The human cisplatin-resistant gastric cancer cell line SGC7901/DDP and its parental cells SGC7901 were purchased from KeyGEN Biotechnology Company (Nanjing, Jiangsu, China). Cells were cultured in RPMI-1640 medium (Gibco, Grand Island, NY, United States) containing 10% fetal calf serum (Gibco, NY, United States) supplemented with 100 U/mL penicillin and 100 μg/mL streptomycin. Cells were cultured in a humidified atmosphere with 5% CO₂ at 37 °C. Cisplatin (Sigma, CA, United States) with final concentration of 800 ng/mL was added to the culture media for SGC7901/DDP cells to maintain the cisplatin-resistant phenotype.

SGC7901/DDP and SGC7901 cells were suspended at a density of 1 × 10⁵ cells/mL and planted into 96-well culture plate. After 24 hours, the cells were treated with freshly prepared DDP. The final concentrations were 133.34 μmol /L, 66.67 μmol/L, 6.67 μmol/L, 0.67 μmol/L and 0.067 μmol/L, because the human peak plasma concentration for DDP has been reported as 6.67 μmol/L[21]. Cell viability was examined after 48 h and was determined by adding 20 μL MTT (5 mg/mL) to each well and incubated for a further 4 h. The resulting formazan crystal was dissolved by addition of 150 μL dimethyl sulfoxide (DMSO) (sigma, Germany) each well, and then plates were shaken for 10 minutes. The absorbance at 490 nm was measured by spectrophotometer (ELx 800; BioTek; Winooski, VT, United States). The inhibition of growth (IC50) for DDP was calculated by the cells relative viability. Each experiment was performed in triplicate.

Cells were harvested when they had grown to 80%-90% confluency and were still in logarithmic phase. Total RNA was extracted from the three matched pairs of SGC7901/DDP and SGC7901 cells using TRIzol reagent (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s instructions. The quality of total RNA was measured by NanoDrop ND-2000 spectrophotometer (Thermo Scientific, Waltham, MA, United States). Total RNA from three paired samples were amplified and transcribed into fluorescent cDNA, and then the fluorescent labeled samples were hybridized to the Agilent LncRNA-mRNA Human Gene Expression Microarray V4.0 (Capital Bio Corp, Beijing, China) which contains 25069 human mRNA according to the manufacturer’s recommendations. The microarray was scanned by an Agilent Microarray Scanner. Image processing was conducted using Agilent Feature Extraction software and raw microarray signals normalized using Agilent Gene-Spring software. The normalized mRNA expression profiles data output was received in Excel spreadsheets. The two group of samples data were analyzed by t-test to get the P-values. FC values representing the differently expressed mRNAs between SGC7901/DDP and their parental cells. Cluster 3.0 software was performed to show differential expression patterns of mRNAs.

Bioinformatics analysis were generated using KOBAS software and STRING 9.1 software. KOBAS software was used to analyze Ontology, Disease and pathways of the dysregulated mRNAs. KOBAS associated with 1 ontology database (Gene Ontology), 5 disease databases (OMIM, KEGG DISEASE, PID Reactome, FunDO, GAD, NHGRI) and 7 pathway databases (KEGG PATHWAY, PID Curated, PID BioCarta, BioCyc, eactome, Panther). The entire analysis process includes two steps: first, bring the input gene ID map to the gene in the databases, and then annotate pathways, disease and function of these genes involved in. Second step, compare the first step results with background (usually the entire genome of the gene, or the entire probe on the chip), and unearth statistically significant enrichment pathways, disease or function. Fisher’s exact test and χ² test were used as statistical tests and the FDR was performed to correct the P-value[22]. Additionally, we used STRING 9.1 software to decipher the protein-protein interaction (PPI) network of the differentially expressed proteins. The PPI network may help in understanding the molecular mechanism of cisplatin resistance. All mRNA microarray data were given by Capital Bio Corp.

To validate the reliability of microarray analysis, we performed quantitative real-time PCR (qRT-PCR). The reverse transcription production cDNA was synthesized using oligo-dT primers and Superscript II reverse transcriptase. PCR was performed with SYBR^® Premix Ex Taq^TM (TaKaRa Bio; Japan) by a Light Cycler PCR system (Agilent Technologies, Palo Alto, CA, United States) according to the manufacturer’s instructions. After amplification, melting curves were analyzed. Beta-actin snRNA used as endogenous control, each sample was done in triplicate. The relative expression levels of target mRNAs were calculated using the 2^-∆∆Ct method (where ∆∆Ct is the difference in threshold cycles for the ∆Ct of SGC7901/DDP sample and SGC7901 sample, and ∆Ct is the difference between the target gene and endogenous control beta-actin). Sequences of primers for qRT-PCR are provided in supporting Table 1.

