The Central Importance of Hub Proteins in a Disease-Gene Network Model: A New Paradigm of Chronic Myeloid Leukemia Disease Study.
Category:- Journal; Year:- 2021
Discipline:- Biotechnology & Genetic Engineering Discipline
School:- Life Science School
Abstract
The 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. Results: The
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. Conclusions: Our 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.