The impact of PKR activation: from neurodegeneration to cancer.

An inverse association between cancer and neurodegeneration is plausible because these biological processes share several genes and signaling pathways. Whereas uncontrolled cell proliferation and decreased apoptotic cell death governs cancer, excessive apoptosis contributes to neurodegeneration. Protein kinase R (PKR), an interferon-inducible double-stranded RNA protein kinase, is involved in both diseases. PKR activation blocks global protein synthesis through eIF2α phosphorylation, leading to cell death in response to a variety of cellular stresses. However, PKR also has the dual role of activating the nuclear factor κ-B pathway, promoting cell proliferation. Whereas PKR is recognized for its negative effects on neurodegenerative diseases, in part, inducing high level of apoptosis, the role of PKR activation in cancer remains controversial. In general, PKR is considered to have a tumor suppressor function, and some clinical data show a correlation between suppressed or inactivated PKR and a poor prognosis for several cancers. However, other studies show high PKR expression and activation levels in various cancers, suggesting that PKR might contribute to neoplastic progression. Understanding the cellular factors and signals involved in the regulation of PKR in these age-related diseases is relevant and may have important clinical implications. The present review highlights the current knowledge on the role of PKR in neurodegeneration and cancer, with special emphasis on its regulation and clinical implications.

Transcriptional profiling of peripheral blood in pancreatic adenocarcinoma patients identifies diagnostic biomarkers.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy associated with poor survival rates. Fast detection of PDAC appears to be the most relevant strategy to improve the long-term survival of patients. AIMS: Our objective was to identify new markers in peripheral blood that differentiates between PDAC patients and healthy controls. METHODS: Peripheral blood samples from PDAC patients (n = 18) and controls (n = 18) were analyzed by whole genome cDNA microarray hybridization. The most relevant genes were validated by quantitative real-time PCR (RT-qPCR) in the same set of samples. Finally, our gene prediction set was tested in a blinded set of new peripheral blood samples (n = 30). RESULTS: Microarray studies identified 87 genes differentially expressed in peripheral blood samples from PDAC patients. Four of these genes were selected for analysis by RT-qPCR, which confirmed the previously observed changes. In our blinded validation study, the combination of CLEC4D and IRAK3 predicted the diagnosis of PDAC with 93 % accuracy, with a sensitivity of 86 % and specificity of 100 %. CONCLUSIONS: Peripheral blood gene expression profiling is an useful tool for the diagnosis of PDAC. We present a validated four-gene predictor set (ANKRD22, CLEC4D, VNN1, and IRAK3) that may be useful in PDAC diagnosis.

Prognosis Relevance of Serum Cytokines in Pancreatic Cancer.

The overall survival of patients with pancreatic ductal adenocarcinoma is extremely low. Although gemcitabine is the standard used chemotherapy for this disease, clinical outcomes do not reflect significant improvements, not even when combined with adjuvant treatments. There is an urgent need for prognosis markers to be found. The aim of this study was to analyze the potential value of serum cytokines to find a profile that can predict the clinical outcome in patients with pancreatic cancer and to establish a practical prognosis index that significantly predicts patients' outcomes. We have conducted an extensive analysis of serum prognosis biomarkers using an antibody array comprising 507 human cytokines. Overall survival was estimated using the Kaplan-Meier method. Univariate and multivariate Cox's proportional hazard models were used to analyze prognosis factors. To determine the extent that survival could be predicted based on this index, we used the leave-one-out cross-validation model. The multivariate model showed a better performance and it could represent a novel panel of serum cytokines that correlates to poor prognosis in pancreatic cancer. B7-1/CD80, EG-VEGF/PK1, IL-29, NRG1-beta1/HRG1-beta1, and PD-ECGF expressions portend a poor prognosis for patients with pancreatic cancer and these cytokines could represent novel therapeutic targets for this disease.

Serum cytokine profile in patients with pancreatic cancer.

OBJECTIVE: Pancreatic ductal adenocarcinoma is a deadly disease because of late diagnosis and chemoresistance. We aimed to find a panel of serum cytokines representing diagnostic and predictive biomarkers for pancreatic cancer. METHODS: A cytokine antibody array was performed to simultaneously identify 507 cytokines in sera of patients with pancreatic cancer and healthy controls. The nonparametric Mann-Whitney U test was used to pairwise compare the controls, the pretreated patients, and the posttreated patients. Fold changes greater than or equal to 1.5 or less than or equal to 1/1.5 were considered significant. Receiver operating characteristic curves were used to assess the performance of the model. A leave-one-out cross-validation was used for estimating prediction error. RESULTS: Comparing the sera of pretreated patients against the control samples, the cytokines fibroblast growth factor 10 (FGF-10/keratinocyte growth factor-2 (KGF-2), chemokine (C-X-C motif) ligand 11 interferon inducible T cell alpha chemokine (I-TAC)/chemokine [C-X-C motif] ligand 11 (CXCL11), oncostatin M (OSM), osteoactivin/glycoprotein nonmetastatic melanoma protein B, and stem cell factor (SCF) were found significantly overexpressed. Besides, the cytokines CD30 ligand/tumor necrosis factor superfamily, member 8 (TNFSF8), chordin-like 2, FGF-10/KGF-2, growth/differentiation factor 15, I-TAC/CXCL11, OSM, and SCF were differentially expressed in response to treatment. CONCLUSIONS: We propose a role for FGF-10/KGF-2, I-TAC/CXCL11, OSM, osteoactivin/glycoprotein nonmetastatic melanoma protein B, and SCF as novel diagnostic biomarkers. CD30 ligand/TNFSF8, chordin-like 2, FGF-10/KGF-2, growth/differentiation factor 15, I-TAC/CXCL11, OSM, and SCF might represent as predictive biomarkers for gemcitabine and erlotinib response of patients with pancreatic cancer.