Key Cell-in-Cell Related Genes are Identified by Bioinformatics and Experiments in Glioblastoma
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| Názov: | Key Cell-in-Cell Related Genes are Identified by Bioinformatics and Experiments in Glioblastoma |
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| Autori: | Zhang F, Ye J, Zhu J, Qian W, Wang H, Luo C |
| Zdroj: | Cancer Management and Research, Vol 16, Pp 1109-1130 (2024) |
| Informácie o vydavateľovi: | Dove Medical Press, 2024. |
| Rok vydania: | 2024 |
| Zbierka: | LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Predmety: | glioblastoma, bioinformatics, cell-in-cell, prognosis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| Popis: | Fenglin Zhang,* Jingliang Ye,* Junle Zhu, Wenbo Qian, Haoheng Wang, Chun Luo Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chun Luo, Email luoc_sh@sina.comPurpose: This study aimed to explore the roles of cell-in-cell (CIC)-related genes in glioblastoma (GBM) using bioinformatics and experimental strategies.Patients and Methods: The ssGSEA algorithm was used to calculate the CIC score for each patient. Subsequently, differentially expressed genes (DEGs) between the CIClow and CIChigh groups and between the tumor and control samples were screened using the limma R package. Key CIC-related genes (CICRGs) were further filtered using univariate Cox and LASSO analyses, followed by the construction of a CIC-related risk score model. The performance of the risk score model in predicting GBM prognosis was evaluated using ROC curves and an external validation cohort. Moreover, their location and differentiation trajectory in GBM were analyzed at the single-cell level using the Seurat R package. Finally, the expression of key CICRGs in clinical samples was examined by qPCR.Results: In the current study, we found that CIC scorelow group had a significantly better survival in the TCGA-GBM cohort, supporting the important role of CICRGs in GBM. Using univariate Cox and LASSO analyses, PTX3, TIMP1, IGFBP2, SNCAIP, LOXL1, SLC47A2, and LGALS3 were identified as key CICRGs. Based on this data, a CIC-related prognostic risk score model was built using the TCGA-GBM cohort and validated in the CGGA-GBM cohort. Further mechanistic analyses showed that the CIC-related risk score is closely related to immune and inflammatory responses. Interestingly, at the single-cell level, key CICRGs were expressed in the neurons and myeloids of tumor tissues and exhibited unique temporal dynamics of expression changes. Finally, the expression of key CICRGs was validated by qPCR using clinical samples from GBM patients.Conclusion: We identified novel CIC-related genes and built a reliable prognostic prediction model for GBM, which will provide further basic clues for studying the exact molecular mechanisms of GBM pathogenesis from a CIC perspective.Keywords: glioblastoma, bioinformatics, cell-in-cell, prognosis |
| Druh dokumentu: | article |
| Popis súboru: | electronic resource |
| Jazyk: | English |
| ISSN: | 1179-1322 |
| Relation: | https://www.dovepress.com/key-cell-in-cell-related-genes-are-identified-by-bioinformatics-and-ex-peer-reviewed-fulltext-article-CMAR; https://doaj.org/toc/1179-1322 |
| Prístupová URL adresa: | https://doaj.org/article/a95e37bd57224b39a56e26e58b898b6b |
| Prístupové číslo: | edsdoj.95e37bd57224b39a56e26e58b898b6b |
| Databáza: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | Abstrakt: | Fenglin Zhang,* Jingliang Ye,* Junle Zhu, Wenbo Qian, Haoheng Wang, Chun Luo Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chun Luo, Email luoc_sh@sina.comPurpose: This study aimed to explore the roles of cell-in-cell (CIC)-related genes in glioblastoma (GBM) using bioinformatics and experimental strategies.Patients and Methods: The ssGSEA algorithm was used to calculate the CIC score for each patient. Subsequently, differentially expressed genes (DEGs) between the CIClow and CIChigh groups and between the tumor and control samples were screened using the limma R package. Key CIC-related genes (CICRGs) were further filtered using univariate Cox and LASSO analyses, followed by the construction of a CIC-related risk score model. The performance of the risk score model in predicting GBM prognosis was evaluated using ROC curves and an external validation cohort. Moreover, their location and differentiation trajectory in GBM were analyzed at the single-cell level using the Seurat R package. Finally, the expression of key CICRGs in clinical samples was examined by qPCR.Results: In the current study, we found that CIC scorelow group had a significantly better survival in the TCGA-GBM cohort, supporting the important role of CICRGs in GBM. Using univariate Cox and LASSO analyses, PTX3, TIMP1, IGFBP2, SNCAIP, LOXL1, SLC47A2, and LGALS3 were identified as key CICRGs. Based on this data, a CIC-related prognostic risk score model was built using the TCGA-GBM cohort and validated in the CGGA-GBM cohort. Further mechanistic analyses showed that the CIC-related risk score is closely related to immune and inflammatory responses. Interestingly, at the single-cell level, key CICRGs were expressed in the neurons and myeloids of tumor tissues and exhibited unique temporal dynamics of expression changes. Finally, the expression of key CICRGs was validated by qPCR using clinical samples from GBM patients.Conclusion: We identified novel CIC-related genes and built a reliable prognostic prediction model for GBM, which will provide further basic clues for studying the exact molecular mechanisms of GBM pathogenesis from a CIC perspective.Keywords: glioblastoma, bioinformatics, cell-in-cell, prognosis |
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| ISSN: | 11791322 |