ArticleHeliyon2024
Construction and experimental verification of a novel nine-glycosylation-related gene prognostic risk model for clear cell renal carcinoma.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning integration of multi-omics data develops a sarcomatoid-related renal cell carcinoma score (SARS) for prognosis stratification and guiding therapy.Translational andrology and urology · 2026Article
- Comprehensive analysis of COLGALT1 in tumor microenvironment regulation and prognosis of clear cell renal cell carcinoma.Clinical and experimental medicine · 2026Article
- The role of adherent-to-suspension transition factors in clear cell renal cell carcinoma progression: a comprehensive analysis.Scientific reports · 2025Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: Patients with clear cell renal carcinoma (ccRCC) typically have poor prognosis. Glycosylation modification plays an important role in ccRCC. This study aimed to develop a novel signature for predicting ccRCC prognosis based on glycosylation-related genes (GRGs). Methods: Differentially expressed GRGs (DE_GRGs) were identified using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus and used for constructing the risk model. The function of key genes was validated. Results: Twenty-two DE_GRGs were intersected between GSE53757 and TCGA. Patients with ccRCC were divided into two clusters based on the expression profile of these DE_GRGs. Significant differences in the infiltration of 10 immune cell types were observed between two subclusters. Subsequently, the prognostic signatures of nine DE_GRGs (CHST9, COLGALT1, FUT3, FUT6, HS3ST2, POMGNT2, ST8SIA4, UGT3A1, and UGT8) were established. Patients in the high-risk group showed a poorer prognosis relative to the low-risk group. According to univariate and multivariate Cox regression analyses, the risk score, stage, and grade could be independent prognostic factors. A nomogram incorporating information on gender, age, risk group, TNM stage, and clinical stage showed accurate prediction in the survival probability. Except for CHST9, HS3ST2, and ST8SIA4, expression patterns of the remaining 6 DE_GRGs in Caki-1 and 786-O cells were confirmed by quantitative real-time PCR. FUT6 overexpression resulted in the inhibition of proliferation, migration, and invasion of ccRCC cells. Conclusion: This study established a nine-DE_GRG-based prognostic signature, which independently predicted ccRCC prognosis. This finding emphasizes that GRGs are stratification factors for the precise prognosis of ccRCC.
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