The Central Importance of Hub Proteins in a Disease-Gene Network Model: A New Paradigm of Chronic Myeloid Leukemia Disease Study

BackgroundThe network biology of disease-gene association provides a holistic framework to decipher the intrinsic complexity of disease signaling pathways into cellular communication level. Different types of studies including large-scale genome-wide association, multifactor dimensional reduction analysis, whole genome, or exome-based sequencing strategies of diseases are striving to connect genes to diseases. Indeed, these approaches have had some accomplishments, but the cellular communication level needs a more streamlining outcome to understand the mechanistic impact of context. The higher-order combination of disease-gene interaction has a great potential to decipher the intricateness of diseases. The molecular interaction pattern of diseases at the genomic and proteomic level offers a revolutionized platform not only to understand the complexity of particular disease modules and pathways but also leading towards design novel therapeutics. ResultsThe enrichment and topology analysis was performed by JEPETTO a plugin of Cytoscape software. We identified the chronic myeloid leukemia (CML) disease signaling pathways that appeared first in the ranking order based on XD-score among the bone, breast, and colon genes set and second at kidney and liver. This result validates the highest proximity between CML and five cancerous tissue gene set clusters. The topology analysis also supports the results while (p<0.0001) is considered to be extremely significant between CML and fives cancerous tissues genes set. Enrichment analysis identified that abl-gene acts as an overlapping node which is the major gene for inducing various mutations in CML. Amazingly, we identified 56 common path expansion/added genes among these five cancerous tissues which can be considered the direct cofactors of CML disease. By relative node degree, resolution, possible ligand, stoichiometry, Q-mean, and Z-score analysis we found 11 hubs proteins like SMAD3, GRB2, TP53, SMAD4, RB1, HDAC1, RAF1, ABL1, SHC1, TGFBR1, RELA which can be regarded for further drug target identification. ConclusionsOur proposed network analysis reflects on the gene set interaction pattern of disease signaling pathways of humans. The integrated multidrug computational and experimental approaches boost up to improve the novel drug target approach. Besides, such a trove can yield unprecedented insights to lead to an enhanced understanding of potential application both in drug target optimization and for drug dislodging.