Dataset Information

Liver-enriched Genes are Associated with the Prognosis of Patients with Hepatocellular Carcinoma.

ABSTRACT: Tissue-enriched genes are highly expressed in one particular tissue type and represent distinct physiological processes. The dynamic profile of tissue-enriched genes during tumorigenesis and progression remains largely unstudied. Here, we identified tissue-enriched genes from 12 tissue types based on RNA sequencing data from the Cancer Genome Atlas (TCGA), and found that the liver had the largest number of such genes among the 12 tissue types. The characteristics of liver-enriched genes were further investigated. Most liver-enriched genes were downregulated and metabolism-related genes, which were associated with pathological stage and dedifferentiation in patients with hepatocellular carcinoma (HCC). Hypermethylation might be a mechanism underlying the downregulation of liver-enriched genes. We constructed a liver-enriched gene set and demonstrated that it is associated with the prognosis of the patients with HCC both in the TCGA cohort and the Gene Expression Omnibus (GEO) datasets. Moreover, we discovered that the degree of the dissimilarity between tumors and normal tissues was correlated with the prognosis of patients with HCC and the biological behaviours of the tumors. These results will help identify prognostic biomarkers of patients with HCC, and enhance our understanding of the molecular mechanisms of hepatocarcinogenesis and progression.

SUBMITTER: Li B

PROVIDER: S-EPMC6060164 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Liver-enriched Genes are Associated with the Prognosis of Patients with Hepatocellular Carcinoma.

Li Binghua B Xu Tiancheng T Liu Chaohui C Meng Gang G Sun Yuchen Y Qian Liyuan L Wang Shaohe S Wei Jiwu J Yu Decai D Ding Yitao Y

Scientific reports 20180725 1

Tissue-enriched genes are highly expressed in one particular tissue type and represent distinct physiological processes. The dynamic profile of tissue-enriched genes during tumorigenesis and progression remains largely unstudied. Here, we identified tissue-enriched genes from 12 tissue types based on RNA sequencing data from the Cancer Genome Atlas (TCGA), and found that the liver had the largest number of such genes among the 12 tissue types. The characteristics of liver-enriched genes were fur ...[more]

PMID: 30046116

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Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most serious malignant tumors threatening human life with a high mortality rate. The liver regenerative capacity after hepatectomy in early-stage HCC patients is influenced by various factors, including surgical methods and energy metabolism. This study aims to provide a prognostic model based on genes related to liver regeneration that can predict the prognosis of non-tumor tissues in HCC patients.Patients and methodsA total of 584 non-tumor tissues from HCC patients were collected from three independent databases. Kaplan-Meier survival curves were used to identify prognostic liver-regeneration genes. Subsequently, a prognostic indicator, designated as the Liver Regeneration score (LR score), was determined using single-sample gene set enrichment analysis (ssGSEA). Independent cohorts were used to verify the relationship between LR score and prognosis in non-tumor tissues of HCC patients. Furthermore, a liver regeneration-related model was established to validate key genes identified through LASSO Cox regression analysis.ResultsWe constructed a gene set comprising 24 liver regeneration-related genes, and the LR score was utilized to predict the prognosis of HCC patients based on its levels in non-tumor tissues. In non-tumor tissues of HCC patients, higher LR scores were associated with improved prognosis. Higher LR scores in non-tumor tissues indicate improved liver metabolism in HCC patients, revealed by Enrichment analysis. LASSO Cox regression analysis identified two key genes, DHTKD1 (dehydrogenase E1 and transketolase domain containing 1) and PHYH (phytanoyl-CoA 2-hydroxylase), and higher expression levels of these genes in non-tumor tissues were correlated with better prognosis. The expression levels of these two genes also changed corresponding to the progression of liver regeneration.ConclusionIn summary, our study has introduced a novel LR gene signature for HCC patients, providing a predictive model for estimating clinical prognosis from non-tumor tissues. The LR score demonstrates promise as a reliable indicator for predicting overall survival in HCC.

Project description:Objective: This study aimed to validate FANCI as a potential marker for both prognosis and therapy in liver hepatocellular carcinoma. Method: FANCI expression data were acquired from GEPIA, HPA, TCGA, and GEO databases. The impact of clinicopathological features was analyzed by UALCAN. The prognosis of Liver Hepatocellular Carcinoma (LIHC) patients with highly expressed FANCI was constructed utilizing Kaplan-Meier Plotter. GEO2R was employed to identify differentially expressed genes (DEGs). Metascape was used to analyze functional pathways correlations. Protein-Protein interaction (PPI) networks were generated by Cytoscape. Furthermore, molecular complex detection (MCODE) was utilized to recognize Hub genes, which were selected to establish a prognostic model. Lastly, the relationship between FANCI and immune cell infiltration in LIHC was examined. Results: Compared to adjacent tissues, FANCI expression levels were significantly higher in LIHC tissues and were positively correlated to the cancer grade, stage, and prior hepatitis B virus (HBV) infection. High expression of FANCI was found to be associated with poor prognosis in LIHC (HR=1.89, p<0.001). DEGs that were positively correlated with FANCI were involved in various processes, including the cell cycle, VEGF pathway, immune system processes, and biogenesis of ribonucleoproteins. MCM10, TPX2, PRC1, and KIF11 were identified as key genes closely related to FANCI and poor prognosis. A reliable five-variable prognostic model was constructed with strong predictive capability. Lastly, a positive correlation was observed between FANCI expression and tumor-infiltration levels of CD8+ T cells, B cells, regulatory T (Tregs), CD4+ T helper 2 (Th2), and macrophage M2 cells. Conclusion: FANCI may hold promise as a potential biomarker for predicting prognostic outcomes, and a valuable therapeutic target for LIHC patients, with a focus on anti-proliferation, anti-chemoresistance, and combination with immunotherapy.

Project description:BackgroundMAPK-RAP1A signaling, which is involved in cancer progression, remains to be defined. Upregulation of MAPK-RAP1A signaling accounts for most cancers that harbor high incident rate, such as non-small cell lung cancer (NSCLC) and pancreatic cancer, especially in hepatocellular carcinoma (HCC). MAPK-RAP1A signaling plays an important function as clinical diagnosis and prognostic value in cancers, and the role of MAPK-RAP1A signaling related with immune infiltration for HCC should be elucidated.MethodsMicroarray data and patient cohort information from The Cancer Genome Atlas (TCGA; n = 425) and International Cancer Genome Consortium (ICGC; n = 405) were selected for validation. The Cox regression and least absolute shrinkage and selection operator (LASSO) were used to construct a clinical prognostic model in this analysis and validation study. We also tested the area under the curve (AUC) of the risk signature that could reflect the status of predictive power by determining model. MAPK-RAP1A signaling is also associated with tumor-infiltrating immune cells (TICs) as well as clinical parameters in HCC. The GSEA and CIBERSORT were used to calculate the proportion of TICs, which should be beneficial for the clinical characteristics (clinical stage, distant metastasis) and positively correlated with the survival of HCC patients.ResultsHCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), and STMN1, RAP1A, FLT3, HSPA8, ANGPT2, and PGF were used as candidate biomarkers for risk scores of HCC. To determine the molecular mechanism of this signature gene association, Gene Set Enrichment Analysis (GSEA) was proposed. Cytokine-cytokine receptor interaction, TGF-β signaling pathway, and Intestinal immune network for IgA production gene sets were closely related in MAPK-RAP1A gene sets. Thus, we established a novel prognostic prediction of HCC to deepen learning of MAPK-RAP1A signaling pathways.ConclusionOur findings demonstrated that HCC patients with enrichment of MAPK-RAP1A signaling were associated with clinical characteristics and favorable T cell gamma delta (Vδ T cells), which may be a novel prognostic prediction of HCC.

Dataset Information

Liver-enriched Genes are Associated with the Prognosis of Patients with Hepatocellular Carcinoma.

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Liver-enriched Genes are Associated with the Prognosis of Patients with Hepatocellular Carcinoma.

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