Dataset Information

Using Prognosis-Related Gene Expression Signature and Connectivity Map for Personalized Drug Repositioning in Multiple Myeloma.

ABSTRACT: BACKGROUND Multiple myeloma (MM) is the second most common hematologic cancer with poor prognosis. Novel therapeutic strategies are needed to decrease the high mortality rate. The aim of this study was to identify prospective agents for MM. MATERIAL AND METHODS A microarray dataset was mined, which contains the transcriptome profiles of 588 MM patients. Univariate Cox analysis was performed to analyze the relationships between genes and clinical outcome. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were determined. Protective and risky genes were uploaded to Connectivity Map (CMAP) database to identify the potentially unknown effects of existing drugs. An example was selected to be docked on the known molecules. RESULTS A total of 1445 genes significantly correlated with the event free survival (EFS) of MM patients were identified and included 676 protective and 769 risky indicators. KEGG pathway analysis revealed that these prognosis-associated genes were enriched in the "cell cycle," "DNA replication," and "P53 signaling pathway". The top t3 most significant potential molecules were vorinostat, trifluoperazine, and thioridazine. CDK1 (cyclin-dependent kinase-1) ranked as the core in the class of prognosis-related genes in MM based on protein-protein interaction (PPI) network analysis. With Sybyl-X 2.0, the majority of the top 10 molecules aforementioned displayed high binding forces with CDK1. Among these molecules, trichostatin A had the greatest ability in combining with CDK1. CONCLUSIONS Genes that mainly accumulate in the cell cycle pathway play an essential role in the prognosis of MM, and these prognosis-related genes also have great value in drug development.

SUBMITTER: Zhu FX

PROVIDER: S-EPMC6510057 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Using Prognosis-Related Gene Expression Signature and Connectivity Map for Personalized Drug Repositioning in Multiple Myeloma.

Zhu Fang-Xiao FX He Yu-Chan YC Zhang Jun-Yan JY Wang Hang-Fei HF Zhong Chen C Wang Xiao-Tao XT

Medical science monitor : international medical journal of experimental and clinical research 20190502

BACKGROUND Multiple myeloma (MM) is the second most common hematologic cancer with poor prognosis. Novel therapeutic strategies are needed to decrease the high mortality rate. The aim of this study was to identify prospective agents for MM. MATERIAL AND METHODS A microarray dataset was mined, which contains the transcriptome profiles of 588 MM patients. Univariate Cox analysis was performed to analyze the relationships between genes and clinical outcome. Gene Ontology (GO) and the Kyoto Encyclop ...[more]

PMID: 31048671

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Project description:BackgroundMultiple myeloma (MM) is a highly malignant hematological tumor with a poor overall survival (OS). Due to the high heterogeneity of MM, it is necessary to explore novel markers for the prognosis prediction for MM patients. Ferroptosis is a form of regulated cell death, playing a critical role in tumorigenesis and cancer progression. However, the predictive role of ferroptosis-related genes (FRGs) in MM prognosis remains unknown.MethodsThis study collected 107 FRGs previously reported and utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to construct a multi-genes risk signature model upon FRGs. The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were carried out to evaluate immune infiltration level. Drug sensitivity was assessed based on the Genomics of Drug Sensitivity in Cancer database (GDSC). Then the synergy effect was determined with Cell counting kit-8 (CCK-8) assay and SynergyFinder software.ResultsA 6-gene prognostic risk signature model was constructed, and MM patients were divided into high and low risk groups. Kaplan-Meier survival curves showed that patients in the high risk group had significantly reduced OS compared with patients in the low risk group. Besides, the risk score was an independent predictor for OS. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the risk signature. Combination of risk score and ISS stage had better prediction performance. Enrichment analysis revealed immune response, MYC, mTOR, proteasome and oxidative phosphorylation were enriched in high risk MM patients. We found high risk MM patients had lower immune scores and immune infiltration levels. Moreover, further analysis found that MM patients in high risk group were sensitive to bortezomib and lenalidomide. At last, the results of the in vitro experiment showed that ferroptosis inducers (RSL3 and ML162) may synergistically enhance the cytotoxicity of bortezomib and lenalidomide against MM cell line RPMI-8226.ConclusionThis study provides novel insights into roles of ferroptosis in MM prognosis prediction, immune levels and drug sensitivity, which complements and improves current grading systems.

Project description:BackgroundDrug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning.MethodsTo overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects.ResultsIn DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmapsConclusionsReliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts.

Project description:BACKGROUND:In patients with limited response to conventional therapeutics, repositioning of already approved drugs can bring new, more effective options. Current drug repositioning methods, however, frequently rely on retrospective computational analyses and genetic testing - time consuming methods that delay application of repositioned drugs. Here, we show how proteomic analysis of liquid biopsies successfully guided treatment of neovascular inflammatory vitreoretinopathy (NIV), an inherited autoinflammatory disease with otherwise poor clinical outcomes. METHODS:Vitreous biopsies from NIV patients were profiled by an antibody array for expression of 200 cytokine-signaling proteins. Non-NIV controls were compared with NIV samples from various stages of disease progression. Patterns were identified by 1-way ANOVA, hierarchical clustering, and pathway analysis. Subjects treated with repositioned therapies were followed longitudinally. RESULTS:Proteomic profiles revealed molecular pathways in NIV pathologies and implicated superior and inferior targets for therapy. Anti-VEGF injections resolved vitreous hemorrhages without the need for vitrectomy surgery. Methotrexate injections reversed inflammatory cell reactions without the side effects of corticosteroids. Anti-IL-6 therapy prevented recurrent fibrosis and retinal detachment where all prior antiinflammatory interventions had failed. The cytokine array also showed that TNF-? levels were normal and that corticosteroid-sensitive pathways were absent in fibrotic NIV, helping explain prior failure of these conventional therapeutic approaches. CONCLUSIONS:Personalized proteomics can uncover highly personalized therapies for autoinflammatory disease that can be timed with specific pathologic activities. This precision medicine strategy can also help prevent delivery of ineffective drugs. Importantly, proteomic profiling of liquid biopsies offers an endpoint analysis that can directly guide treatment using available drugs.

Dataset Information

Using Prognosis-Related Gene Expression Signature and Connectivity Map for Personalized Drug Repositioning in Multiple Myeloma.

Publications

Using Prognosis-Related Gene Expression Signature and Connectivity Map for Personalized Drug Repositioning in Multiple Myeloma.

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