Dataset Information

Biased random walk model for the prioritization of drug resistance associated proteins.

ABSTRACT: Multi-drug resistance is the main cause of treatment failure in cancer patients. How to identify molecules underlying drug resistance from multi-omics data remains a great challenge. Here, we introduce a data biased strategy, ProteinRank, to prioritize drug-resistance associated proteins in cancer cells. First, we identified differentially expressed proteins in Adriamycin and Vincristine resistant gastric cancer cells compared to their parental cells using iTRAQ combined with LC-MS/MS experiments, and then mapped them to human protein-protein interaction network; second, we applied ProteinRank to analyze the whole network and rank proteins similar to known drug resistance related proteins. Cross validations demonstrated a better performance of ProteinRank compared to the method without usage of MS data. Further validations confirmed the altered expressions or activities of several top ranked proteins. Functional study showed PIM3 or CAV1 silencing was sufficient to reverse the drug resistance phenotype. These results indicated ProteinRank could prioritize key proteins related to drug resistance in gastric cancer and provided important clues for cancer research.

SUBMITTER: Guo H

PROVIDER: S-EPMC4454201 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Biased random walk model for the prioritization of drug resistance associated proteins.

Guo Hao H Dong Jiaqiang J Hu Sijun S Cai Xiqiang X Tang Guangbo G Dou Jianhua J Tian Miaomiao M He Fuchu F Nie Yongzhan Y Fan Daiming D

Scientific reports 20150603

Multi-drug resistance is the main cause of treatment failure in cancer patients. How to identify molecules underlying drug resistance from multi-omics data remains a great challenge. Here, we introduce a data biased strategy, ProteinRank, to prioritize drug-resistance associated proteins in cancer cells. First, we identified differentially expressed proteins in Adriamycin and Vincristine resistant gastric cancer cells compared to their parental cells using iTRAQ combined with LC-MS/MS experiment ...[more]

PMID: 26039373

Similar Datasets

Project description:PurposeProstate motion during radiation therapy (ie, intrafraction motion) can cause unwanted loss of radiation dose to the prostate and increased dose to the surrounding organs at risk. A compact but general statistical description of this motion could be useful for simulation of radiation therapy delivery or margin calculations. We investigated whether prostate motion could be modeled with a random walk model.Methods and materialsProstate motion recorded during 548 radiation therapy fractions in 17 patients was analyzed and used for input in a random walk prostate motion model. The recorded motion was categorized on the basis of whether any transient excursions (ie, rapid prostate motion in the anterior and superior direction followed by a return) occurred in the trace and transient motion. This was separately modeled as a large step in the anterior/superior direction followed by a returning large step. Random walk simulations were conducted with and without added artificial transient motion using either motion data from all observed traces or only traces without transient excursions as model input, respectively.ResultsA general estimate of motion was derived with reasonable agreement between simulated and observed traces, especially during the first 5 minutes of the excursion-free simulations. Simulated and observed diffusion coefficients agreed within 0.03, 0.2 and 0.3 mm2/min in the left/right, superior/inferior, and anterior/posterior directions, respectively. A rapid increase in variance at the start of observed traces was difficult to reproduce and seemed to represent the patient's need to adjust before treatment. This could be estimated somewhat using artificial transient motion.ConclusionsRandom walk modeling is feasible and recreated the characteristics of the observed prostate motion. Introducing artificial transient motion did not improve the overall agreement, although the first 30 seconds of the traces were better reproduced. The model provides a simple estimate of prostate motion during delivery of radiation therapy.

Dataset Information

Biased random walk model for the prioritization of drug resistance associated proteins.

Publications

Biased random walk model for the prioritization of drug resistance associated proteins.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets