Circadian rhythm related genes signature in glioma for drug resistance prediction: a comprehensive analysis integrating transcriptomics and machine learning.

BACKGROUND: Gliomas, 24% of all primary brain tumors, have diverse histology and poor survival rates, with about 70% recurring due to acquired or de novo resistance. Insomnia in patients is correlated strongly with circadian rhythm disruptions. The correlation between circadian rhythm disorders and drug resistance of some tumors has been proved. However, the precise mechanism underlying the relationship between glioma and circadian rhythm disorders has not been elucidated.

METHODS: Circadian rhythm-related genes (CRRGs) were identified using the least absolute shrinkage and selection operator (LASSO) regression, and stochastic gradient descent (SGD) was performed to form a circadian rhythm-related score (CRRS) model. The studies of immune cell infiltration, genetic variations, differential gene expression pattern, and single cell analysis were performed for exploring the mechanisms of chemotherapy resistance in glioma. The relationship between CRRGs and chemosensitivity was also confirmed by IC 50 (half maximal inhibitory concentration) analysis.

RESULT: Signatures of 16 CRRGs were screened out and identified. Based on the CRRS model, an optimal comprehensive nomogram was created, exhibiting a favorable potential for predicting drug resistance in samples. Immune infiltration, cell-cell communication, and single cell analysis all indicated that high CRRS group was closely related to innate immune cells. IC50 analysis showed that CRRG knockdown enhanced the chemosensitivity of glioma.

CONCLUSION: A significant correlation between CRRGs, drug resistance of glioma, and innate immune cells was found, which might hold a significant role in the drug resistance of glioma.