Alterations of TP53 are associated with a poor outcome for patients with hepatocellular carcinoma: evidence from a systematic review and meta-analysis

Biomarkers for prognosis of meningioma patients: A systematic review and meta-analysis

Meningioma is the most common primary brain tumor and many studies have evaluated numerous biomarkers for their prognostic value, often with inconsistent results. Currently, no reliable biomarkers are available to predict the survival, recurrence, and progression of meningioma patients in clinical practice. This study aims to evaluate the prognostic value of immunohistochemistry-based (IHC) biomarkers of meningioma patients. A systematic literature search was conducted up to November 2023 on PubMed, CENTRAL, CINAHL Plus, and Scopus databases. Two authors independently reviewed the identified relevant studies, extracted data, and assessed the risk of bias of the studies included. Meta-analyses were performed with the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS). The risk of bias in the included studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool. A total of 100 studies with 16,745 patients were included in this review. As the promising markers to predict OS of meningioma patients, Ki-67/MIB-1 (HR = 1.03, 95%CI 1.02 to 1.05) was identified to associate with poor prognosis of the patients. Overexpression of cyclin A (HR = 4.91, 95%CI 1.38 to 17.44), topoisomerase II α (TOP2A) (HR = 4.90, 95%CI 2.96 to 8.12), p53 (HR = 2.40, 95%CI 1.73 to 3.34), vascular endothelial growth factor (VEGF) (HR = 1.61, 95%CI 1.36 to 1.90), and Ki-67 (HR = 1.33, 95%CI 1.21 to 1.46), were identified also as unfavorable prognostic biomarkers for poor RFS of meningioma patients. Conversely, positive progesterone receptor (PR) and p21 staining were associated with longer RFS and are considered biomarkers of favorable prognosis of meningioma patients (HR = 0.60, 95% CI 0.41 to 0.88 and HR = 1.89, 95%CI 1.11 to 3.20). Additionally, high expression of Ki-67 was identified as a prognosis biomarker for poor PFS of meningioma patients (HR = 1.02, 95%CI 1.00 to 1.04). Although only in single studies, KPNA2, CDK6, Cox-2, MCM7 and PCNA are proposed as additional markers with high expression that are related with poor prognosis of meningioma patients. In conclusion, the results of the meta-analysis demonstrated that PR, cyclin A, TOP2A, p21, p53, VEGF and Ki-67 are either positively or negatively associated with survival of meningioma patients and might be useful biomarkers to assess the prognosis.

Construction of a TP53 mutation-associated ceRNA network as prognostic biomarkers in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) continues to endanger human health worldwide. Regulatory networks of competing endogenous RNAs (ceRNAs) play important roles in HCC. TP53 is the second most often altered gene in HCC and has a significant role in regulating target genes such as miRNAs and lncRNAs.

Methods

Data from patients with TP53 mutation were collected through the cBioPortal database and differential analysis was performed to screen RNAs related to TP53 mutation. The lncRNA-miRNA-mRNA relationship was predicted by the miRcode, miRDB, and TargetScan databases. The ceRNA networks were screened and visualized by Cytoscape. Core ceRNA networks were generated by differential analysis, coexpression analysis, prognostic analysis and subcellular localization. Finally, methylation, mutation, PPI, GSEA, immunity and drug sensitivity analyses of MEX3A were performed to determine the role of MEX3A in HCC.

Results

We identified 1508 DEmRNAs, 85 DEmiRNAs and 931 DElncRNAs and obtained a ceRNA network including 28 lncRNAs, 4 miRNAs and 31 mRNAs. Twenty hub DERNAs in the TP53-altered-related ceRNA network were screened out by Cytoscape and the core ceRNA network (LINC00491/TCL6-hsa-miR-139-5p-MEX3A) was obtained by multiple analyses. In addition, we discovered that the methylation level of MEX3A was decreased and the mutation frequency was raised in HCC. Furthermore, elevated MEX3A expression was associated with alterations in the HCC immunological microenvironment.

Conclusion

We successfully constructed a reciprocal ceRNA network, which could provide new ideas for exploring HCC mechanisms and therapeutic approaches.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

Bulk and single-cell transcriptome reveal the immuno-prognostic subtypes and tumour microenvironment heterogeneity in HCC

BACKGROUND & AIMS

Accumulating evidences suggest tumour microenvironment (TME) profoundly influence clinical outcome in hepatocellular carcinoma (HCC). Existing immune subtypes are susceptible to batch effects, and integrative analysis of bulk and single-cell transcriptome is helpful to recognize immune subtypes and TME in HCC.

METHODS

Based on the relative expression ordering (REO) of 1259 immune-related genes, an immuno-prognostic signature was developed and validated in 907 HCC samples from five bulk transcriptomic cohorts, including 72 in-house samples. The machine learning models based on subtype-specific gene pairs with stable REOs were constructed to jointly predict immuno-prognostic subtypes in single-cell RNA-seq data and validated in another single-cell data. Then, cancer characteristics, immune landscape, underlying mechanism and therapeutic benefits between subtypes were analysed.

RESULTS

An immune-related signature with 29 gene pairs stratified HCC samples individually into two risk subgroups (C1 and C2), which was an independent prognostic factor for overall survival. The machine learning models verified the immune subtypes from five bulk cohorts to two single-cell transcriptomic data. Integrative analysis revealed that C1 had poorer outcomes, higher CNV burden and malignant scores, higher sensitivity to sorafenib, and exhibited an immunosuppressive phenotype with more regulators, e.g., myeloid-derived suppressor cells (MDSCs), Mø_SPP1, while C2 was characterized with better outcomes, higher metabolism, more benefit from immunotherapy, and displayed active immune with more effectors, e.g., tumour infiltrating lymphocyte and dendritic cell. Moreover, both two single-cell data revealed the crosstalk of SPP1-related L-R pairs between cancer and immune cells, especially SPP1-CD44, might lead to immunosuppression in C1.

CONCLUSIONS

The REO-based immuno-prognostic subtypes were conducive to individualized prognosis prediction and treatment options for HCC. This study paved the way for understanding TME heterogeneity between immuno-prognostic subtypes of HCC.

Hepatocellular Carcinoma: Old and Emerging Therapeutic Targets

Liver cancer, predominantly hepatocellular carcinoma (HCC), globally ranks sixth in incidence and third in cancer-related deaths. HCC risk factors include non-viral hepatitis, alcohol abuse, environmental exposures, and genetic factors. No specific genetic alterations are unequivocally linked to HCC tumorigenesis. Current standard therapies include surgical options, systemic chemotherapy, and kinase inhibitors, like sorafenib and regorafenib. Immunotherapy, targeting immune checkpoints, represents a promising avenue. FDA-approved checkpoint inhibitors, such as atezolizumab and pembrolizumab, show efficacy, and combination therapies enhance clinical responses. Despite this, the treatment of hepatocellular carcinoma (HCC) remains a challenge, as the complex tumor ecosystem and the immunosuppressive microenvironment associated with it hamper the efficacy of the available therapeutic approaches. This review explores current and advanced approaches to treat HCC, considering both known and new potential targets, especially derived from proteomic analysis, which is today considered as the most promising approach. Exploring novel strategies, this review discusses antibody drug conjugates (ADCs), chimeric antigen receptor T-cell therapy (CAR-T), and engineered antibodies. It then reports a systematic analysis of the main ligand/receptor pairs and molecular pathways reported to be overexpressed in tumor cells, highlighting their potential and limitations. Finally, it discusses TGFβ, one of the most promising targets of the HCC microenvironment.

Role of immune related genes in predicting prognosis and immune response in patients with hepatocellular carcinoma

Immunotherapy has developed rapidly in recent years. This study aimed to establish a prognostic signature for immune-related genes (IRGs) and explore related potential immunotherapies. The RNA-seq transcriptome profiles and clinicopathological information of patients were obtained from The Cancer Genome Atlas. Differentially expressed IRGs in tumors and normal tissues were screened and a risk score signature was constructed to predict the prognosis in patients with hepatocellular carcinoma (HCC). Receiver operating characteristic curves, survival analyses, and correlation analyses were used to explore the clinical application of this model. We further analyzed the differences in clinical characteristics, immune infiltration, somatic mutations, and treatment sensitivity between the high- and low-risk populations characterized by the prognostic models. The immune cell infiltration score and immune-related pathway activity were calculated using the single sample gene set enrichment analysis (ssGSEA) set enrichment analysis. Gene ontology (GO), Kyoto encyclopedia of genes and genomes, and GSEA were used to explore the underlying mechanisms. We constructed a nine-IRG formula to predict the prognosis in HCC patients. The higher the risk score, the higher the malignancy of the tumor and the worse the prognosis. There were significant differences in immune related processes between the high- and low-risk groups. TP53 and CTNNB1 mutations were significantly different between different risk groups. The expression of model gene was closely related to the sensitivity of tumor cells to chemotherapeutic drugs. This risk score model, which is helpful for the individualized treatment of patients with different risk factors, could be a reliable prognostic tool for HCC patients.

Characterization of stem cell subtypes and prognostic signature in hepatocellular carcinoma

BACKGROUND

Cancer stem cells (CSCs) were linked to cancer aggressiveness and poor prognosis in patients with hepatocellular carcinoma (HCC).

