Evidence map›Paper›PMID 34899849›Full record

ArticleFrontiers in genetics2021

A Novel Immune-Gene Pair Signature Revealing the Tumor Microenvironment Features and Immunotherapy Prognosis of Muscle-Invasive Bladder Cancer.

Xiaonan Zheng, Xianghong Zhou, Hang Xu, Di Jin, Lu Yang, Bairong Shen, Shi Qiu, Jianzhong Ai, Qiang Wei

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2021. 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
1.3field-weighted citation impact, top 20% 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, 6 citations in OpenAlex.

  1. Article
  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

9 authors at 2 institutions in 1 country.

Xiaonan ZhengDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Xianghong ZhouDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Hang XuDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Di JinDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Lu YangDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Bairong ShenInstitute of Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.
Shi QiuDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Jianzhong AiDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Qiang WeiDepartment of Urology, West China Hospital, Sichuan University, Chengdu, China.
Sichuan University · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy has been a milestone for muscle-invasive bladder cancer (MIBC), but only a small portion of patients can benefit from it. Therefore, it is crucial to develop a robust individualized immune-related signature of MIBC to identify patients potentially benefiting from immunotherapy. The current study identified patients from the Cancer Genome Atlas (TCGA) and immune genes from the ImmPort database, and used improved data analytical methods to build up a 45 immune-related gene pair signature, which could classify patients into high-risk and low-risk groups. The signature was then independently validated by a Gene Expression Omnibus (GEO) dataset and IMvigor210 data. The subsequent analysis confirmed the worse survival outcomes of the high-risk group in both training (

Indexed as

immune-related gene pair signatureimmunotherapymuscle-invasive bladder cancerprognosistumor microenvironment

Identifiers

PMID34899849
PMCPMC8664435
OpenAlexW3217325701

What OpenQuestion holds

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LicenceCC BY
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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.