Research ArticleArticles
Open Access

Depletion of DNTTIP2 Induces Cell Cycle Arrest in Pancreatic Cancer Cells

MASATO YOSHIZAWA, ATSUSHI SHIOZAKI and EISHI ASHIHARA
Cancer Genomics & Proteomics January 2024, 21 (1) 18-29; DOI: https://doi.org/10.21873/cgp.20426

Abstract

Background/Aim: Pancreatic cancer is one of the most lethal malignant cancers worldwide and the seventh most common cause of cancer-related death in both sexes. Herein, we analyzed open access data and discovered that expression of a gene called deoxynucleotidyltransferase terminal-interacting protein 2 (DNTTIP2) is linked to prognosis of pancreatic ductal adenocarcinoma (PDAC). We then elucidated the role of DNTTIP2 in the proliferation of pancreatic cancer cells in vitro. Materials and Methods: A WST-8 assay, cell cycle analysis, Annexin-V staining, quantitative reverse transcription-PCR, and western blot analysis were conducted to assess cell proliferation, cell cycle, apoptosis, and expression of DNTTIP2 mRNA and protein, respectively, in DNTTIP2-depleteted MIA-PaCa-2 and PK-1 cells. Results: Depletion of DNTTIP2 induced G1 arrest in MIA-PaCa-2 cells by decreasing expression of special AT-rich sequence binding protein 1 (SATB1) and cyclin-dependent kinase 6 (CDK6). In addition, depletion of DNTTIP2 induced G2 arrest in PK-1 cells by decreasing expression of CDK1. Depletion of DNTTIP2 did not induce apoptosis in MIA-PaCa-2 or PK-1 cells. Conclusion: DNTTIP2 is involved in proliferation of pancreatic cancer cells. Thus, DNTTIP2 is a potential target for inhibiting progression of pancreatic cancers.

Key Words

Pancreatic cancer, one of the most lethal malignant cancers worldwide, is the seventh most common cause of cancer-related death in both sexes (1). Pancreatic cancer is classified broadly into two types: pancreatic ductal adenocarcinoma (PDAC) and pancreatic endocrine tumors. PDAC accounts for more than 90% of all pancreatic tumors (2). Survival rates for patients with pancreatic cancer are extremely poor because it rarely causes symptoms during the early stages; indeed, it is locally advanced in more than one third of patients, and has already metastasized in another half of patients, by the time it is diagnosed (3). Patients with metastatic PDAC receive first-line treatment with gemcitabine (GEM) (4). Between 40% and 50% of these patients receive second-line treatment after disease progression, but the optimal therapy in this setting has not been established (4).

The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalogue and identify major cancer-causing genomic alterations. This database contains a huge amount of publicly available data, giving researchers around the world an immeasurable source of knowledge regarding the genetic and epigenetic profiles of different cancers (5). In the present study, we analyzed the vast amount of cancer data accumulated in TCGA and utilized it to investigate molecules that affect the prognosis of pancreatic cancer.

Analysis of this open access data revealed that expression of a gene called deoxynucleotidyltransferase terminal-interacting protein 2 [DNTTIP2; also known as terminal deoxynucleotidyltransferase (TdT) interacting factor 2 (TdIF2)] plays a role in the prognosis of PDAC patients. The DNTTIP2 (TdIF2) gene is located on chromosome 1 (6). TdIF2, which is localized within the nucleolus where it binds to DNA and core histones, contains an acidic amino acid-rich region within its C-terminus; therefore, it is thought to function as a histone chaperone within the nucleus (7). A previous report examined the association between DNTTIP2 expression and the malignant characteristics of low-grade glioma (8), but there are no other reports regarding other cancers.

Herein, we conducted this study to elucidate the role of the DNTTIP2 gene in cell proliferation in pancreatic cancer cells. We also investigated whether the DNTTIP2 gene could be a potential target molecule for pancreatic cancer treatment, for which no effective molecular target therapy exists yet.

Materials and Methods

Extraction of target genes from large-scale RNA interference (RNAi) datasets. A large-scale RNAi screening dataset (9) (“D2_combined_gene_dep_scores.csv”) was downloaded from the DepMap (10) portal (https://depmap.org/portal/download/all/). The median value of the DEMETER2 score (9, 10) for each gene was calculated using R software (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria). Genes with a median DEMETER2 score <-1 were extracted as genes involved in cancer cell growth.

Analysis of gene expression and overall survival (OS) using a pancreatic adenocarcinoma patient dataset. A total of 177 PDAC patients from the TCGA database were included in the study. Raw clinical and genomic data for Pancreatic Adenocarcinoma (TCGA, Firehose Legacy, paad_tcga.tar.gz) were downloaded from cBioPortal (11, 12) (https://www.cbioportal.org/). The “paad_tcga. tar.gz” was unzipped and the Files “data_bcr_clinical_data_patient.txt” and “data_RNA_Seq_v2_mRNA_median_Zscores.txt” were analyzed using R software. Expression of each gene extracted from each PDAC patient was examined, and the median expression value was calculated by R software. PDAC patients with a gene expression value higher than the calculated median were classified into the high expression group, and those with a gene expression value lower than the calculated median were classified into the low expression group. Kaplan-Meier plots and survival (OS) curve analyses were performed for patients in each of these groups.

Relationship between DNTTIP2 and SATB1. The relationship between DNTTIP2 and SATB1 was investigated using “GSE83744 (13)” data downloaded from the “Gene Expression Omnibus (GEO) (14)” and “GeneMANIA” database (http://genemania.org). When given a single query gene, GeneMANIA identifies genes likely to share function with it based on their interactions (15).

Comparison of SATB1 mRNA expression levels in MIA-PaCa-2 and PK-1 cells. Expression of SATB1 mRNA in MIA-PaCa-2 and PK-1 cells was investigated by analyzing “23 RNA HPA cell line gene data” downloaded from “The Human Protein Atlas” (16) database version 22.0 (https://v22.proteinatlas.org/about/download).

Evaluation of SATB1 function as a transcription factor. The role of SATB1 as a transcription factor for CDK1 and CDK6 was investigated using the “ChIP-Atlas (17, 18) (Peak Browser)” database (https://chip-atlas.org), which visualizes protein binding at a given genomic loci using Integrative Genomics Viewer (IGV) genome browser (19).

