Advances in genomic hepatocellular carcinoma research

Construction of CDKN2A-related competitive endogenous RNA network and identification of GAS5 as a prognostic indicator for hepatocellular carcinoma

BACKGROUND

Competitive endogenous RNA (ceRNA) is an innovative way of gene expression modulation, which plays a crucial part in neoplasia. However, the intricacy and behavioral characteristics of the ceRNA network in hepatocellular carcinoma (HCC) remain dismal.

AIM

To establish a cyclin dependent kinase inhibitor 2A (CDKN2A)-related ceRNA network and recognize potential prognostic indicators for HCC.

METHODS

The mutation landscape of CDKN2A in HCC was first explored using the cBioPortal database. Differential expression analysis was implemented between CDKN2Ahigh and CDKN2Alow expression HCC samples. The targeted microRNAs were predicted by lncBasev3.0, and the targeted mRNAs were predicted by miRDB, and Targetscan database. The univariate and multivariate analysis were utilized to identify independent prognostic indicators.

RESULTS

CDKN2A was frequently mutated and deleted in HCC. The single-cell RNA-sequencing analysis revealed that CDKN2A participated in cell cycle pathways. The CDKN2A-related ceRNA network-growth arrest specific 5 (GAS5)/miR-25-3p/SRY-box transcription factor 11 (SOX11) was successfully established. GAS5 was recognized as an independent prognostic biomarker, whose overexpression was correlated with a poor prognosis in HCC patients. The association between GAS5 expression and methylation, immune infiltration was explored. Besides, traditional Chinese medicine effective components targeting GAS5 were obtained.

CONCLUSION

This CDKN2A-related ceRNA network provides innovative insights into the molecular mechanism of HCC formation and progression. Moreover, GAS5 might be a significant prognostic biomarker and therapeutic target in HCC.

Mechanism-guided fine-tuned microbiome potentiates anti-tumor immunity in HCC

Microbiome, including bacteria, fungi, and viruses, plays a crucial role in shaping distal and proximal anti-tumor immunity. Mounting evidence showed that commensal microbiome critically modulates immunophenotyping of hepatocellular carcinoma (HCC), a leading cause of cancer-related death. However, their role in anti-tumor surveillance of HCC is still poorly understood. Herein, we spotlighted growing interests in how the microbiome influences the progression and immunotherapeutic responses of HCC via changing local tumor microenvironment (TME) upon translocating to the sites of HCC through different "cell-type niches". Moreover, we summarized not only the associations but also the deep insight into the mechanisms of how the extrinsic microbiomes interplay with hosts to shape immune surveillance and regulate TME and immunotherapeutic responses. Collectively, we provided a rationale for a mechanism-guided fine-tuned microbiome to be neoadjuvant immunotherapy in the near future.

Integrated Analysis of N1-Methyladenosine Methylation Regulators-Related lncRNAs in Hepatocellular Carcinoma

N1-methyladenosine (m1A) and long non-coding RNAs (lncRNAs) play significant roles in tumor progression in hepatocellular carcinoma (HCC). However, their association with HCC is still unclear. In this study, lncRNAs related to m1A were extracted from the mRNA expression matrix in The Cancer Genome Atlas (TCGA) database. Five m1A-related lncRNAs (AL031985.3, NRAV, WAC-AS1, AC026412.3, and AC099850.4) were identified based on lasso Cox regression and they generated a prognostic signature of HCC. The prognostic signature was identified as an independent prognosis factor in HCC patients. Moreover, the prognostic signature achieved better performance than TP53 mutation status or tumor mutational burden (TMB) scores in the stratification of patient survival. The immune landscape indicated that most immune checkpoint genes and immune cells were distributed differently between both risk groups. A higher IC50 of chemotherapeutics (sorafenib, nilotinib, sunitinib, and gefitinib) was observed in the high-risk group, and a lower IC50 of gemcitabine in the low-risk group, suggesting the potential of the prognostic signature in chemosensitivity. In addition, fifty-five potential small molecular drugs were found based on drug sensitivity and NRAV expression. Together, five m1A-related lncRNAs generated a prognostic signature that could be a promising prognostic prediction approach and therapeutic response assessment tool for HCC patients.

