A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma
| Autor: | An-Qiang Li, Tian-Kang Guo, Li-Tian Wang, Xiang-Yong Hao, Hao Shi, Yan-Fei Shen, Yuan Deng, Tao Wang, Hui Cai |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Oncology Male medicine.medical_specialty Multivariate analysis Carcinoma Hepatocellular Bioinformatics Biophysics Biology Biochemistry 03 medical and health sciences 0302 clinical medicine Internal medicine medicine Biomarkers Tumor Humans KEGG Molecular Biology Survival analysis Diagnostics & Biomarkers Research Articles Cancer Homeodomain Proteins DNA methylation Receiver operating characteristic Proportional hazards model Liver Neoplasms Computational Biology Cell Biology Methylation hepatocellular carcinoma TCGA Middle Aged medicine.disease GEO Cyclin-Dependent Kinases 030104 developmental biology Homeobox A10 Proteins 030220 oncology & carcinogenesis Hepatocellular carcinoma Female prognosis Transcription Factors |
| Zdroj: | Bioscience Reports |
| ISSN: | 1573-4935 0144-8463 |
| Popis: | Purpose: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data. Methods: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model. Results: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan–Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P Conclusion: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC. |
| Databáze: | OpenAIRE |
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