Dataset Information

Identification of therapeutic targets for inflammation in sickle cell disease (SCD) among Indian patients using gene expression data analysis.

ABSTRACT: Sickle cell disease (SCD) is life-threatening hemoglobinopathy prevalent in India, Sub-Saharan Africa and Middle East. Inflammation plays a pivotal role in disease process and involves intricate interaction among leukocytes, platelets, sickle erythrocytes and vascular endothelium. Available disease modifying therapies are hydroxyl-urea and blood transfusion. Therefore, it is of interest to develop improved pharmacological agents for SCD. We report up-regulated genes in steady state and vaso-occlusive crisis using analysis of gene expression data obtained by microarray experiment for SCD as potential targets. The association of these targets with inflammation in pathway analysis is also documented.

SUBMITTER: Das I

PROVIDER: S-EPMC6143353 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of therapeutic targets for inflammation in sickle cell disease (SCD) among Indian patients using gene expression data analysis.

Das Ipsita I Mishra Hrishikesh H Khodiar Prafulla K PK Patra Pradeep K PK

Bioinformation 20180731 7

Sickle cell disease (SCD) is life-threatening hemoglobinopathy prevalent in India, Sub-Saharan Africa and Middle East. Inflammation plays a pivotal role in disease process and involves intricate interaction among leukocytes, platelets, sickle erythrocytes and vascular endothelium. Available disease modifying therapies are hydroxyl-urea and blood transfusion. Therefore, it is of interest to develop improved pharmacological agents for SCD. We report up-regulated genes in steady state and vaso-occl ...[more]

PMID: 30262980

Similar Datasets

Project description:BackgroundTarget identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market.MethodsTo test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets.ResultsWe observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature.ConclusionsOur in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.

Project description:Esophageal squamous cell carcinoma (ESCC), the predominant histological subtype of esophageal cancer, is characterized by high mortality. Previous work identified important mRNA expression differences between normal and tumor cells; however, to date there are limited ex vivo studies examining expression changes occurring during normal esophageal squamous cell differentiation versus those associated with tumorigenesis. In this study, we used a unique tissue microdissection strategy and microarrays to measure gene expression profiles associated with cell differentiation versus tumorigenesis in twelve cases of patient-matched normal basal squamous epithelial cells (NB), normal differentiated squamous epithelium (ND), and squamous cell cancer. Class comparison and pathway analysis were used to compare NB versus tumor in a search for unique therapeutic targets.As a first step towards this goal, gene expression profiles and pathways were evaluated. Overall, ND expression patterns were markedly different from NB and tumor; whereas, tumor and NB were more closely related. Tumor showed a general decrease in differentially expressed genes relative to NB as opposed to ND that exhibited the opposite trend. FSH and IgG networks were most highly dysregulated in normal differentiation and tumorigenesis, respectively. DNA repair pathways were generally elevated in NB and tumor relative to ND indicating involvement in both normal and pathological growth. PDGF signaling pathway and 12 individual genes unique to the tumor/NB comparison were identified as therapeutic targets, and 10 associated ESCC gene-drug pairs were identified. We further examined the protein expression level and the distribution patterns of four genes: ODC1, POSTN, ASPA and IGF2BP3. Ultimately, three genes (ODC1, POSTN, ASPA) were verified to be dysregulated in the same pattern at both the mRNA and protein levels.These data reveal insight into genes and molecular pathways mediating ESCC development and provide information potentially useful in designing novel therapeutic interventions for this tumor type.

Dataset Information

Identification of therapeutic targets for inflammation in sickle cell disease (SCD) among Indian patients using gene expression data analysis.

Publications

Identification of therapeutic targets for inflammation in sickle cell disease (SCD) among Indian patients using gene expression data analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets