Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis
Abstract From 2020 to 2022, colorectal cancer (CRC) cases increased, making it the third most common cancer and the second leading cause of cancer-related deaths worldwide. Early detection remains a significant challenge due to the lack of reliable diagnostic biomarkers. This study aimed to develop...
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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/s12672-025-02435-0 |
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| author | Li Zhou Lihua Yu Mingjing Liao Tingting Peng Leilei Zhang Chengyun Han Yuan Li Jiwang Zhang |
| author_facet | Li Zhou Lihua Yu Mingjing Liao Tingting Peng Leilei Zhang Chengyun Han Yuan Li Jiwang Zhang |
| author_sort | Li Zhou |
| collection | DOAJ |
| description | Abstract From 2020 to 2022, colorectal cancer (CRC) cases increased, making it the third most common cancer and the second leading cause of cancer-related deaths worldwide. Early detection remains a significant challenge due to the lack of reliable diagnostic biomarkers. This study aimed to develop a robust gene diagnostic model for CRC using publicly available databases, such as GEO and GEPIA2. The approach integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and the application of 113 machine learning combinations derived from 12 algorithms. The most effective model was then validated using independent datasets, which included analyses such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interaction (PPI) networks, and receiver operating characteristic (ROC) curves, along with assessments of immune infiltration and tumor-node-metastasis (TNM) staging. Notably, the glmBoost + RF algorithm identified an eight-gene diagnostic model with high precision, pinpointing key genes such as CLDN1, IFITM1, and FOXQ1, which exhibited strong diagnostic performance (AUC > 0.9). Furthermore, Mendelian randomization (MR) analysis suggested that IFITM1 may be a potential causal gene for CRC, with significant associations to immune cell profiles and established roles in immune regulation and tumor progression. Collectively, these findings highlight IFITM1, SCGN, and FOXQ1 as promising early diagnostic biomarkers and therapeutic targets for CRC, laying a foundation for future research focused on enhancing early detection and intervention strategies in colorectal cancer management. |
| format | Article |
| id | doaj-art-eee50dc1bdd24b74ba884f7dd33544dd |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-eee50dc1bdd24b74ba884f7dd33544dd2025-08-20T03:09:19ZengSpringerDiscover Oncology2730-60112025-05-0116112010.1007/s12672-025-02435-0Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosisLi Zhou0Lihua Yu1Mingjing Liao2Tingting Peng3Leilei Zhang4Chengyun Han5Yuan Li6Jiwang Zhang7Central Sterile Supply Department, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityDepartment of Clinical Laboratory, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityDepartment of Clinical Laboratory, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityDepartment of Clinical Laboratory, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityDepartment of Clinical Laboratory, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityDepartment of Clinical Laboratory, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityCentral Laboratory, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityDepartment of Clinical Laboratory, The Affiliated Yongchuan Hospital of Chongqing Medical UniversityAbstract From 2020 to 2022, colorectal cancer (CRC) cases increased, making it the third most common cancer and the second leading cause of cancer-related deaths worldwide. Early detection remains a significant challenge due to the lack of reliable diagnostic biomarkers. This study aimed to develop a robust gene diagnostic model for CRC using publicly available databases, such as GEO and GEPIA2. The approach integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and the application of 113 machine learning combinations derived from 12 algorithms. The most effective model was then validated using independent datasets, which included analyses such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interaction (PPI) networks, and receiver operating characteristic (ROC) curves, along with assessments of immune infiltration and tumor-node-metastasis (TNM) staging. Notably, the glmBoost + RF algorithm identified an eight-gene diagnostic model with high precision, pinpointing key genes such as CLDN1, IFITM1, and FOXQ1, which exhibited strong diagnostic performance (AUC > 0.9). Furthermore, Mendelian randomization (MR) analysis suggested that IFITM1 may be a potential causal gene for CRC, with significant associations to immune cell profiles and established roles in immune regulation and tumor progression. Collectively, these findings highlight IFITM1, SCGN, and FOXQ1 as promising early diagnostic biomarkers and therapeutic targets for CRC, laying a foundation for future research focused on enhancing early detection and intervention strategies in colorectal cancer management.https://doi.org/10.1007/s12672-025-02435-0Colorectal cancerDiagnostic modelMachine learningIFITM1Mendelian randomization |
| spellingShingle | Li Zhou Lihua Yu Mingjing Liao Tingting Peng Leilei Zhang Chengyun Han Yuan Li Jiwang Zhang Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis Discover Oncology Colorectal cancer Diagnostic model Machine learning IFITM1 Mendelian randomization |
| title | Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis |
| title_full | Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis |
| title_fullStr | Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis |
| title_full_unstemmed | Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis |
| title_short | Integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis |
| title_sort | integrating machine learning and genetic evidence to uncover novel gene biomarkers for colorectal cancer diagnosis |
| topic | Colorectal cancer Diagnostic model Machine learning IFITM1 Mendelian randomization |
| url | https://doi.org/10.1007/s12672-025-02435-0 |
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