You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Gastric cancer (GC) is the fourth most common cancer worldwide, with a total of 989,600 new cases and 738,000 deaths occurred in 2008[@b1]. It is well accepted that gastric carcinogenesis is a multistep and multifactorial process involving genetic and epigenetic alterations in oncogenes and tumor suppressor genes[@b2][@b3].

miRNAs, an important component of epigenetic mechanisms, comprise species of short noncoding RNA and post-transcriptionally modulate gene expression by negatively regulating the stability or translational efficiency of their target mRNAs[@b4]. miRNAs play a crucial role in a broad range of physiological and pathological process, and their aberrant expression has been observed in a variety of diseases, including neurodegenerative disease[@b5], diabetes[@b6] and tumors[@b7]. Increasing studies suggest miRNAs are promising biomarkers for cancer diagnosis, prognosis and therapeutic targets[@b7][@b8].

Our previous genome-wide miRNA microarray study has identified 12 miRNAs including miR-107 significantly dysregulated in GC[@b9]. miR-107 has been reported to play a vital role in cell division, metabolism, stress response, and angiogenesis. Dysregulated of miR-107 is involved in multiple tumors development and progression[@b10]. miR-107 functions as a tumor suppressor by inducing cell cycle arrest in lung cancer and glioma via down-regulating the expression of CDK6[@b11][@b12]. However, miR-107 serves as oncogene for promoting tumor invasion and metastasis in breast and gastric cancer through targeting DICER1[@b13][@b14]. The role of miR-107 in tumors is contradictory and needed to be further addressed. In this study, we sought to determine the role of miR-107 underlying the pathogenesis of GC.

Methods and Materials
=====================

Cell lines and reagents
-----------------------

Human GC cell lines SGC7901 and MGC803 were purchased from the Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences (Shanghai, China). Cells were cultured in DMEM/10% fetal bovine serum and grown at 37 °C and 5% CO~2~.

Small interfering RNA
---------------------

miR-107 inhibitor and negative control (designated si-107 and NC, respectively) were chemically synthesized by GenePharma Tech (Shanghai, China). The sequences of si-107 and NC were 5′-UGAUAGCCCUGUACAAUGCUGCU-3′ and 5′-CAGUAC UUUUGUGUAGUACA-3′, respectively; all the bases were 2′-OMe modified. Small interfering RNA (siRNA) specific for NF1 (sc-36036) and non-specific control siRNA (sc-37007) were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). In all functional analysis, NC and si-107 were used at a concentration of 50 nmol/L, while si-NF1 and its control at 10 nmol/L.

Plasmids construction, transient transfection and luciferase assay
------------------------------------------------------------------

The NF1 Wild-type reporter plasmid was constructed by cloning a 490-bp DNA fragment of *NF1* 3′-untranslated region (UTR), which is containing the two predicted miR-107 target sites, into the *Hpa*I site of the pGL3-promoter vector (Promega, WI, USA; [Fig. 1C](#f1){ref-type="fig"}, upper panel). Based on the Wild plasmid, the Mut-1, Mut-2 and Mut-both mutant constructs were made by removing the first, second and both predicted miR-107 target sites of the *NF1* 3′-UTR fragment, respectively.

The plasmids or in combination with siRNAs were transiently transfected into cells using Lipofectamine 2000 (Invitrogen, CA, USA) following the manufacturer's protocol. As an internal standard, all plasmids were cotransfected with 10 ng pRL-SV40, which contained the Renilla luciferase gene. The pGL3-promoter vector without an insert was used as a negative control. After transfection at 48 h, luciferase activity in lysates was measured with a Dual-Luciferase Reporter Assay System (Promega, WI, USA). The reporter assay was performed with three biological replicates and three technical replicates.

