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miR-3174 Contributes to Apoptosis and Autophagic Cell Death Defects in Gastric Cancer Cells by Targeting ARHGAP10

Gastric cancer (GC) is a major health problem worldwide because of its high morbidity and mortality. Considering the well-established roles of miRNA in the regulation of GC carcinogenesis and progression, we screened differentially expressed microRNAs (miRNAs) by using The Cancer Genome Atlas (TCGA) and the GEO databases. We found that miR-3174 was the most significantly differentially expressed miRNA in GC. Ectopic miR-3174 expression was also detected in clinical GC patient samples and cell lines and associated with poor patient prognosis. Apoptosis and autophagic cell death are two types of programmed cell death, whereas both are deficient in gastric cancer. Our functional analyses demonstrated that miR-3174 inhibited mitochondria-dependent apoptosis and autophagic cell death in GC. Moreover, high expression of miR-3174 also resulted in Cisplatin resistance in GC cells. Using bioinformatics analyses combined with in vitro and in vivo experiments, we determined that miR-3174 directly targets ARHGAP10. Notably, ARHGAP10 promoted mitochondria-dependent apoptosis by enhancing p53 expression, which was followed by Bax trans-activation and caspase cleavage. ARHGAP10 also facilitated autophagic cell death by suppressing mammalian target of rapamycin complex 1 (mTOC1) activity. Our results reveal a potential miRNA-based clinical therapeutic target that may also serve as a predictive marker for GC.


INTRODUCTION
Gastric cancer (GC) is the second highest cause of cancer-related death worldwide and is especially prevalent in East Asia. 1 Despite new diagnostic techniques and improvements in radical lymphadenectomy surgical approaches to treat GC, the prognosis of patients remains poor, and the five-year survival rate remains at approximately 20%. 2 Patients with advanced GC also typically require further treatment after gastrectomy, such as postoperative chemotherapy or targeted therapy. 3 However, the therapeutic effectiveness of perioperative and postoperative chemotherapy remains insufficient, and these modalities increase survival by only 14% and 10%, respectively. 4,5 Furthermore, there is currently a lack of efficient molecular therapeutic targets, such as HER-2 or VEGFR-2. 6,7 It has become a major challenge to identify effective molecular therapeutic targets and potential predictive markers of GC.
Apoptosis and autophagy, two methods for programmed self-destruction of cells, have long been topics of investigation by the research community. 8 Apoptosis, also known as type I cell death, is one of the best-described types of programmed cell death (PCD), and it activates cellular death signaling cascades such as mitochondria-dependent and death receptor-mediated signaling, 9,10 which are followed by nuclear chromatin condensation and nuclear fragmentation. 11 In contrast, autophagy is an intracellular self-catabolic degradation process. Under normal conditions, autophagosomes (APs) package and transport bulk cytoplasm, damaged proteins, and organelles to lysosomes to be digested, recycled, and converted to energy, thereby ultimately supporting cancer cell survival or proliferation. 12 However, excessive autophagy can also induce autophagic cell death (ACD), which is also known as type II cell death, 13 another type of PCD. In early ACD, in contrast to the classic morphological changes caused by apoptosis, large numbers of organelles are digested, but cytoskeletal elements are preserved, and this whole process is caspase independent. 14 Typically, stimuli can induce either apoptosis or ACD; however, both modes of death can be simultaneously induced in response to the same stimulation. 15 These characteristics might be attributed to the variable thresholds of both processes. Recently, several genes and pathways have been reported to be involved in the link between apoptosis and ACD, 16,17 thus indicating that these processes do not occur through isolated pathways. However, cancer cells can develop multiple mechanisms that promote malignant processes, such as continuous proliferation and avoidance of death. 18 Both types of PCD are defective in cancer, and there is currently a lack of suitable therapeutic methods that can simultaneously target both ACD and apoptosis. Thus, there is an urgent need to discover therapeutic targets that can reestablish PCD in cancer cells.
MicroRNAs (miRNAs) play significant roles in many types of diseases 19,20 and regulate the expression of oncogenes and tumor suppressors as well as entire signaling pathways involved in tumorigenesis and cancer progression. 21,22 miRNAs can also potentially serve as clinical therapeutic targets and predictive markers for GC. 23 Using bioinformatics analysis, we discovered a miRNA, miR-3174, that was significantly upregulated in GC tissues and cell lines. Because the role of miR-3174 has seldom been reported previously, we focused on uncovering the biological function of this miRNA in GC.
ARHGAP10, a member of the Rho GTPase-activating protein family (RhoGAP), catalyzes the GTP of Rho GTPases to GDP, 24 such as RhoA 25 and CDC42, 26 and subsequently inhibits the activities of Rho GTPase as well as its downstream signaling cascade. In addition to a RhoGAP domain, ARHGAP10 also possesses a SRC homology 3 (SH3) domain and a Pleckstrin homology (PH) domain. 27 This configuration allows ARHGAP10 to interact with many other protein species and to regulate multiple cellular processes, such as cytoskeleton and cell Figure 1. Identifying Differentially Expressed miRNAs by Using TCGA and the GEO Database (A) Bioinformatics analysis flow diagram for the screening for differentially expressed miRNAs in GC on the basis of TCGA and GEO databases. Eight miRNAs were significantly differentially expressed in GC tissues compared with normal tissues with a fold change R2 and p < 0.05. (B) Volcano plot of the miRNA matrix generated according to TCGA statistics from 491 GC patients. Red puncta represent highly expressed miRNAs in GC tissues in contrast to normal tissues with a fold change R2 and p < 0.05. Green puncta represent downregulated miRNAs in GC with fold change R2 and p < 0.05. (C) A miRNA expression heatmap for 446 GC patients and 45 healthy controls from TCGA was generated by using R with the pheatmap package. (D) miRNA expression heatmap for three GC tissues and three paired adjacent tissues from the GEO78091 dataset, generated by using R with the pheatmap package.
junction formation, 28 cardiac mechanical stress, 29 and vesicular trafficking of Golgi bodies. 26 Luo et al. 30 have found that ARHGAP10 plays a tumor-suppressor role in ovarian cancer by inhibiting the activity of CDC42. Azzato et al. 31 have described single-nucleotide polymorphisms of ARHGAP10 in breast cancer. Collectively, these results indicate an association between ARHGAP10 and cancer; however, the mechanism underlying this relationship is unknown.
In the present study, we provide the evidence that miR-3174 expression is significantly upregulated in GC. Further experiments confirmed that miR-3174 upregulation simultaneously inhibits ACD and apoptosis in GC cells by downregulating the expression of ARHGAP10. Upregulation of miR-3174 expression also contributes to cisplatin resistance in GC.

miR-3174 Is Upregulated in Gastric Cancer and Is Associated with Poor Prognosis
To identify candidate miRNAs with a role in GC, we examined changes in miRNA expression (fold change R2) among 446 GC tissues compared to 45 normal tissues from The Cancer Genome Atlas (TCGA) database and combined these results with the GEO datasets (GEO78091, fold change R2) ( Figure 1A). Five miRNAs were upregulated; three were downregulated in both TCGA ( Figures 1B and  1C) and the GEO libraries ( Figure 1D). RT-PCR was used to further validate the expression of these eight miRNAs (means and standard deviations of each group of miRNA expression profile were displayed in Table S1). Our results showed that the expression of miR-3174, miR-17-5p, and miR-19a-3p was increased (Figures 2A, 2E, and 2G) and that of miR-148a-3p, miR-4732-3p, and miR-125b-1-3p was decreased ( Figures 2B, 2D, and 2F), whereas there was no significant difference in miR-93-5p or miR-20a-5p expression among 50 GC tissues compared with paired adjacent normal tissues ( Figures  2C and 2H). The most significant change in expression was observed for miR-3174, thus prompting us to further examine the role of this miRNA in GC malignancy. High expression of miR-3174 was confirmed in an additional 100 paired GC tissues (Figures 2I and  2J). miR-3174 reads with transcripts per million (Tpm) correction were significantly higher in 446 GC tissues compared with 45 healthy tissues in TCGA ( Figure 2K). In addition, miR-3174 expression was also increased in several GC cell lines compared with the normal stomach mucosa cell line GES-1 (Figure 2l). Using the median expression value of miR-148a-3p as a cutoff point, the cohort was dichotomized into miR-148a-3p high and low group (means or variance values of two groups were shown in Table S1); univariate analysis of clinicopathologic features showed that the relative expression of miR-3174 was correlated with tumor size, tumor node metastasis (TNM) stage, and T stage of GC patients (Table S2); furthermore, high expression of miR-3174 contributed to poor prognosis and significantly lower five-year overall survival ( Figure 2M). These data indicate that miR-3174 is probably an onco-miRNA in GC.

