Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer
Abstract The role of mitochondria in tumorigenesis and progression is has been increasingly demonstrated by numerous studies, but its prognostic value in colorectal cancer (CRC) remains unclear. To address this, we developed a mitochondrial-related gene prognostic model using 101 combinations of 10...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02462-x |
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| author | Shaoke Wang Yien Li Zhihui Wang Changhui Geng Peng Chen Zhengang Li Chenxu Li Xuefeng Bai |
| author_facet | Shaoke Wang Yien Li Zhihui Wang Changhui Geng Peng Chen Zhengang Li Chenxu Li Xuefeng Bai |
| author_sort | Shaoke Wang |
| collection | DOAJ |
| description | Abstract The role of mitochondria in tumorigenesis and progression is has been increasingly demonstrated by numerous studies, but its prognostic value in colorectal cancer (CRC) remains unclear. To address this, we developed a mitochondrial-related gene prognostic model using 101 combinations of 10 machine learning algorithms. Patients in the high-risk group exhibited significantly shorter overall survival time. The high-risk group exhibited elevated tumor immune dysfunction and exclusion score, indicating diminished immunotherapy efficacy. To address the suboptimal treatment outcomes in these patients, we identified PYR-41 and pentostatin as potential therapeutic agents, which are anticipated to enhance therapeutic efficacy in the high-risk group. Additionally, four biomarkers (HSPA1A, CHDH, TRAP1, CDC25C) were validated by quantitative real-time PCR, with significant expression differences between normal intestinal epithelial cells and colon cancer cells. Our prognostic model provides accurate CRC outcome prediction and guides personalized therapeutic strategies. |
| format | Article |
| id | doaj-art-0e288ae67f144e2fa5d66352ac37b98a |
| institution | OA Journals |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-0e288ae67f144e2fa5d66352ac37b98a2025-08-20T02:10:54ZengSpringerDiscover Oncology2730-60112025-05-0116111710.1007/s12672-025-02462-xConstructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancerShaoke Wang0Yien Li1Zhihui Wang2Changhui Geng3Peng Chen4Zhengang Li5Chenxu Li6Xuefeng Bai7Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityDepartment of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin Medical UniversityAbstract The role of mitochondria in tumorigenesis and progression is has been increasingly demonstrated by numerous studies, but its prognostic value in colorectal cancer (CRC) remains unclear. To address this, we developed a mitochondrial-related gene prognostic model using 101 combinations of 10 machine learning algorithms. Patients in the high-risk group exhibited significantly shorter overall survival time. The high-risk group exhibited elevated tumor immune dysfunction and exclusion score, indicating diminished immunotherapy efficacy. To address the suboptimal treatment outcomes in these patients, we identified PYR-41 and pentostatin as potential therapeutic agents, which are anticipated to enhance therapeutic efficacy in the high-risk group. Additionally, four biomarkers (HSPA1A, CHDH, TRAP1, CDC25C) were validated by quantitative real-time PCR, with significant expression differences between normal intestinal epithelial cells and colon cancer cells. Our prognostic model provides accurate CRC outcome prediction and guides personalized therapeutic strategies.https://doi.org/10.1007/s12672-025-02462-xColorectal cancerMitochondrionMachine learningPrognosisBiomarkers |
| spellingShingle | Shaoke Wang Yien Li Zhihui Wang Changhui Geng Peng Chen Zhengang Li Chenxu Li Xuefeng Bai Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer Discover Oncology Colorectal cancer Mitochondrion Machine learning Prognosis Biomarkers |
| title | Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer |
| title_full | Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer |
| title_fullStr | Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer |
| title_full_unstemmed | Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer |
| title_short | Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer |
| title_sort | constructing a mitochondrial related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer |
| topic | Colorectal cancer Mitochondrion Machine learning Prognosis Biomarkers |
| url | https://doi.org/10.1007/s12672-025-02462-x |
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