Using Multi-Omics Analysis to Explore Diagnostic Tool and Optimize Drug Therapy Selection for Patients with Glioma Based on Cross-Talk Gene Signature.

BACKGROUND

The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics. However, biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited. Therefore, we aimed to develop a model that can effectively predict prognosis, differentiate microenvironment signatures, and optimize drug selection for patients with glioma.

MATERIALS AND METHODS

The CIBERSORT algorithm, bulk sequencing analysis, and single-cell RNA (scRNA) analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues. A predictive model was constructed based on cross-talk gene expression, and its effect on prognosis, recurrence prediction, and microenvironment characteristics was validated in multiple cohorts. The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.

RESULTS

A high abundance of M2 macrophages in glioma tissues indicates poor prognosis, and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment. Eight genes involved in the cross-talk between macrophages and cancer cells were identified. Among them, periostin (), chitinase 3 like 1 (), serum amyloid A1 (), and matrix metallopeptidase 9 () were selected to construct a predictive model. The developed model demonstrated significant efficacy in distinguishing patient prognosis, recurrent cases, and characteristics of high inflammation, hypoxia, and immunosuppression. Furthermore, this model can serve as a valuable tool for guiding the use of trametinib.

CONCLUSIONS

In summary, this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma; utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis, recurrence instances, and microenvironment characteristics; and aids in optimizing the application of trametinib in glioma patients.