miR-3174 Inhibits Mitochondria-Dependent Apoptosis in GC Cells
Flow cytometry assays revealed that upregulation of miR-3174 prominently decreased the apoptotic rate in MKN45 cells, whereas repression of miR-3174 improved the apoptotic rate in BGC823 cells. In addition, the pro-apoptotic effect of miR-3174 inhibition was recovered by Z-VAD-FMK, a pan-caspase inhibitor, in BGC823 cells, and the anti-apoptotic effect of miR-3174 reconstitution was similar to the effect of Z-VAD-FMK in MKN45 cells ( Figures 3A and 3B). These results were further verified by caspase-3 activity detection kit and cell viability assay ( Figures 3C and 3D). To confirm that the inhibition of apoptosis caused by miR-3174 was mitochondria dependent, we measured mitochondrial membrane potential (DJm, MMP) by using JC-1 probes. As expected, miR-3174 reconstitution increased, whereas miR-3174 downregulation decreased the DJm compared with that in controls ( Figures 3E and 3F). Besides, levels of cleaved caspase-3 and Bax, both of which play important roles in mitochondriadependent apoptosis, were decreased in MKN45 cells with miR-3174 reconstitution but increased in BGC823 cells with miR-3174 suppression, whereas Bcl-2 was unchanged regardless of altering miR-3174 expression ( Figure 3G, upper panel). Furthermore, another protein that participates in mitochondria-dependent apoptosis pathway, cytochrome c, released from mitochondria to cytosol in miR-3174-suppressed cells but accumulated in mitochondria in miR-3174-overex-pressed cells ( Figure 3G, lower panel). Thus, we conclude that miR-3174 inhibits mitochondria-dependent apoptosis in GC.

miR-3174 Restrains ACD in GC Cells
ACD is another type of PCD in addition to apoptosis, thus prompting us to test whether miR-3174 might regulate cellular autophagy in GC cells. To accomplish this, cells were transfected with lentivirus for GFP-mRFP-LC3 expression. Subsequent confocal microscopy revealed that miR-3174 overexpression significantly diminished both APs (yellow puncta) and autolysosomes (ALs, red puncta) in MKN45 cells, whereas miR-3174 inhibition enhanced APs and ALs in BGC823 cells ( Figures 4A and 4B). Transmission electron microscopy (TEM) detection of characteristic AP with double layer structure or ALs generated by fusion of AP with lysosome showed that reconstituted miR-3174 expression significantly reduced, whereas miR-3174 suppression improved cellular APs or ALs ( Figure 4C). LC3-II turnover assay also indicated that miR-3174 could negatively regulate autophagy in GC cells, both in normal and serum-starved conditions ( Figures 4D and 4E). In addition, the effect of miR-3174 on autophagy was further augmented with chloroquine (CQ) treatment but restrained in the presence of 3-methyladenine (3-MA), a class III PI3K inhibitor ( Figures 4D and 4E). Moreover, overexpression of miR-3174 in MKN45 cells increased protein abundance of SQSTM1/p62 and decreased levels of BECN1, both of which are markers of autophagy, whereas the opposite findings were found in miR-3174-inhibited BGC823 cells ( Figure 4F). CCK-8 assay results showed that the autophagic inhibitors 3-MA and Wortmannin (WMT) as well as the small interfering RNA (siRNA) sequences siBECN1 and siATG5 significantly decreased cell death caused by miR-3174 downregulation in BGC823 cells ( Figure 4H) (the inhibition effectiveness were validated as shown in Figure 4G). All these results reveal that high expression of miR-3174 contributes to death defects in GC cells partly by suppressing ACD. miR-3174 Reinforces the CDDP Resistance in GC Cells cis-diamine dichloroplatinum/cisplatin (CDDP)-based chemotherapy is the first-line regimen for advanced and metastatic GC. On the basis of the relationship between apoptosis or autophagy with chemo-sensitivity in cancer, we speculated that miR-3174 might also decrease the cytotoxicity of CDDP in GC. To test this hypothesis, MKN45 and BGC823 cells resistant to CDDP (referred to as MKN45CDDP and BGC823CDDP cells, respectively) were established. The CDDP-resistant features of these two cell lines were validated through cell viability assays and subsequent IC 50 evaluation (Figures S1B-S1D). miR-3174 expression was significantly upregulated in MKN45 and BGC823 CDDP-resistant cells compared with the corresponding drug-sensitive parental cells ( Figure 5A). miR-3174 reconstitution decreased cell viability with various concentrations of CDDP treatment and elevated the IC 50 in MKN45 and BGC823 cells ( Figures 5B, 5D, 5F, and 5H), whereas downregulation of miR-3174 displayed the reverse effects in MKN45CDDP and BGC823CDDP cells ( Figures 5C, 5E, 5G, and 5I). Colony formation assay verified that miR-3174-reconstituted CDDP-sensitive cells showed stronger long-term viability than control cells (Figures 5J, S2B, and S2C, left panels); the opposite findings were acquired in miR-3174-depressed CDDP-resistant cells ( Figures 5K, S2B, and S2C, right panels). These data indicate that miR-3174 facilitates CDDP resistance in GC cells.

