Identification of a Novel Nomogram to Predict Progression Based on the Circadian Clock and Insights Into the Tumor Immune Microenvironment in Prostate Cancer

Abstract

BackgroundCurrently, 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.MethodsThe 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.ResultsTen 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.ConclusionWe identified ten prognosis-related genes as a promising tool for risk stratification in PCA patients from the fresh perspective of CIC.

Circadian clock‐mediated nuclear receptors in cancer

“…Clock mutations in colorectal cancer occurred in 53% of MSI, which could improve immunotherapy and chemotherapy (Alhopuro et al, 2010; Lin et al, 2020). Likewise, study observed consistent results regarding the impact of MSI on the progression‐free interval in prostate cancer patients (D. Feng et al, 2022). However, the impact of polymorphisms of the circadian rhythm genes on cancer development is highly dependent on the specific polymorphism and types of cancer (Morales‐Santana et al, 2019).…”

“…Clock mutations in colorectal cancer occurred in 53% of MSI, which could improve immunotherapy and chemotherapy (Alhopuro et al, 2010;Lin et al, 2020). Likewise, study observed consistent results regarding the impact of MSI on the progressionfree interval in prostate cancer patients (D. Feng et al, 2022).…”

Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration

“…To date, it has been reported that TR genes are strong diagnostic and prognostic markers in cancers such as glioma [ 17 ], prostate cancer [ 18 ], and stomach adenocarcinoma [ 19 ]. However, studies that investigate whether TR genes can act as specific diagnostic biomarkers for psychiatric disorders to further explore relevant therapeutic targets for psychiatric disorders, especially MDD, are lacking.…”

Cuproptosis-Mediated Patterns Characterized by Distinct Tumor Microenvironment and Predicted the Immunotherapy Response for Gastric Cancer

“…Eight genes were selected by LASSO-Cox regression via the “glmnet” packages, and three hub genes (AHCYL2, ANKRD6, and FDGFRB) were finally screened by multivariate COX analysis with “both step” function. 30 The formula of CuPS was defined as follows: CuPS = −0.205*AHCYL2 exp + 0.231*ANKRD6 exp + 0.238*FDGFRB exp , where “exp” signifies the expression. The packages the “survivalROC” and “survminer” were utilized to compute the receiver operating characteristic curve (ROC) and Kaplan–Meier (K–M) survival curve.…”