DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches
Identifying cancer biomarkers through DNA methylation analysis is an efficient approach toward the detection of aberrant changes in epigenetic regulation associated with early-stage cancer types. Among all cancer types, cancers with relatively low five-year survival rates and high incidence rates we...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1523524/full |
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| author | Yi-Hsuan Tsai Prasenjit Mitra David Taniar Tun-Wen Pai Tun-Wen Pai |
| author_facet | Yi-Hsuan Tsai Prasenjit Mitra David Taniar Tun-Wen Pai Tun-Wen Pai |
| author_sort | Yi-Hsuan Tsai |
| collection | DOAJ |
| description | Identifying cancer biomarkers through DNA methylation analysis is an efficient approach toward the detection of aberrant changes in epigenetic regulation associated with early-stage cancer types. Among all cancer types, cancers with relatively low five-year survival rates and high incidence rates were pancreatic (10%), esophageal (20%), liver (20%), lung (21%), and brain (27%) cancers. This study integrated genome-wide DNA methylation profiles and comorbidity patterns to identify the common biomarkers with multi-functional analytics across the aforementioned five cancer types. In addition, gene ontology was used to categorize the biomarkers into several functional groups and establish the relationships between gene functions and cancers. ALX3, HOXD8, IRX1, HOXA9, HRH1, PTPRN2, TRIM58, and NPTX2 were identified as important methylation biomarkers for the five cancers characterized by low five-year survival rates. To extend the applicability of these biomarkers, their annotated genetic functions were explored through GO and KEGG pathway analyses. The combination of ALX3, NPTX2, and TRIM58 was selected from distinct functional groups. An accuracy prediction of 93.3% could be achieved by validating the ten most common cancers, including the initial five low-survival-rate cancer types. |
| format | Article |
| id | doaj-art-cc0993f44b094d26b8add9db4c76d7bb |
| institution | Kabale University |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj-art-cc0993f44b094d26b8add9db4c76d7bb2025-01-28T06:41:06ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-01-01510.3389/fbinf.2025.15235241523524DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approachesYi-Hsuan Tsai0Prasenjit Mitra1David Taniar2Tun-Wen Pai3Tun-Wen Pai4Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, TaiwanCollege of Information Science and Engineering, The Pennsylvania State University, University Park, PA, United StatesDepartment of Software Systems and Cybersecurity, Monash University, Clayton, VIC, AustraliaDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Computer Science and Engineering, National Taiwan Ocean University, Keelung, TaiwanIdentifying cancer biomarkers through DNA methylation analysis is an efficient approach toward the detection of aberrant changes in epigenetic regulation associated with early-stage cancer types. Among all cancer types, cancers with relatively low five-year survival rates and high incidence rates were pancreatic (10%), esophageal (20%), liver (20%), lung (21%), and brain (27%) cancers. This study integrated genome-wide DNA methylation profiles and comorbidity patterns to identify the common biomarkers with multi-functional analytics across the aforementioned five cancer types. In addition, gene ontology was used to categorize the biomarkers into several functional groups and establish the relationships between gene functions and cancers. ALX3, HOXD8, IRX1, HOXA9, HRH1, PTPRN2, TRIM58, and NPTX2 were identified as important methylation biomarkers for the five cancers characterized by low five-year survival rates. To extend the applicability of these biomarkers, their annotated genetic functions were explored through GO and KEGG pathway analyses. The combination of ALX3, NPTX2, and TRIM58 was selected from distinct functional groups. An accuracy prediction of 93.3% could be achieved by validating the ten most common cancers, including the initial five low-survival-rate cancer types.https://www.frontiersin.org/articles/10.3389/fbinf.2025.1523524/fullcomorbidity patternsupport vector machineearly detectionKEGG pathwaygene ontology |
| spellingShingle | Yi-Hsuan Tsai Prasenjit Mitra David Taniar Tun-Wen Pai Tun-Wen Pai DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches Frontiers in Bioinformatics comorbidity pattern support vector machine early detection KEGG pathway gene ontology |
| title | DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches |
| title_full | DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches |
| title_fullStr | DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches |
| title_full_unstemmed | DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches |
| title_short | DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches |
| title_sort | dna methylation biomarker analysis from low survival rate cancers based on genetic functional approaches |
| topic | comorbidity pattern support vector machine early detection KEGG pathway gene ontology |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1523524/full |
| work_keys_str_mv | AT yihsuantsai dnamethylationbiomarkeranalysisfromlowsurvivalratecancersbasedongeneticfunctionalapproaches AT prasenjitmitra dnamethylationbiomarkeranalysisfromlowsurvivalratecancersbasedongeneticfunctionalapproaches AT davidtaniar dnamethylationbiomarkeranalysisfromlowsurvivalratecancersbasedongeneticfunctionalapproaches AT tunwenpai dnamethylationbiomarkeranalysisfromlowsurvivalratecancersbasedongeneticfunctionalapproaches AT tunwenpai dnamethylationbiomarkeranalysisfromlowsurvivalratecancersbasedongeneticfunctionalapproaches |