The SGC7901 cell line was obtained from the Cancer Institute and Hospital, Chinese Academy of Medical Sciences (CAMS) (Beijing, China). The cells were grown in 90% RPMI-1640, supplemented with 10% FBS, 100 U/ml penicillin, and 100 mg/ml streptomycin and incubated at 37°C in a humidified atmosphere of 5% CO₂.

Matrine (Baoji Fangsheng Pharmaceutical Co., Ltd., Xian, China) was analyzed by HPLC assay and dissolved in phosphate-buffered saline (PBS; Invitrogen-Gibco, Grand Island, NY, USA). SGC-7901 cell cultures were then treated for 24 h with 0.5, 1.0, 1.5, 2.0 and 2.5 mg/ml concentrations of matrine. Subsequently, 20 μl of 5 mg/ml MTT in PBS was added to each well and the plate was incubated at 37°C for 4 h. The plate was then centrifuged at 1,000 × g for 2 min and followed by removal of the medium. One-hundred and fifty microliter of dimethyl sulfoxide (DMSO; Sigma, St. Louis, MO, USA) was then added. After incubation at 37°C for 5 min, absorbance in the control and matrine-treated cells was measured spectrophotometrically at 570 nm using a benchmark microtiter plate reader (Bio-Rad Laboratories, Hercules, CA, USA). Microarray array was subsequently conducted on the control (blank) vs. 1.5 mg/ml matrine (MA) treatment groups.

Total RNA, including miRNA, was isolated using the miRNeasy mini kit (Qiagen, Hilgen, Germany), according to the manufacturer’s instructions. RNA quality and quantity was measured via a NanoDrop spectrophotometer (ND-1000; NanoDrop Technologies, Wilmington, DE, USA) and RNA integrity was determined by gel electrophoresis. The concentration and purity of total RNA were assessed at absorbance readings of 260 and 280 nm, using an ultraviolet spectrophotometer. Only the RNA samples with ratios of A260/A280 >1.8 were used in the present study. After isolating the RNA, the miRCURY Hy3™/Hy5™ Power labeling kit (Exiqon, Copenhagen, Denmark) was used for miRNA labeling, according to the manufacturer’s instructions. One microgram of each sample was 3′-end-labeled with a Hy3™ fluorescent label using T4 RNA ligase, as follows: RNA was placed in 2.0 μl of water and combined with 1.0 μl of CIP buffer and CIP (Exiqon, Vedbaek, Denmark). After incubating for 30 min at 37°C the reaction was terminated by 5-min incubation at 95°C. A volume of 3.0 μl labeling buffer, 1.5 μl fluorescent label (Hy3™), 2.0 μl DMSO, and 2.0 μl labeling enzyme were then added. The mixture was incubated for 1 h at 16°C for labeling, followed by 15-min incubation at 65°C to terminate the labeling reaction. After labeling, the Hy3™-labeled samples were hybridized on the miRCURY™ LNA Array v.18.0 (Exiqon, Copenhagen, Denmark), according to the array manual. A 25 μl volume of the Hy3™-labeled sample mixture and 25 μl hybridization buffer were denatured for 2 min at 95°C, incubated on ice for 2 min and then hybridized to the microarray for 16–20 h at 56°C in a 12-Bay hybridization system (NimbleGen Hybridization System 12; Roche Applied Science, Madison, WI, USA), which provides an active mixing action and constant incubation temperature to improve hybridization uniformity and signal enhancement. Following hybridization, slides were prepared, washed several times using a wash buffer kit (Exiqon), and dried via centrifugation for 5 min at 400 rpm. The slides were scanned using an Axon GenePix 4000B microarray scanner (Axon Instruments, Foster City, CA, USA).

miRBase, miRanda and TargetScan were used to predict the target genes of differentially expressed miRNAs following matrine treatment. The prediction information from the three databases that overlapped was considered the final result. The miRNA target gene prediction databases miRFocus 2.0 (http://mirfocus.org/index.php) and miRWalk (http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/) were also used to analyze plausible KEGG pathway enrichment of dysregulated miRNA targets. The miRFocus 2.0 prediction tools included miRanda, MirTarget2, PicTar, microT and TargetScanS. The experimental validated target gene tools included miRecords, miR2Disease, TarBase and miRTarBase. The prediction databases support number was ≥3 and the Fisher test P-value cut-off was 0.01.

A SYBR-Green RT-PCR assay was used for miRNA quantification. In brief, 1 μg of total RNA, containing miRNA, was polyadenylated using poly(A) polymerase and was reverse transcribed to cDNA using a miScript Reverse Transcription kit, according to the manufacturer’s instructions (GeneCopoeia, Rockville, MD, USA). The miScript Universal primer was provided by the manufacturer (GeneCopoeia) and the miScript SYBR-Green PCR kit was used. RT-PCR was performed using the Bio-Rad CFX96 real-time PCR system. Each reaction was performed in a final volume of 10 μl containing 2 μl cDNA, 0.5 mM of each primer and 1X SYBR-Green PCR Master Mix (GeneCopoeia). The amplification program was as follows: denaturation at 95°C for 10 min, followed by 45 cycles at 94°C for 15 sec, 55°C for 30 sec and 70°C for 30 sec, in which fluorescence was obtained. After completion of the PCR cycles melting curve analyses and electrophoresis, on 2.5% agarose gels were performed to validate the specificity of the expected PCR product. Each sample was analyzed in triplicate. The expression levels of miRNAs were normalized to RNU6B and the relative gene expression was calculated as 2^{−(CTmiRNA-CTRNU6B RNA)}. Each sample was analyzed in triplicate. All the miRNA PCR primers were purchased from GeneCopoeia.

Scanned images of the miRNA Array were imported into GenePix Pro 6.0 software (Axon) for grid alignment and data extraction. Replicated miRNAs were averaged and miRNAs with intensities of ≥50 in all the samples were used to calculate the normalization factor. Expressed data were normalized using the Median normalization. After normalization, differentially expressed miRNAs were identified through fold change filtering. Hierarchical clustering was performed using MeV software (v4.9, TIGR). Results of real-time RT-PCR experiments are expressed as means ± SD.

The antitumor reducing activity of matrine cytotoxicity (0.5, 1.0, 1.5, 2.0 and 2.5 mg/ml) to SGC-7901 cells culture is shown in Fig. 1. Matrine showed a dose-dependent inhibition of the growth of SGC-7901 cells.

After SGC-7901 cells were treated with 1.5 mg/ml matrine for 24 h, 60 miRNAs were downregulated (fold change, ≤0.5) and 68 miRNAs were upregulated (fold change, ≥2.0) (Fig. 2). By searching the miRFocus 2.0 and miRWalk miRNA databases, only 38 of the 60 downregulated miRNAs and 8 of 68 upregulated miRNAs were annotated with their respective expressions in cancer tissues or cells or the predicted and validated target genes. Of the 46 annotated miRNAs, 25 were upregulated and 6 were downregulated in gastric cancer tissue or gastric cancer cell lines by miRNA microarray or RT-PCR (Tables I–III).

The miRBase, miRanda and TargetScan bioinformatical databases were used to predict the target genes of 46 aberrantly expressed miRNAs. The results showed that there are 4,159 overlapped target genes as predicted by the three tools for all the downregulated miRNAs and 367 overlapped target genes for the upregulated miRNAs that exhibited a >2-fold expression change after matrine treatment (Fig. 3). Possible regulation mechanisms of differentially expressed miRNAs following matrine treatment were investigated using the bioinformatics database, miRFocus 2.0, to select clustered enrichment KEGG pathways of plausible targets for each miRNA. A total of 57 enrichment pathways for predicted target genes were identified, including 36 enrichment signaling pathways for downregulated miRNAs. According to the Enrichment Score, the top 10 signaling pathways were the MAPK, focal adhesion, neurotrophin, axon guidance, ECM-receptor interaction, Wnt, amino sugar and nucleotide sugar metabolism, glycosaminoglycan biosynthesis-keratan sulfate, and the p53 signaling pathway, as well as pathways in cancer (Table IV).

The remaining 21 enrichment signaling pathways were associated with the target genes of upregulated miRNAs. According to the Enrichment Score, the top 10 signaling pathways were the Legionellosis, notch, neurotrophin, protein processing in endoplasmic reticulum, TGF-β, small cell lung cancer, epithelial cell signaling in Helicobacter pylori infection, cell cycle, RIG-I-like and pertussis signaling pathways (Table V).

The miRFocus 2.0 database was thoroughly examined for target genes of pathways for MAPK signaling and cell cycles. The results showed that 196 genes are associated with MAPK signaling pathways regulated by the 14 downregulated miRNAs, while 53 genes are associated with cell cycle pathways regulated by the 8 upregulated miRNAs. In this study, of the 159 genes 14 miRNAs were downregulated (Figs. 4 and 5).

After SGC-7901 cells were treated with 1.5 mg/ml matrine for 24 h, 14 miRNAs whose target genes were clustered in the MAPK signaling pathway and 8 miRNAs whose target genes are clustered in pathways of the cell cycle were selected to verify their expression using RT-qPCR. The results showed the alterations of 8 upregulated miRNAs, including miR-192-5p, miR-299-5p, miR-194-5p, miR-300, miR-302a-3p, miR-483-5p, miR-498 and miR-513a-5p whose target genes were clustered in the cell cycle pathway following matrine treatment (1.5 mg/ml), were consistent with alterations identified by miRNA microarray. However, for the 38 down-regulated miRNAs, the alterations of 14 miRNAs following matrine treatment (1.5 mg/ml), including let-7b-5p, let-7c, let-7d-5p, let-7f-5p, miR-19a-3p, miR-19b-3p, miR-20b-5p, miR-21-5p, miR-27a-3p, miR-27b-3p, miR-29c-3p, miR-30e-5p, miR-98-5p and miR-106b-5p whose target genes were clustered in the MAPK signaling pathway, were consistent with alterations identified by miRNA microarray (Tables I and II).