Fig 1.
B, the group including all of the Behçet’s disease patients; BD, Behçet’s disease; C, control group; FC, fold change; MB, Behçet’s disease patients with isolated mucocutaneous manifestations; OB, Behçet’s disease patients with any kind of ocular involvement; RMA, robust multiarray average; VB, Behçet’s disease patients with large vein thrombosis.
Table 1.
Demographic and basic clinical characteristics of the study population.a, b
Fig 2.
Dendrogram and heatmap representations of the results of the initial cluster analysis “MB & VB” (a) and “MB & OB & VB” (b).
For both cases, hierarchical clustering using Euclidean metric and average linkage was employed and both patients and genes were clustered. For sake of simplicity, only the dendrograms for clustering of the patients are shown in the heatmaps. Take note of the position of VB1 (study ID 2, GSM428038) in the MB branch of the dendrograms. Based on these clustering results, VB1 was excluded prior to further analysis. Also, as can be seen in the figure, the cluster analysis successfully clustered distinct expressions of Behçet’s disease and with the exception of VB1, the expression profiling based clustering results were in accordance with the manifestation based clustering of Behçet’s disease patients. The gene sets used for clustering were constituted from the DEGs identified during the corresponding class comparisons (i.e., MB vs VB and MB vs OB vs VB).
Table 2.
Summary of key results of the class comparison analysis.
Fig 3.
Venn diagram representation of the class comparisons MB vs C, OB vs C, and VB vs C.
The number of the common DEGs present in the intersections are shown on the figure. Note that, the intersection “MB vs C” ∩ “OB vs C” ∩ “VB vs C” has no common DEGs. The gene lists of the intersections are as follows: “MB vs C” ∩ “OB vs C”: LTF, OLFM4, CEACAM8; “MB vs C” ∩ “VB vs C”: HBG1, TMEM66, LGALS2, SEC24D, BCL10, EIF1AX, MAP3K4, KRR1, RP2, ABO, ATF1, TAX1BP1, CD69, TLR4; “OB vs C” ∩ “VB vs C”: PRKCQ, TNFAIP3, DDX17, SLC6A8, RGS1, NR4A2, G0S2, OSM. Missing counts in the gene lists occur because of recurring gene symbols and/or probe sets without assigned symbols.
Table 3.
The top 20 most differentially expressed genes in the class comparison analysis.a
Fig 4.
Dendrogram and heatmap representations of the results of the cluster analysis “MB & OB” (a), “MB & VB” (b), “OB & VB” (c), and “MB & OB & VB” (d).
For every case, hierarchical clustering using Euclidean metric and average linkage was employed and both patients and genes were clustered. For ease of demonstration, only the dendrograms for clustering of the patients are shown in the heatmaps. As the figure shows, the algorithm and the gene sets implemented successfully clustered distinct expressions of Behçet’s disease. The gene sets used for clustering were constituted from the DEGs identified during the corresponding class comparisons (i.e., MB vs OB, MB vs VB, OB vs VB, and MB vs OB vs VB).
Table 4.
Summary of key findings of the Gene Ontology term enrichment analysis.a, b, c
Table 5.
Matches between the loci identified in the genome-wide association and the genome-wide linkage studies of Behçet’s disease and the loci of the differentially expressed genes documented in the present study.a
Table 6.
Potentially significant differentially expressed genes with immune/inflammatory functions.a
Fig 5.
Clustered heatmap representations of TNFAIP3 and CD69 expressions in class comparisons MB vs C (a), OB vs C (b), and VB vs C (c).
CD69, cluster of differentiation 69; TNFAIP3, tumor necrosis factor, α-induced protein 3.
Table 7.
Expression patterns of TNFAIP3 and CD69 in BS subsets and controls.