
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39259370
1276
10.1007/s12672-024-01276-7
Analysis
Identification of circadian clock-related immunological prognostic index and molecular subtypes in prostate cancer
Che Lu 1
Li Dengxiong 2
Wang Jie 2
Tuo Zhouting 3
Yoo Koo Han 4
Feng Dechao dechao.feng@ucl.ac.uk

26
Ou Yun 176137214@qq.com

5
Wu Ruicheng ruichengwu.ymedx@gmail.com

2
Wei Wuran weiwuran@scu.edu.cn

2
1 https://ror.org/011ashp19 grid.13291.38 0000 0001 0807 1581 Operating Room, Department of Anesthesiology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041 China
2 grid.412901.f 0000 0004 1770 1022 Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, 610041 China
3 grid.452696.a 0000 0004 7533 3408 Department of Urology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601 China
4 https://ror.org/01zqcg218 grid.289247.2 0000 0001 2171 7818 Department of Urology, Kyung Hee University, Seoul, South Korea
5 grid.412901.f 0000 0004 1770 1022 Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041 China
6 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Division of Surgery & Interventional Science, University College London, London, W1W 7TS UK
11 9 2024
11 9 2024
12 2024
15 42927 6 2024
26 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Evidence suggests that the circadian clock (CIC) is among the important factors for tumorigenesis. We aimed to provide new insights into CIC-mediated molecular subtypes and gene prognostic indexes for prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT).

Methods

PCa data from TCGA was analyzed to identify differentially expressed genes (DEGs) with significant fold changes and p-values. A prognostic index called CIC-related gene prognostic index (CICGPI) was developed through clustering methods and survival analysis and validated on multiple data sets. The diagnostic accuracy of CICGPI for resistance to chemotherapy and radiotherapy was confirmed. Additionally, the interaction between tumor immune environment and CICGPI score was explored, along with their correlation with prognosis.

Results

TOP2A, APOE, and ALDH2 were used to classify the PCa patients into two subtypes. Cluster 2 had a higher risk of biochemical recurrence (BCR) than cluster 1 for PCa patients undergoing RP or RT. A CIC-related gene prognostic index (CICGPI) was constructed using the above three genes for PCa patents in the TCGA database. The CICGPI score showed good prognostic value in the TCGA database and was externally confirmed by PCa patients in GSE116918, MSKCC2010 and GSE46602. In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988). For gene set variation analysis, we observed that both beta alanine metabolism and limonene and pinene degradation were upregulated in cluster 1 for PCa patients undergoing RP or RT. For PCa patients undergoing RP, cell cycle, homologous recombination, mismatch repair, and DNA replication were upregulated in cluster 2. A strongly positive relationship between cancer-related fibroblasts and CICGPI score was observed in PCa patients undergoing RP or RT. Moreover, a high density of CAFs was highly closely associated with poorer BCR-free survival of PCa patients.

Conclusions

In this study, we established CIC-related immunological prognostic index and molecular subtypes, which might be useful for the clinical practice.

Keywords

Prostate cancer
Circadian clock
Molecular subtypes
Tumor immune environment
a regional innovation cooperation project of Sichuan Province23QYCX0136 23QYCX0136 23QYCX0136 23QYCX0136 23QYCX0136 23QYCX0136 23QYCX0136 23QYCX0136 23QYCX0136 Che Lu Li Dengxiong Wang Jie Tuo Zhouting Yoo Koo Han Feng Dechao Ou Yun Wu Ruicheng Wei Wuran issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Prostate cancer (PCa), despite its slow progression, continues to present a considerable public health challenge, with an estimated 299,010 new cases and 35,250 deaths expected in the United States in 2024 [1]. Globally, PCa ranks as the second most common cancer in men for 2022, leading to approximately 390,000 deaths [2]. PCa is predominantly an age-related disease, with a notable rise in incidence among men aged 65 and older [3]. With the aging of the population worldwide, there will be a growing economic strain on national healthcare systems. Notably, research indicates that in the United States, annual medical expenditures on PCa ranged from $24.8 billion to $39.2 billion between 2014 and 2019 [4–6]. The two most common therapies for localised PCa are radical prostatectomy (RP) and radical radiotherapy (RT), with similar rates of biochemical recurrence (BCR) and 5- and 10-year overall survival [7, 8]. Additionally, there are emerging focal Mminimally invasive methods, such as ablation, being utilized to treat PCa [9]. Nevertheless, it is still unclear how extreme therapies affect the development of PCa. As the disease advances, drug resistance may develop in PCa, subsequently impacting patient survival [10]. Significant clinical heterogeneity is linked to PCa, and this may be represented in the temporal and geographic variability both between and within patients [11, 12]. Hence, a crucial step towards customised treatment has been considered to be the molecular characterisation of these variations.

