Tumor growth factor-beta (TGF-beta) is a key mediator of glomerular and tubulointerstitial pathobiology in chronic kidney disease. Its signaling transduction controls a diverse number of biological processes in a dynamic and context-dependent manner. We applied a data mining strategy to deconvolute gene expression patterns across hundreds of microarray data sets to reveal members of the TGF-beta signaling network in human kidney. This strategy is composed of three major steps: (i) select genes known to be involved and expressionally regulated in TGF-beta signaling as "bait"; (ii) select microarray data sets in which the bait genes are strongly co-regulated; (iii) identify (or "fish") additional TGF-beta signaling genes by a non-parametric statistic-based gene scoring system (NP score). The 40 genes with highest NP scores and significant permutation p values were selected for in silico validation, and used to identify a network, in which 35 of these genes were found to be connected by literature- derived relationships. Transcription factors were found to be enriched in the top list. Among them, activated transcription factor 3 (ATF3) had the highest NP score, and was proposed to play a pivotal role in TGF-beta signaling in human kidney. Finally, we implemented a non-parametric pathway ranking (NPPR) tool (Mootha et al., 2003) to rank pathways and identified canonical biological pathways associated with the down-stream of TGF-beta signaling.