Human BIRC7 blocking peptide

Baculoviral IAP repeat containing 7 (BIRC7/Livin/ML-IAP/KIAP; referred to as Livin throughout the present study) and placental growth factor (PlGF) are not detectable in the majority of normal differentiated tissues, but are present in a number of types of cancer, including hepatocellular carcinoma, ovarian cancer and renal cell carcinoma. The aim of the present study was to assess the expression levels of Livin and PlGF in human osteosarcoma specimens and cell lines, and to analyze the functions of Livin and PIGF in the prognosis of osteosarcoma. The expression levels of Livin and PlGF in 48 osteosarcoma specimens and three osteosarcoma cells were determined using immunohistochemistry and reverse transcription-quantitative polymerase chain reaction. The positivity rates of Livin and PlGF in osteosarcoma specimens were 58.3 and 60.4%, respectively, but were 0% in normal bone tissues. The expression levels of Livin and PlGF were increased in MG-63 cells, compared with those in the other cell lines evaluated in the present study. In addition, the expression levels of Livin and PlGF were significantly associated with tumor diameter and Enneking staging, but were independent of tumor site, age and sex of patients. The expression level of Livin was not associated with PlGF. Furthermore, the 5-year overall survival rate was decreased in the Livin or PlGF expression group, compared with that in the non-expression group (P=0.034 and P=0.012, respectively). The expression levels of Livin and PlGF were independent prognostic factors for patients with osteosarcoma. The results of the present study demonstrated that Livin and PlGF may participate in the pathogenesis of osteosarcoma. Therefore, pharmacological inhibition of Livin or PlGF may provide a novel strategy for osteosarcoma treatment.

Background: Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction prediction. Methodology/Principal Findings: Apoptosis pathway related genes were assembled from electronic databases. To assess expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were analysed using public databases and computational prediction of protein interactions. Gene expression data were implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further showed possible new interactions between the single pathway members, which demonstrate the complexity of the apoptosis pathway. Conclusions/Significance: Our data shows that by computational evaluation of public accessible data an acceptable virtual image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC.