ArticleTranslational andrology and urology2025
Integrating transcriptomics, single-cell omics, and deep learning-based histopathological features to identify
Article in Translational andrology and urology, 2025. 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 (BCa) represents the most common malignancy of the urinary system, characterized by a high recurrence rate, with approximately 61% of patients experiencing recurrence within 1 year post-surgery. Current monitoring methods, such as cystoscopy and urine cytology, are constrained by low sensitivity and patient discomfort. This study employed multi-omics data to investigate the role of Methods: This study utilized RNA sequencing (RNA-seq) data and clinical information from The Cancer Genome Atlas (TCGA) to analyze patients with BCa, stratifying them into relapse and non-relapse groups. Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with 1-year BCa recurrence. Subsequently, univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were conducted to select eight prognostic genes, and a risk model was developed and validated in both TCGA and Gene Expression Omnibus (GEO) datasets. Additionally, single-cell RNA sequencing (scRNA-seq) data from Guangdong Provincial Second People's Hospital were analyzed to evaluate gene expression across high and low tumor stromal BCa subtypes and explore the relationship between the expression of these eight genes and clinical features. A deep learning model based on the ResNet50 architecture was developed to predict Results: WGCNA identified gene modules associated with BCa recurrence, with the red module exhibiting a significantly positive correlation with recurrence status. Through univariate and LASSO Cox regression analyses, we selected eight prognosis-related genes. Kaplan-Meier survival analysis demonstrated that these genes effectively differentiated between high- and low-risk groups, with statistically significant survival differences observed in both TCGA and GEO datasets. Further Kaplan-Meier survival analysis of each of the eight genes indicated that high Conclusions: Multi-omics approaches effectively identified the
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.