Abstract
Background/Aim: γ-Glutamylcyclotransferase (GGCT) depletion suppresses breast cancer cell proliferation by inducing cellular senescence. However, the underlying molecular mechanisms have not been fully elucidated. Therefore, the objective of this study was to elucidate the mechanisms by which GGCT depletion suppresses cancer cell proliferation.
Materials and Methods: Human breast cancer MCF-7 cells were transfected with GGCT-specific or control siRNAs. Transcriptomic profiling by RNA sequencing identified differentially expressed genes (q<0.01, |log2 fold change|>1), and Gene Ontology and KEGG analyses characterized affected pathways. Key genes and functional effects on the TGF-β2/SMAD3 axis, cell-cycle progression, and senescence were validated by qRT-PCR, western blotting, and SA-β-Gal assays.
Results: Comprehensive gene expression analysis revealed that depletion of GGCT increases the expression levels of the cell cycle arrest factors CDKN1A (p21Cip1) and CDKN2B (p15INK4b), accompanied by elevated transforming growth factor-β2 (TGFB2) expression. Blocking this pathway through the simultaneous knockdown of TGFB2 was found to significantly restore the growth-inhibitory effect mediated by cellular senescence induced by GGCT depletion. This finding demonstrated that these phenotypes depend on the TGF-β2 pathway. Furthermore, we identified SMAD3 as a TGF-β2 downstream factor essential for the increase in p21Cip1 and p15INK4b and the growth-inhibitory effect induced by GGCT depletion.
Conclusion: Activation of the TGF-β2/SMAD3 pathway is a mechanism by which cellular senescence is induced through GGCT depletion, suggesting that GGCT inhibition represents a promising therapeutic strategy for the treatment of breast cancer.
- γ-Glutamylcyclotransferase
- GGCT
- cellular senescence
- cyclin-dependent kinase inhibitor
- TGF-β
- SMAD
- transcriptome analysis
Introduction
γ-Glutamylcyclotransferase (GGCT) is a key enzyme in glutathione metabolism that catalyzes the conversion of γ-glutamyl dipeptides into 5-oxoproline and free amino acids (1). It was initially identified as chromosome 7 open reading frame 24 (C7orf24), reported to be overexpressed in urothelial carcinoma of the bladder (2). Subsequent studies have demonstrated that GGCT regulates cell proliferation, survival, invasion, migration, and metastasis across various types of cancer in humans (3–9). In addition, He et al. reported amplification of the GGCT gene in multiple cancers, including breast cancer, and showed that oncogenic KRas mutations up-regulate GGCT expression (10). Similarly, mutant NRas was found to induce GGCT expression in glioblastoma stem cells (11), highlighting GGCT as a potential therapeutic target in Ras/MAPK pathway-activated cancers. Furthermore, recent studies have demonstrated that typical oncogenes, namely the c-Myc (12) and c-Jun transcription factors, directly activate the GGCT promoter (11), suggesting that GGCT plays a crucial role in cancer metabolism (11, 12). GGCT depletion by RNA interference has been shown to suppress cell proliferation in several cancer cell lines (11, 13, 14). GGCT-targeting small-interfering RNA (siRNA) or antisense oligonucleotides have demonstrated anti-tumor efficacy in xenograft mouse models (15–17). However, the molecular mechanisms by which GGCT inhibition mediates growth suppression are not fully understood.
Transforming growth factor-β (TGF-β), originally identified in the 1980s as a growth-inhibitory factor produced by various cell types, influences proliferation and differentiation in various cell lines. There are three isoforms of TGF-β (i.e., TGF-β1, TGF-β2, and TGF-β3). These isoforms have high sequence homology and signal through the same receptor complex. TGF-β2 was purified from BSC-1 monkey kidney epithelial cells as an autocrine growth inhibitor and was shown to suppress epidermal cell growth (18). Mutations in TGF-β receptors and signaling molecules have been identified in colorectal and pancreatic cancers, suggesting that disruption of TGF-β signaling contributes to tumorigenesis (19). SMAD3 and SMAD4 function downstream of TGF-β receptors, and mutations in these proteins impair TGF-β-mediated growth inhibition (20). In tumorigenic cells transformed by activated H-Ras, SMAD3 expression is markedly reduced, indicating the involvement of SMAD3 down-regulation in tumor progression (21). TGF-β signaling releases the transcriptional repression of p15INK4b by disrupting the Myc/c-Myc-interacting zinc finger protein-1 (Myc/Miz-1) complex and activates p15INK4b transcription by facilitating the interaction between Smad proteins and Miz-1 (22). Although recent studies have revealed that TGF-β exerts both tumor-suppressive and tumor-promoting effects depending on the cellular context (23), the present study focuses on its tumor-suppressive role, particularly in relation to GGCT depletion.
In this study, we conducted a comprehensive transcriptomic analysis to elucidate the mechanisms by which GGCT depletion suppresses cancer cell proliferation. Furthermore, we investigated whether activation of the TGF-β/SMAD pathway plays a role in the GGCT depletion-induced cellular senescence, which is caused by up-regulation of cyclin-dependent kinase inhibitors (CDKIs).
Materials and Methods
Cell lines and culture conditions. The breast cancer cell line MCF-7 was purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA) and cultured in Dulbecco’s modified Eagle’s medium (D-MEM; FUJIFILM Wako, Osaka, Japan) supplemented with 10% fetal bovine serum (HyClone, South Logan, UT, USA) and 1% penicillin and streptomycin. Cells were maintained at 37°C in an environment with 5% CO2.
RNA interference. Synthesized siRNA targeting human GGCT was purchased from Qiagen (Hilden, Germany) and the following sequence was used: 5′-AATGACTATACAG-GAAAGGTC-3′ (GGCT); other siRNAs, cyclin dependent kinase inhibitor 1A (CDKN1A) (ID# s417), CDKN2B (ID# s2844), SMAD3 (ID# 107877), and TGF-β2 (ID# s533550) were purchased from Thermo Fisher Scientific (Waltham, MA, USA). Scrambled control RNA duplexes (Silencer Select Negative Control #1 siRNA; cat. no. 4390844) were also purchased from Thermo Fisher Scientific. The individual siRNA sequences are provided in Table I. Transient transfections were performed using Lipofectamine RNAi-MAX reagents (Invitrogen, Carlsbad, CA, USA) according to instructions provided by the manufacturer. All siRNAs were used for transfection at a concentration of 10 nM.
Small-interfering RNA (siRNA) sequences used for RNA interference.
Sequencing library construction and sequencing. MCF-7 cells were cultured and transfected with control-siRNA for 1 day or with GGCT-siRNA for 1, 2, or 3 days. Total RNA was extracted from four experimental groups (control-siRNA for 1 day, and GGCT-siRNA for 1, 2, and 3 days), each in triplicate, using the Trizol Plus RNA Purification kit (Thermo Fisher Scientific). To ensure the quality of the samples for sequencing, Bioanalyzer (Agilent, Santa Clara, CA, USA) was used according to instructions provided by the manufacturer; the RNA integrity numbers of all prepared total RNA samples were ≥8. The library of template molecules for sequencing was converted from total RNA using the TruSeq RNA Sample Prep Kitv2 (Illumina, San Diego, CA, USA) according to instructions provided by the manufacturer. The library (4 pM) was subjected to cluster generation on a Single Read Flow Cell v4 (TruSeq SR Cluster Kit v2-cBot-GA) with a cBot generation instrument (Illumina). Sequencing was performed on a Genome Analyzer GAIIx for 58 cycles using Cycle Sequencing v5-GA reagents (Illumina), 58-bp single-end reads.