Real-time PCR analysis
----------------------

Total RNA was isolated from cell lysates according to the instructions provided by the manufacturer of TRIZOL (Invitrogen, CA, USA). Reverse transcription was performed using the TaqMan MicroRNA Reverse Transcription Kit (ABI, CA, USA). The expression level of miR-107 was assessed using the specific TaqMan MicroRNA Assay kit (ABI, CA, USA), and normalized to U6. To determine the expression levels of *NF1* mRNA, the cDNA was amplified by real-time PCR with SYBR Green RT-PCR kit (Takara, Japan). The expression of *GAPDH* was used as an internal control. The following primers were used for amplification: 5′- CGAATGGCACCGAGTCTT AC-3′ (F) and 5′-GACCAGTTGGACGAGCCC -3′ (R) for *NF1*; 5′- GCACCGTCA AGGCTGAGAAC -3′ (F) and 5′- TGGTGAAGACGCCAGTGGA -3′ (R) for *GAPDH*. Real-time PCR was performed in triplicate on ABI 7900HT Real-Time PCR System (ABI, CA, USA). Relative expression was calculated using the comparative Ct method. The Real-time PCR assay was performed with three biological replicates and three technical replicates.

Western blotting
----------------

The total cell lysates were prepared with a detergent lysis buffer (50 mM Tris, pH 7.4; 150 mM NaCl; 1% NP-40; 0.5% sodium deoxycholate; 0.1% SDS; 1 mM PMSF; and 1% phosphatase inhibitor cocktail (P2850, Sigma-Aldrich, MO, USA). Western blots were performed as previously reported[@b15] using antibodies of NF1 (sc-67, Santa Cruz, CA, USA); α-tubulin, p-ERK1/2, and ERK1/2 (Cat. \#2125, 9101, 9102, respectively; Cell Signaling, MA, USA).

Cell growth assay
-----------------

The transfected MGC803 cells were seeded in 96-well plates. Cell culture was continued for 24 h, 36 h and 48 h and subsequently incubated with MTT reagent (5 mg/ml) at 37 °C for 4 h. MTT assay was performed as described elsewhere[@b16].

Wound healing assay
-------------------

The transfected MGC803 cells (5 × 10^5^) were cultured in 6-well plates to monolayer. The cells were then starved in serum-free medium for 12 h before a wound approximately 2 mm in width was made with a cell scraper. The wound was allowed to heal for 3 d in a fresh medium containing 1% fetal bovine serum. The wounded monolayer was photographed at the indicated day using a fluorescent microscopy (IX70, Olympus, Japan) with a 10 × objective. Wound closure was measured as a percentage of original wound width.

Transwell assay
---------------

Transwell assay was performed using 12-well Transwell chambers (Corning Costar, Cambridge, MA, USA) with a pore size of 8 μm. For Transwell migration, Cells (1 × 10^5^) were seeded in serum-free medium in the upper chamber and incubated at 37 °C for 8 h. Afterward, the cells remained in the upper chamber were carefully removed with a cotton swab, whereas the cells having traversed to reverse face of the membrane were fixed in methanol, stained with crystal violet (0.04% in water), and counted. Transwell invasion assay was done under the same conditions as the Transwell migration assays, but in Matrigel-coated transwells (BD Biosciences, MA, USA) and incubation for 24 h.

Statistical analysis
--------------------

Quantified data are presented as mean ± SEM. The difference between two independent means was assessed by *t*-test. All *P*-values are two-sided, and *P* \< 0.05 was considered to statistically significant. Statistical analyses were carried out using SAS software (V.9.1.3; SAS Institute, Cary, NC, USA) and R software (V.2.15.0; The R Foundation for Statistical Computing).

Results
=======

miR-107 is upregulated in GC tissues
------------------------------------

We detected the expression of miR-107 in 55 paired of cancer and normal tissues. The expression level of miR-107 in cancer tissues was significantly increased compared with normal tissues (*P* = 0.0003, [Fig. 1A](#f1){ref-type="fig"}). The same result was found in TCGA ([Fig. 1B](#f1){ref-type="fig"}). As shown in [Table 1](#t1){ref-type="table"}, the aberrant miR-107 expression in GC tissues was associated with tumor sizes and depth of invasion.

NF1 is a target of miR-107
--------------------------

miRNA carries out its biological function via regulating the expression of its target genes through base-pairing with endogenous mRNAs. As an initial step to identify putative miR-107 targets, four commonly used algorithms (i.e., TargetScan, PicTar, Microcosm Targets v5, and miRanda) were applied to predict miR-107 target genes; and finally, there were 22 genes predicted by all four algorithms ([Table 2](#t2){ref-type="table"}). Among these 22 predicted target genes, *NF1* (Neurofibromin 1) stood out for the presence of two evolutionarily conserved binding sites, suggesting collaborative binding and biologically effective interaction ([Fig. 2A](#f2){ref-type="fig"}).

