A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma
Abstract Background Malignant mesothelioma is a type of infrequent tumor that is substantially related to asbestos exposure and has a terrible prognosis. We tried to produce a fibroblast differentiation-related gene set for creating a novel classification and prognostic prediction model of MESO. Met...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Language: | English |
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BMC
2024-03-01
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| Series: | Cell & Bioscience |
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| Online Access: | https://doi.org/10.1186/s13578-023-01180-7 |
| _version_ | 1797259010013921280 |
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| author | Jun Liu Yuwei Lu Yifan Liu Wei Zhang Shuyuan Xian Siqiao Wang Zixuan Zheng Ruoyi Lin Minghao Jin Mengyi Zhang Weijin Qian Jieling Tang Bingnan Lu Yiting Yang Zichang Liu Mingyu Qu Haonan Ma Xinru Wu Zhengyan Chang Jie Zhang Yuan Zhang |
| author_facet | Jun Liu Yuwei Lu Yifan Liu Wei Zhang Shuyuan Xian Siqiao Wang Zixuan Zheng Ruoyi Lin Minghao Jin Mengyi Zhang Weijin Qian Jieling Tang Bingnan Lu Yiting Yang Zichang Liu Mingyu Qu Haonan Ma Xinru Wu Zhengyan Chang Jie Zhang Yuan Zhang |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | Abstract Background Malignant mesothelioma is a type of infrequent tumor that is substantially related to asbestos exposure and has a terrible prognosis. We tried to produce a fibroblast differentiation-related gene set for creating a novel classification and prognostic prediction model of MESO. Method Three databases, including NCBI-GEO, TCGA, and MET-500, separately provide single-cell RNA sequencing data, bulk RNA sequencing profiles of MESO, and RNA sequencing information on bone metastatic tumors. Dimensionality reduction and clustering analysis were leveraged to acquire fibroblast subtypes in the MESO microenvironment. The fibroblast differentiation-related genes (FDGs), which were associated with survival and subsequently utilized to generate the MESO categorization and prognostic prediction model, were selected in combination with pseudotime analysis and survival information from the TCGA database. Then, regulatory network was constructed for each MESO subtype, and candidate inhibitors were predicted. Clinical specimens were collected for further validation. Result A total of six fibroblast subtypes, three differentiation states, and 39 FDGs were identified. Based on the expression level of FDGs, three MESO subtypes were distinguished in the fibroblast differentiation-based classification (FDBC). In the multivariate prognostic prediction model, the risk score that was dependent on the expression level of several important FDGs, was verified to be an independently effective prognostic factor and worked well in internal cohorts. Finally, we predicted 24 potential drugs for the treatment of MESO. Moreover, immunohistochemical staining and statistical analysis provided further validation. Conclusion Fibroblast differentiation-related genes (FDGs), especially those in low-differentiation states, might participate in the proliferation and invasion of MESO. Hopefully, the raised clinical subtyping of MESO would provide references for clinical practitioners. |
| first_indexed | 2024-04-24T23:02:37Z |
| format | Article |
| id | doaj.art-638544b8aa61487ca636dadf12ab223b |
| institution | Directory Open Access Journal |
| issn | 2045-3701 |
| language | English |
| last_indexed | 2024-04-24T23:02:37Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Cell & Bioscience |
| spelling | doaj.art-638544b8aa61487ca636dadf12ab223b2024-03-17T12:39:39ZengBMCCell & Bioscience2045-37012024-03-0114112010.1186/s13578-023-01180-7A gene signature linked to fibroblast differentiation for prognostic prediction of mesotheliomaJun Liu0Yuwei Lu1Yifan Liu2Wei Zhang3Shuyuan Xian4Siqiao Wang5Zixuan Zheng6Ruoyi Lin7Minghao Jin8Mengyi Zhang9Weijin Qian10Jieling Tang11Bingnan Lu12Yiting Yang13Zichang Liu14Mingyu Qu15Haonan Ma16Xinru Wu17Zhengyan Chang18Jie Zhang19Yuan Zhang20Department of Anesthesiology, Shanghai Pulmonary Hospital Affiliated to Tongji UniversityShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineDepartment of Burn Surgery, The First Affiliated Hospital of Naval Medical UniversityDepartment of Burn Surgery, The First Affiliated Hospital of Naval Medical UniversityTongji University School of MedicineTongji University School of MedicineTongji University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineDepartment of Pathology, School of Medicine, Shanghai Tenth People’s Hospital, Tongji UniversityDepartment of Gynecology, Shanghai First Maternity and Infant Hospital, Tongji University School of MedicineDepartment of Pulmonary and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University School of MedicineAbstract Background Malignant mesothelioma is a type of infrequent tumor that is substantially related to asbestos exposure and has a terrible prognosis. We tried to produce a fibroblast differentiation-related gene set for creating a novel classification and prognostic prediction model of MESO. Method Three databases, including NCBI-GEO, TCGA, and MET-500, separately provide single-cell RNA sequencing data, bulk RNA sequencing profiles of MESO, and RNA sequencing information on bone metastatic tumors. Dimensionality reduction and clustering analysis were leveraged to acquire fibroblast subtypes in the MESO microenvironment. The fibroblast differentiation-related genes (FDGs), which were associated with survival and subsequently utilized to generate the MESO categorization and prognostic prediction model, were selected in combination with pseudotime analysis and survival information from the TCGA database. Then, regulatory network was constructed for each MESO subtype, and candidate inhibitors were predicted. Clinical specimens were collected for further validation. Result A total of six fibroblast subtypes, three differentiation states, and 39 FDGs were identified. Based on the expression level of FDGs, three MESO subtypes were distinguished in the fibroblast differentiation-based classification (FDBC). In the multivariate prognostic prediction model, the risk score that was dependent on the expression level of several important FDGs, was verified to be an independently effective prognostic factor and worked well in internal cohorts. Finally, we predicted 24 potential drugs for the treatment of MESO. Moreover, immunohistochemical staining and statistical analysis provided further validation. Conclusion Fibroblast differentiation-related genes (FDGs), especially those in low-differentiation states, might participate in the proliferation and invasion of MESO. Hopefully, the raised clinical subtyping of MESO would provide references for clinical practitioners.https://doi.org/10.1186/s13578-023-01180-7MesotheliomaMESOSingle-cell RNA sequencingFibroblastDifferentiation |
| spellingShingle | Jun Liu Yuwei Lu Yifan Liu Wei Zhang Shuyuan Xian Siqiao Wang Zixuan Zheng Ruoyi Lin Minghao Jin Mengyi Zhang Weijin Qian Jieling Tang Bingnan Lu Yiting Yang Zichang Liu Mingyu Qu Haonan Ma Xinru Wu Zhengyan Chang Jie Zhang Yuan Zhang A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma Cell & Bioscience Mesothelioma MESO Single-cell RNA sequencing Fibroblast Differentiation |
| title | A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma |
| title_full | A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma |
| title_fullStr | A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma |
| title_full_unstemmed | A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma |
| title_short | A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma |
| title_sort | gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma |
| topic | Mesothelioma MESO Single-cell RNA sequencing Fibroblast Differentiation |
| url | https://doi.org/10.1186/s13578-023-01180-7 |
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