Analysis of metabolites and metabolic pathways in breast cancer in a Korean prospective cohort: the Korean Cancer Prevention Study-II.

INTRODUCTION: Since blood is in contact with all tissues in the body and is considered to dynamically reflect the body's pathophysiological status, serum metabolomics changes are important and have diagnostic value in early cancer detection. OBJECTIVES: In this prospective study, we investigated the application of metabolomics to differentiate subjects with incident breast cancer (BC) from subjects who remained free of cancer during a mean follow-up period of 7 years with the aim of identifying valuable biomarkers for BC. METHODS: Baseline serum samples from 84 female subjects with incident BC (BC group) and 88 cancer-free female subjects (control group) were used. Metabolic alterations associated with BC were investigated via metabolomics analysis of the baseline serum samples using ultra-performance liquid chromatography-linear-trap quadrupole-Orbitrap mass spectrometry. RESULTS: A total of 57 metabolites were identified through the metabolic analysis. Among them, 20 metabolite levels were significantly higher and 22 metabolite levels were significantly lower in the BC group than in the control group at baseline. Ten metabolic pathways, including amino acid metabolism, arachidonic acid (AA) metabolism, fatty acid metabolism, linoleic acid metabolism, and retinol metabolism, showed significant differences between the BC group and the control group. Logistic regression revealed that the incidence of BC was affected by leucine, AA, prostaglandin (PG)J2, PGE2, and γ-linolenic acid (GLA). CONCLUSIONS: This prospective study showed the clinical relevance of dysregulation of various metabolisms on the incidence of BC. Additionally, leucine, AA, PGJ2, PGE2, and GLA were identified as independent variables affecting the incidence of BC.

On Constructing Seminal Paper Genealogy.

Let us consider that someone is starting a research on a topic that is unfamiliar to them. Which seminal papers have influenced the topic the most? What is the genealogy of the seminal papers in this topic? These are the questions that they can raise, which we try to answer in this paper. First, we propose an algorithm that finds a set of seminal papers on a given topic. We also address the performance and scalability issues of this sophisticated algorithm. Next, we discuss the measures to decide how much a paper is influenced by another paper. Then, we propose an algorithm that constructs a genealogy of the seminal papers by using the influence measure and citation information. Finally, through extensive experiments with a large volume of a real-world academic literature data, we show the effectiveness and efficiency of our approach.