Hypoxia, an important component of the tumor microenvironment, plays a crucial role in the occurrence and progression of cancer. However, to the best of our knowledge, a systematic analysis of a hypoxia-related prognostic signature for breast cancer is lacking and is urgently required. Therefore, in the present study, RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and served as a discovery cohort. Cox proportional hazards regression analysis was performed to construct a 14-gene prognostic signature (PFKL, P4HA2, GRHPR, SDC3, PPP1R15A, SIAH2, NDRG1, BTG1, TPD52, MAFF, ISG20, LALBA, ERRFI1 and VHL). The hypoxia-related signature successfully predicted survival outcomes of the discovery cohort (P<0.001 for the TCGA dataset). Three independent Gene Expression Omnibus databases (GSE10886, GSE20685 and GSE96058) were used as validation cohorts to verify the value of the predictive signature (P=0.007 for GSE10886, P=0.021 for GSE20685, P<0.001 for GSE96058). In the present study, a robust predictive signature was developed for patients with breast cancer, and the findings revealed that the 14-gene hypoxia-related signature could serve as a potential prognostic biomarker for breast cancer.

Tumor microenvironment (TME) plays an essential role in the development and metastasis of breast cancer (BC). More studies are needed on the differences and functions of immune components and matrix components. In this study, we calculated the proportion of tumorinfiltrating immune cells (TICs) and the proportion of immune and matrix components of BC patients from The Cancer Genome Atlas (TCGA). We performed Cox regression analysis and constructed protein-protein interaction (PPI) network based on the differentially expressed genes (DEGs) and obtained the most crucial gene CD52. CD52 significantly upregulated and affected the prognosis of BC patients. Gene set enrichment analysis (GSEA) suggested that the genes in the CD52 high-expression group were mainly enriched in immune-related pathways, while those in the CD52 low-expression group were mainly enriched in metabolic pathways. TICs analyses showed that there should be a positive correlation between CD52 expression and CD8+ T cells, activated memory CD4+ T cells, macrophage M1, and Gamma Delta T cells. It indicated that CD52 might be an essential factor in maintaining the immune-dominant position of TME. These results suggest that CD52 might be a potential biomarker for prognosis and provide a new therapeutic target for BC patients.

Gliomas are one of the most common types of brain cancers. Numerous efforts have been devoted to studying the mechanisms of glioma genesis and identifying biomarkers for diagnosis and treatment. To help further investigations, we present a comprehensive database named GliomaDB. GliomaDB includes 21,086 samples from 4303 patients and integrates genomic, transcriptomic, epigenomic, clinical, and gene-drug association data regarding glioblastoma multiforme (GBM) and low-grade glioma (LGG) from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), the Memorial Sloan Kettering Cancer Center Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), the US Food and Drug Administration (FDA), and PharmGKB. GliomaDB offers a user-friendly interface for two main types of functionalities. The first comprises queries of (i) somatic mutations, (ii) gene expression, (iii) microRNA (miRNA) expression, and (iv) DNA methylation. In addition, queries can be executed at the gene, region, and base level. Second, GliomaDB allows users to perform survival analysis, coexpression network visualization, multi-omics data visualization, and targeted drug recommendations based on personalized variations. GliomaDB bridges the gap between glioma genomics big data and the delivery of integrated information for end users, thus enabling both researchers and clinicians to effectively use publicly available data and empowering the progression of precision medicine in glioma. GliomaDB is freely accessible at http://bigd.big.ac.cn/gliomaDB.