Dataset Information

Prognostic profile of patients with non-viral hepatocellular carcinoma: A comparative study with hepatitis C virus-related hepatocellular carcinoma using data mining analysis.

ABSTRACT: Various factors are associated with the prognosis of patients with non-viral hepatocellular carcinoma (HCC). The present study aimed to investigate the prognosis of patients with non-viral HCC compared with that of patients with hepatitis C virus-related (HCV)-HCC and the features associated with prognosis of patients with non-viral HCC using data mining analyses. Patients with non-viral HCC (n=182, age 70.4±8.9 years) and HCV-HCC (n=612, age 70±8.4 years) were enrolled and the overall survival was compared between the non-viral HCC and HCV-HCC groups. The present study performed random forest and decision tree analyses to identify features that distinguish prognosis between the non-viral HCC and HCV-HCC groups. The median survival of the non-viral HCC group was significantly shorter than the HCV-HCC group (1,553 vs. 2,304 days, P<0.01). In the multivariate analysis, the non-viral HCC group was an independent risk factor for survival (HR 1.42, 95% CI 1.08-1.87, P=0.013). In the random forest analysis, the high-ranking distinguishable factors were 'number of tumors' and 'HCC stage' in the non-viral HCC group and 'albumin' and 'total bilirubin' in the HCV-HCC group. The decision tree analysis revealed that, in patients with HCC stage >I, the survival period in the non-viral HCC group was significantly shorter than the HCV-HCC group (HR 1.39, 95% CI 1.07-1.81, P=0.0132). The prognosis of patients with non-viral HCC was poorer than patients with HCV-HCC. In addition, data mining analysis revealed that tumor-related variables had the highest importance for survival in patients with non-viral HCC.

SUBMITTER: Noda Y

PROVIDER: S-EPMC6540186 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Prognostic profile of patients with non-viral hepatocellular carcinoma: A comparative study with hepatitis C virus-related hepatocellular carcinoma using data mining analysis.

Noda Yu Y Kawaguchi Takumi T Kuromatsu Ryoko R Komukai Sho S Nakano Masahito M Niizeki Takashi T Koga Hironori H Kawaguchi Atsushi A Torimura Takuji T

Oncology letters 20190425 1

Various factors are associated with the prognosis of patients with non-viral hepatocellular carcinoma (HCC). The present study aimed to investigate the prognosis of patients with non-viral HCC compared with that of patients with hepatitis C virus-related (HCV)-HCC and the features associated with prognosis of patients with non-viral HCC using data mining analyses. Patients with non-viral HCC (n=182, age 70.4±8.9 years) and HCV-HCC (n=612, age 70±8.4 years) were enrolled and the overall survival ...[more]

PMID: 31289492

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Project description:Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV-associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV-associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV-associated HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV-associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein-to-protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV-associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.

Project description:BackgroundApoptosis-related genes(Args)play an essential role in the occurrence and progression of hepatocellular carcinoma(HCC). However, few studies have focused on the prognostic significance of Args in HCC. In the study, we aim to explore an efficient prognostic model of Asian HCC patients based on the Args.MethodsWe downloaded mRNA expression profiles and corresponding clinical data of Asian HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The Args were collected from Deathbase, a database related to cell death, combined with the research results of GeneCards、National Center for Biotechnology Information (NCBI) databases and a lot of literature. We used Wilcoxon-test and univariate Cox analysis to screen the differential expressed genes (DEGs) and the prognostic related genes (PRGs) of HCC. The intersection genes of DEGs and PGGs were seen as crucial Args of HCC. The prognostic model of Asian HCC patients was constructed by least absolute shrinkage and selection operator (lasso)- proportional hazards model (Cox) regression analysis. Kaplan-Meier curve, Principal Component Analysis (PCA) analysis, t-distributed Stochastic Neighbor Embedding (t-SNE) analysis, risk score curve, receiver operating characteristic (ROC) curve, and the HCC data of ICGC database and the data of Asian HCC patients of Kaplan-Meier plotter database were used to verify the model.ResultsA total of 20 of 56 Args were differentially expressed between HCC and adjacent normal tissues (p < 0.05). Univariate Cox regression analysis showed that 10 of 56 Args were associated with survival time and survival status of HCC patients (p < 0.05). There are seven overlapping genes of these 20 and 10 genes, including BAK1, BAX, BNIP3, CRADD, CSE1L, FAS, and SH3GLB1. Through Lasso-Cox analysis, an HCC prognostic model composed of BAK1, BNIP3, CSE1L, and FAS was constructed. Kaplan-Meier curve, PCA, t-SNE analysis, risk score curve, ROC curve, and secondary verification of ICGC database and Kaplan-Meier plotter database all support the reliability of the model.ConclusionsLasso-Cox regression analysis identified a 4-gene prognostic model, which integrates clinical and gene expression and has a good effect. The expression of Args is related to the prognosis of HCC patients, but the specific mechanism remains to be further verified.

