Evidence map›Paper›PMID 36263004›Full record

ArticleDisease markers2022

Prognostic Signature GXYLT2 Is Correlated with Immune Infiltration in Bladder Cancer.

Shuo Wu, Shipei Qiu, Wei Chen, Liucheng Ding, Lejun Wu

Abstract read
In one paragraph

Article in Disease markers, 2022. 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
–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

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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. 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.

Shuo WuDepartment of Urology, The First People's Hospital of Hangzhou, Linan District, Hangzhou, China.ORCID https://orcid.org/0000-0001-5591-967X
Shipei QiuDepartment of Urology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Wei ChenDepartment of Urology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Liucheng DingDepartment of Urology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID https://orcid.org/0000-0001-6691-2967
Lejun WuDepartment of Urology, The First People's Hospital of Hangzhou, Linan District, Hangzhou, China.ORCID https://orcid.org/0000-0002-4867-5125

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: GXYLT2 (glucoside xylosyltransferase 2) was known as an important gene that regulates classical Notch signaling and is involved in progression in human tumors. However, the correlation between GXYLT2 expression and bladder cancer remains unclear. Methods: GXYLT2 expression was analyzed by ONCOMINE database, GEPIA database, and TIMER database. The Cancer Genome Atlas (TCGA) was utilized to confirm relationships between GXYLT2 and molecular subtypes of BLCA (bladder cancer). We discovered prognostic value of GXYLT2 in BLCA using GEPIA, LinkedOmics database, and Kaplan-Meier Plotter database. Subsequently, correlations between GXYLT2 and tumor immune infiltration were investigated through TIMER and TISIDB website. We then downloaded data of patients with BLCA from TCGA website, to conduct functional annotations and to construct protein-protein interaction network through STRING and Enrich web servers. Results: Significant differences were observed between GXYLT2 expression of bladder cancer and normal tissues. GXYLT2 was a poor prognostic biomarker in BLCA with impact on diverse clinical characteristics. We found that GXYLT2 was closely related to tumor immune infiltrated cells and immune genes. Functional annotations indicated that GXYLT2 was linked to immune-related pathways. Conclusions: The results suggested that GXYLT2 was associated with a poor prognosis and tumor immune cell infiltration of BLCA. GXYLT2 could be a promising therapeutic target in bladder cancer.

Indexed as

Urinary Bladder NeoplasmsBiomarkersGene Expression Regulation, NeoplasticGlucosidesHumansPrognosisBiomarkersGlucosides

Identifiers

PMID36263004
PMCPMC9576427

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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.