tRF-Val-CAC-016 modulates the transduction of CACNA1d-mediated MAPK signaling pathways to suppress the proliferation of gastric carcinoma

As a new kind of non-coding RNAs (ncRNAs), tRNA derivatives play an important role in gastric carcinoma (GC). Nevertheless, the underlying mechanism tRNA derivatives were involved in was rarely illustrated. We screened out the tRNA derivative, tRF-Val-CAC-016, based on the tsRNA sequencing and demonstrated the effect tRF-Val-CAC-016 exerted on GC proliferation in vitro and in vivo. We applied Dual-luciferase reporter assay, RIP assay, and bioinformatic analysis to discover the downstream target of tRF-Val-CAC-016. Then CACNA1d was selected, and the oncogenic characteristics were verified. Subsequently, we detected the possible regulation of the canonical MAPK signaling pathway to further explore the downstream mechanism of tRF-Val-CAC-016. As a result, we found that tRF-Val-CAC-016 was low-expressed in GC, and upregulation of tRF-Val-CAC-016 could significantly suppress the proliferation of GC cell lines. Meanwhile, tRF-Val-CAC-016 regulated the canonical MAPK signaling pathway by targeting CACNA1d. tRF-Val-CAC-016 modulates the transduction of CACNA1d-mediated MAPK signaling pathways to suppress the proliferation of gastric carcinoma. This study discussed the function and mechanism of tRF-Val-CAC-016 in GC for the first time. The pioneering work has contributed to our present understanding of tRNA derivative, which might provide an alternative mean for the targeted therapy of GC. AKy7v1UFnnMKc3sU1zEgwP Video abstract Video abstract


Background
More than 70% of new cases of gastric cancer (GC) occurred in developing countries, and about 50% of GC cases occurred in eastern Asia, mainly in China. The number of incidences and deaths of gastric cancer in China accounted for 42.6% and 45.0% of the global statistical data, respectively, ranking 5th in morbidity and 6th in mortality among 183 countries globally [1]. Therefore, it is essential to explore the treatment approaches for GC. tsRNAs, as a new kind of discovered ncRNAs, have attracted increasing attention in cancer research. tsR-NAs were classified into tRFs and tiRNAs based on the Open Access cleavage sites of tRNAs by the ribonucleases such as Dicer and RNase Z [2,3]. Meanwhile, with the illustration for the fundamental structures of tRFs and tiRNAs, it aroused great interest of many researchers in the aspect of biological characteristics and potential therapeutic strategies of tsRNAs in several kinds of carcinomas [4][5][6][7][8]. tsRNAs have been demonstrated as mRNA regulators by binding to the 3′UTR of specific mRNAs, similar to miRNAs [9].
CACNA1d has been verified as an oncogene in several articles [10][11][12]. It was involved in the MAPK signaling pathways to regulate the proliferation of certain carcinomas [13]. As one of the calcium voltage-gated channel subunits, CACNA1d also played a role in the modulation of calcium ion concentration inside and outside the cells [14]. Furthermore, the MAPK signaling pathways was widely accepted as the canonical pathway to regulate the progression of GC in many types of studies [15][16][17][18][19]. The bioinformatic analysis indicated that CACNA1d was located in the cell membrane and participated in the MAPK signaling pathways, consistent with a few previous studies [13,20].
Concerning the present study, we screened out the tRF-Val-CAC-016 as the research target, and it was significantly down-regulated in GC tissues. Furthermore, we found MAPK signaling pathways and CACNA1d were regulated by tRF-Val-CAC-016. Therefore, we hypothesize that tRF-Val-CAC-016 modulates the transduction of CACNA1d-mediated MAPK signaling pathways to suppress the proliferation of gastric carcinoma.

Clinical samples
All GC samples were collected in Jiangsu Cancer Hospital with informed consents from these patients. Meanwhile, this study was appraised and authorized by the ethics committee of Jiangsu Cancer Hospital (NYDLS-2019-919). In this study, we classifed and analyzed the collected GC samples from 40 patients according to the data such as age, gender, tumor size, histology, and stage. We grouped the age and tumor size with median and mean, respectively. The eighth edition American Joint Committee on Cancer (AJCC) was applied to implement the classification of TNM stage. Details were presented in Table 1. All samples were snap-frozen in the refrigerator at − 80 °C, and the Declaration of Helsinki was obeyed.