METHODS

We integrated two external HCC cohorts to develop the stem cell subtypes according to unsupervised clustering with 26 stem cell gene sets. Between the subtypes, differences in prognosis, clinical characteristics, recognized HCC subtypes, metabolic profile, immune-related features, somatic mutation, and drug sensitivity were examined. The prognostic signature was created, and validated by numerous cohorts, and used to assess the efficacy of immunotherapy and transcatheter arterial chemoembolization (TACE) treatment. The nomogram was developed based on the signature and clinical features. We further examined the function of KIF20A in HCC and proved that KIF20A had the potential to regulate the stemness of HCC cells through western blot.

RESULTS

Low stem cell patterns, a good prognosis, positive clinical features, specific molecular subtypes, low metastatic characteristics, and an abundance of metabolic and immunological aspects were associated with Cluster 1, whereas Cluster 2 was the reverse. Chemotherapy and immunotherapy were more effective in Cluster 1. Cluster 1 and CTNNB1 and ALB mutation were more closely. Additionally, the prognosis, immunotherapeutic, and TACE therapy responses were all worse in the high-risk group. The nomogram could predict the survival probability of HCC patients. KIF20A was discovered to be overexpressed in HCC and was revealed to be connected to the stemness of the HepG2 cell line.

CONCLUSIONS

Two stem cell subgroups with different prognoses, metabolic, and immunological characteristics in HCC patients were identified. We also created a 7-gene prognostic signature and a nomogram to estimate the survival probability. The function of KIF20A in HCC stemness was initially examined.

Characterization of the metabolic alteration-modulated tumor microenvironment mediated by TP53 mutation and hypoxia

BACKGROUND

TP53 mutation and hypoxia play an essential role in cancer progression. However, the metabolic reprogramming and tumor microenvironment (TME) heterogeneity mediated by them are still not fully understood.

METHODS

The multi-omics data of 32 cancer types and immunotherapy cohorts were acquired to comprehensively characterize the metabolic reprogramming pattern and the TME across cancer types and explore immunotherapy candidates. An assessment model for metabolic reprogramming was established by integration of multiple machine learning methods, including lasso regression, neural network, elastic network, and survival support vector machine (SVM). Pharmacogenomics analysis and in vitro assay were conducted to identify potential therapeutic drugs.

RESULTS

First, we identified metabolic subtype A (hypoxia-TP53 mutation subtype) and metabolic subtype B (non-hypoxia-TP53 wildtype subtype) in hepatocellular carcinoma (HCC) and showed that metabolic subtype A had an "immune inflamed" microenvironment. Next, we established an assessment model for metabolic reprogramming, which was more effective compared to the traditional prognostic indicators. Then, we identified a potential targeting drug, teniposide. Finally, we performed the pan-cancer analysis to illustrate the role of metabolic reprogramming in cancer and found that the metabolic alteration (MA) score was positively correlated with tumor mutational burden (TMB), neoantigen load, and homologous recombination deficiency (HRD) across cancer types. Meanwhile, we demonstrated that metabolic reprogramming mediated a potential immunotherapy-sensitive microenvironment in bladder cancer and validated it in an immunotherapy cohort.

CONCLUSION

Metabolic alteration mediated by hypoxia and TP53 mutation is associated with TME modulation and tumor progression across cancer types. In this study, we analyzed the role of metabolic alteration in cancer and propose a predictive model for cancer prognosis and immunotherapy responsiveness. We also explored a potential therapeutic drug, teniposide.

Mutational Landscape and Precision Medicine in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fourth most common malignancy worldwide and exhibits a universal burden as the incidence of the disease continues to rise. In addition to curative-intent therapies such as liver resection and transplantation, locoregional and systemic therapy options also exist. However, existing treatments carry a dismal prognosis, often plagued with high recurrence and mortality. For this reason, understanding the tumor microenvironment and mutational pathophysiology has become the center of investigation for disease control. The use of precision medicine and genetic analysis can supplement current treatment modalities to promote individualized management of HCC. In the search for personalized medicine, tools such as next-generation sequencing have been used to identify unique tumor mutations and improve targeted therapies. Furthermore, investigations are underway for specific HCC biomarkers to augment the diagnosis of malignancy, the prediction of whether the tumor environment is amenable to available therapies, the surveillance of treatment response, the monitoring for disease recurrence, and even the identification of novel therapeutic opportunities. Understanding the mutational landscape and biomarkers of the disease is imperative for tailored management of the malignancy. In this review, we summarize the molecular targets of HCC and discuss the current role of precision medicine in the treatment of HCC.

Assessments of TP53 and CTNNB1 gene hotspot mutations in circulating tumour DNA of hepatitis B virus-induced hepatocellular carcinoma

Background: Hepatitis B virus (HBV) infection is one of the major causes of chronic liver disease, which progresses from chronic hepatitis B (CHB) to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Early detection and laboratory-based screening of hepatocellular carcinoma are still major challenges. This study was undertaken to determine whether the cancer hallmark gene signatures that are released into circulation as circulating tumour DNA (ctDNA) can be used as a liquid biopsy marker for screening, early detection, and prognosis of HCC. Methods: A total of 130 subjects, including HBV-HCC (n = 80), HBV-cirrhotic and non-cirrhotic (n = 35), and healthy (n = 15) controls, were evaluated for TP53 and beta-catenin (CTNNB1) gene hotspot mutations in ctDNA by Sanger-based cycle sequencing and droplet digital PCR (ddPCR) assays. Mutation detection frequency, percentage mutant fractions, and their association with tumour stage, mortality, and smoking habits were determined. Results: Sanger-based cycle sequencing was carried out for 32 HCC patients. Predict SNP Tools analysis indicated several pathogenic driver mutations in the ctDNA sequence, which include p.D228N, p.C229R, p.H233R, p.Y234D, p.S240T, p.G245S, and p.R249M for TP53 gene exon 7 and p.S33T for CTNNB1 gene exon 3. The TP53 c.746G>T (p.R249M) mutation was detected predominately (25% cases) by sequencing, but there was no dominant mutation at position c.747G>T (p.R249S) that was reported for HBV-HCC patients. A dual-probe ddPCR assay was developed to determine mutant and wild-type copy numbers of TP53 (p.R249M and p.R249S) and CTNNB1 (p.S45P) and their percentage mutant fraction in all 130 subjects. The TP53 R249M and CTNNB1 S45P mutations were detected in 31.25% and 26.25% of HCC patients, respectively, with a high mutant-to-wild-type fraction percentage (1.81% and 1.73%), which is significant as compared to cirrhotic and non-cirrhotic patients. Poor survival was observed in HCC patients with combined TP53 and CTNNB1 gene driver mutations. The TP53 R249M mutation was also significantly (p < 0.0001) associated with smoking habits (OR, 11.77; 95% CI, 3.219-36.20), but not the same for the TP53 R249S mutation. Conclusion: Screening of ctDNA TP53 and CTNNB1 gene mutations by ddPCR may be helpful for early detection and identifying the risk of HCC progression.

M7G methylated core genes (METTL1 and WDR4) and associated RNA risk signatures are associated with prognosis and immune escape in HCC

N7 methylguanosine (m7G) has a crucial role the development of hepatocellular carcinoma (HCC). This study aimed to investigate the impact of the m7G methylation core genes (METTL1 and WDR4) and associated RNA risk signatures on HCC. we found m7G methylation core genes (METTL1 and WDR4) were upregulated in four HCC cell lines, and downregulation of METTL1 and WDR4 attenuated HCC cell proliferation, migration, and invasion. Moreover, METTL1 and WDR4 are upregulated in HCC tissues, and that there is a significant positive correlation between them. METTL1 and WDR4 were identified as independent prognostic markers for HCC by employing overall survival (OS), disease-specific survival (DSS), Progression Free Interval survival (PFI), and univariate/multivariate Cox analyses. We identified 1479 coding RNAs (mRNAs) and 232 long non-coding RNAs (lncRNAs) associated with METTL1 / WDR4 by using weighted coexpression network analysis (WGCNA) and co-clustering analysis. The least absolute shrinkage and selection operator (lasso) were used to constructing mRNA and lncRNA risk signatures associated with the METTL1 / WDR4. These risk were independent poor prognostic factors in HCC. Furthermore, we found that METTL1 / WDR4 expression and mRNA / lncRNA risk scores were closely associated with TP53 mutations. Clinicopathological features correlation results showed that METTL1 / WDR4 expression and mRNA / lncRNA risk score were associated with the stage and invasion depth (T) of HCC. To predict the overall survival of HCC individuals, we constructed a nomogram with METTL1/WDR4 expression, mRNA/lncRNA risk score, and clinicopathological features. In addition, we combined single-cell sequencing datasets and immune escape-related checkpoints to construct an immune escape-related protein-protein interaction(PPI) network. In conclusion, M7G methylated core genes (METTL1 and WDR4) and associated RNA risk signatures are associated with prognosis and immune escape in HCC.

