Dataset features


Application: Gene expression microarray analysis
Number of samples: 12
Release date: Apr 11 2017
Last update date: Jul 26 2018
Access: Public
Diseases: Autoimmune Diseases, Lymphoma, Nervous System Diseases, Salivary Gland Diseases, Sjogren's Syndrome, Lymphoma, B-Cell
Dataset link Transcriptome analysis of salivary gland epithelial cell lines derived from patients with primary Sjögren’s syndrome.

Experimental Protocol

Long-term cultured non-neoplastic secondary SGEC lines were established from minor salivary gland biopsies that were obtained with informed consent from patients with dryness complaints during their routine diagnostic work-up for SS. All SGEC lines studied (n=12) were of ductal type and were established and maintained under the same culture conditions in serum-free keratinocyte basal medium, as previously described (Dimitriou et al, Eur J Oral Sci, 2002, 110:21-30). The exclusive epithelial nature and ductal epithelial origin of cultured SGEC lines was verified by morphology, as well as by the uniform and consistent expression of epithelial specific markers and the absence of markers indicative of other types of cells. Transcriptome analyses were performed in total RNA specimens isolated from the non-neoplastic SGEC lines derived from 3 non-SS sicca controls (control-SGEC lines) and from 9 SS patients (SS-SGEC lines), using the Affymetrix microarray technology (GeneChipHuGene 1.0ST arrays with 28,869 annotated genes). The SS-SGEC lines studied were selected on the basis of the intensity of lymphoepithelial infiltrates in the respective MSG biopsies (all with sialadenitis focus score ≥1), and consisted of two subgroups; SS-Group-1 (n=3) derived from biopsies with moderate mononuclear infiltrations (focus score<2) and SS-Group-2 (n=6) derived from biopsies with heavy mononuclear infiltrations (focus score ≥2). Evidence of type-1 disease (high risk for lymphoma development) was exclusively manifested by patients belonging in SS-Group-2. Microarray analysis was performed with the R statistical environment version 2.13 using the Bioconductor package. RMA normalization was performed in the microarray and identification of differentially expressed genes was conducted with Student’s t-test (p-value<0.05). Gene ontology annotation of differentially expressed genes, signal pathway analysis and network construction was performed with the use of Ingenuity Pathway Analysis (IPA) software and he Kyoto Encyclopedia of Genes and Genomes (KEGG).








Menelaos Manoussakis