Transcript quantification and differential expression analysis. Sequencing raw reads were filtered by CASAVA Software 1.8.2 (Illumina) to produce 51-bp and remove shortened <49-bp sequence data in fastq format, which were processed using Cutadapt version 1.2.1. The clean single-end reads from each sample were aligned to the reference genome (Ensembl GRCh37) using TopHat version 2.0.10. Cufflinks was used to calculate the fragments per kilobase of exon model per million mapped reads (FPKM) value of mRNAs in each sample. DEGs were determined between the control-siRNA (1 day) and GGCT-siRNA (1, 2, and 3 days) groups using Cuffdiff, applying filter criteria of q<0.01 and |log2(fold change)| >1. The datasets generated and/or analyzed during the current study are available in the DNA Data Bank of Japan (DDBJ) repository under accession number DRA010859.
Functional annotation and pathway analysis. The Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Resources (DAVID; version 6.8, https://david.ncifcrf.gov/home.jsp) was used to construct gene annotations for DEGs in relation to biological process. The function and interactions among DEGs were elucidated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The cut-off p-Value to examine the significance of gene-term were set at p-values <0.1, and the top 10 terms and pathways (according to p-value), neglecting apparently unrelated pathways, were presented in a bar chart.
Quantitative reverse transcription PCR. Total RNA was extracted from cells lysed by Trizol (Invitrogen) and purified with PureLink DNase Kits (Invitrogen) according to instructions provided by the manufacturer. cDNA was synthesized by reverse transcription using the SuperScript™ VILO™ cDNA synthesis Kit (Thermo Fisher Scientific). The expression levels were quantified using the LightCycler 480 SYBG Master I Mix and LightCycler 480 System II (Roche Diagnostics GmbH, Mannheim, Germany). Gene expression data were normalized using the levels of the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene. The primer sequences used in this experiment are provided in Table II. All analyses were performed in triplicate.
Primer sequences used for qRT-PCR.
Antibodies. Antibodies against various proteins were obtained from the following sources: mouse monoclonal antibodies; p15INK4b (1:500; cat. no. sc-271791; Santa Cruz Biotechnology, Dallas, TX, USA); p21WAF1/CIP1 (1:500; cat. no. 556430; BD Biosciences, San Jose, CA, USA); α-tubulin (1:1,000; cat. no. T9026; Sigma–Aldrich Co. LCC, Tokyo, Japan); GGCT (1:500; cat. no. HPA020735; Sigma–Aldrich Co. LCC); TGF-β2 (1:1,000; cat no. 19999-1-AP; Proteintech, Rosemont, IL, USA), phospho-SMAD2 (1:500; cat. no. 3108; Cell Signaling Technology, Danvers, MA, USA); phospho-SMAD3 (1:500; cat. no. 9520; Cell Signaling Technology); SMAD2/3 (1:500; cat. no. 3102; Cell Signaling Technology). The secondary antibodies included horseradish peroxidase-conjugated (HRP-conjugated) horse anti-mouse IgG (1:5,000; cat. no. 7076; Cell Signaling Technology) and HRP-conjugated goat anti-rabbit IgG (1:5,000; cat. no. 7074; Cell Signaling Technology).
Western blotting analysis. Proteins were extracted from cell culture using RIPA buffer (Nacalai Tesque, Kyoto, Japan) supplemented with a protease inhibitor cocktail (Nacalai Tesque) and PhosSTOP (Roche Diagnostics GmbH). Subsequently, they were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and electro-transferred onto membrane filters [Immuno-Blot polyvinylidene difluoride (PVDF) membranes, Bio-Rad Laboratories, Richmond, CA, USA]. The filters were incubated overnight with primary antibodies in tris-buffered saline with Tween 20 (TBST) containing 2% bovine serum albumin and incubated for 1 h in HRP-conjugated anti-mouse or anti-rabbit antibodies (dilution: 1:5,000) in TBST containing 2% bovine serum albumin. Immunoreactivity was detected using the Luminata Classico Western HRP substrate (Millipore Corporation, Burlington, MA, USA) with LAS4000 (FUJIFILM Wako) and quantified with MultiGauge software (FUJIFILM Wako), using an anti-α-tubulin antibody as the internal control.
