Gene expression-based recurrence prediction of hepatitis B virus-related human hepatocellular carcinoma

Prognostic implications of alpha-fetoprotein and C-reactive protein elevation in hepatocellular carcinoma following resection (PACE): a large cohort study of 2770 patients

BACKGROUND

Routine clinical staging for hepatocellular carcinoma (HCC) incorporates liver function, general health, and tumor morphology. Further refinement of prognostic assessments and treatment decisions may benefit from the inclusion of tumor biological marker alpha-fetoprotein (AFP) and systemic inflammation indicator C-reactive protein (CRP).

METHODS

Data from a multicenter cohort of 2770 HCC patients undergoing hepatectomy were analyzed. We developed the PACE risk score (Prognostic implications of AFP and CRP Elevation) after initially assessing preoperative AFP and CRP's prognostic value. Subgroup analyzes were performed in BCLC cohorts A and B using multivariable Cox analysis to evaluate the prognostic stratification ability of the PACE risk score and its complementary utility for BCLC staging.

RESULTS

Preoperative AFP ≥ 400ng/mL and CRP ≥ 10 mg/L emerged as independent predictors of poorer prognosis in HCC patients who underwent hepatectomy, leading to the creation of the PACE risk score. PACE risk score stratified patients into low, intermediate, and high-risk groups with cumulative 5-year overall (OS) and recurrence-free survival (RFS) rates of 59.6%/44.9%, 43.9%/38.4%, and 20.6%/18.0% respectively (all P < 0.001). Increased PACE risk scores correlated significantly with early recurrence and extrahepatic metastases frequency (all P < 0.001). The multivariable analysis identified intermediate and high-risk PACE scores as independently correlating with poor postoperative OS and RFS. Furthermore, the PACE risk score proficiently stratified the prognosis of BCLC stages A and B patients, with multivariable analyses demonstrating it as an independent prognostic determinant for both stages.

CONCLUSION

The PACE risk score serves as an effective tool for postoperative risk stratification, potentially supplementing the BCLC staging system.

Tumor biology, immune infiltration and liver function define seven hepatocellular carcinoma subtypes linked to distinct drivers, survival and drug response

BACKGROUND & AIMS

Tumor heterogeneity is jointly determined by the components of the tumor ecosystem (TES) including tumor cells, immune cells, stromal cells, and non-cellular components. We aimed to identify subtypes using TES-related genes and determine subtype specific drivers and treatments for hepatocellular carcinoma (HCC).

METHODS

We collected 68 genesets depicting tumor biology, immune infiltration, and liver function, totaling 2831 genes, and collected mRNA profiles and clinical data for over 6000 tumors from 65 datasets in the GEO, TCGA, ICGC, and several other databases. We designed a three-step clustering pipeline to identify subtypes. The microenvironment, genomic alteration, and drug response differences were systematically compared among subtypes.

RESULTS

Seven subtypes (TES-1/2/3/4/5/6/7) were revealed in 159 tumors from the CHCC-HBV cohort. We constructed a single sample classifier using paired genes (sscpgsTES). TES subtypes were significantly associated with multiple clinical variables including etiology, and survival in 14 of 17 cohorts and the meta-cohort. TES-1 had the poorest prognosis and highest proliferation level. Both TES-2 and TES-7 were immune-enriched, however, TES-2 had a significantly worse prognosis, and hypoxic and immunosuppressive microenvironment. TES-4 had activated Wnt pathway, driven by CTNNB1 mutation. Good prognosis TES-6 exhibited the best differentiation. TES-5 and TES-3 were considered as novel subclasses by comparing with ten previous subtyping systems. TES-5 tumors had high AFP but good overall survival, and ∼45% of them harbored AXIN1 mutation. TES-3 was immune and stromal desert, may be driven by high copy number alteration burden, and had the poorest response to immune checkpoint inhibitor. TES-1 and TES-2 had significantly lower response to transarterial chemoembolization, but they showed significantly higher sensitivity to compound YM-155.

CONCLUSIONS

Tumor ecosystem subtypes expand existing HCC subtyping systems, have distinct drivers, prognosis, and treatment vulnerabilities.

CD24 blockade as a novel strategy for cancer treatment

The CD24 protein is a heat-stable protein with a small core that undergoes extensive glycosylation. It is expressed on the surface of various normal cells, including lymphocytes, epithelial cells, and inflammatory cells. CD24 exerts its function by binding to different ligands. Numerous studies have demonstrated the close association of CD24 with tumor occurrence and progression. CD24 not only facilitates tumor cell proliferation, metastasis, and immune evasion but also plays a role in tumor initiation, thus, serving as a marker on the surface of cancer stem cells (CSCs). Additionally, CD24 induces drug resistance in various tumor cells following chemotherapy. To counteract the tumor-promoting effects of CD24, several treatment strategies targeting CD24 have been explored, such as the use of CD24 monoclonal antibodies (mAb) alone, the combination of CD24 and chemotoxic drugs, or the combination of these drugs with other targeted immunotherapeutic techniques. Regardless of the approach, targeting CD24 has demonstrated significant anti-tumor effects. Therefore, the present study focuses on anti-tumor therapy and provides a comprehensive review of the structure and fundamental physiological function of CD24 and its impact on tumor development, and suggests that targeting CD24 may represent an effective strategy for treating malignant tumors.

Comprehensive characterization of ferroptosis in hepatocellular carcinoma revealing the association with prognosis and tumor immune microenvironment

Background

Ferroptosis is a type of regulatory cell death (RCD) mode that depends on iron-mediated oxidative damage. It has the potential to improve the efficacy of tumor immunotherapy by modulating the tumor microenvironment (TME). Currently, immunotherapy has significantly improved the overall treatment strategy for advanced hepatocellular carcinoma (HCC), but the distinct immune microenvironment and high tolerance to the immune make massive differences in the immunotherapy effect of HCC patients. As a result, it is imperative to classify HCC patients who may benefit from immune checkpoint therapy. Simultaneously, the predictive value of ferroptosis in HCC and its potential role in TME immune cell infiltration also need to be further clarified.

Methods

Three ferroptosis molecular models were built on the basis of mRNA expression profiles of ferroptosis-related genes (FRGs), with notable variations in immunocyte infiltration, biological function, and survival prediction. In order to further investigate the predictive impact of immunotherapy response in HCC patients, the ferroptosis score was constructed using the principal component analysis (PCA) algorithm to quantify the ferroptosis molecular models of individual tumors.

Results

In HCC, there were three totally different ferroptosis molecular models. The ferroptosis score can be used to assess genetic variation, immunotherapy response, TME characteristics, and prognosis. Notably, tumors with low ferroptosis scores have extensive tumor mutations and immune exhaustion, which are associated with a poor prognosis and enhanced immunotherapy response.

Conclusions

Our study indicates that ferroptosis plays an indispensable role in the regulation of the tumor immune microenvironment. For HCC, the ferroptosis score is an independent prognostic indicator. Assessing the molecular model of ferroptosis in individual tumors will assist us in better understanding the characteristics of TME, predicting the effect of immunotherapy in HCC patients, and thus guiding a more reasonable immunotherapy program.

A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.

