Dataset Information

Combined signature of nine immune-related genes: a novel risk score for predicting prognosis in hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most common internal malignancies worldwide and is associated with a poor prognosis. There is an urgent need to identify diagnostic and prognostic biomarkers of HCC pathogenesis and progression. Accordingly, in this study, we analyzed differentially expressed immune-related genes (IRGs) from 329 patients with HCC from The Cancer Genome Atlas datasets. Functional analysis revealed that the IRGs had potential effects on tumor immune processes, such as inflammatory responses and growth factor activity. In the training group, we constructed a nine-IRG formula to predict prognosis in patients with HCC. To validate the protein and mRNA levels of these IRGs, we used the Human Protein Atlas database and quantitative PCR analysis and found that most protein expression levels matched the corresponding mRNA expression levels. Furthermore, we also validated the prognostic value of the new risk model in another independent cohort (n = 277) from a Gene Expression Omnibus dataset (GSE14520). Our data suggested that there was a significant association between our risk model and patient prognosis. Stratification analysis showed that the nine-IRG signature was significantly associated with overall survival in men. Finally, the signature was found to be correlated with various clinicopathological features. Intriguingly, the prognostic index based on the IRGs reflected infiltration by several types of immune cells. In summary, our data provided evidence that the nine-IRG signature could serve as an independent biomarker to predict prognosis in patients with HCC.

SUBMITTER: Tang Y

PROVIDER: S-EPMC7191166 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Combined signature of nine immune-related genes: a novel risk score for predicting prognosis in hepatocellular carcinoma.

Tang Yunliang Y Zeng Zhenguo Z Wang Jiao J Li Guoyong G Huang Chao C Dong Xiaoyang X Feng Zhen Z

American journal of translational research 20200415 4

Hepatocellular carcinoma (HCC) is one of the most common internal malignancies worldwide and is associated with a poor prognosis. There is an urgent need to identify diagnostic and prognostic biomarkers of HCC pathogenesis and progression. Accordingly, in this study, we analyzed differentially expressed immune-related genes (IRGs) from 329 patients with HCC from The Cancer Genome Atlas datasets. Functional analysis revealed that the IRGs had potential effects on tumor immune processes, such as i ...[more]

PMID: 32355535

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Project description:Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, and its prognosis remains unsatisfactory. The identification of new and effective markers is helpful for better predicting the prognosis of patients with HCC and for conducting individualized management. The oncogene Aurora kinase A (AURKA) is involved in a variety of tumors; however, its role in liver cancer is poorly understood. The aim of this study was to establish AURKA-related gene signatures for predicting the prognosis of patients with HCC. Methods: We first analyzed the expression of AURKA in liver cancer and its prognostic significance in different data sets. Subsequently, we selected genes with prognostic value related to AURKA and constructed a gene signature based on them. The predictive ability of the gene signature was tested using the HCC cohort development and verification data sets. A nomogram was constructed by integrating the risk score and clinicopathological characteristics. Finally, the influence of the gene signature on the immune microenvironment in HCC was comprehensively analyzed. Results: We found that AURKA was highly expressed in HCC, and it exhibited prognostic value. We selected eight AURKA-related genes with prognostic value through the protein-protein interaction network and successfully constructed a gene signature. The nine-gene signature could effectively stratify the risk of patients with HCC and demonstrated a good ability in predicting survival. The nomogram showed good discrimination and consistency of risk scores. In addition, the high-risk group showed a higher percentage of immune cell infiltration (i.e., macrophages, myeloid dendritic cells, neutrophils, and CD4+T cells). Moreover, the immune checkpoints SIGLEC15, TIGIT, CD274, HAVCR2, and PDCD1LG2 were also higher in the high-risk group versus the low-risk group. Conclusions: This gene signature may be useful prognostic markers and therapeutic targets in patients with HCC.

Project description:Ferroptosis is an iron-dependent cell death process that plays important regulatory roles in the occurrence and development of cancers, including hepatocellular carcinoma (HCC). Moreover, the molecular events surrounding aberrantly expressed long non-coding RNAs (lncRNAs) that drive HCC initiation and progression have attracted increasing attention. However, research on ferroptosis-related lncRNA prognostic signature in patients with HCC is still lacking. In this study, the association between differentially expressed lncRNAs and ferroptosis-related genes, in 374 HCC and 50 normal hepatic samples obtained from The Cancer Genome Atlas (TCGA), was evaluated using Pearson's test, thereby identifying 24 ferroptosis-related differentially expressed lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model were used to construct and validate a prognostic risk score model from both TCGA training dataset and GEO testing dataset (GSE40144). A nine-lncRNA-based signature (CTD-2033A16.3, CTD-2116N20.1, CTD-2510F5.4, DDX11-AS1, LINC00942, LINC01224, LINC01231, LINC01508, and ZFPM2-AS1) was identified as the ferroptosis-related prognostic model for HCC, independent of multiple clinicopathological parameters. In addition, the HCC patients were divided into high-risk and low-risk groups according to the nine-lncRNA prognostic signature. The gene set enrichment analysis enrichment analysis revealed that the lncRNA-based signature might regulate the HCC immune microenvironment by interfering with tumor necrosis factor α/nuclear factor kappa-B, interleukin 2/signal transducers and activators of transcription 5, and cytokine/cytokine receptor signaling pathways. The infiltrating immune cell subtypes, such as resting memory CD4(+) T cells, follicular helper T cells, regulatory T cells, and M0 macrophages, were all significantly different between the high-risk group and the low-risk group as indicated in Spearman's correlation analysis. Moreover, a substantial increase in the expression of B7H3 immune checkpoint molecule was found in the high-risk group. Our findings provided a promising insight into ferroptosis-related lncRNAs in HCC and a personalized prediction tool for prognosis and immune responses in patients.