Cell cycle analysis by flow cytometry. The cells were seeded in 10 cm culture dishes and transfected with individual siRNAs. After incubation for 72 h at 37°C, the cells were collected and washed twice with phosphate-buffered saline. Cells were then fixed in 70% ethanol at 4°C for 30 minutes, and the nuclei were stained with propidium iodide–RNase solution (IMMUNOSTEP S.L., Salamanca, Spain). A total of 10,000 nuclei were analyzed for each sample using a FACS Calibur flow cytometer (BD Biosciences). All analyses were performed in triplicate.
Trypan blue dye exclusion test. One day after seeding, the cells were transfected with individual siRNAs. The cell number was determined using a hemocytometer and the standard trypan blue dye exclusion assay was used with 0.4% trypan blue solution (FUJIFILM Wako).
Senescence-associated β-galactosidase (SA-β-Gal) assay. The Senecence Detection Kit was obtained from Cell Biolabs, Inc. (OZ Bioscience, San Diego, CA, USA). Cells were seeded in 12-well plates and transfected with siRNAs as described above. Four days after transfection, the cells were incubated with the staining solution at 37°C overnight, and SA-β-Gal-positive cells were counted. Each evaluation was performed by counting more than 100 cells in at least five fields per well.
Statistical analysis. Data are presented as the mean and standard error. Statistical analyses were performed using two-tailed Student’s t-tests for pairwise comparisons and one-way ANOVA followed by Tukey’s post hoc test for multiple group comparisons. Differences were considered statistically significant at p<0.05. Asterisks indicate comparisons versus the control group (*p<0.05, **p<0.01, ***p<0.001), and daggers indicate comparisons versus the GGCT-knockdown group (†p<0.05, ††p<0.01, †††p<0.001).
Results
Antiproliferative effects and transcriptomic profiling of GGCT-depleted cells. To elucidate the mechanism underlying GGCT depletion-mediated growth suppression, we performed siRNA-mediated knockdown (KD) of GGCT in human breast cancer MCF-7 cells. Quantitative PCR and western blotting confirmed a significant reduction in GGCT mRNA and protein levels following the GGCT-siRNA transfection (Figure 1A, B). Cell viability assays conducted at 1, 4, and 7 days post-transfection revealed a marked suppression of cell proliferation at days 4 and 7 (Figure 1C, D). To investigate gene expression changes in response to GGCT-KD, high-throughput transcriptomic sequencing was performed at days 1, 2, and 3 post-siRNA transfection, comparing profiles to those of control cells. Hierarchical clustering analysis revealed time-dependent transcriptional alterations, with pronounced changes observed between days 1 and 2 (Figure 1E). A total of 2,160, 4,906, and 5,578 transcripts were differentially expressed at days 1, 2, and 3, respectively. Applying stringent filtering criteria [q-value <0.01 and log2(fold change) >1], we identified 195 (53 up-regulated, 142 down-regulated), 912 (363 up-regulated, 549 down-regulated), and 1,180 (624 up-regulated, 556 down-regulated) DEGs at days 1, 2, and 3, respectively (Figure 1F). These results suggest that gene expression patterns markedly change from day 1 to day 2 following siGGCT transfection, leading to inhibition of cell proliferation.
Knockdown of GGCT inhibited the proliferation and caused significant changes in gene expression profiles detected by transcriptomic high-throughput sequencing in MCF-7 cells. (A) mRNA expression of GGCT was analyzed by qRT-PCR 3 days after transfection. n=3 per group; *p<0.05, **p<0.01, and ***p<0.001 using two-tailed Student’s t-test for pairwise comparisons. (B) Western blotting analysis of GGCT and α-tubulin 4 days after transfection of MCF-7 cells with GGCT-siRNA or non-target control siRNA. (C) The relative survival number of trypan blue-negative viable MCF-7 cells at 1, 4, 7 days post-transfection. n=3 per group; *p<0.05, **p<0.01, and ***p<0.001 using one-way ANOVA followed by Tukey’s post hoc test. (D) Representative images at 4 days post-transfection. (E) Hierarchical clustering analysis of differentially expressed genes (DEGs) detected by RNA-seq analysis. The color scale indicates log10(FPKM) and intensity increases from green to red, indicating down-regulation and up-regulation, respectively. (F) Numbers of significantly up-regulated (red) and down-regulated (green) DEGs in siGGCT-transfected MCF-7 cells at days 1, 2, and 3 post-transfection, identified by RNA-seq analysis using the filtering criteria of |log2 fold change| >1 and q-value <0.01. Scale bar: 50 μm. ANOVA, Analysis of variance; FC, fold change; FPKM, fragments per kilobase of exon model per million mapped reads; GGCT, γ-glutamylcyclotransferase; qRT-PCR, quantitative reverse-transcription-polymerase chain reaction; siRNA, small-interfering RNA.
GGCT-KD regulates genes involved in cell cycle progression. To explore biological processes and pathways associated with GGCT-dependent transcriptional changes, we performed GO and KEGG pathway analyses using the DAVID platform. GO analysis revealed that up-regulated genes were enriched in interferon signaling, viral response, and regulation of viral genome replication. In contrast, down-regulated genes were associated with cell division, DNA replication, mitotic nuclear division, DNA repair, and G1/S transition. KEGG analysis identified significant alterations in pathways related to DNA replication, cell cycle, mismatch repair, homologous recombination, and base excision repair (Figure 2A). Notably, 37 DEGs were mapped to the cell cycle pathway according to KEGG pathway analysis (Table III). Among them were TGF-β2, CDKN1A (encoding p21Cip1), and CDKN2B (encoding p15INK4b), which were up-regulated. Both p21Cip1 and p15INK4b are known to inhibit cyclin–CDK complexes, thereby regulating G1/S transition and S-phase progression (Figure 2B). Quantitative PCR confirmed the transcriptomic data, showing consistent up-regulation of TGF-β2, CDKN2B, and CDKN1A mRNA levels (Figure 2C). Among the three TGF-β isoforms, only TGFB2 satisfied the predefined thresholds for differential expression. TGFB1 and TGFB3 did not meet the DEG criteria (q-value and fold-change cutoffs) and were thus not classified as DEGs.
GO and KEGG pathway enrichment analyses of DEGs in MCF-7 cells after GGCT knockdown. (A) GO enrichment analysis for biological processes (up-regulated and down-regulated genes), and KEGG pathway analysis of DEGs, using samples collected 3 days after knockdown. The top 10 biological process terms ranked according to p-Values are shown. (B) Hypothetical schema of cell cycle regulation following GGCT knockdown in MCF-7 cells, constructed based on DEGs enriched in the KEGG pathway ‘hsa04110: Cell cycle’ (http://www.kegg.jp). (C) Relative mRNA expression patterns of GGCT, TGF-β2, CDKN1A (p21Cip1), and CDKN2B (p15INK4b) were measured with qRT-PCR. n=3 per group; *p<0.05, **p<0.01, and ***p<0.001 using two-tailed Student’s t-test for pairwise comparisons. CDKN1A, Cyclin dependent kinase inhibitor 1A; CDKN2B, cyclin dependent kinase inhibitor 2B; DEGs, differentially expressed genes; GGCT, γ-glutamylcyclotransferase; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; qRT-PCR, quantitative reverse-transcription-polymerase chain reaction; TGF-β2, transforming growth factor-β2.
