Literature DB >> 30591553

A Large-Cohort, Longitudinal Study Determines Precancer Disease Routes across Different Cancer Types.

Jessica X Hu1, Marie Helleberg2, Anders B Jensen1, Søren Brunak3,4, Jens Lundgren5.   

Abstract

Although many diseases are associated with cancer, the full spectrum of temporal disease correlations across cancer types has not yet been characterized. A population-wide study of longitudinal disease trajectories is needed to interrogate the general medical histories of patients with cancer. Here we performed a retrospective study covering a 20-year period, using 6.9 million patients from the Danish National Patient Registry linked to 0.7 million patients with cancer from the Danish Cancer Registry. Statistical analysis identified all significant disease associations occurring prior to cancer diagnoses. These associations were used to build frequently occurring, longitudinal disease trajectories. Across 17 cancer types, a total of 648 significant diagnoses correlated directly with a cancer, while 168 diagnosis trajectories of time-ordered steps were identified for seven cancer types. The most common diseases across cancer types involved cardiovascular, obesity, and genitourinary diseases. A comprehensive, publicly available web tool of interactive illustrations for all cancer disease associations is provided. By exploring the precancer landscape using this large dataset, we identify disease associations that can be used to derive mechanistic hypotheses for future cancer research. SIGNIFICANCE: This study offers an innovative approach to examine prediagnostic disease and cancer development in a large national population-based setting and provides a publicly available tool to foster additional cancer surveillance research. ©2018 American Association for Cancer Research.

Entities:  

Year:  2018        PMID: 30591553     DOI: 10.1158/0008-5472.CAN-18-1677

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  4 in total

1.  Transcriptomic signals in blood prior to lung cancer focusing on time to diagnosis and metastasis.

Authors:  Therese H Nøst; Marit Holden; Tom Dønnem; Hege Bøvelstad; Charlotta Rylander; Eiliv Lund; Torkjel M Sandanger
Journal:  Sci Rep       Date:  2021-04-01       Impact factor: 4.379

2.  Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model.

Authors:  Kadri Künnapuu; Solomon Ioannou; Kadri Ligi; Raivo Kolde; Sven Laur; Jaak Vilo; Peter R Rijnbeek; Sulev Reisberg
Journal:  JAMIA Open       Date:  2022-03-16

Review 3.  Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.

Authors:  Barbara Kenner; Suresh T Chari; David Kelsen; David S Klimstra; Stephen J Pandol; Michael Rosenthal; Anil K Rustgi; James A Taylor; Adam Yala; Noura Abul-Husn; Dana K Andersen; David Bernstein; Søren Brunak; Marcia Irene Canto; Yonina C Eldar; Elliot K Fishman; Julie Fleshman; Vay Liang W Go; Jane M Holt; Bruce Field; Ann Goldberg; William Hoos; Christine Iacobuzio-Donahue; Debiao Li; Graham Lidgard; Anirban Maitra; Lynn M Matrisian; Sung Poblete; Laura Rothschild; Chris Sander; Lawrence H Schwartz; Uri Shalit; Sudhir Srivastava; Brian Wolpin
Journal:  Pancreas       Date:  2021-03-01       Impact factor: 3.243

4.  Characterisation, identification, clustering, and classification of disease.

Authors:  A J Webster; K Gaitskell; I Turnbull; B J Cairns; R Clarke
Journal:  Sci Rep       Date:  2021-03-08       Impact factor: 4.379

  4 in total

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