Novel Nanotechnology Approaches to Overcome Drug Resistance in the Treatment of Hepatocellular Carcinoma: Glypican 3 as a Useful Target for Innovative Therapies

Hepatocellular carcinoma (HCC) is the second most lethal tumor, with a 5-year survival rate of 18%. Early stage HCC is potentially treatable by therapies with curative intent, whereas chemoembolization/radioembolization and systemic therapies are the only therapeutic options for intermediate or advanced HCC. Drug resistance is a critical obstacle in the treatment of HCC that could be overcome by the use of targeted nanoparticle-based therapies directed towards specific tumor-associated antigens (TAAs) to improve drug delivery. Glypican 3 (GPC3) is a member of the glypican family, heparan sulfate proteoglycans bound to the cell surface via a glycosylphosphatidylinositol anchor. The high levels of GPC3 detected in HCC and the absence or very low levels in normal and non-malignant liver make GPC3 a promising TAA candidate for targeted nanoparticle-based therapies. The use of nanoparticles conjugated with anti-GPC3 agents may improve drug delivery, leading to a reduction in severe side effects caused by chemotherapy and increased drug release at the tumor site. In this review, we describe the main clinical features of HCC and the common treatment approaches. We propose the proteoglycan GPC3 as a useful TAA for targeted therapies. Finally, we describe nanotechnology approaches for anti-GPC3 drug delivery systems based on NPs for HCC treatment.

Development of a prognostic gene signature for hepatocellular carcinoma

Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns. Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package "limma". Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan-Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the "rms" package. Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes, IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. In conclusion, the prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

Absent in melanoma 1-like (AIM1L) serves as a novel candidate for overall survival in hepatocellular carcinoma

Identifying biomarkers for hepatocellular carcinoma (HCC) survival is of great importance for the early detection, monitoring, and predicting for prognosis. This study aimed to investigate the candidate biomarkers for predicting overall survival (OS) in HCC patients. Using RTCGAToolbox, top 50 upregulated differential expressed genes (DEGs) were identified. The least absolute shrinkage and selection operator (LASSO) and Cox models were used to select powerful candidate genes, and log rank method was used to address the survivor functions of potential biomarkers. Selected by LASSO model, ANLN, TTK, AIM1L and person neoplasm cancer status might be candidate parameters associated with OS in HCC patients. After adjusting person neoplasm cancer status, ANLN and TTK levels in Cox model, AIM1L was identified as a risk factor for predicting OS in HCC patients (HR = 1.5, P = 0.037). Validated in The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) series, AIM1L was significantly overexpressed in tumor tissues compared to nontumor tissues (all P < 0.0001). HCC patients with high AIM1L in tumor tissues had significantly unfavorable OS compared to those with low AIM1L in TCGA, ICGC, Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier Plotter datasets (all P < 0.05). Conclusively, AIM1L is upregulated in tumor samples and serves as a novel candidate for predicting unfavorable OS in HCC patients.

Advances in Multi-Omics Applications in HBV-Associated Hepatocellular Carcinoma

Hepatitis B virus (HBV) specifically infects liver cells, leading to progressive liver cirrhosis and significantly increasing the risk of hepatocellular carcinoma (HCC). The maturity of sequencing technology, improvement in bioinformatics data analysis and progress of omics technologies had improved research efficiency. The occurrence and progression of HCC are affected by multisystem and multilevel pathological changes. With the application of single-omics technologies, including genomics, transcriptomics, metabolomics and proteomics in tissue and body fluid samples, and even the novel development of multi-omics analysis on a single-cell platform, HBV-associated HCC changes can be better analyzed. The review summarizes the application of single omics and combined analysis of multi-omics data in HBV-associated HCC and proposes the importance of multi-omics analysis in the type of HCC, which provide the possibility for the precise diagnosis and therapy of HBV-associated HCC.

