Identification Of Functional Clusters And Application Of Clusters For Exploring Drug Targets And Sensitivity Biomarkers

Cellular function in tumor or normal tissues are mostly conducted in a highly modular manner and the potential mechanism can be elucidated by advanced approaches derived from biological networks.Study of the modular organization of interactome or regularity networks,such as protein-protein interactions(PPIs)or co-expression network,may benefit further explorations of the underlying molecular network mechanisms related to human diseases.This network medicine framework provides a global system-level view for discovering the potential causes of human diseases and obtaining a better understanding of the correlation between each disease and its molecular functional communities.Moreover,the results produced by network approaches will offer an efficient way for human disease treatment.First,we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms.We subsequently compared the performance of these algorithms for target gene prediction,which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods,shortest path and diffusion correlation.In addition,we validated the proportion of perturbed genes in clusters by finding candidate antibreast cancer drugs and confirming our predictions using literature evidence and cases in the Clinical Trials.gov.Our results indicate that the Walktrap(CW)clustering algorithm achieved the best performance overall in our comparative study.Secondly,we applied a meta-analytical framework to compute co-expression between each pair of isoforms in two large datasets of RNA-seq profiles of breast cancer cell lines.Then,hallmark-related direct(HRD)networks were built by integrating breast cancer type-specific isoform co-expression(BCIC)network and hallmark-related isoforms.Next,we explored the associations between isoform biomarkers and the functional clusters of the HRD network.The crucial isoform-based biomarkers for drugs were identified by functional clusters analysis and elucidated by combining isoform expression profiles with clinical information for breast cancer in The Cancer Genome Atlas.Third,we integrated a breast cancer type-specific isoform coexpression network with gene perturbation signatures in the MCF7 cell line in the Connectivity Map database using the ‘shortest path' drug target prioritization method.We used a leukemia cancer network and differential expression data for drugs in the HL-60 cell line to test the robustness of the detection algorithm for target major isoforms.We further analyzed the properties of target major isoforms for each multi-isoform gene using pharmacogenomic datasets,proteomic data and the principal isoforms defined by the APPRIS and STRING datasets.Then,we tested our predictions for the most promising target major protein isoforms of DNMT1,MGEA5 and P4HB4 based on expression data and topological features in the coexpression network.Interestingly,these isoforms are not annotated as principal isoforms in APPRIS.Lastly,we tested the affinity of the target major isoform of MGEA5 for streptozocin through in silico docking.Our findings will pave the way for more effective and targeted therapies via studies of drug targets at the isoform level.Forth,we aimed to identify higher degree isoforms(HDIs)of multi-isoform genes(MIGs)in cancer by applying a meta-analytical framework to calculate co-expression between each pair of isoforms in two large datasets of RNA-seq profiles from breast cancer,lung cancer,leukemia,and colon cancer cell lines.Then,we compared HDIs with isoforms identified by proteomic data and prognostic and predictive evidence in various cancers.In addition,we separately analyzed the associations between HDIs and non-HDIs(n HDIs)of the same genes according to transcript expression and drug responses in various cancer type cell lines.Collectively,these results indicated the complex properties of HDIs per gene identified by cancer type-based isoform–isoform co-expression networks and showed the potential of HDIs as novel therapeutic targets for cancer treatment.