Background
Melatonin (MT) can have a direct or indirect impact on the expression of numerous crucial genes in cancer cells. Consequently, this paper strived to investigate MT-associated prognostic genes in Glioblastoma (GBM) and their mechanisms of operation.
Methods
Based on the survival of samples in training set and the expression of 34 MT-related genes (MT-RGs), univariate Cox regression analysis and consistent cluster analysis were used to identify differential prognostic MT-RGs for follow-up analysis. Then, prognostic genes were obtained by least absolute shrinkage and selection operator (LASSO) regression analysis to construct risk model. On the basis of risk model, GBM patients in training set were sorted to high- and low-risk cohorts. Further, to predict patient survival, we combined risk score with other clinical characteristics to access independent prognostic factors for the construction of prognostic model. Moreover, immune cell infiltration analysis and drug sensitization were performed to probe the potential mechanism of prognostic genes. Additionally, single-cell analysis was utilized to recognize cell types in GBM patients, and pseudotemporal trajectories of predictive genes were predicted in key cell types.
Results
The GBM patients in the training set were divided into two clusters (Cluster 1 and Cluster 2). Based on two clusters and the expression of MT-RGs, 8 differential prognostic MT-RGs were selected by wilcoxon test. Ultimately, ECE1, CYP1B1, IRAK1, SIRT1, and ACHE were defined as prognostic genes via LASSO. Importantly, risk score and age associated with GBM prognosis were identified as independent prognostic factor to establish the prognostic model, which might be an effective tool for predicting survival in GBM patients. For immune analysis, neutrophils were markedly more abundant in high-risk cohort. And 198 drugs (e.g. Temozolomide, Cispiatin, Etoposide) were identified between two risk cohorts. Moreover, 7 cell types (Myeloid, Neoplastic, Oligodendrocytes, Astrocytes, Vcascular, OPCs, and Neurons, and Myeloid) were annotated in single-cell dataset. Pseudotemporal analysis revealed that ECE1, CYP1B1 and ACHE expression steadily decreased on Myeloid, Vascular and Neoplastic.
Conclusion
A new prognostic model for GBM associated with MT was created and verified, which might furnish new opinions for the study of MT-RGs in GBM.