Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation

Indolent cancer and pattern of progression: Two missing parameters in trial design for hepatology

The indolent and aggressive behaviors of HCC might have a role in clinical trial (CT) results; however, the indolent HCC is less analyzed compared to others cancer. Indolent profile could be characterized as follows: (1) patients with low risk of progression itself due to the HCC molecular profile and/or due to the interaction between cancer cell their microenvironment; (2) patients who achieve objective response or present spontaneous regression; and (3) patients who develop radiological progression with no consequence on either the liver function or general status, and without trigger a change in the tumor stage. Patients with "indolent HCC" generally never develop cancer-related symptoms neither die for HCC-related causes. Thus, we hypothesize that the imbalance in the proportion of "indolent" versus "aggressive HCC" between arms or the underestimation/overestimation of HCC behavior at baseline in single-arm CT could be associated with CT failure or under-overestimation of trial results. The "indolent progression" may also explain the discrepancy between radiological progression-based end points and survival. Moreover, we discuss the related causes that explain the indolent profile of HCC and propose (1) refining the progression-related end point by the pattern of progression to minimize the limitations of the current end points; (2) considering alternative statistical tools for survival analysis such as milestone survival, or restricted mean survival time to capture the value of indolent HCC. According to these considerations, we propose incorporating novel end points into the single arm of phase I/II CT as exploratory analysis or as a secondary end point in phase III CT.

TPR is a prognostic biomarker and potential therapeutic target associated with immune infiltration in hepatocellular carcinoma

Liver cancer is the fourth leading cause of cancer-related mortality worldwide and hepatocellular carcinoma (HCC) is the most common primary liver cancer. In the present study, it was demonstrated that translocated promoter region (TPR) was upregulated in tumor tissues and associated with prognosis and immune infiltration in HCC. The clinical outcome of patients with HCC with aberrant expression of TPR was examined using multiple databases, including Gene Expression Omnibus, The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, Kaplan-Meier (KM) Plotter and Xiantao tool. The clinicopathologic characteristics of patients from TCGA database that were associated with overall survival were assessed using Cox regression and KM analysis. The potential hallmarks associated with TPR expression were further predicted by Metascape and Gene Set Enrichment Analysis, and the relationship between TPR and immune infiltration was explored using the Tumor-Immune System Interactions Database and the Tumor Immune Estimation Resource. The results demonstrated that TPR expression was higher in HCC and its overexpression was associated with a worse prognosis, alongside a correlation with several clinical features. Furthermore, cell differentiation, a prospective new hallmark of cancer, was differentially enriched in the high TPR expression phenotype pathway. Moreover, TPR may also modulate the tumor immune microenvironment as it was significantly associated with immunoregulators and chemokines, as well as different tumor infiltration immune cells. According to the in vitro experiments, TPR silencing inhibited the phosphorylation of AKT and the proliferation of HCC cells. In summary, TPR may be a new marker and target for HCC therapy.

The transcription factor ChREBP Orchestrates liver carcinogenesis by coordinating the PI3K/AKT signaling and cancer metabolism

Cancer cells integrate multiple biosynthetic demands to drive unrestricted proliferation. How these cellular processes crosstalk to fuel cancer cell growth is still not fully understood. Here, we uncover the mechanisms by which the transcription factor Carbohydrate responsive element binding protein (ChREBP) functions as an oncogene during hepatocellular carcinoma (HCC) development. Mechanistically, ChREBP triggers the expression of the PI3K regulatory subunit p85α, to sustain the activity of the pro-oncogenic PI3K/AKT signaling pathway in HCC. In parallel, increased ChREBP activity reroutes glucose and glutamine metabolic fluxes into fatty acid and nucleic acid synthesis to support PI3K/AKT-mediated HCC growth. Thus, HCC cells have a ChREBP-driven circuitry that ensures balanced coordination between PI3K/AKT signaling and appropriate cell anabolism to support HCC development. Finally, pharmacological inhibition of ChREBP by SBI-993 significantly suppresses in vivo HCC tumor growth. Overall, we show that targeting ChREBP with specific inhibitors provides an attractive therapeutic window for HCC treatment.

Comprehensive analysis and validation of SNX7 as a novel biomarker for the diagnosis, prognosis, and prediction of chemotherapy and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Studies have demonstrated that Sorting nexin 7 (SNX7) functions as an anti-apoptotic protein in liver tissue and plays a crucial role in the survival of hepatocytes during early embryonic development. However, its diagnostic and prognostic value as well as the predictive value of chemotherapy and immunotherapy have not been reported in hepatocellular carcinoma (HCC).

METHODS

SNX7 mRNA expression and its diagnostic efficacy were examined in GEO datasets, and the findings were further confirmed in TCGA, ICGC cohorts, and cell lines. The protein level of SNX7 was determined using CPTAC and HPA databases, and the results were validated through immunohistochemistry (IHC). Survival analyses were performed in TCGA and ICGC cohorts, and the results were subsequently validated via Kaplan-Meier Plotter. The response to chemotherapy and immunotherapy was predicted via GDSC dataset and TIDE algorithm, respectively. R packages were employed to explore the relationship between SNX7 expression and immune infiltration, m6A modification, as well as the functional enrichment of differentially expressed genes (DEGs).

RESULTS

The expression of SNX7 at both mRNA and protein levels was significantly upregulated in HCC tissues. SNX7 exhibited superior diagnostic efficacy compared to AFP alone for HCC detection, and combining it with AFP improved the diagnostic accuracy for HCC. High SNX7 was associated with unfavorable outcomes, including poor overall survival, disease-specific survival, progression-free survival, and advanced pathological stage, in patients with HCC, and SNX7 was identified as an independent risk factor for HCC. Moreover, elevated SNX7 expression was positively correlated with increased sensitivity to various chemotherapy drugs, including sorafenib, while it was associated with resistance to immunotherapy in HCC patients. Correlation analysis revealed a relationship between SNX7 and multiple m6A-related genes and various immune cells. Finally, enrichment analysis demonstrated strong associations of SNX7 with critical biological processes, such as cell cycle regulation, cellular senescence, cell adhesion, DNA replication, and mismatch repair pathway in HCC.

CONCLUSIONS

Our study highlights the association of SNX7 with the immune microenvironment and its potential influence on HCC progression. SNX7 emerges as a promising novel biomarker for the diagnosis, prognosis, and prediction of response to chemotherapy and immunotherapy in patients with HCC.

Clinically conserved genomic subtypes of gastric adenocarcinoma

Gastric adenocarcinoma (GAC) is a lethal disease characterized by genomic and clinical heterogeneity. By integrating 8 previously established genomic signatures for GAC subtypes, we identified 6 clinically and molecularly distinct genomic consensus subtypes (CGSs). CGS1 have the poorest prognosis, very high stem cell characteristics, and high IGF1 expression, but low genomic alterations. CGS2 is enriched with canonical epithelial gene expression. CGS3 and CGS4 have high copy number alterations and low immune reactivity. However, CGS3 and CGS4 differ in that CGS3 has high HER2 activation, while CGS4 has high SALL4 and KRAS activation. CGS5 has the high mutation burden and moderately high immune reactivity that are characteristic of microsatellite instable tumors. Most CGS6 tumors are positive for Epstein Barr virus and show extremely high levels of methylation and high immune reactivity. In a systematic analysis of genomic and proteomic data, we estimated the potential response rate of each consensus subtype to standard and experimental treatments such as radiation therapy, targeted therapy, and immunotherapy. Interestingly, CGS3 was significantly associated with a benefit from chemoradiation therapy owing to its high basal level of ferroptosis. In addition, we also identified potential therapeutic targets for each consensus subtype. Thus, the consensus subtypes produced a robust classification and provide for additional characterizations for subtype-based customized interventions.

