Dataset Information

Genomewide identification of a novel six-LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma.

ABSTRACT: The current prognostic long noncoding RNA (lncRNA) signatures for hepatocellular carcinoma (HCC) are still controversial and need to be optimized by systematic bioinformatics analyses with suitable methods and appropriate patients. Therefore, we performed the study to establish a credible lncRNA signature for HCC outcome prediction and explore the related mechanisms. Based on the lncRNA profile and the clinical data of carefully selected HCC patients (n = 164) in TCGA, six of 12727 lncRNAs, MIR22HG, CTC-297N7.9, CTD-2139B15.2, RP11-589N15.2, RP11-343N15.5, and RP11-479G22.8 were identified as the independent predictors of patients' overall survival in HCC by sequential univariate Cox and 1000 times Cox LASSO regression with 10-fold CV, and multivariate Cox analysis with 1000 times bootstrapping. In the Kaplan-Meier analysis with patients trichotomized by the six-lncRNA signature, high-risk patients showed significantly shorter survival than mid- and low-risk patients (log-rank test P < 0.0001). According to the ROCs, the six-lncRNA signature showed superior predictive capacity than the two existing four-lncRNA combinations and the traditional prognostic clinicopathological parameter TNM stage. Furthermore, low MIR22HG and CTC-297N7.9, but high CTD-2139B15.2, RP11-589N15.2, RP11-343N15.5, and RP11-479G22.8, were, respectively, demonstrated to be related with the malignant phenotypes of HCC. Functionally, the six lncRNAs were disclosed to involve in the regulation of multiple cell cycle and stress response-related pathways via mediating transcription regulation and chromatin modification. In conclusion, our study identified a novel six-lncRNA signature for resectable HCC prognosis prediction and indicated the underlying mechanisms of HCC progression and the potential functions of the six lncRNAs awaiting further elucidation.

SUBMITTER: Wu Y

PROVIDER: S-EPMC6308084 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Genomewide identification of a novel six-LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma.

Wu Ying Y Wang Peng-Shuo PS Wang Ben-Gang BG Xu Lu L Fang Wan-Xia WX Che Xiao-Fang XF Qu Xiu-Juan XJ Liu Yun-Peng YP Li Zhi Z

Cancer medicine 20181030 12

The current prognostic long noncoding RNA (lncRNA) signatures for hepatocellular carcinoma (HCC) are still controversial and need to be optimized by systematic bioinformatics analyses with suitable methods and appropriate patients. Therefore, we performed the study to establish a credible lncRNA signature for HCC outcome prediction and explore the related mechanisms. Based on the lncRNA profile and the clinical data of carefully selected HCC patients (n = 164) in TCGA, six of 12727 lncRNAs, MIR2 ...[more]

PMID: 30378276

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Project description:Background:The prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes. Methods:Six gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed. Results:A twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P?<?0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes. Conclusions:We identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

Project description:Hepatocellular carcinoma (HCC), the most frequently occurring type of cancer, is strongly associated with metabolic disorders. In this study, we aimed to characterize the metabolic features of HCC and normal tissue adjacent to the tumor (NAT). By using samples from The Cancer Genome Atlas (TCGA) liver cancer cohort and comparing 85 well-defined metabolic pathways obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG), 70 and 7 pathways were found to be significantly downregulated and upregulated, respectively, in HCC, revealing that tumor tissue lacks the ability to maintain normal metabolic levels. Through unsupervised hierarchical clustering of metabolic pathways, we found that metabolic heterogeneity correlated with prognosis in HCC samples. Thus, using the least absolute shrinkage and selection operator (LASSO) and filtering independent prognostic genes by the Cox proportional hazards model, a six-gene-based metabolic score model was constructed to enable HCC classification. This model showed that high expression of LDHA and CHAC2 was associated with an unfavorable prognosis but that high ADPGK, GOT2, MTHFS, and FTCD expression was associated with a favorable prognosis. Patients with higher metabolic scores had poor prognoses (p value = 2.19e-11, hazard ratio = 3.767, 95% CI = 2.555-5.555). By associating the score level with clinical features and genomic alterations, it was found that NAT had the lowest metabolic score and HCC with tumor stage III/IV the highest. qRT‒PCR results for HCC patients also revealed that tumor samples had higher score levels than NAT. Regarding genetic alterations, patients with higher metabolic scores had more TP53 gene mutations than those with lower metabolic scores (p value = 8.383e-05). Validation of this metabolic score model was performed using another two independent HCC cohorts from the Gene Expression Omnibus (GEO) repository and other TCGA datasets and achieved good performance, suggesting that this model may be used as a reliable tool for predicting the prognosis of HCC patients.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the leading causes of tumor-related death. MicroRNAs (miRNAs) belong to a subfamily of functional non-coding RNAs (ncRNAs) and are essential regulators of tumorigenesis. They affect tumor-related therapeutic response, tumor metastasis, and clinical outcomes of several human malignant tumors. However, the prognostic value of miRNAs and their role in the tumor immune microenvironment (TIME) of HCC have not been clarified.Materials and methodsRaw RNA-sequencing data (mRNA and miRNA) and clinicopathological characteristics of HCC samples were downloaded from the TCGA-GDC database. The Perl programming language, R software, Cytoscape software, and several online databases were used to clarify the clinical significance and biological functions of miRNAs and their target genes in HCC.ResultsA total of 424 mRNA-sequencing samples and 425 miRNA-sequencing samples were obtained from the TCGA database. There were 344 HCC cases with complete information in the TCGA dataset and they were randomly categorized into two subgroups. Six miRNAs were identified as independent prognostic biomarkers for HCC patients by univariate and multivariate Cox regression analysis. The constructed prognostic signature, which contains these six miRNAs, was significantly correlated with overall survival (OS). In addition, this prognostic signature is superior to single miRNA in predicting short-term prognosis of HCC patients. We also found that the prognostic signature was significantly associated with tumor-related immune cell infiltration, TIME, and immunotherapeutic response. Furthermore, a total of 4568 potential target genes of six miRNAs were identified. The miRNA-mRNA co-expression network, protein-protein interaction (PPI) network, and functional and pathway enrichment analysis demonstrated that these miRNA-related target genes have important biological effects during the initiation and progression of HCC.ConclusionsThis study demonstrates that the miRNA signature can accurately predict the prognosis of HCC patients and provide a basis for novel immunotherapy treatments.

Project description:BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.ResultsA total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan-Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.ConclusionsWe propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.

Dataset Information

Genomewide identification of a novel six-LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma.

Publications

Genomewide identification of a novel six-LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma.

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