The role of the circadian clock (CIC) in carcinogenesis is growing, and irregularities in the circadian rhythm encourage the development of metabolic diseases and cancer markers [13], such as increased metabolic demands, immune evasion, resistance to apoptosis, and inflammatory tumor-supportive niches [14]. Retinal ganglion cells send light signals to the neurons in the suprachiasmatic nuclei (SCN) region. These neurons' clocks synchronise CIC in peripheral organs and other areas of the brain. They also synchronise diurnal rhythms in several hormones and body temperature [15]. The transcription-translation feedback loops are interlocked and comprise two activators and repressors that make up the fundamental clock mechanism [16]. Transcription repressors Cryptochrome (CRY1 and CRY2) and Period (PER1, PER2, and PER3) are expressed when transcription activators BMAL1 and CLOCK/NPAS2 attach to DNA sequences [16]. On the other hand, CRY and PER protein complexes block the activity of CLOCK-BMAL1, which blocks their own transcription. Furthermore, β-TRCP and casein kinase 1δ/ε degrade PER proteins. AMPK and FBXL3-driven post-translational processes will cause CRYs to break down in the nucleus; CK1δ/ε and FBXL21 control CRYs' breakdown in the cytoplasm [16]. The transcription factors REV-ERBα/β and RORα/β/γ proteins are activated by CLOCK/BMAL in an interlocked loop, and the ROR and REV-ERB proteins function as repressors and activators of BMAL1 transcription, respectively [16]. Many genes are regulated by CIC in response to extracellular stress, and vice versa. Currently, a small number of genes are involved in the regulation of transcription, translation, post-translational modification, subcellular localization, and degradation of core clock proteins [16, 17].

PCa cells rely on androgens for their growth and survival, primarily through the androgen receptor (AR) [18]. Androgen deprivation therapy is a commonly used treatment for PCa due to the role of androgens in promoting cancer growth [19]. The CIC gene plays a role in regulating androgen production, while Bmal1 gene-deficient mice exhibit notably reduced testosterone levels [20]. Bmal1 protein expression is specific to mouse Leydig cells and is closely associated with circadian rhythm oscillations, particularly in relation to testosterone circadian rhythm [21]. As of right now, the majority of research on CIC has only looked at the correlation between PCa risk and polymorphisms in the CIC core genes, primarily CRY1, CRY2, ARNTL, RORA, PER3, CSNK1E, and NPAS2 [22, 23]. Cao et al. [24]. found that PER1 physically interacted with AR, and PER1 overexpression led to significant growth inhibition and apoptosis in PCa cells. Compared with normal prostate cells, Per2 expression is decreased in PCa cells, and overexpression of Per2 also inhibits the growth of PCa cells [25]. In addition, Li et al. [26] showed that PER3 expression levels were highly correlated with the prognosis of PCa patients clinically, and PER3 negatively regulated the stemness of PCa stem cells mechanistically via the WNT/β-catenin pathway. Shafi et al. [27] discovered that the expression of CYR1 is influenced by androgens and plays a role in the advancement of PCa by rhythmically regulating DNA repair mechanisms. Even with these encouraging outcomes, not much research has been done on how CIC affects PCa. In this work, we have contributed new knowledge to the field of gene prognostic indices and CIC-mediated molecular subtypes for PCa patients receiving RP or RT.

Methods

Identifying key genes of molecular subtypes and prognostic index

Prior to extracting the message RNA (mRNA) matrix, we downloaded the standardized PCa data in the TCGA database from the UCSC XENA [28]. We determined the differentially expressed genes (DEGs), which were classified as llogFCl > 0.4 and p.adj. < 0.05, using the R package "limma". A fold change of approximately 1.5-fold (log2 fold change of approximately 0.4) is considered significant in biological studies. In multiple hypothesis testing, a p.adj < 0.05 is widely acknowledged to control the false discovery rate. Using the log-rank test, we were able to identify the genes linked to BCR-free survival for PCa patients undergoing RP. For the purpose of mutual authentication, we merged four datasets (GSE46602 [29], GSE32571 [30], GSE62872 [31], and GSE116918 [32]), which can be seen in our previous study [33]. Using the above methods, we used three datasets [29–31] containing 360 tumor and 209 normal samples to obtain the DEGs, and the prognostic genes were further acquired through the 248 PCa patients undergoing RT in GSE116918 [32]. Using the common DEGs associated with CIC [34] and BCR-free survival in the TCGA database and GSE116918 [32], PCa patients in TCGA, MSKCC2010 [35] or GSE116918 [32] were clustered using the R package “ConsensusClusterPlus”. The consensus matrix K values were used to determine the number of clusters, and the consistency of the prognostic analysis in the aforementioned three cohorts determined which clusters were best. In this work, the ideal clusters were identified when the consensus matrix K equaled 2. Depending on the situation, we used the t test, chi-square test, Fisher's exact test, or Wilcoxon rank sum test to examine the clinical connection between the two groups. Furthermore, we employed Cox regression analysis to ascertain the classification's independent prognostic value. If a variable's p value in the univariable Cox regression analysis was less than 0.1, it was included in the multivariate Cox regression analysis. For the purpose of gene set variation analysis (GSVA), we calculated the enrichment scores of the related pathways and molecular mechanisms of each sample through the R packages “GSVA” [36] and “c2. cp. kegg. v7.4. symbols. gmt” subset from the molecular signature database [37]. Five gene sets were the minimum and five thousand the maximum, respectively. The "wilcox.text" programme was then utilised to assess how each pathway differed between the two clusters. We deemed p. adj. < 0.01 and false discovery rate < 0.01 to be statistically significant, given the fold change of 1.5.

