1,720,984 research outputs found

    Prognostic role of clusterin in resected adenocarcinomas of the lung

    No full text
    Prognostic role of clusterin in resected adenocarcinomas of the lung. Panico F, Casali C, Rossi G, Rizzi F, Morandi U, Bettuzzi S, Davalli P, Corbetta L, Storelli ES, Corti A, Fabbri LM, Astancolle S, Luppi F. Source Section of Respiratory Diseases, Department of Oncology, Haematology & Pulmonology, University of Modena and Reggio Emilia, Modena, Italy. Abstract RATIONALE: Clusterin expression may change in various human malignancies, including lung cancer. Patients with resectable non-small cell lung cancer (NSCLC), including adenocarcinoma, have a poor prognosis, with a relapse rate of 30-50% within 5 years. Nuclear factor kB (Nf-kB) is an intracellular protein involved in the initiation and progression of several human cancers, including the lung. OBJECTIVES: We investigate the role of clusterin and Nf-kB expression in predicting the prognosis of patients with early-stage surgically resected adenocarcinoma of the lung. FINDINGS: The level of clusterin gradually decreased from well-differentiated to poorly differentiated adenocarcinomas. Clusterin expression was significantly higher in patients with low-grade adenocarcinoma, in early-stage disease and in women. Clusterin expression was inversely related to relapse and survival in both univariate and multivariate analyses. Finally, we observed an inverse correlation between Nf-kB and clusterin. CONCLUSIONS: Clusterin expression represents an independent prognostic factor in surgically resected lung adenocarcinoma and was proven to be a useful biomarker for fewer relapses and longer survival in patients in the early stage of disease. The inverse correlation between Nf-kB and clusterin expression confirm the previously reported role of clusterin as potent down regulator of Nf-kB

    Going Beyond Counting First Authors in Author Co-citation Analysis

    Full text link
    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
    corecore