Cell culture. The human pancreatic cancer cell line MIA-PaCa-2 was purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA). The human pancreatic cancer cell line PK-1 was purchased from the Cell Resource Center for Biomedical Research Institute of Development, Aging and Cancer at Tohoku University (Sendai, Japan). MIA-PaCa-2 cells were cultured in low-glucose Dulbecco’s modified Eagle’s medium (DMEM, FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan), and PK-1 cells were cultured in Roswell Park Memorial Institute-1640 medium (RPMI-1640, FUJIFILM Wako Pure Chemical Corporation). Both media were supplemented with 10% heat-inactivated fetal bovine serum (FBS, Thermo Fisher Scientific, Tokyo, Japan) and 1% penicillin-streptomycin (FUJIFILM Wako Pure Chemical Corporation). Cells were maintained in a fully humidified incubator at 37°C/5% CO2. All cell lines were tested using the Venor GeM Classic Mycoplasma Detection Kit (Minerva Biolabs, Berlin, Germany) and were confirmed negative for mycoplasma contamination.

Knockdown of DNTTIP2 and SATB1 using small interfering RNAs (siRNAs). SiRNAs targeting DNTTIP2 (s26947, s26949) or SATB1 (s12480, s12481), as well as a negative control siRNA (product number: 4390846), were purchased from Thermo Fisher Scientific. Cancer cells were seeded on a 6-well plate (Thermo Fisher Scientific) or a 6 cm dish (Thermo Fisher Scientific) and transfected with 5 nM of each siRNA in Opti-MEM (Thermo Fisher Scientific) containing lipofectamine RNAiMAX (Thermo Fisher Scientific). Herein, the DNTTIP2 siRNA (s26947, s26949), SATB1 siRNA (s12480, s12481), and negative control siRNA are referred to as siDNTTIP2 #1, siDNTTIP2 #2, siSATB1 #1, siSATB1 #2, and siCtrl, respectively. SiRNAs sequences used in this study are shown in Table I.

View this table:
Table I.

Small interfering RNAs (siRNAs) sequences used in this study.

Cell proliferation assay. The effect of siDNTTIP2 #1 and #2 on cell proliferation was evaluated in a WST-8 assay using Cell Counting Kit-8 (CCK-8; DOJINDO, Kumamoto, Japan) and GloMax EXPLORER (GloMax, Promega, Madison, WI, USA). We performed the WST-8 assay with n=three independent biological replicates, each containing four technical replicates. Briefly, MIA-PaCa-2 (3.0×102 cells in 100 μl of medium) or PK-1 cells (1.0×103 cells in 100 μl of medium) were seeded into each well of a flat-bottomed 96-well plate (Thermo Fisher Scientific) and treated with siCtrl, siDNTTIP2 #1, or siDNTTIP2 #2 (5 nM each) for 72 h. After 69 h, 10 μl of CCK-8 reagent was added to each well. Three hours later, absorbance at 450 nm was measured using GloMax.

Cell cycle analysis. Cell cycle analysis was performed using propidium iodide (PI) staining, followed by analysis using an LSRFortessa X-20 cytometer [Becton, Dickinson and Company (BD), Franklin Lakes, NJ, USA]. Data were analyzed by FlowJo software (version 10.4.2; BD). We performed the cell cycle analysis with n=three independent biological replicates, each containing one technical replicate. Briefly, MIA-PaCa-2 cells were seeded at 6.0×104 cells in 3 ml of medium into 6 cm dishes and treated with siCtrl, siDNTTIP2 #1, or siDNTTIP2 #2 (5 nM each) for 48 h. PK-1 cells were seeded at 6.0×104 cells in 3 ml of medium into 6 cm dishes and treated with siCtrl, siDNTTIP2 #1, or siDNTTIP2 #2 (5 nM each) for 72 h. The cells were then analyzed as described previously (20).

Apoptosis measurement. Apoptosis was measured using the eBioscience Annexin V-FITC Apop Kit (Thermo Fisher Scientific) and an LSRFortessa X-20 cytometer. Data were analyzed using FlowJo software. We performed the apoptosis measurement with n=three independent biological replicates, each containing one technical replicate. MIA-PaCa-2 cells were seeded at 3.0×104 cells in 3 ml of medium into 6 cm dishes and PK-1 cells were seeded at 6.0×104 cells in 3 ml of medium into 6 cm dishes. Seeded MIA-PaCa-2 and PK-1 cells were treated with siCtrl, siDNTTIP2 #1, or siDNTTIP2 #2 (5 nM each) for 72 h. Apoptosis analysis was performed in accordance with the instructions in the kit.

RNA extraction and cDNA synthesis. RNA extraction and cDNA synthesis were performed using the NucleoSpin RNA (Takara Bio Inc., Kusatsu, Japan) and a ReverTra Ace qPCR RT Kit (TOYOBO CO., LTD., Osaka, Japan), respectively. Briefly, MIA-PaCa-2 cells were seeded at 4.0×104 cells in 2 ml of medium into each well of a 6-well plate and treated with siCtrl, siDNTTIP2 #1, siDNTTIP2 #2, siSATB1 #1, or siSATB1 #2 (5 nM each) for 48 h. PK-1 cells were seeded at 8.0×104 cells in 2 ml of medium into each well of the 6-well plate and treated with siCtrl, siDNTTIP2 #1, or siDNTTIP2 #2 (5 nM each) for 48 h. PK-1 cells were seeded at 4.0×104 cells in 2 ml of medium into each well of the 6-well plate and treated with siCtrl, siDNTTIP2 #1, siDNTTIP2 #2, siSATB1 #1, or siSATB1 #2 (5 nM each) for 72 h. RNA extraction and cDNA synthesis were then performed in accordance with the instructions supplied with the kits.

Quantitative reverse transcription-PCR (qRT-PCR). Human CDK1, CDK4, CDK6, DNTTIP2, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and SATB1 mRNA transcript levels were measured by real-time PCR using the Brilliant III Ultra-Fast SYBR Green QPCR Master Mix (Agilent Technologies, Santa Clara, CA, USA) and the Thermal Cycler Dice Real Time System II (TaKaRa Bio Inc.). We performed the qRT-PCR with n=three independent biological replicates, each containing three technical replicates. GAPDH was used as a loading control. Oligo DNAs for each gene were designed using primerBLAST (21) and purchased from Thermo Fisher Scientific. To evaluate SATB1 mRNA transcripts in PK-1 cells, 80 ng of cDNA was used in qRT-PCR reaction comprising 50 cycles. Other mRNA transcripts in PK-1 cells and mRNA transcripts in MIA-PaCa-2 cells were evaluated using 5 ng of cDNA and a qRT-PCR reaction comprising 40 cycles. Primer sequences used for qRT-PCR in this study are shown in Table II.

