cg04448376, cg24387542, cg08548498, and cg14621323 as a Novel Signature to Predict Prognosis in Kidney Renal Papillary Cell Carcinoma
| Autor: | Ying-Ying Zhang, Ying-Lei Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Male Article Subject Computational biology Biology General Biochemistry Genetics and Molecular Biology 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Gene expression Biomarkers Tumor Humans KEGG Survival rate Gene Carcinoma Renal Cell General Immunology and Microbiology General Medicine Methylation DNA Methylation Middle Aged Prognosis Kidney Neoplasms 030104 developmental biology chemistry 030220 oncology & carcinogenesis DNA methylation Biomarker (medicine) Medicine Female Transcriptome DNA Research Article |
| Zdroj: | BioMed Research International BioMed Research International, Vol 2020 (2020) |
| ISSN: | 2314-6141 2314-6133 |
| Popis: | Introduction. DNA methylation plays a vital role in prognosis prediction of cancers. In this study, we aimed to identify novel DNA methylation site biomarkers and create an efficient methylated site model for predicting survival in kidney renal papillary cell carcinoma (KIRP). Methods. DNA methylation and gene expression profile data were downloaded from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Differential methylated genes (DMGs) and differential expression genes (DEGs) were identified and then searched for the hub genes. Cox proportional hazards regression was applied to identify DNA methylated site biomarkers from the hub genes. Kaplan–Meier survival and ROC analyses were used to validate the effective prognostic ability of the methylation gene site biomarker. The biomarker sites were validated in the GEO cohorts. The GO and KEGG annotation was done to explore the biological function of DNA methylated site signature. Results. Nine DMGs with opposite expression patterns containing 47 methylated sites were identified. Finally, four methylated sites were identified using the hazard regression model (cg04448376, cg24387542, cg08548498, and cg14621323) located in UTY, LGALS9B, SLPI, and PFN3, respectively. These sites classified patients into high- and low-risk groups in the training cohort. The 5-year survival rates for patients with low-risk and high-risk scores were 97.5% and 75.9% (P<0.001). The prognostic accuracy and signature methylation sites were validated in the test (TCGA,n=87) and GEO cohorts (n=14). Multivariate regression analysis showed that the signature was an independent prediction prognostic factor for KIRP. Based on this analysis, we developed methylated site signature nomogram that predicts an individual’s risk of survival. Functional analysis suggested that these signature genes are involved in the biological processes of protein binding. Conclusions. Our study demonstrated that the methylated gene site signature might be a powerful prognostic tool for evaluating survival rate and guiding tailored therapy for KIRP patients. |
| Databáze: | OpenAIRE |
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