Combining WGCNA and machine learning to construct immune-related EMT patterns to predict HCC prognosis and immune microenvironment

Hepatocellular carcinoma (HCC) is a malignancy with a very high mortality rate. Because of its high heterogeneity, there is an urgent need to find biomarkers that accurately predict prognosis. Epithelial-mesenchymal transition (EMT) is closely associated with frequent recurrence and high mortality of HCC. Therefore, it is necessary to comprehensively analyze the prognostic value and immunological properties of EMT gene in HCC. In our study, we performed bioinformatics analysis of the TCGA and ICGC liver cancer cohorts and identified the module genes of immune-associated EMTs (iEMT) by Weighted Gene Co-Expression Network Analysis (WGCNA). Further we used machine learning (support vector machines-recursive feature elimination and Lasso) to identify three central iEMT genes (ARMC9, ADAM15 and STC2) and construct iEMT_score. Subsequently, in the training and validation cohorts, it was demonstrated that the overall survival (OS) of patients in the high iEMT_score group was worse than that of patients in the low iEMT_score group. Based on this, we have constructed a nomogram that is easy for clinicians to use. In addition, our study explored differences in pathway enrichment, immunological properties, and sensitivity to common chemotherapy and targeted drugs in different subgroups of iEMT_score. Finally, we showed through in vitro experiments that knockdown of ARMC9 could significantly inhibit the proliferation, migration and invasion of HCC cells BEL7402. Taken together, our findings suggest that iEMT_score is an excellent biomarker for predicting prognosis and provide some new insights for personalized treatment of HCC patients.

Role of p53 suppression in the pathogenesis of hepatocellular carcinoma

In the world, hepatocellular carcinoma (HCC) is among the top 10 most prevalent malignancies. HCC formation has indeed been linked to numerous etiological factors, including alcohol usage, hepatitis viruses and liver cirrhosis. Among the most prevalent defects in a wide range of tumours, notably HCC, is the silencing of the p53 tumour suppressor gene. The control of the cell cycle and the preservation of gene function are both critically important functions of p53. In order to pinpoint the core mechanisms of HCC and find more efficient treatments, molecular research employing HCC tissues has been the main focus. Stimulated p53 triggers necessary reactions that achieve cell cycle arrest, genetic stability, DNA repair and the elimination of DNA-damaged cells’ responses to biological stressors (like oncogenes or DNA damage). To the contrary hand, the oncogene protein of the murine double minute 2 (MDM2) is a significant biological inhibitor of p53. MDM2 causes p53 protein degradation, which in turn adversely controls p53 function. Despite carrying wt-p53, the majority of HCCs show abnormalities in the p53-expressed apoptotic pathway. High p53 in-vivo expression might have two clinical impacts on HCC: (1) Increased levels of exogenous p53 protein cause tumour cells to undergo apoptosis by preventing cell growth through a number of biological pathways; and (2) Exogenous p53 makes HCC susceptible to various anticancer drugs. This review describes the functions and primary mechanisms of p53 in pathological mechanism, chemoresistance and therapeutic mechanisms of HCC.

Comprehensive analysis of the correlation of the pan-cancer gene HAUS5 with prognosis and immune infiltration in liver cancer

Liver hepatocellular carcinoma (LIHC) is one of the most common malignancies and places a heavy burden on patients worldwide. HAUS augmin-like complex subunit 5 (HAUS5) is involved in the occurrence and development of various cancers. However, the functional role and significance of HAUS5 in LIHC remain unclear. The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE) and Gene Expression Omnibus (GEO) databases were used to analyze the mRNA expression of HAUS5. The value of HAUS5 in predicting LIHC prognosis and the relationship between HAUS5 and clinicopathological features were assessed by the Kaplan-Meier plotter and UALCAN databases. Functional enrichment analyses and nomogram prediction model construction were performed with the R packages. The LinkedOmics database was searched to reveal co-expressed genes associated with HAUS5. The relationship between HAUS5 expression and immune infiltration was explored by searching the TISIDB database and single-sample gene set enrichment analysis (ssGSEA). The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (HPA) databases were used to evaluate HAUS5 protein expression. Finally, the effect of HAUS5 on the proliferation of hepatoma cells was verified by CCK-8, colony formation and EdU assays. HAUS5 is aberrantly expressed and associated with a poor prognosis in most tumors, including LIHC. The expression of HAUS5 is significantly correlated with clinicopathological indicators in patients with LIHC. Functional enrichment analysis showed that HAUS5 was closely related to DNA replication, cell cycle and p53 signaling pathway. HAUS5 may serve as an independent risk factor for LIHC prognosis. The nomogram based on HAUS5 had area under the curve (AUC) values of 0.74 and 0.77 for predicting the 3-year and 5-year overall survival (OS) of LIHC patients. Immune correlation analysis showed that HAUS5 was significantly associated with immune infiltration. Finally, the results of in vitro experiments showed that when HAUS5 was knocked down, the proliferation of hepatoma cells was significantly decreased. The pan-oncogene HAUS5 is a positive regulator of LIHC progression and is closely associated with a poor prognosis in LIHC. Moreover, HAUS5 is involved in immune infiltration in LIHC. HAUS5 may be a new prognostic marker and therapeutic target for LIHC patients.

Prognosis and personalized medicine prediction by integrated whole exome and transcriptome sequencing of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a clinically and genetically heterogeneous disease. To better describe the clinical value of the main driver gene mutations of HCC, we analyzed the whole exome sequencing data of 125 patients, and combined with the mutation data in the public database, 14 main mutant genes were identified. And we explored the correlation between the main mutation genes and clinical features. Consistent with the results of previous data, we found that TP53 and LRP1B mutations were related to the prognosis of our patients by WES data analysis. And we further explored the associated characteristics of TP53 and LRP1B mutations. However, it is of great clinical significance to tailor a unique prediction method and treatment plan for HCC patients according to the mutation of TP53. For TP53 wild-type HCC patients, we proposed a prognostic risk model based on 11 genes for better predictive value. According to the median risk score of the model, HCC patients with wild-type TP53 were divided into high-risk and low-risk groups. We found significant transcriptome changes in the enrichment of metabolic-related pathways and immunological characteristics between the two groups, suggesting the predictability of HCC immunotherapy by using this model. Through the CMap database, we found that AM580 had potential therapeutic significance for high-risk TP53 wild-type HCC patients.

Construction and systematic evaluation of a machine learning-based cuproptosis-related lncRNA score signature to predict the response to immunotherapy in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is a common malignant cancer with a poor prognosis. Cuproptosis and associated lncRNAs are connected with cancer progression. However, the information on the prognostic value of cuproptosis-related lncRNAs is still limited in HCC.

Methods

We isolated the transcriptome and clinical information of HCC from TCGA and ICGC databases. Ten cuproptosis-related genes were obtained and related lncRNAs were correlated by Pearson's correlation. By performing lasso regression, we created a cuproptosis-related lncRNA prognostic model based on the cuproptosis-related lncRNA score (CLS). Comprehensive analyses were performed, including the fields of function, immunity, mutation and clinical application, by various R packages.

Results

Ten cuproptosis-related genes were selected, and 13 correlated prognostic lncRNAs were collected for model construction. CLS was positively or negatively correlated with cancer-related pathways. In addition, cell cycle and immune related pathways were enriched. By performing tumor microenvironment (TME) analysis, we determined that T-cells were activated. High CLS had more tumor characteristics and may lead to higher invasiveness and treatment resistance. Three genes (TP53, CSMD1 and RB1) were found in high CLS samples with more mutational frequency. More amplification and deletion were detected in high CLS samples. In clinical application, a CLS-based nomogram was constructed. 5-Fluorouracil, gemcitabine and doxorubicin had better sensitivity in patients with high CLS. However, patients with low CLS had better immunotherapeutic sensitivity.

Conclusion

We created a prognostic CLS signature by machine learning, and we comprehensively analyzed the signature in the fields of function, immunity, mutation and clinical application.

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

BACKGROUND

The TP53 gene is estimated to be mutated in over 50% of tumors, with the majority of tumors exhibiting abnormal TP53 signaling pathways. However, the exploration of TP53 mutation-related LncRNAs in Hepatocellular carcinoma (HCC) remains incomplete. This study aims to identify such LncRNAs and enhance the prognostic accuracy for Hepatoma patients.

MATERIAL AND METHODS

Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database.

RESULTS

In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability.

CONCLUSION

The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.

Six-Transmembrane Epithelial Antigen of Prostate 4: An Indicator of Prognosis and Tumor Immunity in Hepatocellular Carcinoma

Purpose

The six-transmembrane epithelial antigen of prostate 4 (STEAP4) has been linked to tumor progression via its involvement in inflammatory responses, oxidative stress, and metabolism. However, STEAP4 has rarely been studied in hepatocellular carcinoma (HCC). We explored STEAP4 expression associated with tumor prognosis to understand its role in tumor biology in HCC.

Patients and Methods

STEAP4 mRNA and protein expressions were primarily analyzed using bioinformatics tools based on The Cancer Genome Atlas database to understand the expression pattern, molecular mechanism, prognostic impact, and association with immune cell infiltration. We further investigated the association between STEAP4 protein expression and clinicopathological parameters and their predictive value in HCC patients using immunohistochemical staining of tissue microarrays.

Results

The expression of STEAP4 mRNA and protein in HCC tissues was significantly lower than in normal liver tissues. Reduced expression of STEAP4 was linked to advanced HCC stages, poor recurrence-free survival (RFS), and overall survival. Furthermore, reduced STEAP4 expression was a significant predictor of worse RFS in univariate and multivariate analyses in the immunohistochemical cohort. GO, KEGG, and GSEA analyses revealed that STEAP4 is related to numerous biological processes and pathways, including drug metabolism, DNA replication, RNA metabolism, and immune response. In terms of the immune system, the decreased level of STEAP4 was correlated with the immunosuppressive microenvironment.

Conclusion

Our data indicated that reduced STEAP4 expression was significantly associated with tumor aggressiveness and poor prognosis, possibly because of its link to various biological processes and induction of HCC immune evasion. Therefore, STEAP4 expression may serve as a potential prognostic biomarker for cancer progression and immunity, as well as a therapeutic target in HCC.

Molecular subtyping and IMScore based on immune-related pathways, oncogenic pathways, and DNA damage repair pathways for guiding immunotherapy in hepatocellular carcinoma patients

Background

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. Although immunotherapy provides hope for advanced HCC patients, the outcomes are not satisfactory and vary by individual case. In this study, we sought to establish novel molecular subtypes and a stable model based on tumor-related pathways for guiding the immunotherapy in HCC patients.

Methods

A total of 15 pathways including immune pathways, stromal pathways, oncogenic pathways, and DNA damage repair pathways were used to construct molecular subtypes through consensus clustering. Immune characteristics, gene mutations, and genomic alterations including copy number variations and homologous recombination deficiency (HRD) were analyzed in different clusters. The Tumor Immune Dysfunction and Exclusion (TIDE) framework was used to predict the response to immunotherapy. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were employed to screen prognostic genes for constructing a risk model.

Results

Three clusters/subtypes were constructed including Immune-E, Immune-D and Stromal-E. Immune-D had the worst prognosis and high enrichment of HRD pathways. Immune-E had higher immune infiltration, higher expression of major histocompatibility complex (MHC)-related genes, and higher expression of PD1, PDL1, CTLA4, and LAG3. TP53 alterations frequently occurred in Immune-D. Immune-E had a relatively high response to immunotherapy and was sensitive to chemotherapeutic drugs. Moreover, we constructed an IMScore model that was effective to classify HCC patients into different risk groups, and the IMScore had a better performance than the TIDE score.

Conclusions

This study revealed the complex interaction among the tumor microenvironment (TME), genomic alterations, and tumor-related pathways by exploring the molecular difference of 3 subtypes. The IMScore model has potential to provide guidance for immunotherapy in HCC patients.

Computer analysis of regulation of hepatocarcinoma marker genes hypermethylated by HCV proteins

Hepatitis C virus (HCV) is a risk factor that leads to hepatocellular carcinoma (HCC) development. Epigenetic changes are known to play an important role in the molecular genetic mechanisms of virus-induced oncogenesis. Aberrant DNA methylation is a mediator of epigenetic changes that are closely associated with the HCC pathogenesis and considered a biomarker for its early diagnosis. The ANDSystem software package was used to reconstruct and evaluate the statistical significance of the pathways HCV could potentially use to regulate 32 hypermethylated genes in HCC, including both oncosuppressor and protumorigenic ones identified by genome-wide analysis of DNA methylation. The reconstructed pathways included those affecting protein-protein interactions (PPI), gene expression, protein activity, stability, and transport regulations, the expression regulation pathways being statistically significant. It has been shown that 8 out of 10 HCV proteins were involved in these pathways, the HCV NS3 protein being implicated in the largest number of regulatory pathways. NS3 was associated with the regulation of 5 tumor-suppressor genes, which may be the evidence of its central role in HCC pathogenesis. Analysis of the reconstructed pathways has demonstrated that following the transcription factor inhibition caused by binding to viral proteins, the expression of a number of oncosuppressors (WT1, MGMT, SOCS1, P53) was suppressed, while the expression of others (RASF1, RUNX3, WIF1, DAPK1) was activated. Thus, the performed gene-network reconstruction has shown that HCV proteins can influence not only the methylation status of oncosuppressor genes, but also their transcriptional regulation. The results obtained can be used in the search for pharmacological targets to develop new drugs against HCV-induced HCC.

Integrated systemic analysis of FAM72A to identify its clinical relevance, biological function, and relationship to drug sensitivity in hepatocellular carcinoma

Background

The family with sequence similarity 72 member A (FAM72A) protein has been identified as an effector of multiple pathological processes in many cancers. The value of FAM72A in HCC remains largely unknown.

Methods

Data from TCGA-LIHC, ICGC-LIRI-JP, IMvigor210, cBioPortal, GeneMANIA, and TIMER were processed and visualized to explore the association between FAM72A and the prognosis, stemness phenotype, mutational burden, immune cell infiltration, and drug sensitivity in HCC patients. Potential pathways were also revealed. Furthermore, we experimentally verified the results in vivo and in vitro using immunohistochemistry, western blotting, and CCK-8 assays.

Results

First, FAM72A mRNA expression was significantly upregulated in HCC. High FAM72A expression was independently associated with a poor prognosis. Experimental validation confirmed that FAM72A was remarkably overexpressed in HCC patients and mice. Moreover, FAM72A knockdown suppressed HCC cell proliferation. In addition, the frequency of TP53 mutations was significantly higher in the high FAM72A expression group. Subsequently, the enrichment analysis revealed that FAM72A was closely related to immune processes and mTOR pathways. Silencing FAM72A increased the expression levels of mTOR in HCC cell lines. The FAM72A-mTOR pathway was strongly associated with a poor prognosis for patients with HCC. Patients with high FAM72A expression levels might be more resistant to sorafenib. Furthermore, the expression of FAM72A and mTOR was significantly associated with the abundance of some tumor-infiltrating immune cells, especially CD4+ T cells. Finally, patients with high levels of FAM72A and mTOR were more sensitive to immunotherapy.

Conclusions

FAM72A, a member of the FAM72 family, might be a prognostic and immunotherapeutic target for HCC patients.

DNAJA1- and conformational mutant p53-dependent inhibition of cancer cell migration by a novel compound identified through a virtual screen

Cancers are frequently addicted to oncogenic missense mutant p53 (mutp53). DNAJA1, a member of heat shock protein 40 (HSP40), also known as J-domain proteins (JDPs), plays a crucial role in the stabilization and oncogenic activity of misfolded or conformational mutp53 by binding to and preventing mutp53 from proteasomal degradation. However, strategies to deplete mutp53 are not well-established, and no HSP40/JDPs inhibitors are clinically available. To identify compounds that bind to DNAJA1 and induce mutp53 degradation, we performed an in silico docking study of ~10 million of compounds from the ZINC database for the J-domain of DNAJA1. A compound 7-3 was identified, and its analogue A11 effectively reduced the levels of DNAJA1 and conformational mutp53 with minimal effects on the levels of wild-type p53 and DNA-contact mutp53. A11 suppressed migration and filopodia formation in a manner dependent on DNAJA1 and conformational mutp53. A mutant DNAJA1 with alanine mutations at predicted amino acids (tyrosine 7, lysine 44, and glutamine 47) failed to bind to A11. Cells expressing the mutant DNAJA1 became insensitive to A11-mediated depletion of DNAJA1 and mutp53 as well as A11-mediated inhibition of cell migration. Thus, A11 is the first HSP40/JDP inhibitor that has not been previously characterized for depleting DNAJA1 and subsequently conformational mutp53, leading to inhibition of cancer cell migration. A11 can be exploited for a novel treatment against cancers expressing conformational mutp53.

TP53 mutation-associated immune infiltration and a novel risk score model in HNSCC

In HNSCC, few studies have focused on the relationship between wild-type TP53 and mutant TP53-related immunity and prognosis. Our objective was to explore how TP53 mutation regulates the immunophenotype of HNSCC and thus affects the prognosis of HNSCC. Cox and Lasso regression were used to establish a prognostic model of TP53-related immune genes, on which basis a nomogram was used to establish a clinical prediction model, and ROC curves were further used to evaluate the effectiveness of the model. The risk of death in the TP53_WT group was only 0.68 times that in the TP53_Mut group (HR = 0.68, CI: 0.5–0.91, P < 0.05). T cells, CD8 T cells, cytotoxic lymphocytes, B lineage, NK cells, myeloid dendritic cells, and fibroblasts were significantly different between the TP53_Mut and TP53_WT groups (all P < 0.05). Time - dependent ROC curves of nomogram were plotted for 1-, 3-, and 5-year survival to further verify the predictive power of the nomogram for prognosis, and the AUCs were 0.78, 0.82, and 0.83, respectively. We showed there are significant differences in the immune microenvironment associated with wild-type TP53 and mutant TP53. The immune model associated with TP53 mutation has a good prediction ability for the prognosis of HNSCC and may be of reference value for other tumors with high mutation rate of TP53. Notably, the effect of TP53 mutation on the prognosis of HNSCC could be illustrated from an immunologic perspective.

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The differences in the immune microenvironment associated with wild-type TP53 and mutant TP53, affect the patient's sensitivity to immunotherapy.

Molecular Classification of Hepatocellular Carcinoma Using Wnt-Hippo Signaling Pathway-Related Genes

Simple Summary

The characters of Taiwanese hepatocellular carcinoma (HCC) are different from other parts of the world. We characterized the molecular features of HCC using a newly developed classification system based on the expression of the Wnt–Hippo signaling pathway-related genes. We analyzed the data in terms of prognostic value, transcriptome features, immune infiltration, and clinical characteristics, and compared the resulting subclasses with previously published classifications. A new molecular classification method based on a 272 gene panel of Wnt–Hippo pathways that may provide a new target for the treatment.

Abstract

In Taiwan, a combination of hepatitis B and C infection, economic boom-related food and alcohol overconsumption, and Chinese medicine prescriptions has led to a high rate of hepatocellular carcinoma (HCC). However, the causative factors and underlying tumor biology for this unique HCC environment have not been identified. Wnt and Hippo signaling pathways play an important regulatory role in HCC development, and their functions are generally considered as positive and negative regulators of cell proliferation, respectively. In this study, we characterized the molecular features of HCC using a newly developed classification system based on the expression of the Wnt–Hippo signaling pathway-related genes. RNA sequencing (RNA-Seq) was performed on liver tumor tissues from 100 patients with liver cancer. RNA-Seq data for 272 previously characterized Wnt–Hippo signaling pathway-related genes were used for hierarchical clustering. We analyzed the data in terms of prognostic value, transcriptome features, immune infiltration, and clinical characteristics, and compared the resulting subclasses with previously published classifications. Four subclasses of HCC (HCCW1–4) were identified. Subclass HCCW1 displayed the highest PCDHB4 expression. Subclass HCCW2 displayed lower Edmondson–Steiner grades (I and II) and CTNNB1 mutation frequencies. Subclass HCCW3 was associated with a good prognosis, the highest PCDHGB7 expression, high CD8+ naïve T cells abundance, and relatively low TP53 mutation rates. Subclass HCCW4 was associated with a poor prognosis, the highest PCDHB2 and PCDHB6 expression, a relatively high abundance of Th1 cells, NKT and class-switched memory B cells, relatively low enrichment of cDC, iDC, and CD4+ memory T cells, and high Edmondson–Steiner grades (III and IV). We also identified Wnt–Hippo signaling pathway-related genes that may influence immune cell infiltration. We developed a panel of 272 Wnt–Hippo signaling pathway-related genes to classify HCC into four groups based on Taiwanese HCC and The Cancer Genome Atlas Liver Hepatocellular Carcinoma datasets. This novel molecular classification system may aid the treatment of HCC.

Identification of chromatin organization-related gene signature for hepatocellular carcinoma prognosis and predicting immunotherapy response

BACKGROUND

Chromatin organization is associated with tumorigenesis; however, information on its role in hepatocellular carcinoma (HCC) is limited. Moreover, although immune checkpoint inhibitors (ICIs) have proven effective against HCC, the optimal index remains unknown. In this study, we aimed to construct a chromatin organization-related gene signature (CORGS) for prognosis and predicting response to ICIs in HCC.

METHODS

HCC-related data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Construction (ICGC). Chromatin organization-related genes (CORGs) were retrieved from Gene Set Enrichment Analysis. Differentially expressed genes (DEGs) and prognostic genes were then applied to select candidate genes using advanced statistical methods, including learning vector quantization, random forest, and lasso regression. Subsequently, the CORGS was established based on chromatin organization-related hub genes using multivariate Cox regression analysis, evaluated with Kaplan-Meier survival curves, and verified in 64 samples of HCC patients from Fujian Provincial Hospital (FPH) via quantitative PCR. Subsequently, functional enrichment analysis, tumor somatic mutation analysis, and tumor immune analysis were performed to evaluate the potential value of the CORGS.

RESULTS

Three hundred and thirty-nine CORGs were identified as DEGs, and 186 were associated with HCC prognosis (all P < 0.05). Four intersection genes were selected to establish the CORGS using TCGA cohort, which was found to serve as an independent risk factor for HCC patients. CORGS was then validated in an ICGC cohort. In addition, CORGS reliability was verified in 64 samples from HCC patients and 26 adjacent non-tumorous tissues, collected from the FPH. The CORGS was also associated with tumor immune microenvironment characteristics and ICI response. Moreover, data from "IMvigor 210" revealed that more patients in the low CORGS group responded to atezolizumab compared to high CORGS patients (P < 0.05). Finally, a nomogram of tumor characteristics and the CORGS was established, exhibiting superior discrimination and calibration compared to the current staging system and published models.

CONCLUSIONS

CORGS may serve as an effective predictive biomarker for HCC as well as a potential index of the tumor immune microenvironment and ICI response.

The Potential Predictive Biomarkers for Advanced Hepatocellular Carcinoma Treated With Anti-Angiogenic Drugs in Combination With PD-1 Antibody

Background

Based on molecular biomarkers, anti-angiogenic drugs in combination with programmed cell death protein 1 (PD-1) antibodies can screen the potentially beneficial populations with hepatocellular carcinoma (HCC) and predict the efficacy after treatment. Therefore, we aimed to study predictive molecular biomarkers to improve the effectiveness of immuno-targeted combination therapy for HCC.

Patients and Methods

Baseline clinical data, blood samples, and imaging data of the first evaluation after two cycles of treatment were collected for 40 patients with advanced HCC who underwent combination therapy, and then these data were compared according to the efficacy. Since 15 patients had complete hematology samples, we additionally tested the T lymphocyte subpopulations of these 15 patients and also compared them according to the efficacy. In addition, we also selected five patients who benefited the most from the combination therapy and five patients with the worst curative effect for gene detection based on survival time and efficacy evaluation. Finally, the relationship between certain clinical characteristics, laboratory indicators, specific T lymphocyte subpopulations, gene mutations and the response of immuno-targeted combination therapy for HCC was evaluated.

Results

The high levels of CD3⁺CD4⁺CD279⁺, CD3⁺CD8⁺CD45RO⁺CD62L⁺T lymphocytes and tumor mutational burden (TMB) were associated with good efficacy of the combination therapy (P=0.03, P<0.01 and P=0.03). The high levels of CD3⁺CD4⁺CD28⁺ T lymphocytes were associated with poor efficacy of the combination therapy (P=0.02). The high mutation frequency of TP53 and ARID1A appeared in the non-response cohort. In addition, amplification mutation of 11q13-CCND1, FGF3, FGF4, and FGF19 was found in a patient with hyperprogression (HP).

Conclusions

The certain clinical characteristics, laboratory indicators, specific T lymphocyte subpopulations, and gene mutations established in this paper were potential predictive biomarkers for HCC patients treated with combination therapy.

Arsenic concentration in topsoil of central Chile is associated with aberrant methylation of P53 gene in human blood cells: a cross-sectional study

Gene expression can be modified in people who are chronically exposed to high concentrations of heavy metals. The soil surrounding the Ventanas Industrial Complex, located on the coastal zone of Puchuncaví and Quintero townships (Chile), contain heavy metal concentrations (As, Cu, Pb, Zn, among others) that far exceed international standards. The aim of this study was to determine the potential association of the heavy metals in soils, especially arsenic, with the status of methylation of four tumor suppressor genes in permanent residents in those townships. To study the methylation status in genes p53, p16, APC, and RASSF1A, we took blood samples from adults living in areas near the industrial complex for at least 5 years and compared it to blood samples from adults living in areas with normal heavy metal concentrations of soils. Results indicated that inhabitants of an area with high levels of heavy metals in soil have a significantly higher proportion of methylation in the promoter region of the p53 tumor suppressor gene compared with control areas (p-value: 0.0035). This is the first study to consider associations between heavy metal exposure in humans and aberrant DNA methylation in Chile. Our results suggest more research to support consistent decision-making on processes of environmental remediation or prevention of exposure.

Silencing the ADAM9 Gene through CRISPR/Cas9 Protects Mice from Alcohol-Induced Acute Liver Injury

Alcoholic liver injury is a major global public health concern at present. The ADAM9 gene plays a crucial role in the occurrence and development of various liver diseases, but its role in acute alcoholic liver injury remains ambiguous. In this study, a chimeric single-guide RNA targeting the genomic regions of mouse ADAM9 was designed using the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technology. Next, the role of ADAM9 in acute alcoholic liver injury in vitro in cultured mouse cells and in vivo in a hydrodynamic injection-based alcoholic liver injury mouse model was documented. The findings of this study suggest that ADAM9 induces by regulating cell proliferation, apoptosis, and stress metabolism in mice. Thus, inhibiting the expression of ADAM9 gene using CRISPR/Cas9 can attenuate alcohol-induced acute liver injury in mice.

Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics

Background: TP53 mutations are the most frequent mutations in hepatocellular carcinoma (HCC) and affect the occurrence and development of this cancer type. Therefore, it is essential to clarify the function and mechanism of TP53 mutations in HCC.

Methods: We performed a sequence of bioinformatic analyses to elucidate the characteristics of TP53 mutations in HCC. We downloaded the data of hepatocellular carcinoma from The Cancer Genome Atlas database and used different R packages for serial analyses, including gene mutation analysis, copy number variation analysis, analysis of the tumor mutational burden and microsatellite instability, differential gene expression analysis, and functional enrichment analysis of TP53 mutations, and performed gene set enrichment analysis. We established a protein-protein interaction network using the STRING online database and used the Cytoscape software for network visualization, and hub gene screening. In addition, we performed anticancer drug sensitivity analysis using data from the Genomics of Drug Sensitivity in Cancer. Immune infiltration and prognosis analyses were also performed.

Results: Missense mutations accounted for a great proportion of HCC mutations, the frequency of single nucleotide polymorphisms was high, and C > T was the most common form of single nucleotide variations. TP53 had a mutation rate of 30% and was the most commonly mutated gene in HCC. In the TP53 mutant group, the tumor mutational burden (p < 0.001), drug sensitivity (p < 0.05), ESTIMATE score (p = 0.038), and stromal score (p < 0.001) dramatically decreased. The Cytoscape software screened ten hub genes, including CT45A1, XAGE1B, CT55, GAGE2A, PASD1, MAGEA4, CTAG2, MAGEA10, MAGEC1, and SAGE1. The prognostic model showed a poor prognosis in the TP53 mutation group compared with that in the wild-type group (overall survival, p = 0.023). Univariate and multivariate cox regression analyses revealed that TP53 mutation was an independent risk factor for the prognosis of HCC patients (p <0.05). The constructed prognostic model had a favorable forecast value for the prognosis of HCC patients at 1 and 3 years (1-year AUC = 0.752, 3-years AUC = 0.702).