Glutamine synthetase limits β-catenin-mutated liver cancer growth by maintaining nitrogen homeostasis and suppressing mTORC1

Glutamine synthetase (GS) catalyzes de novo synthesis of glutamine that facilitates cancer cell growth. In the liver, GS functions next to the urea cycle to remove ammonia waste. As a dysregulated urea cycle is implicated in cancer development, the impact of GS's ammonia clearance function has not been explored in cancer. Here, we show that oncogenic activation of β-catenin (encoded by CTNNB1) led to a decreased urea cycle and elevated ammonia waste burden. While β-catenin induced the expression of GS, which is thought to be cancer promoting, surprisingly, genetic ablation of hepatic GS accelerated the onset of liver tumors in several mouse models that involved β-catenin activation. Mechanistically, GS ablation exacerbated hyperammonemia and facilitated the production of glutamate-derived nonessential amino acids, which subsequently stimulated mechanistic target of rapamycin complex 1 (mTORC1). Pharmacological and genetic inhibition of mTORC1 and glutamic transaminases suppressed tumorigenesis facilitated by GS ablation. While patients with hepatocellular carcinoma, especially those with CTNNB1 mutations, have an overall defective urea cycle and increased expression of GS, there exists a subset of patients with low GS expression that is associated with mTORC1 hyperactivation. Therefore, GS-mediated ammonia clearance serves as a tumor-suppressing mechanism in livers that harbor β-catenin activation mutations and a compromised urea cycle.

Integrating cell interaction with transcription factors to obtain a robust gene panel for prognostic prediction and therapies in cholangiocarcinoma

Objective: The efficacy of immunotherapy for cholangiocarcinoma (CCA) is blocked by a high degree of tumor heterogeneity. Cell communication contributes to heterogeneity in the tumor microenvironment. This study aimed to explore critical cell signaling and biomarkers induced via cell communication during immune exhaustion in CCA.

Methods: We constructed empirical Bayes and Markov random field models eLBP to determine transcription factors, interacting genes, and associated signaling pathways involved in cell-cell communication using single-cell RNAseq data. We then analyzed the mechanism of immune exhaustion during CCA progression.

Results: We found that VEGFA-positive macrophages with high levels of LGALS9 could interact with HAVCR2 to promote the exhaustion of CD8+ T cells in CCA. Transcription factors SPI1 and IRF1 can upregulate the expression of LGALS9 in VEGFA-positive macrophages. Subsequently, we obtained a panel containing 54 genes through the model, which identified subtype S2 with high expression of immune checkpoint genes that are suitable for immunotherapy. Moreover, we found that patients with subtype S2 with a higher mutation ratio of MUC16 had immune-exhausted genes, such as HAVCR2 and TIGIT. Finally, we constructed a nine-gene eLBP-LASSO-COX risk model, which was designated the tumor microenvironment risk score (TMRS).

Conclusion: Cell communication-related genes can be used as important markers for predicting patient prognosis and immunotherapy responses. The TMRS panel is a reliable tool for prognostic prediction and chemotherapeutic decision-making in CCA.

Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers

BACKGROUND

Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications.

METHODS

We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures.

RESULTS

Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively.

CONCLUSION

Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.

An MMP-based risk score strongly distinguishes prognosis in hepatocellular carcinoma after resection

Aim: To first explore the prognostic value of MMP11 and MMP15 in hepatocellular carcinoma. Methods: MMP11/MMP15 expression was immunohistochemically detected and correlated with clinicopathologic variables and survival and confirmed in publicly available databases. An MMP-based risk score (MMPRS) was established. Results: Tumoral MMP11/MMP15 expression was higher and univariately associated with crucial clinicopathologic parameters, overall survival and disease-free survival in all patients and/or many subsets. Multivariately, MMP11/MMP15 expression remained significant. Their overexpression and prognostic value were confirmed in the Ualcan and Kaplan-Meier plotter databases. Critically, the novel MMPRS integrating MMP11, MMP15 and tumor-node-metastasis stage identified subgroups with the best and worst prognoses, with much higher predictive power. Conclusion: MMP11 and MMP15 served as prognosticators in hepatocellular carcinoma. MMPRS might work more accurately.

Liver cancer heterogeneity modeled by in situ genome editing of hepatocytes

Mechanistic study and precision treatment of primary liver cancer (PLC) are hindered by marked heterogeneity, which is challenging to recapitulate in any given liver cancer mouse model. Here, we report the generation of 25 mouse models of PLC by in situ genome editing of hepatocytes recapitulating 25 single or combinations of human cancer driver genes. These mouse tumors represent major histopathological types of human PLCs and could be divided into three human-matched molecular subtypes based on transcriptomic and proteomic profiles. Phenotypical characterization identified subtype- or genotype-specific alterations in immune microenvironment, metabolic reprogramming, cell proliferation, and expression of drug targets. Furthermore, single-cell analysis and expression tracing revealed spatial and temporal dynamics in expression of pyruvate kinase M2 (Pkm2). Tumor-specific knockdown of Pkm2 by multiplexed genome editing reversed the Warburg effect and suppressed tumorigenesis in a genotype-specific manner. Our study provides mouse PLC models with defined genetic drivers and characterized phenotypical heterogeneity suitable for mechanistic investigation and preclinical testing.

Recurrence Risk of Liver Cancer Post-hepatectomy Using Machine Learning and Study of Correlation With Immune Infiltration

Postoperative recurrence of liver cancer is the main obstacle to improving the survival rate of patients with liver cancer. We established an mRNA-based model to predict the risk of recurrence after hepatectomy for liver cancer and explored the relationship between immune infiltration and the risk of recurrence after hepatectomy for liver cancer. We performed a series of bioinformatics analyses on the gene expression profiles of patients with liver cancer, and selected 18 mRNAs as biomarkers for predicting the risk of recurrence of liver cancer using a machine learning method. At the same time, we evaluated the immune infiltration of the samples and conducted a joint analysis of the recurrence risk of liver cancer and found that B cell, B cell naive, T cell CD4⁺ memory resting, and T cell CD4⁺ were significantly correlated with the risk of postoperative recurrence of liver cancer. These results are helpful for early detection, intervention, and the individualized treatment of patients with liver cancer after surgical resection, and help to reveal the potential mechanism of liver cancer recurrence.

An Overview of the Genomic Characterization of Hepatocellular Carcinoma

Tumor classifications based on alterations in the genome, epigenome, or proteome have revealed distinct tumor subgroups that are associated with clinical outcomes. Several landmark studies have demonstrated that such classifications can significantly improve patient outcomes by enabling tailoring of therapy to specific alterations in cancer cells. Since cancer cells accumulate numerous alterations in many cancer-related genes, it is a daunting task to find and confirm important cancer-promoting alterations as therapeutic targets or biomarkers that can predict clinical outcomes such as survival and response to treatments. To aid further advances, we provide here an overview of the current understanding of molecular and genomic subtypes of hepatocellular carcinoma (HCC). System-level integration of data from multiple studies and development of new technical platforms for analyzing patient samples hold great promise for the discovery of new targets for treatment and correlated biomarkers, leading to personalized medicine for treatment of HCC patients.

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges

Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

Expression, clinicopathologic and prognostic significance of plasminogen activator inhibitor 1 in hepatocellular carcinoma

OBJECTIVES

Thus far, biological roles of plasminogen activator inhibitor 1 (PAI1) in hepatocellular carcinoma (HCC) remain controversial. Moreover, its expression, clinicopathologic and prognostic significance in HCC have not been comprehensively investigated, therefore needing further evidence.