We used the clustered genes to establish a CIC-related gene prognostic index (CICGPI) through Cox regression analysis using PCa patients in TCGA. CICGPI score = −2.854 + 0.280*TOP2A + 0.259*APOE-0.191*ALDH2. We used MSKCC2010 [35], GSE116918 [32] and GSE46602 [38] to externally confirm the prognostic value of the CICGPI score. Moreover, GSE42913 [39] and GSE53902 [40] were employed to examine the diagnostic accuracy of chemoresistance and radioresistance of the CICGPI score, respectively. We used the EPIC and ESTIMATE algorithms [41, 42] to explore the immune-infiltrating cells and scores of the tumor immune environment (TME). The association between the TME parameters and immunological checkpoints and the CICGPI score was examined using Spearman's correlation. Furthermore, the correlation between the two clusters and the CICGPI score in respect to the CIC core genes was studied. In Fig. 1, we display the study's flowchart.Fig. 1 The flowchart of this study. DEGs differentially expressed genes, GEO Gene Expression Omnibus, CICGPI circadian clock-related gene prognostic index, CIC circadian clock

Statistical analysis

R 3.6.3 software and the appropriate packages were used to conduct all of the analyses. The log-rank test was used to conduct the survival analysis, which is shown as a Kaplan–Meier curve. Additionally, in cases where the continuous variables failed the Shapiro–Wilk normality test, Spearman analysis was employed to evaluate the relationships between them. One standard for statistical significance was two-sided p < 0.05. Significant marks were as follows: no significance (ns), p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001.

Results

Molecular subtypes

We used 498 tumor and 52 normal tissues in the TCGA database and 360 tumor and 209 normal samples in the GEO datasets (GSE46602 [29], GSE32571 [30], and GSE62872 [31]) to identify DEGs (Fig. 2A, B). We detected 47 DEGs associated with CIC (Fig. 2C). Subsequently, 430 PCa patients undergoing RP in the TCGA database (Fig. 2D) and 248 PCa patients undergoing RT in GSE116918 [32] (Fig. 2E) were used to identify DEGs associated with BCR-free survival. Finally, we found that TOP2A, APOE, and ALDH2 were related to BCR-free survival in both TCGA database and GSE116918 [32], among which TOP2A and APOE were upregulated in the tumor tissues, and ALDH2 was downregulated in the tumor tissues when compared to normal tissues (Fig. 2A, B). For PCa patients undergoing RP in the TCGA database, we found that the three genes could clearly classify these patients into two clusters (Fig. 2F), as well as GSE116918 [32] (Fig. 2G) and MSKCC2010 [35] (Fig. 2H). Cluster 2 had a significantly higher risk of BCR than cluster 1 (HR: 2.78, 95% CI 1.65–4.70, p < 0.001; Fig. 2I). Similar results were observed in PCa patients undergoing RT in GSE116918 [32] (Fig. 2J, K) and MSKCC2010 [35] (Fig. 2L). Furthermore, we found that this classification was an independent risk factor for PCa patients undergoing RP or RT through multivariate Cox regression analysis incorporating clusters and clinical features (Table 1). For PCa patients in the TCGA database, cluster 2 was highly associated with greater age, larger Gleason score, residual tumor, and advanced T stage and N stage (Table 2). Similarly, we found that cluster 2 was highly associated with a larger Gleason score, BCR, metastasis, and advanced T stage for PCa patients in GSE116918 [32] (Table 2). For GSVA analysis, we observed that both beta alanine metabolism and limonene and pinene degradation were upregulated in cluster 1 for PCa patients undergoing RP or RT (Fig. 2M, N). For PCa patients undergoing RT, propanoate metabolism and valine leucine and isoleucine degradation were highly enriched in cluster 1 as well (Fig. 2N). For PCa patients undergoing RP, cell cycle, homologous recombination, mismatch repair, and DNA replication were upregulated in cluster 2 (Fig. 2M).Fig. 2 Identification of circadian clock-mediated molecular subtypes and functional analysis. A volcan plot showing DEGs between tumor and normal tissues in the prostate cancer patients from the TCGA database; B volcan plot showing DEGs between tumor and normal tissues in the prostate cancer patients from the GSE116918; C Venn plot showing the intersection of circadian clock-related genes and DEGs from the TCGA database and GSE116918; D forest plot showing the DEGs associated with BCR-free survival for prostate cancer patients undergoing radical prostatectomy in the TCGA database; E forest plot showing the DEGs associated with BCR-free survival for prostate cancer patients undergoing radical radiotherapy in the GSE116918; F cluster result for prostate cancer patients undergoing radical prostatectomy in the TCGA database using the TOP2A, APOE, and ALDH2; G cluster result for prostate cancer patients undergoing radical radiotherapy in the GSE116918 using the TOP2A, APOE, and ALDH2; H cluster result for prostate cancer patients undergoing radical prostatectomy in the MSKCC2010 using the TOP2A, APOE, and ALDH2; I Kaplan‒Meier curve showing cluster 2 had higher risk of BCR than cluster 1 for prostate cancer patients undergoing radical prostatectomy in the TCGA database; J Kaplan‒Meier curve showing cluster 2 had higher risk of BCR than cluster 1 for prostate cancer patients undergoing radical radiotherapy in GSE116918; K Kaplan‒Meier curve showing cluster 2 had higher risk of metastasis than cluster 1 for prostate cancer patients undergoing radical radiotherapy in GSE116918; L Kaplan‒Meier curve showing cluster 2 had higher risk of BCR than cluster 1 for prostate cancer patients undergoing radical prostatectomy in MSKCC2010; M gene set variation analysis for prostate cancer patients undergoing radical prostatectomy in the TCGA database; N gene set variation analysis for prostate cancer patients undergoing radical radiotherapy in the GSE116918. BCR biochemical recurrence, DEGs differentially expressed genes, GEO Gene Expression Omnibus

