combining dysregulated pathways with Z<0.05 and dysregulated pathway pairs with Z<0.2, individual networks were constructed. The optimal main network with the highest AUC value was identified. In the HCV group, the network was identified with an AUC value of 0.98, including 41 pairs of pathways, and in the HBV group, the network was identified with an AUC value of 0.94, including eight pairs of pathways. In addition, four pairs were identified in both groups. In conclusion, the optimal networks of HCV and HBV groups were identified with the highest AUC values. The use of these networks is expected to assist in diagnosing patients effectively at an early stage.

Paired assessment of liver telomere lengths in hepatocellular cancer is a reliable predictor of disease persistence.

In the present study, we used a small series of highly defined patients, where we had matched timed peripheral blood samples (PBS), as well as paired liver biopsies obtained during collection of blood samples from patients with diagnosed hepatocellular carcinoma (HCC) and compared the correlation between the changes of telomere lengths in these defined samples. Patients included had either HCC alone or in conjunction with either pre-existing hepatitis B virus (HBV) or hepatitis C virus (HCV) infection. PCR-based assay incorporating primers to the telomeric hexamer repeats to polymerize and detect telomeric DNA was used. The average telomere length for each independent assessment was measured by seeing the differences in the intensity of the sample's telomere signal (T) to the signal from a single-copy gene (S-, ß-globin) to estimate the standard ratio. Our results provide the first convincing evidence that PBS may be utilized to assay telomere shortening as a predictor for disease persistence in HCC resulting after HBV or HCV infection, but not in non-infectious cause-stimulated HCC. These findings provide incipient opportunity to develop telomere length assessment as a biomarker tool for prediction of HCC in patients with HBV or HCV infection, as well as to gauge responses to chemotherapy and other treatment modalities.

Detecting critical state before phase transition of complex biological systems by hidden Markov model.

MOTIVATION: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages, i.e. before-transition state, pre-transition state and after-transition state, which can be considered as three different Markov processes. RESULTS: By exploring the rich dynamical information provided by high-throughput data, we present a novel computational method, i.e. hidden Markov model (HMM) based approach, to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process), thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets, and further identify the pre-transition states as well as their critical modules for three real datasets, i.e. the acute lung injury triggered by phosgene inhalation, MCF-7 human breast cancer caused by heregulin and HCV-induced dysplasia and hepatocellular carcinoma. Both functional and pathway enrichment analyses validate the computational results. AVAILABILITY AND IMPLEMENTATION: The source code and some supporting files are available at https://github.com/rabbitpei/HMM_based-method CONTACTS: lnchen@sibs.ac.cn or liyj@scut.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.