METHODS

PAI1 expression was measured, using tissue microarray-based immunohistochemical staining, in matched HCC and adjacent liver samples from 178 patients with HCC after curative resection. The correlations of PAI1 H-scores with clinicopathologic variables and survival were further evaluated. Its prognostic value was finally confirmed in some public databases.

RESULTS

It was found that PAI1 expression was significantly higher in HCC than in adjacent liver tissues. Moreover, high PAI1 expression was more frequent in those with multiple lesions. Univariate analyses showed that PAI1 expression was negatively associated with both overall and relapse-free survival. Although PAI1 expression was not statistically significant in multivariate Cox regression test, combination of it with TNM stage effectively distinguished survival and relapse, and served as an independent prognostic factor. In the online available datasets of HCC and liver cancer used, SERPINE1, the gene encoding PAI1, was also revealed to be prognostic.

CONCLUSIONS

Our data suggested that high PAI1 expression was predictive for unfavorable biological behavior and long-term prognosis in HCC.

Low ARID1A Expression is Associated with Poor Prognosis in Hepatocellular Carcinoma

AT-rich interactive domain 1A (ARID1A) is one of the most frequently mutated genes in hepatocellular carcinoma (HCC), but its clinical significance is not clarified. We aimed to evaluate the clinical significance of low ARID1A expression in HCC. By analyzing the gene expression data of liver from Arid1a-knockout mice, hepatic Arid1a-specific gene expression signature was identified (p < 0.05 and 0.5-fold difference). From this signature, a prediction model was developed to identify tissues lacking Arid1a activity and was applied to gene expression data from three independent cohorts of HCC patients to stratify patients according to ARID1A activity. The molecular features associated with loss of ARID1A were analyzed using The Cancer Genome Atlas (TCGA) multi-platform data, and Ingenuity Pathway Analysis (IPA) was done to uncover potential signaling pathways associated with ARID1A loss. ARID1A inactivation was clinically associated with poor prognosis in all three independent cohorts and was consistently related to poor prognosis subtypes of previously reported gene signatures (highly proliferative, hepatic stem cell, silence of Hippo pathway, and high recurrence signatures). Immune activity, indicated by significantly lower IFNG6 and cytolytic activity scores and enrichment of regulatory T-cell composition, was lower in the ARID1A-low subtype than ARID1A-high subtype. Ingenuity pathway analysis revealed that direct upstream transcription regulators of the ARID1A signature were genes associated with cell cycle, including E2F group, CCND1, and MYC, while tumor suppressors such as TP53, SMAD3, and CTNNB1 were significantly inhibited. ARID1A plays an important role in immune activity and regulating multiple genes involved in HCC development. Low-ARID1A subtype was associated with poor clinical outcome and suggests the possibility of ARID1A as a prognostic biomarker in HCC patients.

Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Pathways Associated With Clinical Outcome in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for approximately 85-90% of all liver cancer cases and has poor relapse-free survival. There are many gene expression studies that have been performed to elucidate the genetic landscape and driver pathways leading to HCC. However, existing studies have been limited by the sample size and thus the pathogenesis of HCC is still unclear. In this study, we performed an integrated characterization using four independent datasets including 320 HCC samples and 270 normal liver tissues to identify the candidate genes and pathways in the progression of HCC. A total of 89 consistent differentially expression genes (DEGs) were identified. Gene-set enrichment analysis revealed that these genes were significantly enriched for cellular response to zinc ion in biological process group, collagen trimer in the cellular component group, extracellular matrix (ECM) structural constituent conferring tensile strength in the molecular function group, protein digestion and absorption, mineral absorption and ECM-receptor interaction. Network system biology based on the protein-protein interaction (PPI) network was also performed to identify the most connected and important genes based on our DEGs. The top five hub genes including osteopontin (SPP1), Collagen alpha-2(I) chain (COL1A2), Insulin-like growth factor I (IGF1), lipoprotein A (LPA), and Galectin-3 (LGALS3) were identified. Western blot and immunohistochemistry analysis were employed to verify the differential protein expression of hub genes in HCC patients. More importantly, we identified that these five hub genes were significantly associated with poor disease-free survival and overall survival. In summary, we have identified a potential clinical significance of these genes as prognostic biomarkers for HCC patients who would benefit from experimental approaches to obtain optimal outcome.

Prognostic significance of an invasive leader cell-derived mutation cluster on chromosome 16q

BACKGROUND

Intratumoral heterogeneity is defined by subpopulations with varying genotypes and phenotypes. Specialized, highly invasive leader cells and less invasive follower cells are phenotypically distinct subpopulations that cooperate during collective cancer invasion. Because leader cells are a rare subpopulation that would be missed by bulk sequencing, a novel image-guided genomics platform was used to precisely select this subpopulation. This study identified a novel leader cell mutation signature and tested its ability to predict prognosis in non-small cell lung cancer (NSCLC) patient cohorts.

METHODS

Spatiotemporal genomic and cellular analysis was used to isolate and perform RNA sequencing on leader and follower populations from the H1299 NSCLC cell line, and it revealed a leader-specific mutation cluster on chromosome 16q. Genomic data from patients with lung squamous cell carcinoma (LUSC; n = 475) and lung adenocarcinoma (LUAD; n = 501) from The Cancer Genome Atlas were stratified by 16q mutation cluster (16qMC) status (16qMC+ vs 16qMC-) and compared for overall survival (OS), progression-free survival (PFS), and gene set enrichment analysis (GSEA).

RESULTS

Poorer OS, poorer PFS, or both were found across all stages and among early-stage patients with 16qMC+ tumors within the LUSC and LUAD cohorts. GSEA revealed 16qMC+ tumors to be enriched for the expression of metastasis- and survival-associated gene sets.

CONCLUSIONS

This represents the first leader cell mutation signature identified in patients and has the potential to better stratify high-risk NSCLC and ultimately improve patient outcomes.

Mitoribosome Defect in Hepatocellular Carcinoma Promotes an Aggressive Phenotype with Suppressed Immune Reaction

Mitochondrial ribosomes (mitoribosomes), the specialized translational machinery for mitochondrial genes, exclusively encode the subunits of the oxidative phosphorylation (OXPHOS) system. Although OXPHOS dysfunctions are associated with hepatic disorders including hepatocellular carcinoma (HCC), their underlying mechanisms remain poorly elucidated. In this study, we aimed to investigate the effects of mitoribosome defects on OXPHOS and HCC progression. By generating a gene signature from HCC transcriptome data, we developed a scoring system, i.e., mitoribosome defect score (MDS), which represents the degree of mitoribosomal defects in cancers. The MDS showed close associations with the clinical outcomes of patients with HCC and with gene functions such as oxidative phosphorylation, cell-cycle activation, and epithelial-mesenchymal transition. By analyzing immune profiles, we observed that mitoribosomal defects are also associated with immunosuppression and evasion. Taken together, our results provide new insights into the roles of mitoribosome defects in HCC progression.

