The important tumor suppressor role of PER1 in regulating the cyclin–CDK–CKI network in SCC15 human oral squamous cell carcinoma cells
Authors Fu X, Li H, Yang K, Chen D, Tang H
Received 20 November 2015
Accepted for publication 4 March 2016
Published 15 April 2016 Volume 2016:9 Pages 2237—2245
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Manfred Beleut
Peer reviewer comments 3
Editor who approved publication: Dr Jianmin Xu
Xiao-Juan Fu, Han-Xue Li, Kai Yang, Dan Chen, Hong Tang
Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
Background: Accumulating evidence suggests that the abnormal expression of the circadian clock gene PER1 is closely related to the development and progression of cancer. However, the exact molecular mechanism by which the abnormal expression of PER1 induces carcinogenesis is unclear. This study was conducted to investigate the alterations in downstream cell cycle genes, cell cycle distribution, cell proliferation, apoptosis, and in vivo tumorigenicity in SCC15 oral squamous cell carcinoma cells after PER1 downregulation.
Materials and methods: A stable SCC15 cell line was established to constitutively express shRNA targeting PER1. Quantitative real-time polymerase chain reaction (PCR) and Western blot analyses were conducted to estimate PER1 mRNA and protein expression. The expression of PER1, P53, CyclinD1, CyclinE, CyclinA2, CyclinB1, cyclin-dependent kinase (CDK) 1, CDK2, CDK4, CDK6, P16, P21, WEE1, and CDC25 mRNA was detected by quantitative real-time PCR. Cell cycle distribution, cell proliferation, and apoptosis were determined by flow cytometry. The in vivo tumorigenicity of SCC15 cells was evaluated in female BALB/c nu/nu mice.
Results: PER1 downregulation resulted in significantly increased mRNA expression levels of CyclinD1, CyclinE, CyclinB1, CDK1, and WEE1 (P<0.05), and significantly decreased mRNA expression levels of P53, CyclinA2, P16, P21, and CDC25 (P<0.05) compared to control cells. Additionally, PER1 downregulation led to significantly fewer cells in S phase (P<0.05), but significantly more cells in G2/M phase (P<0.05) compared to the control group. After PER1 downregulation, the cell proliferation index was significantly higher (P<0.05), and the apoptotic index was significantly lower (P<0.05). The in vivo tumorigenicity of SCC15 cells was significantly enhanced by PER1 downregulation (P<0.05).
Conclusion: PER1 is an important tumor suppressor gene which acts by regulating the Cyclin-CDK-cyclin-dependent kinase inhibitor regulatory network. An in-depth characterization of this gene may further illuminate the molecular mechanisms responsible for the development and progression of cancer, thus providing novel molecular targets for cancer treatment.
Keywords: oral cancer, cell cycle, circadian clock, PER1, gene
Most cellular activities, such as hormone secretion, cell proliferation, and intracellular metabolism, periodically fluctuate over a period of ~24 hours, which is known as the circadian rhythm.1–4 The circadian rhythm is the result of long-term biological evolution within the external environment, and it is one of the basic characteristics of life. The periodic expression of intracellular circadian clock genes is responsible for circadian rhythms.1–3,5 To date, 14 circadian clock genes have been reported, including PER1, PER2, PER3, CRY1, CRY2, CLOCK, BMAL1, TIM, CK1ε, NPAS2, REV-ERBs, DEC1, DEC2, and RORs.1,3,5,6 Circadian rhythms, which are driven by the periodic expression of these circadian clock genes, coordinate the complex activities of life such that they occur in an orderly manner. As an important clock gene, PER1 plays an critical role in regulating and maintaining the stability of circadian rhythms.7,8 Recent studies have shown that PER1 is involved in organismal circadian rhythms and regulates many crucial downstream cyclins.9,10 Alterations in PER1 gene expression are closely related to the development and progression of cancer.9–18
Cell cycle disorder is the main contributor to tumorigenesis.5,19 The normal cell cycle operates in strict chronological order through the G1-S-G2-M phases.5,20–22 At the molecular level, the normal operation of the cell cycle is dependent on the cyclin/cyclin-dependent kinase (CDK)/cyclin-dependent kinase inhibitor (CKI) regulatory network.5,19–21 The cyclin family includes CyclinA–Y, and CyclinA2, CyclinB1, CyclinD1, and CyclinE play major roles in regulating the cell cycle. The CDK family includes CDK1–16, and CDK1, CDK2, CDK4, and CDK6 play major roles in cell cycle regulation. Ink4 and Cip/Kip family members are CKIs; P16 belongs to the Ink4 family, and P21 belongs to the Cip/Kip family. Both of these CKIs have important roles in regulating the cell cycle.19,20 CDKs are at the core of the cyclin–CDK–CKI regulatory network. Cyclins and CKIs positively and negatively, respectively, regulate the function of CDKs.19,20
Recent studies have shown that PER1 expression is low in many types of cancer.11–18 Our previous work showed low PER1 gene expression in human oral squamous cell carcinoma (OSCC) sample tissues, and PER1 expression was closely related to clinical stage and lymph node metastasis.23 Studies have shown that PER1 can regulate many important downstream cyclins,9,10 leading to altered cell cycle progression and proliferative capacity, which are closely related to the development and progression of cancer.5,9–11 With respect to the cyclin–CDK–CKI regulatory network, most studies have focused on the PER1-mediated regulation of cyclins, such as CyclinB1, CyclinD1, CyclinE, and WEE1.9,10 However, the mechanism by which PER1 regulates CDKs and CKIs remains poorly understood. We speculate that PER1 may regulate various molecules in the cyclin–CDK–CKI regulatory network. With the aim of further investigating the relationship between the PER1 gene and cancer, we evaluated the interaction between the cell cycle and circadian rhythm. Alterations in cell cycle distribution, cell proliferation, apoptosis, and in vivo tumorigenicity were detected after the PER1 gene was downregulated in SCC15 human OSCC cells. Variations in important cyclin–CDK–CKI regulatory network molecules were observed, which further elucidated the molecular mechanism by which PER1 is involved in cancer development.
Materials and methods
SCC15 human OSCCs were acquired from Chongqing Medical University of Life Sciences (Chongqing, People’s Republic of China) and cultured in Dulbecco’s Modified Eagle’s Medium (DMEM)/F12 medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific), 100 IU/mL penicillin, and 100 μg/mL streptomycin at 37°C in a humidified atmosphere of 5% CO2. The experiment was approved by the ethics committee of Chongqing Medical University.
Plasmid construction and identification
Based on the mRNA sequence of the human PER1 gene (GeneID: NM_002616) in the GenBank database (http://www.ncbi.nlm.nih.gov/genbank/) and siRNA design principles,24 RNA interference target sequences in the PER1 gene were selected, and a BLAST genome homology analysis was performed (http://www.ncbi.nlm.nih.gov/BLAST/). Three target sites with potential interference function were identified for the PER1 gene. These three PER1-siRNA sequences (PER1-I, CAGCACCACTAAGCGTAAATG; PER1-II, CCAGCACCACTAAGCGTAAAT; and PER1-III, CCATGGACATGTCCACCTATA) and one control sequence (Control, CCTAAGGTTAAGTCGCCCTCG) were designed using BLOCK-iT™ RNAi Designer software (Thermo Fisher Scientific). The control sequence was not predicted to have an interference effect on any gene according to a GenBank database search. Using DNA ligase, the CCGG sequence was added to the 5′ distal end, and the TTTTTG sequence was added to the 3′ distal end. The sense and antisense sequences were connected with a loop sequence (CTCGAG). The synthesized short hairpin RNA (shRNA) was amplified via reverse transcription polymerase chain reaction (RT-PCR). The obtained DNA fragments were subcloned into the pUM-T vector (BioTeke, Beijing, People’s Republic of China) between the EcoRI and AgeI restriction enzyme sites. After verification with Chromas software (Technelysium, Brisbane, OLD, Australia), the complete coding sequence of PER1 was inserted into the PLKO.1 vector (PLKO.1-PER1).
