VALIDATION OF A RAT MODEL FOR ANALYZING MicroRNA IN CHONDROSARCOMA

Purpose: We compared the expression of microRNAs (miRNAs) in rat chondrosarcoma relative to normal rat cartilage with miRNA expression in human chondrosarcoma relative to normal human cartilage. We did this in order to answer the question: Are the miRNA profiles of chondrosarcoma in humans and rats similar enough to allow for the rat model to be valid for study of chondrosarcoma in humans? Methods: Twenty rats were sacrificed, cartilage was collected, RNA extracted and 233 miRNAs analyzed. The expression of these miRNAs was then compared to their expression in normal human tissue and chondrosarcoma. Integrative correlation coefficients were applied to the data in whole to measure the similarity between the rat and human miRNA. Results: For normal human versus normal rat cartilage the integrative correlation coefficient was 0.84 (95% CI: 0.82–0.87). The integrative correlation coefficient for chondrosarcoma in human versus rat tissue was 0.70 (95% CI: 0.65–0.74). Conclusions: Our findings suggest that human and rat normal cartilage, as well as chondrosarcoma genetics, are sufficiently similar for use of rat models to study chondrosarcoma (integrative correlation coefficient [Formula: see text]). We also revealed similarities between specific miRNA, especially those with the most similar fold-change (miR26b, 126, 145, 195, and 320).

MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data

Cross-study validation of gene expression investigations is critical in genomic analysis. We developed an R package and associated object definitions to merge and visualize multiple gene expression datasets. Our merging functions use arbitrary character IDs and generate objects that can efficiently support a variety of joint analyses. Visualization tools support exploration and cross-study validation of the data, without requiring normalization across platforms. Tools include “integrative correlation" plots that is, scatterplots of all pairwise correlations in one study against the corresponding pairwise correlations of another, both for individual genes and all genes combined. Gene-specific plots can be used to identify genes whose changes are reliably measured across studies. Visualizations also include scatterplots of gene-specific statistics quantifying relationships between expression and phenotypes of interest, using linear, logistic and Cox regression.