Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P05109 (S100A8)
 1,212 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Gene expression patterns in ductal carcinoma in situ (DCIS) and invasive and metastatic breast tumors have been determined using serial analysis of gene expression (SAGE). The purpose of this approach was to identify biologically and clinically meaningful subgroups of DCIS with a high risk of progression to invasive disease. The analyses have led to the identification of several differentially expressed genes, such as HIN-1, dermcidin and S100A7 (psoriasin). The aim of the present study was further to delineate the expression profile of S100 genes using information from 22 breast epithelial SAGE libraries. We demonstrated the down-regulation of S100A6 and S100A10 in breast cancer, irrespective of pathological stage. S100P and S100Z were both up-regulated in cancer; whereas S100A7, S100A8 and S100A9 were strongly up-regulated only in DCIS. The hierarchical clustering of S100 gene expression in these 22 libraries revealed two major groups with distinguishable S100 gene expression profiles. One of them was characterized by the high concomitant expression of S100A7, S100A8 and S100A9. Using SAGE informatics, we found 21 genes with a high positive correlation to S100A7 expression in libraries representing different categories of tissues archived at SAGE Genie, suggesting a function of psoriasin that is not tissue specific. Like S100A7, several of these genes displayed cation-binding properties. We also report the strong correlation in the breast epithelial SAGE libraries between the expression of S100A7 and genes reported as being up-regulated in DCIS, as well as in the inflammatory skin disorder, psoriasis; including RGS5, UPK1A, TMPRSS3, S100A9, p53, SCCA1, SCCA2 and KRT17.
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PMID:Cluster analysis of S100 gene expression and genes correlating to psoriasin (S100A7) expression at different stages of breast cancer development. 1627 1

Cigarette smoke creates a molecular field of injury in epithelial cells that line the respiratory tract. We hypothesized that transcriptome sequencing (RNA-Seq) will enhance our understanding of the field of molecular injury in response to tobacco smoke exposure and lung cancer pathogenesis by identifying gene expression differences not interrogated or accurately measured by microarrays. We sequenced the high-molecular-weight fraction of total RNA (>200 nt) from pooled bronchial airway epithelial cell brushings (n = 3 patients per pool) obtained during bronchoscopy from healthy never smoker (NS) and current smoker (S) volunteers and smokers with (C) and without (NC) lung cancer undergoing lung nodule resection surgery. RNA-Seq libraries were prepared using 2 distinct approaches, one capable of capturing non-polyadenylated RNA (the prototype NuGEN Ovation RNA-Seq protocol) and the other designed to measure only polyadenylated RNA (the standard Illumina mRNA-Seq protocol) followed by sequencing generating approximately 29 million 36 nt reads per pool and approximately 22 million 75 nt paired-end reads per pool, respectively. The NuGEN protocol captured additional transcripts not detected by the Illumina protocol at the expense of reduced coverage of polyadenylated transcripts, while longer read lengths and a paired-end sequencing strategy significantly improved the number of reads that could be aligned to the genome. The aligned reads derived from the two complementary protocols were used to define the compendium of genes expressed in the airway epithelium (n = 20,573 genes). Pathways related to the metabolism of xenobiotics by cytochrome P450, retinol metabolism, and oxidoreductase activity were enriched among genes differentially expressed in smokers, whereas chemokine signaling pathways, cytokine-cytokine receptor interactions, and cell adhesion molecules were enriched among genes differentially expressed in smokers with lung cancer. There was a significant correlation between the RNA-Seq gene expression data and Affymetrix microarray data generated from the same samples (P < 0.001); however, the RNA-Seq data detected additional smoking- and cancer-related transcripts whose expression was were either not interrogated by or was not found to be significantly altered when using microarrays, including smoking-related changes in the inflammatory genes S100A8 and S100A9 and cancer-related changes in MUC5AC and secretoglobin (SCGB3A1). Quantitative real-time PCR confirmed differential expression of select genes and non-coding RNAs within individual samples. These results demonstrate that transcriptome sequencing has the potential to provide new insights into the biology of the airway field of injury associated with smoking and lung cancer. The measurement of both coding and non-coding transcripts by RNA-Seq has the potential to help elucidate mechanisms of response to tobacco smoke and to identify additional biomarkers of lung cancer risk and novel targets for chemoprevention.
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PMID:Characterizing the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq. 2163 47