Risk stratification and early detection biomarkers for precision HCC screening

Hepatocellular carcinoma (HCC) mortality remains high primarily due to late diagnosis as a consequence of failed early detection. Professional societies recommend semi-annual HCC screening in at-risk patients with chronic liver disease to increase the likelihood of curative treatment receipt and improve survival. However, recent dynamic shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population for the screening, whereas annual incidence rate has become substantially lower. Thus, with the contemporary HCC etiologies, the traditional screening approach might not be practical and cost-effective. HCC screening consists of (i) definition of rational at-risk population, and subsequent (ii) repeated application of early detection tests to the population at regular intervals. The suboptimal performance of the currently available HCC screening tests highlights an urgent need for new modalities and strategies to improve early HCC detection. In this review, we overview recent developments of clinical, molecular, and imaging-based tools to address the current challenge, and discuss conceptual framework and approaches of their clinical translation and implementation. These encouraging progresses are expected to transform the current "one-size-fits-all" HCC screening into individualized precision approaches to early HCC detection and ultimately improve the poor HCC prognosis in the foreseeable future.

BAIAP2L2 is a novel prognostic biomarker related to migration and invasion of HCC and associated with cuprotosis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and its pathophysiological mechanisms remain unknown. IRSp53 family members, such as BAIAP2L1, participate in the progression of multiple tumors. However, the role of BAIAP2L2 in HCC remains unclear. This study comprehensively analyzed the potential role of BAIAP2L2 in HCC using bioinformatic techniques. The expression of BAIAP2L2 in HCC was analyzed using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), and Human Protein Atlas (HPA) databases and in vitro experiments. In addition, the prognostic value of BAIAP2L2 in HCC was analyzed using the TCGA database. TCGA and GEO database were used to analyze the role of BAIAP2L2 in immune features. We also explored the function of BAIAP2L2 in methylation and cuprotosis. The CellMiner database was used to analyze the relationship between BAIAP2L2 expression and drug sensitivity. Our study revealed that BAIAP2L2 is overexpressed in HCC and promotes the migration and invasion of HCC cells. BAIAP2L2 may affect the prognosis of HCC by regulating immunity, methylation, and cuprotosis. BAIAP2L2 is a novel HCC prognostic gene involved in immune infiltration associated with cuprotosis and may be a potential prognosis and therapeutic target for HCC.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Plasma-only circulating tumor DNA analysis detects minimal residual disease and predicts early relapse in hepatocellular carcinoma patients undergoing curative resection

Background

Minimal residual disease (MRD) is considered an essential factor leading to relapse within 2 years (early relapse) after radical surgery, which is challenging to be detected by conventional imaging. Circulating tumor DNA (ctDNA) provides a novel approach for detecting MRD and predicting clinical outcomes. Here, we tried to construct a fixed panel for plasma-only ctDNA NGS to enable tumor-uninformed MRD detection in hepatocellular carcinoma (HCC).

Methods

Here, we performed the followings: (i) profiling genomic alteration spectrum of ctDNA from the Chinese HCC cohort consisting of 493 individuals by NGS; (ii) screening of MRD monitoring genes; and (iii) performance evaluation of MRD monitoring genes in predicting early relapse in the ZJZS2020 cohort comprising 20 HCC patients who underwent curative resection.

Results

A total of 493 plasma samples from the Chinese HCC cohort were detected using a 381/733-gene NGS panel to characterize the mutational spectrum of ctDNA. Most patients (94.1%, 464/493) had at least one mutation in ctDNA. The variants fell most frequently in TP53 (45.1%), LRP1B (20.2%), TERT (20.2%), FAT1 (16.2%), and CTNNB1 (13.4%). By customized filtering strategy, 13 MRD monitoring genes were identified, and any plasma sample with one or more MRD monitoring gene mutations was considered MRD-positive. In the ZJZS2020 cohort, MRD positivity presented a sensitivity of 75% (6/8) and a specificity of 100% (6/6) in identifying early postoperative relapse. The Kaplan-Meier analysis revealed a significantly short relapse-free survival (RFS; median RFS, 4.2 months vs. NR, P=0.002) in the MRD-positive patients versus those with MRD negativity. Cox regression analyses revealed MRD positivity as an independent predictor of poor RFS (HR 13.00, 95% CI 2.60-69.00, P=0.002).

Conclusions

We successfully developed a 13-gene panel for plasma-only MRD detection, which was effective and convenient for predicting the risk of early postoperative relapse in HCC.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Effects of Subnormothermic Regulated Hepatic Reperfusion on Mitochondrial and Transcriptomic Profiles in a Porcine Model

OBJECTIVE

We sought to investigate the biological effects of pre-reperfusion treatments of the liver after warm and cold ischemic injuries in a porcine donation after circulatory death model.

SUMMARY OF BACKGROUND DATA

Donation after circulatory death represents a severe form of liver ischemia and reperfusion injury that has a profound impact on graft function after liver transplantation.

METHODS

Twenty donor pig livers underwent 60 minutes of in situ warm ischemia after circulatory arrest and 120 minutes of cold static preservation prior to simulated transplantation using an ex vivo perfusion machine. Four reperfusion treatments were compared: Control-Normothermic (N), Control- Subnormothermic (S), regulated hepatic reperfusion (RHR)-N, and RHR-S (n = 5 each). The biochemical, metabolic, and transcriptomic profiles, as well as mitochondrial function were analyzed.

RESULTS

Compared to the other groups, RHR-S treated group showed significantly lower post-reperfusion aspartate aminotransferase levels in the reperfusion effluent and histologic findings of hepatocyte viability and lesser degree of congestion and necrosis. RHR-S resulted in a significantly higher mitochondrial respiratory control index and calcium retention capacity. Transcriptomic profile analysis showed that treatment with RHR-S activated cell survival and viability, cellular homeostasis as well as other biological functions involved in tissue repair such as cytoskeleton or cytoplasm organization, cell migration, transcription, and microtubule dynamics. Furthermore, RHR-S inhibited organismal death, morbidity and mortality, necrosis, and apoptosis.

CONCLUSION

Subnormothermic RHR mitigates IRI and preserves hepatic mitochondrial function after warm and cold hepatic ischemia. This organ resuscitative therapy may also trigger the activation of protective genes against IRI. Sub- normothermic RHR has potential applicability to clinical liver transplantation.

YAP1 is essential for malignant mesothelioma tumor maintenance

Malignant pleural mesothelioma, a tumor arising from the membrane covering the lungs and the inner side of the ribs, is a cancer in which genetic alterations of genes encoding proteins that act on or are part of the Hippo-YAP1 signaling pathway are frequent. Dysfunctional Hippo signaling may result in aberrant activation of the transcriptional coactivator protein YAP1, which binds to and activates transcription factors of the TEAD family. Recent studies have associated elevated YAP1 protein activity with a poor prognosis of malignant mesothelioma and its resistance to current therapies, but its role in tumor maintenance is unclear. In this study, we investigate the dependence of malignant mesothelioma on YAP1 signaling to maintain fully established tumors in vivo. We show that downregulation of YAP1 in a dysfunctional Hippo genetic background results in the inhibition of YAP1/TEAD-dependent gene expression, the induction of apoptosis, and the inhibition of tumor cell growth in vitro. The conditional downregulation of YAP1 in established tumor xenografts leads to the inhibition of YAP1-dependent gene transcription and eventually tumor regression. This effect is only seen in the YAP1-activated MSTO-211H mesothelioma xenograft model, but not in the Hippo-independent HCT116 colon cancer xenograft model. Our data demonstrate that, in the context of a Hippo pathway mutated background, YAP1 activity alone is enough to maintain the growth of established tumors in vivo, thus validating the concept of inhibiting the activated YAP1-TEAD complex for the treatment of malignant pleural mesothelioma patients.

Liver cancer risk-predictive molecular biomarkers specific to clinico-epidemiological contexts

Hepatocellular carcinoma (HCC) risk prediction is increasingly important because of the low annual HCC incidence in patients with the rapidly emerging non-alcoholic fatty liver disease or cured HCV infection. To date, numerous clinical HCC risk biomarkers and scores have been reported in literature. However, heterogeneity in clinico-epidemiological context, e.g., liver disease etiology, patient race/ethnicity, regional environmental exposure, and lifestyle-related factors, obscure their real clinical utility and applicability. Proper characterization of these factors will help refine HCC risk prediction according to certain clinical context/scenarios and contribute to improved early HCC detection. Molecular factors underlying the clinical heterogeneity encompass various features in host genetics, hepatic and systemic molecular dysregulations, and cross-organ interactions, which may serve as clinical-context-specific biomarkers and/or therapeutic targets. Toward the goal to enable individual-risk-based HCC screening by incorporating the HCC risk biomarkers/scores, their assessment in patient with well-defined clinical context/scenario is critical to gauge their real value and to maximize benefit of the tailored patient management for substantial improvement of the poor HCC prognosis.

