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A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

ABSTRACT: BACKGROUND:Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism. METHODS:Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival (OS) were identified using Cox regression and LASSO analysis. Then the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. CIBERSORT was used for estimating the fractions of immune cell types. RESULTS:A total of 397 hypoxia-related DEGs in HCC were detected and three genes (PDSS1, CDCA8 and SLC7A11) among them were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response. Meanwhile, the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1. CONCLUSIONS:Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

SUBMITTER: Zhang B

PROVIDER: S-EPMC7487492 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

Zhang Baohui B Tang Bufu B Gao Jianyao J Li Jiatong J Kong Lingming L Qin Ling L

Journal of translational medicine 20200904 1

<h4>Background</h4>Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.<h4>Methods</h4>Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome ...[more]

PMID: 32887635

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Project description:BackgroundLung squamous cell carcinoma (LUSC) is a malignant tumour of the lung epithelium. A hypoxic environment can promote tumour cell proliferation and invasion. Therefore, this study aims to explore hypoxia-related genes and construct reliable models to predict the prognosis, cellular processes, immune microenvironment and target compounds of lung squamous carcinoma.MethodsThe transcriptome data and matched clinical information of LUSC were retrieved from The Cancer Genome Atlas (TCGA) database. The GSVA algorithm calculated each LUSC patient's hypoxia score, and all LUSC patients were divided into the high hypoxia score group and low hypoxia score group. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to screen out differentially expressed hypoxia-related genes (DE-HRGs) in LUSC microenvironment, and the underlying regulatory mechanism of DE-HRGs in LUSC was explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hereafter, we established a prognosis-related genetic signature for DE-HRGs using univariate and multivariate Cox regression analyses. The relationship between gene signature and immune cells was further evaluated. Finally, the Comparative Toxicogenomics Database (CTD) was utilized to predict the targeted drugs for the prognostic genes.ResultsWe obtained 376 DE-HRGs. Functional enrichment analysis indicated that the DE-HRGs were involved in the cell cycle-related regulatory processes. Next, we developed and validated 3 HRGs-based prognostic signature for LUSC, including HELLS, GPRIN1, and FAM83A. Risk score is an independent prognostic factor for LUSC. Functional enrichment analysis and immune landscape analysis suggested that the risk scoring system might be involved in altering the immune microenvironment of LUSC patients to influence patient outcomes. Ultimately, a total of 92 potential compounds were predicted for the three prognostic genes.ConclusionIn summary, we developed and validated a hypoxia-related model for LUSC, reflecting the cellular processes and immune microenvironment characteristics and predicting the prognostic outcomes and targeted compounds.

Dataset Information

A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

Publications

A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

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