NF1 is a tumor suppressor, and loss of NF1 expression has been linked to tumor development and progression[@b17][@b18]. To confirm that NF1 is a target of miR-107 in GC, the endogenous NF1 levels were measured in MGC803 cells at 48 h after miR-107 knockdown. The results showed that NF1 mRNA expression was significantly increased after miR-107 knockdown, compared with the negative control ([Fig. 2B](#f2){ref-type="fig"}). Cichowski *et al.*[@b19] reported that NF1 degradation could be rapidly triggered in response to growth factors and re-elevated shortly after growth factor treatment. Replicate growth factor treatment of MGC803 cells with 10% goat serum revealed that NF1 was rapidly degraded within 5 min and quickly re-elevated in the NC processing group, whereas evident degradation of NF1 was seen in 10 min in the si-107 treated group, possibly attributing to a rise in the expression of NF1 induced by miR-107 knockdown ([Fig. 2C](#f2){ref-type="fig"}).

miR-107 downregulates NF1 expression by directly targeting its 3′-UTR
---------------------------------------------------------------------

To establish a direct interaction between miR-107 and the 3′-UTR of *NF1*, we cloned the *NF1* 3′-UTR portion containing the two miR-107 target sites into a firefly luciferase reporter construct, designated as Wild-type Reporter ([Fig. 3A](#f3){ref-type="fig"}), and used it for transient transfection into MGC803 cells. A significant reduction (87%) in the luciferase activity of the Wild-type Reporter was observed compared with the pGL3-promoter reporter ([Fig. 3B](#f3){ref-type="fig"}). To evaluate whether the reduction of luciferase activity was associated with miR-107 targeting, the Wild-type Reporter was co-transfected with si-107 (miR-107 inhibitor) or NC (negative control) into the SGC7901 and MGC803 cells. The results demonstrated a significant increase in luciferase activity in both cell lines (1.48-fold for SGC7901 and 1.71-fold for MGC803) treated with si-107 compared with their NC-treated counterparts ([Fig. 3C](#f3){ref-type="fig"}), indicating a direct interaction between miR-107 and the *NF1* 3′-UTR. As mentioned above, there are two potential miR-107 target sites within the *NF1* 3′-UTR. In order to identify the *bona fide* miR-107 target site, we mutated the first, second and both predicted target sites of the Wild-type Reporter, designated as Mut-1, Mut-2 and Mut-both reporter. Likewise, the three mutated reporters were co-transfected with si-107 or NC into MGC803 and SGC7901 cells, respectively, while the Wild-type Reporter was used as a positive control. The results revealed that in both cell lines, the luciferase activities were only increased in the cells co-transfected with the Wild and Mut-2 reporters after miR-107 knockdown compared with their NC-treated counterparts ([Fig. 3D](#f3){ref-type="fig"}), indicating that the first predicted target site is the authentic miR-107 target site.

Regulation by miR-107 results in *NF1* mRNA degradation
-------------------------------------------------------

*NF1* 3′-UTR sequence analysis revealed that there is an AU-rich element (ARE element, ATTTA) adjacent to the second predicted miR-107 target site ([Fig. 4A](#f4){ref-type="fig"}). It was reported that miRNA could regulate the mRNA stability through binding to the ARE element in the 3′-UTR of mRNA[@b20]. In order to determine whether the increased expression of *NF1* mRNA was a consequence of the enhanced mRNA stability after miR-107 knockdown, we measured the mRNA stability of *NF1*. As shown in [Fig. 4B](#f4){ref-type="fig"}, the remaining *NF1* mRNA expression was more in the si-107 treated group than in NC-treated group (1.08 ± 0.11 vs. 0.66 ± 0.09, *P* \< 0.05) after ActD treatment for 2 h, implying that si-107 treatment significantly increased the stability of *NF1* mRNA in contrast with the negative control (NC) and miR-107 may have an effect on *NF1* mRNA decay and subsequent protein expression.

