The human PC cell line PANC-1 was obtained from Procell (http://www.procell.com.cn; Wuhan, China) and cultured in monolayers in Dulbecco's modified Eagle's medium (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) supplemented with 10% fetal bovine serum (Hyclone; GE Healthcare Life Sciences, Logan, UT, USA) in a humidified incubator at 37°C and a 5% CO₂ atmosphere. The GSP extract, obtained from JF-NATURAL (Tianjin, China; cat. no. J011003), contained monomeric (9.5%), dimeric (12.8%), trimeric (76.7%) and oligomeric (1%) procyanidins. The 100 µg GSPs extract was dissolved in 100 µl dimethylsulfoxide (DMSO; Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) for 10 min at room temperature prior to addition to the cell culture media. The maximum concentration of DMSO in the media did not exceed 0.1%. PANC-1 cells were treated with 20 µg/ml GSP for 3, 12 and 24 h at 37°C. Additionally, cells were treated with DMSO for 3, 12 and 24 h at 37°C served as controls.

GSP-treated PANC-1 cells were plated in 96-well cell culture plates at 5×10³ cells/well and incubated for 24 h at 37°C. Subsequently, 50 µl MTT solution (5 mg/ml; Sigma-Aldrich; Merck KGaA) was added to each well and the cells were incubated for a further 4 h at 37°C. Following 3 min centrifugation at 5,500 × g at 4°C, the supernatant was removed from each well. The colored formazan crystals produced by MTT in each well were dissolved in 150 µl DMSO and the optical density values were measured at 490 nm.

GSP-induced apoptosis in PC cells was determined by flow cytometry using an Annexin V-fluorescein isothiocyanate (FITC) Apoptosis detection kit (BD Biosciences, Franklin Lakes, NJ, USA). Following treatment with GSPs for 48 h at 37°C, cells (2×10⁵) were harvested, washed twice with PBS and incubated with Annexin V-FITC and propidium iodide for 10 min in the dark at room temperature. The stained cells were then detected and analyzed by a MoFLO XDP flow cytometer (Beckman Coulter, Inc., Brea, CA, USA) and the Cell Quest 3.3 software (BD Biosciences).

Total RNA was extracted from the GSP- or DMSO-treated PANC-1 cells using TRIzol^® reagent (Invitrogen; Thermo Fisher Scientific, Inc.). The RNA was further purified by two 15 min phenol-chloroform (1:1) (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) at 4°C treatments and then treated with RQ1DNase (Promega Corporation, Madison, WI, USA) for 30 min at 37°C to remove DNA. The quality and quantity of the purified RNA were monitored by measuring the absorbance at 260 and 280 nm, and the A260:A280 ratio using a Smartspec Plus Spectrophotometer (Bio-Rad Laboratories, Inc., Hercules, CA, USA). The integrity of RNA was verified by 1.5% agarose gel electrophoresis.

For each sample, 10 µg total RNA was used for RNA-seq library preparation. Polyadenylated mRNAs were purified and concentrated with oligo(dT)-conjugated magnetic beads (Invitrogen; Thermo Fisher Scientific, Inc.), according to the manufacturer's protocol prior to directional RNA-seq library preparation. The libraries were prepared using the purified mRNAs by the TruSeq Stranded Total RNA LT Sample Prep Kit (Illumina, Inc., San Diego, CA, USA), according to the the manufacturer's protocol. The Nextseq 500 system (Illumina, Inc., San Diego, CA, USA) was used to collect data from 151-base pair pair-end sequencing from ABlife Inc. (https://www.ablife.cc; Wuhan, China).

Using TopHat 2.0 software (22), reads were successfully mapped against the current human reference genome (GRCH38). Reads per kilobase per million mapped reads (RPKM) was used to calculate the expression level of genes. To measure the RPKM value and screen out the DEGs, the edgeR 3.22 software package of Bioconductor software (23) was used to specifically analyze the differential expression of genes using RNA-seq data. The genes in every sample with RPKM values <0.1 were removed prior to analysis. A fold change (FC)≥2 or ≤0.5 and P≤0.01 indicated a statistically significant DEG.

To predict the gene function and calculate the functional category distribution frequency, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was employed using the DAVID bioinformatics database (https://david.ncifcrf.gov) (24). Networks were constructed by calculating the Pearson's correlation coefficient (PCC) for the expression levels of the DEGs. Cytoscape 3.5.1 software was used to display the co-expression network (25).

To validate the RNA-seq data, RT-qPCR was performed for selected DEGs and normalization was achieved with the human reference gene GAPDH. The primers used are presented in Table I. The same RNA samples for RNA-seq were used for RT-qPCR, and RNA extraction was performed in accordance with the aforementioned protocol and materials. In each pooled sample, l µg total RNA was reverse transcribed using a PrimeScript™ RT Reagent kit (Takara Bio, Inc., Otsu, Japan), according to the manufacturer's protocol. qPCR was performed with a S1000 Thermal Cycler (Bio-Rad Laboratories, Inc.) and Bestar SYBR^® Green RT-PCR Master mix (DBI Bioscience, Shanghai, China), according to the manufacturer's protocols. The following thermocycling conditions were used: 95°C for 10 min, followed by 38 cycles of 95°C for 15 sec and 60°C for 1 min. PCR amplifications were performed in triplicate for each sample, and the results were quantified by 2^−∆∆Cq method (26).

The data were analyzed using Microsoft Excel 2007 software (https://www.microsoft.com; Microsoft Corporation, Redmond, WA, USA). All data are presented as the mean ± standard deviation. For comparisons between two groups, statistically significant differences between means were identified by paired Student's t-test. For multiple comparisons, the significance was determined by simple one-way analysis of variance followed by a Tukey's post-hoc test. P<0.05 was considered to indicate a statistically significant difference.

The datasets (GSE85610) generated in the present study are available from the National Center for Biotechnology Information Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/).

Using flow cytometry, the present study identified that treatment with 0, 20, 40 or 60 µg/ml GSP significantly induced apoptosis of the PC cell line PANC-1 in a dose-dependent manner (Fig. 1A and B). Additionally, the effect of treatment with GSPs on the viability of PC cells was determined with an MTT assay. A significant dose-dependent decrease in the viability of PC cells was revealed 24 h after treatment (Fig. 1C) Furthermore, it was identified that treatment with GSPs was associated with necrosis (upper left quadrants) only in a small number of PC cells in a dose-dependent manner (Fig. 1A).

To investigate how GSPs regulate gene expression and avoid high levels of necrotic cells, PANC-1 cells were treated with 20 µg/ml GSP for 3, 12 and 24 h, and the RNA samples termed S3, S12 and S24 were prepared, respectively. Cells treated with DMSO for 3, 12 and 24 h served as controls and the control RNA samples termed C3, C12 and C24 were prepared. Two biological replicates were generated at each time point, therefore, a total of 12 cDNA libraries (S3-A, S3-B, C3-A, C3-B, S12-A, S12-B, C12-A, C12-B, S24-A, S24-B, C24-A and C24-B) were constructed for RNA-seq.

Using Illumina NextSeq 500, >0.65 billion pair-end reads were generated; corresponding to ~54.6 million sequence reads per sample (Table II). Using TopHat 2.0 software (22), 68.4% of all reads were successfully mapped against the current human reference genome (GRCH38).

Using edgeR 22.0 software (23), a total of 966, 3,543 and 4,944 statistically significant DEGs were identified in S3 vs. C3, S12 vs. S12 and S24 vs. C24, respectively (Fig. 1D and E), which indicates marked changes to the transcriptome in PANC-1 cells following treatment with GSPs. Additionally, 456 common DEGs were identified between all three comparisons.

To identify the primary functions in which the DEGs are involved, GO enrichment analysis was performed (Fig. 2). A total of 24, 52 and 64 GO terms were identified in S3 vs. C3, S12 vs. C12 and S24 vs. C24, respectively (P<0.05; data not shown).

For all three comparisons, numerous DEGs were enriched in the following terms: ‘Mitotic cell cycle’ (GO:0000278), ‘G2/M transition of mitotic cell cycle’ (GO:0000086), ‘mitosis’ (GO:0007067) and ‘cell cycle’ (GO:0007049) (Fig. 2B, D and F). This indicates that treatment with GSPs exhibits an effect on the CC of PANC-1 cells. To determine how GSPs affect the CC of PANC-1 cells, the expression levels of DEGs enriched in the aforementioned GO terms were analyzed by RNA-seq. This demonstrated that the expression levels of a number of DEGs associated with the CC, including cyclin dependent kinase 1 (CDK1) and mouse double minute 2 homolog (MDM2), decreased in GSP-treated cells, compared with the control cells (Fig. 3). CDK1 serves a key role in the eukaryotic CC by regulating the centrosome cycle and the onset of mitosis, and modulating the G2-M transition, G1 progression and G1-S transition via an association with multiple interphase cyclins, including CDC7, CDC20 and CDC25A (27). MDM2 can inhibit CC arrest and apoptosis by binding to transcriptional activation domain of TP53 (28).

The results of RT-qPCR were consistent with the results of RNA-seq (Fig. 3B). PCC values were >0.9, which indicates that treatment with GSPs may disrupt the CC of PANC-1 cells. Additionally, certain genes associated with the CC were identified to be upregulated in cells treated with GSPs, compared with control cells. For example, the mitogen-activated protein kinase 3 (MAPK3) gene was revealed as an upregulated DEG in S12 vs. C12 and S24 vs. C24 (data not shown). MAPK3 serves a role in a signaling cascade that mediates various cellular processes, including proliferation, differentiation and CC progression, in response to a variety of extracellular signals (29).

There are intricate connections between CC regulation and transcriptional control, and progression of the CC is partially controlled at the transcriptional level in eukaryotes (30). Therefore, disruption of the transcription machinery may induce disruption of the CC.

GO analysis revealed that the DEGs identified in S3 vs. C3, S12 vs. C12 and S24 vs. C24 were enriched in the ‘regulation of transcription, DNA-dependent’ (GO:0006355) and ‘transcription, DNA-dependent’ (GO:0006351) terms (Fig. 2B, D and F). This indicates that normal regulation of transcription may be disrupted in PANC-1 cells following treatment with GSPs. Furthermore, the expression levels of multiple DEGs associated with transcriptional regulation, including E2F transcription factor 3 (E2F3) and hypoxia inducible factor 1 α subunit (HIF1A), decreased in GSP-treated cells, compared with control cells (Fig. 3). E2F3 is a transcription factor that recognizes a specific sequence motif in DNA and interacts directly with the retinoblastoma protein to modulate the expression of genes involved in the CC (31). HIF1A functions as a master transcriptional regulator of the adaptive response to hypoxia, which serves as a stimulus for CC arrest (32). A number of genes associated with regulation of transcription were also identified to be upregulated in GSP-treated cells, compared with control cells. For example, JunB proto-oncogene, a family member of the activator protein 1 transcription factors, was identified as an upregulated DEG in S3 vs. C3, S12 vs. C12 and S24 vs. C24 (data not shown). These observations indicate that the transcriptional machinery in PANC-1 cells may be disrupted following treatment with GSPs, which may result in disruption of the CC.

Accurate DNA replication and repair are crucial in the normal CC. The DEGs identified in S3 vs. C3, S12 vs. C12 and S24 vs. C24 were enriched in the ‘DNA-dependent DNA replication’ (GO: 0006261), ‘DNA replication’ (GO: 0006260), ‘DNA repair’ (GO: 0006281) and ‘double-strand break repair via homologous recombination’ (GO: 0000724) GO terms (Fig. 2B, D and F). The expression levels of numerous DEGs enriched in the aforementioned terms, including ataxia telangiectasia and Rad3-related (ATR), bloom syndrome RecQ like helicase (BLM), nibrin (NBN) and RAD50 double strand break repair protein (RAD50), were decreased in the PANC-1 cells treated with GSPs, compared with the control cells (Fig. 3). ATR serves a critical role in the DNA damage response (33). NBN serves a role in the control of the intra-S-phase checkpoint (34). Additionally, evidence indicates that NBN is involved in the G1 and G2 checkpoints, and is involved in DNA double-strand break repair and DNA damage-induced checkpoint activation (35). BLM serves a role in DNA replication and repair (36). RAD50, in cooperation with other molecules, is important in DNA double-strand break repair, CC checkpoint activation, telomere maintenance and meiotic recombination (37). A number of genes associated with DNA replication and repair were also identified to be upregulated in GSP-treated cells, compared with control cells. For example, the ubiquitin C gene, which serves an important role in DNA repair (38), was revealed to be an upregulated DEG in S12 vs. C12 and S24 vs. C24 (data not shown). These observations indicate that the regulation of DNA replication and repair may be disrupted in PANC-1 cells following treatment with GSPs, which may result in disruption of the CC.

The aforementioned results revealed that disruption of transcription and DNA replication may affect the CC of PC cells, and induce apoptosis of cancer cells. To determine the co-expression associations of the DEGs, a network was constructed using the PCCs of the common DEGs (PPC≥0.95; P<0.01), which were enriched in the ‘mitotic cell cycle’ (GO:0000278), ‘G2/M transition of mitotic cell cycle’ (GO:0000086), ‘mitosis’ (GO:0007067), ‘cell cycle’ (GO:0007049), ‘regulation of transcription, DNA-dependent’ (GO:0006355), ‘transcription, DNA-dependent’ (GO:0006351), ‘DNA-dependent DNA replication’ (GO:0006261), ‘DNA replication’ (GO:0006260), ‘DNA repair’ (GO:0006281) and ‘double-strand break repair via homologous recombination’ (GO:0000724) pathways (Fig. 4). The co-expression associations between these DEGs indicated that disorders of transcription, DNA replication, DNA repair and CC worked together to induce apoptosis, resulting in the anticancer effects of GSPs.