We are analyzing https://link.springer.com/article/10.1186/s12967-020-02492-9.

Title:
A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients | Journal of Translational Medicine
Description:
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.
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hcc, expression, fig, article, pubmed, hypoxiarelated, analysis, model, google, scholar, genes, prognostic, immune, patients, gene, cancer, recurrence, cas, tumor, signature, prognosis, survival, nomogram, hypoxia, cohort, level, tcga, cell, cells, carcinoma, hepatocellular, high, degs, samples, additional, file, data, group, clinical, diagnostic, showed, predictive, results, higher, central, regression, prediction, roc, performed, database,

Topics {✒️}

hypoxia–glycolysis–lactate-related gene signature real-time pcr analyses article download pdf transcriptional network-based biomarkers kaplan–meier plotter clinical decision-making benefits time‐dependent roc analysis time-dependent roc analysis kaplan–meier survival analysis methylation-driven prognostic model hepatocellular carcinoma based time-dependent roc curves time‐dependent roc curves dna methylation-driven genes lasso-penalized cox regression small-cell lung cancer hepatocellular carcinoma occurrence lasso‐penalized cox analysis kaplan–meier plot showed absolute log2-fold change hepatocellular carcinoma patients hypoxia-related prognostic signature k-means clustering algorithm mrna expression profile low-risk group based vertical axis represents hypoxia-related degs dataset hypoxia-related recurrence signature violin plots showing hypoxia-related degs clustering analyzed hypoxia-related genes hypoxia-related recurrence model progression-free survival analysis cancer genome atlas explore key targets high-risk group based privacy choices/manage cookies full access hypoxia-related prognosis signature kaplan meier curves hypoxic environments remains gene expression profiles mrna expression profiles inter-cluster variation coefficient hepatocellular carcinoma low-risk groups ling qin multivariate cox regression horizontal axis represents nf-kappa

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         description: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. 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. 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. 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.
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