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      Aberrant FAM64A mRNA expression is an independent predictor of poor survival in pancreatic cancer

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      1 , 2 , 3 , 1 , 1 , *
      PLoS ONE
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          Abstract

          FAM64A, a marker of cell proliferation, has been investigated as a potential biomarker in several cancers. In the present study, we examined the value of FAM64A expression in the diagnosis and prognosis of pancreatic cancer through bioinformatics analysis of data obtained from The Cancer Genome Atlas (TCGA) database. The diagnostic value of FAM64A expression in pancreatic cancer tissue was deteremined through receiver operating characteristic (ROC) curve analysis, and based on the obtained cut-off value, patients were divided into two groups (high FAM64A expression and low FAM64A expression). Chi-square and Fisher exact tests were applied to identify associations between FAM64A expression and clinical features. Moreover, the effect of FAM64A expression in the survival of pancreatic cancer patients was observed by Kaplan-Meier and Cox analyses. Gene set enrichment analysis (GSEA) was performed using the TCGA dataset. Our results showed that high FAM64A expression in pancreatic cancer was associated with survival status, overall survival (OS), and recurrence. The area under the ROC curve was 0.736, which indicated modest diagnostic value. Patients with higher FAM64A expression had significantly shorter OS and recurrence-free survival (RFS) times. Multivariate survival analysis demonstrated that high FAM64A expression was an independent risk factor for OS and RFS. GSEA identified mitotic spindles, myc targets, MTORC1 signaling, G2M checkpoint, E2F targets, DNA repair, glycolysis and unfolded protein response as differentially enriched with the high FAM64A expression phenotype. In conclusion, high FAM64A mRNA expression is an independent risk factor for poor prognosis in pancreatic cancer.

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          Modeling Survival Data: Extending the Cox Model

          This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.
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            Challenges in diagnosis of pancreatic cancer

            Pancreatic cancer is a growing source of cancer related death, yet has poor survival rates which have not improved in the last few decades. Its high mortality rate is attributed to pancreatic cancer biology, difficulty in early diagnosis and the lack of standardised international guidelines in assessing suspicious pancreatic masses. This review aims to provide an update in the current state of play in pancreatic cancer diagnosis and to evaluate the benefits and limitations of available diagnostic technology. The main modalities discussed are imaging with computed tomography, magnetic resonance imaging, endoscopic ultrasound and positron emission tomography and tissue acquisition with fine needle aspiration. We also review the improvements in the techniques used for tissue acquisition and the opportunity for personalised cancer medicine. Screening of high risk individuals, promising biomarkers and common mimickers of pancreatic cancer are also explored, as well as suggestions for future research directions to allow for earlier detection of pancreatic cancer. Timely and accurate diagnosis of pancreatic cancer can lead to improvements in the current poor outcome of this disease.
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              The novel CALM interactor CATS influences the subcellular localization of the leukemogenic fusion protein CALM/AF10.

              The Clathrin Assembly Lymphoid Myeloid leukemia gene (CALM or PICALM) was first identified as the fusion partner of AF10 in the t(10;11)(p13;q14) translocation, which is observed in acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL) and malignant lymphoma. The CALM/AF10 fusion protein plays a crucial role in t(10;11)(p13;q14) associated leukemogenesis. Using the N-terminal half of CALM as a bait in a yeast two-hybrid screen, a novel protein named CATS (CALM interacting protein expressed in thymus and spleen) was identified. Multiple tissue Northern blot analysis showed predominant expression of CATS in thymus, spleen and colon. CATS codes for two protein isoforms of 238 and 248 amino acids (aa). The interaction between CALM and CATS could be confirmed using pull down assays, co-immunoprecipitation and colocalization experiments. The CATS interaction domain of CALM was mapped to aa 221-335 of CALM. This domain is contained in the CALM/AF10 fusion protein. CATS localizes to the nucleus and shows a preference for nucleoli. Expression of CATS was able to markedly increase the nuclear localization of CALM and of the leukemogenic fusion protein CALM/AF10. The possible implications of these findings for CALM/AF10-mediated leukemogenesis are discussed.
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                Author and article information

                Contributors
                Role: Data curationRole: MethodologyRole: VisualizationRole: Writing – original draft
                Role: Writing – original draft
                Role: Formal analysis
                Role: Investigation
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 January 2019
                2019
                : 14
                : 1
                : e0211291
                Affiliations
                [1 ] Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, Jilin, P.R. China
                [2 ] Department of Hand and Foot surgery, The First Hospital of Jilin University, Changchun, Jilin, P.R. China
                [3 ] Department of Pathophysiology, College of Basic Medical Sciences, Jilin University, Changchun, Jilin, P.R. China
                University of South Alabama Mitchell Cancer Institute, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-4018-5404
                Article
                PONE-D-18-30380
                10.1371/journal.pone.0211291
                6351057
                30695070
                06c40b12-673a-47bc-8d76-69f900683dd1
                © 2019 Jiao et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 21 October 2018
                : 10 January 2019
                Page count
                Figures: 5, Tables: 5, Pages: 13
                Funding
                The authors received no specific funding for this work.
                Categories
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                Medicine and Health Sciences
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                Cancers and Neoplasms
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