Bioinformatics and systems biology analysis of genes network involved in OLP (Oral Lichen Planus) pathogenesis.

Background: Genes involved in different biological processes form complex interaction networks. However, only few of them have a high number of interactions with the other genes in the network and therefore they may play a major role. In previous bioinformatics and experimental studies, these genes were identified and termed as "leader genes". In the current ab initio theoretical study, genes involved in human OLP (Oral Lichen Planus) pathogenesis are identified and ranked according to their number of interactions, in order to obtain a broader view of its molecular mechanisms and to plan targeted experimentations. Methods:,Genes involved or potentially involved in OLP were identified by systematically. querying several databases until the identification of a final set of genes. Interactions among these genes were mapped and given a significance score using STRING database. For each gene, significance scores were summed to obtain a weighted number of links (WNL) and subsequently genes were clustered according to this parameter. The genes in the highest cluster were termed as leader genes; the other ones were ranked as class B genes, class C genes, and so on. This study was complemented by a topological analysis of the network, carried out using Cytoscape, BinGO and FANMOD software. Results: The interactions in the obtained network showed power law behaviour, in agreement with the scale-free topology theory of the biological graphs. 132 genes were identified and five of them (namely, JUN, EGFR, FOS, IL2, ITGB4) were classified as leaders. Interestingly, all of them but EGFR were up-regulated and were widely distributed in the network (in term of topological parameters such as stress, eccentricity and radiality) and showed higher topological coefficients than the other genes. Conclusions: Even with the limitations of any ab initio analysis, this study can suggest targeted experimentation, focused on the leader genes and therefore simpler to be analysed than mass scale molecular genomics. Moreover, it may suggest new potential risk factors and therapeutic targets.