Identification of the molecular subtypes and construction of risk models in neuroblastoma
Abstract The heterogeneity of neuroblastoma directly affects the prognosis of patients. Individualization of patient treatment to improve prognosis is a clinical challenge at this stage and the aim of this study is to characterize different patient populations. To achieve this, immune-related cell c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35401-3 |
| _version_ | 1827895192595726336 |
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| author | Enyang He Bowen Shi Ziyu Liu Kaili Chang Hailan Zhao Wei Zhao Hualei Cui |
| author_facet | Enyang He Bowen Shi Ziyu Liu Kaili Chang Hailan Zhao Wei Zhao Hualei Cui |
| author_sort | Enyang He |
| collection | DOAJ |
| description | Abstract The heterogeneity of neuroblastoma directly affects the prognosis of patients. Individualization of patient treatment to improve prognosis is a clinical challenge at this stage and the aim of this study is to characterize different patient populations. To achieve this, immune-related cell cycle genes, identified in the GSE45547 dataset using WGCNA, were used to classify cases from multiple datasets (GSE45547, GSE49710, GSE73517, GES120559, E-MTAB-8248, and TARGET) into subgroups by consensus clustering. ESTIMATES, CIBERSORT and ssGSEA were used to assess the immune status of the patients. And a 7-gene risk model was constructed based on differentially expressed genes between subtypes using randomForestSRC and LASSO. Enrichment analysis was used to demonstrate the biological characteristics between different groups. Key genes were screened using randomForest to construct neural network and validated. Finally, drug sensitivity was assessed in the GSCA and CellMiner databases. We classified the 1811 patients into two subtypes based on immune-related cell cycle genes. The two subtypes (Cluster1 and Cluster2) exhibited distinct clinical features, immune levels, chromosomal instability and prognosis. The same significant differences were demonstrated between the high-risk and low-risk groups. Through our analysis, we identified neuroblastoma subtypes with unique characteristics and established risk models which will improve our understanding of neuroblastoma heterogeneity. |
| first_indexed | 2024-03-12T22:18:00Z |
| format | Article |
| id | doaj.art-84fd2d1dc942494cbbbc123993230967 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T22:18:00Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-84fd2d1dc942494cbbbc1239932309672023-07-23T11:13:25ZengNature PortfolioScientific Reports2045-23222023-07-0113111910.1038/s41598-023-35401-3Identification of the molecular subtypes and construction of risk models in neuroblastomaEnyang He0Bowen Shi1Ziyu Liu2Kaili Chang3Hailan Zhao4Wei Zhao5Hualei Cui6Tianjin Medical UniversityTianjin Medical UniversityTianjin Medical UniversityTianjin Medical UniversityTianjin Medical UniversityTianjin Medical UniversityTianjin Medical UniversityAbstract The heterogeneity of neuroblastoma directly affects the prognosis of patients. Individualization of patient treatment to improve prognosis is a clinical challenge at this stage and the aim of this study is to characterize different patient populations. To achieve this, immune-related cell cycle genes, identified in the GSE45547 dataset using WGCNA, were used to classify cases from multiple datasets (GSE45547, GSE49710, GSE73517, GES120559, E-MTAB-8248, and TARGET) into subgroups by consensus clustering. ESTIMATES, CIBERSORT and ssGSEA were used to assess the immune status of the patients. And a 7-gene risk model was constructed based on differentially expressed genes between subtypes using randomForestSRC and LASSO. Enrichment analysis was used to demonstrate the biological characteristics between different groups. Key genes were screened using randomForest to construct neural network and validated. Finally, drug sensitivity was assessed in the GSCA and CellMiner databases. We classified the 1811 patients into two subtypes based on immune-related cell cycle genes. The two subtypes (Cluster1 and Cluster2) exhibited distinct clinical features, immune levels, chromosomal instability and prognosis. The same significant differences were demonstrated between the high-risk and low-risk groups. Through our analysis, we identified neuroblastoma subtypes with unique characteristics and established risk models which will improve our understanding of neuroblastoma heterogeneity.https://doi.org/10.1038/s41598-023-35401-3 |
| spellingShingle | Enyang He Bowen Shi Ziyu Liu Kaili Chang Hailan Zhao Wei Zhao Hualei Cui Identification of the molecular subtypes and construction of risk models in neuroblastoma Scientific Reports |
| title | Identification of the molecular subtypes and construction of risk models in neuroblastoma |
| title_full | Identification of the molecular subtypes and construction of risk models in neuroblastoma |
| title_fullStr | Identification of the molecular subtypes and construction of risk models in neuroblastoma |
| title_full_unstemmed | Identification of the molecular subtypes and construction of risk models in neuroblastoma |
| title_short | Identification of the molecular subtypes and construction of risk models in neuroblastoma |
| title_sort | identification of the molecular subtypes and construction of risk models in neuroblastoma |
| url | https://doi.org/10.1038/s41598-023-35401-3 |
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