Novel Gene Signatures as Prognostic Biomarkers for Predicting the Recurrence of Hepatocellular Carcinoma

Simple Summary

A high percentage of patients who undergo surgical resection for hepatocellular carcinoma (HCC) experience recurrence. Therefore, identification of accurate molecular markers for predicting recurrence of HCC is important. We analyzed recurrence and non-recurrence HCC tissues using two public omics datasets comprising microarray and RNA-sequencing and found novel gene signatures associated with recurrent HCC. These molecules might be used to not only predict for recurrence of HCC but also act as potential prognostic indicators for patients with HCC.

Abstract

Hepatocellular carcinoma (HCC) has a high rate of cancer recurrence (up to 70%) in patients who undergo surgical resection. We investigated prognostic gene signatures for predicting HCC recurrence using in silico gene expression analysis. Recurrence-associated gene candidates were chosen by a comparative analysis of gene expression profiles from two independent whole-transcriptome datasets in patients with HCC who underwent surgical resection. Five promising candidate genes, CETN2, HMGA1, MPZL1, RACGAP1, and SNRPB were identified, and the expression of these genes was evaluated using quantitative reverse transcription PCR in the validation set (n = 57). The genes CETN2, HMGA1, RACGAP1, and SNRPB, but not MPZL1, were upregulated in patients with recurrent HCC. In addition, the combination of HMGA1 and MPZL1 demonstrated the best area under the curve (0.807, 95% confidence interval [CI] = 0.681–0.899) for predicting HCC recurrence. In terms of clinicopathological correlation, CETN2, MPZL1, RACGAP1, and SNRPB were upregulated in patients with microvascular invasion, and the expression of MPZL1 and SNRPB was increased in proportion to the Edmonson tumor differentiation grade. Additionally, overexpression of CETN2, HMGA1, and RACGAP1 correlated with poor overall survival (OS) and disease-free survival (DFS) in the validation set. Finally, Cox regression analysis showed that the expression of serum alpha-fetoprotein and RACGAP1 significantly affected OS, whereas platelet count, microvascular invasion, and HMGA1 expression significantly affected DFS. In conclusion, HMGA1 and RACGAP1 may be potential prognostic biomarkers for predicting the recurrence of HCC after surgical resection.

Implications of Stemness Features in 1059 Hepatocellular Carcinoma Patients from Five Cohorts: Prognosis, Treatment Response, and Identification of Potential Compounds

Simple Summary

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. However, comprehensive interpretations of transcriptomic stemness features in HCC patients have not been conducted in multiple cohorts. Our aim was to interpret clinical and therapeutic implications of transcriptional stemness features and explore potential compounds for HCC treatment. We found that transcriptional stemness indexes (mRNAsi) were independently associated with worse HCC prognosis. The HCC stemness risk model (HSRM) developed in this study significantly predicted prognosis and treatment response in various HCC cohorts. Analysis of two stemness subtypes suggested several liver-specific metabolic pathways, and mutations of TP53 and RB1 were associated with HCC transcriptional stemness. Moreover, we also identified potential compounds that target HCC transcriptional stemness. Our findings comprehensively characterized transcriptional stemness as a risk factor in HCC progression and treatment.

Abstract

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. In this study, five HCC cohorts with 1059 patients were collected to calculate transcriptional stemness indexes (mRNAsi) by the one-class logistic regression machine learning algorithm. In the TCGA-LIHC cohort, we found mRNAsi was an independent prognostic factor, and 626 mRNAsi-related genes were identified by Spearman correlation analysis. The HCC stemness risk model (HSRM) was trained in the TCGA-LIHC cohort and significantly discriminated overall survival in four independent cohorts. HSRM was also significantly associated with transarterial chemoembolization treatment response and rapid tumor growth in HCC patients. Consensus clustering was conducted based on mRNAsi-related genes to divide 1059 patients into two stemness subtypes. On gene set variation analysis, samples of subtype I were found enriched with pathways such as DNA replication and cell cycle, while several liver-specific metabolic pathways were inhibited in these samples. Somatic mutation analysis revealed more frequent mutations of TP53 and RB1 in the subtype I samples. In silico analysis suggested topoisomerase, cyclin-dependent kinase, and histone deacetylase as potential targets to inhibit HCC stemness. In vitro assay showed two predicted compounds, Aminopurvalanol-a and NCH-51, effectively suppressed oncosphere formation and impaired viability of HCC cell lines, which may shed new light on HCC treatment.

Identification and Validation of an Immune-related Prognostic Signature for Hepatocellular Carcinoma

BACKGROUND AND AIMS

The immune system plays vital roles in hepatocellular carcinoma (HCC) initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients.

METHODS

Gene expression data were retrieved from The Cancer Genome Atlas database. The IRPS was established via least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset.

RESULTS

A total of 62 genes were identified as candidate immune-related prognostic genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in the ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be independent risk factors influencing prognosis of HCC patients. The relationships between the IRPS and infiltration of immune cells demonstrated that the IRPS was associated with immune cell infiltration. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.

CONCLUSIONS

The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

Clinical significance of FAT1 gene mutation and mRNA expression in patients with head and neck squamous cell carcinoma

The FAT1 gene functions as a tumor suppressor or promoter and remains incompletely understood. We examined the clinical significance of FAT1 in head and neck squamous cell carcinoma (HNSCC) using four publicly available HNSCC cohorts and one HNSCC cohort enrolled at a tertiary medical center. We developed FAT1 signatures reflecting FAT1 mutations and mRNA expression using one cohort. Patients with HNSCC were classified into FAT1‐associated low risk (FAT1‐LR; n = 195) and FAT1‐associated high risk (FAT1‐HR; n = 371) subgroups. The five‐year overall survival and recurrence‐free survival rates were significantly lower in the FAT1‐HR subgroup than in the FAT1‐LR subgroup (P = 0.01 and 0.003, respectively). The clinical significance of FAT1 was validated using four independent cohorts. Cox proportional hazards models showed that the FAT1 signature was an independent prognostic factor for HNSCC patients. In addition, FAT1 signature was associated with the response to radiotherapy, advanced stage, and human papilloma virus (HPV) status in HNSCC patients. In conclusion, the FAT1 gene signature was associated with prognosis of HNSCC and may help to provide personalized treatments for HNSCC patients.

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system

Hepatocellular carcinoma (HCC) is a malignancy with a dismal survival rate. The novel autoantibodies panel may provide new insights for the diagnosis of HCC. Biomarkers screened by two methods (bioinformatics and the antigen‐antibody system) were taken as candidate tumor‐associated antigens (TAAs). Enzyme‐linked immunosorbent assay was used to detect the corresponding autoantibodies in 888 samples of verification and validation cohorts. The verification cohort was used to verify the autoantibodies. Samples in the validation cohort were randomly divided into a train set and a test set with the ratio of 6:4. A diagnostic model was established by support vector machines within the train set. The test set further verified the model. Eleven TAAs were selected (AAGAB, C17orf75, CDC37L1, DUSP6, EID3, PDIA2, RGS20, PCNA, TAF7L, TBC1D13, and ZIC2). The titer of six autoantibodies (PCNA, AAGAB, CDC37L1, TAF7L, DUSP6, and ZIC2) had a significant difference in any of the pairwise comparisons among the HCC, liver cirrhosis, and normal control groups. The titer of these autoantibodies had an increasing tendency. Finally, an optimum diagnostic model was constructed with the six autoantibodies. The AUCs were 0.826 in the train set and 0.773 in the test set. The area under the curve (AUC) of this panel for diagnosing early HCC was 0.889. The diagnostic ability of the panel reduced with the progress of HCC. The positive rate of the panel in diagnosing alpha‐fetoprotein (AFP)‐negative patients was 75.6%. For early HCC, the sensitivity of the combination of AFP with the panel was 90.9% and superior to 53.2% of AFP alone. The novel immunodiagnosis panel combining AFP may be a new approach for the diagnosis of HCC, especially for early‐HCC cases.

Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients

Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k‐top scoring pairs (k‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant.

Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

Background

Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence.

Methods

The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database.

Results

Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group.

Conclusions

Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08827-z.

Bioinformatics screening of biomarkers related to liver cancer

Background

Liver cancer is a common malignant tumor in China, with high mortality. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, mRNA quantification and the clinical data of liver cancer. However, the hub genes, which can be served as biomarkers for diagnosis and treatment of early liver cancer, are not well screened.

Results

Here we present a new method for getting 6 key genes, aiming to diagnose and treat the early liver cancer. We firstly analyzed the different expression microarrays based on TCGA database, and a total of 1564 differentially expressed genes were obtained, of which 1400 were up-regulated and 164 were down-regulated. Furthermore, these differentially expressed genes were studied by using GO and KEGG enrichment analysis, a PPI network was constructed based on the STRING database, and 15 hub genes were obtained. Finally, 15 hub genes were verified by applying the survival analysis method on Oncomine database, and 6 key genes were ultimately identified, including PLK1, CDC20, CCNB2, BUB1, MAD2L1 and CCNA2. The robustness analysis of four independent data sets verifies the accuracy of the key gene’s classification of the data set.

Conclusions

Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new method to identify the 6 key genes for diagnosis and treatment of early liver cancer, and these key genes can help us understand the pathogenesis of liver cancer more deeply.

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.

Development and Validation of a Model Including Distinct Vascular Patterns to Estimate Survival in Hepatocellular Carcinoma

IMPORTANCE

Because of tumor heterogeneity, traditional clinical variables remain insufficient to predict recurrence, which impairs long-term survival among patients undergoing radical hepatectomy for hepatocellular carcinoma (HCC). Vessels encapsulating tumor clusters (VETC) constitute a novel vascular pattern distinct from microvascular invasion (MVI), representing biological aggressiveness of HCC.

OBJECTIVE

To establish a model to estimate individualized recurrence-free survival (RFS) in HCC by integrating VETC and MVI.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study included 498 patients undergoing radical hepatectomy for HCC from 5 academic centers in China from January 1, 2013, to December 31, 2016, and consisted of 3 cohorts: training (243 [48.8%]), internal validation (122 [24.5%]), and external validation (133 [26.7%]). Follow-up was completed on March 30, 2020, and the data were analyzed from December 1 to 31, 2020.

EXPOSURES

VETC, MVI, tumor number, and maximum tumor size.

MAIN OUTCOMES AND MEASURES

The primary end point was RFS. The risk score for relative recurrence and nomogram for absolute RFS probability were derived from the final model, which contained variables recommended by multivariate least absolute shrinkage and selection operator Cox proportional hazards regression analysis. Their performance was quantified using the Harrell concordance index (C index), the time-dependent area under the receiver operating characteristic curve, and calibration curves and was compared with 6 prognostic systems. Recurrence-free survival was estimated by the Kaplan-Meier method, and RFS curves were compared using a log-rank test.

RESULTS

Among the 498 patients, 432 (86.7%) were men; the mean (SD) age at diagnosis was 51.4 (11.3) years. Independent predictors for RFS identified included VETC, MVI, tumor number, and maximum tumor size, which were incorporated into the multivariate model (VMNS model). The C index (0.702; 95% CI, 0.653-0.752) for the VMNS score of the training cohort was significantly higher than those of 6 conventional systems (0.587 [95% CI, 0.535-0.638] to 0.657 [95% CI, 0.606-0.708]). Different recurrence risk groups defined by the VMNS score showed significantly different 2-year RFS (low-risk group, 81.4% [SE, 0.036]; medium-risk group, 62.1% [SE, 0.054]; high-risk group, 30.1% [SE, 0.079]; P < .001). Calibration curves of the VMNS nomogram showed good agreement between the nomogram-predicted RFS probability and actual RFS proportion. The internal and external validation cohorts confirmed the results.

CONCLUSIONS AND RELEVANCE

The VMNS model enabled individualized prognostication of RFS in patients with HCC undergoing curative resection.

Immune-associated molecular occurrence and prognosis predictor of hepatocellular carcinoma: an integrated analysis of GEO datasets

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the second most common cause of cancer-related deaths worldwide. As immune response failure is the main factor in the occurrence and poor prognosis of HCC, our study aimed to develop an immune-associated molecular occurrence and prognosis predictor (IMOPP) of HCC. To that end, we discovered a 4-gene immune-associated gene signature: C-C motif chemokine ligand 14 (CCL14), kallikrein B1 (KLKB1), vasoactive intestinal peptide receptor 1 (VIPR1), and cluster of differentiation 4 (CD4). When tested on three cohorts as an immune-associated molecular occurrence predictor (IMOP), it had high sensitivity, specificity, and area under the receiver operating characteristics curve. When tested as an immune-associated molecular prognosis predictor (IMPP), it stratified the HCC prognosis for overall survival (Kaplan-Meier analysis, log rank P = 0.0016; Cox regression, HR = 1.832, 95% CI = 1.173-2.859, P = 0.008) and disease-free survival (Kaplan-Meier analysis, log rank P = 0.0227). IMPP also significantly correlated with the clinicopathological characteristics of HCC; integrating it with clinicopathological characteristics improved the accuracy of a nomogram for overall survival prediction (C-index: 0.7097 vs. 0.6631). In HCC tumor microenviroments, the proportion of CD8⁺ T cells significantly differed between IMOP-stratified groups. We conclude that IMOPP can potentially predict the occurrence of HCC in high-risk populations and improve prognostic accuracy by providing new biomarkers for risk stratification. In addition, we believe that the IMOP mechanism may be related to its effect on the proportion of CD8⁺ T cells in tumor-infiltrating lymphocytes.

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.

Methods

The differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.

Results

A total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.

Conclusion

Our study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Identification of HCC-Related Genes Based on Differential Partial Correlation Network

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death, but its pathogenesis is still unclear. As the disease is involved in multiple biological processes, systematic identification of disease genes and module biomarkers can provide a better understanding of disease mechanisms. In this study, we provided a network-based approach to integrate multi-omics data and discover disease-related genes. We applied our method to HCC data from The Cancer Genome Atlas (TCGA) database and obtained a functional module with 15 disease-related genes as network biomarkers. The results of classification and hierarchical clustering demonstrate that the identified functional module can effectively distinguish between the disease and the control group in both supervised and unsupervised methods. In brief, this computational method to identify potential functional disease modules could be useful to disease diagnosis and further mechanism study of complex diseases.

Pathological predictive factors for late recurrence of hepatocellular carcinoma in chronic liver disease

BACKGROUND & AIMS

Late recurrence of hepatocellular carcinoma (HCC) is regarded as de novo HCC from chronic hepatitis. This study investigated clinicopathological and molecular factors to develop a nomogram for predicting late HCC recurrence (>2 years after curative resection).

METHODS

The training and validation cohorts included HCC patients with a major aetiology of hepatitis B who underwent curative resection. Clinicopathological features including lobular and porto-periportal inflammatory activity, fibrosis and liver cell change were evaluated. Proteins encoded by genes related to late recurrence were identified using a reverse phase protein array of 95 non-tumourous liver tissues. Immunoexpression of phosphorylated signal transducer and activator of transcription 3 (pSTAT3), plasminogen activator inhibitor-1, phosphorylated extracellular signal-regulated kinase 1/2 (pERK1/2) and spleen tyrosine kinase (SYK) was measured.