Conclusion: This study further deepened our understanding of TP53-mutated HCC, provided new insights into a precise individualized therapy for HCC, and has particular significance for prognosis prediction.

Glycolytic and fatty acid oxidation genes affect the treatment and prognosis of liver cancer

BACKGROUND

Metabolic reprogramming is a feature of tumour cells and is essential to support their rapid proliferation. The glycolytic activity of liver cancer cells is significantly higher than that of normal liver cells, and the rapidly proliferating tumour cells are powered by aerobic glycolysis. Lipid metabolism reprogramming enables tumour cells to meet their needs for highly proliferative growth and is an important driving force for the development of hepatocellular carcinoma (HCC).

AIM

To explore the influence of different metabolic subtypes of HCC and analyse their significance in guiding prognosis and treatment based on the molecular mechanism of glycolysis and fatty acid oxidation (FAO).

METHODS

By downloading related data from public databases including the Cancer Genome Atlas (TCGA), the Molecular Signatures Database, and International Cancer Genome Consortium, we utilised unsupervised consensus clustering to divide TCGA Liver Hepatocellular Carcinoma samples into four metabolic subgroups and compared single nucleotide polymorphism, copy number variation, tumour microenvironment, and Genomics of Drug Sensitivity in Cancer and Tumour Immune Dysfunction and Exclusion between different metabolites. The differences and causes of survival and the clinical characteristics between them were analysed, and a prognostic model was established based on glycolysis and FAO genes. Combined with the clinical features, a Norman diagram was created to compare the pros and cons of each model.

RESULTS

In the four metabolic subgroups, with the increase in glycolytic expression, the median survival of patients showed the worst results, while FAO showed the best. When comparing the follow-up analysis of each group, we considered that the differences between them might be related to reactive oxygen species, somatic copy number variation of key genes, and immune microenvironment. It was also found that the FAO group and the low-risk group had better efficacy and response to immune checkpoint blockade treatment and anti-tumour drugs.

CONCLUSION

There are obvious differences in genes, chromosomes, and clinical characteristics between metabolic subgroups. The establishment of a prognostic model could predict patient prognosis and guide clinical treatment.

A Prognostic Gene Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma is the third most common cause of cancer-related deaths in China and immune-based therapy can improve patient outcomes. In this study, we investigated the relationship between immunity-associated genes and hepatocellular carcinoma from the prognostic perspective. The data downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) and the Gene Expression Omnibus (GEO) was screened for gene mutation frequency using the maftools package. Immunity-associated eight-gene signature with strong prognostic ability was constructed and proved as an independent predictor of the patient outcome in LIHC. Seven genes in the immune-related eight-gene signature were strongly associated with the infiltration of M0 macrophages, resting mast cells, and regulatory T cells. Our research may provide clinicians with a quantitative method to predict the prognosis of patients with liver cancer, which can assist in the selection of the optimal treatment plan.

Zinc Finger E-Box Binding Homeobox 2 as a Prognostic Biomarker in Various Cancers and Its Correlation with Infiltrating Immune Cells in Ovarian Cancer

This study investigated the expression of zinc finger E-box binding homeobox 2 (ZEB2), its prognostic significance in various cancers, and the correlation between ZEB2 and infiltrating immune cells and ZEB2-related proteins in ovarian cancer (OV). The Gene Expression Profiling Interactive Analysis tool was used to analyze RNA sequencing data and cancer survival rates, based on normal and tumor tissue data available in The Cancer Genome Atlas (TCGA) database. The Kaplan–Meier plotter and PrognoScan databases were used to analyze the prognostic value of ZEB2 in OV (n = 1144). The Tumor Immune Estimation Resource was used to investigate the correlation between ZEB2 and infiltrating immune cells in various cancers, including OV. High ZEB2 expression was associated with a poorer prognosis in OV. In OV, ZEB2 is positively correlated with CD8+T cells, neutrophils, macrophages, and dendritic cell invasion; and ZEB2 is negatively correlated with tumor-infiltrating B cells. The STRING database was used to investigate the correlations with ZEB2-related proteins. The results reveal that ZEB2 was positively correlated with SMAD1 and SMAD2 in OV. Our findings may serve as a potential prognostic biomarker, and provide novel insights into the tumor immunology in OV. Thus, ZEB2 may be a potential diagnostic and therapeutic target in OV.

p53 Modulation of Autophagy Signaling in Cancer Therapies: Perspectives Mechanism and Therapeutic Targets

The key tumor suppressor protein p53, additionally known as p53, represents an attractive target for the development and management of anti-cancer therapies. p53 has been implicated as a tumor suppressor protein that has multiple aspects of biological function comprising energy metabolism, cell cycle arrest, apoptosis, growth and differentiation, senescence, oxidative stress, angiogenesis, and cancer biology. Autophagy, a cellular self-defense system, is an evolutionarily conserved catabolic process involved in various physiological processes that maintain cellular homeostasis. Numerous studies have found that p53 modulates autophagy, although the relationship between p53 and autophagy is relatively complex and not well understood. Recently, several experimental studies have been reported that p53 can act both an inhibitor and an activator of autophagy which depend on its cellular localization as well as its mode of action. Emerging evidences have been suggested that the dual role of p53 which suppresses and stimulates autophagy in various cencer cells. It has been found that p53 suppression and activation are important to modulate autophagy for tumor promotion and cancer treatment. On the other hand, activation of autophagy by p53 has been recommended as a protective function of p53. Therefore, elucidation of the new functions of p53 and autophagy could contribute to the development of novel therapeutic approaches in cancer biology. However, the underlying molecular mechanisms of p53 and autophagy shows reciprocal functional interaction that is a major importance for cancer treatment and manegement. Additionally, several synthetic drugs and phytochemicals have been targeted to modulate p53 signaling via regulation of autophagy pathway in cancer cells. This review emphasizes the current perspectives and the role of p53 as the main regulator of autophagy-mediated novel therapeutic approaches against cancer treatment and managements.

Overweight/obesity-related transcriptomic signature as a correlate of clinical outcome, immune microenvironment, and treatment response in hepatocellular carcinoma

BACKGROUNDS

The pandemic of overweight and obesity (quantified by body mass index (BMI) ≥ 25) has rapidly raised the patient number of non-alcoholic fatty hepatocellular carcinoma (HCC), and several clinical trials have shown that BMI is associated with the prognosis of HCC. However, whether overweight/obesity is an independent prognostic factor is arguable, and the role of overweight/obesity-related metabolisms in the progression of HCC is scarcely known.

MATERIALS AND METHODS

In the present study, clinical information, mRNA expression profile, and genomic data were downloaded from The Cancer Genome Atlas (TCGA) as a training cohort (TCGA-HCC) for the identification of overweight/obesity-related transcriptome. Machine learning and the Cox regression analysis were conducted for the construction of the overweight/obesity-associated gene (OAG) signature. The Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and the Cox regression analysis were performed to assess the prognostic value of the OAG signature, which was further validated in two independent retrospective cohorts from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Subsequently, functional enrichment, genomic profiling, and tumor microenvironment (TME) evaluation were utilized to characterize biological activities associated with the OAG signature. GSE109211 and GSE104580 were retrieved to evaluate the underlying response of sorafenib and transcatheter arterial chemoembolization (TACE) treatment, respectively. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic response.

RESULTS

Overweight/obesity-associated transcriptome was mainly involved in metabolic processes and noticeably and markedly correlated with prognosis and TME of HCC. Afterward, a novel established OAG signature (including 17 genes, namely, GAGE2D, PDE6A, GABRR1, DCAF8L1, DPYSL4, SLC6A3, MMP3, RIBC2, KCNH2, HTRA3, PDX1, ATHL1, PRTG, SHC4, C21orf29, SMIM32, and C1orf133) divided patients into high and low OAG score groups with distinct prognosis (median overall survival (OS): 24.87 vs. 83.51 months, p < 0.0001), and the values of area under ROC curve (AUC) in predicting 1-, 2-, 3-, and 4-year OS were 0.81, 0.80, 0.83, and 0.85, respectively. Moreover, the OAG score was independent of clinical features and also exhibited a good ability for prognosis prediction in the ICGC-LIHC-JP cohort and GSE54236 dataset. Expectedly, the OAG score was also highly correlated with metabolic processes, especially oxidative-related signaling pathways. Furthermore, abundant enrichment of chemokines, receptors, MHC molecules, and other immunomodulators as well as PD-L1/PD-1 expression among patients with high OAG scores indicated that they might have better responses to immunotherapy. However, probably exclusion of T cells from infiltrating tumors resulting in lower infiltration of effective T cells would restrict immunotherapeutic effects. In addition, the OAG score was significantly associated with the response of sorafenib and TACE treatment.

CONCLUSIONS

Overall, this study comprehensively disclosed the relationship between BMI-guided transcriptome and HCC. Moreover, the OAG signature had the potential clinical applications in the future to promote clinical management and precision medicine of HCC.

Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, genome instability-associated lncRNAs (GILncRNAs) and their performance in clinical prognostic significance in hepatocellular carcinoma (HCC) are rarely reported. Our study constructed a computational framework integrating somatic mutation information and lncRNA expression profiles of HCC genome and we identified 88 GILncRNAs of HCC. Function enrichment analysis revealed that GILncRNAs were involved in various metabolism processes and genome instability of cancer. A genome instability-derived lncRNA-based gene signature (GILncSig) was constructed using training set data. The performance of GILncSig for outcome prediction was validated in testing set and The Cancer Genome Atlas (TCGA) set. The multivariate cox regression analysis and stratification analysis demonstrated GILncSig could serve as an independent prognostic factor for the overall survival of HCC patients. The time-dependent Receiver Operating Characteristic (ROC) curve illustrated GILncSig outperformed two recently published lncRNA signatures for overall survival prediction. The combination of GILncSig and tumor protein p53 (TP53) mutation status exhibited better prognostic performance in survival evaluation compared to TP53 mutation status alone. AC145343.1 was further validated to be a risk factor for HCC in vitro among GILncSig. Overall, our study provided a novel approach for identification of genome instability-associated lncRNAs and established an independent risk score system for outcome prediction of HCC patients, which provided a new insight for exploring in-depth mechanism and potential therapy strategy.

Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, genome instability-associated lncRNAs (GILncRNAs) and their performance in clinical prognostic significance in hepatocellular carcinoma (HCC) are rarely reported. Our study constructed a computational framework integrating somatic mutation information and lncRNA expression profiles of HCC genome and we identified 88 GILncRNAs of HCC. Function enrichment analysis revealed that GILncRNAs were involved in various metabolism processes and genome instability of cancer. A genome instability-derived lncRNA-based gene signature (GILncSig) was constructed using training set data. The performance of GILncSig for outcome prediction was validated in testing set and The Cancer Genome Atlas (TCGA) set. The multivariate cox regression analysis and stratification analysis demonstrated GILncSig could serve as an independent prognostic factor for the overall survival of HCC patients. The time-dependent Receiver Operating Characteristic (ROC) curve illustrated GILncSig outperformed two recently published lncRNA signatures for overall survival prediction. The combination of GILncSig and tumor protein p53 (TP53) mutation status exhibited better prognostic performance in survival evaluation compared to TP53 mutation status alone. AC145343.1 was further validated to be a risk factor for HCC in vitro among GILncSig. Overall, our study provided a novel approach for identification of genome instability-associated lncRNAs and established an independent risk score system for outcome prediction of HCC patients, which provided a new insight for exploring in-depth mechanism and potential therapy strategy.

Epigenetic mechanisms of liver tumor resistance to immunotherapy

Hepatocellular carcinoma (HCC) is the most common primary liver tumor, which stands fourth in rank of cancer-related deaths worldwide. The incidence of HCC is constantly increasing in correlation with the epidemic in diabetes and obesity, arguing for an urgent need for new treatments for this lethal cancer refractory to conventional treatments. HCC is the paradigm of inflammation-associated cancer, since more than 80% of HCC emerge consecutively to cirrhosis associated with a vast remodeling of liver microenvironment. In the recent decade, immunomodulatory drugs have been developed and have given impressive results in melanoma and later in several other cancers. In the present review, we will discuss the recent advancements concerning the use of immunotherapies in HCC, in particular those targeting immune checkpoints, used alone or in combination with other anti-cancers agents. We will address why these drugs demonstrate unsatisfactory results in a high proportion of liver cancers and the mechanisms of resistance developed by HCC to evade immune response with a focus on the epigenetic-related mechanisms.

Construction of a Novel Gene-Based Model for Survival Prediction of Hepatitis B Virus Carriers With HCC Development

Despite the effectiveness of hepatitis B virus (HBV) vaccination in reducing the prevalence of chronic HBV infection as well as the incidence of acute hepatitis B, fulminant hepatitis, liver cirrhosis and hepatocellular carcinoma (HCC), there was still a large crowd of chronically infected populations at risk of developing cirrhosis or HCC. In this study, we established a comprehensive prognostic system covering multiple signatures to elevate the predictive accuracy for overall survival (OS) of hepatitis B virus carriers with HCC development. Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and multivariate COX analysis, along with a suite of other online analyses were successfully applied to filtrate a three-gene signature model (TP53, CFL1, and UBA1). Afterward, the gene-based risk score was calculated based on the Cox coefficient of the individual gene, and the prognostic power was assessed by time-dependent receiver operating characteristic (tROC) and Kaplan–Meier (KM) survival analysis. Furthermore, the predictive power of the nomogram, integrated with the risk score and clinical parameters (age at diagnosis and TNM stage), was revealed by the calibration plot and tROC curves, which was verified in the validation set. Taken together, our study may be more effective in guiding the clinical decision-making of personalized treatment for HBV carriers.

The effect of the TP53 and RB1 mutations on the survival of hepatocellular carcinoma patients with different racial backgrounds

Background

Racial disparities in the survival of patients with hepatocellular carcinoma (HCC) exist. Gene mutations have a profound effect on carcinogenesis, are easily affected by environment and etiology factors, and may result in survival divergences among patients with different racial backgrounds. This report explores the effects of gene mutations on the survival of American Caucasians and Asian patients.

Methods

The sequencing and clinical data of 336 HCC patients were obtained from The Cancer Genome Atlas (TCGA) database. The sequencing data was subject to gene mutation profiling, and an analysis of immune cell infiltration was conducted. A multivariate analysis was performed to assess the independent effects of gene mutations on patients' overall survival (OS) and disease-free survival (DFS).

Results

Asian HCC patients had a significantly higher level of TP53 mutation frequency than Caucasian HCC patients (Asian vs. Caucasian, 39% vs. 23%; P=0.003). The TP53 mutation was associated with shorter OS [hazard ratio (HR), 2.33; 95% confidence interval (CI), 1.36-3.97; P=0.002] and DFS (HR, 2.2; 95% CI, 1.38-3.51; P<0.001) in Caucasian HCC patients, but had no effect on Asian HCC patients' survival. Compared to Asian HCC patients, Caucasian HCC patients with the TP53 mutation had a decreased proportion of infiltrating M2 macrophages and activating natural killer (NK) cells, and an increased proportion of follicular helper T cells. The RB1 mutation was associated with shorter OS (HR, 3.37; 95% CI, 1.73-6.57; P<0.001) in Asian HCC patients, and shorter DFS (HR, 2.11; 95% CI, 1.15-3.88; P=0.017) in Caucasian HCC patients. Asian HCC patients with the RB1 mutation had a decreased proportion of infiltrating CD8 T cells.

Conclusions

The effects of the TP53 and RB1 mutations on survival differ among Asian and Caucasian HCC patients.

Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

BACKGROUND

The high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.

MATERIAL AND APPROACH

This study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan-Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.

RESULTS

A prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.

CONCLUSION

Our study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

Identification of NAA40 as a Potential Prognostic Marker for Aggressive Liver Cancer Subtypes

Liver hepatocellular carcinoma (LIHC) is a leading cause of cancer-related mortality. In this study we initially interrogated the Cancer Genome Atlas (TCGA) dataset to determine the implication of N-terminal acetyltransferases (NATs), a family of enzymes that modify the N-terminus of the majority of eukaryotic proteins, in LIHC. This examination unveiled NAA40 as the NAT family member with the most prominent upregulation and significant disease prognosis for this cancer. Focusing on this enzyme, which selectively targets histone proteins, we show that its upregulation occurs from early stages of LIHC and is not specifically correlated with any established risk factors such as viral infection, obesity or alcoholic disease. Notably, in silico analysis of TCGA and other LIHC datasets found that expression of this epigenetic enzyme is associated with high proliferating, poorly differentiating and more aggressive LIHC subtypes. In particular, NAA40 upregulation was preferentially linked to mutational or non-mutational P53 functional inactivation. Accordingly, we observed that high NAA40 expression was associated with worse survival specifically in liver cancer patients with inactivated P53. These findings define NAA40 as a NAT with potentially oncogenic functions in LIHC and uncover its prognostic value for aggressive LIHC subtypes.

Toll-like receptor 5 and 8 in hepatocellular carcinoma

Toll-like receptors (TLRs) are components of innate immunity, but also have a role in carcinogenesis. The prognostic value of TLR5 and TLR8 tumor expression was examined in contrast with known risk markers Ki67 and p53. All HCC patients from Oulu University Hospital with available representative tumor sample were included in this study (n = 182). TLR5, TLR8, Ki67, and p53 expression were investigated by immunohistochemistry. The relation between patient survival and TLR, Ki67, and p53 expression was calculated with Cox regression adjusted for confounding factors. TLR5 cytoplasm intensity was associated with 5-year overall (strong 0.0% vs weak 23.4%, p < 0.001) and disease-specific (strong 0.0% vs weak 34.9%, p < 0.001) survival. TLR5 nuclei percentage was associated with poor 5-year disease-specific survival (high 16.3% vs low 31.5%, p = 0.022). In adjusted analysis, strong TLR5 cytoplasm intensity was an independent risk factor for poor 5-year overall (adjusted HR 1.88, 95% CI 1.26-2.81) and disease-specific (adjusted HR 2.00, 95% CI 1.27-3.15) survival. High Ki67 and p53 expression associated with 5-year overall- and disease-specific survival. TLR8 was not associated with patient survival. This study suggests that TLR5 expression is independently prognostic in HCC with similar point estimate as previously known p53.

Correlationship between Ki67, VEGF, and p53 and Hepatocellular Carcinoma Recurrence in Liver Transplant Patients

Background and Aims

Patients with hepatocellular carcinoma (HCC) who undergo orthotopic liver transplantation (OLT) are at risk for posttransplant tumor recurrence. The aim of this study was to evaluate the correlation between the expression of Ki67, VEGF, and p53 in HCC and clinicopathological characteristics of HCC patients, as well as their predictive value for HCC recurrence after OLT.

