Evidence map›Paper›PMID 32448176›Full record

ReviewBMC cancer2020

Text mining in a literature review of urothelial cancer using topic model.

Hsuan-Jen Lin, Phillip C-Y Sheu, Jeffrey J P Tsai, Charles C N Wang, Che-Yi Chou

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

8 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Article
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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

5 authors at 1 institution in 2 countries.

Hsuan-Jen LinDepartment of Biomedical Informatics, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, Taiwan.
Phillip C-Y SheuDepartment of Biomedical Informatics, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, Taiwan.
Jeffrey J P TsaiDepartment of Biomedical Informatics, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, Taiwan.
Charles C N WangDepartment of Biomedical Informatics, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, Taiwan.
Che-Yi ChouDivision of Nephrology, Asia University Hospital, Taichung, Taiwan. cychou.chou@gmail.com.ORCID http://orcid.org/0000-0003-4029-9097
Asia University · TW

Funding

Asia University ASIA-107-AUH-01
6 · The paper itself

Abstract

backgroundUrothelial cancer (UC) includes carcinomas of the bladder, ureters, and renal pelvis. New treatments and biomarkers of UC emerged in this decade. To identify the key information in a vast amount of literature can be challenging. In this study, we use text mining to explore UC publications to identify important information that may lead to new research directions.

methodWe used topic modeling to analyze the titles and abstracts of 29,883 articles of UC from Pubmed, Web of Science, and Embase in Mar 2020. We applied latent Dirichlet allocation modeling to extract 15 topics and conducted trend analysis. Gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to identify UC related pathways.

resultsThere was a growing trend regarding UC treatment especially immune checkpoint therapy but not the staging of UC. The risk factors of UC carried in different countries such as cigarette smoking in the United State and aristolochic acid in Taiwan and China. GMCSF, IL-5, Syndecan-1, ErbB receptor, integrin, c-Met, and TRAIL signaling pathways are the most relevant biological pathway associated with UC.

conclusionsThe risk factors of UC may be dependent on the countries and GMCSF, IL-5, Syndecan-1, ErbB receptor, integrin, c-Met, and TRAIL signaling pathways are the most relevant biological pathway associated with UC. These findings may provide further UC research directions.

Indexed as

Models, TheoreticalData MiningHumansKidney PelvisPrognosisRisk FactorsUrologic NeoplasmsLDA2vecResearch trendsText miningTopic modelingUrothelial carcinoma

Identifiers

PMID32448176
PMCPMC7245902
OpenAlexW3027680453

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.