Objective: The aim of this study was to identify hub genes associated with immune cell infiltration in breast cancer through bioinformatic analyses of multiple datasets.
Methods: Nonparametric (NOISeq) and robust rank aggregation-ranked parametric (EdgeR) methods were used to assess robust differentially expressed genes across multiple datasets. Protein-protein interaction network, GO, KEGG enrichment, and sub-network analyses were performed to identify immune-associated hub genes in breast cancer.
Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data.
View Article and Find Full Text PDFMacrophages enhance glioma development and progression by shaping the tumor microenvironment. Class A1 scavenger receptor (SR-A1), a pattern recognition receptor primarily expressed in macrophages, is up-regulated in many human solid tumors. We found that SR-A1 expression in 136 human gliomas was positively correlated with tumor grade (P<0.
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