To establish an OSCC line that constitutively expressed shRNA targeting PER1, SCC15 cells were detached by trypsinization, plated in six-well plates (5×105 cells/well) and allowed to grow for 24 hours. At 70%–80% confluence, the cells were transfected with the PER1-shRNA (I–III)-PLKO.1 recombinant plasmid using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s protocol. At 24 hours post-transfection, stable clones were selected by treatment with puromycin (2.0 μg/mL, Thermo Fisher Scientific) for 2 weeks. The cells were divided into the following five groups: the PER1-shRNA-I, PER1-shRNA-II, and PER1-shRNA-III groups, which included SCC15 cells transfected with the PER1-shRNA-I, PER1-shRNA-II, and PER1-shRNA-III plasmids, respectively; the control group (Control-shRNA) of SCC15 cells transfected with an empty plasmid; and the SCC15 group (blank), which consisted of untreated SCC15 cells.
Western blot analysis
Total protein was extracted using meta-pentaerythritol ester rosin lysis buffer (Thermo Scientific, Rockford, IL, USA), and the protein concentration was determined using the bicinchoninic acid (BCA) method (Pierce, Rockford, IL, USA). Equal amounts of protein from each sample were fractionated by 10% SDS-PAGE and transferred onto PVDF membranes (Millipore, Bedford, MA, USA). The membranes were blocked with 5% skim milk and subsequently incubated overnight at 4°C with a rabbit anti-human PER1 polyclonal antibody (1:200) (GeneTex, Inc., Irvine, CA, USA) and a mouse anti-human GAPDH monoclonal antibody (1:3,000) (Pik-day Biotechnology Research Institute, Beijing, People’s Republic of China). After washing, the membranes were incubated with horseradish peroxidase-conjugated secondary antibodies for 1 hour at room temperature. The proteins were visualized with an enhanced chemiluminescence kit (Millipore) according to the manufacturer’s instructions. The gray values were analyzed using Quantity One software (Bio-Rad Laboratories Inc., Hercules, CA, USA), and the gray value ratio of PER1 to the internal reference GAPDH band was calculated. The experiment was repeated three times.
Quantitative real-time PCR
Total RNA was extracted from each group according to the instructions for RNAiso Plus (TaKaRa Bio Inc., Kusatsu, Japan). The quantity of total RNA was estimated using a Nanodrop ND 2000 (Thermo Scientific). RNA (10 μL) was reverse-transcribed with a PrimeScript RT Reagent Kit (TaKaRa) on an S1000™ Thermal Cycler (Bio-Rad Laboratories Inc.) according to the manufacturer’s instructions. The cDNA was used as a template for quantitative real-time PCR (qRT-PCR) in a C-1000™ Thermal Cycler (Bio-Rad Laboratories Inc.) with SsoAdvanced Universal SYBR Green Supermix (Bio-Rad Laboratories Inc.) according to the manufacturer’s instructions. The following conditions were used for qRT-PCR: 3 minutes at 95°C, followed by 40 cycles of 10 seconds at 95°C and 30 seconds at 60°C. Primer sequences for the following target genes were designed using Primer Premier 5.0 software: PER1, P53, CyclinD1, CyclinE, CyclinA2, CyclinB1, CDK1, CDK2, CDK4, CDK6, P16, P21, WEE1, CDC25, and GAPDH (reference gene) (Table 1). The designed primers were obtained from Sangon Biotech (Shanghai, People’s Republic of China). To determine the inter-assay precision, three cDNA replicates from each sample were run on 3 separate days. The mRNA expression of each gene was calculated by the Pfaffl method,25 and GAPDH served as the endogenous reference for normalizing target gene expression. A no template control was included to check the reagents and to assay for DNA contamination in each sample.
Table 1 The primers for real-time quantitative reverse transcription-polymerase chain reaction analysis of the genes
Based on the PER1 mRNA and protein results, the PER1-shRNA-I group served as the experimental group. Cells in the PER1-shRNA-I, Control-shRNA, and SCC15 groups were harvested by centrifugation, adjusted to 1×106 cells/mL, and washed with phosphate-buffered saline (PBS).
Cell cycle distribution and cell proliferation assays
Cells were fixed with 225 μL of ice-cold 70% ethanol and treated with 1 mg/mL RNAse for 30 minutes. Intracellular DNA was labeled with propidium iodide (50 μg/mL) and incubated at 4°C in the dark. The samples were then analyzed using a flow cytometer (FACSVantage SE; BD Biosciences, San Jose, CA, USA) to detect cell cycle distribution and to determine the proliferation index (PI), which was calculated using the following formula: PI = (S + G2/M)/(G0/G1 + S + G2/M) ×100%.
Cells were stained with 200 μL of Annexin V-fluorescein isothiocyanate (Thermo Fisher Scientific) at 4°C for 15 minutes and then with 1 mL of propidium iodide for 5 minutes. The proportion of apoptotic cells was determined by flow cytometry. The apoptotic index (AI) was calculated using the following formula: AI = (apoptotic cells/total cells measured) ×100%. The aforementioned experiment was repeated three times.
In vivo tumorigenicity experiment
Ten specific pathogen-free female BALB/c nu/nu mice (4–6 weeks old, 18–22 g) were purchased from the Institute of Experimental Animals (Chongqing Medical University). The mice were randomly divided into the experimental group (PER1-shRNA-I) and the control group (SCC15). PER1-shRNA-I and SCC15 cells (5×106 cells/mL) were harvested by centrifugation, and the suspensions (0.2 mL) were subcutaneously injected into the right flank of mice in the experimental and control groups. After 3 weeks of in vivo tumor growth, the mice were killed by cervical dislocation. The tumors were removed and weighed using an electronic balance (A250 type; Denver Instrument Co., Denver, CO, USA). The maximum length diameter (a) and minimum minor diameter (b) were measured using a vernier caliper, and the tumor volume (V) was calculated according to the following formula: V = 0.5 × a × b2. The tumor was then fixed with 4% paraformaldehyde, embedded in paraffin, sectioned, and visualized at 200× magnification after hematoxylin and eosin (HE) staining. All experimental procedures were approved by the Experimental Animal Use Management Committee of the Medical Laboratory Animal Institute of Chongqing Medical University.
All data were analyzed using SPSS 17.0 software (SPSS Inc., Chicago, IL, USA). The in vivo tumorigenicity in the two groups was compared using a Student’s t-test. The experimental data in multiple groups were analyzed by one-way analysis of variance (ANOVA) with pairwise comparisons, followed by the least significant difference test (LSD-t). The results are presented as the mean ± standard deviation (SD). P<0.05 was considered statistically significant.
Establishment of a stable SCC15 cell line with downregulated PER1
To explore the role of PER1 in the development and progression of human OSCC, we established stable SCC15 cell lines that constitutively expressed shRNA targeting PER1. After screening with puromycin, SCC15 cells were subjected to qRT-PCR and Western blot analyses for PER1 expression. The mRNA expression levels of PER1 in the PER1-shRNA-I, PER1-shRNA-II, PER1-shRNA-III, Control-shRNA, and SCC15 cells were 1.00±0.00, 1.45±0.09, 1.50±0.17, 2.11±0.31, and 2.20±0.34, respectively, and the gray value ratios of PER1 protein expression in these cells were 1.05±0.21, 1.5±0.15, 1.63±0.11, 2.18±0.24, and 2.21±0.33, respectively (Figure 1). The PER1 mRNA and protein expression levels were significantly lower in the PER1-shRNA-I group than in the other groups (P<0.05). This finding suggested that PER1 downregulation was most effective in the PER1-shRNA-I group, and this group was therefore used in further experiments.
Alterations in the mRNA expression of cell cycle-related genes in SCC15 cells
SCC15 cells were transfected with a PER1-shRNA plasmid or an empty vector, and the mRNA expression levels of cell cycle-related genes were examined by qRT-PCR (Figure 2). The mRNA expression levels of CyclinD1, CyclinE, CyclinB1, CDK1, and WEE1 were significantly higher in the PER1-shRNA-I group than in the Control-shRNA and SCC15 groups (P<0.05). The mRNA expression levels of P53, CyclinA2, P16, P21, and CDC25 were significantly lower in the PER1-shRNA-I group than in the Control-shRNA and SCC15 groups (P<0.05). The mRNA expression levels of CDK2, CDK4, and CDK6 were not significantly different in the three groups (P>0.05).
Alterations in SCC15 cell cycle distribution, proliferation, and apoptosis
Flow cytometry was performed to investigate the alterations in cell cycle distribution, cell proliferation, and apoptosis after downregulation of the PER1 gene (Figure 3). The PI of the PER1-shRNA-I group was significantly higher than that of the Control-shRNA and SCC15 groups (P<0.05), while the AI was significantly lower in the PER1-shRNA-I group than in the Control-shRNA and SCC15 groups (P<0.05). Compared to control cells, the number of cells in S phase was significantly lower in the PER1-shRNA-I group (P<0.05), and the number of cells in G2/M phase was significantly increased (P<0.05).
Downregulation of PER1 enhances the in vivo tumorigenicity of SCC15 cells
Tumors were removed and weighed using an electronic balance (Figure 4). The average tumor volume in the PER1-shRNA-I and SCC15 groups was 0.25±0.09 cm3 and 0.08±0.02 cm3 (P<0.05), respectively, and the mean tumor weight was 0.50±0.16 g and 0.18±0.04 g (P<0.05), respectively. These results suggested that the in vivo tumorigenicity of SCC15 cells was increased after PER1 downregulation.
Cell cycle disorders alter the balance between cell proliferation and apoptosis, which is the main cause of tumorigenesis.5,19 Cell cycle progression through G1-S-G2-M is precisely regulated by the cyclin–CDK–CKI regulatory network.5,20–22 CDKs are at the core of cell cycle regulation by this network. In each phase of the cell cycle, different cyclins combine with specific CDKs to form cyclin–CDK complexes, which activate other CDKs and promote cell cycle progression. Different CKIs combine with the corresponding CDKs or cyclin–CDK complexes to inhibit CDK activity, thus inhibiting cell cycle transitions.19,20 If the synergistic regulation between the positive (cyclins) and negative (CKIs) regulatory factors is abnormal, the balance between cell proliferation and apoptosis will change, leading to carcinogenesis.
Previously published studies have demonstrated that the abnormal expression of the PER1 gene is closely related to the development and progression of cancer,9–11 and this association is due to the PER1-mediated regulation of important downstream cyclins. Yang et al reported that downregulation of the PER1 gene in breast cancer cells in mice increases CyclinD1 and CyclinE expression levels, cell proliferation, and growth capacity.9 Overexpression of the PER1 gene in HCT116 colon cancer cells decreases the expression levels of CyclinB1 and WEE1, thus promoting apoptosis and inhibiting cell proliferation.10 Cao et al also found that overexpression of the PER1 gene in prostate cancer cells inhibits cell proliferation and promotes apoptosis.11 However, Sato et al showed that PER1 was an antiapoptotic factor in pancreatic cancer, liver cancer, and gingival cancer.26,27 These differences may stem from different experimental methods and conditions, or PER1 expression may exhibit tissue or cell specificity. However, in the cyclin–CDK–CKI regulatory network, the mechanism by which PER1 regulates CDKs and CKIs remains poorly understood.
