Research Article Prognosis Relevance of Serum Cytokines in Pancreatic Cancer Carolina Torres, 1 Ana Linares, 1 Maria José Alejandre, 1 Rogelio J. Palomino-Morales, 1 Octavio Caba, 2 Jose Prados, 3 Antonia Aránega, 3 Juan R. Delgado, 4 Antonio Irigoyen, 4 Joaquina Martínez-Galán, 4 Francisco M. Ortuño, 5 Ignacio Rojas, 5 and Sonia Perales 1 1 Department of Biochemistry and Molecular Biology I, University of Granada, 18071 Granada, Spain 2 Department of Health Sciences, University of Jaen, 23071 Jaen, Spain 3 Department of Human Anatomy and Embryology, University of Granada, 18012 Granada, Spain 4 Oncology Service, Virgen de las Nieves Hospital, 18014 Granada, Spain 5 Department of Computer Architecture and Computer Technology (CITIC-UGR), University of Granada, 18071 Granada, Spain Correspondence should be addressed to Carolina Torres; ctp@ugr.es Received 4 November 2014; Revised 5 January 2015; Accepted 12 January 2015 Academic Editor: Wen-Bin Wu Copyright © 2015 Carolina Torres et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te 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 refect signifcant improvements, not even when combined with adjuvant treatments. Tere is an urgent need for prognosis markers to be found. Te aim of this study was to analyze the potential value of serum cytokines to fnd a profle that can predict the clinical outcome in patients with pancreatic cancer and to establish a practical prognosis index that signifcantly 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. Te 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. 1. Introduction Pancreatic ductal adenocarcinoma (PDAC) accounts for only 2.68% of all cancers, but it represents the fourth leading cancer-related death worldwide just remaining afer lung and bronchus, prostate, and colorectum cancers in men and afer lung and bronchus, breast, and colorectum cancers in women [1]. Te dreadful prognosis of patients with this disease, less than 5% reaching 5 years of survival afer diagnosis, is due to the little impact of the available chemotherapy on the course of the disease and to tumor metastasis at presentation. Te development of the disease is a result of a complex and does not yet fully understood process encompassing the accumulation of mutations and the alteration of multiple pathways. Tis could partly explain the clinical heterogeneity of this disease and the great diference seen in the outcomes between individual patients. Tereby, there is a trend towards tailored therapies to specifc genetic characteristics of indi- vidual tumors, not only for PDAC but also for the majority of the cancers [2, 3]. Troughout past years there has not been remarkable survival improvement in PDAC patients; consequently it is urgent that novel biomarkers are identifed for PDAC in order to reduce its mortality rate [4, 5]. As defned by the NIH Biomarker Working Group, a biological marker (biomarker) is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention [6]. In PDAC, three types of biomarkers are desirable: those that help in the detection of the disease onset (diagnosis biomarkers); those that predict responses to treatments (predictive biomarkers); and those that forecast the likely course of the disease, Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 518284, 12 pages http://dx.doi.org/10.1155/2015/518284