View this table:
Table II.

Primer sequences used for quantitative reverse transcription-PCR in this study.

Western blot analysis. MIA-PaCa-2 cells were seeded at 4.0×104 cells in 2 ml of medium into each well of a 6-well plate and treated with siCtrl, siDNTTIP2 #1, or siDNTTIP2 #2 (5 nM each) for 48 h. PK-1 cells were seeded at 4.0×104 cells in 2 ml of medium into each well of a 6-well plate and treated with siCtrl, siDNTTIP2 #1, or siDNTTIP2 #2 (5 nM, respectively) for 72 h. Cells were then treated as described previously (22, 23). We performed the western blot analysis with n=three independent biological replicates, each containing one technical replicate. Briefly, samples (containing 20 μg of protein) were first separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and blotted with primary antibodies specific for the following proteins: CDK1 (65182-1-Ig, Proteintech, Rosemont, IL, USA), CDK6 (#13331, Cell Signaling Technology, Danvers, MA, USA), DNTTIP2 (HPA044502, Atlas Antibodies, Zurich, Switzerland), Vinculin (sc-73614, Santa Cruz Biotechnology, Dallas, TX, USA), and GAPDH (#2118, Cell Signaling Technology). GAPDH and Vinculin were used as loading controls.

The study flow chart. We showed the study flow chart including key nodes of the experimental processes undertaken (Figure 1).

Figure 1.
Figure 1.

Study flow chart. We used open access databases to identify DNTTIP2 as a molecule that plays a significant role in the proliferation of pancreatic cancer cells. We performed the experimental validation to reveal the role of DNTTIP2 gene in the proliferation of pancreatic cancer cells.

Statistical analysis. Data in bar graphs are presented as the mean+standard deviation (SD; n=three independent biological replicates) or as the mean+standard error of the mean (SEM; n=three independent biological replicates). All analyses were conducted using R software. OS was assessed using the Kaplan-Meier method and data were compared using the log-rank test. Differences between three groups were compared using one-way analysis of variance (ANOVA), and multiple comparisons of means were performed using Holm’s correction (24). p<0.05 was considered significant.

Results

Selection of the DNTTIP2 gene. We analyzed large-scale RNAi screening datasets (“D2_combined_gene_dep_scores.csv”) and extracted genes with median DEMETER2 scores <-1; these genes were thought to be involved in cancer cell proliferation. We extracted 157 genes from PDAC patients, constructed Kaplan-Meier plots, and performed survival (OS) curve analyses. Patients were divided into groups showing high or low expression of each gene. The results showed that the prognosis of the DNTTIP2 low expression group (N=88) was significantly better than that of the DNTTIP2 high expression group (N=89, p=0.0004, Figure 2A). The relationships between expression of DNTTIP2 mRNA and clinicopathological characteristics are shown in Table III. After analyzing the data, we decided to focus our research on DNTTIP2; our hypothesis was that reduced expression of DNTTIP2 would inhibit proliferation of pancreatic cancer cells.

Figure 2.
Figure 2.

Effect of DNTTIP2 on survival of patients with PDAC, and the effects of depleting DNTTIP2 on proliferation of pancreatic cancer cells. (A) Survival of the DNTTIP2 high expression (N=89) and low expression (N=88) groups of PDAC patients. OS was assessed using the Kaplan-Meier method, and data were compared using the log-rank test (p=0.0004). (B) Expression of DNTTIP2 mRNA in MIA-PaCa-2 cells treated with DNTTIP2 siRNA s26947 (siDNTTIP2 #1) and DNTTIP2 siRNA s26949 (siDNTTIP2 #2) for 48 h. Data are presented as the mean+standard error of the mean (SEM) of three independent biological replicates, each with three technical replicates. ***p<0.001 [vs. negative control siRNA (siCtrl)]. (C, D) Expression of DNTTIP2 mRNA transcripts in PK-1 cells treated with siDNTTIP2 #1 and #2 for 48 and 72 h. Data are presented as the mean+SEM of three independent biological replicates, each with three technical replicates. ***p<0.001 (vs. siCtrl). (E, F) Expression of DNTTIP2 protein by MIA-PaCa-2 cells treated with siDNTTIP2 #1 and #2 for 48 h, and by PK-1 cells treated with siDNTTIP2 #1 and #2 for 72 h. Data of three independent biological replicates, each with one technical replicate, are shown. (G, H) Proliferation of MIA-PaCa-2 and PK-1 cells treated with siDNTTIP2 #1 and #2 for 72 h. Data are presented as the mean+SEM of three independent biological replicates, each with four technical replicates. ***p<0.001 (vs. siCtrl).

View this table:
Table III.

Relationship between DNTTIP2 mRNA expression and clinicopathological characteristics.

SiDNTTIP2 #1 and #2 inhibit proliferation of MIA-PaCa-2 and PK-1 cells. qRT-PCR and western blotting were conducted to verify whether siRNA (siDNTTIP2 #1 and #2) knocked down DNTTIP2. Knockdown of DNTTIP2 was confirmed by measurement of mRNA in MIA-PaCa-2 cells after 48 h of exposure to siRNAs (Figure 2B), and in PK-1 cells after exposure to siDNTTIP2 #1 and #2 siRNA for 48 and 72 h (Figure 2C and D). Knockdown of DNTTIP2 was confirmed by measuring expression of protein in MIA-PaCa-2 cells after 48 h (Figure 2E) and in PK-1 cells after 72 h (Figure 2F). Next, we performed a WST-8 assay to investigate whether siDNTTIP2 #1 and #2 affected proliferation of MIA-PaCa-2 and PK-1 cells. Proliferation of MIA-PaCa-2 and PK-1 cells was inhibited significantly after 72 h of treatment with siDNTTIP2 #1 or #2 (Figure 2G and H). Thus, depleting DNTTIP2 inhibits proliferation of pancreatic cancer cells.

