ArticleBriefings in bioinformatics2023
Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Artificial Intelligence-Driven Antimicrobial Peptide Discovery: Prediction, Generation, Mining and Optimization.Probiotics and antimicrobial proteins · 2026Review
- Integrated Transcriptomic Analyses Identify Four Prognosis-Associated Genes in Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- Past achievements and future perspectives of personalized vaccines and the role of dendritic cells.Immunologic research · 2026Review
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Bibliometric analysis of single-cell RNA sequencing in tumor microenvironment.Discover oncology · 2026Review
- A computational framework integrating multi-omics and machine learning for identifying glycolytic gene markers in breast cancer.Discover oncology · 2026Article
- Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.Discover oncology · 2026Article
- CMsiRNAdb: a database of chemically modified SiRNA silencing efficiency for nucleic acid drug design.BMC bioinformatics · 2026Article
- Development and validation of a prognostic model based on T cell signature genes in colon cancer using single-cell RNA sequencing.Clinical and experimental medicine · 2026Article
- Integrating single-cell RNA-seq and machine learning to dissect polyamine metabolism in metabolic dysfunction-associated steatotic liver disease.Frontiers in medicine · 2026Article
- Single-cell RNA-seq and machine learning identify HMGN2 as a lactylation-associated hub gene in heart failure.PloS one · 2026Article
- Integrating bulk, single-cell, and spatial transcriptomics to identify a novel pyroptosis-related gene signature for predicting prognosis and tumor immune landscape in triple-negative breast cancer.Frontiers in immunology · 2026Article
- Improving B-cell Linear Epitope PredictionCurrent drug targets · 2026Article
- Integrated single-cell and transcriptomic profiling identifies machine-learning-based pyroptosis biomarkers in IBD.Frontiers in immunology · 2026Article
- Combining multi-omics analysis methods to identify biomarkers for mitophagy involved in immune checkpoint inhibitors-related myocarditis.Frontiers in immunology · 2026Article
- Investigating tryptophan metabolism in colorectal cancer using Single-cell RNA sequencing based on machine learning techniques.PloS one · 2026Article
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- Integrative Single-Cell and Machine Learning Analysis Develops a Glutamine Metabolism-Based Prognostic Model and Identifies MSMO1 as a Therapeutic Target in Osteosarcoma.Biomolecules · 2025Article
- IGFBP7 and CCT2 are novel lactylation-driven mediators of endothelial-to-mesenchymal transition in idiopathic pulmonary fibrosis.Functional & integrative genomics · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-cell and bulk transcriptome data, we systematically investigated significance of the shared pyroptotic signature at both single-cell and bulk levels in HCC prognosis. Based on the pyroptotic signature, a robust PR risk system was constructed to quantify the prognostic risk of individual patient. To further verify capacity of the pyroptotic signature on predicting patients' prognosis, an attention mechanism-based deep neural network classification model was constructed. The mechanisms of prognostic difference in the patients with distinct PR risk were dissected on tumor stemness, cancer pathways, transcriptional regulation, immune infiltration and cell communications. A nomogram model combining PR risk with clinicopathologic data was constructed to evaluate the prognosis of individual patients in clinic. The PR risk could also evaluate therapeutic response to neoadjuvant therapies in HCC patients. In conclusion, the constructed PR risk system enables a comprehensive assessment of tumor microenvironment characteristics, accurate prognosis prediction and rational therapeutic options in HCC.
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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 Socratic graph.