E2F1-Induced lncRNA BAIAP2-AS1 Overexpression Contributes to the Malignant Progression of Hepatocellular Carcinoma via miR-361-3p/SOX4 Axis

Currently, plenty of researches have revealed that long noncoding RNAs (lncRNAs) can act as crucial roles during the progression of various tumors, including hepatocellular carcinoma (HCC). Here, we measured the expression of lncRNA BAIAP2 antisense RNA 1(BAIAP2-AS1) as well as its contribution to the developments of HCC. In this study, the expressions of BAIAP2-AS1 and SOX4 were distinctly upregulated in HCC cells and tissues, and high BAIAP2-AS1 may be a novel biomarker for HCC. E2F1 activated BAIAP2-AS1 expression. The silence of BAIAP2-AS1 inhibited the proliferation and metastasis of HepG2 and PLC5 cells. Assays for relationship verification showed that BAIAP2-AS1 regulated the expression of SOX4 and miR-361-3p. Rescue experiments further confirmed the positive interaction between miR-361-3p and BAIAP2-AS1 as well as between miR-361-3p and SOX4. Overall, BAIAP2-AS1 modulated the miR-361-3p/SOX4 axis to promote the development of HCC. Thus, our study offers a potential therapeutic target for treating HCC.

Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning

Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can predict the point mutations of six important genes (AUC 0.68-0.85) and copy number alteration of another six genes (AUC 0.69-0.79). Additionally, the trained models can predict the activities of three out of ten canonical pathways (AUC 0.65-0.79). Next, we visualized the weight maps of tumor tiles in WSI to understand the decision-making process of deep-learning models via a self-attention mechanism. We further validated our models on liver and lung cancers that are related to metastatic breast cancer. Our results provide insights into the association between pathological image features, molecular outcomes, and targeted therapies for breast cancer patients.

Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

Using Integrated Bioinformatics Analysis to Identify Abnormally Methylated Differentially Expressed Genes in Hepatocellular Carcinoma

Objective

For the identification of abnormally methylated differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC), this study integrated four microarray datasets to investigate the fundamental mechanisms of tumorigenesis.

Methods

We obtained the expression (GSE76427, GSE57957) and methylation (GSE89852, GSE54503) profiles from Gene Expression Omnibus (GEO). The abnormally MDEGs were identified by using R software. We used the clusterProfiler package for the functional and pathway enrichment analysis. The String database was used to build the protein–protein interaction (PPI) network and visualize it in Cytoscape. MCODE was employed in the module analysis. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) were employed to validate results. Lastly, we used cBioPortal software to examine the hub genetic alterations.

Results

We identified 162 hypermethylated, down-regulated genes and 190 hypomethylated, up-regulated genes. Up-regulated genes with low methylation were enriched in biological processes, such as keratinocyte proliferation, and calcium homeostasis. Pathway analysis was enriched in the AMPK and PI3K-Akt signaling pathways. The PPI network identified PTK2, VWF, and ITGA2 as hypomethylated, high-expressing hub genes. Down-regulated genes with high methylation were related to responses to peptide hormones and estradiol, multi-multicellular organism process. Pathway analysis indicated enrichment in camp, oxytocin signaling pathways. The PPI network identified CFTR, ESR1, and CXCL12 as hypermethylated, low-expressing hub genes. Upon verification in TCGA databases, we found that the expression and methylation statuses of the hub genes changed significantly, and it was consistent with our results.

Conclusion

The novel abnormally MDEGs and pathways in HCC were identified. These results helped us further understand the molecular mechanisms underlying HCC invasion, metastasis, and development. Hub genes can serve as biomarkers for an accurate diagnosis and treatment of HCC, and PTK2, VWF, ITGA2, CFTR, ESR1, and CXCL12 are included.

KIF14 and KIF23 Promote Cell Proliferation and Chemoresistance in HCC Cells, and Predict Worse Prognosis of Patients with HCC

Background

Hepatocellular carcinoma (HCC) is one of the most common human malignant tumors. The prognosis of HCC patients is still unsatisfying. In this study, we performed the integrated bioinformatics analysis to identify potential biomarkers and biological pathways in HCC.

Methods

Gene expression profiles were obtained from the Gene Expression Omnibus database (GSE55048, GSE55758, and GSE56545) for the screening of the common differentially expressed genes (DEGs) between HCC tissues and matched non-tumor tissues. DEGs were subjected to Gene Ontology, KEGG pathway, and Reactome pathway analysis. The hub genes were identified by using protein–protein interaction (PPI) network analysis. The hub genes in HCC were further subjected to overall survival analysis of HCC patients. The hub genes were further validated by in vitro functional assays.

