ArticleFrontiers in immunology2026
Identification of succinylation-related genes in bladder cancer: integration of single-cell and transcriptomic data.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Bladder cancer (BLCA) exhibits a poor prognosis, highlighting the urgent need for reliable prognostic genes. Although succinylation is linked to tumor progression, its role in BLCA remains understudied. This study aimed to identify and validate prognostic succinylation-related genes (SRGs) in BLCA and elucidate their impact on the tumor microenvironment (TME). Methods: SRGs were initially identified through transcriptomic sequencing of 15 paired BLCA and adjacent normal tissues (Soochow-BLCA cohort) and further refined by integrating single-cell and bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. A prognostic risk model was developed using LASSO and multivariable Cox regression, incorporating clinical factors for nomogram construction. Mechanistic insights were obtained through functional enrichment, immune profiling, somatic mutation, and drug sensitivity analyses. The Scissor algorithm mapped bulk transcriptome risk signatures to single-cell resolution, enabling identification of high-risk cell subpopulations. Pseudotime and cell-cell communication analyses characterized dynamic expression patterns of the core genes. Finally, the expression profiles and functional roles of the core genes were validated using RT-qPCR and CCK-8 proliferation assays Results: KCTD16, CD3D and GSDMB were identified as prognostic genes. The risk score derived from these genes, in combination with age and N stage, was incorporated into a risk model that exhibited robust predictive accuracy (AUC > 0.7). Expression of these genes differed significantly between non-muscle-invasive and muscle-invasive subtypes. The high-risk group displayed enhanced immune evasion (higher TIDE score, Conclusions: The succinylation-related prognostic model accurately predicts outcomes in BLCA, reveals links to immune escape and TME, and highlights the pivotal role of epithelial cells, offering potential targets for individualized therapy.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.