Integrating Large-Scale Bulk and Single-Cell RNA Sequencing with Machine Learning: Unveiling the Cytokine Landscape of Glioblastoma and Constructing a Prognostic Feature Model.

INTRODUCTION Cytokines play an important role in modulating the tumor microenvironment (TME) in glioblastoma multiforme (GBM). However, little work has focused on developing a prognostic model for GBM using cytokine signatures.

Methods

Herein, we combined several GBM datasets, such as GSE163120, TCGA-GBM, CGGA-693, CGGA-325, GSE16011, the Rembrandt dataset, and GTEx. We combined the characteristic genes of different cell types in GBM samples with cytokine-associated genes identified in our previous research to identify single-cell cytokine-related genes (scCRGs). The enrichment scores of scCRGs in each cell were computed, and cells were stratified into a high-expression group and a low-expression group based on the median enrichment score. Differentially expressed scCRGs in the high-expression group were identified using the "limma" R package. Then, 117 combinations of machine learning (ML) algorithms were used to construct the new cytokine-related signature (CRS) prediction model. Moreover, we used consensus clustering algorithms to perform novel clustering analyses on GBM and subsequently conducted comprehensive immune profiling, response-to-immune-therapy prediction, and drug-sensitivity evaluation. Finally, we validated the key molecules in the model using tissue microarrays.

Results

We identified 17 scCRGs that are mostly highly expressed in microglia. The new CRS model we proposed demonstrated strong predictive performance across the three independent cohorts, outperforming existing GBM models. Based on the CRS model, we identified distinct subgroups of GBM patients who may respond favorably to immunotherapy. The key gene AEBP1 in the model is highly expressed in GBM tissues.

Discussion

Here, we present, for the first time, a highly reproducible and patient-specific prognostic prediction model based on a cytokine-related gene signature, combining several ML techniques and a large set of bioinformatic features. Compared with other published models, the proposed model is more stable and produces better predictions. Based on the model, we could identify separate subgroups of GBM patients who might respond better to immunotherapy, providing actionable information for the development of precision medicine approaches to treating GBM.

Conclusion

CRS has the potential to be an effective and promising strategy for improving clinical outcomes in patients with GBM.