Results

A total of 544 common differentially expressed genes were screened from three datasets. Gene Ontology, KEGG and Reactome analysis results showed that DEGs are significantly associated with the biological process of cell cycle, cell division, and DNA replication. PPI network analysis identified 20 hub genes from the DEGs. These hub genes except CENPE were all significantly up-regulated in the HCC tissues when compared to non-tumor tissues. The Kaplan–Meier survival analysis results showed that the high expression of the 20 hub genes was associated with shorter survival of the HCC patients. Further validation studies showed that knockdown of KIF14 and KIF23 both suppressed the proliferative potential, increased the caspase-3/-7 activity, up-regulated Bax expression, and promoted the invasive and migratory abilities in the HCC cells. In addition, knockdown of KIF14 and KIF23 enhanced chemosensitivity to cisplatin and sorafenib in the HCC cells. Finally, the high expression of KIF14 and KIF23 was associated with shorter progression-free survival, recurrence-free survival, and disease-specific survival of patients with HCC.

Conclusion

In conclusion, the present study performed the integrated bioinformatics analysis and showed that KIF14 and KIF23 silence attenuated cell proliferation, invasion, and migration, and promoted chemosensitivity of HCC cells. KIF14 and KIF23 may serve as potential biomarkers for predicting the worse prognosis of patients with HCC.

MFAP2 Promotes the Proliferation of Cancer Cells and Is Associated With a Poor Prognosis in Hepatocellular Carcinoma

Backgrounds:

Microfibril-associated protein 2 (MFAP2) is an extracellular matrix protein that regulates the function of microfibrils by interacting with fibrillin. MFAP2 has been reported to play an important role in metabolic diseases and has been shown to be significantly overexpressed in head and neck squamous cell carcinoma and Hepatocellular carcinoma (HCC). However, the molecular function and prognostic value of MFAP2 have never been reported in HCC or other tumors.

Methods:

In the present study, expression characteristics of MFAP2 in HCC, its influence on the development of HCC, as well as its function and potential mechanism in HCC were verified by Quantitative reverse transcription-polymerase chain reaction, bioinformatics data mining and in vitro cell experiments.

Results:

MFAP2 was prominently high-expressed in HCC and associated with cancer stages. HCC patients with higher MFAP2 expression displayed lower overall survival (OS) and disease-specific survival(DSS), while there was no significant difference in recurrence-free survival (RFS). In vitro experiments showed that downregulation of MFAP2 inhibited proliferation, migration level of HCC cells. Transcription factors, DNA methyltransferases, immune factors may interact with MFAP2 mRNA to promote tumor progression in HCC.

Conclusion:

These findings suggest that MFAP2 may play a key role in the development of HCC. Therefore, MFAP2 may be a valuable prognostic marker and an effective anticancer target in HCC.

Genetically Engineered Mouse Models of Liver Tumorigenesis Reveal a Wide Histological Spectrum of Neoplastic and Non-Neoplastic Liver Lesions

Genetically engineered mouse models (GEMM) are an elegant tool to study liver carcinogenesis in vivo. Newly designed mouse models need detailed (histopathological) phenotyping when described for the first time to avoid misinterpretation and misconclusions. Many chemically induced models for hepatocarcinogenesis comprise a huge variety of histologically benign and malignant neoplastic, as well as non-neoplastic, lesions. Such comprehensive categorization data for GEMM are still missing. In this study, 874 microscopically categorized liver lesions from 369 macroscopically detected liver "tumors" from five different GEMM for liver tumorigenesis were included. The histologic spectrum of diagnosis included a wide range of both benign and malignant neoplastic (approx. 82%) and non-neoplastic (approx. 18%) lesions including hyperplasia, reactive bile duct changes or oval cell proliferations with huge variations among the various models and genetic backgrounds. Our study therefore critically demonstrates that models of liver tumorigenesis can harbor a huge variety of histopathologically distinct diagnosis and, depending on the genotype, notable variations are expectable. These findings are extremely important to warrant the correct application of GEMM in liver cancer research and clearly emphasize the role of basic histopathology as still being a crucial tool in modern biomedical research.