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A set of down-regulated MRPs in HCC cause mitoribosomal defects

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Mitoribosomal defects are linked to aggressive molecular features and poor prognosis

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Mitoribosomal defects in HCC are associated with immunosuppression and evasion

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TGF-β signaling pathway is a crucial mechanism to mediate mitoribosomal defects in HCC

High MMP14 expression is predictive of poor prognosis in resectable hepatocellular carcinoma

Matrix metalloproteinase 14 (MMP14) has been found to play multiple biological roles in cancers, including hepatocellular carcinoma (HCC). Up to now, its expression, clinicopathological and prognostic implications in HCC have not been comprehensively investigated. In the present study, MMP14 expression was detected, using tissue microarray-based immunohistochemical staining, in paired HCC and adjacent liver (AL) samples from 260 patients who underwent radical hepatectomy. The associations of MMP14 staining H-scores with clinicopathological parameters, overall and disease-free survival were then evaluated. Finally, its expression and prognostic value were confirmed in some online publicly available databases. It was shown that MMP14 expression was significantly higher in HCC than in AL tissues (p=0.035). Furthermore, MMP14 expression correlated positively with tumour size, Edmondson-Steiner grade and α-fetoprotein level (p<0.05). For survival, MMP14 expression was negatively associated with both overall and disease-free survival in univariate analyses (p<0.05), while it remained statistically significant for disease-free survival by multivariate Cox regression test. In the Ualcan and Kaplan-Meier Plotter databases, MMP14 was also revealed to be overexpressed and prognostic. Taken together, our study indicated that high MMP14 expression was predictive for unfavourable biological behaviours and long-term prognosis in resectable HCC.

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

Background

Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility.

Results

The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems.

Conclusion

This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github.com/LokmaneChebouba/key-pipeand contains as testing data all the data used in this paper.

A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC.

METHODS

We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice.

RESULTS

Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice.

CONCLUSION

This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

Discovering master regulators in hepatocellular carcinoma: one novel MR, SEC14L2 inhibits cancer cells

Identification of master regulator (MR) genes offers a relatively rapid and efficient way to characterize disease-specific molecular programs. Since strong consensus regarding commonly altered MRs in hepatocellular carcinoma (HCC) is lacking, we generated a compendium of HCC datasets from 21 studies and identified a comprehensive signature consisting of 483 genes commonly deregulated in HCC. We then used reverse engineering of transcriptional networks to identify the MRs that underpin the development and progression of HCC. After cross-validation in different HCC datasets, systematic assessment using patient-derived data confirmed prognostic predictive capacities for most HCC MRs and their corresponding regulons. Our HCC signature covered well-established liver cancer hallmarks, and network analyses revealed coordinated interaction between several MRs. One novel MR, SEC14L2, exerted an anti-proliferative effect in HCC cells and strongly suppressed tumor growth in a mouse model. This study advances our knowledge of transcriptional MRs potentially involved in HCC development and progression that may be targeted by specific interventions.

Genomic Perspective on Mouse Liver Cancer Models

Selecting the most appropriate mouse model that best recapitulates human hepatocellular carcinoma (HCC) allows translation of preclinical mouse studies into clinical studies. In the era of cancer genomics, comprehensive and integrative analysis of the human HCC genome has allowed categorization of HCC according to molecular subtypes. Despite the variety of mouse models that are available for preclinical research, there is a lack of evidence for mouse models that closely resemble human HCC. Therefore, it is necessary to identify the accurate mouse models that represent human HCC based on molecular subtype as well as histologic aggressiveness. In this review, we summarize the mouse models integrated with human HCC genomic data to provide information regarding the models that recapitulates the distinct aspect of HCC biology and prognosis based on molecular subtypes.

Overexpression of CDCA5, KIF4A, TPX2, and FOXM1 Coregulated Cell Cycle and Promoted Hepatocellular Carcinoma Development

This study aimed to identify key functional modules and genes in functional module involved in hepatocellular carcinoma (HCC) development. The microarray data set GSE54236 was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between HCC, and normal samples were identified by Limma. DAVID was used to identify the gene ontology terms these genes enriched. The co-expression network was constructed based on Pearson correlation coefficient between gene expression values, and the functional modules these DEGs obviously enriched in were recognized through GraphWeb. Then, based on the genes related to the development of HCC, the DEGs interacting with HCC-associated genes were spotted. Finally, survival analysis and real-time quantitative polymerase chain reaction were performed. Totally, 427 upregulated (e.g., cell division cycle associated 5 [CDCA5], kinesin family member 4A [KIF4A], TPX2 microtubule nucleation factor [TPX2]) and 313 downregulated (e.g., metallothionein 1E [MT1E]) DEGs were identified in HCC. Besides, CDCA5, KIF4A, and TPX2 had interacting relationship and played important roles in HCC development by interrelating with HCC-related gene, forkhead box M1 (FOXM1). Furthermore, CDCA5, KIF4A, TPX2, and FOXM1 obviously enriched in cell cycle-related functional module, whereas MT1E enriched in mineral absorption module in Kyoto Encyclopedia of Genes and Genomes. CDCA5, KIF4A, and TPX2 expression were increased in HCC cells, and their high expressions were related to poor prognosis. Overexpression of CDCA5, KIF4A, TPX2, and FOXM1 coregulated cell cycle and thereby promoted the development of HCC. The finding provided potential targets for the study and treatment of HCC.

Tspan15 Is a New Stemness-Related Marker in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the second cause of cancer-related deaths worldwide. A clearer understanding of the molecular mechanisms underlying tumor growth and invasiveness remains crucial for developing new therapies. Here, the expression of tetraspanins, a family of plasma membrane organizers involved in tumor progression, has been addressed. Integrative approaches combining transcriptomics and bioinformatics allow demonstrating the induced and heterogeneous expression of Tspan15 in HCC. Tspan15 positive tumors exhibit signatures related to hepatic progenitor cells as well as recurrence of cancer. Immunohistochemistry experiments confirm Tspan15 expression in the subset of HCC expressing stemness-related markers such as EpCAM and Cytokeratin-19. Functional networks reveal that most of these genes expressed in correlation to Tspan15 support cell proliferation. Furthermore, Tspan15 overexpression in the hepatoma cell line HepG2 significantly increases cell proliferation. A quantitative proteomic analysis of the secretome reveals a higher abundance of the protein connective tissue growth factor (CTGF), a pleiotropic matricellular signaling protein. Proteomic profiling of Tspan15 complexes allows identifying numerous membrane proteins including several growth factor receptors. Finally, Tspan15 increases ERK1/2 phosphorylation that directly controls CTGF expression and secretion. In conclusion, Tspan15 is a new stemness-related marker in HCC which exhibits high potential of tumor growth and recurrence.

Identification of hedgehog signaling as a potential oncogenic driver in an aggressive subclass of human hepatocellular carcinoma: A reanalysis of the TCGA cohort

Hepatocellular carcinoma (HCC) is a heterogeneous disease and the second most common cause of cancer-related death worldwide. Marked developments in genomic technologies helped scientists to understand the heterogeneity of HCC and identified multiple HCC-related molecular subclasses. An integrative analysis of genomic datasets including 196 patients from The Cancer Genome Atlas (TCGA) group has recently reported a new HCC subclass, which contains three subgroups (iCluster1, iCluster2, and iCluster3). However, the transcriptional molecular characteristics underlying the iClusters have not been thoroughly investigated. Herein, we identified a more aggressive subset of HCC patients in the iCluster1, and re-clustered the TCGA samples into novel HCC subclasses referred to as aggressive (Ag), moderate-aggressive (M-Ag), and less-aggressive (L-Ag) subclasses. The Ag subclass had a greater predictive power than the TCGA iCluster1, and a higher level of alpha fetoprotein, microscopic vascular invasion, immune infiltration, isocitrate dehydrogenase 1/2 mutation status, and a worse survival than M-Ag and L-Ag subclasses. Global transcriptomic analysis showed that activation of hedgehog signaling in the Ag subclass may play key roles in tumor development of aggressive HCC. GLI1, a key transcriptional regulator of hedgehog signaling upregulated in the Ag subclass, was correlated with poor prognosis of HCC, and may be a potential prognostic biomarker and therapeutic target for Ag subclass HCC patients.