SiDNTTIP2 #1 and #2 induce G1 arrest in MIA-PaCa-2 cells and G2 arrest in PK-1 cells. We next analyzed the effects of siDNTTIP2 #1 and #2 on the cell cycle of MIA-PaCa-2 and PK-1 cells. siDNTTIP2 #1 and #2 induced G1 arrest in MIA-PaCa-2 cells after 48 h (Figure 3A). We exposed PK-1 cells to siDNTTIP2 #1 and #2 for 72 h because no change was observed after exposure for 48 h (data not shown). G2 arrest was observed after 72 h (Figure 3B). We concluded therefore that depleting DNTTIP2 induces cell cycle arrest in MIA-PaCa-2 and PK-1 cells.

Figure 3.
Figure 3.

Cell cycle and apoptosis analysis after depletion of DNTTIP2 from pancreatic cancer cells. (A) Cell cycle analysis of MIA-PaCa-2 cells treated with siDNTTIP2 #1 and #2 for 48 h. The data in histograms are representative of three independent experiments. Data in the bar graphs are expressed as the mean+standard deviation (SD) of three independent biological replicates, each with one technical replicate. *p<0.05; **p<0.01 (vs. siCtrl). (B) Cell cycle analysis of PK-1 cells treated with siDNTTIP2 #1 and #2 for 72 h. The data in the histograms are representative of three independent experiments. Data in the bar graphs are presented as the mean+SD of three independent biological replicates, each with one technical replicate. *p<0.05 (vs. siCtrl). (C, D) Apoptosis of MIA-PaCa-2 and PK-1 cells treated with siDNTTIP2 #1 and #2 for 72 h. Data in the dot plots are representative of three independent experiments. Data in the bar graphs are presented as the mean+SD of three independent biological replicates, each with one technical replicate. NS: Not significant; *p<0.05; **p<0.01 (vs. siCtrl).

SiDNTTIP2 #1 and #2 did not induce the apoptosis in MIA-PaCa-2 and in PK-1 cells. Next, we analyzed the effect of siDNTTIP2 #1 and #2 on induction of apoptosis in MIA-PaCa-2 and PK-1 cells. Apoptosis was not induced significantly in MIA-PaCa-2 and PK-1 cells after 72 h of treatment with siDNTTIP2 #1 and #2 (Figure 3C and D). Although there were some significant differences in induction, the increase in the number of cells that underwent apoptosis was small. We concluded therefore that depleting DNTTIP2 does not induce apoptosis effectively in MIA-PaCa-2 or PK-1 cells.

SiDNTTIP2 #1 and #2 inhibit expression of CDK1 and CDK6 in MIA-PaCa-2 and PK-1 cells. We evaluated the expression of CDK1 and CDK6 mRNA and protein in MIA-PaCa-2 cells treated with siDNTTIP2 #1 and #2 for 48 h, and in PK-1 cells treated with siDNTTIP2 #1 and #2 for 72 h. CDK4/6 regulates G1/S phase transition, whereas CDK1 regulates G2/M phase transition. qRT-PCR revealed a significant reduction in CDK1 and CDK6 mRNA transcripts in MIA-PaCa-2 treated with siDNTTIP2 #1 and #2 for 48 h and in PK-1 cells treated with siDNTTIP2 #2 for 72 h (Figure 4A and B). We also found no significant reduction in expression of mRNA transcripts of CDK4 in DNTTIP2-depleted MIA-PaCa-2 and PK-1 cells (Figure 4C). Furthermore, we evaluated expression of CDK1 and CDK6 protein in MIA-PaCa-2 cells treated with siDNTTIP2 #1 and #2 for 48 h, and in PK-1 cells treated with siDNTTIP2 #1 and #2 for 72 h by Western blotting. Expression of CDK1 and CDK6 protein decreased in DNTTIP2-depleted MIA-PaCa-2 and PK-1 cells (Figure 4D and E). Thus, depleting DNTTIP2 reduces expression of CDK1 and CDK6 in MIA-PaCa-2 and PK-1 cells.

Figure 4.
Figure 4.

mRNA and protein levels of cell cycle-regulating molecules in pancreatic cancer cells depleted of DNTTIP2. Evaluation of (A) CDK1, (B) CDK6, and (C) CDK4 mRNA by qRT-PCR. MIA-PaCa-2 cells were treated with siDNTTIP2 #1 and #2 for 48 h, and PK-1 cells were treated with siDNTTIP2 #1 and #2 for 72 h. Data are presented as the mean+SEM of three independent biological replicates, each with three technical replicates. NS: Not significant; *p<0.05; **p<0.01; ***p<0.001 (vs. siCtrl). (D) CDK1 and CDK6 protein expression in MIA-PaCa-2 cells treated with siDNTTIP2 #1 and #2 for 48 h. Data of three independent biological replicates, each with one technical replicate, are shown. (E) CDK1 and CDK6 protein expression in PK-1 cells treated with siDNTTIP2 #1 and #2 for 72 h. Data of three independent biological replicates, each with one technical replicate, are shown.

SiDNTTIP2 #1 and #2 decrease expression of SATB1 mRNA transcripts in MIA-PaCa-2 and PK-1 cells. We then analyzed data from “GSE83744” to find out which molecules have a relationship with DNTTIP2. We found a relationship between DNTTIP2 and SATB1 in the SALE immortalized lung cell line (data not shown). Next, we searched the “GeneMANIA” database (which is used to investigate relationships between genes) and found that expression of DNTTIP2 is associated with that of SATB1 (data not shown). Therefore, we next evaluated expression of SATB1 mRNA in MIA-PaCa-2 and PK-1 cells treated with siDNTTIP2 #1 and #2 for 48 h. qRT-PCR revealed that expression of SATB1 mRNA transcripts decreased in DNTTIP2-depleted MIA-PaCa-2 and PK-1 cells (Figure 5A and B).

Figure 5.
Figure 5.

Relationship between DNTTIP2 and SATB1. (A, B) Expression of SATB1 mRNA, as assessed by qRT-PCR. MIA-PaCa-2 and PK-1 cells were treated with siDNTTIP2 #1 and #2 for 48 h. Data are presented as the mean+SEM of three independent biological replicates, each with three technical replicates. ***p<0.001 (vs. siCtrl). MIA-PaCa-2 cells were treated with SATB1 siRNA s12480 (siSATB1 #1) and SATB1 siRNA s12481 (siSATB1 #2) for 48 h, and expression of SATB1 (C), CDK6 (D), and CDK1 (E) mRNA was assessed by qRT-PCR. Data are presented as the mean+SEM of three independent biological replicates, each with three technical replicates. *p<0.05; ***p<0.001 (vs. siCtrl). PK-1 cells were treated with siSATB1 #1 and #2 for 72 h, and expression of SATB1 (F), CDK6 (G), and CDK1 (H) mRNA was evaluated by qRT-PCR. Data are presented as the mean+SEM of three independent biological replicates, each with three technical replicates. NS: Not significant; *p<0.05; **p<0.01; ***p<0.001 (vs. siCtrl).

SiSATB1 #1 and #2 decrease expression of CDK6 mRNA transcripts in MIA-PaCa-2 and PK-1 cells. The “ChIP-Atlas (Peak Browser)” database was used to investigate whether SATB1 is a transcription factor for CDK1 or CDK6. The results showed that SATB1 is not a transcription factor for CDK1; however, it is a transcription factor for CDK6 (data not shown). Therefore, we evaluated expression of mRNA transcripts of SATB1, CDK1, and CDK6 in MIA-PaCa-2 and PK-1 cells treated with siSATB1 #1 and #2. Expression of SATB1 and CDK6 mRNA fell significantly in MIA-PaCa-2 cells treated with siSATB1 #1 and #2 for 48 h (Figure 5C and D). Expression levels of CDK1 mRNA also decreased significantly, but to a lesser extent than that of CDK6 (Figure 5E). PK-1 cells were treated with siSATB1 #1 and #2 for 72 h because we noted no change in expression of CDK6 mRNA transcripts after 48 h (data not shown). After 72 h, we noted a significant decrease in the amounts of SATB1 and CDK6 mRNA transcripts (Figure 5F and G), but not in the amounts of CDK1 mRNA transcripts (Figure 5H). In addition, depletion of SATB1 by siSATB1 decreased the amount of CDK6 mRNA, but not that of CDK1 mRNA. These data suggest that DNTTIP2 regulates CDK6 through SATB1 but regulates CDK1 directly.

Discussion

Studies are being conducted that focus on the expression of specific genes and proteins to predict cancer prognosis, metastasis, and recurrence in pancreatic cancer studies (25, 26). We concentrated on PDAC patients with high or low expression of specific genes and conducted the study to investigate whether the expression of specific genes was involved in the prognosis of PDAC patients.

Herein, we used open access databases to identify DNTTIP2 as a molecule that plays a major role in the prognosis of PDAC. Depletion of DNTTIP2 led to reduced expression of CDK1 and CDK6, and induced G1 arrest in MIA-PaCa-2 cells and G2 arrest in PK-1 cells.

Since depletion of DNTTIP2 induced G1 arrest and did not induce G2 arrest in MIA-PaCa-2 cells treated with siDNTTIP2 #1 and #2 for 48 h, we evaluated the expression of CDK1 and CDK6 [CDK1 regulates G2/M phase transition (27) and CDK6 regulates G1/S phase transition (28)] mRNA and protein in MIA-PaCa-2 cells treated with siDNTTIP2 #1 and #2 for 48 h. We did not find evidence of cycle arrest in PK-1 cells treated with siDNTTIP2 #1 and #2 for 48 h; rather, these cells arrested in G2 after treatment with siDNTTIP2 #1 and #2 for 72 h. Therefore, we also evaluated expression of CDK1 and CDK6 mRNA and protein in PK-1 cells treated with siDNTTIP2 #1 or #2 for 72 h.

We also found that depletion of DNTTIP2 decreased expression of SATB1 mRNA. SATB1 is a nuclear protein, chromatin organizer, and transcription factor (29). Studies of human cancer suggest that SATB1 is an important factor that promotes growth and invasion of breast cancers (30), colorectal cancers (31), and pancreatic cancers (32). Depletion of SATB1 from Primary Human Epidermal Keratinocytes neonatal (HEKn) reduces expression of CDK6 protein (33). Furthermore, we showed that depletion of SATB1 decreases expression of CDK6 mRNA, but not that of CDK1. These results suggest that DNTTIP2 suppresses proliferation of pancreatic cancer cells by regulating CDK6 through SATB1, and by regulating CDK1 directly (Figure 6). Depletion of DNTTIP2 induced G1 arrest in MIA-PaCa-2 cells and G2 arrest in PK-1 cells. To explain this discrepancy, we estimated the expression levels of SATB1 mRNA in MIA-PaCa-2 and PK-1 cells using “The Human Protein Atlas” database. The results showed that expression of SATB1 mRNA is much higher in MIA-PaCa-2 cells (11.9 normalized transcripts per million (TPM)) than in PK-1 (1.0 normalized TPM) cells. Therefore, we speculate that differences in expression of SATB1 mRNA in these two cell lines are the reason for cell cycle arrest at different phases.

Figure 6.
Figure 6.

Proposed mechanism by which DNTTIP2 regulates CDK1 and CDK6. DNTTIP2 regulates CDK6 through SATB1 and regulates CDK1 directly. DNTTIP2 also regulates SATB1. CCNA: Cyclin A; CCNB: cyclin B; CCND: cyclin D; CDK1: cyclin-dependent kinase 1; CDK4: cyclin-dependent kinase 4; CDK6: cyclin-dependent kinase 6; DNTTIP2: deoxynucleotidyltransferase terminal-interacting protein 2; OS: overall survival; PDAC: pancreatic ductal adenocarcinoma; qRT-PCR: quantitative reverse transcription-PCR; RNAi: RNA interference; SATB1: special AT-rich sequence binding protein 1; TCGA: the cancer genome atlas.

Although depletion of DNTTIP2 inhibited proliferation of the two pancreatic cancer cell lines, we did not observe apoptotic cell death. Depletion of DNTTIP2 decreased expression of SATB1 mRNA. The previous report proposes that SATB1 plays a role in the GEM resistance of PDAC cells (34); thus, GEM resistance might be overcome by depletion of DNTTIP2. Future studies should evaluate the effects of GEM on DNTTIP2-depleted pancreatic cancer cells. SATB1 is also involved in invasion and migration of various cancers (31, 32, 35). For example, SATB1 regulates expression of the MYC oncogene in pancreatic cancer cells (32) and plays a crucial role in regulating genes that control epithelial-mesenchymal transition (EMT) in bladder cancer (35). Based on these previous observations, we speculate that metastasis of pancreatic cancer cells might be suppressed by depletion of DNTTIP2. Future studies should evaluate the anti-metastatic effects of DNTTIP2-depletion both in vitro and in vivo.

Conclusion

In conclusion, depleting DNTTIP2 induces cell cycle arrest in MIA-PaCa-2 cells by inhibiting expression of CDK6, and in PK-1 cells by inhibiting expression of CDK1. Taken together, these findings strongly suggest that DNTTIP2 plays a major role in proliferation of pancreatic cancer. DNTTIP2 is a potential target for inhibiting progression of pancreatic cancer.

Acknowledgements

We thank Shigekuni Hosogi and Yuki Toda for helpful discussions. The results shown here are partly derived from data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Footnotes

  • Conflicts of Interest

    The Authors declare no conflicts of interest regarding this study.

  • Authors’ Contributions

    M.Y. performed the experiments and analyzed data; A.S. instructed M.Y. on how to culture pancreatic cancer cell lines; A.S. and E.A. provided intellectual guidance; M.Y. and E.A. wrote the manuscript.

  • Received October 8, 2023.
  • Revision received November 20, 2023.
  • Accepted November 28, 2023.

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).

References

  1. ↵
    1. Ferlay J,
    2. Soerjomataram I,
    3. Dikshit R,
    4. Eser S,
    5. Mathers C,
    6. Rebelo M,
    7. Parkin DM,
    8. Forman D,
    9. Bray F
    : Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 136(5): E359-86, 2015. DOI: 10.1002/ijc.29210
    OpenUrlCrossRefPubMed
  2. ↵
    1. Hidalgo M,
    2. Cascinu S,
    3. Kleeff J,
    4. Labianca R,
    5. Löhr JM,
    6. Neoptolemos J,
    7. Real FX,
    8. Van Laethem JL,
    9. Heinemann V
    : Addressing the challenges of pancreatic cancer: Future directions for improving outcomes. Pancreatology 15(1): 8-18, 2015. DOI: 10.1016/j.pan.2014.10.001
    OpenUrlCrossRefPubMed
  3. ↵
    1. Gillen S,
    2. Schuster T,
    3. Meyer Zum Büschenfelde C,
    4. Friess H,
    5. Kleeff J
    : Preoperative/neoadjuvant therapy in pancreatic cancer: a systematic review and meta-analysis of response and resection percentages. PLoS Med 7(4): e1000267, 2010. DOI: 10.1371/journal.pmed.1000267
    OpenUrlCrossRefPubMed
  4. ↵
    1. Seufferlein T,
    2. Bachet JB,
    3. Van Cutsem E,
    4. Rougier P, ESMO Guidelines Working Group
    : Pancreatic adenocarcinoma: ESMO–ESDO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 23(Suppl 7): vii33-40, 2012. DOI: 10.1093/annonc/mds224
    OpenUrlCrossRefPubMed
  5. ↵
    1. Tomczak K,
    2. Czerwińska P,
    3. Wiznerowicz M
    : The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19(1A): A68-A77, 2015. DOI: 10.5114/wo.2014.47136
    OpenUrlCrossRefPubMed
  6. ↵
    1. Coltell O,
    2. Sorlí JV,
    3. Asensio EM,
    4. Barragán R,
    5. González JI,
    6. Giménez-Alba IM,
    7. Zanón-Moreno V,
    8. Estruch R,
    9. Ramírez-Sabio JB,
    10. Pascual EC,
    11. Ortega-Azorín C,
    12. Ordovas JM,
    13. Corella D
    : Genome-wide association study for serum Omega-3 and Omega-6 polyunsaturated fatty acids: Exploratory analysis of the sex-specific effects and dietary modulation in mediterranean subjects with metabolic syndrome. Nutrients 12(2): 310, 2020. DOI: 10.3390/nu12020310
    OpenUrlCrossRef
  7. ↵
    1. Koiwai K,
    2. Noma S,
    3. Takahashi Y,
    4. Hayano T,
    5. Maezawa S,
    6. Kouda K,
    7. Matsumoto T,
    8. Suzuki M,
    9. Furuichi M,
    10. Koiwai O
    : TdIF2 is a nucleolar protein that promotes rRNA gene promoter activity. Genes Cells 16(7): 748-764, 2011. DOI: 10.1111/j.1365-2443.2011.01524.x
    OpenUrlCrossRefPubMed
  8. ↵
    1. Liu YJ,
    2. Zeng SH,
    3. Qian WH,
    4. Tao MX,
    5. Zhu YY,
    6. Li JP
    : DNTTIP2 expression is associated with macrophage infiltration and malignant characteristics in low-grade glioma. Pharmgenomics Pers Med 15: 261-275, 2022. DOI: 10.2147/PGPM.S356326
    OpenUrlCrossRef
  9. ↵
    1. McFarland JM,
    2. Ho ZV,
    3. Kugener G,
    4. Dempster JM,
    5. Montgomery PG,
    6. Bryan JG,
    7. Krill-Burger JM,
    8. Green TM,
    9. Vazquez F,
    10. Boehm JS,
    11. Golub TR,
    12. Hahn WC,
    13. Root DE,
    14. Tsherniak A
    : Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. Nat Commun 9(1): 4610, 2018. DOI: 10.1038/s41467-018-06916-5
    OpenUrlCrossRefPubMed
  10. ↵
    1. Tsherniak A,
    2. Vazquez F,
    3. Montgomery PG,
    4. Weir BA,
    5. Kryukov G,
    6. Cowley GS,
    7. Gill S,
    8. Harrington WF,
    9. Pantel S,
    10. Krill-Burger JM,
    11. Meyers RM,
    12. Ali L,
    13. Goodale A,
    14. Lee Y,
    15. Jiang G,
    16. Hsiao J,
    17. Gerath WFJ,
    18. Howell S,
    19. Merkel E,
    20. Ghandi M,
    21. Garraway LA,
    22. Root DE,
    23. Golub TR,
    24. Boehm JS,
    25. Hahn WC
    : Defining a cancer dependency map. Cell 170(3): 564-576.e16, 2017. DOI: 10.1016/j.cell.2017.06.010
    OpenUrlCrossRefPubMed
  11. ↵
    1. Cerami E,
    2. Gao J,
    3. Dogrusoz U,
    4. Gross BE,
    5. Sumer SO,
    6. Aksoy BA,
    7. Jacobsen A,
    8. Byrne CJ,
    9. Heuer ML,
    10. Larsson E,
    11. Antipin Y,
    12. Reva B,
    13. Goldberg AP,
    14. Sander C,
    15. Schultz N
    : The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5): 401-404, 2012. DOI: 10.1158/2159-8290.CD-12-0095
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Gao J,
    2. Aksoy BA,
    3. Dogrusoz U,
    4. Dresdner G,
    5. Gross B,
    6. Sumer SO,
    7. Sun Y,
    8. Jacobsen A,
    9. Sinha R,
    10. Larsson E,
    11. Cerami E,
    12. Sander C,
    13. Schultz N
    : Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269): pl1, 2013. DOI: 10.1126/scisignal.2004088
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Berger AH,
    2. Brooks AN,
    3. Wu X,
    4. Shrestha Y,
    5. Chouinard C,
    6. Piccioni F,
    7. Bagul M,
    8. Kamburov A,
    9. Imielinski M,
    10. Hogstrom L,
    11. Zhu C,
    12. Yang X,
    13. Pantel S,
    14. Sakai R,
    15. Watson J,
    16. Kaplan N,
    17. Campbell JD,
    18. Singh S,
    19. Root DE,
    20. Narayan R,
    21. Natoli T,
    22. Lahr DL,
    23. Tirosh I,
    24. Tamayo P,
    25. Getz G,
    26. Wong B,
    27. Doench J,
    28. Subramanian A,
    29. Golub TR,
    30. Meyerson M,
    31. Boehm JS
    : High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30(2): 214-228, 2016. DOI: 10.1016/j.ccell.2016.06.022
    OpenUrlCrossRefPubMed
  14. ↵
    1. Edgar R,
    2. Domrachev M,
    3. Lash AE
    : Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30(1): 207-210, 2002. DOI: 10.1093/nar/30.1.207
    OpenUrlCrossRefPubMed
  15. ↵
    1. Franz M,
    2. Rodriguez H,
    3. Lopes C,
    4. Zuberi K,
    5. Montojo J,
    6. Bader GD,
    7. Morris Q
    : GeneMANIA update 2018. Nucleic Acids Res 46(W1): W60-W64, 2018. DOI: 10.1093/nar/gky311
    OpenUrlCrossRefPubMed
  16. ↵
    1. Uhlen M,
    2. Zhang C,
    3. Lee S,
    4. Sjöstedt E,
    5. Fagerberg L,
    6. Bidkhori G,
    7. Benfeitas R,
    8. Arif M,
    9. Liu Z,
    10. Edfors F,
    11. Sanli K,
    12. von Feilitzen K,
    13. Oksvold P,
    14. Lundberg E,
    15. Hober S,
    16. Nilsson P,
    17. Mattsson J,
    18. Schwenk JM,
    19. Brunnström H,
    20. Glimelius B,
    21. Sjöblom T,
    22. Edqvist PH,
    23. Djureinovic D,
    24. Micke P,
    25. Lindskog C,
    26. Mardinoglu A,
    27. Ponten F
    : A pathology atlas of the human cancer transcriptome. Science 357(6352): eaan2507, 2017. DOI: 10.1126/science.aan2507
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Oki S,
    2. Ohta T,
    3. Shioi G,
    4. Hatanaka H,
    5. Ogasawara O,
    6. Okuda Y,
    7. Kawaji H,