Project description:BackgroundHepatocellular carcinoma (HCC) has a high incidence and poor prognosis. Cuproptosis is a novel type of cell death, which differs from previously reported types of cell death such as apoptosis, autophagy, proptosis, ferroptosis, necroptosis, etc. Long non-coding RNAs (lncRNAs) play multiple roles in HCC.MethodsWe downloaded information from The Cancer Genome Atlas (TCGA) database, and obtained cuproptosis-related genes from published studies. The cuproptosis-related lncRNAs were obtained by correlation analysis, and subsequently used to construct a prognostic cuproptosis-related lncRNA signature. Analyses of overall survival (OS), progression-free survival (PFS), receiver operating characteristic (ROC) curve with the area under the curve (AUC) values and the index of concordance (c-index) curve were used to evaluate the signature. The tumor microenvironment (TME) was analyzed by ESTIMATE algorithm. The immune cell data was downloaded from the Tumor Immune Estimation Resource (TIMER) 2.0 database. Immune-related pathways were analyzed by single-sample gene set enrichment analysis (ssGSEA) algorithm. Immunophenoscore (IPS) scores from The Cancer Immunome (TCIA) database were used to evaluate immunotherapy response. The "pRRophetic" was employed to screen drugs for high-risk patients. The candidate lncRNA expression levels were detected by Real Time Quantitative PCR.ResultsWe constructed a cuproptosis-related lncRNA signature containing seven lncRNAs: AC125437.1, PCED1B-AS1, PICSAR, AP001372.2, AC027097.1, LINC00479, and SLC6A1-AS1. This signature had excellent accuracy, and was independent of the stratification of clinicopathological features. Further study showed that high-risk tumors under this signature had higher TMB, fewer TME components and higher tumor purity. The tumors with high risk were not enriched in immune cell infiltration or immune process pathways, and high-risk patients had a poor response to immunotherapy. Moreover, 29 drugs such as sorafenib, dasatinib and paclitaxel were screened for high-risk HCC patients to improve their prognosis. The expression levels of the candidate lncRNAs in HCC tissue were significantly increased (except PCED1B-AS1).ConclusionsOur prognostic cuproptosis-related lncRNA signature was accurate and effective for predicting the prognosis of HCC. The immunotherapy was unsuitable for high-risk HCC patients with this signature.

Project description:BackgroundHepatocellular carcinoma (HCC) remains a growing threat to global health. Necroptosis is a newly discovered form of cell necrosis that plays a vital role in cancer development. Thus, we conducted this study to identify a predictive signature of HCC based on necroptosis-related genes.MethodsThe tumor samples in the liver hepatocellular carcinoma (LIHC) cohort from The Cancer Genome Atlas (TCGA) database were subtyped using the consensus clustering algorithm. Univariate Cox regression and LASSO-Cox analysis were performed to identify a gene signature from genes differentially expressed between tumor clusters. Then, we integrated the TNM stage and the prognostic model to build a nomogram. The gene signature and the nomogram were externally validated in the GSE14520 cohort from the Gene Expression Omnibus (GEO) and the LIRP-JP cohort from the International Cancer Genome Consortium (ICGC). Evaluations of predictive performance evaluations were conducted using Kaplan-Meier plots, time-dependent receiver operating characteristic curves, principal component analyses, concordance indices, and decision curve analyses. The tumor microenvironment was estimated using eight published methods. Finally, we forecasted the sensitivity of HCC patients to immunotherapy and chemotherapy based on this gene signature.ResultsWe identified two necroptosis-related clusters and a 10-gene signature (MTMR2, CDCA8, S100A9, ANXA10, G6PD, SLC1A5, SLC2A1, SPP1, PLOD2, and MMP1). The gene signature and the nomogram had good predictive ability in the TCGA, ICGC, and GEO cohorts. The risk score was positively associated with the levels of necroptosis and immune cell infiltrations (especially of immunosuppressive cells). The high-risk group could benefit more from immunotherapy and some chemotherapeutics than the low-risk group.ConclusionThe necroptosis-related gene signature provides a new method for the risk stratification and treatment optimization of HCC. The nomogram can further improve predictive accuracy.

Dataset Information

Combined signature of nine immune-related genes: a novel risk score for predicting prognosis in hepatocellular carcinoma.

Publications

Combined signature of nine immune-related genes: a novel risk score for predicting prognosis in hepatocellular carcinoma.

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