ArticleBMC urology2025
Machine learning model in multi-omics perspective demystifies the prognostic significance of crotonylation heterogeneity in clear cell renal cell carcinoma.
Article in BMC urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
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- An integrated machine learning and mendelian randomization approach identifies SERPING1 as a prognostic biomarker associated with CD8 + T-cell infiltration in DLBCL.Discover oncology · 2026Article
- Integration of multi-omics and machine learning to identify core genes in PANoptosisof lung adenocarcinoma and their mechanisms in the tumor microenvironment and therapeutic potential.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Ethnicity-specific molecular subtypes and a machine-learning risk model in Asian patients with non-muscle-invasive bladder cancer.Scientific reports · 2026Article
- Multi-omics analysis reveals TM4SF19 as a diagnostic and prognostic biomarker in bladder cancer.Discover oncology · 2026Article
- A prognostic exosome-related mRNAs risk signature correlates with the immune microenvironment in breast cancer.Discover oncology · 2026Article
- Characterization of telomere-related gene subtypes in lung adenocarcinoma and their implications for prognosis and treatment.Discover oncology · 2026Article
- Construction of a prognostic prediction model for diffuse large B-cell lymphoma patients based on ferroptosis-related LncRNAs.Discover oncology · 2026Article
- Deciphering the potential pathogenic mechanisms of 3-BHA in ovarian cancer through integrated bioinformatics and machine learning strategies.Discover oncology · 2026Article
- Development and validation of an interpretable prognostic model for bladder cancer based on lactylation associated genes using SHAP analysis.Discover oncology · 2026Article
- Integrative analysis of myeloid cell signatures identifies a prognostic risk model and potential mechanisms in bladder cancer.Biology direct · 2026Article
- Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.Discover oncology · 2026Article
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Abstract
backgroundCrotonylation, a post-translational modification, is implicated in cancer progression, but its prognostic significance in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to demystify crotonylation heterogeneity and establish a robust prognostic model for ccRCC.
methodsUsing multi-omics approaches, we analyzed transcriptomic data from TCGA-KIRC and GEO cohorts (GSE40435, GSE167573, GSE29609). Crotonylation scores were calculated via ssGSEA, with related gene modules identified through WGCNA. We integrated 10 machine learning algorithms to develop a prognostic model. Immune microenvironment was profiled using Cibersort, mutation landscapes via maftools, and drug sensitivity through oncoPredict. Spatial transcriptomics and single-cell data were analyzed for expression patterns, validated by qRT-PCR in 786-O and HK-2 cell lines.
resultsDysregulation of 16/18 crotonylation-related genes was observed in ccRCC. WGCNA revealed crotonylation related modules significantly enriched in angiogenesis, calcium/Ras signaling, and cancer stemness pathways. A 5-gene prognostic model (PLCL1, DNASE1L3, CD248, CDH13, PDGFD) demonstrated robust stratification: High-risk patients showed poorer overall survival, higher Treg infiltration, elevated tumor mutation burden and increased sensitivity to several chemotherapy approaches like Cisplatin. Molecular docking identified diacetylmorphine as a potential therapeutic agent (binding energy: -7.278 kcal/mol with DNASE1L3). Spatial/single-cell analyses confirmed cell-type-specific gene expression and the diffferential expression between tumor and normal cell lines was validated by qRT-PCR.
conclusionThis study establishes a crotonylation-based prognostic model that effectively stratifies ccRCC risk and elucidates key mechanisms linking crotonylation heterogeneity to immune evasion, mutational burden, and metabolic reprogramming. The model offers clinical utility for personalized therapy selection.
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