Table 1 The results of COX regression analysis incorporating clusters and clinical features

Characteristics	Total(N)	Univariate analysis	Multivariate analysis	
Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value	
TCGA database	
 Cluster	
  Cluster 1	248	Reference				
  Cluster 2	182	2.793 (1.624–4.803)	 < 0.001	2.003 (1.054–3.806)	0.034	
  Age	430	1.016 (0.978–1.055)	0.426			
 Gleason score	
  GS = 6	39	Reference				
  GS = 7	206	1.072 (0.242–4.756)	0.927	8866774.603 (0.000-Inf)	0.996	
  GS = 8	59	3.763 (0.832–17.011)	0.085	23446112.391 (0.000-Inf)	0.996	
  GS = 9	126	4.833 (1.157–20.194)	0.031	21775926.870 (0.000-Inf)	0.996	
 T stage	
  T2	155	Reference				
  T3	261	5.208 (2.230–12.163)	 < 0.001	2.979 (1.002–8.861)	0.050	
  T4	8	6.140 (1.235–30.532)	0.027	1.329 (0.135–13.134)	0.808	
 Race	
  White	355	Reference				
  Asian	11	0.673 (0.147–3.079)	0.610			
  Black or African American	50	0.648 (0.288–1.460)	0.296			
 N stage	
  N0	306	Reference				
  N1	69	1.822 (1.001–3.313)	0.049	44428698.033 (0.000-Inf)	0.999	
 Residual tumor	
  No	273	Reference				
  Yes	146	1.781 (1.050–3.019)	0.032	1.212 (0.644–2.283)	0.552	
GSE116918	
 Cluster	
  Cluster 1	132	Reference				
  Cluster 2	116	2.208 (1.278–3.816)	0.005	1.990 (1.090–3.632)	0.025	
  Age	248	0.976 (0.937–1.017)	0.252			
 T stage	
  T1	51	Reference				
  T2	76	1.504 (0.577–3.917)	0.404	1.253 (0.475–3.310)	0.649	
  T3	92	2.599 (1.075–6.282)	0.034	2.179 (0.889–5.342)	0.089	
  T4	4	11.987 (2.963–48.501)	 < 0.001	7.630 (1.807–32.218)	0.006	
 Gleason score	
  GS = 6	42	Reference				
  GS = 7	99	1.876 (0.760–4.632)	0.173			
  GS = 8	52	2.031 (0.771–5.346)	0.151			
  GS = 9	55	2.458 (0.951–6.355)	0.064			
GS Gleason score, CI confidence interval

Table 2 The clinical correlation of the clusters for prostate cancer patients in the TCGA database and GSE116918

Characteristic	Cluster 1	Cluster 2	P value	
TCGA database	
 Samples	248	182		
 Age, median (IQR)	60 (55.75, 65)	63 (57, 67)	0.002	
 Gleason score, n (%)			 < 0.001	
  GS = 6	33 (7.7%)	6 (1.4%)		
  GS = 7	141 (32.8%)	65 (15.1%)		
  GS = 8	29 (6.7%)	30 (7%)		
  GS = 9	45 (10.5%)	81 (18.8%)		
 T stage, n (%)			 < 0.001	
  T2	123 (29%)	32 (7.5%)		
  T3	119 (28.1%)	142 (33.5%)		
  T4	2 (0.5%)	6 (1.4%)		
 Race, n (%)			0.144	
  Asian	6 (1.4%)	5 (1.2%)		
  Black or African American	35 (8.4%)	15 (3.6%)		
  White	196 (47.1%)	159 (38.2%)		
 N stage, n (%)			 < 0.001	
  N0	180 (48%)	126 (33.6%)		
  N1	24 (6.4%)	45 (12%)		
 Positive lymphnodes, n (%)			0.001	
  No	167 (46.6%)	121 (33.8%)		
  Yes	25 (7%)	45 (12.6%)		
 Residual tumor, n (%)			0.012	
  No	169 (40.3%)	104 (24.8%)		
  Yes	71 (16.9%)	75 (17.9%)		
GSE116918	
 Samples	132	116		
 Age, median (IQR)	67 (63, 72)	69 (64, 73)	0.175	
 T stage, n (%)			0.003	
  T1	36 (16.1%)	15 (6.7%)		
  T2	38 (17%)	38 (17%)		
  T3	41 (18.4%)	51 (22.9%)		
  T4	0 (0%)	4 (1.8%)		
 Gleason score, n (%)			0.002	
  GS = 6	32 (12.9%)	10 (4%)		
  GS = 7	55 (22.2%)	44 (17.7%)		
  GS = 8	24 (9.7%)	28 (11.3%)		
  GS = 9	21 (8.5%)	34 (13.7%)		
 Biochemical recurrence, n (%)			0.005	
  No	112 (45.2%)	80 (32.3%)		
  Yes	20 (8.1%)	36 (14.5%)		
 Metastasis, n (%)			0.005	
  No	127 (51.2%)	99 (39.9%)		
  Yes	5 (2%)	17 (6.9%)		
IQR interquartile range, GS Gleason score

CICGPI score and immune correlation analysis

To quantify risk and guide clinical practice, we established the CICGPI score using 430 PCa patients in the TCGA database, which were classified into high- and low-risk groups according to the median CICGPI score. We found that the BCR risk of high-risk patients was 2.29 times that of low-risk patients (Fig. 3A). PCa patients undergoing RT in the GSE116918 [32] or RP in the GSE46602 [38] were used to externally validate the prognostic values of the CICGPI score. Surprisingly, we detected similar results. For PCa patients in GSE116918 [32], high-risk patents were more prone to BCR (HR: 2.64, 95% CI 1.56–4.46, p = 0.001; Fig. 3B) than low-risk patients. For PCa patients in MSKCC2010 [30, 31], high-risk patients had higher BCR risk (HR: 3.52, 95% CI 1.83–6.78, p < 0.001; Fig. 3C) than low-risk patients, as well as GSE46602 [38] (HR: 2.47, 95% CI 1.06–5.77, p = 0.031; Fig. 3D). Moreover, we found that the CICGPI score had certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781; Fig. 3E) and radioresistance (AUC: 0.988; Fig. 3F) using PCa cells from GSE42913 [39] and GSE53902 [40], respectively.Fig. 3 CICGPI score and immune analysis. A Kaplan‒Meier curve showing high-risk group had higher risk of BCR than low-risk group for prostate cancer patients undergoing radical prostatectomy in the TCGA database; B Kaplan‒Meier curve showing high-risk group had higher risk of BCR than low-risk group for prostate cancer patients undergoing radical radiotherapy in the GSE116918; C Kaplan‒Meier curve showing high-risk group had higher risk of metastasis than low-risk group for prostate cancer patients undergoing radical prostatectomy in MSKCC2010; D Kaplan‒Meier curve showing high-risk group had higher risk of BCR than low-risk group for prostate cancer patients undergoing radical prostatectomy in the GSE46602; E the diagnostic accuracy of CICGPI score for tumor chemoresistance using the GSE42913; F the diagnostic accuracy of CICGPI score for tumor radioresistance using the GSE53902; G radar plot showing the relationship of CICGPI score with TME parameters for prostate cancer patients undergoing radical prostatectomy in the TCGA database; H radar plot showing the relationship of CICGPI score with TME parameters for prostate cancer patients undergoing radical radiotherapy in the GSE116918; I radar plot showing the relationship of CICGPI score with stimulator checkpoints for prostate cancer patients undergoing radical prostatectomy in the TCGA database; J radar plot showing the relationship of CICGPI score with inhibitor checkpoints for prostate cancer patients undergoing radical prostatectomy in the TCGA database; K radar plot showing the relationship of CICGPI score with stimulator checkpoints for prostate cancer patients undergoing radical radiotherapy in the GSE116918; L radar plot showing the relationship of CICGPI score with inhibitor checkpoints for prostate cancer patients undergoing radical radiotherapy in the GSE116918. BCR biochemical recurrence, CICGPI circadian clock-related gene prognostic index, TME tumor immune environment. Prostate cancer patients were divided into high- and low-risk groups according to the median CICGPI score

In terms of TME analysis, we found that the CICGPI score was significantly associated with cancer-associated fibroblasts (CAFs, r: 0.45), macrophages (r: 0.34), stromal score (r: 0.22), immune (r: 0.2), and estimate score (r: 0.24) for PCa patients undergoing RP (Fig. 3G). For PCa patients undergoing RT, we detected that the CCIGPI score was significantly related to CAFs (r: 0.2) (Fig. 3H). In addition, we found that the CICGPI score was positively associated with most stimulator (Fig. 3I) and inhibitor checkpoints (Fig. 3J) for PCa patients undergoing RP. Similar results were observed in PCa patients undergoing RT (Fig. 3K, L).

Correlation analysis of CIC core genes

We observed that most CIC core genes were differentially expressed in the two clusters for PCa patients undergoing RP (Fig. 4A), while only RORA was downregulated in cluster 2 for PCa patients undergoing RT (Fig. 4B). Similar results were detected in the Spearman analysis of the CICGPI score and CIC core genes for PCa patients undergoing RP (Fig. 4C) or RT (Fig. 4D). According to the correlation coefficient of l0.3 l, the CICGPI score had higher correlations with PER1 (r: -0.41), PER2 (r: -0.32), and CRY2 (r: −0.3) than other CIC core genes for PCa patients undergoing RP. Owing to the higher correlations of CAFs with CICGPI scores for PCa patients undergoing RP or RT, we divided these patients into high- and low-expression groups according to the median CAF score. We found that CAFs were significantly associated with BCR-free survival for PCa patients undergoing RP (HR: 1.97, 95% CI 1.18–3.30, p = 0.012; Fig. 4E) or RT (HR: 2.09, 95% CI 1.23–3.54, p = 0.006; Fig. 4F). Moreover, we found that CAFs were significantly associated with NR1D1 (r: 0.32), CRY1 (r: 0.11), RORA (r: 0.31), NR1D2 (r: 0.15), NPAS2 (r: 0.22), ANRTL (r: 0.1), PER1 (−0.1), and CSNK1D (r: −0.17) in PCa patients undergoing RP (Fig. 4G). In terms of PCa patients undergoing RT, we observed that CAFs were significantly related to ARNTL (r: 0.17), NPAS2 (r: 0.20), NR1D2 (r: 0.22), PER3 (r: 0.15), RORA (r: 0.22), and PER2 (r: −0.27) (Fig. 4H).Fig. 4 Correlations of CIC core genes with clusters and CICGPI score. A Comparisons between cluster 1 and cluster 2 for CIC core genes in prostate cancer patients undergoing radical prostatectomy in the TCGA database; B comparisons between cluster 1 and cluster 2 for CIC core genes in prostate cancer patients undergoing radical radiotherapy in GSE116918; C radar plot showing the relationship of CICGPI score with CIC core genes for prostate cancer patients undergoing radical prostatectomy in the TCGA database; D radar plot showing the relationship of CICGPI score with CIC core genes for prostate cancer patients undergoing radical radiotherapy in GSE116918; E Kaplan‒Meier curve showing that patients with high-expression CAFs had a higher risk of BCR than those with low-expression CAFs for prostate cancer patients undergoing radical prostatectomy in the TCGA database; F Kaplan‒Meier curve showing that patients with high-expression CAFs had a higher risk of BCR than those with low-expression CAFs for prostate cancer patients undergoing radical radiotherapy in GSE116918; G radar plot showing the relationship of CAF level with CIC core genes for prostate cancer undergoing radical prostatectomy in the TCGA database; H radar plot showing the relationship of CAF level with CIC core genes for prostate cancer patients undergoing radical radiotherapy in GSE116918. BCR biochemical recurrence, CICGPI circadian clock-related gene prognostic index, CAFs cancer-related fibroblasts. Prostate cancer patients were divided into high- and low-expression groups according to the median CAF level

Discussion

The homeostatic function of steroid hormones and their receptors, whose disruption is linked to a variety of pathogenic diseases, including malignancies, is dependent on the circadian rhythm [24, 43]. The association between night shift work and PCa risk in earlier epidemiological studies is still up for debate [41, 42]. In our earlier work, we suggested that if retinal neurons receive light signals constantly, the synthesis of PER1, PER2, and CRY2 will not diminish, and that as a result, the production of androgen and AR will not increase [24, 44]. As a result, there is little to no increase in PCa risk. There are currently some reported associations between PCa risk and certain polymorphisms in the CIC core gene [22, 23]. The deeper position of CIC core genes and the increasing number of genes regulated by CIC components make it crucial to uncover CIC-mediated prognostic genes in order to better guide clinical practice. Three important genes (TOP2A, APOE, and ALDH2) were found in this investigation, and we also first postulated CIC-mediated molecular subtypes with strong predictive value. Additionally, we developed and externally validated the CICGPI score to measure the progression risk for PCa patients having RP or RT. Two of the three genes found in this study, APOE and TOP2A, have been linked to the advancement of PCa, but the underlying mechanism related to CIC remains elusive. Studies have shown that elevated levels of TOP2A are linked to higher Gleason scores and serve as an independent prognostic indicator for BCR [45]. Schaefer et al. demonstrated that increased TOP2A expression promotes the progression of PCa by triggering chromosomal rearrangements and boosting androgen signaling [46]. Additionally, Kirk et al. discovered that TOP2A is markedly up-regulated in metastatic prostate tumors, making it a valuable prognostic marker for identifying aggressive PCa [47]. APOE is significantly upregulated in localized PCa, with its elevation correlating with a higher Gleason score and increased tumor invasiveness [48]. A multinational ecological study revealed a strong association between APOE4 alleles and both PCa morbidity and mortality [49]. Reactive oxygen species (ROS) overproduction and lipid peroxidation are known to promote carcinogenesis. ALDH2 is linked to the detoxification of reactive aldehydes, including 4-hydroxy-2-nonenal (4-HNE), malondialdehyde, and acrolein, produced by ROS-mediated lipid peroxidation [50]. The prognosis and chemoradiotherapy sensitivity of numerous malignancies have been shown to be significantly correlated with ALDH2 [50, 51]. According to our earlier research, ALDH2 may be a biomarker that can indicate a patient's best outcome for PCa patients [5, 44, 52]. Overall, this work offered a significant chance to progress our knowledge of the connections between PCa patients' CIC and TOP2A, APOE, and ALDH2, which merited additional investigation.

After undergoing aggressive therapy, about 75% of men will develop BCR without showing obvious signs of metastatic illness [53], and recurrence occurs in about 50% of PCa patients who receive palliative radiation therapy following BCR [54–56]. The explanation could be that following radiation therapy, PCa patients' conditions will worsen even more since their cancer cells become less susceptible to radiation. Remarkably, we discovered that the CICGPI score was highly accurate in diagnosing and differentiating between radioresistant and susceptible PCa cells. Additionally, this score demonstrated a certain level of diagnostic accuracy in differentiating between PCa chemoresistance and no chemoresistance. Carnosine, anserine, balenine, and dihydrouracil break down in vivo to generate beta-alanine, which has been shown to be a potential anti-tumor drug with many anti-cancer actions in renal and cervical tumour cells [57]. A limonene and pinene-rich essential oil exhibits concentration-dependent antiproliferative properties together with antioxidant activity, causing G0/G1 arrest in prostate and cervical cancer cells [58]. Consistent with previous studies [58], we discovered that in PCa patients receiving RP or RT, beta alanine metabolism and the breakdown of limonene and pinene provided protection. A prior study demonstrated that DNA replication stress, a cause of genome instability and a characteristic of precancerous and cancerous cells, is produced by any circumstance that results in significant levels of DNA damage [59]. A mostly conserved mechanism of chromosomal damage repair, homologous recombination serves important roles in replication fork repair and protection as well as the elimination of harmful lesions [60, 61]. Homologous recombination errors demonstrated evidence of accelerated ageing and led to the development and progression of PCa [60]. Mismatch repair and homologous recombination are closely related processes. During genome duplication in mitotic cells, homologous recombination and mismatch repair are also important processes. While homologous recombination helps maintain the replication fork and repairs spontaneous DNA double-strand breaks and genotoxic lesions that damage both DNA strands, mismatch repair corrects polymerase misincorporation errors. When there are too many mismatched nucleotides in the heteroduplex DNA, a process known as homeologous recombination, mismatch repair inhibits homologous recombination [62]. In general, DNA damage and the cell cycle—characteristics of cellular senescence—are tightly linked to homologous recombination, mismatch repair, and DNA replication, indicating many connections to PCa [63, 64]. Similar pathways associated in the progression of PCa patients undergoing RP were also discovered in this investigation. Clinically, elevated CAF expression is linked to a worse BCR-free survival and an advanced stage in PCa patients [65]. Mechanistically, prostatic CAFs may secrete CXCL12, which could cause cancer in normal human prostatic epithelial cells in vitro [63]. Similarly, in PCa patients having RP or RT, there was a clear positive correlation seen between CAFs and CICGPI score. Furthermore, a higher CAF density was substantially correlated with a worse BCR-free survival rate for PCa patients. Furthermore, in PCa patients receiving RP as opposed to RT, there was a strong correlation between the CICGPI score and macrophages, CD4 + T cells, stromal score, immunological score, and ESTIMATE score. We believed that these outcomes were partly due to senescence or lethal effects of radiation on TME cells. Additionally, we saw a strong correlation between the CICGPI score and the majority of immunological checkpoints, suggesting a close relationship between PCa progression and TME development and providing some support for the data regarding the CICGPI score's association with TME. The effectiveness of immunotherapy in PCa is not as high as in other solid malignancies, possibly due to the tumor microenvironment having fewer immune-infiltrating cells or the lower tumor mutation load in PCa [66, 67]. This indicates that successful immunotherapy for PCa may require multiple approaches. In addition to identifying potential genomic alterations as biomarkers, combining therapies to enhance immunotherapy effectiveness is gaining traction. Checkmate650 is assessing the combination of nivolumab and ipilimumab in treating newly diagnosed metastatic castration-resistant PCa, achieving an overall response rate of up to 25% [68]. Progress is also being made in vaccine treatments for PCa, such as ADXS31-142, which utilizes Listeria bacteria to deliver PSA antigen fused with a potent immune stimulant directly to antigen-presenting cells of the immune system [69]. This approach stimulates an immune response against PSA-expressing cells, with study results indicating that combining ADXS31-142 with pembrolizumab significantly improves overall survival compared to using either drug alone [69]. Research has shown that circadian drug delivery strategies are effective in treating various cancers. In a study on patients with advanced non-small cell lung cancer, cisplatin-based circadian administration significantly decreased the occurrence of leukopenia and neutropenia, as well as gastrointestinal toxicity [70]. In patients with metastatic bladder cancer, the circadian timing of combination chemotherapy may also provide benefits [71]. Through regulating the expression of circadian rhythm genes and optimizing drug administration timing, cancer treatment effectiveness can be enhanced, side effects minimized, and personalized cancer treatment strategies developed. Based on our research findings, the CICGPI score shows promise as a valuable tool for prognostic assessment and risk stratification in clinical settings. It effectively distinguishes between high-risk and low-risk PCa patient groups, with a significant increase in the risk of BCR observed in the high-risk group. This risk stratification can inform clinical decision-making by guiding the selection of more aggressive treatment options and monitoring plans. Additionally, the CICGPI score demonstrates predictive value for chemotherapy resistance, aiding in the prediction of patient response to chemotherapy. This supports the development of personalized treatment plans tailored to maximize benefits for patients. Furthermore, the correlation of the CICGPI score with immune parameters in the TME and its association with the expression of multiple immune checkpoints suggest its potential in identifying patients who may benefit from immunotherapy strategies, such as immune checkpoint inhibitors. This offers a foundation for the selection and optimization of immunotherapy, particularly considering the increasing significance of immunotherapy in the treatment of PCa.