Project description:Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies and a major cause of cancer involved death worldwide. Prognosis remains poor because of high recurrence rates and lack of effective relapse prevention strategies. Notch pathway plays an important role in tumor progression and metastasis, and it is associated with the prognosis of cancer. A total of 465 hepatitis B virus (HBV)-related HCC patients who underwent surgery were enrolled. Single nucleotide polymorphisms (SNP) of Notch pathway receptors were genotyped using Sanger DNA sequencing. Kaplan-Meier curves and the Cox proportional hazards regression model were adopted to analyze the association of polymorphisms and mRNA expression with clinical and pathological features, respectively. Four SNPs (rs1043996 in Notch3 and rs422951, rs520692, rs3830041 in Notch4) were significantly associated with overall survival (OS) (P = 0.023, P = 0.042, P = 0.028, and P = 0.001 respectively). Patients carrying the AA genotype in rs1043996 and TT/TC genotypes in rs422951 and rs520692 significantly decreased risks of death, compared to those carrying the AG/GG genotype in rs1043996 and CC genotype in rs422951 and rs520692, respectively. Patients carrying the TT genotype in rs3830041 showed poorer OS, compared with those carrying the TC/CC genotype. A haplotype block (rs422951 was in strong LD with rs520692, r2 = 0.843) was identified in Notch4. Notch3 mRNA expression significantly increased in tumor tissue, compared with nontumor normal tissue (P < 0.0001). Moreover, higher expression of Notch3 was associated with poorer OS (HR = 2.11, 95% CI = 1.32-3.37, P = 0.002) and shorter recurrence time of HBV-related HCC (HR = 1.96, 95% CI = 1.31-2.93, P = 0.001). Our findings collectively indicate that Notch receptors variants (rs1043996 in Notch3 and rs422951, rs520692, rs3830041 in Notch4) are independent predictive targets for OS in HBV-related HCC patients. Notch3 expression is a potential prognostic biomarker of OS and recurrence-free survival (RFS) prediction in HBV-related HCC patients following surgical treatment.

Project description:BackgroundPredicting the long-term prognosis of individuals who experienced sorafenib treatment following partial hepatectomy due to hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is difficult. This work aims to create an effective prognostic nomogram for HBV related HCC patients who are receiving sorafenib treatment as adjuvant therapy after surgery.MethodsA total of 233 HBV-related HCC patients treated with or without sorafenib following partial hepatectomy at the Eastern Hepatobiliary Surgery Hospital from 2008 to 2013 were matched with propensity score matching analysis. The optimal cut-off point of the overall survival (OS) factor level was determined by x-tile. The selection of indicators was based on clinical findings. The Cox regression model with an interaction term was employed for evaluating the predictive value. Using a multivariate Cox proportional hazards model, a nomogram was subsequently formulated to analyze 111 patients treated with sorafenib. The nomogram's discriminative ability and predictive accuracy were determined using the concordance index (C-index), calibration, and ROC curve.ResultsThe matched sorafenib cohort of 111 patients and control cohort of 118 patients were analyzed. Subgroup analysis revealed that low GPC3, pERK, pAKT, serum AFP levels, without MVI, under 50 years old, male, TNM stage I/II and BCLC stage 0/A were significantly associated with a better OS in patients subjected to sorafenib treatment compared to those without sorafenib treatment after surgery. Multivariate analysis of the sorafenib cohort revealed GPC3, pERK, pAKT, serum AST, and BCLC stage as independent factors for OS, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.73(95% CI, 0.67-0.78). The area under the ROC curve (AUC) for the nomogram to predict the survival for 1, 3, and 5-year was 0.726, 0.816, and 0.823, respectively.ConclusionThis proposed nomogram shows the potential to make a precise prediction regarding the prognosis of HBV-related HCC patients and may help to stratify patients for personalized therapy following partial hepatectomy.

Dataset Information

Prognostic profile of patients with non-viral hepatocellular carcinoma: A comparative study with hepatitis C virus-related hepatocellular carcinoma using data mining analysis.

Publications

Prognostic profile of patients with non-viral hepatocellular carcinoma: A comparative study with hepatitis C virus-related hepatocellular carcinoma using data mining analysis.

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