Evidence map›Paper›PMID 40589607›Full record

ArticleJournal of inflammation research2025

Machine Learning and Experimental Validation Reveal MYH11 as a Novel Prognostic Biomarker and Therapeutic Target in Bladder Cancer.

Zhiyong Tan, Xiaorong Chen, Shi Fu, Yinglong Huang, Haihao Li, Chen Gong, Dihao Lv, Chadanfeng Yang, Jiansong Wang, Mingxia Ding and 1 more

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Zhiyong Tan *Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.ORCID 0000-0002-7771-0962
Xiaorong Chen *Department of Kidney Transplantation, The Third Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.
Shi Fu *Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Yinglong Huang *Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Haihao Li *Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Chen GongDepartment of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Dihao LvDepartment of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Chadanfeng YangDepartment of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Jiansong WangDepartment of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Mingxia DingDepartment of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.
Haifeng WangDepartment of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Bladder cancer (BCa) is one of the top ten most common cancers, yet its underlying mechanisms remain unclear. This study aimed to explore the potential molecular mechanisms of BCa using multi-omics and single-cell analysis. Methods: First, differential analysis of transcriptome data related to BCa from public databases was performed, and a risk model was then developed using 101 different machine learning algorithms to determine prognostic genes, followed by independent prognostic analysis to construct a nomogram. Immune infiltration analysis was performed to explore the impact of prognostic genes on the tumor microenvironment. Metabolomics, proteomics, and post-translational modification data from BCa tumor and adjacent non-tumor tissues were used to explore the relationships between prognostic genes and various omics levels. Finally, single-cell analysis identified key cells involved in BCa pathogenesis, and in vitro experiments validated the expression and function of key genes. Results: The risk model constructed by 8 prognostic genes identified using 101 algorithms effectively predicted the survival outcomes of BCa patients. Furthermore, risk scores, pathological T stage, and pathological N stage were confirmed as independent prognostic factors for the nomogram construction. Interestingly, high-risk patients showed a significantly lower response to PD-L1 treatment, with higher TIDE scores. Omics analysis revealed a close relationship between prognostic genes and proteomics, metabolomics, and post-translational modifications. Specifically, FLNC and MYH11 may influence BCa progression through phosphorylation and succinylation. Single-cell analysis identified fibroblasts as key cells in BCa. Functional experiments showed that MYH11 knockdown promoted cell proliferation, migration, and invasion. Conclusion: This study identified 8 prognostic genes to construct a risk model, and suggest that MYH11 is a potential diagnostic and prognostic biomarker for BCa.

Indexed as

bladder cancermulti-omicsMYH11single-cell sequencing analysistherapeutic target

Identifiers

PMID40589607
PMCPMC12206902

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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.