ArticleBrain and behavior2022
Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma.
Article in Brain and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 12 citations in OpenAlex.
- Defining treatment-resistant brain cancer: Genetic screening to identify oncogene-driven immunomodulation and therapy resistance.Cancer gene therapy · 2026Review
- A novel neutrophil extracellular trap-related gene signature for predicting glioblastoma prognosis.Scientific reports · 2025Article
- Current Applications of Single-Cell RNA Sequencing in Glioblastoma: A Scoping Review.Brain sciences · 2025Review
- The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery.European journal of medical research · 2023Review
- Functional polymorphisms of the TET1 gene increase the risk of neuroblastoma in Chinese children.Journal of cellular and molecular medicine · 2023Article
- Comparative oncology: overcoming human cancer through companion animal studies.Experimental & molecular medicine · 2023Review
- 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 · 2022Article
- BUB1 Is Identified as a Potential Therapeutic Target for Pancreatic Cancer Treatment.Frontiers in public health · 2022Article
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5 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioblastoma (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.
methodsWe 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.
resultsWe 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.
conclusionWe 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.
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