MTT test and qRT-PCR statistical analysis was performed using GraphPad Prism software (v. 5.0a; GraphPad Software, La Jolla, CA, United States). We used one-way analysis of variance (ANOVA) followed by Student’s t-test to assess the statistical significance of differences between different cell groups. The threshold for statistical significance was P-values < 0.05. Fold changes of mRNAs validated by qRT-PCR in SGC7901/DDP cells compared with SGC7901 cells are shown as mean ± SD.

To determine the chemotherapy sensitivity of SGC7901/DDP and SGC7901 cell line to cisplatin, varying concentrations of cisplatin were added into the 96-well plates and incubated for 48 h. From these data, half maximal inhibitory concentration (IC50) cisplatin dose was calculated. IC50 cisplatin doses for SGC7901/DDP and SGC7901 (after 48 h in DDP-containing media) were 43.47 ± 0.21 μmol/L and 1.24 ± 0.02 μmol/L, respectively, and the resistance index for SGC7901/DDP cell lines was 35.12, confirming that these cells are refractory to cisplatin. Cell viability was checked by MTT assay (Figure 1).

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Figure 1 Cell viability treated with different concentrations of cisplatin for 48 h. MTT assay for SGC7901 cells and SGC7901/DDP cells treated with cisplatin (133.34, 66.67, 6.67, 0.67 and 0.067 μmol/L, respectively).

To show mRNA expression profile in cisplatin-resistant SGC7901/DDP cells, we used a stringency cutoff to identify significantly differently mRNAs (P < 0.05, FC ≥ 2) and two-dimensional hierarchical clustering 3.0 to represent expression profiles between samples (Figure 2). The results indicated that 1308 mRNAs were significantly differentially expressed in SGC7901/DDP cells compared with SGC7901 cells. Among these transcripts, 578 mRNAs were upregulated, and 730 mRNAs were downregulated.

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Figure 2 mRNA expression levels from microarray. A: The volcano plot image showed the mRNA expression levels of microarray in SGC7901/DDP cells compared with SGC7901 cells. Black dots: equally expressed mRNAs between SGC7901/DDP cells and SGC7901 cells (FC ≤ 2); red dots: mRNAs were over-expressed in SGC7901/DDP cells compared with SGC7901 cells (FC ≥ 2); green dots: mRNAs in SGC7901/DDP cells were down-expressed compared to SGC7901 cells (P-values < 0.05, FC ≥ 2). Fold changes of these mRNAs in SGC7901/DDP cells compared with SGC7901 cells are shown as mean ± SD; B: Two-dimensional hierarchical clustering image of the 1308 dysregulated mRNAs in the SGC7901/DDP cells compared with the SGC7901 cells, each row represents an mRNA, each column represents a sample. 7901-1, 7901-2 and 7901-3 represent the three samples of SGC7901 cells, DDP-1, DDP-2 and DDP-3 represent the three samples of SGC7901/DDP cells. Red: Higher expression levels; green: Lower expression levels.

First, we concentrated on validating the microarray results. From the abnormally expressed (P < 0.05) mRNAs obtained from the microarray analyses, we selected 8 upregulated mRNAs (HIPK2, PDE3B, FGF2, TWIST1, ZEB2, VEGFC, SPHK1, BAX) and 5 downregulated (PTEN, HTRA1, CCL5, TGM2, TLR4) mRNAs for qRT-PCR validation. The relative fold-changes (SGC7901/DDP vs SGC7901) detected by qRT-PCR were consistent with the microarray results (Figure 3), indicating the dependability of our microarray platform.

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Figure 3 Quantitative real-time polymerase chain reaction validation of the microarray results of the 13 mRNAs. Relative fold changes in expression between SGC7901/DDP cells and SGC7901 cells were in agreement with microarray.

To depict comprehensively the properties of the differentially expressed mRNA in SGC7901/DDP cells, GO annotation and enrichment analysis was performed to evaluate which cellular components, molecular functions and biological processes may be are affected by this dysregulation. The GO enrichment analysis showed that the differentially expressed genes were involved in a variety of functions, including locomotion, chemotaxis, cell adhesion, regulation of cell migration, extracellular matrix disassembly, response to xenobiotic chemotaxis, localization of cell adhesion and blood vessel morphogenesis (Figure 4A).

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Figure 4 Bioinformatic analysis of differentially expressed mRNAs. Gene ontology analysis of mRNAs dysregulated in SGC7901/DDP cells compared with SGC7901 cells. A: Top 30 molecular functions of the dysregulated mRNAs may associated with. Gene ontology analysis include biological processes, cellular components and molecular function; B: Gene ontology enriched diseases. Top 30 diseases annotations of dysregulated mRNAs may involve in. The disease enrich system include 5 disease databases: OMIM, KEGG disease, FunDO, GAD and NHGRI GWAS Catalog.

Additionally, 59 human diseases were significant enriched (P < 0.05) in five human disease databases (KEGG DISEASE, FunDO, GAD, NHGRI GWAS Catalog and OMIM) (Figure 4B, Table 2). Furthermore, it is worth noting that in KEGG disease database, gastric cancer is the most highly enriched disease, and the input genes include DCC, CD44, CDH1, VEGFC, EGF, TGFA.

To determine which pathway might be involved in drug resistance formation, KEGG pathway analysis was used to authenticate pathways and understand biological functions of significantly differentially expressed genes. The result indicated that the differentially expressed mRNAs were enriched for 233 pathways, including the Rap1 signaling pathway, PI3K-Akt signaling pathway, ECM-receptor interaction, TNF signaling pathway, and pathways in cancer, among others (Figure 5, Table 3). Cluster 3.0 software were performed the heat-map.This finding identified many candidate pathways and input genes that may play an important role in resistance mechanism.

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Figure 5 Heat-map of gene ontology enriched cisplatin resistance pathways and input mRNAs which significantly altered in SGC7901/DDP cells compared with SGC7901 cells. A: PI3K-Akt signaling pathway and input genes; B: MAPK signaling pathway and input genes; C: Notch signaling pathway and input genes; D: ErbB signaling pathway and input genes; E: Jak-STAT signaling pathway and input genes; F: NF-kappa B signaling pathway and input genes; G: HIF-1 signaling pathway and input genes; H: MicroRNAs in cancer and input genes. Each row represents an mRNA, and each column represents a sample. The intensity of the color indicates the relative levels of mRNAs. Red: Higher expression levels; green: Lower expression levels. The name of the input mRNAs which significantly altered (P < 0.05, FC ≥ 2) is present at the right of the figure.

The STRING 9.1 software (Search Tool for the Retrieval of Interacting Genes) was used to perceive functional relations and generate networks of differential expression of proteins (Figure 6). For all of the 1002 differentially expressed proteins, we extracted a network containing 443 upregulated and 559 downregulated proteins which functionally associated with each other. We found that interacting proteins which participate in angiogenesis, toll-like receptor signaling pathway and cell adhesion had a high level of co-expression.

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Figure 6 Interaction network analyses of differentially express proteins. In the network, nodes represents proteins, lines as functional associations between the abnormal expressed proteins and the thickness of the lines indicates the level of confidence in association reported.

Cisplatin-contained chemotherapy is one of the most frequently used for advanced gastric cancer; however, this chemotherapeutic agent is often limited due to drug resistance and result unsatisfactory prognosis. Research increasingly suggests that abnormal expression of biological pathway and proteins associated with cisplatin resistance. This demonstrated that more bioinformatics study is needed to predict targets for gastric cancer with cisplatin.

Bioinformatics analysis demonstrated that some mRNAs which related to the biological behavior abnormal expression in SGC7901/DDP cells. These mRNAs have already been shown to play important roles in the process of cisplatin resistance of various cancers, including gastric cancer.

The authors performed bioinformatics analysis of mRNA expression profile in SGC7901/DDP cells compared with SGC7901 cells, and found that many mRNAs and pathways in SGC7901/DDP cells expressed abnormally, these may participate in and predict cisplatin resistance in gastric cancer.

These results suggest that targeting the differently expression mRNA may provide more selective approaches to reverse cisplatin resistance of therapeutic targets.

The definition of cisplatin resistance: in the clinic, if a patient who have disease recurrence within the first months after the recent cisplatin dose, the patient is considered cisplatin resistance; in cells, generally, resistance index > 20 exhibited high resistance, resistance index 5-15 is moderate resistance, resistance index < 5 represent low or no resistance. Correct P: Using Benjamini Hochberg FDR method for correction of p values. Fold change (FC): gene expression in SGC7901 / DDP cells compared with SGC7901 cells.

The paper is a good study on mRNAs expression profile in SGC7901/DDP cells. The investigators shown that many mRNAs was abnormal expressed in SGC7901/DDP cells and these mRNAs enriched in many biological process which have already been shown to play important roles in the process of cisplatin resistance in human cancer.