The gene for SOST itself was not detectably differentially expressed in this experiment based on the microarray data, highlighting the sensitivity of the causal analysis.
Our findings, derived from a different type of analysis of microarray data, highlight the importance of analyzing coordinated gene expression to find molecular pathways involved in melanoma progression.
A more detailed analysis of the microarray data highlighted an interesting finding: several lipid-metabolizing genes showing differential expression in the livers of infected animals are direct or indirect targets of miR-122 (Table S3).
Bioinformatics analysis of microarray data highlighted distinct responses to CDV exposure in PHKs compared to HPV+ cervical carcinoma cells, on one hand, and to HPV- immortalized keratinocytes, on the other hand.
A list of the primers for the genes analyzed is shown in the additional file 4. Overall, the fold changes observed by real time RT-PCR were in good agreement with the predicted fold changes from the microarray data highlighted by a significant Spearman's correlation (rho = 0.9286; P = 0.0067).
Using a simple regression model, the rough PPI network was further refined with the microarray data to highlight the independent protein association for samples with and without lung cancer relative to their respective data sets.
The results from the microarray data also highlighted the involvement of a number of genes encoding components of the TLR signalling pathway including CD14 (2.48 fold), IKBKB (1.98 fold) and NFKB1 (2.32 fold), all of which were significantly increased in expression (P ≤ 0.05).
We considered 25 literature-based connections as required connections, based on previous literature data that is consistent with the current microarray data, which are highlighted in the resulting network.
To expand the functional significance of the As-responsive genes in Col-0 and Ws-2, we used MapMan representations to highlight microarray data and biochemical pathways.
Although in this study we applied our approach to microarray data analysis, the issues highlighted here are also relevant to data acquired with RNA-seq technology (Mortazavi et al., 2008).
