Biomedical subjects

Diogo F C Patrão

Publications and source records attributed to Diogo F C Patrão.

3 recordsLinked to original sources

PubMed2006

BayBoots: a model-free Bayesian tool to identify class markers from gene expression data.

One of the goals of gene expression experiments is the identification of differentially expressed genes among populations that could be used as markers. For this purpose, we implemented a model-free Bayesian approach in a user-friendly and freely available web-based tool called BayBoots. In spite of a common misunderstanding that Bayesian and model-free approaches are incompatible, we merged them in the BayBoots implementation using the Kernel density estimator and Rubin 's Bayesian Bootstrap. We used the Bayes error rate (BER) instead of the usual P values as an alternative statistical index to rank a class marker's discriminative potential, since it can be visualized by a simple graphical representation and has an intuitive interpretation. Subsequently, Bayesian Bootstrap was used to assess BER 's credibility. We tested BayBoots on microarray data to look for markers for Trypanosoma cruzi strains isolated from cardiac and asymptomatic patients. We found that the three most frequently used methods in microarray analysis: t-test, non-parametric Wilcoxon test and correlation methods, yielded several markers that were discarded by a time-consuming visual check. On the other hand, the BayBoots graphical output and ranking was able to automatically identify markers for which classification performance was consistent. BayBoots is available at: http://www.vision.ime.usp.br/~rvencio/BayBoots.

Animals↗

PubMed2006

JDP1 (DNAJC12/Hsp40) expression in breast cancer and its association with estrogen receptor status.

The members of the DnaJ/Hsp40 proteins are highly conserved through evolution, expressed in several tissues and act as co-chaperone regulating protein folding, transport, translational initiation and gene expression. Recently, using cDNA microarray we identified differences in the expression of the JDP1 (DNAJC12) gene, a member of the DnaJ/Hsp40 family, between ER-positive and ER-negative breast tumours. In this study, using quantitative real-time PCR (qPCR) we evaluated the expression pattern of the JDP1 gene in a series of 72 primary breast tumours and investigated the effects of 17beta-estradiol on the expression of the JDP1 in MCF-7 breast cancer cells. Three patterns of JDP1 mRNA expression were identified in the primary breast tumours analysed: normal expression was found in 14% of the cases, under-expression in 50%, and over-expression in 36% of the cases. High levels of JDP1 mRNA expression were significantly associated with estrogen receptor-positive status (p=0.02). No relationship was found between JDP1 mRNA expression and any other clinicopathological characteristics of the patients. Sequence analysis of the promoter region of the JDP1 gene revealed the presence of potential estrogen response elements (EREs), suggesting it to be under the control of estrogen action. We also assessed the effects of 17beta-estradiol (10 nM) on JDP1 mRNA expression in MCF-7 breast cancer cells. The JDP1 transcripts were found to be up-regulated in a time-dependent fashion in MCF-7 cells exposed to 17beta-estradiol treatment. Here we show for the first time that JDP1 is a estrogen target gene and that its expression might be used as a marker of the ER transactivation activity and may have a predictive value for response to hormonal therapy.

Adult↗

PubMed2004

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE).

BACKGROUND: An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. RESULTS: We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. CONCLUSION: Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

Astrocytoma↗