Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma
Abstract Background Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5‐year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-05-01
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| Series: | Brain and Behavior |
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| Online Access: | https://doi.org/10.1002/brb3.2575 |
| _version_ | 1797737292210634752 |
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| author | Wenwen Lai Defu Li Jie Kuang Libin Deng Quqin Lu |
| author_facet | Wenwen Lai Defu Li Jie Kuang Libin Deng Quqin Lu |
| author_sort | Wenwen Lai |
| collection | DOAJ |
| description | Abstract Background Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5‐year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA‐seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice. Methods We studied single‐cell RNA‐seq dataset and The Cancer Genome Atlas‐glioblastoma multiforme (TCGA‐GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model. Results We identified 2128 DEGs from the single‐cell RNA‐seq dataset and 6461 DEGs from the bulk RNA‐seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low‐risk group showed better OS than those in the high‐risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients. Conclusion We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA‐seq dataset and a bulk RNA‐seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients. |
| first_indexed | 2024-03-12T13:26:33Z |
| format | Article |
| id | doaj.art-7c807b440c2a40419057c081d1232d0e |
| institution | Directory Open Access Journal |
| issn | 2162-3279 |
| language | English |
| last_indexed | 2024-03-12T13:26:33Z |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj.art-7c807b440c2a40419057c081d1232d0e2023-08-25T04:42:55ZengWileyBrain and Behavior2162-32792022-05-01125n/an/a10.1002/brb3.2575Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastomaWenwen Lai0Defu Li1Jie Kuang2Libin Deng3Quqin Lu4Jiangxi Provincial Key Laboratory of Preventive Medicine Nanchang University Nanchang ChinaJiangxi Provincial Key Laboratory of Preventive Medicine Nanchang University Nanchang ChinaJiangxi Provincial Key Laboratory of Preventive Medicine Nanchang University Nanchang ChinaJiangxi Provincial Key Laboratory of Preventive Medicine Nanchang University Nanchang ChinaJiangxi Provincial Key Laboratory of Preventive Medicine Nanchang University Nanchang ChinaAbstract Background Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5‐year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA‐seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice. Methods We studied single‐cell RNA‐seq dataset and The Cancer Genome Atlas‐glioblastoma multiforme (TCGA‐GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model. Results We identified 2128 DEGs from the single‐cell RNA‐seq dataset and 6461 DEGs from the bulk RNA‐seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low‐risk group showed better OS than those in the high‐risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients. Conclusion We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA‐seq dataset and a bulk RNA‐seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.https://doi.org/10.1002/brb3.2575glioblastomaoverall survivalprognostic modelsingle‐cell RNA‐seqbulk RNA‐seq |
| spellingShingle | Wenwen Lai Defu Li Jie Kuang Libin Deng Quqin Lu Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma Brain and Behavior glioblastoma overall survival prognostic model single‐cell RNA‐seq bulk RNA‐seq |
| title | Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma |
| title_full | Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma |
| title_fullStr | Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma |
| title_full_unstemmed | Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma |
| title_short | Integrated analysis of single‐cell RNA‐seq dataset and bulk RNA‐seq dataset constructs a prognostic model for predicting survival in human glioblastoma |
| title_sort | integrated analysis of single cell rna seq dataset and bulk rna seq dataset constructs a prognostic model for predicting survival in human glioblastoma |
| topic | glioblastoma overall survival prognostic model single‐cell RNA‐seq bulk RNA‐seq |
| url | https://doi.org/10.1002/brb3.2575 |
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