ArticleTranslational andrology and urology2025
Integrated single-cell genomics, transcriptomics, and pathomics to identify potential biomarkers in muscle-invasive bladder cancer.
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. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Immunohistochemistry-Based Molecular Classification of Muscle-Invasive Bladder Urothelial Carcinoma: Survival Trends Across Molecular Subtypes.International journal of molecular sciences · 2026Article
- Pan-cancer screening and integrative multi-omics and deep learning reveal the prognostic significance of an IBD-CRC shared host-microbe signature in bladder urothelial carcinoma.Translational oncology · 2026Article
- A Deep Learning-Generated Mixed Tumor-Stroma Ratio for Prognostic Stratification and Multi-omics Profiling in Bladder Cancer.Research (Washington, D.C.) · 2026Article
- Integrative multiomics dissection of LSM7 as a prognostic biomarker and therapeutic target in hepatocellular carcinoma.Science progressArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of multi-omics approaches provides a powerful strategy for understanding cancer. By combining these methods, researchers gain insights into tumor diversity, gene activity, and the tumor microenvironment, which are essential for advancing cancer biology, improving early detection, refining prognostic tools, and developing targeted treatments. This study aims to explore key biomarkers in muscle-invasive bladder cancer (MIBC) and to develop a predictive model for better understanding disease progression and therapeutic responses. Methods: Single-cell analysis of MIBC samples from public datasets identified basal-related genes. Using Cox analysis of The Cancer Genome Atlas (TCGA)-bladder urothelial carcinoma (BLCA) clinical data and the expression matrix, and combining it with weighted gene co-expression network analysis (WGCNA) of another MIBC dataset, a disintegrin and metalloprotease protein 17 ( Results: Conclusions: Multi-omics approaches effectively identify biomarkers for MIBC, with
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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.