Evidence map›Paper›PMID 35991125›Full record

ArticleJournal of immunology research2022

The Role of the Key Differentially Mutated Gene

Jun Jiang, Yan Zhan, Jun Li

Open access · goldAbstract read
In one paragraph

Article in Journal of immunology research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Jun Jiang *Department of Oncology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.ORCID https://orcid.org/0000-0001-9613-5089
Yan Zhan *Department of Oncology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.ORCID https://orcid.org/0000-0001-7756-9017
Jun LiDepartment of Oncology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.ORCID https://orcid.org/0000-0003-4817-0693
Central Hospital of Wuhan · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tumor microenvironment (TME) has been a major focus of research in recent years as a crucial factor in the development and progression of bladder cancer. Unfortunately, the precise composition of TME, particularly the immunological and stromal components, remains unknown. In this work, we downloaded the RNA-seq expression profiles and somatic mutation data of 433 bladder cancer cases from The Cancer Genome Atlas (TCGA) and then employed a comprehensive bioinformatics approach to evaluate them. Firstly, the expression profiles were used to predict the scores and then the content of immune and stromal cells via the estimate package in R software. We then identified differentially expressed genes (DEGs) and differentially mutated genes (DMGs) according to the high-stromal score cohort and low-stromal score cohort. Finally, fibroblast growth factor receptor 3 (FGFR3) was the main differentially mutated gene in bladder carcinoma that we discovered after conducting a cross-study on DEGs and DMGs. Follow-up investigation revealed that

Indexed as

Receptor, Fibroblast Growth Factor, Type 3Urinary Bladder NeoplasmsComputational BiologyHumansMutationNeoplasm StagingPrognosisTumor MicroenvironmentFGFR3 protein, humanReceptor, Fibroblast Growth Factor, Type 3

Identifiers

PMID35991125
PMCPMC9391163
OpenAlexW4291301630

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.