A novel mesothelioma molecular classification based on malignant cell differentiation
Abstract Background The high heterogeneity and multi-directional poor differentiation of tumor cells in mesothelioma (MESO) contributes to tumor growth and malignant biological behaviors. However, a molecular classification based on differentiated states of tumor cells remains void. Methods We perfo...
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BMC
2025-06-01
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| Series: | Cancer Cell International |
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| Online Access: | https://doi.org/10.1186/s12935-025-03816-9 |
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| author | Jun Liu Yifan Liu Yuwei Lu Wei Zhang Jiale Yan Bingnan Lu Yuntao Yao Shuyuan Xian Donghao Lyu Jiaying Shi Yuanan Li Xinru Wu Chenguang Bai Jie Zhang Yuan Zhang |
| author_facet | Jun Liu Yifan Liu Yuwei Lu Wei Zhang Jiale Yan Bingnan Lu Yuntao Yao Shuyuan Xian Donghao Lyu Jiaying Shi Yuanan Li Xinru Wu Chenguang Bai Jie Zhang Yuan Zhang |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | Abstract Background The high heterogeneity and multi-directional poor differentiation of tumor cells in mesothelioma (MESO) contributes to tumor growth and malignant biological behaviors. However, a molecular classification based on differentiated states of tumor cells remains void. Methods We performed dimensionality reduction analysis on the single-cell RNA sequencing profiles available from the GEO database, to visualize the cell types in MESO. Multi-omics analysis was done to supplement the plausibility of classification. We also constructed regulatory networks to detect the function of important tumor cell differential genes (TCDGs) in the MESO. Results Following twice dimensionality reduction analysis and clustering, eight malignant cell subtypes in the MESO were visualized. According to the expression of TCDGs, MESO was classified into three subtypes (Malignant differentiation-related MESO, Benign differentiation-related MESO, and Neutral differentiation-related MESO) with prognostic differences. The prediction model was built by 12 key TCDGs (ALDH2, HP, CASP1, RTP4, PDZK1IP1, TOP2A, LOXL2, CKS2, SPARC, TLCD3A, C6orf99, and SERPINH1) and validated with high accuracy. In the regulatory networks of MESO subtypes, RTP4, CASP1, MYO1B, SLC7A5, LOXL2, and GHR were labeled as key genes. A total of 14 potential inhibitors were predicted. Clinical specimens validated the reliability of the clinical subtyping of MESO patients. Conclusion The novel molecular classification system and the prognostic prediction model might benefit the management of MESO patients. |
| format | Article |
| id | doaj-art-7a93158bf13a46a6ae498c3c4bbda4fd |
| institution | Kabale University |
| issn | 1475-2867 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj-art-7a93158bf13a46a6ae498c3c4bbda4fd2025-08-20T03:27:10ZengBMCCancer Cell International1475-28672025-06-0125112110.1186/s12935-025-03816-9A novel mesothelioma molecular classification based on malignant cell differentiationJun Liu0Yifan Liu1Yuwei Lu2Wei Zhang3Jiale Yan4Bingnan Lu5Yuntao Yao6Shuyuan Xian7Donghao Lyu8Jiaying Shi9Yuanan Li10Xinru Wu11Chenguang Bai12Jie Zhang13Yuan Zhang14Department of Anesthesiology, Shanghai Pulmonary Hospital Affiliated to Tongji University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Jiao Tong University School of MedicineDepartment of Burn Surgery, The First Affiliated Hospital of Naval Medical UniversityDepartment of Gynecology, Shanghai First Maternity and Infant Hospital Affiliated to Tongji University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineDepartment of Burn Surgery, The First Affiliated Hospital of Naval Medical UniversityDepartment of Urology, Xinhua Hospital Affiliated to Shanghai 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, The First Affiliated Hospital of Naval Medical UniversityDepartment of Gynecology, Shanghai First Maternity and Infant Hospital Affiliated to Tongji University School of MedicineDepartment of Pulmonary and Critical Care Medicine, Shanghai Pulmonary Hospital Affiliated to Tongji University School of MedicineAbstract Background The high heterogeneity and multi-directional poor differentiation of tumor cells in mesothelioma (MESO) contributes to tumor growth and malignant biological behaviors. However, a molecular classification based on differentiated states of tumor cells remains void. Methods We performed dimensionality reduction analysis on the single-cell RNA sequencing profiles available from the GEO database, to visualize the cell types in MESO. Multi-omics analysis was done to supplement the plausibility of classification. We also constructed regulatory networks to detect the function of important tumor cell differential genes (TCDGs) in the MESO. Results Following twice dimensionality reduction analysis and clustering, eight malignant cell subtypes in the MESO were visualized. According to the expression of TCDGs, MESO was classified into three subtypes (Malignant differentiation-related MESO, Benign differentiation-related MESO, and Neutral differentiation-related MESO) with prognostic differences. The prediction model was built by 12 key TCDGs (ALDH2, HP, CASP1, RTP4, PDZK1IP1, TOP2A, LOXL2, CKS2, SPARC, TLCD3A, C6orf99, and SERPINH1) and validated with high accuracy. In the regulatory networks of MESO subtypes, RTP4, CASP1, MYO1B, SLC7A5, LOXL2, and GHR were labeled as key genes. A total of 14 potential inhibitors were predicted. Clinical specimens validated the reliability of the clinical subtyping of MESO patients. Conclusion The novel molecular classification system and the prognostic prediction model might benefit the management of MESO patients.https://doi.org/10.1186/s12935-025-03816-9Mesothelioma (MESO)Single-cell RNA sequencing (scRNA-seq)Tumor heterogeneityGene classificationPrognostic prediction model |
| spellingShingle | Jun Liu Yifan Liu Yuwei Lu Wei Zhang Jiale Yan Bingnan Lu Yuntao Yao Shuyuan Xian Donghao Lyu Jiaying Shi Yuanan Li Xinru Wu Chenguang Bai Jie Zhang Yuan Zhang A novel mesothelioma molecular classification based on malignant cell differentiation Cancer Cell International Mesothelioma (MESO) Single-cell RNA sequencing (scRNA-seq) Tumor heterogeneity Gene classification Prognostic prediction model |
| title | A novel mesothelioma molecular classification based on malignant cell differentiation |
| title_full | A novel mesothelioma molecular classification based on malignant cell differentiation |
| title_fullStr | A novel mesothelioma molecular classification based on malignant cell differentiation |
| title_full_unstemmed | A novel mesothelioma molecular classification based on malignant cell differentiation |
| title_short | A novel mesothelioma molecular classification based on malignant cell differentiation |
| title_sort | novel mesothelioma molecular classification based on malignant cell differentiation |
| topic | Mesothelioma (MESO) Single-cell RNA sequencing (scRNA-seq) Tumor heterogeneity Gene classification Prognostic prediction model |
| url | https://doi.org/10.1186/s12935-025-03816-9 |
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