Molecular predictors of prevention of recurrence in HCC with sorafenib as adjuvant treatment and prognostic factors in the phase 3 STORM trial

OBJECTIVE

Sorafenib is the standard systemic therapy for advanced hepatocellular carcinoma (HCC). Survival benefits of resection/local ablation for early HCC are compromised by 70% 5-year recurrence rates. The phase 3 STORM trial comparing sorafenib with placebo as adjuvant treatment did not achieve its primary endpoint of improving recurrence-free survival (RFS). The biomarker companion study BIOSTORM aims to define (A) predictors of recurrence prevention with sorafenib and (B) prognostic factors with B level of evidence.

DESIGN

Tumour tissue from 188 patients randomised to receive sorafenib (83) or placebo (105) in the STORM trial was collected. Analyses included gene expression profiling, targeted exome sequencing (19 known oncodrivers), immunohistochemistry (pERK, pVEGFR2, Ki67), fluorescence in situ hybridisation (VEGFA) and immunome. A gene signature capturing improved RFS in sorafenib-treated patients was generated. All 70 RFS events were recurrences, thus time to recurrence equalled RFS. Predictive and prognostic value was assessed using Cox regression models and interaction test.

RESULTS

BIOSTORM recapitulates clinicopathological characteristics of STORM. None of the biomarkers tested (related to angiogenesis and proliferation) or previously proposed gene signatures, or mutations predicted sorafenib benefit or recurrence. A newly generated 146-gene signature identifying 30% of patients captured benefit to sorafenib in terms of RFS (p of interaction=0.04). These sorafenib RFS responders were significantly enriched in CD4+ T, B and cytolytic natural killer cells, and lacked activated adaptive immune components. Hepatocytic pERK (HR=2.41; p=0.012) and microvascular invasion (HR=2.09; p=0.017) were independent prognostic factors.

CONCLUSION

In BIOSTORM, only hepatocytic pERK and microvascular invasion predicted poor RFS. No mutation, gene amplification or previously proposed gene signatures predicted sorafenib benefit. A newly generated multigene signature associated with improved RFS on sorafenib warrants further validation.

TRIAL REGISTRATION NUMBER

NCT00692770.

MicroRNA-1271 functions as a potential tumor suppressor in hepatitis B virus-associated hepatocellular carcinoma through the AMPK signaling pathway by binding to CCNA1

Hepatocellular carcinoma (HCC) is mainly associated with hepatitis B virus (HBV) infection and characterized by metastasizing and infiltrating adjacent and distant tissues. Notably, microRNA-1271 (miR-1271) is a tumor suppressor in various cancers. Therefore, we evaluate the ability of miR-1271 to influence cell proliferation, migration, invasion, and apoptosis in HBV-associated HCC through the Adenosine monophosphate-activated protein kinase (AMPK) signaling pathway via targeting CCNA1. HBV-associated HCC and adjacent normal tissues were collected to identify the expression of miR-1271 and CCNA1. To verify the relationship between miR-1271 and CCNA1, we used bioinformatics prediction and the dual-luciferase reporter gene assay. The effects of miR-1271 on HBV-associated HCC cell behaviors were investigated by treatment of the miR-1271 mimic, the miR-1271 inhibitor, or small interfering RNA against CCNA1. The HBV-DNA quantitative assay, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromid assay, scratch test, transwell assay, and flow cytometry were used to detect HBV-DNA replication, cell proliferation, invasion, migration, and apoptosis. MiR-1271 showed a low expression, whereas CCNA1 showed a high expression in HBV-associated HCC tissues. We identified that miR-1271 targeted and negatively regulated CCNA1. Upregulated miR-1271 and downregulated CCNA1 inhibited the HBV-associated HCC cell HBV-DNA replication, proliferation, migration, and invasion, while accelerating apoptosis by activating the AMPK signaling pathway. MiR-1271 promotes the activation of the AMPK signaling pathway by binding to CCNA1, whereby miR-1271 suppresses HBV-associated HCC progression. This study points to a potential therapeutic approach of downregulation of miR-1271 in HBV-associated HCC treatment.

NELFE-Dependent MYC Signature Identifies a Unique Cancer Subtype in Hepatocellular Carcinoma

The MYC oncogene is dysregulated in approximately 30% of liver cancer. In an effort to exploit MYC as a therapeutic target, including in hepatocellular carcinoma (HCC), strategies have been developed on the basis of MYC amplification or gene translocation. Due to the failure of these strategies to provide accurate diagnostics and prognostic value, we have developed a Negative Elongation Factor E (NELFE)-Dependent MYC Target (NDMT) gene signature. This signature, which consists of genes regulated by MYC and NELFE, an RNA binding protein that enhances MYC-induced hepatocarcinogenesis, is predictive of NELFE/MYC-driven tumors that would otherwise not be identified by gene amplification or translocation alone. We demonstrate the utility of the NDMT gene signature to predict a unique subtype of HCC, which is associated with a poor prognosis in three independent cohorts encompassing diverse etiologies, demographics, and viral status. The application of gene signatures, such as the NDMT signature, offers patients access to personalized risk assessments, which may be utilized to direct future care.

Genetic variants and Expression of Cytochrome p450 Oxidoreductase Predict Postoperative Survival in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma

Our current study investigates the prognostic values of genetic variants and mRNA expression of cytochrome p450 oxidoreductase (POR) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). A total of 19 candidate single nucleotide polymorphisms (SNPs) located in the exons of POR were genotyped using Sanger sequencing from 476 HBV-related HCC patients who underwent hepatectomy between 2003 and 2013. The mRNA expression of POR in 212 patients with HBV-related HCC was obtained from GSE14520 dataset. Survival analysis was performed to investigate the association of POR variants and mRNA expression with overall survival (OS) and recurrence-free survival (RFS). Nomograms were used to predict the prognosis of HBV-related HCC patients. Gene set enrichment analysis (GSEA) was used to investigate the mechanism of POR in HBV-related HCC prognosis. The polymorphism POR-rs1057868 was significantly associated with HBV-related HCC OS (CT/TT vs. CC, hazard ratio [HR] = 0.69, 95% confidence interval [CI] = [0.54, 0.88], P = 0.003), but not significantly associated with RFS (CT/TT vs. CC, P = 0.378). POR mRNA expression was also significantly associated with HBV-related HCC OS (high vs. low, HR = 0.61, 95% CI = [0.38, 0.97], P = 0.036), but not significantly associated with the RFS (high vs. low, P = 0.201). Two nomograms were developed to predict the HBV-related HCC OS. Furthermore, GSEA suggests that multiple gene sets were significantly enriched in liver cancer survival and recurrence, as well as POR-related target therapy in the liver. In conclusion, our study suggests that POR-rs1057868 and mRNA expression may serve as a potential postoperative prognosis biomarker in HBV-related HCC.

