Evidence map›Paper›PMID 25392682›Full record

ArticleCancer informatics2014

Mining cancer-specific disease comorbidities from a large observational health database.

Yang Chen, Rong Xu

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Applications of network analysis to routinely collected health care data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018
    Pooled it
  2. Article
  3. Comorbidity in Older Patients Hospitalized with Cancer in Northeast China based on Hospital Discharge Data.International journal of environmental research and public health · 2020
    Article
  4. Article
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  8. Interrogating Patient-level Genomics and Mouse Phenomics towards Understanding Cytokines in Colorectal Cancer Metastasis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017
    Article
  9. Article
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  12. Disease Comorbidity Network Guides the Detection of Molecular Evidence for the Link Between Colorectal Cancer and Obesity.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015
    Article
  13. Multimorbidity and mortality: A data science perspective.Journal of multimorbidity and comorbidity
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Yang ChenDivision of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.
Rong XuDivision of Medical Informatics, Case Western Reserve University, Cleveland, OH, USA.
Case Western Reserve University · US

Funding

CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR024989 · NCRR · CASE WESTERN RESERVE UNIVERSITY · PI DAVIS, PAMELA B · 2007 to 2011
$53.7M
TRAINING IN COMPUTATIONAL GENOMIC EPIDEMIOLOGY OF CANCERR25CA094186 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI LI, LI · 2002 to 2016
$6.8M
NCI NIH HHS R25 CA094186NCRR NIH HHS UL1 RR024989
6 · The paper itself

Abstract

Cancer comorbidities often reflect the complex pathogenesis of cancers and provide valuable clues to discover the underlying genetic mechanisms of cancers. In this study, we systematically mine and analyze cancer-specific comorbidity from the FDA Adverse Event Reporting System. We stratified 3,354,043 patients based on age and gender, and developed a network-based approach to extract comorbidity patterns from each patient group. We compared the comorbidity patterns among different patient groups and investigated the effect of age and gender on cancer comorbidity patterns. The results demonstrated that the comorbidity relationships between cancers and non-cancer diseases largely depend on age and gender. A few exceptions are depression, anxiety, and metabolic syndrome, whose comorbidity relationships with cancers are relatively stable among all patients. Literature evidences demonstrate that these stable cancer comorbidities reflect the pathogenesis of cancers. We applied our comorbidity mining approach on colorectal cancer and detected its comorbid associations with metabolic syndrome components, diabetes, and osteoporosis. Our results not only confirmed known cancer comorbidities but also generated novel hypotheses, which can illuminate the common pathophysiology between cancers and their co-occurring diseases.

Indexed as

cancer comorbiditycancer informaticscolorectal cancerdata miningnetwork analysissystems biology

Identifiers

PMID25392682
PMCPMC4216041
OpenAlexW2023575634

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.