Genome‑wide copy number analysis of circulating tumor cells in breast cancer patients with liver metastasis

The genome‑wide copy number analysis of circulating tumor cells (CTCs) provides a promising prognostic biomarker for survival in breast cancer liver metastasis (BCLM) patients. The present study aimed to confirm the prognostic value of the presence of CTCs in BCLM patients. We previously developed an assay for the genome‑wide pattern differences in copy number variations (CNVs) as an adjunct test for the routine imaging and histopathologic diagnosis methods to distinguish newly diagnosed liver metastases and recurrent liver metastases. Forty‑three breast cancer patients were selected for this study in which 23 newly diagnosed and 20 recurrent liver metastases were diagnosed by histopathology and 18F‑FDG PET/CT imaging. CTCs were counted from all patients using the CellSearch system and were confirmed by cytomorphology and three‑color immunocytochemistry. Genomic DNA of single CTCs was amplified using multiple annealing and looping based amplification cycles (MALBAC). Then, we compared the CTC numbers of newly diagnosed and recurrent BCLM patients using Illumina platforms. A high CTC frequency (>15 CTCs/7.5 ml blood) was found to be correlated with disease severity and metastatic progression, which suggests the value for CTCs in the diagnosis of BCLM in comparison with pathohistology and PET/CT imaging (P>0.05). Moreover, CTCs isolated from BCLM patients remained an independent prognostic detection factor associated with overall survival (P=0.0041). Comparison between newly diagnosed and recurrent liver metastases revealed different frequencies of CNVs (P>0.05). Notably, the CNV pattern of isolated CTCs of recurrent BCLM patients was similar to recurrent liver metastases (nearly 82% of the gain/loss regions). Functional enrichment analysis identified 25 genes as a CNV signature of BCLM. Among them, were defensin and β‑defensin genes, which are significantly associated with anti‑angiogenesis and immunomodulation signaling pathways. High CTC frequencies are effective in the evaluation and differentiation between newly diagnosed liver metastases from recurrent liver metastases. Future clinical studies will be necessary to fully determine the prognostic potential of CTC cluster signatures in patients with BCLM.

Inhibition of HIF1A-AS1 promoted starvation-induced hepatocellular carcinoma cell apoptosis by reducing HIF-1α/mTOR-mediated autophagy

BACKGROUND

Hepatocellular carcinoma (HCC) is still a major health burden in China considering its high incidence and mortality. Long non-coding RNAs (lncRNAs) were found playing vital roles in tumor progression, suggesting a new way of diagnosis and prognosis prediction, or treatment of HCC. This study was designed to investigate the role of HIF1A-AS1 during the progression of HCC and to explore its related mechanisms.

METHODS

The expression of HIF1A-AS1 was detected in 50 paired carcinoma tissues and adjacent normal tissues by quantitative real-time PCR assay. HCC cell apoptosis was induced by nutrient-deficient culture medium and detected by Cell Counting Kit-8 and flow cytometer assays. HIF1A-AS1 inhibition in HCC cells was accomplished by small interfering RNA transfection.

RESULTS

HIF1A-AS1 was overexpressed in HCC tissues and was associated with tumor size, TNM stage, and lymph node metastasis. Compared with the low HIF1A-AS1 group, the high HIF1A-AS1 group had a shorter overall survival and a worse disease-free survival. HIF1A-AS1 expression was significantly higher in HCC cell lines (7721 and Huh7) than that in normal hepatocyte cell line L02 under normal culture condition. However, under nutrient-deficient condition, HIF1A-AS1 expression was significantly increased in both HCC and normal hepatocyte cell lines and was increased with the prolongation of nutrient-free culture. Inhibition of HIF1A-AS1 promoted starvation-induced HCC cell apoptosis. Furthermore, inhibition of HIF1A-AS1 could also reduce starvation-induced HCC cell autophagy. The expression of HIF-1α and phosphorylated mTOR was significantly decreased in HCC cells after HIF1A-AS1 inhibition.

CONCLUSIONS

HIF1A-AS1, overexpressed in HCC and associated with HCC prognosis, could regulate starvation-induced HCC cell apoptosis by reducing HIF-1α/mTOR-mediated autophagy, promoting HCC cell progression.

