Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia
Abstract Lysosomes have a tight connection to cancer and can eliminate cancer cells. The dismal prognosis of acute myeloid leukemia (AML) patients may thus be improved by a thorough examination of the function of lysosome-related genes (LRGs). By using a variety of machine learning methods including...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-03302-8 |
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| _version_ | 1849235008873562112 |
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| author | Gongchang Li Yangyang Miao Fang Yuan Weiran Zhang Yali Wu Liqiang Zhu |
| author_facet | Gongchang Li Yangyang Miao Fang Yuan Weiran Zhang Yali Wu Liqiang Zhu |
| author_sort | Gongchang Li |
| collection | DOAJ |
| description | Abstract Lysosomes have a tight connection to cancer and can eliminate cancer cells. The dismal prognosis of acute myeloid leukemia (AML) patients may thus be improved by a thorough examination of the function of lysosome-related genes (LRGs). By using a variety of machine learning methods including random forest approach, LASSO-COX regression, and extreme gradient boosting (XGBoost), we create a prognostic six-LRGs-related signature (HPS1, BCAN, SLC2A8, DOC2A, CHMP4C, and SLC29A3), which categorized AML patients into two groups with significant survival and tumor microenvironment (TME) differences. Data from the ICGC and TARGET cohorts were used as test cohorts for the validation of the prognostic LRGs-related signature. We also discovered that chemotherapeutic susceptibility was connected to the LRGs-related signature. Finally, we evaluated gene expression levels in the LRGs-related signature between normal and AML samples and confirmed the elevation of CHMP4C expression in 90 clinical samples. In summary, a six-LRGs-related signature was developed to predict the prognosis of AML patients, and more research is necessary to determine whether this signature has therapeutic promise as an anti-AML target. |
| format | Article |
| id | doaj-art-6356b605d9a842aa8c327dcb2d43e4cd |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-6356b605d9a842aa8c327dcb2d43e4cd2025-08-20T04:02:56ZengSpringerDiscover Oncology2730-60112025-08-0116111210.1007/s12672-025-03302-8Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemiaGongchang Li0Yangyang Miao1Fang Yuan2Weiran Zhang3Yali Wu4Liqiang Zhu5The Second Affiliated Hospital of Zhengzhou UniversityZhengzhou Central Hospital Afflicted of Zhengzhou UniversityThe Second Affiliated Hospital of Zhengzhou UniversityThe Second Affiliated Hospital of Zhengzhou UniversityThe Second Affiliated Hospital of Zhengzhou UniversityThe Second Affiliated Hospital of Zhengzhou UniversityAbstract Lysosomes have a tight connection to cancer and can eliminate cancer cells. The dismal prognosis of acute myeloid leukemia (AML) patients may thus be improved by a thorough examination of the function of lysosome-related genes (LRGs). By using a variety of machine learning methods including random forest approach, LASSO-COX regression, and extreme gradient boosting (XGBoost), we create a prognostic six-LRGs-related signature (HPS1, BCAN, SLC2A8, DOC2A, CHMP4C, and SLC29A3), which categorized AML patients into two groups with significant survival and tumor microenvironment (TME) differences. Data from the ICGC and TARGET cohorts were used as test cohorts for the validation of the prognostic LRGs-related signature. We also discovered that chemotherapeutic susceptibility was connected to the LRGs-related signature. Finally, we evaluated gene expression levels in the LRGs-related signature between normal and AML samples and confirmed the elevation of CHMP4C expression in 90 clinical samples. In summary, a six-LRGs-related signature was developed to predict the prognosis of AML patients, and more research is necessary to determine whether this signature has therapeutic promise as an anti-AML target.https://doi.org/10.1007/s12672-025-03302-8LysosomeAMLMachine learningPrognosisCHMP4C |
| spellingShingle | Gongchang Li Yangyang Miao Fang Yuan Weiran Zhang Yali Wu Liqiang Zhu Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia Discover Oncology Lysosome AML Machine learning Prognosis CHMP4C |
| title | Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia |
| title_full | Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia |
| title_fullStr | Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia |
| title_full_unstemmed | Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia |
| title_short | Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia |
| title_sort | lysosome derived biomarkers for predicting survival outcome in acute myeloid leukemia |
| topic | Lysosome AML Machine learning Prognosis CHMP4C |
| url | https://doi.org/10.1007/s12672-025-03302-8 |
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