Generating evidence-backed hypotheses in silico for novel prostate cancer biomarkers, Part III

The story of how, within just a few weeks, a scientist can uncover plausible biomarkers for a complex disease using IPA® for in silico research. We invite you to register for a webinar to hear Dr. Billaud present this in silico study live on September 21 or to listen to a pre-recorded video recording of his presentation.

By Jean-Noel Billaud, Associate Staff Scientist, Ingenuity. September 13, 2011

(View Part 1 and Part 2)

Part III: Analysis of published mRNA data: Functions, networks and transcription factors

IPA has powerful analysis capabilities across any type of ‘omics data. I uploaded recent gene expression (mRNA) data sets from a leader in the field of PCa research. This study (which I’ll refer to throughout this series as Study 1) looked for a gene signature for PCa that represents potential biomarkers for this disease.

The data sets were deposited in GEO under GSE 22606. I would like to acknowledge publicly the authors (Dr. Heemers et. al.) of this elegant study published in Cancer Research, issue of March 1, 2011, PMID: 21324924.  This group identified a novel indirect mechanism of androgen action in which effects of androgens on PCa cells are mediated by the transcription factor (TF): Serum Response Factor or SRF. According to the authors: “The microarray data analysis focused on identifying genes which, for each replicate, show at least 2-fold androgen-dependent changes in expression, rely entirely on the presence of SRF for androgen-dependence, and show basal expression that is not affected by loss of SRF.” The cell line used for this study is LNcaP, which is an androgen-sensitive prostate adenocarcinoma cell line.

The analysis showed that 158 unique genes (178 probe sets) were modulated in an SRF-dependent manner after androgen treatment: 113 (131 probe sets) were upregulated, 45 (47 probe sets) were downregulated (see Figure 4). In the example shown in the Figure 5, IPA indicated that the top molecular and cellular functions were associated with well-described phenomenon or biological processes in tumorigenesis: cell cycle is affected, cellular movement and cellular morphology are also involved. Indeed the actin cytoskeleton is constantly reshaped and reorganized to help the tumor cell to divide or to migrate.

This analysis is strengthened by the transcription factor analysis in IPA (see Figure 5), which determines the overlap of the downstream genes of well-described transcription factors with the uploaded data set. This analysis linked, for instance, 5 genes (3 ribosomal proteins, another transcription factor CITED2 with a potential role in osteotropism, and CRIM1, a transmembrane protein involved in capillary formation and maintenance during angiogenesis) with myc, a major transcription factor involved in cell cycle progression, apoptosis and cellular transformation. This feature helped confirm and identify key factors in the tumor process.

In summary, using the Core Analysis and the Transcription Factor analysis features, IPA helped me understand the relevant biological processes and dissect important molecular pathways relevant to prostate cancer. Next week, I will to integrate an miRNA data set with mRNA expression data set by using the new microRNA Target Filter in IPA. This process will combine two independent studies in IPA and identify convergent and relevant key molecular aspects that may be useful for identifying potential biomarker for PCa.

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