Relationship between platelet-based models and the prognosis of patients with malignant hepatic tumors

Platelets (PLTs) are involved in tumor growth, metabolism and vascular activation. PLT-based models have been reported to have significant value on the recurrence of malignant hepatic tumors. The present study aimed to investigate the effect of PLT count and 18 PLT-based models on the prognosis of patients with malignant hepatic tumors. The clinical data from 189 patients with malignant hepatic tumors were retrospectively analyzed and used to calculate the scores of the 18 PLT-based models. Receiver operating characteristic curve was used to determine the suitable cut-off values of mortality and recurrence in patients with malignant hepatic tumors. The overall survival and cumulative recurrence rates of patients were calculated using Kaplan-Meier survival curves and the difference was analyzed using log-rank test. Multivariate analysis was performed to determine the independent risk factors of recurrence-free survival and overall survival. In the present study, 11 models were considered as predictors of mortality (P<0.05) and six models were considered as predictors of recurrence (P<0.05). The results from multivariate analysis demonstrated that vascular cancer embolus, uric acid >231 µmol/l, hemoglobin >144 g/l and the Lok index model >0.695 were considered as independent risk factors of mortality (P<0.05). Furthermore, vascular cancer embolus, PLT to lymphocyte ratio (PLR) >175 and fibrosis-4 (FIB-4) >4.82 were independent factors of recurrence (P<0.05). In addition, the results from this study indicated that the Lok-index could be considered as a predictor of the overall survival rate. In conclusion, the FIB-4 and PLR model may be valuable for predicting the recurrence-free rate of patients with malignant hepatic tumors.

The Effect of Protein FAM172A on Proliferation in HepG2 Cells and Investigation of the Possible Molecular Mechanism

Background

In our previous study, we found that the FAM172A recombinant protein could promote proliferation of L02 cells. However, the underlying mechanisms are still unknown. The present study was aimed at investigating the effect of FAM172A on proliferation of HepG2 cells and exploring the possible molecular mechanisms and its role in hepatocellular carcinoma (HCC).

Methods

Cell proliferation was measured by MTT assay. Western blot test was carried out to investigate the mechanism. Rabbit antibodies against FAM172A and membrane proteins isolated from lysate of HepG2 cell were coprecipitated and the resultant precipitates were analyzed by mass spectrum.

Results

The MTT assay showed that recombinant protein FAM172A isoform 1 (FAM172A-1) could induce HepG2 cell proliferation at the concentration of 10-100 ng/mL, while protein FAM172A isoform 3 (FAM172A-3) was at the concentration of 80-100 ng/mL. Western blot demonstrated that both FAM172A-1 and FAM172A-3 could activate the mitogen-activated protein kinase/extracellular signal-regulated protein kinase (MAPK/ERK) pathway and the phosphatidylinositol 3-kinase/threonine-protein kinase (PI3K/Akt) pathway. Mass spectrum analysis suggested that there were some membrane proteins interacting with FAM172A. Several candidate interacting proteins might mediate proliferation signals induced by FAM172A recombinant protein, including seven membrane proteins.

Conclusion

In conclusion, FAM172A recombinant protein could induce proliferation of HepG2 cells, in which the MAPK/ERK and PI3K/Akt signaling pathways might be involved. The role of FAM172A in HepG2 cell proliferation also indicated its possible involvement in HCC. The receptor of FAM172A on cells still needs to be exploited.

Systematic profiling of a novel prognostic alternative splicing signature in hepatocellular carcinoma

Alternative splicing (AS) is a pervasive and vital mechanism involved in the progression of cancer by expanding genomic encoding capacity and increasing protein complexity. However, the systematic analysis of AS in hepatocellular carcinoma (HCC) is lacking and urgently required. In the present study, genome‑wide AS events with corresponding clinical information were profiled in 290 patients with HCC from the Cancer Genome Atlas and SpliceSeq software. Functional enrichment analyses revealed the pivotal biological process of AS regulation. Univariate Cox regression analyses were performed, followed by stepwise forward multivariate analysis to develop the prognostic signatures. Spearman's correlation analyses were also used to construct potential regulatory network between the AS events and aberrant splicing factors. A total of 34,163 AS events were detected, among which 1,805 AS events from 1,314 parent genes were significantly associated with the overall survival (OS) of patients with HCC, and their parent genes serve crucial roles in HCC‑related oncogenic processes, including the p53 signaling pathway, AMPK signaling pathway and HIF‑1 signaling pathway. A prognostic AS signature was established that was found to be an independent prognostic factor for OS in stratified cohorts, harboring a noteworthy ability to distinguish between the distinct prognoses of patients with HCC (high‑risk vs. low‑risk, 827 vs. 3,125 days, P<2e‑16). Time‑dependent receiver‑-operator characteristic curves confirmed its robustness and clinical efficacy, with the area under the curves maintained >0.9 for short‑term and long‑term prognosis prediction. The splicing correlation network suggested a trend in the interactions between splicing factors and prognostic AS events, further revealing the underlying mechanism of AS in the oncogenesis of HCC. In conclusion, the present study provides a comprehensive portrait of global splicing alterations involved in the progression and HCC in addition to valuable prognostic factors for patients, which may represent as underappreciated hallmark and provide novel clues of therapeutic targets in HCC.

Roles and Regulation of Long Noncoding RNAs in Hepatocellular Carcinoma

Next-generation sequencing has uncovered thousands of long noncoding RNAs (lncRNA). Many are reported to be aberrantly expressed in various cancers, including hepatocellular carcinoma (HCC), and play key roles in tumorigenesis. This review provides an in-depth discussion of the oncogenic mechanisms reported to be associated with deregulated HCC-associated lncRNAs. Transcriptional expression of lncRNAs in HCC is modulated through transcription factors, or epigenetically by aberrant histone acetylation or DNA methylation, and posttranscriptionally by lncRNA transcript stability modulated by miRNAs and RNA-binding proteins. Seventy-four deregulated lncRNAs have been identified in HCC, of which, 52 are upregulated. This review maps the oncogenic roles of these deregulated lncRNAs by integrating diverse datasets including clinicopathologic features, affected cancer phenotypes, associated miRNA and/or protein-interacting partners as well as modulated gene/protein expression. Notably, 63 deregulated lncRNAs are significantly associated with clinicopathologic features of HCC. Twenty-three deregulated lncRNAs associated with both tumor and metastatic clinical features were also tumorigenic and prometastatic in experimental models of HCC, and eight of these mapped to known cancer pathways. Fifty-two upregulated lncRNAs exhibit oncogenic properties and are associated with prominent hallmarks of cancer, whereas 22 downregulated lncRNAs have tumor-suppressive properties. Aberrantly expressed lncRNAs in HCC exert pleiotropic effects on miRNAs, mRNAs, and proteins. They affect multiple cancer phenotypes by altering miRNA and mRNA expression and stability, as well as through effects on protein expression, degradation, structure, or interactions with transcriptional regulators. Hence, these insights reveal novel lncRNAs as potential biomarkers and may enable the design of precision therapy for HCC.

Expression of genes that control core fucosylation in hepatocellular carcinoma: Systematic review

BACKGROUND

Changes in N-linked glycosylation have been observed in the circulation of individuals with hepatocellular carcinoma. In particular, an elevation in the level of core fucosylation has been observed. However, the mechanisms through which core fucose is increased are not well understood. We hypothesized that a review of the literature and related bioinformatic review regarding six genes known to be involved in the attachment of core fucosylation, the synthesis of the fucosylation substrate guanosine diphosphate (GDP)-fucose, or the transport of the substrate into the Golgi might offer mechanistic insight into the regulation of core fucose levels.

AIM

To survey the literature to capture the involvement of genes regulating core N-linked fucosylation in hepatocellular carcinoma

METHODS

The PubMed biomedical literature database was searched for the association of hepatocellular carcinoma and each of the core fucose-related genes and their protein products. We also queried The Cancer Genome Atlas Liver hepatocellular carcinoma (LIHC) dataset for genetic, epigenetic and gene expression changes for the set of six genes using the tools at cBioportal.

RESULTS

A total of 27 citations involving one or more of the core fucosylation-related genes (FPGT, FUK, FUT8, GMDS, SLC35C1, TSTA3) and hepatocellular carcinoma were identified. The same set of gene symbols was used to query the 371 patients with liver cancer in the LIHC dataset to identify the frequency of mRNA over or under expression, as well as non-synonymous mutations, copy number variation and methylation level. Although all six genes trended to more samples displaying over expression relative to under-expression, it was noted that a number of tumor samples had undergone amplification of the genes of the de novo synthesis pathway, GMDS (27 samples) and TSTA3 (78 samples). In contrast, the other four genes had undergone amplification in 2 or fewer samples.

CONCLUSION

Amplification of genes involved in the de novo pathway for generation of GDP-fucose, GMDS and TSTA3, likely contributes to the elevated core fucose observed in hepatocellular carcinoma.