Plasminogen activator inhibitor 2 (PAI2) inhibits invasive potential of hepatocellular carcinoma cells in vitro via uPA- and RB/E2F1-related mechanisms

BACKGROUND

Plasminogen activator inhibitor 2 (PAI2) has been shown to be associated with invasive phenotypes and prognosis in hepatocellular carcinoma (HCC). However, its biological roles and underlying mechanisms in invasion of HCC have not been explored. The present study aimed to address the issues.

METHODS

First, sub-lines in that PAI2 was stably overexpressed and silenced were established based on MHCC97H and BEL7402 cell lines, respectively. Wound-healing and transwell assays were applied to evaluate cell migration and invasion. Urokinase-type plasminogen activator (uPA) activity was measured using an ELISA kit. Real-time RT-PCR and western blotting were used to show gene expression at mRNA and protein levels. E2F1 expression in human specimens was determined by tissue microarray-based immunohistochemical staining.

RESULTS

The sub-lines, MHCC97H-PAI2 and BEL7402-siPAI2, were successfully established. The two sub-lines carried much lower and higher migration and invasion powers, respectively, in contrast to the controls. In MHCC97H-PAI2 sub-line, intra-medium uPA activity was significantly decreased, while RB expression was obviously elevated, compared with the controls. The BEL7402-siPAI2 sub-line presented the opposite trend. To identify the role of RB/E2F1 pathway, we transiently overexpressed E2F1 in MHCC97H-PAI2 sub-line, and largely reversed the inhibitory effects of PAI2 on cell migration and invasion, through regulating multiple matrix metalloproteinases and epithelial-mesenchymal transition. In HCC specimens, E2F1 expression was much higher in tumor than in non-tumor tissues, and was significantly related to Edmondson-Steiner grade, overall as well as tumor-free survival.

CONCLUSIONS

Our data suggest that PAI2 inhibits invasive potential of HCC cells via uPA- and RB/E2F1-related mechanisms.

An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

Background: Nowadays, clinical treatment outcomes of patients with hepatocellular carcinoma (HCC) have been improved. However, due to the complexity of the molecular mechanisms, the recurrence rate and mortality in HCC inpatients are still at a high level. Therefore, there is an urgent need in screening biomarkers of HCC to show therapeutic effects and improve the prognosis. Methods: In this study, we aim to establish a gene signature that can predict the prognosis of HCC patients by downloading and analyzing RNA sequencing data and clinical information from three independent public databases. Firstly, we applied the limma R package to analyze biomarkers by the genetic data and clinical information downloaded from the Gene Expression Omnibus database (GEO), and then used the least absolute shrinkage and selection operator (LASSO) Cox regression and survival analysis to establish a gene signature and a prediction model by data from the Cancer Genome Atlas (TCGA). Besides, messenger RNA (mRNA) and protein expressions of the six-gene signature were explored using Oncomine, Human Protein Atlas (HPA) and the International Cancer Genome Consortium (ICGC). Results: A total of 8,306 differentially expressed genes (DEGs) were obtained between HCC (n = 115) and normal tissues (n = 52). Top 5,000 significant genes were selected and subjected to the weighted correlation network analysis (WGCNA), which constructed nine gene co-expression modules that assign these genes to different modules by cluster dendrogram trees. By analyzing the most significant module (red module), six genes (SQSTM1, AHSA1, VNN2, SMG5, SRXN1, and GLS) were screened by univariate, LASSO, and multivariate Cox regression analysis. By a survival analysis with the HCC data in TCGA, we established a nomogram based on the six-gene signature and multiple clinicopathological features. The six-gene signature was then validated as an independent prognostic factor in independent HCC cohort from ICGC. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the six-gene signature and nomogram. Besides, overexpression of the six genes at the mRNA and protein levels was validated using Oncomine and HPA, respectively. Conclusion: The predictive six-gene signature and nomograms established in this study can assist clinicians in selecting personalized treatment for patients with HCC.

Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis

BACKGROUND

Redox metabolism is often considered a potential target for cancer treatment, but a systematic examination of redox responses in hepatocellular carcinoma (HCC) is missing.

METHODS

Here, we employed systems biology and biological network analyses to reveal key roles of genes associated with redox metabolism in HCC by integrating multi-omics data.

FINDINGS

We found that several redox genes, including 25 novel potential prognostic genes, are significantly co-expressed with liver-specific genes and genes associated with immunity and inflammation. Based on an integrative analysis, we found that HCC tumors display antagonistic behaviors in redox responses. The two HCC groups are associated with altered fatty acid, amino acid, drug and hormone metabolism, differentiation, proliferation, and NADPH-independent vs -dependent antioxidant defenses. Redox behavior varies with known tumor subtypes and progression, affecting patient survival. These antagonistic responses are also displayed at the protein and metabolite level and were validated in several independent cohorts. We finally showed the differential redox behavior using mice transcriptomics in HCC and noncancerous tissues and associated with hypoxic features of the two redox gene groups.

INTERPRETATION

Our integrative approaches highlighted mechanistic differences among tumors and allowed the identification of a survival signature and several potential therapeutic targets for the treatment of HCC.

Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes

Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/β-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

Clinical significance of APOB inactivation in hepatocellular carcinoma

Recent findings from The Cancer Genome Atlas project have provided a comprehensive map of genomic alterations that occur in hepatocellular carcinoma (HCC), including unexpected mutations in apolipoprotein B (APOB). We aimed to determine the clinical significance of this non-oncogenetic mutation in HCC. An Apob gene signature was derived from genes that differed between control mice and mice treated with siRNA specific for Apob (1.5-fold difference; P < 0.005). Human gene expression data were collected from four independent HCC cohorts (n = 941). A prediction model was constructed using Bayesian compound covariate prediction, and the robustness of the APOB gene signature was validated in HCC cohorts. The correlation of the APOB signature with previously validated gene signatures was performed, and network analysis was conducted using ingenuity pathway analysis. APOB inactivation was associated with poor prognosis when the APOB gene signature was applied in all human HCC cohorts. Poor prognosis with APOB inactivation was consistently observed through cross-validation with previously reported gene signatures (NCIP A, HS, high-recurrence SNUR, and high RS subtypes). Knowledge-based gene network analysis using genes that differed between low-APOB and high-APOB groups in all four cohorts revealed that low-APOB activity was associated with upregulation of oncogenic and metastatic regulators, such as HGF, MTIF, ERBB2, FOXM1, and CD44, and inhibition of tumor suppressors, such as TP53 and PTEN. In conclusion, APOB inactivation is associated with poor outcome in patients with HCC, and APOB may play a role in regulating multiple genes involved in HCC development.

