Dataset Information

A Molecular Subtype Model for Liver HBV-Related Hepatocellular Carcinoma Patients Based on Immune-Related Genes.

ABSTRACT: Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with a very poor prognosis. Immunotyping is of great significance for predicting HCC outcomes and guiding immunotherapy. Therefore, we sought to establish a reliable prognostic model for HBV-related HCC based on immune scores. We identified immune-related modules of The Cancer Genome Atlas LIHC and GSE14520 data sets through weighted gene co-expression network analysis and evaluated HCC through a non-negative matrix factorization algorithm. Through further bioinformatics analyses, we identified causes for prognostic differences between subtypes. The results illustrate a significant difference in prognosis based on immunotypes, which may stem from metabolic disorders and increased tumor invasion associated with the high expression of genes related to stem cell characteristics. In conclusion, we identified a novel HBV-related HCC immune subtype and determined its immunological characteristics, which provides ideas for further individualized immunotherapy research.

SUBMITTER: Zhang Q

PROVIDER: S-EPMC7538624 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Molecular Subtype Model for Liver HBV-Related Hepatocellular Carcinoma Patients Based on Immune-Related Genes.

Zhang Qiyao Q Yu Xiao X Zheng Qingyuan Q He Yuting Y Guo Wenzhi W

Frontiers in oncology 20200923

Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with a very poor prognosis. Immunotyping is of great significance for predicting HCC outcomes and guiding immunotherapy. Therefore, we sought to establish a reliable prognostic model for HBV-related HCC based on immune scores. We identified immune-related modules of The Cancer Genome Atlas LIHC and GSE14520 data sets through weighted gene co-expression network analysis and evalu ...[more]

PMID: 33072587

Similar Datasets

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with poor prognosis. Increasing evidence has revealed that immune cells and checkpoints in the tumor microenvironment (TME) and aging are associated with the prognosis of HCC. However, the association between aging and the tumor immune microenvironment (TIME) in HCC is still unclear.MethodsRNA expression profiles and clinical data concerning HCC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Based on differentially expressed aging-related genes (DEAGs), unsupervised clustering was used to identify a novel molecular subtype in HCC. The features of immune cell infiltration and checkpoints were further explored through CIBERSORTx. Enrichment analysis and both univariate and multivariate Cox analyses were conducted to construct a 3-gene model for predicting prognosis and chemosensitivity. Finally, the mRNA and protein expression levels of the 3 genes were verified in HCC and other cancers through database searches and experiments.ResultsEleven differentially expressed AGs (GHR, APOC3, FOXM1, PON1, TOP2A, FEN1, HELLS, BUB1B, PPARGC1A, PRKDC, and H2AFX) correlated with the prognosis of HCC were used to divide HCC into two subtypes in which the prognosis was different. In cluster 2, which had a poorer prognosis, the infiltration of naive B cells and monocytes was lower in the TCGA and GEO cohorts, while the infiltration of M0 macrophages was higher. In addition, the TCGA cohort indicated that the microenvironment of cluster 2 had more immunosuppression through immune checkpoints. Enrichment analysis suggested that the MYC and E2F targets were positively associated with cluster 2 in the TCGA and GEO cohorts. Additionally, 3 genes (HMGCS2, SLC22A1, and G6PD) were screened to construct the prognostic model through univariate/multivariate Cox analysis. Then, the model was validated through the TCGA validation set and GEO dataset (GSE54236). Cox analysis indicated that the risk score was an independent prognostic factor and that patients in the high-risk group were sensitive to multiple targeted drugs (sorafenib, gemcitabine, rapamycin, etc.). Finally, significantly differential expression of the 3 genes was detected across cancers.ConclusionWe systematically described the immune differences in the TME between the molecular subtypes based on AGs and constructed a novel three-gene signature to predict prognosis and chemosensitivity in patients with HCC.

Project description:Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis. Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups. Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score. Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

Dataset Information

A Molecular Subtype Model for Liver HBV-Related Hepatocellular Carcinoma Patients Based on Immune-Related Genes.

Publications

A Molecular Subtype Model for Liver HBV-Related Hepatocellular Carcinoma Patients Based on Immune-Related Genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets