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  3. The Circadian Rhythm Gene Network Could Distinguish Molecular Profile and Prognosis for Glioblastoma

The Circadian Rhythm Gene Network Could Distinguish Molecular Profile and Prognosis for Glioblastoma

  • Int J Mol Sci. 2025 Jun 19;26(12):5873. doi: 10.3390/ijms26125873.
Fangzhu Wan 1 2 3,  Zongpu Zhang 4 5 6,  Jinsen Zhang 7 8 9 10,  Jiyi Hu 1 2 3,  Weixu Hu 1 2 3,  Jing Gao 1 2 11,  Minjie Fu 7 8 9 10,  Yuan Feng 7 8 9 10,  Lin Kong 1 2 3
Affiliations

Affiliations

  • 1 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Shanghai Cancer Center, Shanghai 201321, China.
  • 2 Shanghai Key Laboratory of Radiation Oncology, Shanghai 201321, China.
  • 3 Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201321, China.
  • 4 Department of Thoracic Surgery and State Key Laboratory of Genetics and Development of Complex Phenotypes, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
  • 5 Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China.
  • 6 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
  • 7 Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China.
  • 8 Neurosurgical Institute of Fudan University, Shanghai 200040, China.
  • 9 Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China.
  • 10 Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China.
  • 11 Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China.
PMID: 40565338 DOI: 10.3390/ijms26125873
Abstract

Increasing evidence highlights the role of aberrant circadian rhythm gene expression in glioblastoma (GBM) progression, but the impact of the circadian rhythm gene network on GBM molecular profiles and prognosis remains unclear. A total of 1042 GBM samples from six public datasets, TCGA and CGGA, were analyzed, with GBM samples stratified into three circadian core-gene patterns using unsupervised clustering based on the expression profiles of 17 circadian rhythm genes. The Limma R package identified differentially expressed genes (DEGs) among the three patterns, and a secondary clustering system, termed circadian-related gene pattern, was established based on DEGs. A circadian risk score was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, and the efficiency of these patterns and the circadian risk score in distinguishing molecular profiles and predicting prognosis was systematically analyzed. The relationship between the circadian risk score and response to immune or targeted therapy was examined using the GSE78200 and IMvigor210 datasets. The results showed that GBM patients were clustered into three circadian core-gene patterns based on the expression profiles of 17 core circadian genes, with distinct molecular profiles, malignant characteristics, and patient prognoses among the patterns. Thirty-two DEGs among these patterns were identified and termed circadian-related genes, and secondary clustering based on these 32 DEGs classified GBM samples into two circadian-related gene patterns, which also predicted molecular profiles and prognosis. A circadian risk scoring system was established, allowing the calculation of individual risk scores based on the expression of 10 genes, where GBM patients with lower circadian risk scores had prolonged overall survival and less aggressive molecular subtypes, while higher circadian risk scores correlated with better responses to MAPK-targeted therapy. In conclusion, this study established two clustering patterns based on 17 circadian rhythm genes or 32 circadian-related genes, enabling the rapid classification of GBM patients with distinct molecular profiles and prognoses, while the circadian risk scoring system effectively predicted survival, molecular profiles, and therapeutic responses for individual GBM patients, demonstrating that the circadian rhythm gene network can distinguish molecular profiles and prognosis in GBM.

Keywords

LASSO; circadian rhythm; glioblastoma; proneural subtype; unsupervised cluster analysis.

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