WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation Scores
Genetic and genomic variations are primary drivers of tumor development. Identifying driver genes from numerous passenger genes across pan-cancer poses a significant challenge due to varying mutation loads. While independent studies have elucidated cancer-associated mutation patterns within specific...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807179/ |
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| author | Yanjie Ren Azlan Mohd Zain Yan Zhang Rozita Abdul Jalil Mahadi Bahari Norfadzlan Bin Yusup Mazlina Abdul Majid Azurah A. Samah Didik Dwi Prasetya Nurhafizah Moziyana Mohd Yusop |
| author_facet | Yanjie Ren Azlan Mohd Zain Yan Zhang Rozita Abdul Jalil Mahadi Bahari Norfadzlan Bin Yusup Mazlina Abdul Majid Azurah A. Samah Didik Dwi Prasetya Nurhafizah Moziyana Mohd Yusop |
| author_sort | Yanjie Ren |
| collection | DOAJ |
| description | Genetic and genomic variations are primary drivers of tumor development. Identifying driver genes from numerous passenger genes across pan-cancer poses a significant challenge due to varying mutation loads. While independent studies have elucidated cancer-associated mutation patterns within specific cancer types, a systematic approach to integrating these mutation data for assessing the impact of gene mutations has been lacking. This study addresses this gap by integrating pan-cancer genomic somatic mutation data and introducing a novel mutation weight fusion (WeiFu) score calculation method. WeiFu computes frequency and weighted fusion scores by cancer type, facilitating the identification of potential driver genes. Evaluation results on an integrated pan-cancer dataset comprising 29 different cancer types demonstrate that WeiFu significantly outperforms current well-known approaches in prediction accuracy, sensitivity, and specificity. Notably, WeiFu recovers 277 known cancer genes among the top 500 ranked candidates and successfully identifies potential driver genes supported by strong evidence. Consequently, WeiFu shows considerable promise for identifying driver genes within the rapidly expanding corpus of cancer genomic data. |
| format | Article |
| id | doaj-art-de41c8b0a5c54f3b957d32715b225676 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-de41c8b0a5c54f3b957d32715b2256762025-01-16T00:01:30ZengIEEEIEEE Access2169-35362024-01-011219476219477310.1109/ACCESS.2024.352055010807179WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation ScoresYanjie Ren0https://orcid.org/0009-0001-6849-7864Azlan Mohd Zain1https://orcid.org/0000-0003-2004-3289Yan Zhang2Rozita Abdul Jalil3https://orcid.org/0009-0003-2597-4039Mahadi Bahari4https://orcid.org/0000-0003-0301-374XNorfadzlan Bin Yusup5https://orcid.org/0000-0002-8913-8203Mazlina Abdul Majid6https://orcid.org/0000-0001-9068-7368Azurah A. Samah7https://orcid.org/0000-0002-3639-5038Didik Dwi Prasetya8https://orcid.org/0000-0002-3540-2961Nurhafizah Moziyana Mohd Yusop9https://orcid.org/0000-0003-0394-7710Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaHebei Institute of Mechanical and Electrical Technology, Xingtai, ChinaFaculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, MalaysiaFaculty of Management, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, MalaysiaFaculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pahang, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Engineering, State University of Malang, Malang, IndonesiaFaculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, MalaysiaGenetic and genomic variations are primary drivers of tumor development. Identifying driver genes from numerous passenger genes across pan-cancer poses a significant challenge due to varying mutation loads. While independent studies have elucidated cancer-associated mutation patterns within specific cancer types, a systematic approach to integrating these mutation data for assessing the impact of gene mutations has been lacking. This study addresses this gap by integrating pan-cancer genomic somatic mutation data and introducing a novel mutation weight fusion (WeiFu) score calculation method. WeiFu computes frequency and weighted fusion scores by cancer type, facilitating the identification of potential driver genes. Evaluation results on an integrated pan-cancer dataset comprising 29 different cancer types demonstrate that WeiFu significantly outperforms current well-known approaches in prediction accuracy, sensitivity, and specificity. Notably, WeiFu recovers 277 known cancer genes among the top 500 ranked candidates and successfully identifies potential driver genes supported by strong evidence. Consequently, WeiFu shows considerable promise for identifying driver genes within the rapidly expanding corpus of cancer genomic data.https://ieeexplore.ieee.org/document/10807179/Driver genepan-cancersomatic mutationcancer incidence weighting |
| spellingShingle | Yanjie Ren Azlan Mohd Zain Yan Zhang Rozita Abdul Jalil Mahadi Bahari Norfadzlan Bin Yusup Mazlina Abdul Majid Azurah A. Samah Didik Dwi Prasetya Nurhafizah Moziyana Mohd Yusop WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation Scores IEEE Access Driver gene pan-cancer somatic mutation cancer incidence weighting |
| title | WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation Scores |
| title_full | WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation Scores |
| title_fullStr | WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation Scores |
| title_full_unstemmed | WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation Scores |
| title_short | WeiFu: A Novel Pan-Cancer Driver Gene Identification Method Using Incidence-Weighted Mutation Scores |
| title_sort | weifu a novel pan cancer driver gene identification method using incidence weighted mutation scores |
| topic | Driver gene pan-cancer somatic mutation cancer incidence weighting |
| url | https://ieeexplore.ieee.org/document/10807179/ |
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