Methods

60 patients who underwent OLT and were found to have HCC in the liver explant. The expression of Ki67, VEGF, and p53 in HCC was detected by immunohistochemistry.

Results

Ki67 was associated with the tumor number and the grade of differentiation at baseline. VEGF was associated with the diameter and number of tumors, tumor differentiation, and lymph node metastasis. p53 was associated with the tumor diameter and tumor encapsulation. The expression of Ki67, VEGF, and p53 in HCC was correlated with the tumor recurrence after OLT, respectively. Among them, VEGF was an independent predictor for tumor recurrence after OLT.

Conclusion

Ki67, VEGF, and p53 are associated with the recurrence of HCC after OLT. VEGF independently predicts the recurrence of HCC.

Genotypic Characteristics of Hepatoblastoma as Detected by Next Generation Sequencing and Their Correlation With Clinical Efficacy

BACKGROUND

Hepatoblastoma (HB) is the most common malignant embryonic liver tumor type in children under 3 years of age. In the present study, the next generation sequencing (NGS) method was used to detect the genotype characteristics of HB and summarize the correlation between the common mutation genotypes noted in this disease and the clinical treatment and prognosis. The results may aid clinical prognosis and the successful application of targeted drugs.

METHODS

Initially, DNA was extracted from tumor tissue specimens and peripheral blood derived from 19 pediatric patients with HB. Subsequently, DNA panel and NGS methods were used to detect tumor diagnosis and the expression levels of treatment-associated genes, followed by the summary of genotype characteristics. In addition, in order to further assess the application of immunotherapy in HB, immunohistochemical detection of programmed cell death 1 ligand 1 (PDL1) was performed in combination with tumor mutation burden (TMB) and DNA mismatch repair status analysis. Furthermore, the clinical treatment effect and prognosis of the pediatric patients were statistically analyzed according to the characteristics of the genotype. Overall prognosis and prognostic analyses in different groups were performed by Kaplan-Meier and log-rank tests, respectively. Finally, expression validation and diagnostic analysis of commonly reported genes were performed in the GSE75271 dataset, which was obtained from the Gene Expression Omnibus (GEO) database.

RESULTS

In the present study, certain mutated genes, including nuclear factor erythroid 2-related factor 2 (NFE2L2), catenin β1 (CTNNB1), MYCN, tumor protein p53, axis inhibition protein 1 (AXIN1) and adenomatous polyposis coli (APC) were associated with the pathogenesis of HB. During TMB and DNA mismatch repair status analyses, pediatric patients had a low TMB. All of them did not present with microsatellite instability. The immunohistochemical results indicated lower expression levels of PDL1 in HB. The complete remission (CR) rate of pediatric patients in the gene abnormality group was lower than that of the non-reported disease-associated gene abnormality group. The 2-year overall survival rate and disease-free survival rate of 19 pediatric patients with HB were 72.1% and 42.4%, respectively. Receiver operating characteristic (ROC) analysis demonstrated that CTNNB1, NFE2L2, AXIN1, APC, MYCN and insulin growth factor 2 (IGF2) may be potential biomarkers that could be used for the diagnosis of HB.

CONCLUSION

The genotype changes in HB were more common and the CR rate of the pediatric patients with an altered genotype was lower than that of pediatric patients without an altered genotype. In addition, pediatric patients with HB exhibited lower TMB compared with adult patients. Moreover, the data indicated that CTNNB1, NFE2L2, AXIN1, APC, MYCN and IGF2 may be potential biomarkers that can be used for the diagnosis of HB.

Thymoquinone Selectively Induces Hepatocellular Carcinoma Cell Apoptosis in Synergism With Clinical Therapeutics and Dependence of p53 Status

Thymoquinone (TQ) is a natural compound extracted from the black seeds of Nigella sativa Linn. belonging to the Ranunculaceae family. TQ exhibits anti-inflammatory and antineoplastic activities against various cancers. Many therapeutics in hepatocellular carcinoma (HCC) treatments, such as doxorubicin (DOX) and cisplatin (DDP), exhibit considerable side effects on patients. We investigated cytotoxic effects of TQ, alone or in combination with DDP and DOX to HCC cells. TQ exhibited selective killing to HCC HepG2 and SMMC-7721 cells, but relatively low toxicity to normal liver HL-7702 cells. Importantly, when used with DOX or DDP, TQ showed synergistic inhibition of HCC cells, but not HL-7702 cells. We also discovered that Hep3B cells with a p53 null status were more sensitive to TQ than HepG2 and SMMC-7721 cells harboring wild type p53. Consistently, shRNA-mediated p53 silencing in HepG2 cells dramatically enhanced TQ-induced apoptosis, measured by caspase 3 and PARP cleavage. Furthermore, TQ-stimulated increase of reactive oxygen species (ROS) in p53-depleted cells was more pronounced than that in cells with intact p53. In summary, we discovered that TQ synergistically improves the anti-cancer activity of DOX and DDP, and loss of p53 sensitizes HCC cells to TQ-induced apoptosis.

Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology

TP53 mutation is one of the most common genetic changes in hepatocellular carcinoma (HCC). It is of great clinical significance to tailor specialized prognostication approach and to explore more therapeutic options for TP53-mutant HCCs. In this study, a total of 1135 HCC patients were retrospectively analyzed. We developed a random forest-based prediction model to estimate TP53 mutational status, tackling the problem of limited sample size in TP53-mutant HCCs. A multi-step process was performed to develop robust poor prognosis-associated signature (PPS). Compared with previous established population-based signatures, PPS manifested superior ability to predict survival in TP53-mutant patients. After in silico screening of 2249 drug targets and 1770 compounds, we found that three targets (CANT1, CBFB and PKM) and two agents (irinotecan and YM-155) might have potential therapeutic implications in high-PPS patients. The results of drug targets prediction and compounds prediction complemented each other, presenting a comprehensive view of potential treatment strategy. Overall, our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy.

Quantification of intratumoral collagen and elastin fibers within hepatocellular carcinoma tissues finds correlations with clinico-patho-radiological features

AIM

Emerging evidence suggests a promising role for tumor stromal factors in characterizing patients with various types of malignancies, including hepatocellular carcinoma (HCC). We quantified the amount of collagen and elastin fibers in HCC samples with the aim of clarifying the clinico-patho-radiological significance of fiber deposition in HCC.

METHODS

We computed the amount of collagen and elastin fibers using digital image analysis of whole-slide images of Elastica van Gieson-stained tissues from 156 surgically resected HCCs. Furthermore, we assessed the correlations between the fiber content of HCC samples and clinical, pathological, and radiological features, including immunohistochemistry-based molecular subtypes and immunosubtypes.

RESULTS

The intratumoral area ratio of collagen in HCC tissues (median 3.4%, range 0.1-22.2%) was more than threefold that of elastin (median 0.9%, range 0.1-9.0%); there was a strong positive correlation between the amounts of collagen and elastin. Higher levels of combined collagen and elastin were significantly associated with the confluent multinodular macroscopic tumor type, the absence of a fibrous capsule, intratumoral steatosis, scirrhous tumor stroma, dense inflammatory-cell infiltrates, and the biliary/stem cell markers-positive HCC subtype. The associations of higher collagen levels with radiological findings, including heterogeneous enhancement and persistent enhancement on dynamic computed tomography, were significant. In contrast, the associations of radiological findings with elastin fibers were not significant. Intratumoral fibrous stroma in HCC comprised septum-like and perisinusoidal fibrosis; these two forms represented distinct distribution patterns of fibers and fibroblasts.

CONCLUSION

Quantitative analysis suggested that stromal fiber-rich HCCs likely represent a distinct clinico-patho-radiological entity.

Comprehensive investigation of p53, p21, nm23, and VEGF expression in hepatitis B virus-related hepatocellular carcinoma overall survival after hepatectomy

Objective: The goal of our current study is to assess the immunohistochemical of p53, p21, nm23, and VEGF expression in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) prognosis after hepatectomy, as well as the prospective molecular mechanisms of prognostic indicator. Methods: There were 419 HBV-related HCC patients who were from southern China of Guangxi province and were used to evaluate the immunohistochemical expression for these biomarkers in prognosis. A genome-wide expression microarray dataset of HBV-related HCC were obtained from GSE14520. Results: In our study, the expression of p53, p21, and nm23 in cancer tissues of patients with hepatitis B-related hepatocellular carcinoma did not affected the clinical outcome of 2 years, 5 years or overall. Patients with high expression of VEGF had a worse overall survival after 2 years of surgery than patients with low expression (adjusted P=0.040, adjusted HR = 1.652, 95% CI = 1.024-2.665). Survival analysis of VEGF in GSE14520 cohort also demonstrated that VEGF mRNA expression also significantly associated with HBV-related HCC OS (adjusted P=0.035, adjusted HR =1.651, 95% CI =1.035-2.634). The prospective molecular mechanisms by co-expression analysis suggested that VEGF might be correlated to regulation of cell proliferation, cell growth and apoptotic process, Rap1 signaling pathway, HIF-1 signaling pathway, PPAR signaling pathway, cell cycle. Whereas the GSEA suggested that VEGF might involve in the regulation of HIF and HIF1A pathway, and TP53 regulation pathway. Conclusion: Our findings suggested that VEGF might be a prognostic indicator of HBV-related HCC, and we also identified the VEGF prospective molecular mechanisms through the whole genome co-expression and GSEA approaches.