ArticleClinical and experimental medicine2024
Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response.
Article in Clinical and experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Epithelial-Mesenchymal Transition Markers in Clear Cell Renal Cell Carcinoma: Expression Patterns and Prognostic Significance.Journal of personalized medicine · 2026Article
- Single-cell transcriptomic insights into ccRCC: a stemness gene signature for prognosis and treatment response prediction.Discover oncology · 2025Article
- A lactate related signature for predicting prognosis and tumor microenvironment in lung adenocarcinoma.Discover oncology · 2025Article
- Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework.Discover oncology · 2025Article
- Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma.Journal of cellular and molecular medicine · 2025Article
- Development of a novel prognostic signature based on cytotoxic T lymphocyte-evasion genes for hepatocellular carcinoma patient management.Discover oncology · 2025Article
- Prognostic value of positive lymph node ratio, tumor deposit, and perineural invasion in advanced colorectal signet-ring cell carcinoma.Frontiers in molecular biosciences · 2025Article
- Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.Frontiers in immunology · 2025Article
- Novel Machine Learning Approaches Revolutionize Pancreatic Malignancy Prognosis: Exploring Programed Cell Death.Mediators of inflammation · 2025Article
- Single-cell and spatial transcriptomics integration reveals FAM49B promotes tumor-associated macrophages polarization in colorectal cancer via the MK pathway.Frontiers in immunology · 2025Article
- Machine learning-based prediction of gastroparesis risk following complete mesocolic excision.Discover oncology · 2024Article
- Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.Frontiers in immunology · 2024Article
- From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.Frontiers in immunology · 2024Article
- Leveraging single-cell and multi-omics approaches to identify MTOR-centered deubiquitination signatures in esophageal cancer therapy.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Renal cell carcinoma (RCC) is characterized by a variety of subtypes, each defined by unique genetic and morphological features. This study utilizes single-cell RNA sequencing to explore the molecular heterogeneity of RCC. A highly proliferative cell subset, termed as "Prol," was discovered within RCC tumors, and its increased presence was linked to poorer patient outcomes. An artificial intelligence network, encompassing traditional regression, machine learning, and deep learning algorithms, was employed to develop a Prol signature capable of predicting prognosis. The signature demonstrated superior performance in predicting RCC prognosis compared to other signatures and exhibited pan-cancer prognostic capabilities. RCC patients with high Prol signature scores exhibited resistance to targeted therapies and immunotherapies. Furthermore, the key gene CEP55 from the Prol signature was validated by both proteinomics and quantitative real time polymerase chain reaction. Our findings may provide new insights into the molecular and cellular mechanisms of RCC and facilitate the development of novel biomarkers and therapeutic targets.
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