List of DEGs participating in the cell cycle pathway based on the KEGG pathway analysis.
p15INK4b and p21Cip1 induce cell cycle arrest and suppress proliferation upon GGCT knockdown. Next, we analyzed the cell cycle by GGCT, p15INK4b, and p21Cip1 KD using flow cytometry. Western blotting confirmed that GGCT-KD significantly up-regulated p15INK4b and p21Cip1 protein expression (Figure 3A). Flow cytometry revealed that GGCT-KD increased the proportion of cells in the G0/G1 phase (from 68.3% to 86.6%) and decreased the proportion of cells in the S-phase (from 19.4% to 5.3%), indicating G0/G1 arrest. Simultaneous knockdown (Co-KD) of p15INK4b and p21Cip1 restored cell cycle distribution to control levels (Figure 3B, C). We next examined whether this co-KD could also reverse the growth suppression caused by GGCT-KD. The double KD of p15INK4b and p21Cip1 significantly restored cell proliferation compared with single KD (Figure 3D, E).
Up-regulation of p15INK4b and p21Cip1 contributes to G0/G1 cell cycle arrest and subsequent cell growth inhibition in GGCT-depleted MCF-7 cells. (A) The levels of p15INK4b and p21Cip1 proteins were normalized to levels of α-tubulin and the fold change relative to the control was calculated at 4 days after the indicated siRNA transfection. (B) The cell cycle distribution and (C) representative histograms in FACS analysis in MCF-7 cells 4 days after transfection with the indicated siRNAs. n=3 per group; *p<0.05; **p<0.01, and ***p<0.001 vs. control, †p<0.05; ††p<0.01, and †††p<0.001 vs. GGCT, one-way ANOVA followed by Tukey’s post hoc test. (D) The number of viable cells in the trypan blue dye exclusion test and (E) representative images of MCF-7 cells at 7 days after transfection with the indicated siRNAs. Scale bar: 50 μm; n=3 per group; *p<0.05, **p<0.01, and ***p<0.001, using one-way ANOVA followed by Tukey’s post hoc test. ANOVA, Analysis of variance; FACS, fluorescence-activated cell sorting; GGCT, γ-glutamylcyclotransferase; PCR, polymerase chain reaction; siRNA, small-interfering RNA.
TGF-β2 regulates p15INK4b and p21Cip1 expression through activation of SMAD signaling. We next focused on TGF-β2, whose expression was significantly elevated in the cell cycle signaling pathway, to elucidate the pathway controlling the increased CDKI expression induced by GGCT inhibition. Quantitative PCR and western blotting confirmed that GGCT KD increased TGF-β2 expression, and simultaneous KD of TGF-β2 abolished the induction (Figure 4A, B). TGF-β activates receptor-regulated SMADs (R-SMADs; primarily SMAD2 and SMAD3, which are directly phosphorylated by activated TGF-β receptors), and these R-SMADs translocate into the nucleus to regulate the expression of p15INK4b and p21Cip1 (24). Therefore, GGCT and TGF-β2 were simultaneously depleted, and the expression levels and phosphorylation status of R-SMADs were examined. KD of GGCT increased SMAD3 expression and phosphorylation, and the simultaneous KD of TGF-β2 significantly suppressed both the increased expression and phosphorylation of SMAD3 (Figure 4C). GGCT depletion also increased phosphorylation of SMAD2 without altering total SMAD2 levels. In contrast, both total and phosphorylated SMAD3 were robustly up-regulated, indicating coordinated activation of SMAD3. Moreover, significantly reduced expression of p15INK4b and p21Cip1 was observed following inhibition of SMAD3 phosphorylation (Figure 4D). Importantly, the growth inhibition induced by GGCT depletion was restored by silencing TGF-β2 or SMAD3 (Figure 4E, F), indicating that GGCT-mediated growth suppression is regulated via the TGF-β2/SMAD3 signaling pathway.
TGF-β2/SMAD signaling axis regulates p15INK4b and p21Cip1 expression in GGCT-depleted MCF-7 cells. (A) mRNA expression levels of GGCT and TGF-β2 were analyzed by qRT-PCR 3 days after transfection with GGCT- and/or TGF-β2-siRNA. (B) Western blot analysis of GGCT and TGF-β2 expression 4 days after transfection with GGCT- and/or TGF-β2-siRNA. (C) Western blotting analysis of p15INK4b, p21Cip1, phospho-SMAD2 (pSMAD2), SMAD2, phospho-SMAD3 (pSMAD3), SMAD3, GGCT, and α-tubulin in MCF-7 at 4 days after transfection with GGCT-siRNA and/or TGF-β2-siRNA, or non-target control siRNA. (D) Western blotting analysis of p15INK4b, p21Cip1, pSMAD3, SMAD3, GGCT, and α-tubulin in MCF-7 cells at 4 days after transfection with GGCT-siRNA and/or SMAD3-siRNA, or non-target control siRNA. (E) The number of viable cells in the trypan blue dye exclusion test and (F) representative images of MCF-7 cells at 4 days after transfection with the indicated siRNAs. Scale bar: 200 μm; n=3 per group; *p<0.05, **p<0.01, and ***p<0.001 vs. control, †p<0.05; ††p<0.01, and †††p<0.001 vs. GGCT, one-way ANOVA followed by Tukey’s post hoc test. ANOVA, Analysis of variance; GGCT, γ-glutamylcyclotransferase; pSMAD, phosphorylated SMAD; qRT-PCR, quantitative reverse-transcription-polymerase chain reaction; siRNA, small-interfering RNA.
GGCT depletion induces cellular senescence via the induction of CDKIs and TGF-β2. Prolonged cell cycle arrest mediated by CDK inhibitors p21Cip1 and p15INK4b induces cellular senescence (25, 26). In GGCT-depleted cells, morphological changes indicative of cellular senescence (cell hypertrophy and flattening) and an increase in SA-β-Gal-positive cells were observed (Figure 5A). Simultaneous KD of p15INK4b and p21Cip1 significantly reversed the induction of the SA-β-Gal-positive senescent cells (Figure 5A, B). Furthermore, the double KD of TGF-β2 and GGCT suppressed morphological changes associated with cellular senescence and the increase in SA-β-Gal-positive cells caused by GGCT depletion (Figure 5C, D). These data suggest that the mechanism underlying the growth-inhibitory effect of GGCT depletion involves activation of the TGF-β2 pathway and the subsequent induction of cellular senescence via p15INK4b and p21Cip1 up-regulation.
p15INK4b, p21Cip1, and their upstream regulator TGF-β2 are involved in the induction of cellular senescence following GGCT-KD in MCF-7 cells. (A) Representative images of SA-β-Gal staining 4 days after transfection with the indicated siRNAs, including simultaneous knockdown of GGCT, p15, and p21. Scale bar: 50 μm. (B) The proportion of SA-β-Gal-positive cells in MCF-7 cells are shown. (C) Representative images of SA-β-Gal staining at 4 days after transfection with the indicated siRNAs, including simultaneous knockdown of GGCT and TGF-β2. Scale bar: 50 μm. (D) The proportion of SA-β-Gal-positive cells in MCF-7 cells are shown. n=3 per group; *p<0.05, **p<0.01, ***p<0.001, using one-way ANOVA followed by Tukey’s post hoc test. ANOVA, Analysis of variance; GGCT, γ-glutamylcyclotransferase; KD, knockdown; SA-β-Gal, senescence-associated β-galactosidase; siRNA, small-interfering RNA; TGF-β2, transforming growth factor-β2.