RESULTS

Late recurrence occurred in 74/402 (18%) and 47/243 (19%) in the training and validation cohorts respectively. Cirrhosis, moderate/severe lobular inflammatory activity, and expression of pSTAT3, pERK1/2, and SYK proteins correlated to the gene signature of hepatocyte injury and regeneration were independently associated with late recurrence, with odds ratios (95% confidence intervals) of 2.0 (1.2-3.3), 21.1 (4.3-102.7) and 6.0 (2.1-17.7) respectively (P < .05 for all). A nomogram based on these variables (histological parameters and immunohistochemical marker combinations) showed high reliability in both the training and validation cohorts (Harrell's C index: 0.701 and 0.716; 95% confidence intervals: 0.64-0.76 and 0.64-0.79 respectively).

CONCLUSIONS

The combination of pSTAT3, pERK1/2 and SYK immunoexpression with high lobular inflammatory activity and cirrhosis (fibrosis) predicts late HCC recurrence.

Prognostic Model for the Risk Stratification of Early and Late Recurrence in Hepatitis B Virus-Related Small Hepatocellular Carcinoma Patients with Global Histone Modifications

Background and Aim

To assess the profile of global histone modifications in small hepatocellular carcinoma (small HCC) and identify its prognostic value in predicting recurrence.

Methods

The expression profiles of global histone modifications, including H2AK5AC, H2BK20AC, H3K4me2, H3K9AC, H3K18AC, H4K12AC, and H4R3me2, were evaluated with immunohistochemistry in 335 HBV related small HCC patients. Two histone signature classifiers were then developed using least absolute shrinkage and selection operator Cox regression. A nomogram was built using the classifier and independent risk factors. The performances of the classifier and nomogram were assessed by receiver operating characteristic curves.

Results

Histone modifications were more pronounced in tumor tissues than in adjacent liver tissues. In tumor tissues, the risk score built based on the seven-histone signature exhibited satisfactory prediction efficiency, with an AUC = 0.71 (0.63–0.79) for 2-year survival in the training cohort. Patients with a high risk score had shorter recurrence-free survival than those with a low risk score (HR: 1.96, 95% CI: 1.24–3.08, p = 0.004; HR: 1.95, 95% CI: 1.12–3.42, p = 0.019; and HR: 1.97, 95% CI: 1.39–2.80, p < 0.001 for the training, validation and total cohorts, respectively). Furthermore, the statistical nomogram built using the histone classifier for early recurrence had a C-index = 0.68. In non-neoplastic liver tissues, the hepatic signature based on H3K4me2 and H4R3me2 was related to late recurrence (HR: 2.00, 95% CI: 1.15–3.48, p = 0.01).

Conclusion

Global histone modifications in tumor and adjacent liver tissues are novel predictors of early and late recurrence, respectively, in HBV-related small HCC patients.

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Objective

The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC).

Methods

Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes.

Results

We identified 11 hub genes (CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway.

Conclusions

In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

SNRPC promotes hepatocellular carcinoma cell motility by inducing epithelial-mesenchymal transition

The therapeutic outcome of hepatocellular carcinoma (HCC) remains unsatisfactory because of poor response and acquired drug resistance. To better elucidate the molecular mechanisms of HCC, here we used three Gene Expression Omnibus datasets to identify potential oncogenes, and thereby identified small nuclear ribonucleoprotein polypeptide C (SNRPC). We report that SNRPC is highly up‐regulated in HCC tissues as determined using immunohistochemistry assays of samples from a cohort of 224 patients with HCC, and overexpression of SNRPC was correlated with multiple tumors, advanced stage, and poor outcome. Kaplan–Meier analysis confirmed that patients with high SNRPC expression exhibited shorter survival in four independent HCC cohorts (all P < 0.05). Furthermore, SNRPC mutations are significantly more frequent in HCC tissues than in normal liver tissues and are an early event in the development of HCC. Functional network analysis suggested that SNRPC is linked to the regulation of ribosome, spliceosome, and proteasome signaling. Subsequently, gain‐ and loss‐of‐function assays showed that SNRPC promotes the motility and epithelial–mesenchymal transition of HCC cells in vitro. SNRPC expression was negatively correlated with the infiltration of CD4⁺ T cells, macrophage cells, and neutrophil cells (all P < 0.05), as determined by analyzing the TIMER (Tumor IMmune Estimation Resource) database. In conclusion, our findings suggest that SNRPC has a potential role in epithelial–mesenchymal transition and motility in HCC.

A blood-based prognostic liver secretome signature and long-term hepatocellular carcinoma risk in advanced liver fibrosis

Background

Accurate non-invasive prediction of long-term hepatocellular carcinoma (HCC) risk in advanced liver fibrosis is urgently needed for cost-effective HCC screening; however, this currently remains an unmet need.

Methods

A serum-protein-based prognostic liver secretome signature (PLSec) was bioinformatically derived from previously validated hepatic transcriptome signatures and optimized in 79 patients with advanced liver fibrosis. We independently validated PLSec for HCC risk in 331 cirrhosis patients with mixed etiologies (validation set 1 [V1]) and thereafter developed a score with clinical prognostic variables. The score was then validated in two independent cohorts: validation set 2 (V2): 164 patients with advanced liver fibrosis due to hepatitis C virus (HCV) infection cured after direct-acting antiviral therapy; validation set 3 (V3): 146 patients with advanced liver fibrosis with successfully-treated HCC and cured HCV infection.

Findings

An 8-protein blood-based PLSec recapitulated transcriptome-based hepatic HCC risk status. In V1, PLSec was significantly associated with incident HCC risk (adjusted hazard ratio [aHR], 2.35; 95% confidence interval [CI], 1.30-4.23). A composite score with serum alpha-fetoprotein (PLSec-AFP) was defined in V1, and validated in V2 (adjusted odds ratio, 3.80 [95%CI, 1.66-8.66]) and V3 (aHR, 3.08 [95%CI, 1.78-5.31]; c-index, 0.74). PLSec-AFP outperformed AFP alone (Brier score, 0.165 vs. 0.186 in V2; 0.196 vs. 0.206 in V3, respectively).

Conclusions

The blood-based PLSec-AFP can accurately stratify patients with advanced liver fibrosis for long-term HCC risk and thereby guide risk-based tailored HCC screening.

Clinical and Molecular Prediction of Hepatocellular Carcinoma Risk

Prediction of hepatocellular carcinoma (HCC) risk becomes increasingly important with recently emerging HCC-predisposing conditions, namely non-alcoholic fatty liver disease and cured hepatitis C virus infection. These etiologies are accompanied with a relatively low HCC incidence rate (~1% per year or less), while affecting a large patient population. Hepatitis B virus infection remains a major HCC risk factor, but a majority of the patients are now on antiviral therapy, which substantially lowers, but does not eliminate, HCC risk. Thus, it is critically important to identify a small subset of patients who have elevated likelihood of developing HCC, to optimize the allocation of limited HCC screening resources to those who need it most and enable cost-effective early HCC diagnosis to prolong patient survival. To date, numerous clinical-variable-based HCC risk scores have been developed for specific clinical contexts defined by liver disease etiology, severity, and other factors. In parallel, various molecular features have been reported as potential HCC risk biomarkers, utilizing both tissue and body-fluid specimens. Deep-learning-based risk modeling is an emerging strategy. Although none of them has been widely incorporated in clinical care of liver disease patients yet, some have been undergoing the process of validation and clinical development. In this review, these risk scores and biomarker candidates are overviewed, and strategic issues in their validation and clinical translation are discussed.

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Background

Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.

Methods

We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.

Results

Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset.

Conclusion

Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Transcriptomic analysis on the effects of melatonin in gastrointestinal carcinomas

BACKGROUND

Melatonin has been shown with anticancer property and therapeutic potential for tumors. However, there lacks a systematic study on the molecular pathways of melatonin and its antitumor effects in gastrointestinal carcinomas.

METHODS

Using the gene expression profiles of four cancer cell lines from three types of gastrointestinal carcinomas before and after melatonin treatment, including gastric carcinoma (GC), colorectal carcinoma (CRC) and hepatocellular carcinoma (HCC), differentially expressed genes (DEGs) and biological pathways influenced by melatonin were identified. The qRT-PCR analyses were performed to validate the effects of melatonin on 5-FU resistance-related genes in CRC.

RESULTS

There were 17 pathways commonly altered by melatonin in the three cancer types, including FoxO signaling pathways enriched by the upregulated DEGs and cell cycle signaling pathways enriched by the downregulated DEGs, confirmed the dual role of melatonin to tumor growth, pro-apoptosis and anti-proliferation. DEGs upregulated in the three types of cancer tissues but reversely downregulated by melatonin were commonly enriched in RNA transport, spliceosome and cell cycle signaling pathways, which indicate that melatonin might exert antitumor effects through these pathways. Our results further showed that melatonin can downregulate the expression levels of 5-FU resistance-related genes, such as thymidylate synthase in GC and ATR, CHEK1, BAX and MYC in CRC. The qRT-PCR results demonstrated that melatonin enhanced the sensitivity of CRC 5-FU resistant cells by decreasing the expression of ATR.

CONCLUSIONS

Melatonin exerts the effects of pro-apoptosis and anti-proliferation on gastrointestinal carcinomas, and might increase the sensitivity of 5-FU in GC and CRC patients.

Omics-derived hepatocellular carcinoma risk biomarkers for precision care of chronic liver diseases

Precise hepatocellular carcinoma (HCC) risk prediction will play increasingly important roles with the contemporary HCC etiologies, that is, non-alcoholic fatty liver disease and resolved hepatitis C virus infection. Because the HCC incidence rate in this emerging patient population is relatively low (~1% per year), identification of a subset of patients at the highest risk is critical to concentrate the effort and resources of regular HCC screening to those who most need it. Omics profiling has been derived using several candidate HCC risk biomarkers, which could refine HCC screening by enabling individual risk-based personalized or risk-stratified patient management. Various types of biomolecules have been explored as sources of information to predict HCC risk at various time horizons. Germline DNA polymorphisms likely reflect race/ethnicity- and/or etiology-specific susceptibility to HCC development or chronic liver disease progression toward carcinogenesis. Transcriptomic dysregulations in the diseased liver capture functional molecular status supporting oncogenesis such as inflammatory pathway and myofibroblast activation. Circulating nucleic acids, proteins, and metabolites could serve as less-invasive measures of molecular HCC risk. Characterization of gut microbiota could also inform HCC risk estimation. Each biomarker could have its niche of clinical application depending on logistics of use, performance, and costs with a goal to eventually improve patient prognosis as a part of the whole algorithm of chronic liver disease management.

Epithelial-mesenchymal transition gene signature is associated with prognosis and tumor microenvironment in head and neck squamous cell carcinoma

In this study we assessed the clinical significance of an epithelial-mesenchymal transition (EMT) gene signature and explored its association with the tumor microenvironment related to immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Genes were selected when mRNA levels were positively or negatively correlated with at least one well-known EMT marker. We developed an EMT gene signature consisting of 82 genes. The patients were classified into epithelial or mesenchymal subgroups according to EMT signature. The clinical significance of the EMT signature was validated in three independent cohorts and its association with several immunotherapy-related signatures was investigated. The mesenchymal subgroup showed worse prognosis than the epithelial subgroup, and significantly elevated PD-1, PD-L1, and CTLA-4 levels, and increased interferon-gamma, cytolytic, T cell infiltration, overall immune infiltration, and immune signature scores. The relationship between PD-L1 expression and EMT status in HNSCC after treatment with TGF-β was validated in vitro. In conclusion, the EMT gene signature was associated with prognosis in HNSCC. Additionally, our results suggest that EMT is related to immune activity of the tumor microenvironment with elevated immune checkpoint molecules.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91–0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

LncRNA BCYRN1/miR-490-3p/POU3F2, served as a ceRNA network, is connected with worse survival rate of hepatocellular carcinoma patients and promotes tumor cell growth and metastasis

Backgrounds

LncRNA Brain Cytoplasmic RNA 1 (BCYRN1) has been certified to modulate cancer cells growth and aggressiveness in several tumors. However, research about function of BCYRN1 in hepatocellular carcinoma (HCC) is limited. Therefore, our research intends to explore the function of BCYRN1 in HCC.

Methods

HepG2 and BEL-7402 cell lines were employed for later function experiments. Differently expression levels of BCYRN1, miR-490-3p, and POU class 3 homeobox 2 (POU3F2) were determined on the base of TCGA dataset including 375 HCC patients and 50 normal. 370 cases of patients, which have fairly complete clinical data, were utilized for survival analysis of BCYRN1, miR-490-3p, or POU3F2 by Kaplan–Meier method. Relative expression pattern of BCYRN1 was examined by quantitative real time polymerase chain reaction (qRT-PCR), and relative expression level of POU3F2 was assessed by qRT-PCR and western blot. Cell biological behaviors were analyzed by cell counting kit-8, cloning formation, and transwell assays. Bioinformatics software and dual luciferase assay were applied to predict and confirm the targeted relationship between BCYRN1 and miR-490-3p, as well as miR-490-3p and POU3F2. Further associations among BCYRN1, miR-490-3p, and POU3F2 were analyzed by rescue assays.

Results

Our results exhibited that BCYRN1 was over expressed in HCC samples, which was connected with unfavorable prognosis in HCC patients. In addition, a series of experiments exhibited that overexpression of BCYRN1 significantly expedited HCC cells growth, clone formation, and movement abilities, and vice versa. Moreover, targeted relationships between BCYRN1 and miR-490-3p, as well as miR-490-3p and POU3F2 were affirmed by dual luciferase assay. Furthermore, POU3F2 expression was negatively connected with the expression of miR-490-3p and positively associated with BCYRN1 expression. Whilst, either overexpression of miR-490-3p or knockdown of POU3F2 could remarkably inhibit the increasing trends of proliferation, clone formation, invasion, and migration abilities induced by BCYRN1 in HCC cells.

Conclusions

BCYRN1, served as a competing endogenous RNA, up-regulated the expression of POU3F2 to promote the development of HCC through sponging miR-490-3p, supplying novel molecular targets and underlying prognostic biomarkers for HCC therapy.

Identification of prognostic biomarker in predicting hepatocarcinogenesis from cirrhotic liver using protein and gene signatures

INTRODUCTION

Cirrhosis primes the liver for hepatocellular carcinoma (HCC) development. However, biomarkers that predict HCC in cirrhosis patients are lacking. Thus, we aimed to identify a biomarker directly from protein analysis and relate it with transcriptomic data to validate in larger cohorts.

MATERIAL AND METHOD

Forty-six patients who underwent hepatectomy for HCC that arose from cirrhotic liver were enrolled. Reverse-phase protein array and microarray data of these patients were analyzed. Clinical validation was performed in two independent cohorts and functional validation using cell and tissue microarray (TMA).

RESULTS

Systematic analysis performed after selecting 20 proteins from 201 proteins with AUROC >70 effectively categorized patients into high (n = 20) or low (n = 26) risk HCC groups. Proteome-derived late recurrence (PDLR)-gene signature comprising 298 genes that significantly differed between high and low risk groups predicted HCC well in a cohort of 216 cirrhosis patients and also de novo HCC recurrence in a cohort of 259 patients who underwent hepatectomy. Among 20 proteins that were selected for analysis, caveolin-1 (CAV1) was the most dominant protein that categorized the patients into high and low risk groups (P < .001). In a multivariate analysis, compared with other clinical variables, the PDLR-gene signature remained as a significant predictor of HCC (HR 1.904, P = .01). In vitro experiments revealed that compared with mock-transduced immortalized liver cells, CAV1-transduced cells showed significantly increased proliferation (P < .001) and colony formation in soft agar (P < .033). TMA with immunohistochemistry showed that tissues with CAV1 expression were more likely to develop HCC than tissues without CAV1 expression (P = .047).

CONCLUSION

CAV1 expression predicts HCC development, making it a potential biomarker and target for preventive therapy.

Preoperative risk stratification for early recurrence of HBV-related hepatocellular carcinoma after deceased donor liver transplantation: a five-eight model development and validation

Background

Early recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT) is associated with poor surgical outcomes. This study aims to construct a preoperative model to predict individual risk of post-LT HCC recurrence.

Methods

Data of 748 adult patients who underwent deceased donor LT for HCC between January 2015, and February 2019 were collected retrospectively from the China Liver Transplant Registry database and randomly divided into training (n = 486) and validation(n = 262) cohorts. A multivariate analysis was performed and the five-eight model was developed.

Results

A total of 748 patients were included in the study; of them, 96% had hepatitis B virus (HBV) and 84% had cirrhosis. Pre-LT serum alpha-fetoprotein (AFP), tumor number and largest tumor diameter were incorporated to construct the 5–8 model which can stratify patients accurately according to their risk of recurrence into three prognostic subgroups; low-(0–5 points), medium-(6–8 points) and high-risk (> 8 points) with 2-year post-LT recurrence rate of (5,20 and 51%,p <  0.001) respectively. The 5–8 model was better than Milan, Hangzhou, and AFP-model for prediction of HCC early recurrence. These findings were confirmed by the results of the validation cohort.

Conclusions

The 5–8 model is a simple validated and accurate tool for preoperative stratification of early recurrence of HCC after LT.

Genome-wide screening diagnostic biomarkers and the construction of prognostic model of hepatocellular carcinoma

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in decades, the overall survival (OS) of HCC remains dissatisfactory, so it is particularly important to find better diagnostic and prognostic biomarkers. In this study, we found a more reliable potential diagnostic biomarkers and constructed a more accurate prognostic evaluation model based on integrated transcriptome sequencing analysis of multiple independent data sets. First, we performed quality evaluation and differential analysis on seven Gene Expression Omnibus (GEO) data sets, and then comprehensively analyzed the differentially expressed genes with a robust rank aggregation algorithm. Next, Least absolute shrinkage and selection operator (LASSO) regression was used to establish an 8-gene prognostic risk score (RS) model. Finally, the prognostic model was further validated in the GEO data set. Also, RS has independence on other clinicopathological characteristics but has similarities in prognostic assessment compared with the T stage. Moreover, the combination of T stage and prognostic RS model based on the 8-gene had a better prognostic evaluation effect. In brief, our research suggest that the prognostic risk model of 8 genes has important clinical significance in HCC patients, and can further enrich the prognostic guidance value of the traditional T stage.

An Immune Gene Expression Signature Associated With Development of Human Hepatocellular Carcinoma Identifies Mice That Respond to Chemopreventive Agents

BACKGROUND & AIMS

Cirrhosis and chronic inflammation precede development of hepatocellular carcinoma (HCC) in approximately 80% of cases. We investigated immune-related gene expression patterns in liver tissues surrounding early-stage HCCs and chemopreventive agents that might alter these patterns to prevent liver tumorigenesis.

METHODS

We analyzed gene expression profiles of nontumor liver tissues from 392 patients with early-stage HCC (training set, N = 167 and validation set, N = 225) and liver tissue from patients with cirrhosis without HCC (N = 216, controls) to identify changes in expression of genes that regulate the immune response that could contribute to hepatocarcinogenesis. We defined 172 genes as markers for this deregulated immune response, which we called the immune-mediated cancer field (ICF). We analyzed the expression data of liver tissues from 216 patients with cirrhosis without HCC and investigated the association between this gene expression signature and development of HCC and outcomes of patients (median follow-up, 10 years). Human liver tissues were also analyzed by histology. C57BL/6J mice were given a single injection of diethylnitrosamine (DEN) followed by weekly doses of carbon tetrachloride to induce liver fibrosis and tumorigenesis. Mice were then orally given the multiple tyrosine inhibitor nintedanib or vehicle (controls); liver tissues were collected and histology, transcriptome, and protein analyses were performed. We also analyzed transcriptomes of liver tissues collected from mice on a choline-deficient high-fat diet, which developed chronic liver inflammation and tumors, orally given aspirin and clopidogrel or the anti-inflammatory agent sulindac vs mice on a chow (control) diet.

RESULTS

We found the ICF gene expression pattern in 50% of liver tissues from patients with cirrhosis without HCC and in 60% of nontumor liver tissues from patients with early-stage HCC. The liver tissues with the ICF gene expression pattern had 3 different features: increased numbers of effector T cells; increased expression of genes that suppress the immune response and activation of transforming growth factor β signaling; or expression of genes that promote inflammation and activation of interferon gamma signaling. Patients with cirrhosis and liver tissues with the immunosuppressive profile (10% of cases) had a higher risk of HCC (hazard ratio, 2.41; 95% confidence interval, 1.21-4.80). Mice with chemically induced fibrosis or diet-induced steatohepatitis given nintedanib or aspirin and clopidogrel down-regulated the ICF gene expression pattern in liver and developed fewer and smaller tumors than mice given vehicle.

CONCLUSIONS

We identified an immune-related gene expression pattern in liver tissues of patients with early-stage HCC, called the ICF, that is associated with risk of HCC development in patients with cirrhosis. Administration of nintedanib or aspirin and clopidogrel to mice with chronic liver inflammation caused loss of this gene expression pattern and development of fewer and smaller liver tumors. Agents that alter immune regulatory gene expression patterns associated with carcinogenesis might be tested as chemopreventive agents in patients with cirrhosis.

mTOR/HDAC1 Crosstalk Mediated Suppression of ADH1A and ALDH2 Links Alcohol Metabolism to Hepatocellular Carcinoma Onset and Progression in silico

Hepatocellular carcinoma (HCC) is ranked the third deadliest cancer worldwide whose molecular pathogenesis is not fully understood. Although deregulated metabolic pathways have been implicated in HCC onset and progression, the mechanisms triggering this metabolic imbalance are yet to be explored. Here, we identified a gene signature coding catabolic enzymes (Cat-GS) involved in key metabolic pathways like amino acid, lipid, carbohydrate, drug, and retinol metabolism as suppressed in HCC. A higher expression of deregulated Cat-GS is associated with good survival and less aggressive disease state in HCC patients. On the other hand, we identified mTOR signaling as a key determinant in HCC onset and progression, whose hyperactivation is found associated with poor survival and aggressive disease state in HCC patients. Next, out of Cat-GS, we established two key regulators of alcohol metabolism, alcohol dehydrogenase 1A (ADH1A) and aldehyde dehydrogenase 2 (ALDH2), as being transcriptionally suppressed by histone deacetylase 1 (HDAC1) at the downstream of mTORC1 signaling. Suppressed ADH1A and ALDH2 expression aligns well with HCC-specific molecular profile and can efficiently predict disease onset and progression, whereas higher ADH1A and ALDH2 expression is associated with good survival and less aggressive disease state in HCC patients. Overall, our in silico findings suggest that transcriptional suppression of alcohol metabolism regulators, ADH1A and ALDH2, at the downstream of mTOR signaling is, in part, responsible for triggering oncogenic transformation of hepatocytes resulting in disease onset and progression in HCC.

TLX3 repressed SNAI1-induced epithelial-mesenchymal transition by directly constraining STAT3 phosphorylation and functionally sensitized 5-FU chemotherapy in hepatocellular carcinoma

TLX3 has an established role as a sequence-specific transcription factor with vital functions in the nervous system. Although several studies have shown that TLX3 is aberrantly up-regulated in leukemia, its expression and function in hepatocellular carcinoma (HCC) remain unknown. We found that TLX3 expression was decreased in 68/100 (68%) HCC cases and negatively correlated with the expression of p-STAT3, SNAI1, and Vimentin, while it was positively associated with E-cadherin expression. ITRAQ proteomic profiling revealed significantly less TLX3 expression in primary HCC tumors than in portal vein tumor thrombi. Comparison of Kaplan-Meier curves showed that down-regulation of TLX3 in HCC was associated with poor post-surgical survival. TLX3 over-expression inhibited HCC cell viability, proliferation, migration, invasion and enhanced 5-FU treatment, whereas silencing TLX3 produced the opposite results. Further experiments showed that TLX3 attenuated the EMT phenotype. In vivo experiments showed that knockdown of TLX3 promoted the growth of HCC xenografts and attenuated the anti-tumor effects of 5-FU treatment. Gene expression microarray analysis revealed that TLX3 inhibited IL-6/STAT3 signaling. In additional mechanistic studies TLX3 reversed the EMT phenotype of HCC cells by binding to STAT3, inhibiting STAT3 phosphorylation, and down-regulating SNAI1 expression. Taken together, loss of expression of TLX3 induces EMT by enhancing IL-6/STAT3/SNAI1 signaling, and accelerates HCC progression while also attenuated the effect of 5-FU on HCCs.

Hepatocellular Carcinoma Risk Stratification by Genetic Profiling in Patients with Cirrhosis

Prediction of future hepatocellular carcinoma (HCC) risk in the sizable chronic liver disease population is an urgent unmet need to enable regular HCC screening for early detection. Germline deoxyribonucleic acid polymorphisms likely represent etiology-specific host factors that determine HCC susceptibility, including single nucleotide polymorphisms in EGF, IFNL3, MICA, and TLL1 in hepatitis C with or without active viral infection, and PNPLA3, TM6SF2, and MBOAT7 in metabolic liver diseases. Transcriptome-based prognostic liver signature in diseased liver tissue has been associated with long-term HCC risk in viral and metabolic etiologies. Transcriptomic signatures of hepatic injury and specific cell type such as aggregated lymphocytes also predict HCC development. Circulating factors such as proteins and their chemical modification, nucleotides, and metabolites may serve for less-invasive assessment of short- or long-term HCC risk. These biomarkers will enable individual HCC risk-based personalized clinical management for cost-effective early HCC detection and improvement of patient survival.

Systems biology based meth-miRNA-mRNA regulatory network identifies metabolic imbalance and hyperactive cell cycle signaling involved in hepatocellular carcinoma onset and progression

Background

Hepatocellular carcinoma (HCC) is one of the leading cause of cancer associated deaths worldwide. Independent studies have proposed altered DNA methylation pattern and aberrant microRNA (miRNA) levels leading to abnormal expression of different genes as important regulators of disease onset and progression in HCC. Here, using systems biology approaches, we aimed to integrate methylation, miRNA profiling and gene expression data into a regulatory methylation-miRNA–mRNA (meth-miRNA–mRNA) network to better understand the onset and progression of the disease.

Methods

Patients’ gene methylation, miRNA expression and gene expression data were retrieved from the NCBI GEO and TCGA databases. Differentially methylated genes, and differentially expressed miRNAs and genes were identified by comparing respective patients’ data using two tailed Student’s t-test. Functional annotation and pathway enrichment, miRNA–mRNA inverse pairing and gene set enrichment analyses (GSEA) were performed using DAVID, miRDIP v4.1 and GSEA tools respectively. meth-miRNA–mRNA network was constructed using Cytoscape v3.5.1. Kaplan–Meier survival analyses were performed using R script and significance was calculated by Log-rank (Mantel-Cox) test.

Results

We identified differentially expressed mRNAs, miRNAs, and differentially methylated genes in HCC as compared to normal adjacent tissues by analyzing gene expression, miRNA expression, and methylation profiling data of HCC patients and integrated top miRNAs along with their mRNA targets and their methylation profile into a regulatory meth-miRNA–mRNA network using systems biology approach. Pathway enrichment analyses of identified genes revealed suppressed metabolic pathways and hyperactive cell cycle signaling as key features of HCC onset and progression which we validated in 10 different HCC patients’ datasets. Next, we confirmed the inverse correlation between gene methylation and its expression, and between miRNA and its targets’ expression in various datasets. Furthermore, we validated the clinical significance of identified methylation, miRNA and mRNA signatures by checking their association with clinical features and survival of HCC patients.

Conclusions

Overall, we suggest that simultaneous (1) reversal of hyper-methylation and/or oncogenic miRNA driven suppression of genes involved in metabolic pathways, and (2) induction of hyper-methylation and/or tumor suppressor miRNA driven suppression of genes involved in cell cycle signaling have potential of inhibiting disease aggressiveness, and predicting good survival in HCC.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0804-3) contains supplementary material, which is available to authorized users.

Upregulated MMP28 in Hepatocellular Carcinoma Promotes Metastasis via Notch3 Signaling and Predicts Unfavorable Prognosis

MMP28 belongs to the matrix metalloproteinases (MMPs) family and functions in tissue homeostasis and development. Although many other MMPs have been reported to regulate tumor progression, the roles of MMP28 in cancer remain largely elusive. In this study, we investigated the potential roles of MMP28 in hepatocellular carcinoma (HCC). The upregulation of MMP28 was first determined by the analysis on different public datasets. Further quantitative real-time PCR (qPCR) analysis, western blot (WB) assay and immunohistochemistry (IHC) assay on tumor and tumor-adjacent samples from HCC patients confirmed the aberrant elevation of MMP28 in HCC. Pathological analysis showed that increased MMP28 was associated with tumor size, vascular invasion, TNM stage and overall survival in HCC patients. Meanwhile, upregulated MMP28 was identified as an independent prognosis factor in multivariate analysis, and the incorporation of MMP28 expression with TNM staging system established a novel model to improve the accuracy of the predictions. In vivo and in vitro data revealed that MMP28 promoted migration and invasion of HCC cells, and enhanced epithelial-mesenchymal transition (EMT) via elevating zinc finger E-box binding homeobox (ZEB) homologues levels. Furthermore, we determined that Notch3 signaling was critical for the functions of MMP28 in HCC. In conclusion, upregulated MMP28 in HCC promoted migration and invasion and predicted poor prognosis for HCC patients, and the effects of MMP28 depended on Notch3 signaling.

Clinical Significance of CUB and Sushi Multiple Domains 1 Inactivation in Head and Neck Squamous Cell Carcinoma

Although the genetic alteration of CUB and Sushi multiple domains 1 (CSMD1) is known to be associated with poor prognosis in several cancers, there is a lack of clinical relevance in head and neck cancer. The aim of this study was to offer insight into the clinical significance of CSMD1, utilizing a multimodal approach that leverages publicly available independent genome-wide expression datasets. CSMD1-related genes were found and analyzed to examine the clinical significance of CSMD1 inactivation in the HNSCC cohort of publicly available databases. We analyzed the frequency of somatic mutations, clinicopathologic characteristics, association with immunotherapy-related gene signatures, and the pathways of gene signatures. We found 363 CSMD1-related genes. The prognosis of the CSMD1-inactivated subgroup was poor. FBXW7, HLA-A, MED1, NOTCH2, NOTCH3, and TP53 had higher mutation rates in the CSMD1-inactivated subgroups. The Interferon-gamma score and immune signature score were elevated in CSMD1-inactivated subgroups. We identified several CSMD1-related pathways, such as the phosphatidylinositol signaling system and inositol phosphate metabolism. Our study using three large and independent datasets suggests that CSMD1-related gene signatures are associated with the prognosis of HNSCC patients.

Long noncoding RNA HOTTIP expression predicts tumor recurrence in hepatocellular carcinoma patients following liver transplantation

Background

The long noncoding RNA HOTTIP has recently been described as a biomarker of poor prognosis in patients with hepatocellular carcinoma (HCC).

Methods

In the present study, we evaluated the clinical significance of HOTTIP expression in predicting the rate of tumor recurrence in HCC patients after liver transplantation (LT). We examined the expression pattern and single nucleotide polymorphism (SNP) genotype of HOTTIP in HCC samples from 155 patients underwent LT, and its correlation with clinical parameters and patient prognosis was analyzed. HOTTIP was suppressed using siRNA to explore the role HOTTIP plays in tumor progression.

Results

The expression level of HOTTIP in cancer tissues was higher than in adjacent noncancerous tissues. Multivariate analyses revealed that HOTTIP expression was an independent prognostic factor for tumor recurrence and lower overall survival times in HCC patients after LT. Patients who beyond the Milan criteria and exhibit decreased HOTTIP expression experienced longer recurrence-free survival and overall survival. HOTTIP rs2071265 is associated with an earlier recurrence in HCC patients. Moreover, the suppression of HOTTIP in liver cancer cell lines reduced cell invasion rates and increased chemosensitivity.

Conclusions

In summary, the high expression level of HOTTIP in HCC could serve as a candidate biomarker for predicting poor prognosis in HCC patients underwent liver transplant therapy. Furthermore, HOTTIP might be a potential therapeutic target.