Evidence map›Paper›PMID 37895248›Full record

ArticleGenes2023

Survival and Enrichment Analysis of Epithelial-Mesenchymal Transition Genes in Bladder Urothelial Carcinoma.

Waleed Ali, Weirui Xiao, Daniel Jacobs, Andre Kajdacsy-Balla

Open access · goldAbstract read
In one paragraph

Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 40% 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

3 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

4 authors at 2 institutions in 1 country.

Waleed AliAlbert Einstein College of Medicine, New York, NY 10461, USA.
Weirui XiaoAlbert Einstein College of Medicine, New York, NY 10461, USA.
Daniel JacobsAlbert Einstein College of Medicine, New York, NY 10461, USA.
Andre Kajdacsy-BallaProfessor of Pathology, University of Illinois at Chicago College of Medicine, Chicago, IL 60607, USA.
Albert Einstein College of Medicine · USUniversity of Illinois Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The escalating prevalence of bladder cancer, particularly urothelial carcinoma, necessitates innovative approaches for prognosis and therapy. This study delves into the significance of genes related to epithelial-mesenchymal transition (EMT), a process inherently linked to carcinogenesis and comparatively better studied in other cancers. We examined 1184 EMT-related gene expression levels in bladder urothelial cancer cases through the TCGA dataset. Genes shown to be differentially expressed in relation to survival underwent further network and enrichment analysis to uncover how they might shape disease outcomes. Our in silico analysis revealed a subset of 32 genes, including those significantly represented in biological pathways such as VEGF signaling and bacterium response. In addition, these genes interact with genes involved in the JAK-STAT signaling pathway. Additionally, some of those 32 genes have been linked to immunomodulators such as chemokines CCL15 and CCL18, as well as to various immune cell infiltrates. Our findings highlight the prognostic utility of various EMT-related genes and identify possible modulators of their effect on survival, allowing for further targeted wet lab research and possible therapeutic intervention.

Indexed as

Carcinoma, Transitional CellUrinary Bladder NeoplasmsEpithelial-Mesenchymal TransitionHumansPrognosisUrinary Bladderbladder urothelial carcinomaepithelial–mesenchymal transitionin silicoTCGAVEGF

Identifiers

PMID37895248
PMCPMC10606556
OpenAlexW4387230441

What Socratic 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 Socratic graph.