Integrated Genomic Comparison of Mouse Models Reveals Their Clinical Resemblance to Human Liver Cancer

Hepatocellular carcinoma (HCC) is a heterogeneous disease. Mouse models are commonly used as preclinical models to study hepatocarcinogenesis, but how well these models recapitulate molecular subtypes of human HCC is unclear. Here, integration of genomic signatures from molecularly and clinically defined human HCC (n = 11) and mouse models of HCC (n = 9) identified the mouse models that best resembled subtypes of human HCC and determined the clinical relevance of each model. Mst1/2 knockout (KO), Sav1 KO, and SV40 T antigen mouse models effectively recapitulated subtypes of human HCC with a poor prognosis, whereas the Myc transgenic model best resembled human HCCs with a more favorable prognosis. The Myc model was also associated with activation of β-catenin. E2f1, E2f1/Myc, E2f1/Tgfa, and diethylnitrosamine (DEN)-induced models were heterogeneous and were unequally split into poor and favorable prognoses. Mst1/2 KO and Sav1 KO models best resemble human HCC with hepatic stem cell characteristics. Applying a genomic predictor for immunotherapy, the six-gene IFNγ score, the Mst1/2 KO, Sav1 KO, SV40, and DEN models were predicted to be the least responsive to immunotherapy. Further analysis showed that elevated expression of immune-inhibitory genes (Cd276 and Nectin2/Pvrl2) in Mst1/2 KO, Sav1 KO, and SV40 models and decreased expression of immune stimulatory gene (Cd86) in the DEN model might be accountable for the lack of predictive response to immunotherapy.Implication: The current genomic approach identified the most relevant mouse models to human liver cancer and suggests immunotherapeutic potential for the treatment of specific subtypes. Mol Cancer Res; 16(11); 1713-23. ©2018 AACR.

Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer

Gastric cancer (GC) has a high incidence and mortality rate. If discovered late, GC tends to have a poor prognosis. Improvements in the prognostic accuracy of GC through combined analysis of multiple relevant genes and clinical factors may solve this problem. In the present study, GSE62254 (including 300 GC tissues), obtained from the Gene Expression Omnibus database, was used as a training set, and the mRNA-sequencing data of GC (including 384 GC tissues) downloaded from the Cancer Genome Atlas database served as a validation set. Based on the t-test and Wilcoxon test, the significantly differentially expressed genes (DEGs) were obtained by screening the intersecting DEGs. The prognosis-associated genes and clinical factors were identified using Cox regression analysis in the R survival package. The optimal prognosis-associated pathways were examined using the Cox-proportional hazards (Cox-PH) model in the R penalized package. Finally, risk prediction models were constructed and validated using the Cox-PH model and the Kaplan-Meier method, respectively. There were a total of 382 significant DEGs, including 268 upregulated genes and 114 downregulated genes. A total of 50 prognosis-associated genes were identified, 16 optimal prognosis-associated pathways (including mitochondrial pathway and the tyrosine-protein kinase JAK-signal transducer and activator of transcription signaling pathway, which involve caspase 7, phosphoinositide-3-kinase regulatory subunit 3, peroxisome proliferator-activated receptor γ and collagen triple helix repeat containing 1) and four prognosis-associated clinical factors [including Pathologic_N, Pathologic_stage, mutL homolog 1 (MLH1) mutation and recurrence]. The pathway- and clinical-factor-based risk prediction model exhibited marked prognostic accuracy. The clinical-factor-based risk prediction model with improved P-values for prognosis prediction may be superior to the pathway-based risk prediction model in predicting the prognosis of GC patients.

Altered capicua transcriptional repressor gene expression exhibits distinct prognostic value for isocitrate dehydrogenase-mutant oligodendroglial tumors

To evaluate the prognostic significance of the altered expression of capicua transcriptional repressor (CIC) in isocitrate dehydrogenase (IDH)-mutant oligodendroglial tumors, a cohort of 54 IDH-mutant oligodendroglial tumors (designated as the Xijing cohort) were examined by immunohistochemistry (IHC), and two public expression data sets from The Cancer Genome Atlas (TCGA; n=265) and the Gene Expression Omnibus (GEO; n=45) were analyzed in the present study. The prognostic value was evaluated by survival analysis and Cox hazards models. Overall survival (OS) was investigated with Kaplan-Meier curves and log-rank tests. Gene set enrichment analysis (GSEA) was also performed to characterize the functional profiles of each subgroup. It was revealed that in IDH-mutant, 1p/19q co-deleted oligodendroglial tumors, higher CIC expression (at mRNA and protein levels) was associated with a more favorable OS time (log-rank P-values: TCGA, P=0.034; GEO, P=0.012; Xijing cohort, P=0.029). By contrast, among IDH-mutant, 1p/19q intact tumors, higher CIC expression was associated with poorer OS time (log-rank P-values: TCGA, P=0.007; GEO, P=0.017; Xijing cohort, P=0.012). To the best of our knowledge, this is the first study demonstrating the distinct prognostic value of altered CIC expression with regard to the 1p/19q status among IDH-mutant oligodendroglial tumors. The dual roles of CIC may be influenced by its transcriptional regulatory activity and the consequent functional profiles. Additionally, a simple risk classification scheme based on CIC expression alone is proposed for the optimal prediction of prognosis in patients with oligodendroglial tumors.

Human hepatocellular carcinomas with a periportal phenotype have the lowest potential for early recurrence after curative resection

Hepatocellular carcinomas (HCCs) exhibit a diversity of molecular phenotypes, raising major challenges in clinical management. HCCs detected by surveillance programs at an early stage are candidates for potentially curative therapies (local ablation, resection, or transplantation). In the long term, transplantation provides the lowest recurrence rates. Treatment allocation is based on tumor number, size, vascular invasion, performance status, functional liver reserve, and the prediction of early (<2 years) recurrence, which reflects the intrinsic aggressiveness of the tumor. Well-differentiated, potentially low-aggressiveness tumors form the heterogeneous molecular class of nonproliferative HCCs, characterized by an approximate 50% β-catenin mutation rate. To define the clinical, pathological, and molecular features and the outcome of nonproliferative HCCs, we constructed a 1,133-HCC transcriptomic metadata set and validated findings in a publically available 210-HCC RNA sequencing set. We show that nonproliferative HCCs preserve the zonation program that distributes metabolic functions along the portocentral axis in normal liver. More precisely, we identified two well-differentiated, nonproliferation subclasses, namely periportal-type (wild-type β-catenin) and perivenous-type (mutant β-catenin), which expressed negatively correlated gene networks. The new periportal-type subclass represented 29% of all HCCs; expressed a hepatocyte nuclear factor 4A-driven gene network, which was down-regulated in mouse hepatocyte nuclear factor 4A knockout mice; were early-stage tumors by Barcelona Clinic Liver Cancer, Cancer of the Liver Italian Program, and tumor-node-metastasis staging systems; had no macrovascular invasion; and showed the lowest metastasis-specific gene expression levels and TP53 mutation rates. Also, we identified an eight-gene periportal-type HCC signature, which was independently associated with the highest 2-year recurrence-free survival by multivariate analyses in two independent cohorts of 247 and 210 patients.

CONCLUSION

Well-differentiated HCCs display mutually exclusive periportal or perivenous zonation programs. Among all HCCs, periportal-type tumors have the lowest intrinsic potential for early recurrence after curative resection. (Hepatology 2017;66:1502-1518).

Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results

OBJECTIVES

In this preliminary study, we examined whether imaging-based phenotypes are associated with reported predictive gene signatures in hepatocellular carcinoma (HCC).

METHODS

Thirty-eight patients (M/F 30/8, mean age 61 years) who underwent pre-operative CT or MR imaging before surgery as well as transcriptome profiling were included in this IRB-approved single-centre retrospective study. Eleven qualitative and four quantitative imaging traits (size, enhancement ratios, wash-out ratio, tumour-to-liver contrast ratios) were assessed by three observers and were correlated with 13 previously reported HCC gene signatures using logistic regression analysis.

