Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis
Abstract Background Osteosarcoma (OS) is the most common primary bone malignancy with variable molecular biology and prognosis. However, our understanding of the association between cell types and OS progression remains poor. Methods We generated a human OS cell atlas by integrating over 110,000 sin...
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2025-02-01
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| Online Access: | https://doi.org/10.1186/s12885-025-13714-y |
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| author | Feng Liu Tingting Zhang Yongqiang Yang Kailun Wang Jinlan Wei Ji-Hua Shi Dong Zhang Xia Sheng Yi Zhang Jing Zhou Faming Zhao |
| author_facet | Feng Liu Tingting Zhang Yongqiang Yang Kailun Wang Jinlan Wei Ji-Hua Shi Dong Zhang Xia Sheng Yi Zhang Jing Zhou Faming Zhao |
| author_sort | Feng Liu |
| collection | DOAJ |
| description | Abstract Background Osteosarcoma (OS) is the most common primary bone malignancy with variable molecular biology and prognosis. However, our understanding of the association between cell types and OS progression remains poor. Methods We generated a human OS cell atlas by integrating over 110,000 single cells from 17 samples. Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. The Scissor algorithm and gene enrichment analyses were conducted to delve into cell-intrinsic molecular characteristics linked to OS prognosis. Moreover, the study investigated the impact of ATF6α in OS aggressiveness through genetic and pharmacological loss of function analyses. Lastly, the CellChat algorithm was employed to investigate cell-cell communications. Results Utilizing the high-quality human OS cell atlas, we identified tumor purity as a prognostic indicator and developed a robust tumor purity prediction model. We respectively delineated cancer cell- and immune cell-intrinsic molecular characteristics associated with OS prognosis at single-cell resolution. Interestingly, tumor cells with activated unfolded protein response (UPR) pathway were significantly associated with disease aggressiveness. Notably, ATF6α emerged as the top-activated transcription factor for this tumor subcluster. Subsequently, we confirmed that ATF6α was markedly associated with OS progression, while both genetic and pharmacological inhibition of ATF6α impaired the survival of HOS cells. Lastly, we depicted the landscape of signal crosstalk between the UPR-related subcluster and other cell types within the tumor microenvironment. Conclusion In summary, our work provides novel insights into the molecular biology of OS, and offers valuable resource for OS biomarker discovery and treatment strategy development. |
| format | Article |
| id | doaj-art-e2a2c3746f7a4867a88722ae2d3999b7 |
| institution | DOAJ |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-e2a2c3746f7a4867a88722ae2d3999b72025-08-20T03:10:57ZengBMCBMC Cancer1471-24072025-02-0125111910.1186/s12885-025-13714-yIntegrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosisFeng Liu0Tingting Zhang1Yongqiang Yang2Kailun Wang3Jinlan Wei4Ji-Hua Shi5Dong Zhang6Xia Sheng7Yi Zhang8Jing Zhou9Faming Zhao10Department of Hand/Foot/Ankle Surgery, Qujing Affiliated Hospital of Kunming Medical UniversitySchool of Life and Health Sciences, Hainan UniversityDepartment of Hand/Foot/Ankle Surgery, Qujing Affiliated Hospital of Kunming Medical UniversitySchool of Life and Health Sciences, Hainan UniversitySchool of Life and Health Sciences, Hainan UniversityDepartment of Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Breast and Thyroid Surgery, Shandong Provincial Hospital, Shandong First Medical UniversityDepartment of Hand/Foot/Ankle Surgery, Qujing Affiliated Hospital of Kunming Medical UniversityDepartment of Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologySchool of Life and Health Sciences, Hainan UniversityAbstract Background Osteosarcoma (OS) is the most common primary bone malignancy with variable molecular biology and prognosis. However, our understanding of the association between cell types and OS progression remains poor. Methods We generated a human OS cell atlas by integrating over 110,000 single cells from 17 samples. Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. The Scissor algorithm and gene enrichment analyses were conducted to delve into cell-intrinsic molecular characteristics linked to OS prognosis. Moreover, the study investigated the impact of ATF6α in OS aggressiveness through genetic and pharmacological loss of function analyses. Lastly, the CellChat algorithm was employed to investigate cell-cell communications. Results Utilizing the high-quality human OS cell atlas, we identified tumor purity as a prognostic indicator and developed a robust tumor purity prediction model. We respectively delineated cancer cell- and immune cell-intrinsic molecular characteristics associated with OS prognosis at single-cell resolution. Interestingly, tumor cells with activated unfolded protein response (UPR) pathway were significantly associated with disease aggressiveness. Notably, ATF6α emerged as the top-activated transcription factor for this tumor subcluster. Subsequently, we confirmed that ATF6α was markedly associated with OS progression, while both genetic and pharmacological inhibition of ATF6α impaired the survival of HOS cells. Lastly, we depicted the landscape of signal crosstalk between the UPR-related subcluster and other cell types within the tumor microenvironment. Conclusion In summary, our work provides novel insights into the molecular biology of OS, and offers valuable resource for OS biomarker discovery and treatment strategy development.https://doi.org/10.1186/s12885-025-13714-yOsteosarcomaSingle-cell RNA-seqUnfolded protein responseATF6αPrognosis |
| spellingShingle | Feng Liu Tingting Zhang Yongqiang Yang Kailun Wang Jinlan Wei Ji-Hua Shi Dong Zhang Xia Sheng Yi Zhang Jing Zhou Faming Zhao Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis BMC Cancer Osteosarcoma Single-cell RNA-seq Unfolded protein response ATF6α Prognosis |
| title | Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis |
| title_full | Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis |
| title_fullStr | Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis |
| title_full_unstemmed | Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis |
| title_short | Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis |
| title_sort | integrated analysis of single cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis |
| topic | Osteosarcoma Single-cell RNA-seq Unfolded protein response ATF6α Prognosis |
| url | https://doi.org/10.1186/s12885-025-13714-y |
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