The G1 phase initiation is critical for cell cycle progression. CyclinD1 binds to and activates CDK4 or CDK6, thus initiating the transition through G1 phase. CyclinE then binds to and activates CDK2, thus driving the transition from G1 to S phase.19,22 P16, which belongs to the Ink4 family of CKIs, competes with CyclinD1 for binding to CDK4 or CDK6 to specifically inhibit their activity. P21, which belongs to the Cip/Kip family of CKIs and has broad inhibitory kinase activity, can inhibit a variety of cyclin–CDK complexes. For example, P21 inhibits the CyclinE–CDK2 complex in G1 phase and thus retards cell cycle progression.19,20,28 In this study, although the expression levels of CDK2, CDK4, and CDK6 did not change significantly after PER1 downregulation in SCC15 cells, CyclinD1 and CyclinE expression levels were significantly increased, thereby activating CDK2, CDK4, and CDK6. At the same time, the expression levels of P16 and P21 were significantly reduced, thus weakening the inhibition of CDK2, CDK4, and CDK6. Therefore, the ability to transition through G1 phase was enhanced, and the cell cycle transition from G1 to S phase was accelerated. This study also revealed that the in vivo tumorigenic capacity of SCC15 cells was increased by PER1 downregulation.
In the S phase of the cell cycle, CyclinA2 binds to and activates CDK2, and the CyclinA2–CDK2 complex drives the cell cycle through S phase and promotes the S/G2 transition.19,22 This study showed that CDK2 expression did not significantly change after PER1 downregulation in SCC15 cells, but CyclinA2 expression was significantly reduced. Furthermore, the expression of P21, which inhibits the activity of the CyclinA2–CDK2 complex, was significantly reduced, leading to the weakening of the continued role of the CyclinA2–CDK2 complex in the cell cycle. Thus, S phase progression was accelerated, as evidenced by the decreased number of cells in this phase.
In G2, CyclinB1 binds to and activates CDK1, and the resulting CDK1–CyclinB1 complex drives the G2/M transition. CDC25 can enhance CDK1 activity to promote progression through the G2/M transition.19,22 WEE1 can inhibit the activity of CDK1, thus delaying the cell cycle in M phase or arresting the cell cycle in G2/M phase.19,29 This study showed that the expression levels of CDK1, CyclinB1, and WEE1 were significantly increased after PER1 downregulation in SCC15 cells, while P21 and CDC25 expression levels were significantly reduced. Thus, three aspects of the Cyclin–CDK–CKI regulatory network were changed. Combined with the finding of an increased number of cells in G2/M phase, these data demonstrated that the negative regulatory function of CyclinB1–CDK1 complex by WEE1 was responsible for the enhanced cell cycle progression.
In the Cyclin–CDK–CKI regulatory network, P53 is an important G1/S phase checkpoint regulator. P53 is activated in cells with DNA damage, leading to cell cycle arrest to enable repair, and cells with irreversible DNA damage are driven at the G1/S phase checkpoint to undergo apoptosis.19,29,30 This study showed that P53 expression was reduced after PER1 downregulation in SCC15 cells, thus weakening the repair capacity of cells with DNA damage and decreasing the induction of apoptosis in these cells. The number of cells with unrepaired DNA damage that entered the S phase was increased. As a result, the integrity and stability of the cellular genome were affected, promoting malignant transformation. This study also demonstrated that the PI was significantly increased, the AI was significantly decreased, and the in vivo tumorigenic capacity was enhanced after downregulation of the PER1 gene in SCC15 cells.
This study revealed that PER1, a clock gene, is an important tumor suppressor. After downregulating PER1 in SCC15 cells, the mRNA expression of CyclinD1, CyclinE, CyclinB1, CDK1, and WEE1 was significantly increased, while that of P53, CyclinA2, P16, P21, and CDC25 was significantly decreased. As a result, the cellular proliferative capacity was enhanced, and apoptosis decreased. Moreover, cell cycle progression changed, and in vivo tumorigenesis was enhanced. This study demonstrated that PER1 plays a crucial role in three aspects of the cyclin–CDK–CKI regulatory network. The results significantly expand our present understanding of PER1. An in-depth evaluation of the translational and posttranslational modifications of the PER1 gene is likely to further clarify the interaction between circadian rhythm and cell cycle, to illuminate the relationship between circadian rhythm and tumorigenesis, and to provide novel molecular targets for cancer treatment.
The authors sincerely thank Xiao-Li Su for participation in the experiment. The authors also acknowledge Hua Ye for providing instructions for the data analysis.
The authors report no conflicts of interest in this work.
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