Currently, the impact of the circadian rhythm on the tumorigenesis and progression of prostate cancer (PCA) has yet to be understood. In this study, we first established a novel nomogram to predict PCA progression based on circadian clock (CIC)-related genes and provided insights into the tumor immune microenvironment.
The TCGA and Genecards databases were used to identify potential candidate genes. Lasso and Cox regression analyses were applied to develop a CIC-related gene signature. The tumor immune microenvironment was evaluated through appropriate statistical methods and the GSCALite database.
Ten genes were identified to construct a gene signature to predict progression probability for patients with PCA. Patients with high-risk scores were more prone to progress than those with low-risk scores (hazard ratio (HR): 4.11, 95% CI: 2.66-6.37; risk score cut-off: 1.194). CLOCK, PER (1, 2, 3), CRY2, NPAS2, RORA, and ARNTL showed a higher correlation with anti-oncogenes, while CSNK1D and CSNK1E presented a greater relationship with oncogenes. Overall, patients with higher risk scores showed lower mRNA expression of PER1, PER2, and CRY2 and higher expression of CSNK1E. In general, tumor samples presented higher infiltration levels of macrophages, T cells and myeloid dendritic cells than normal samples. In addition, tumor samples had higher immune scores, lower stroma scores and lower microenvironment scores than normal samples. Notably, patients with higher risk scores were associated with significantly lower levels of neutrophils, NK cells, T helper type 1, and mast cells. There was a positive correlation between the risk score and the tumor mutation burden (TMB) score, and patients with higher TMB scores were more prone to progress than those with lower TMB scores. Likewise, we observed similar results regarding the correlation between the microsatellite instability (MSI) score and the risk score and the impact of the MSI score on the progression-free interval. We observed that anti-oncogenes presented a significantly positive correlation with PD-L1, PD-L2, TIGIT and SIGLEC15, especially PD-L2.
We identified ten prognosis-related genes as a promising tool for risk stratification in PCA patients from the fresh perspective of CIC.