    8. Nakaki R,
    9. Sese J,
    10. Meno C
    : ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep 19(12): e46255, 2018. DOI: 10.15252/embr.201846255
    OpenUrlAbstract/FREE Full Text
  18. ↵
    1. Zou Z,
    2. Ohta T,
    3. Miura F,
    4. Oki S
    : ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating ChIP-seq, ATAC-seq and Bisulfite-seq data. Nucleic Acids Res 50(W1): W175-W182, 2022. DOI: 10.1093/nar/gkac199
    OpenUrlCrossRefPubMed
  19. ↵
    1. Robinson JT,
    2. Thorvaldsdóttir H,
    3. Winckler W,
    4. Guttman M,
    5. Lander ES,
    6. Getz G,
    7. Mesirov JP
    : Integrative genomics viewer. Nat Biotechnol 29(1): 24-26, 2011. DOI: 10.1038/nbt.1754
    OpenUrlCrossRefPubMed
  20. ↵
    1. Imayoshi N,
    2. Yoshioka M,
    3. Tanaka K,
    4. Yang SM,
    5. Akahane K,
    6. Toda Y,
    7. Hosogi S,
    8. Inukai T,
    9. Okada S,
    10. Maloney DJ,
    11. Nakahata T,
    12. Takita J,
    13. Kato I,
    14. Ashihara E
    : CN470 is a BET/CBP/p300 multi-bromodomain inhibitor and has an anti-tumor activity against MLL-rearranged acute lymphoblastic leukemia. Biochem Biophys Res Commun 590: 49-54, 2022. DOI: 10.1016/j.bbrc.2021.12.078
    OpenUrlCrossRef
  21. ↵
    1. Ye J,
    2. Coulouris G,
    3. Zaretskaya I,
    4. Cutcutache I,
    5. Rozen S,
    6. Madden TL
    : Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics 13: 134, 2012. DOI: 10.1186/1471-2105-13-134
    OpenUrlCrossRefPubMed
  22. ↵
    1. Fukuda H,
    2. Nakamura S,
    3. Chisaki Y,
    4. Takada T,
    5. Toda Y,
    6. Murata H,
    7. Itoh K,
    8. Yano Y,
    9. Takata K,
    10. Ashihara E
    : Daphnetin inhibits invasion and migration of LM8 murine osteosarcoma cells by decreasing RhoA and Cdc42 expression. Biochem Biophys Res Commun 471(1): 63-67, 2016. DOI: 10.1016/j.bbrc.2016.01.179
    OpenUrlCrossRef
  23. ↵
    1. Yoshizawa M,
    2. Nakamura S,
    3. Sugiyama Y,
    4. Tamai S,
    5. Ishida Y,
    6. Sueyoshi M,
    7. Toda Y,
    8. Hosogi S,
    9. Yano Y,
    10. Ashihara E
    : 6-hydroxythiobinupharidine inhibits migration of LM8 osteosarcoma cells by decreasing expression of LIM domain kinase 1. Anticancer Res 39(12): 6507-6513, 2019. DOI: 10.21873/anticanres.13865
    OpenUrlAbstract/FREE Full Text
  24. ↵
    1. Holm S
    : A simple sequentially rejective multiple test procedure. Scand J Stat 6(2): 65-70, 1979. Available at: https://www.jstor.org/stable/4615733 [Last accessed on October 8, 2023]
    OpenUrl
  25. ↵
    1. Isohookana J,
    2. Haapasaari KM,
    3. Soini Y,
    4. Karihtala P
    : KDM4D predicts recurrence in exocrine pancreatic cells of resection margins from patients with pancreatic adenocarcinoma. Anticancer Res 38(4): 2295-2302, 2018. DOI: 10.21873/anticanres.12474
    OpenUrlAbstract/FREE Full Text
  26. ↵
    1. Nasir A,
    2. Ahmed MK,
    3. Saller JJ,
    4. Henderson-Jackson EB,
    5. Malafa MP,
    6. Yeatman TJ,
    7. Coppola D
    : p21 protein outperforms clinico-pathological criteria in predicting liver metastases in pancreatic endocrine tumors. Cancer Genomics Proteomics 20(6): 522-530, 2023. DOI: 10.21873/cgp.20402
    OpenUrlAbstract/FREE Full Text
  27. ↵
    1. Sunada S,
    2. Saito H,
    3. Zhang D,
    4. Xu Z,
    5. Miki Y
    : CDK1 inhibitor controls G2/M phase transition and reverses DNA damage sensitivity. Biochem Biophys Res Commun 550: 56-61, 2021. DOI: 10.1016/j.bbrc.2021.02.117
    OpenUrlCrossRef
  28. ↵
    1. Crozier L,
    2. Foy R,
    3. Mouery BL,
    4. Whitaker RH,
    5. Corno A,
    6. Spanos C,
    7. Ly T,
    8. Gowen Cook J,
    9. Saurin AT
    : CDK4/6 inhibitors induce replication stress to cause long-term cell cycle withdrawal. EMBO J 41(6): e108599, 2022. DOI: 10.15252/embj.2021108599
    OpenUrlCrossRef
  29. ↵
    1. Alvarez JD,
    2. Yasui DH,
    3. Niida H,
    4. Joh T,
    5. Loh DY,
    6. Kohwi-Shigematsu T
    : The MAR-binding protein SATB1 orchestrates temporal and spatial expression of multiple genes during T-cell development. Genes Dev 14(5): 521-535, 2000. DOI: 10.1101/gad.14.5.521
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Liu X,
    2. Zheng Y,
    3. Qiao C,
    4. Qv F,
    5. Wang J,
    6. Ding B,
    7. Sun Y,
    8. Wang Y
    : Expression of SATB1 and HER2 in breast cancer and the correlations with clinicopathologic characteristics. Diagn Pathol 10: 50, 2015. DOI: 10.1186/s13000-015-0282-4
    OpenUrlCrossRef
  31. ↵
    1. Zhang Y,
    2. Tian X,
    3. Ji H,
    4. Guan X,
    5. Xu W,
    6. Dong B,
    7. Zhao M,
    8. Wei M,
    9. Ye C,
    10. Sun Y,
    11. Yuan X,
    12. Yang C,
    13. Hao C
    : Expression of SATB1 promotes the growth and metastasis of colorectal cancer. PLoS One 9(6): e100413, 2014. DOI: 10.1371/journal.pone.0100413
    OpenUrlCrossRefPubMed
  32. ↵
    1. Chen Z,
    2. Li Z,
    3. Li W,
    4. Zong Y,
    5. Zhu Y,
    6. Miao Y,
    7. Xu Z
    : SATB1 promotes pancreatic cancer growth and invasion depending on MYC activation. Dig Dis Sci 60(11): 3304-3317, 2015. DOI: 10.1007/s10620-015-3759-9
    OpenUrlCrossRef
  33. ↵
    1. Lena AM,
    2. Mancini M,
    3. Rivetti di Val Cervo P,
    4. Saintigny G,
    5. Mahé C,
    6. Melino G,
    7. Candi E
    : MicroRNA-191 triggers keratinocytes senescence by SATB1 and CDK6 downregulation. Biochem Biophys Res Commun 423(3): 509-514, 2012. DOI: 10.1016/j.bbrc.2012.05.153
    OpenUrlCrossRefPubMed
  34. ↵
    1. Wei L,
    2. Ye H,
    3. Li G,
    4. Lu Y,
    5. Zhou Q,
    6. Zheng S,
    7. Lin Q,
    8. Liu Y,
    9. Li Z,
    10. Chen R
    : Cancer-associated fibroblasts promote progression and gemcitabine resistance via the SDF-1/SATB-1 pathway in pancreatic cancer. Cell Death Dis 9(11): 1065, 2018. DOI: 10.1038/s41419-018-1104-x
    OpenUrlCrossRefPubMed
  35. ↵
    1. Wan F,
    2. Cheng C,
    3. Wang Z,
    4. Xiao X,
    5. Zeng H,
    6. Xing S,
    7. Chen X,
    8. Wang J,
    9. Li S,
    10. Zhang Y,
    11. Xiang W,
    12. Zhu Z,
    13. Johnson C,
    14. Zhu Z
    : SATB1 overexpression regulates the development and progression in bladder cancer through EMT. PLoS One 10(2): e0117518, 2015. DOI: 10.1371/journal.pone.0117518
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
Depletion of DNTTIP2 Induces Cell Cycle Arrest in Pancreatic Cancer Cells
MASATO YOSHIZAWA, ATSUSHI SHIOZAKI, EISHI ASHIHARA
Cancer Genomics & Proteomics Jan 2024, 21 (1) 18-29; DOI: 10.21873/cgp.20426
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords