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1.
Int Immunopharmacol ; 89(Pt A): 107135, 2020 Dec.
Article in English | MEDLINE | ID: mdl-33189609

ABSTRACT

Tumour mutation burden (TMB) and the immune microenvironment (IME) are reportedly associated with immunotherapy responses, but this relationship remains unclear in hepatocellular carcinoma (HCC). We classified HCC patients in the liver hepatocellular carcinoma cohort from The Cancer Genome Atlas into low- and high-TMB groups and evaluated differences in immune infiltrates. Additionally, differentially expressed genes in the low- and high-TMB groups were identified, and functional analyses were conducted. A risk score model was constructed based on three differentially expressed immune genes (DEIGs). The Tumor Immune Estimation Resource database was utilized to analyse how the IME was affected by the three hub DEIGs. Finally, a prognostic nomogram combining risk scores and stages was established and externally validated with the International Cancer Genome Consortium and GSE14520 cohorts. High-TMB (top 20%) patients exhibited a worse prognosis (P = 0.017). Follicular helper cells (P = 0.001) and activated natural killer cells (P = 0.003) were enriched in high-TMB patients, while resting dendritic cells (P = 0.002) were enriched in low-TMB samples. A risk score model was generated with three hub DEIGs (CCR7, STC2 and S100A9) to predict overall survival in HCC cohorts. Moreover, copy number variations mainly reduced infiltration levels. The nomogram performed better than the risk score model in the training and validation datasets. Higher TMB was associated with IME diversification and worse prognosis in HCC. Mutations in three hub TMB-associated DEIGs correlated with lower immune cell infiltration.


Subject(s)
Carcinoma, Hepatocellular/metabolism , Liver Neoplasms/metabolism , Tumor Microenvironment , Biomarkers, Tumor , DNA Copy Number Variations , Gene Expression Regulation, Neoplastic , Humans , Liver Neoplasms/genetics , Mutation
2.
J Cell Physiol ; 234(10): 18837-18848, 2019 08.
Article in English | MEDLINE | ID: mdl-30916406

ABSTRACT

The aberrant expression of long noncoding RNAs (lncRNAs) has drawn increasing attention in the field of hepatocellular carcinoma (HCC) biology. In the present study, we obtained the expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in 371 HCC tissues and 50 normal tissues from The Cancer Genome Atlas (TCGA) and identified hepatocarcinogenesis-specific differentially expressed genes (DEGs, log fold change ≥ 2, FDR < 0.01), including 753 lncRNAs, 97 miRNAs, and 1,535 mRNAs. Because the specific functions of lncRNAs are closely related to their intracellular localizations and because the cytoplasm is the main location for competitive endogenous RNA (ceRNA) action, we analyzed not only the interactions among these DEGs but also the distributions of lncRNAs (cytoplasmic, nuclear or both). Then, an HCC-associated deregulated ceRNA network consisting of 37 lncRNAs, 10 miRNAs, and 26 mRNAs was constructed after excluding those lncRNAs located only in the nucleus. Survival analysis of this network demonstrated that 15 lncRNAs, 3 miRNAs, and 16 mRNAs were significantly correlated with the overall survival of HCC patients (p < 0.01). Through multivariate Cox regression and lasso analysis, a risk score system based on 13 lncRNAs was constructed, which showed good discrimination and predictive ability for HCC patient survival time. This ceRNA network-construction approach, based on lncRNA distribution, not only narrowed the scope of target lncRNAs but also provided specific candidate molecular biomarkers for evaluating the prognosis of HCC, which will help expand our understanding of the ceRNA mechanisms involved in the early development of HCC.


Subject(s)
Carcinoma, Hepatocellular/genetics , Cytoplasm/metabolism , Disease Progression , Gene Regulatory Networks , Liver Neoplasms/genetics , RNA, Long Noncoding/genetics , Gene Expression Regulation, Neoplastic , Gene Ontology , Humans , Kaplan-Meier Estimate , Linear Models , Liver Neoplasms/pathology , MicroRNAs/genetics , MicroRNAs/metabolism , Prognosis , RNA, Long Noncoding/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , Risk Factors , Survival Analysis
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