Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation
Baohua Zhu1, Ziyang Mo1, Yi Bao2
1Department of Urology, The First Affiliated Hospital, Naval Medical University, Second Military Medical University, Shanghai, China.
Background:
Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors of the urinary system. Protein acetylation plays a key role in regulating cellular processes and cancer signaling pathways. This study explores the potential biological mechanisms of ccRCC from the perspective of acetylation.
Methods:
This study obtained RNA-seq data and clinical information of ccRCC from TCGA and ICGC, and single-cell RNA sequencing datasets from the GEO database. Ten machine learning algorithms and their 101 combinations were used to analyze the prognostic significance of acetylation-related differentially expressed genes (DEGs) and to construct a prognostic risk model. GSEA was used to analyze the enrichment of different signaling pathways in high-risk and low-risk groups, and the correlation between immune infiltration and risk scores was assessed. Finally, the function of the key gene GCNT4 was verified through cell experiments.
Results:
This study identified 84 acetylation-regulated key genes with significant expression differences between tumor and normal tissues, closely linked to patient prognosis. The LASSO + RSF combination model performed best, and the model could accurately predict patient prognosis. The survival of patients in the high-risk group was significantly worse than that in the low-risk group. High expression of GCNT4 was associated with better survival prognosis and was expressed at higher levels in normal tissues than tumor tissues. Overexpression of GCNT4 significantly inhibited the proliferation, invasion, and migration of renal cancer cells and may affect acetylation by regulating the levels of O-GlcNAc modification in cells.
Conclusion:
This study constructed a ccRCC acetylation homeostasis model via transcriptome analysis and machine learning, validating GCNT4 as a key gene. High expression of GCNT4 is associated with better survival prognosis and affects acetylation by regulating O-GlcNAc modification levels, inhibiting the proliferation and migration of renal cancer cells, providing a new potential target for the treatment of ccRCC.
Insights
This study identifies key acetylation-related genes in clear cell renal cell carcinoma (ccRCC) and develops a prognostic model. The gene GCNT4 shows potential as a therapeutic target, inhibiting tumor growth and migration.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Clear cell renal cell carcinoma (ccRCC) is a prevalent urinary system malignancy.
- Protein acetylation is crucial in regulating cellular processes and cancer pathways.
- This research investigates ccRCC mechanisms through the lens of acetylation.
Purpose of the Study:
- To explore potential biological mechanisms of ccRCC related to protein acetylation.
- To identify acetylation-regulated genes impacting ccRCC prognosis.
- To construct a predictive model for ccRCC patient outcomes.
Main Methods:
- Utilized RNA-seq and single-cell RNA sequencing data from TCGA, ICGC, and GEO databases.
- Employed machine learning algorithms to analyze acetylation-related differentially expressed genes (DEGs) and build a prognostic risk model.
- Conducted Gene Set Enrichment Analysis (GSEA) and assessed immune infiltration correlations. Verified GCNT4 function via cell experiments.
Main Results:
- Identified 84 acetylation-regulated key genes linked to ccRCC prognosis.
- A LASSO+RSF model accurately predicted patient survival, with high-risk groups showing worse outcomes.
- High GCNT4 expression correlated with better prognosis, inhibited cancer cell proliferation and invasion, and may regulate O-GlcNAc modification.
Conclusions:
- Developed a ccRCC acetylation homeostasis model using transcriptome analysis and machine learning.
- Validated GCNT4 as a key gene associated with improved survival.
- GCNT4's role in regulating O-GlcNAc modification offers a potential therapeutic strategy for ccRCC.
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