ARHGAP10 Is a Direct Downstream Target Gene of miR-3174
To identify miR-3174 target genes, we screened TCGA for genes that were significantly downregulated in GC (fold change R2) ( Figure 6A) and combined these results with predictive target information regarding miR-3174 obtained from TargetScan (http://www. targetscan.org/). Using this strategy, 345 genes were selected. Subsequently, we identified 466 genes that may increase CDDP sensitivity in 28 GC cell lines based on the Genomics of Drug Sensitivity in Cancer (GDSC) database ( Figure 6B; Table S3; p < 0.05) and combined these data with the previous 345 genes. Ultimately, 21 genes were identified that meet the following criteria: (1) are downregulated in 375 GC tissues compared with 32 normal gastric tissues on TCGA ( Figure 6C), (2) exhibit CDDP sensitization effect according to GDSC, and (3) are the potential targets of miR-3174 according to TargetScan ( Figure 6D). Negative correlations between miR-3174 expression and these 21 potential targets were subsequently verified in GC and normal tissues by the statistics acquired from TCGA (Figure 6E). We chose four genes (ZNF471, LDOC1, CLMP, and ARHGAP10) whose expression was significantly negatively correlated with miR-3174 (p % 0.01) for experimental confirmation (Figures 6F-6I). RT-PCR results validated that expression of CLMP and ARHGAP10 was downregulated in GC tissues compared with normal adjacent tissues ( Figures 7A and 7B), whereas no significant changes in ZNF471 and LDOC1 expression were observed ( Figures 7C and  7D). Furthermore, CLMP and ARHGAP expression levels showed a clear negative correlation with miR-3174 expression in the GC tissues ( Figures 7E and 7F), whereas no clear relationship was observed between ZNF471 or LDOC1 expression and miR-3174 expression ( Figures 7G and 7H). Moreover, the augmentation or inhibition of miR-3174 expression respectively downregulated or upregulated the expression of CLMP and ARHGAP10, but not ZNF471 and LDOC1, at both the mRNA and protein level ( Figures 7I and 7J). Luciferase reporter assays further revealed that the bioluminescence intensities in miR-3174-reconstituted BGC823 and MKN45 cells decreased after transfection with the wild-type 3 0 UTRs of ARHGAP10 and CLMP. However, there were no significant differences after transfected wild-type 3 0 UTRs of ZNF471 or LDOC1 and mutant-type of 3 0 UTRs of these four genes in cells with or without miR-3174 reconstitution ( Figures 7K and 7L). Constructs specific to these four genes were then transfected into GC cells ( Figure S2D), and cell viability was measured with CCK-8 assays. Besides, only interference sequence of ARHGAP10 or LDOC1 treatment could increase the viability of MKN45 cells ( Figure 7M). Collectively, we further focus on ARHGAP10, a potential tumor suppressor in GC and direct downstream target of miR-3174.

ARHGAP10 Restores the Effects of miR-3174 on Apoptosis and ACD as well as CDDP Cytotoxicity in GC Cells
JC-1 staining revealed that ARHGAP10 overexpression decreased MMP levels compared with that in the control group, and, in MKN45 cells, reduced the MMP level that was upregulated by miR-3174 reconstitution ( Figures 8A, left panel, and S3A). In addition, ARHGAP10 repression not only increased MMP level but also restored the downregulation of MMP caused by miR-3174 inhibition in BGC823 cells ( Figures 8A, right panel, and S3B). Flow cytometry assay showed that upregulation of ARHGAP10 expression promoted apoptosis in MKN45 cells and reverted the apoptotic inhibition effect of miR-3174 ( Figures 8B and S3C). However, miR-3174 downregulation showed reverse findings in BGC823 cells ( Figures 8C and S4D). GFP-mRFP-LC3 detection and LC-3 turnover assay demonstrated that ARHGAP10 could antagonize effect of miR-3174 on autophagy in GC cells ( Figures 8D-8G, S3E, and S3F). mTOR signaling is one of the most common pathways that regulates autophagy, thus prompting us to examine this pathway through western blotting. The results showed that ARHGAP10 overexpression decreased intracellular levels of p-p70s6k, a result indicative of mTORC1 activity, although there were no significant differences in total mTORC1 or p-AKT. This finding suggested that ARHGAP10 reconstitution promoted autophagy by inhibiting mTORC1 activity. In addition, ARHGAP10 overexpression increased BECN1, Bax, and cleaved caspase-3 levels and decreased p62 and pan-caspase-3 (inactivated form) levels in MKN45 cells, results consistent with our previous findings (Figure 8H). p53 is an upstream mediator of Bax, and as expected, increased p53 levels were detected after ARHGAP10 reconstitution   Figure 8H). These results suggested that ARHGAP10 promotes apoptosis by inducing p53-mediated Bax trans-activation and were further validated in ANTI-NC-and ANTI-3174-transfected BGC823 cells with or without ARHGAP10 repression ( Figure 8I). CCK-8 assay demonstrated that ARHGAP10 reconstitution decreased cell viability and ameliorated the cell death defect caused by miR-3174 in MKN45 cells ( Figure 8K, left panel). These results were also verified in BGC823 cells with or without miR-3174 inhibition ( Figure 8L, left panel). Next, correlations between ARHGAP10 expression and the IC 50 values of various anti-cancer drugs in 28 GC cell lines in the GDSC database were calculated. ARHGAP10 expression was significantly negatively correlated with the IC 50 value of CDDP in all tested cell lines ( Figure 8J; Table S4; p < 0.01). Both the effects of ARHGAP10 on CDDP sensitivity and antagonizing CDDP resistance induced by miR-3174 were further determined in CDDPresistant or -sensitive cells. (Figures 8K and 8L, right panel). Collectively, these data show that miR-3174 inhibits mTORC1-mediated ACD and p53-regulated apoptosis as well as CDDP cytotoxicity through post-transcriptional suppression of ARHGAP10.