miR-107 regulates cell proliferation, migration and invasion by targeting NF1
-----------------------------------------------------------------------------

It has reported that NF1 acts as a tumor suppressor to arrest cell growth, migration, and invasion[@b18][@b21]. We next examined the role of the interaction between miR-107 and NF1 on cell growth, migration, and invasion of GC cells. Apart from si-107 and its negative control, the MGC803 cells were further treated with si-NF1 ([Fig. 5A](#f5){ref-type="fig"}, left panel) to evaluate whether the biological effect of miR-107 is through directly targeting NF1. The cell growth assay indicated that the cells treated with si-107 illustrated a moderate reduction in cell growth (25%) compared with its negative control ([Fig. 5A](#f5){ref-type="fig"}, right panel). Furthermore, the cells with miR-107 knockdown displayed a significant reduction in cell migration using wound healing (46%, [Fig. 5B](#f5){ref-type="fig"}) and Transwell migration (44%; [Fig. 5C](#f5){ref-type="fig"}, panel a) assays. A strong decrease in invasive ability of the cells transfected with si-107 was also observed by Transwell invasion assay (70%; [Fig. 5C](#f5){ref-type="fig"}, panel b). It was noteworthy that si-NF1 could reverse inhibition of cell proliferation ([Fig. 4A](#f4){ref-type="fig"}, right panel), migration and invasion ([Fig. 5](#f5){ref-type="fig"}, panel a and b, respectively) by miR-107 knockdown, indicating that miR-107 exerted its function by directly targeting NF1.

Discussion
==========

Recent study indicates that the role of miR-107 in tumor development and progression is contradictory and differs in a context-dependent manner. miR-107 could function as tumor suppressor gene by inducing cell cycle arrest in lung cancer and glioma[@b11][@b12], whereas serve as oncogene for promoting tumor invasion and metastasis in breast and gastric cancer[@b13][@b14].

In GC, Li *et al.*[@b14] found that miR-107 was upregulated in GC and promoted tumor invasion and metastasis by negatively regulating DICER1. Song *et al.*[@b22] showed that miR-107 was capable of advancing proliferation of GC cells by targeting CDK8. However, Feng *et al.*[@b23] reported an opposite role of miR-107 that it was down-regulated in GC and acted as a tumor suppressor to oppose proliferation and invasion of GC cells. In our present study, functional analyses revealed that miR-107 stimulated cell growth, migration, and invasion, in agreement with Li *et al.*[@b14] and Song *et al.*[@b22], indicating that miR-107 could function as onco-miRNA in GC through multiple targets, including Dicer, CDK8, and NF1. The biological processes and molecular mechanisms underlying miR-107 still remain unclear in GC, and further functional studies are warranted to address these unsolved issues.

NF1 is a GTPase which converts active Ras-GTP to its inactive form, thereby negatively regulating Ras signaling[@b24]. Loss of NF1 expression by mutation[@b25], copy number alteration[@b26], or miRNA regulation[@b18] can result in constitutive activation of Ras, which can mediate signal transduction via multiple pathways, including Ras/Raf/MEK/ERK pathway, leading to various cancer phenotypes like decreased apoptosis and increased proliferation and migration[@b27]. It is biologically plausible that the observed miR-107 phenotype in GC may be attributable to ERK activation induced by decreased NF1 expression. Recently, Lenarduzzi *et al.*[@b18] reported that miR-193b enhanced tumor progression via down regulation of NF1, which in turn leading to activation of ERK, resulting in proliferation, migration, invasion, and tumor formation.

miRNAs exert their function by repressing translation and/or triggering degradation of mRNA targets[@b28]. It is reported that miRNA is involved in the ARE-mediated mRNA instability[@b20]. By bioinformatics analysis, we identified an ARE element adjacent to the second predicted miR-107 target site in *NF1* 3′-UTR. Our results showed that *NF1* mRNA stability was enhanced after miR-107 knockdown. We proposed that miR-107 was involved in the ARE-mediated mRNA instability. Further studies on the role of direct interaction between miR-107 and ARE element in the regulation of *NF1* mRNA stability are warranted. There is a predicted HuR binding site adjacent to the second predicted miR-107 target site in *NF1* 3′-UTR ([Fig. 3A](#f3){ref-type="fig"}). Haeussler *et al.*[@b29] found that mRNA binding protein HuR could interact with ARE element in the 3′-UTR of *NF1*, thereby negatively regulating the expression of mRNA on the posttranscriptional level. Therefore, the role of HuR in the regulation of *NF1* mRNA stability by miR-107 also merits further investigation.

Some limitations in this study should be addressed. First, the function of miR-107 was evaluated through miR-107 knockdown in a loss-of-function model. Gain-of-function studies via overexpression of miR-107 in GC cell lines are needed to verify our findings. Second, future *in vivo* studies are needed to validate the role of miR-107 in tumor development and progression. It is reported by Li *et al.* that silencing the expression of miR-107 could suppress the migration and invasion of GC cell in nude mice[@b30]. We propose that the miR-107 may play an important role *in vivo* in GC carcinogenesis through multiple targets including NF1.

In summary, our data demonstrated that miR-107 targeted NF1, and suppression of miR-107 enhanced proliferation, migration and invasion of GC, whereas repression of NF1 promoted these phenotypes. We propose that miR-107 may serve as a useful therapeutic strategy for advanced GC.

Additional Information
======================

**How to cite this article**: Wang, S. *et al.* miR-107 regulates tumor progression by targeting NF1 in gastric cancer. *Sci. Rep.* **6**, 36531; doi: 10.1038/srep36531 (2016).

This study was partly supported by National Natural Science Foundation of China (81230068, 81373091, 81302502, 81302490, and 81473049), Natural Science Foundation of Jiangsu Province (BK2012842, BK20130641 and BK2011194), the Key Program for Basic Research of Jiangsu Provincial Department of Education (12KJA330002), Specialized Research Fund for the Doctoral Program of Higher education of China (20130092120063), Jiangsu Provincial Science and Technology Innovation Team, Collaborative Innovation Center For Cancer Personalized Medicine and the Priority Academic Program Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).

**Author Contributions** Z.Z. and M.W. conceived and designed the experiments. S.W., G.M., H.Z., and N.T. performed the experiments. C.L., D.W., and H.C. analyzed the data. F.Q., W.G., Q.Z., G.T. and J.Z. contributed reagents/materials/analysis tools. S.W wrote the paper.

![The difference of miR-107 expression between the gastric carcinoma and paired corresponding non-cancerous tissues.\
(**A**) The expression of miR-107 in 55 paired of gastric carcinoma and corresponding normal tissues. (**B**) The expression of miR-107 in TCGA (n = 422).](srep36531-f1){#f1}

![miR-107 targets NF1.\
(**A**) Schematic diagram of the *NF*1 3′-UTR containing two putative miR-107 binding sites. Below, sequence of mature miR-107 aligned to the two binding sites, showing evolutionary conservation in the seed-pairing sequence between human and mouse. (**B**) Down-regulation of miR-107 by si-107 resulted in an increased NF1 expression in MGC803 cells. (**C**) Down-regulation of miR-107 by si-107 resulted in an increased NF1 expression in MGC803 cells. MGC803 cells pre-treated with NC or si-107 for 48 h were serum starved for 12 h, and exposed to 10% serum for increasing lengths of time (0--30 min), and thereafter the expression of NF1 was evaluated. a-tubulin was used as internal control. \**P* \< 0.05.](srep36531-f2){#f2}

![miR-107 negatively regulates NF1 expression by directly targeting its 3′-UTR.\
(**A**) Partial sequences of *NF1* 3′-UTR containing wild or mutated putative miR-107 target sites were fused to the firefly luciferase coding sequence. Bars, predicted miR-107 seed-pairing sequences. (**B**) The pGL3-NF1 3′-UTR reporter (Wild) and pGL3-promoter plasmids were transfected into MGC803 cells respectively to compare their luciferase activity. (**C**) The Wild plasmid was co-transfected into MGC803 cells with NC or si-107. (**D**) The Wild and three mutated plasmids (Mut-1, Mut-2 and Mut-both) were respectively co-transfected into SGC7901 and MGC803 cells with NC or si-107. Luciferase activity was normalized to a simultaneously transfected Renilla expression plasmid. \**P* \< 0.05, \*\**P* \< 0.01 and \*\*\**P* \< 0.001.](srep36531-f3){#f3}

![miR-107 negatively regulates NF1 expression by degrading its mRNA stability.\
(**A**) The predicted ARE element and HuR binding site within the *NF1* 3′-UTR. (**B**) mRNA stability of NF1 in MGC803 cells treated with NC or si-107was measured by real-time PCR.](srep36531-f4){#f4}

![miR-107 knockdown inhibits cell growth, migration and invasion through NF1 upregulation.\
(**A**) MGC803 cells were transfected with NC or si-107, or si-107 in combination with si-NF1. At 48 h post-transfection, cells were seeded in 96-well plates and their growth was evaluated by MTT assay at indicated time points (right panel). Left panel, the interference effect of si-NF1 on NF1 expression. (**B**) MGC803 cells pre-treated with si-107 or NC for 48 h migrated into the wounded area. Left panel shows representative images; right panel shows the quantification of migration rate. (**C**) Transwell migration (a) and invasion (b) assays were performed in MGC803 cells pre-treated with NC, si-107, or si-107 in combination with si-NF1 for 48 h. Means ± SEM from three independent experiments. \**P* \< 0.05, \*\**P* \< 0.01, and \*\*\**P* \< 0.001.](srep36531-f5){#f5}

###### The relationship between miR-107 expression and clinicopathological feature of 55 GC patients.

  Clinicopathological variables    Number of each group   MiR-107 expression   *P* value  
  ------------------------------- ---------------------- -------------------- ----------- -----------
  Age(years)                                                                              
   \<60                                     18                    8               10         0.465
   ≥60                                      37                    15              22            
  Sex                                                                                     
   Male                                     42                    20              22         0.695
   Female                                   13                    7                6            
  Tumor size                                                                              
   ≤5 cm                                    32                    12              20       **0.015**
   \>5 cm                                   23                    15               8            
  Tumor site                                                                              
   Cardia                                   25                    12              13         0.694
   Non-cardia                               30                    16              14            
  Histological type                                                                       
   Diffuse                                  33                    15              18         0.509
   Intestinal                               22                    12              10            
  Depth of invasion                                                                       
   T1+T2                                    15                    4               11       **0.018**
   T3+T4                                    40                    25              15            
  Lymph nodedistant metastasis                                                            
   N0+N1                                    18                    11               7         0.783
   N2+N3                                    37                    24              13            
  Distant metastasis                                                                      
   M0                                       43                    22              21         0.659
   M1                                       12                    7                5            
  TNM                                                                                     
   I+II                                     16                    10               6         0.379
   III+IV                                   39                    29              10            

###### Computational prediction of miR-107 targets.

  Genes       TargetScan5.1   Microcosm Target V5   PicTar[a](#t2-fn1){ref-type="fn"}   miRanda[b](#t2-fn2){ref-type="fn"}
  ---------- --------------- --------------------- ----------------------------------- ------------------------------------
  NF1               3                 54                           442                                 735
  UPF2             24                 460                          655                                 358
  CACNA2D1         72                 506                          742                                 289
  ZBTB10           141                64                           365                                  98
  BAZ2A            148                21                           432                                  21
  HTR4             149                373                          111                                 130
  KIAA1033         158                559                          508                                  24
  KIF23            173                995                          732                                 959
  TLK1             186                884                          193                                  38
  TGFBR3           219                18                           603                                 165
  LRP1B            250                110                          589                                 553
  C20orf39         253                137                          600                                 984
  SH3GL2           254                 8                           475                                 134
  RNF125           261                71                           748                                 271
  WNT3A            269                337                          319                                 935
  DLL1             285                 4                           445                                 394
  RGS4             290                414                          252                                 216
  SYT6             291                474                          524                                 693
  OGT              294               1026                           2                                  915
  MTMR4            302                920                          200                                 227
  VAMP8            357                49                           516                                 839
  CCNE1            420                 2                           324                                 1441

The figure in the table indicated the rank order of each gene in the respectivemiRNA targets prediction software.

^a^Last updated March 2007.

^b^Last updated June 2005.

[^1]: These authors contributed equally to this work.