| Autor: |
Ray O. Bahado‐Singh, Onur Turkoglu, Buket Aydas, Sangeetha Vishweswaraiah |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Cancer Medicine, Vol 12, Iss 19, Pp 19644-19655 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2045-7634 |
| DOI: |
10.1002/cam4.6604 |
| Popis: |
Abstract Background Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell‐free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC. Methods The Illumina Infinium HD Assay was used for genome‐wide DNA methylation profiling of cfDNA in treatment‐naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis. Results In total, we identified 4556 significantly differentially methylated CpGs (q‐value |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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