miR-3174 Regulates ARHGAP10 Expression Level to Moderate GC Progression In Vivo
To investigate the roles of miR-3174 and ARHGAP10 in vivo, we generated xenografts in nude mice (Figures 9A and 9D). Xenograft tumors formed from miR-3174-overexpressing MKN45 cells showed significantly faster growth ( Figure 9B), whereas miR-3174-suppressed BGC823 xenografts were smaller in volume than those generated from control cells ( Figure 9C). Furthermore, miR-3174 expression was negatively correlated with AHRGAP10 mRNA expression in each group of xenografts ( Figures S4A and S4B). Additionally, overexpression of ARHGAP10 in miR-3174-reconstituted MKN45 cells restrained the acceleration of tumor growth caused by miR-3174 (Figure 9E), and suppression of ARHGAP10 in miR-3174-downregulated BGC823 cells displayed reverse effects ( Figure 9F) (mRNA levels of ARHGAP10 were shown in Figure S4C). Immunohistochemistry (IHC) staining of xenografts revealed the negative relationship between protein levels of ARHGAP10 and miR-3174 expression (Figure 9G). The IHC scores for ARHGAP10 in these xenografts were also calculated and showed similar results ( Figure 9H). The protein levels of ARHGAP10 were further measured in 50 paired GC tissues by using tissue microarrays ( Figure 9I). The results demonstrated higher levels of ARHGAP10 in tumor tissues compared with paracarcinoma tissues ( Figure 9J). In addition, the percentages of ARHGAP10-positive cells in these tissues were clearly negatively correlated with the expression of miR-3174 ( Figure 9K). Collectively, these findings illustrate that miR-3174 is a tumor suppressor in GC that functions through inhibition of ARHGAP10 expression in vivo.

DISCUSSION
A single miRNA can influence multiple biological processes by simultaneously regulating several cellular signaling pathways, thus indicating the vast clinical potential of miRNA-based therapy. Using bioinformatics analysis, we identified eight differentially expressed miRNAs in GC. Through further validation in 150 paired GC clinical tissue samples, we determined that miR-3174 is significantly upregulated in GC and correlates with significantly decreased five-year survival. Stark et al. 32 first detected miR-3174 as an essential miRNA expressed in melanoma; however, the precise role of this miRNA was not previously reported, thus prompting us to investigate its function in GC. We specifically explored the effect of miR-3174 expression on the malignant progression of GC. However, it should be noted that miR-3174 may influence other cellular process or biological functions in GC beyond what is reported here, and additional studies into the function of this molecule should be performed.
Apoptosis (type I cell death), ACD (type II cell death), and necrosis (type III cell death) are all forms of PCD. It has been reported that ACD acts as a compensatory mechanism when apoptosis is inhibited. 33 However, cancer cells show severe defects in both type I and II cell death, and the mechanisms underlying these defects remain unclear. Herein, we determined that miR-3174 simultaneously inhibits apoptosis and ACD in GC and has a role in preventing the death of GC cells. In addition, we also demonstrated that specific targets of miR-3174 can reverse these effects.
Because of its GTPase-activating proteins (GAP) domain, ARHGAP10 shows potent Rho GTPase inhibition activity, in agreement with previous findings that ARHGAP10 promotes tumorigenesis in ovarian cancer by suppressing the activity of CDC42. 30 Additionally, ARHGAP10 also possesses an SH3 domain, and a close relationship between this domain and cancer progression has been reported. 34,35 Thus, beyond its effects in suppressing CDC42, we hypothesized that ARHGAP10 may regulate the expression of other genes and consequently prevent cancer. Interestingly, we found that ARHGAP10 promotes both cell apoptosis and ACD in GC. The ability of mTORC1, a nutrient/ energy/redox sensor, to repress autophagy by phosphorylating autophagy protein-13 (Atg13) has been well established. 36,37 Moreover, the AKT-mTOR signaling cascade is one of the best-defined cascades that are differentially expressed during GC tumorigenesis and progression. 38,39 In accordance with these relationships, we discovered that ARHGAP10 decreased the activity of mTORC1, but not AKT, and this decreased activity was accompanied by massive autophagy and ACD. In addition, we found that ARHGAP10 exerts mitochondria-dependent promotion of apoptosis in GC cells, which is potentially regulated by miR-3174. Wild-type of p53, one of the best-known tumor-suppressors, trans-activates several pro-apoptotic factors, such as Bax, thus inducing mitochondria-dependent apoptosis. 40 Besides, p53 translocates to the outer mitochondrial membrane, where it regulates additional downstream targets such as BCL-2, thereby promoting apoptosis. 41 As expected, elevated expression of both p53 and Bax was observed after ARHGAP10 overexpression. These data illustrate that ARHGAP10 accelerates mitochondriadependent apoptosis by enhancing the expression of p53 and subsequent Bax trans-activation in GC. Our results were consistent with those of another study reporting that DLC1, another member of the RhoGAP, promotes mitochondria-dependent apoptosis in nasopharyngeal carcinoma. 42 Despite these findings, the details of the functional role of ARHGAP10 in cancer remain unclear. Because of its complex structure, we suspect that ARHGAP10 may also be involved in other regulatory mechanisms in GC.
Because of the effect of ARHAGP10 on CDDP sensitivity in GC elucidated here, we examined the relationship between ARHGAP10 expression and the IC 50 values of all anti-cancer drugs in 28 different GC cell lines by using data from the GDSC database. 43 In addition to CDDP, we identified three anti-cancer drugs, Crizotinib, AG-014699, and QL-VIII-58, whose IC 50 values were also significantly negatively correlated with ARHGAP10 expression in GC cells. In contrast, the IC 50 values of TAE684, BMS-536924, BMS-509744, and A-443654 had a positive correlation with expression of ARHGAP10, thus suggesting that ARHGAP10 may contribute to GC cells developing resistance to these drugs. Collectively, these findings suggest that ARHGAP10 may be a predictive marker for GC treatment.
In conclusion, by performing bioinformatics analysis of TCGA as well as the GEO database and combining the results with the data obtained from GC tissue and cell experiments, we identified miR-3174, which showed significantly increased expression in GC cells and was correlated with poor prognosis. The oncogenic role of miR-3174 in GC is driven by the simultaneous suppression of p53/Bax-mediated mitochondria-dependent apoptosis and mTORC1-regulated ACD as well as attenuation of CDDP cytotoxicity. Further experiments including data from TCGA, TargetScan, and the GDSC database demonstrated that miR-3174 functions by directly targeting ARHGAP10 ( Figure 10). Collectively, these results may partially explain why GC cells show a death-resistant phenotype and further indicate that the miR-3174-ARHGAP10 axis may be an effective therapeutic target and predictive marker for GC.

Tissue Samples and Cell Culture
In total, 150 GC tissue samples were obtained from patients with stage I-III disease who underwent R0 resection at The First Affiliated Hospital of Nanjing Medical University. The specimen collection process followed HIPAA guidelines. Five-year survival analysis and univariate analysis of clinicopathological characteristics were performed in 140 patients who had the complete follow-up information and were divided into two groups by the median relative expression of miR-3174 (0.1297358).
To generate CDDP-resistant cell lines, MKN45 and BGC823 cells were cultured with gradually increasing doses of CDDP. The dose at which the cells became insensitive to CDDP was identified, and the cultures were maintained at that dose for another week. Then the cells were stimulated by the addition of 0.5 mM increments of CDDP until the concentration reached 10 mM. The cells were then maintained in culture with this concentration.

CCK-8 and Apoptosis Assays
For Cell Counting Kit-8 assays, approximately 1,000 GC cells were seeded per well into 96-well plates. After 48 hr of incubation, the cells were cultured with 10% CCK-8 (Dojindo, Kumamoto, Japan) reagent for another 2 hr, and the absorbance was detected using a microplate reader.
For flow cytometry, GC cells were plated into 6-well plates (2 Â 10 5 cells/well) and placed into an incubator for 48 hr. The cells were further incubated with Annexin V (3 mL) and propidium iodide (3 mL) for 15 min and examined using a flow cytometer (Gallios, Beckman, USA).
After transfection of GC cells with different lentiviruses or plasmids, the caspase-3 activity was detected by a Caspase 3 Activity Assay Kit (C1115, Beyotime, China) according to the manufacturer's protocol.

qRT-PCR
Total RNA was extracted from cell lines and frozen tissues with TRIzol reagent (15596018, Invitrogen, USA) according to the manufacturer's protocol. A New Poly(A) Tailing Kit (Thermo Fisher Scientific, China) was used to reverse transcribe miRNAs into cDNA. mRNA was reverse-transcribed into cDNA with a PrimeScript RT Master Mix Kit (TaKaRa, RR036A, Japan). cDNA was amplified with a 7500 Real-Time PCR System (7500, Applied Biosystems, USA) with Universal SYBR Green Master Mix (4913914001, Roche, Shanghai, China).

Western Blotting
Total protein was extracted from GC cells and tissues and then separated on 10% SDS polyacrylamide gels and transferred to polyvinylidene difluoride (PVDF) membranes. The membranes were blocked in 5% non-fat powdered milk for 2 hr. After being blocked, the membranes were incubated with primary antibodies in dilution buffer at 4 C overnight and then incubated with secondary antibodies for 2 hr at room temperature and developed with HRP substrate (WBKL0100, Millipore, USA). b-actin was used as an internal control.

AP Detection by Using GFP-mRFP-LC3
After transfection with different lentiviruses encoding miR-3174 or ARHGAP10-expression plasmids, GC cells were infected with lentivirus particles for expression of GFP-mRFP-LC3 (Genechem, Shanghai, China) and subjected to puromycin selection for another 2 months. For AP or AL detection, cells were plated into a 35-mm confocal culture dish. Hoechst 33342 staining was then used to determine nucleus; the location and quantity of APs in GC cells were detected by confocal microscopy (LSM5, Zeiss, Germany, 63Â oil-immersion objective) at 48 hr after planking (we detected cellular autophagic level in MKN45 and BGC823 cells at 0 hr, 24 hr, 48 hr, 72 hr, and 96 hr, respectively, and found that it is highest at 48 hr after GFP-mRFP-LC3 transfection). The quantification of puncta per cell was averaged in five fields. mRFP proteins are more resistant to the acidic environment in lysosomes than GFP proteins; thus, yellow puncta represent AP (Merge), and red puncta represent ALs (Figures 4A, 8D, and 8E).

TEM
In brief, cells were fixed with 2.5% glutaraldehyde overnight at 4 C after being treated with 1% OsO 4 and then were dehydrated with ethanol and embedded in Epon. The embedded materials were then sectioned; the sections were further stained with 0.3% lead citrate and imaged with an electron microscope (JEOL, Tokyo, Japan, 2,500Â or 8,800Â magnification).

Xenograft Model of GC and IHC
Five-week-old female BALB/c nude mice were obtained from the Laboratory Animal Centre of Nanjing Medical University and raised under pathogen-free conditions. In brief, 2 Â 10 6 GC cells in 100 mL of PBS were subcutaneously injected into the mice, and the resultant xenograft tumors were measured every 7 days and harvested 4 weeks later.
For IHC assays, sections of GC xenografts and tissue samples from patients were prepared according to a previously described protocol. 45 The sections were then observed with an IHC Imager (DM4000B, Leica, Germany). Cells positively stained with different types of antibodies were detected in an average of five fields per slice, and the following scores used to describe the overall proportion were measured: <10% = 0, 10%-25% = 1, 26%-50% = 2, 51%-75% = 3 and >75% = 4. For analysis of clinicopathological characteristics, the patients were divided into two groups on the basis of the median relative expression of miR-3174: a miR-3174-high group (expression > 0.1297358, n = 70) and a miR-3174-low group (expression < 0.1297358, n = 70).
Bioinformatics Analysis miRNA expression levels reported for 491 (446 GC patients and 45 healthy subjects) individuals included in TCGA database (https:// cancergenome.nih.gov/) were integrated by using Perl, and a matrix describing differentially expressed miRNA (fold change R2, p value < 0.05) was generated using the edgeR package (Bioconductor software) after Tpm correction and p value adjustment. A matrix of differentially expressed mRNA (fold change R2, p value < 0.05) was also similarly generated for a cohort of 407 patients (375 GC patients and 32 healthy controls).
To screen for differentially expressed miRNA in the GEO database, the GEO78091 dataset, which contains three GC tissues and paired adjacent normal tissues, was selected and analyzed using R with the limma package (Bioconductor software). Candidate mRNAs were selected with a standard method, and differentially expressed mRNAs were defined as having a fold change R2 and a p value < 0.05.
To identify potential genes influencing cisplatin sensitivity, we integrated the IC 50 statistics of multiple types of anti-cancer drugs in 28 different GC cell lines on the basis of a gene-expression matrix obtained from the GDSC (http://www.cancerrxgene.org) database by using Perl. Correlations between gene expression (number of genes, 17,419) and the IC 50 value of CDDP in 28 GC cell lines and correlations between ARHGAP10 expression and the IC 50 values of various types of anti-cancer drugs (number of drugs, 265) were assessed using Pearson correlation coefficient analysis in R. A Z test was used to calculate p values. Genes with R < 0 and a p value < 0.05 are shown as green puncta), and those with R > 0 and a p value < 0.05 are shown as red puncta ( Figure 6B). Drugs with R < 0 and a p value < 0.01 are shown in green font and as green puncta, and those with R > 0 and a p value < 0.01 are shown as red puncta ( Figure 8J).

