Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases
The combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify novel biomarkers, and develop targeted therapeutic strategies. Polycythemia vera (PV) is a chronic myeloproliferative neoplasm associated with a risk of acute myeloid leukemia (AML) transformation. H...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Health
2025-06-01
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| Series: | Blood Science |
| Online Access: | http://journals.lww.com/10.1097/BS9.0000000000000226 |
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| author | Hua Liu Sheng Lin Pei-Xuan Chen Juan Min Xia-Yang Liu Ting Guan Chao-Ying Yang Xiao-Juan Xiao De-Hui Xiong Sheng-Jie Sun Ling Nie Han Gong Xu-Sheng Wu Xiao-Feng He Jing Liu |
| author_facet | Hua Liu Sheng Lin Pei-Xuan Chen Juan Min Xia-Yang Liu Ting Guan Chao-Ying Yang Xiao-Juan Xiao De-Hui Xiong Sheng-Jie Sun Ling Nie Han Gong Xu-Sheng Wu Xiao-Feng He Jing Liu |
| author_sort | Hua Liu |
| collection | DOAJ |
| description | The combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify novel biomarkers, and develop targeted therapeutic strategies. Polycythemia vera (PV) is a chronic myeloproliferative neoplasm associated with a risk of acute myeloid leukemia (AML) transformation. However, the chronic nature of disease transformation complicates longitudinal high-throughput sequencing studies of patients with PV before and after AML transformation. This study aimed to develop a diagnostic model for malignant transformation of chronic proliferative diseases, addressing the challenges of early detection and intervention. Integrated public datasets of PV and AML were analyzed to identify differentially expressed genes (DEGs) and construct a weighted correlation network. Machine-learning algorithms screen genes for potential biomarkers, leading to the development of diagnostic models. Clinical specimens were collected to validate gene expression. cMAP and molecular docking predicted potential drugs. In vitro experiments were performed to assess drug efficacy in PV and AML cells. CIBERSORT and single-cell RNA-sequencing (scRNA-seq) analyses were used to explore the impact of hub genes on the tumor microenvironment. We identified 24 genes shared between PV and AML, which were enriched in immune-related pathways. Lactoferrin (LTF) and G protein-coupled receptor 65 (GPR65) were integrated into a nomogram with a robust predictive power. The predicted drug vemurafenib inhibited proliferation and increased apoptosis in PV and AML cells. TME analysis has linked these biomarkers to macrophages. Clinical samples were used to confirm LTF and GPR65 expression levels. We identified shared genes between PV and AML and developed a diagnostic nomogram that offers a novel avenue for the diagnosis and clinical management of AML-related PV. |
| format | Article |
| id | doaj-art-c9812f46fbce4e428174f63738ff0cec |
| institution | Kabale University |
| issn | 2543-6368 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wolters Kluwer Health |
| record_format | Article |
| series | Blood Science |
| spelling | doaj-art-c9812f46fbce4e428174f63738ff0cec2025-08-20T03:53:38ZengWolters Kluwer HealthBlood Science2543-63682025-06-0172e0022610.1097/BS9.0000000000000226202506000-00011Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseasesHua Liu0Sheng Lin1Pei-Xuan Chen2Juan Min3Xia-Yang Liu4Ting Guan5Chao-Ying Yang6Xiao-Juan Xiao7De-Hui Xiong8Sheng-Jie Sun9Ling Nie10Han Gong11Xu-Sheng Wu12Xiao-Feng He13Jing Liu14a Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinac Department of Hematology, Xiangya Hospital, Central South University, Changsha 410078, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, Chinaa Shenzhen Health Development Research and Data Management Center, Shenzhen 518028, ChinaThe combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify novel biomarkers, and develop targeted therapeutic strategies. Polycythemia vera (PV) is a chronic myeloproliferative neoplasm associated with a risk of acute myeloid leukemia (AML) transformation. However, the chronic nature of disease transformation complicates longitudinal high-throughput sequencing studies of patients with PV before and after AML transformation. This study aimed to develop a diagnostic model for malignant transformation of chronic proliferative diseases, addressing the challenges of early detection and intervention. Integrated public datasets of PV and AML were analyzed to identify differentially expressed genes (DEGs) and construct a weighted correlation network. Machine-learning algorithms screen genes for potential biomarkers, leading to the development of diagnostic models. Clinical specimens were collected to validate gene expression. cMAP and molecular docking predicted potential drugs. In vitro experiments were performed to assess drug efficacy in PV and AML cells. CIBERSORT and single-cell RNA-sequencing (scRNA-seq) analyses were used to explore the impact of hub genes on the tumor microenvironment. We identified 24 genes shared between PV and AML, which were enriched in immune-related pathways. Lactoferrin (LTF) and G protein-coupled receptor 65 (GPR65) were integrated into a nomogram with a robust predictive power. The predicted drug vemurafenib inhibited proliferation and increased apoptosis in PV and AML cells. TME analysis has linked these biomarkers to macrophages. Clinical samples were used to confirm LTF and GPR65 expression levels. We identified shared genes between PV and AML and developed a diagnostic nomogram that offers a novel avenue for the diagnosis and clinical management of AML-related PV.http://journals.lww.com/10.1097/BS9.0000000000000226 |
| spellingShingle | Hua Liu Sheng Lin Pei-Xuan Chen Juan Min Xia-Yang Liu Ting Guan Chao-Ying Yang Xiao-Juan Xiao De-Hui Xiong Sheng-Jie Sun Ling Nie Han Gong Xu-Sheng Wu Xiao-Feng He Jing Liu Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases Blood Science |
| title | Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases |
| title_full | Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases |
| title_fullStr | Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases |
| title_full_unstemmed | Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases |
| title_short | Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases |
| title_sort | integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases |
| url | http://journals.lww.com/10.1097/BS9.0000000000000226 |
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