Establishment of a prognostic model for predicting short-term disease-free survival in cases of hepatitis B-related hepatocellular carcinoma with the TP53 249Ser mutation in southern China

Mutational and transcriptional alterations and clinicopathological factors predict the prognosis of stage I hepatocellular carcinoma

Background

The prognosis of hepatocellular carcinoma (HCC) has been extensively studied. However, the impact on prognosis of stage I HCC has not been well studied at clincopathological, mutational and transcriptional levels.

Methods

Here we first characterized the influencing factors of prognosis of stage I HCC patients by downloading and analyzing the whole-exome somatic mutation data, messenger ribonucleic acid (mRNA) transcription data, along with demographic and clinical information of 163 stage I HCC patients from the TCGA database. The relationship between the influencing factors and HCC prognosis was studied in detail, and a prediction Nomogram model was established. Figures and tables were plotted using the R software.

Results

TP53, CTNNB1, TTN, MUC16 and ALB were the top mutated genes in stage I HCC. A series of co-mutations and mutually exclusive mutations were identified. Twenty-nine genes with significant stratification on prognosis were identified, including highly mutated LRP1B, ARID1A and PTPRQ. Patients with wild type (WT) genes unanimously exhibited significantly better overall survival rate than those with mutants. Patients with the top 10% tumor mutational burden (TMB) exhibited significantly worse prognosis than the rest 90%. Further characterization of transcriptional profile revealed that membrane functions, cell skeleton proteins, ion channels, receptor function and cell cycle were comprehensively altered in stage I HCC. Univariate and multivariate analyses were performed at clinicopathological, mutational and transcriptional levels. The combined analysis revealed sex, race, TMB, neoplasm histologic grade, Child–Pugh grade, MMRN1, OXT and COX6A2 transcription as independent risk factors. These factors were used to establish a Nomogram model to predict the prognosis of individual HCC patients.

Conclusions

The influencing factors of prognosis of stage I HCC have been characterized for the first time at clinicopathological, mutational and transcriptional levels. A Nomogram model has been established to predict the prognosis. Further validation is needed to confirm the effectiveness and reliability of the model.

Screening of a novel autophagy-related prognostic signature and therapeutic targets in hepatocellular carcinoma

BACKGROUND

Many studies have indicated that autophagy plays an important role in multiple cancers, including hepatocellular carcinoma (HCC). This study aimed to establish a prognostic signature for HCC based on autophagy-related genes (ARGs) to predict the prognosis of patients.

METHODS

The list of ARGs was derived from screening National Center for Biotechnology Information (NCBI)-Gene and Molecular Signatures Database (MSigDB) datasets. Differential analysis was conducted via the R limma package in HCC patients based on The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to identify key prognostic ARGs via the survival package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by clusterProfiler package. The Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was used to conduct immune analysis. Finally, the correlation between the prognostic model and clinical characteristics was also assessed, including age, tumor-node-metastasis (TNM) stages, and tumor grades.

RESULTS

Firstly, 106 differential ARGs were identified and 10 candidates were further confirmed via Cox regression analysis, including BAMBI, HIF1A, SERPINE1, EZH2, SLC9A3R1, IGFBP3, HSPB8, DAB2, CXCL1 and PRNP. The receiver operating characteristic (ROC) curve analysis revealed that the ARGs risk model had a well diagnostic positive rate with 1-year area under the curve (AUC) =0.688 and 3-year AUC =0.674. Correlation analysis indicated that only advanced tumor stages were positively associated with high ARGs scores with P=0.0227. There were also significant differences in tumor purity (P=6.71e-05), infiltrating cell analysis (P=7.77e-05), immune analysis (P=7.9e-05), and stromal cells analysis (P=0.0015) in high- and low-risk ARGs samples. The genes HIF1A, IGFBP3, and DAB2 were found to have high frequent missense mutations in samples with high-risk ARGs scores. Lastly, we also established a nomogram to predict overall survival (OS) of HCC by integrating ARGs scores and other clinical parameters.

CONCLUSIONS

Our study established an autophagy-related signature for predicting the prognosis of HCC patients, providing a thorough understanding of the underlying mechanisms of autophagy in HCC.

ANTXR1 as a potential prognostic biomarker for hepatitis B virus-related hepatocellular carcinoma identified by a weighted gene correlation network analysis

BACKGROUND

With high incidence and mortality rates, hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors worldwide. Chronic hepatitis B virus (HBV) infection is a leading cause of HCC, especially for Asians and blacks. However, the molecular mechanisms underlying HBV-related HCC are unclear. This study sought to identify novel prognostic biomarkers and explore the potential pathogenesis of HBV-related HCC.

METHODS

The gene expression profiles and corresponding clinical information of HCC from The Cancer Genome Atlas Liver Hepatocellular Carcinoma data set were analyzed by a weighted gene co-expression network analysis. Correlations between the co-expression modules and clinical traits were calculated. Next, key modules associated with HBV infection were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted for the genes in the key modules. The hub genes were identified based on the protein-protein interaction (PPI) network via the Cytoscape. Finally, an overall survival (OS) analysis was performed.

RESULTS

The two modules (i.e., the brown and yellow modules) most relevant to HBV infection were constructed. A functional enrichment analysis revealed that the genes in the two modules were mainly enriched in HCC-related pathways, such as the phosphatidylinositol-3-kinase and protein kinase B signaling pathway, focal adhesion, human papillomavirus infection, the Rap1 signaling pathway, and the cyclic guanosine monophosphate-dependent protein kinase (cGMP-PKG) signaling pathway. Ten hub genes [i.e., COL3A1, ANTXR1, COL14A1, THBS2, ADAMTS2, AEBP1, PRELP, EMILIN1, DCN and PODN] in the brown module, and 10 hub genes [i.e., USP34, SEC24C, ZNF770, STAG1, TSTD2, PKD1P6, CCNK, GFT2I, NT5C2 and SMG6] in the yellow module were identified. Among the hub genes, ANTXR1 (Anthrax-toxin receptor 1) was significantly correlated with HBV-related HCC patients' OS.

CONCLUSIONS

ANTXR1 represents a potential therapeutic target for HBV-related HCC. This study offers novel insights into the molecular mechanisms of HBV-induced tumorigenesis, which needs to be further validated by basic experiments and large-scale cohort studies.