Evidence map›Paper›PMID 28693282›Full record

ArticleOncology letters2017

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis.

Dong-Qing Zhang, Chang-Kuo Zhou, Shou-Zhen Chen, Yue Yang, Ben-Kang Shi

Abstract read
In one paragraph

Article in Oncology letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

5 authors.

Dong-Qing ZhangDepartment of Urology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China.
Chang-Kuo ZhouDepartment of Urology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China.
Shou-Zhen ChenDepartment of Urology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China.
Yue YangDepartment of Urology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China.
Ben-Kang ShiDepartment of Urology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included

Indexed as

bladder cancerco-expression networkhub genespathway enrichment analysisreverse transcription-quantitative polymerase chain reaction

Identifiers

PMID28693282
PMCPMC5494668

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.