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Simple Summary There is a tremendous amount of gene expression information available for prostate cancer, but very few tools exist to combine the disparate datasets generated across sample types and technical platforms. We present a method of integrating different types of expression data from different study cohorts to increase analytic power, and improve our understanding of the molecular changes underlying the development and progression of prostate cancer from normal to advanced disease. Using this approach, we identified nine additional disease stage-specific candidate genes with prognostic significance, which were not identified in any one study alone. We have developed a free online tool summarizing our results, and making the complete combined dataset available for further translational research. Abstract Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research.

Obesity-induced adipose inflammation has been demonstrated to be a key cause of insulin resistance. Peptides derived from bovine α-lactalbumin have been shown to inhibit the activities of dipeptidyl peptidase IV (DPP-IV) and angiotensin converting enzyme (ACE), scavenge 2,2′-azinobis [3-ethylbenzothiazoline-6-sulfonate] (ABTS+) radical and stimulate glucagon-like peptide-2 secretion. In the present study, the effects of bovine α-lactalbumin hydrolysates (α-LAH) on adipose insulin resistance and inflammation induced by high-fat diet (HFD) were investigated. The insulin resistance model was established by feeding C57BL/6J mice with HFD (60% kcal from fat) for eight weeks. Then, the mice were fed with HFD and bovine α-LAH of different doses (100 mg/kg b.w., 200 mg/kg b.w. and 400 mg/kg b.w.) for another 12 weeks to evaluate its protective effects against HFD-induced insulin resistance. The oral glucose tolerance test (OGTT) and intraperitoneal insulin tolerance test (ipITT) were conducted after intervention with α-LAH for 10 weeks and 11 weeks, respectively. Results showed that bovine α-LAH significantly reduced body weight, blood glucose, serum insulin, and HOMA-IR (homeostatic model assessment of insulin resistance) levels, lowered the area-under-the-curve (AUC) during OGTT and ipITT, and downregulated inflammation-related gene [tumor necrosis factor (TNF)-α, interleukin (IL)-6, monocyte chemoattractant protein (MCP)-1] expression in adipose tissues of HFD-fed C57BL/6J mice. Furthermore, bovine α-LAH also suppressed insulin receptor substrate 1 (IRS-1) serine phosphorylation (Ser307, Ser612), enhanced protein kinase B (known as Akt) phosphorylation, and inhibited the activation of inhibitor of kappaB kinase (IKK) and mitogen activated protein kinase (MAPK) signaling pathways in adipose tissues of HFD-fed C57BL/6J mice. These results suggested that bovine α-LAH could ameliorate adipose insulin resistance and inflammation through IKK and MAPK signaling pathways in HFD-fed C57BL/6J mice.