Statistical Analysis
All experiments were replicated three times, and statistical analysis was performed with SPSS 19.0. All data are shown as the mean ± SEM. Unpaired Student's t tests were used to analyze the statistical significance of the differences between results. The relative expression of miRNAs or genes in tissue samples was calculated by using a Mann-Whitney U test and/or Wilcoxon signed-rank test. Overall survival rates were evaluated with Kaplan-Meier curves and log-rank tests. A p value < 0.05 was defined as statistically significant. were plated into a 35-mm confocal culture dish, and autophagic-associated puncta were measured in a manner similar to that described in Figure 4A (63Â objective magnification; scale bar, 20 mm) after 48 hr. (F and G) LC3-II protein levels were detected in MKN45 (F) and BGC823 (G) cells by western blotting with or without chloroquine (CQ, 10 mM for 2 hr) stimulation. (H and I) The protein levels of p-Akt, mTORC1, p70s6k, p-p70s6k, BECN1, p62, p53, Bax, pan-caspase-3 (inactivated form of caspase-3), and cleaved caspase-3 (c-caspase-3) in GC cells (H, MKN45; I, BGC823) were assessed with western blotting. (J) Correlations between ARHGAP10 expression and the IC 50 values for anti-cancer drugs in 28 GC cell lines obtained from the GDSC database were measured. Anti-cancer drugs (IC 50 values) that were significantly negatively correlated with ARHGAP10 expression are marked in green (p < 0.01), and those whose IC 50 values were significantly positively correlated with ARHGAP10 expression are marked in red (p < 0.01). (K and L) Left panels, cell viability was measured using CCK-8 assays in MKN45 (K) or BGC823 (L) cells with the treatments shown in the figure. Right panels, IC 50 values for CDDP were also calculated in MKN45 or BGC823 CDDP-sensitive cells (K) and their CDDP-resistant cells (L) after exposure to CDDP for 48 hr. b-actin was used as an internal control. Graph represents mean ± SEM; *p < 0.05, **p < 0.01, and ***p < 0.001.

CONFLICTS OF INTEREST
The authors declare that they have no competing interests.  Upregulation in GC Cells Contributes to Apoptosis and ACD Deficiency miR-3174 expression is upregulated in GC cells and leads to cell death defects by directly targeting ARHGAP10, which could not only promote mitochondriadependent apoptosis by enhancing p53 expression, followed by Bax trans-activation and caspase cleavage, but also facilitate autophagic cell death by suppressing mammalian target of rapamycin complex 1 (mTOC1) activity. Table S3. Potential genes associated with GC cells CDDP sensitivity or resistance on GDSC. The correlations between gene expressions with IC50 values of CDDP in 28 kinds of GC cells based on GDSC database were measured using Pearson correlation coefficient analysis in R language library followed by Z-test to acquire P-value. Genes meeting the following requirements were showed: P-value＜0.05. "1005" represents Cisplatin (CDDP) on GDSC. (the table was supplied separately as an Excel file: Supplemental Table S3)   Table S4. Candidate drugs whose IC50 in GC cells were related to ARHGAP10 expression in GDSC. The correlations between IC50 values of all types of anti-cancer drugs with ARHGAP10 expressions in 28 kinds of GC cells based on GDSC database were calculated using Pearson correlation coefficient analysis in R language library followed by Z-test to acquire P-value. Genes meeting the following requirements were showed: P-value＜0.05. "1005" represents Cisplatin (CDDP) on GDSC.