OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma
Leiomyosarcoma (LMS) is one of the most aggressive tumors originating from smooth muscle cells, characterized by a high recurrence rate and frequent distant metastasis. Despite advancements in targeted therapies and immunotherapies, these interventions have failed to significantly improve the long-...
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| Format: | Article |
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Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina
2025-06-01
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| Series: | Biomolecules & Biomedicine |
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| Online Access: | https://www.bjbms.org/ojs/index.php/bjbms/article/view/12342 |
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| author | Jingrong Deng Changfa Shu Dong Wang Richard Nimbona Xingping Zhao Dabao Xu |
| author_facet | Jingrong Deng Changfa Shu Dong Wang Richard Nimbona Xingping Zhao Dabao Xu |
| author_sort | Jingrong Deng |
| collection | DOAJ |
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Leiomyosarcoma (LMS) is one of the most aggressive tumors originating from smooth muscle cells, characterized by a high recurrence rate and frequent distant metastasis. Despite advancements in targeted therapies and immunotherapies, these interventions have failed to significantly improve the long-term prognosis for LMS patients. Here, we identified OncoImmune differential expressed genes (DEGs) that influence monocytes differentiation and the progression of LMS, revealing varied immune activation states of LMS patients. Using a machine learning approach, we developed a prognostic model based on OncoImmune hub DEGs, which offers a moderate accuracy in predicting risk levels among LMS patients. Mechanistically, we found that ATRX mutation may regulate coiled-coil domain-containing protein 69 (CCDC69) expression, leading to functional alterations in mast cells and immune unresponsiveness through the modulation of various immune-related signaling pathways. This machine learning-based prognostic model, centered on seven OncoImmune hub DEGs, along with ATRX gene status, represents promising biomarkers for predicting prognosis, molecular characteristics, and immune features in LMS.
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| format | Article |
| id | doaj-art-d89a04e2aeea45b09040db098365775e |
| institution | Kabale University |
| issn | 2831-0896 2831-090X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina |
| record_format | Article |
| series | Biomolecules & Biomedicine |
| spelling | doaj-art-d89a04e2aeea45b09040db098365775e2025-08-20T03:24:56ZengAssociation of Basic Medical Sciences of Federation of Bosnia and HerzegovinaBiomolecules & Biomedicine2831-08962831-090X2025-06-0110.17305/bb.2025.12342OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcomaJingrong Deng0Changfa Shu1https://orcid.org/0000-0002-6216-5623Dong Wang2Richard Nimbona3Xingping Zhao4Dabao Xu5Department of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, ChinaDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Branch of National Clinical Research Center for Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Center for Gynecological Disease and Reproductive Health, Furong Laboratory, Changsha, Hunan, ChinaDepartment of Orthopedics, The Third Xiangya Hospital of Central South University, Changsha, Hunan, ChinaDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, ChinaDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Branch of National Clinical Research Center for Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Center for Gynecological Disease and Reproductive Health, Furong Laboratory, Changsha, Hunan, ChinaDepartment of Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Branch of National Clinical Research Center for Obstetrics and Gynecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China; Center for Gynecological Disease and Reproductive Health, Furong Laboratory, Changsha, Hunan, China Leiomyosarcoma (LMS) is one of the most aggressive tumors originating from smooth muscle cells, characterized by a high recurrence rate and frequent distant metastasis. Despite advancements in targeted therapies and immunotherapies, these interventions have failed to significantly improve the long-term prognosis for LMS patients. Here, we identified OncoImmune differential expressed genes (DEGs) that influence monocytes differentiation and the progression of LMS, revealing varied immune activation states of LMS patients. Using a machine learning approach, we developed a prognostic model based on OncoImmune hub DEGs, which offers a moderate accuracy in predicting risk levels among LMS patients. Mechanistically, we found that ATRX mutation may regulate coiled-coil domain-containing protein 69 (CCDC69) expression, leading to functional alterations in mast cells and immune unresponsiveness through the modulation of various immune-related signaling pathways. This machine learning-based prognostic model, centered on seven OncoImmune hub DEGs, along with ATRX gene status, represents promising biomarkers for predicting prognosis, molecular characteristics, and immune features in LMS. https://www.bjbms.org/ojs/index.php/bjbms/article/view/12342LeiomyosarcomaLMSmonocyte differentiationATRXimmune responsemachine learning |
| spellingShingle | Jingrong Deng Changfa Shu Dong Wang Richard Nimbona Xingping Zhao Dabao Xu OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma Biomolecules & Biomedicine Leiomyosarcoma LMS monocyte differentiation ATRX immune response machine learning |
| title | OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma |
| title_full | OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma |
| title_fullStr | OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma |
| title_full_unstemmed | OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma |
| title_short | OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma |
| title_sort | oncoimmune machine learning model predicts immune response and prognosis in leiomyosarcoma |
| topic | Leiomyosarcoma LMS monocyte differentiation ATRX immune response machine learning |
| url | https://www.bjbms.org/ojs/index.php/bjbms/article/view/12342 |
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