Discussion
In the present study, we identified key genes and pathways involved in the anti-tumor mechanism mediated by GGCT depletion using transcriptome and bioinformatics analyses. Using the human breast cancer cell line MCF-7, we identified gene expression changes induced by GGCT depletion. We also performed GO and KEGG pathway analyses on the DEGs. We found that GGCT depletion induces CDKIs p15INK4b and p21Cip1 through the TGF-β/SMAD signaling pathway. This response was shown to induce cell cycle arrest at the G0/G1 phase, subsequently leading to the induction of cellular senescence.
It has been reported that GGCT depletion induces cell cycle arrest via p21Cip1 or p16ink4a induction in a cell type-dependent manner, thereby suppressing cancer cell proliferation (13, 14, 27). Consistent with these reports, GGCT knockdown has also been shown to suppress the proliferation of murine glioblastoma stem cells by down-regulating Desert Hedgehog signaling (28). However, the mechanism by which GGCT regulates CDKI expression is not fully understood. In particular, the involvement of p15INK4b has not been reported previously. In this study, we identified that induction of p15INK4b is crucial as a novel CDKI contributing to cell proliferation suppression by GGCT depletion. Moreover, we discovered for the first time that activation of the TGF-β/SMAD signaling pathway is essential for the up-regulation of CDKIs.
TGF-β is a multifunctional cytokine that plays a crucial role in cellular functions such as proliferation, differentiation, adhesion, and migration. R-SMADs activated by the TGF-β signaling cascade translocate into the nucleus and regulate the expression of target genes (29). The inhibition of cell proliferation is one of the most extensively studied cellular events downstream of TGF-β signaling (23, 30). TGF-β maintains the hypo-phosphorylated state of RB protein and has attracted attention as a potential target for cancer therapy (31). However, anti-cancer drugs that directly target the TGF-β signaling pathway have not been developed thus far (32). GGCT is highly expressed in cancer cells, indicating that activation of TGF-β signaling via GGCT inhibition may be a strategy for the treatment of cancer. Ongoing efforts to identify emerging biomarkers and therapeutic targets in breast cancer have been reported (33), underscoring the importance of discovering new molecules involved in tumor biology. In this context, the high expression of GGCT in cancer cells suggests that GGCT may also represent a promising diagnostic and therapeutic target. Future analyses using GGCT inhibitors (34-36) should verify whether in vivo GGCT inhibition induces cellular senescence, a phenomenon characterized by TGF-β2/SMAD3 pathway activation and increased p15INK4b and p21Cip1 expression, and whether this would be useful for cancer treatment. One limitation of this study is that all experiments were conducted exclusively in the MCF-7 cell line. Given the context-dependent nature of TGF-β signaling in breast cancer, further validation in multiple cell lines will be essential to assess the generalizability of these results.
Conclusion
Bioinformatics analysis of transcriptome data, including detailed time series, demonstrated that the induction of p15INK4b and p21Cip1 expression, particularly through the activation of the TGF-β2/SMAD3 signaling pathway, plays a crucial role in the cellular senescence-mediated antiproliferative effect of GGCT depletion. These findings support the potential of GGCT inhibition as an anti-cancer therapy.
Acknowledgements
This study was supported by the Japan Society for the Promotion of Science (grant numbers: 20K07623, 22K06756, 23K06642, and 21K09342). The Authors would like to thank Mr. Masafumi Suzaki and Ms. Masami Katsurai for their support in conducting the experiments.
Footnotes
Conflicts of Interest
The Authors declare no conflicts of interest pertaining to the present study.
Authors’ Contributions
SKu, TI, KK, AW, MN, TK, and KY performed the experiments. SKu, MM, and HI drafted the manuscript. KT and HI designed and supervised the study. SN and SKa designed and supervised the study and wrote the manuscript.
Artificial Intelligence (AI) Disclosure
No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.
- Received November 14, 2025.
- Revision received November 30, 2025.
- Accepted December 8, 2025.
- Copyright © 2026 The Author(s). Published by the International Institute of Anticancer Research.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
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