RESULTS

Thirty-nine HCC tumours (mean size 5.7 ± 3.2 cm) were assessed. Significant positive associations were observed between certain imaging traits and gene signatures of aggressive HCC phenotype (G3-Boyault, Proliferation-Chiang profiles, CK19-Villanueva, S1/S2-Hoshida) with odds ratios ranging from 4.44-12.73 (P <0.045). Infiltrative pattern at imaging was significantly associated with signatures of microvascular invasion and aggressive phenotype. Significant but weak associations were also observed between each enhancement ratio and tumour-to-liver contrast ratios and certain gene expression profiles.

CONCLUSIONS

This preliminary study demonstrates a correlation between phenotypic imaging traits with gene signatures of aggressive HCC, which warrants further prospective validation to establish imaging-based surrogate markers of molecular phenotypes in HCC.

KEY POINTS

• There are associations between imaging and gene signatures of aggressive hepatocellular carcinoma. • Infiltrative type is associated with gene signatures of microvascular invasion and aggressiveness. • Infiltrative type may be a surrogate marker of microvascular invasion gene signature.

Identification of a gene signature associated with radiotherapy and prognosis in gliomas

Glioma is one of the most common primary brain tumors with poor prognosis. Although radiotherapy is an important treatment method for gliomas, the efficacy is still limited by the high occurrence of radioresistance and the underlying molecular mechanism is unclear. Here, we performed a data mining work based on four glioma expression datasets. These datasets were classified into training set and validation set. Radiotherapy-induced differential expressed genes and prognosis-associated genes were screened using different classifiers. The Kaplan-Meier curves along with the two-sided Log Rank (Mantel-Cox) test were used to evaluate overall survival. We found the gene expression profiles of gliomas between those patients received radiotherapy and those patients without received radiotherapy were quite different. A 20-gene signature was identified, which was associated with radiotherapy.Furthermore, a novel 5-gene signature (HOXC10, LOC101928747, CYB561D2, RPL36A and RPS4XP2) as an independent predictor of glioma patients’ prognosis was further derived from the 20-gene signature. These findings provided a new insight into the molecular mechanism of radioresistance in gliomas. The 5-gene signature might represent therapeutic target for gliomas.

Mutations acquired by hepatocellular carcinoma recurrence give rise to an aggressive phenotype

Recurrence of hepatocellular carcinoma (HCC) even after curative resection causes dismal outcomes of patients. Here, to delineate the driver events of genomic and transcription alteration during HCC recurrence, we performed RNA-Seq profiling of the paired primary and recurrent tumors from two patients with intrahepatic HCC. By comparing the mutational and transcriptomic profiles, we identified somatic mutations acquired by HCC recurrence including novel mutants of GOLGB1 (E2721V) and SF3B3 (H804Y). By performing experimental evaluation using siRNA-mediated knockdown and overexpression constructs, we demonstrated that the mutants of GOLGB1 and SF3B3 can promote cell proliferation, colony formation, migration, and invasion of liver cancer cells. Transcriptome analysis also revealed that the recurrent HCCs reprogram their transcriptomes to acquire aggressive phenotypes. Network analysis revealed CXCL8 (IL-8) and SOX4 as common downstream targets of the mutants. In conclusion, we suggest that the mutations of GOLGB1 and SF3B3 are potential key drivers for the acquisition of an aggressive phenotype in recurrent HCC.

Network analysis of HBV‑ and HCV‑induced hepatocellular carcinoma based on Random Forest and Monte Carlo cross‑validation

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer‑associated 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 cross‑validation 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.

Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole-exome sequencing and DNA copy number analyses, and we analyzed 196 HCC cases by DNA methylation, RNA, miRNA, and proteomic expression also. DNA sequencing and mutation analysis identified significantly mutated genes, including LZTR1, EEF1A1, SF3B1, and SMARCA4. Significant alterations by mutation or downregulation by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1.

Hepatocellular Carcinomas Originate Predominantly from Hepatocytes and Benign Lesions from Hepatic Progenitor Cells

Hepatocellular carcinoma (HCC) is an aggressive primary liver cancer. However, its origin remains a debated question. Using human data and various hepatocarcinogenesis mouse models, we show that, in early stages, transformed hepatocytes, independent of their proliferation status, activate hepatic progenitor cell (HPC) expansion. Genetic lineage tracing of HPCs and hepatocytes reveals that, in all models, HCC originates from hepatocytes. However, whereas in various models tumors do not emanate from HPCs, tracking of progenitors in a model mimicking human hepatocarcinogenesis indicates that HPCs can generate benign lesions (regenerative nodules and adenomas) and aggressive HCCs. Mechanistically, galectin-3 and α-ketoglutarate paracrine signals emanating from oncogene-expressing hepatocytes instruct HPCs toward HCCs. α-Ketoglutarate preserves an HPC undifferentiated state, and galectin-3 maintains HPC stemness, expansion, and aggressiveness. Pharmacological or genetic blockage of galectin-3 reduces HCC, and its expression in human HCC correlates with poor survival. Our findings may have clinical implications for liver regeneration and HCC therapy.

Prognostic Biomarker Identification Through Integrating the Gene Signatures of Hepatocellular Carcinoma Properties

Many molecular classification and prognostic gene signatures for hepatocellular carcinoma (HCC) patients have been established based on genome-wide gene expression profiling; however, their generalizability is unclear. Herein, we systematically assessed the prognostic effects of these gene signatures and identified valuable prognostic biomarkers by integrating these gene signatures. With two independent HCC datasets (GSE14520, N = 242 and GSE54236, N = 78), 30 published gene signatures were evaluated, and 11 were significantly associated with the overall survival (OS) of postoperative HCC patients in both datasets. The random survival forest models suggested that the gene signatures were superior to clinical characteristics for predicting the prognosis of the patients. Based on the 11 gene signatures, a functional protein-protein interaction (PPI) network with 1406 nodes and 10,135 edges was established. With tissue microarrays of HCC patients (N = 60), we determined the prognostic values of the core genes in the network and found that RAD21, CDK1, and HDAC2 expression levels were negatively associated with OS for HCC patients. The multivariate Cox regression analyses suggested that CDK1 was an independent prognostic factor, which was validated in an independent case cohort (N = 78). In cellular models, inhibition of CDK1 by siRNA or a specific inhibitor, RO-3306, reduced cellular proliferation and viability for HCC cells. These results suggest that the prognostic predictive capacities of these gene signatures are reproducible and that CDK1 is a potential prognostic biomarker or therapeutic target for HCC patients.

•

Eleven gene signatures have promising values for the OS prediction of HCC patients who undergo the surgical treatments.

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Prediction results for the 11 gene signatures were in moderate concordance, and genes included were functionally linked.

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CDK1 is an independent prognostic biomarker and a potential therapeutic target for HCC patients.

Various gene signatures for hepatocellular carcinoma (HCC) patients have been reported; however, their generalizability is unclear. Using two HCC patient datasets, we assessed the prognostic values of these gene signatures and identified 11 gene signatures that were associated with overall survival for postoperative HCC patients in both cohorts. Genes derived from these signatures formed a functional protein-protein interaction network with 1,406 nodes and 10,135 edges, and the expression levels of three core genes, RAD21, CDK1, and HDAC2, in the network were negatively associated with the overall survival of HCC patients. Further studies suggested that CDK1 is an independent prognostic factor and that it is a potential therapeutic target for HCC patients.