Oncogenic mechanisms, on the other hand, directly degrade circadian rhythms. As the literature review notes, circadian disturbance has lately been recognized as an independent risk factor for cancer and recognized as a carcinogen [16]. The circadian clock plays a crucial role in regulating immune function within the TME. This includes controlling the recruitment and activity of important immune cells like T cells and natural killer cells. Natural killer cells exhibit rhythmic killing activity, cytokine expression, and cytolytic factor expression. Disruption of circadian rhythms, as highlighted by Aiello et al., leads to the loss of diurnal patterns in M1 and M2 macrophages, cytokine levels, and ultimately promotes tumor growth [72, 73]. Additionally, Fortier et al. discovered that circadian rhythm influences antigen-specific immune responses in T cells, resulting in significant changes in proliferation and function, particularly in mice with circadian rhythm gene mutations [74]. By regulating the secretion of hormones like cortisol and melatonin, circadian rhythms not only suppress functional cellular immunity but also trigger overactive inflammatory responses [75]. These responses can in turn facilitate tumor growth, angiogenesis, and metastasis. In fact, we found that patients in the high-risk group, or cluster 2, had greater levels of CRY1, CSNK1E, and CLOCK, and lower levels of PER1, CRY2, and PER2 among PCa patients receiving RP. In line with our earlier research [44], the CIC period was shortened by all of the aforementioned modifications, and androgen excess was caused by the downregulation of PER1 and PER2 in the liver through sex hormone-binding globulin and insulin-like growth factor-binding protein 4 [76]. Furthermore, CRY2 may work with FBXL3 to degrade c-MYC, and CRY1 could not take the place of CRY2 in this process to promote c-MYC degradation [77]. Moreover, CRY2 downregulation was highly associated with an increased risk of PCa [78]. Interestingly, CRY1 is a pro-tumorigenic factor that rhythmically regulates DNA repair in human tumours ex vivo as well as in vitro [27], which was consistent with our findings. Regarding PCa patients following radiation therapy, we discovered that high-risk patients primarily had negative associations with RORA, which may be the pivotal gene regulating CIC. RORA may stimulate BMAL1 transcription, and BMAL1 downregulation may impede the circadian rhythms of immune or tumour cells [16, 79]. Regarding the significance of CAFs for PCa patients in both this and our earlier study [80], we hypothesized that one significant factor in the development of PCa may be the circadian disruption of CAFs. Additionally, tumor cells' circadian disruption was not negligible. It is important to proceed with caution when interpreting our results, as they call for additional investigation.

In the upcoming decades, as the world's population ages and the incidence and mortality rate of PCa have been rising rapidly, PCa in older men will contribute significantly to the disease burden [81–83]. In order to identify clinically high-risk patients and prevent unnecessary medical resource waste, this article sought to exploratorily propose molecular subtypes at the genetic level along with a corresponding formula that has a better predictive effect on BCR-free survival for PCa patients undergoing RP or RT. Two high-throughput sequencing and microarray sequencing systems are combined in our work. The results are more dependable and have significant clinical significance for mutual verification. It is envisaged that this study will deepen our understanding of the relationship between CIC and PCa progression by providing new insights into CIC-related genes.

We must acknowledge, nevertheless, that this work was restricted to the following aspects. Firstly, the ability to provide ongoing follow-up data that may provide prompt knowledge of the benefits of real-world practice was limited by retrospective research studying big public databases. We did not conduct biological experiments to verify our results. More convincing data might also come from studying the biological function of important CIC-related genes in PCa through in vitro and in vivo studies. Lastly, we did not investigate the possible roles of important CIC-related genes in PCa through CIC-related pathways or in relation to circadian rhythm. Subsequent investigations ought to explore the intricate mechanisms that underlie the association between PCa and genes linked to CIC.

Conclusions

In patients with PCa receiving RP or RT, we created CIC-mediated molecular subtypes and determined a strong correlation between CICGPI score and BCR-free survival.

Acknowledgements

We appreciated the Figdraw (www.figdraw.com) and Chengdu Basebiotech Co.,Ltd for their assistance in drawing and data process.

Author contributions

LC, DCF, RCW and WRW proposed the project, conducted data analysis, interpreted the data, and wrote the manuscript; LC, KHY, YO, DXL, RCW, JW, and ZTT conducted data analysis, interpreted the data; DCF, YO, RCW and WRW supervised the project, and interpreted the data. All authors reviewed and edited the manuscript.

Funding

This program was supported by the Chinese Scholarship Council (Grant No. 202206240086) and a regional innovation cooperation project of Sichuan Province (Grant No. 23QYCX0136). The funders had no role in the study design, data collection or analysis, preparation of the manuscript, or the decision to publish.

Availability of data and materials

The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Declarations

Ethics approval and consent to participate

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to declare.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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