Evidence map›Paper›PMID 36672577›Full record

ArticleBiomedicines2022

Molecular Subtyping in Muscle-Invasive Bladder Cancer on Predicting Survival and Response of Treatment.

Tanan Bejrananda, Jirakrit Saetang, Surasak Sangkhathat

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Trial
  2. Bladder cancer biomarkers.Exploration of targeted anti-tumor therapy · 2025
    Review
  3. Article
  4. 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.

Tanan BejranandaDepartment of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0003-0853-2653
Jirakrit SaetangDepartment of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0003-2769-8149
Surasak SangkhathatDepartment of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.
Prince of Songkla University · TH

Funding

Health Systems Research Institute 63-105
6 · The paper itself

Abstract

Molecular classifications for urothelial bladder cancer appear to be promising in disease prognostication and prediction. This study investigated the novel molecular subtypes of muscle invasive bladder cancer (MIBC). Tumor samples and normal tissues of MIBC patients were submitted for transcriptome sequencing. Expression profiles were clustered using K-means clustering and principal component analysis. The molecular subtypes were also applied to The Cancer Genome Atlas (TCGA) dataset and analyzed for clinical outcome correlation. Three molecular subtypes of MIBC were discovered, clusters A, B, and C. The most differentially upregulated genes in cluster A were BDKRB1, EDNRA, AVPR1A, PDGFRB, and TNC, while the most upregulated genes in cluster C were collagen-related genes, PDGFRB, and PRKG1. For cluster B, COL6A3, COL1A2, COL6A2, tenascin C, and fibroblast growth factor 2 were statistically suppressed. When the centroids of clustering on PCA were applied to TCGA data, the clustering significantly predicted survival outcomes. Cluster B had the best overall survival (OS), and cluster C was associated with poor OS but exhibited the best response to perioperative chemotherapy. Among all groups, cluster B had a better pathologic response to neoadjuvant chemotherapy (40%). Based on the results of the present study, the novel clusters of subtype MIBC appear potentially suitable for integration into clinical practice.

Indexed as

molecular subtypesmuscle-invasive bladder cancerthe cancer genome atlas (TCGA)transcriptomic analysis

Identifiers

PMID36672577
PMCPMC9856180
OpenAlexW4313415130

What OpenQuestion holds

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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.