Dataset Information

More Accurate Prediction of Metastatic Pancreatic Cancer Patients' Survival with Prognostic Model Using Both Host Immunity and Tumor Metabolic Activity.

ABSTRACT: Neutrophil to lymphocyte ratio (NLR) and standard uptake value (SUV) by 18F-FDG PET represent host immunity and tumor metabolic activity, respectively. We investigated NLR and maximum SUV (SUVmax) as prognostic markers in metastatic pancreatic cancer (MPC) patients who receive palliative chemotherapy.We reviewed 396 MPC patients receiving palliative chemotherapy. NLR was obtained before and after the first cycle of chemotherapy. In 118 patients with PET prior to chemotherapy, SUVmax was collected. Cut-off values were determined by ROC curve.In multivariate analysis of all patients, NLR and change in NLR after the first cycle of chemotherapy (?NLR) were independent prognostic factors for overall survival (OS). We scored the risk considering NLR and ?NLR and identified 4 risk groups with different prognosis (risk score 0 vs 1 vs 2 vs 3: OS 9.7 vs 7.9 vs 5.7 vs 2.6 months, HR 1 vs 1.329 vs 2.137 vs 7.915, respectively; P<0.001). In PET cohort, NLR and SUVmax were independently prognostic for OS. Prognostication model using both NLR and SUVmax could define 4 risk groups with different OS (risk score 0 vs 1 vs 2 vs 3: OS 11.8 vs 9.8 vs 7.2 vs 4.6 months, HR 1 vs 1.536 vs 2.958 vs 5.336, respectively; P<0.001).NLR and SUVmax as simple parameters of host immunity and metabolic activity of tumor cell, respectively, are independent prognostic factors for OS in MPC patients undergoing palliative chemotherapy.

SUBMITTER: Choi Y

PROVIDER: S-EPMC4699704 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

More Accurate Prediction of Metastatic Pancreatic Cancer Patients' Survival with Prognostic Model Using Both Host Immunity and Tumor Metabolic Activity.

Choi Younak Y Oh Do-Youn DY Park Hyunkyung H Kim Tae-Yong TY Lee Kyung-Hun KH Han Sae-Won SW Im Seock-Ah SA Kim Tae-You TY Bang Yung-Jue YJ

PloS one 20160104 1

<h4>Introduction</h4>Neutrophil to lymphocyte ratio (NLR) and standard uptake value (SUV) by 18F-FDG PET represent host immunity and tumor metabolic activity, respectively. We investigated NLR and maximum SUV (SUVmax) as prognostic markers in metastatic pancreatic cancer (MPC) patients who receive palliative chemotherapy.<h4>Methods</h4>We reviewed 396 MPC patients receiving palliative chemotherapy. NLR was obtained before and after the first cycle of chemotherapy. In 118 patients with PET prior ...[more]

PMID: 26727470

Similar Datasets

Project description:Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.

Dataset Information

More Accurate Prediction of Metastatic Pancreatic Cancer Patients' Survival with Prognostic Model Using Both Host Immunity and Tumor Metabolic Activity.

Publications

More Accurate Prediction of Metastatic Pancreatic Cancer Patients' Survival with Prognostic Model Using Both Host Immunity and Tumor Metabolic Activity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets