Abstract
We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772–0.817) and validation cohort (C-index, 0.770; 0.751–0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.
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Data Availability
All data that support the findings of this study are openly available in TCGA (http://www.ncbi.nlm.nih.gov/geo/) and GEO database (http://www.ncbi.nlm.nih.gov/geo/).
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Acknowledgements
Sincere gratitude would be sent to the academic workers who were responsible for the creation, update, and maintenance of the TCGA and CGGA databases.
Funding
This work was supported by National Key R&D Program of China, Ministry of Science and Technology of the People’s Republic of China (Grant Nos. 2022YFC2407100, 2022YFC2407101).
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Conception/design: Xi-Lin Yang, Zheng Zeng, Fu-Quan Zhang, Xin Lian. Provision of study material or patients: Xi-Lin Yang, Chen Wang, Zheng Zeng. Collection and/or assembly of data: Xi-Lin Yang, Zheng Zeng, Guang-Yu Wang, Yun-Long Sheng. Data analysis and interpretation: Xi-Lin Yang, Zheng Zeng, Chen Wang, Yun-Long Sheng. Manuscript writing: Xi-Lin Yang. Manuscript revision: Fu-Quan Zhang, Xin Lian. Final approval of manuscript: all authors.
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Yang, XL., Zeng, Z., Wang, C. et al. Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms. J Mol Neurosci 74, 48 (2024). https://doi.org/10.1007/s12031-024-02218-2
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DOI: https://doi.org/10.1007/s12031-024-02218-2