Evidence map›Paper›PMID 39112614›Full record

ArticleScientific reports2024

Predicting prognosis and drug sensitivity in bladder cancer: an insight into Pan-programmed cell death patterns regulated by M6A modifications.

Rongjiang Wang, Zhaojun Li, Junwen Shen

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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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

3 authors.

Rongjiang WangThe Department of Urology, The First Affiliated Hospital of Huzhou Normal College, Huzhou, 31300, Zhejiang, China.
Zhaojun LiThe Department of Urology, The First Affiliated Hospital of Huzhou Normal College, Huzhou, 31300, Zhejiang, China.
Junwen ShenThe Department of Urology, The First Affiliated Hospital of Huzhou Normal College, Huzhou, 31300, Zhejiang, China. wind-301@163.com.

Funding

Huzhou Municipal Science and Technology Bureau 2022GZ62
6 · The paper itself

Abstract

The team aimed to explore the possible functional significance of M6A regulation in Pan-programmed cell death (PCD) among patients with bladder cancer (BLCA). In BLCA patients, the analysis was conducted on the13 patterns of programmed cell death (PCD) and the regulation of M6A. Transcriptome, genomics, and clinical data were collected from TCGA-BLCA, GEO32548, and IMvigor210. Consensus clustering analysis, functional enrichment analysis, and other prognostic tools were used to validate the Pan-PCD. Finally, in vitro experiments and transcription sequencing were performed to understand the potential influence of the PI3K pathway on Pan-PCD in BLCA patients. Diverse PCD patterns were simultaneously activated, and M6A regulators exhibited significant variability in bladder malignant tissues. The machine learning algorithm established an 8-gene M6A-related Pan-PCD signature. This signature was validated in three independent datasets, and BLCA patients with higher risk scores had worse prognosis. An unsupervised clustering approach identified activated and suppressed Pan-PCD subgroups of BLCA patients, with distinct responses to immunotherapy and drug sensitivity. In addition, the PI3K pathway was identified as a key mechanism for various forms of programmed cell death, encompassing apoptosis, pyroptosis, autophagy, and cell death dependent on lysosomes. This research revealed that the Pan-PCD model was a more promising approach for BLCA patients under M6A regulation. A new signature from M6A-related Pan-PCD was proposed, with prognostic value for survival or drug sensitivity. The PI3K pathway was a key mechanism for multiple PCDs in BLCA patients.

Indexed as

ApoptosisUrinary Bladder NeoplasmsAutophagyCell Line, TumorDrug Resistance, NeoplasmGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMalePhosphatidylinositol 3-KinasesPrognosisSignal TransductionTranscriptomePhosphatidylinositol 3-KinasesBladder cancerM6A regulationPI3K pathwayProgrammed cell death

Identifiers

PMID39112614
PMCPMC11306778

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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.