Figure 2

Enrichment analysis of microRNA for Pathway Maps, Gene Ontology, Disease by Biomarker and Network processes in liver diseases. Pathway analysis was carried out via MetaCore software, differentially expressed miRNA data for Cirrhosis, Low-grade dysplastic nodule, High-grade dysplastic nodule, Early stage Hepatocellular carcinoma, and Advanced stage Hepatocellular carcinoma were uploaded to MetaCore server and the most significantly affected pathways were created using comparative enrichment analysis. The gene content were aligned between all listed experiments above. The intersection set of experiments is defined as “common” and marked as a blue/white striped bar. The unique genes for the experiments are marked as colored bars. The genes from the “similar” set are present in all but one (any) file. The parameters for comparison are set as above. Enrichment analysis consists of matching gene IDs of possible targets for the “common”, “similar” and “unique” sets with gene IDs in functional ontologies in MetaCore. The probability of a random intersection between set IDs the size of the target list with ontology entities estimated in p-value of hypergeometric intersection. The lower p-value means higher relevance of the entity to the dataset, which shows in a higher rating for the entity (A) there is a unique signature in advanced hepatocellular carcinoma. (B) Pathway Maps: Comparative and enrichment pathway analysis showed most of the miRNAs enriched in various disease stages were involved the oncogenic pathways (The results were obtained using MetaCore pathways analysis tool; GeneGo/Thomson Reuters). Top five common pathways were listed (B1–B5). Experimental data was visualized on the maps as blue (for downregulation) and red (upregulation) histograms. The height of the histogram corresponds to the relative expression value for a particular gene/protein (Pathway maps were obtained from MetaCore pathways analysis tool; GeneGo/Thomson Reuters).