tRFs and tiRNAs sequencing profiles
The purity and concentration of total RNA samples were determined with NanoDrop ND-1000. Total RNA samples are pretreated to remove some RNA modifications that interfere with small RNA-seq library construction. Subsequently, total RNA of each sample was sequentially ligated to 3′ and 5′ small RNA adapters. cDNA was then synthesized and amplified using Illumina's proprietary RT primers and amplification primers. Subsequently, ~ 134-160 bp PCR amplified fragments were extracted and purified from the PAGE gel. Finally, the completed libraries were quantified by Agilent 2100 Bioanalyzer. The libraries were denatured and diluted and were then loaded onto reagent cartridge and forwarded to sequencing run on Illumina NextSeq 500 system using NextSeq 500/550 V2 kit (#FC-404-2005, Illumina), according to the manufacturer's instructions. We then screened out the tRFs and tiRNAs based on the fold-change (log 2 FC ≥ 1 or log 2 FC ≤ − 1) of expression data and p-value (p < 0.05) of differentially expressed tsRNAs. Hierarchical clustering, Volcano plots, and Correlation Analysis were then used to present the essential data. GO and KEGG analysis was then conducted to sieve the significant target genes and signaling pathways. Subsequently, we applied the Gene Set Enrichment Analysis (GSEA) further to illustrate the potential characteristics of selected target genes.
In addition, to comprehensively verify the expression levels and prognostic data of selected genes, we downloaded the GC-related data in TCGA (TCGA-STAD) and GEO databases (GSE65801). Finally, we imported all bioinformatic data into R software to select and verify the target genes.

Cell culture and transfection
We

Cell proliferation assays
In the CCK-8 assay, 100 μL cell suspension containing 1000 cells was seeded in each well of 96-well plates, then 10 μL CCK-8 reagent (Dojindo, Laboratories, Japan) was added into each well and incubated for two hours. We then measured the OD value in 450 nm by the microplate reader at 0, 24, 48, 72, and 96 h. In the cell colony formation assay, 2 ml cell suspension containing 4000 processed cells were seeded into each well of 6-well plates. After the incubation for 10-14 days, the cells were fixed with 4% paraformaldehyde for 20 min at room temperature (RT) and were then stained with 1% crystal violet. Images were captured with a microscope, and the number of colonies was counted with Image J software.

Cell cycle determination
We harvested the transfected cells and resuspended them in 70% ethanol. Afterward, cells were centrifuged, and the cell pellets were resuspended in PI-solution to conduct the flow cytometry analysis.

EdU assay
Ethynyl-2′-deoxyuridine (EdU) assay was undertaken with the Cell-Light EdU Apollo In Vitro Kit (Ribobio, Guangzhou, China). Each well of 96-well plates was seeded with 4 × 10 3 -1.0 × 10 5 cells, and these GC cells were then incubated in the EdU solution (50 μM) for two hours. Subsequently, the cell fixation was performed with 4% paraformaldehyde, and cells were then washed with 50 μL glycine (2 mg/mL). Analogously, we added 100 μL penetrant (0.5% TritonX-100) into each well to incubate these cells for 10 min. Next, we pipetted 1 × Apollo reagent (100 μL) into each well to initiate the staining reaction. Nuclei were stained with DAPI or 1 × Hoe-chst33342, and images were captured with a fluorescence microscope.

Dual-luciferase reporter assay
GC cells were co-transfected with plasmids WT-CACNA1d-3′UTR or MUT-CACNA1d-3′UTR, and tRF-Val-CAC-016 mimics or mimic control with lipofectamine2000 (Invitrogen, USA). We detected the luciferase activities of firefly and renilla with the Dual-Luciferase Reporter Assay System (Promega, Madison, USA). To begin with, we obtained the cell lysates using the Passive Lysis Buffer. After preparing Luciferase Assay Reagent II (LAR II) and Stop & Glo Reagent, we carefully transferred 20 µl of cell lysate into the luminometer tube containing LAR II to record the firefly luciferase activity measurement. Then we added 100 µl of Stop & Glo Reagent to record the renilla luciferase activity measurement.

RNA-binding protein immunoprecipitation (RIP)
We purchased the Magna RIP ™ RNA-Binding Protein Immunoprecipitation Kit (Merck, Darmstadt, Germany). First, we lysed GC cells in RIP lysis buffer (0.5 M Tris-HCl, pH 7.4, 1.5 M NaCl, 2.5% deoxycholic acid, 10% NP-40, 10 mM EDTA) and dispensed 200 μL each of the lysates into nuclease-free microcentrifuge tubes and stored them at − 80 °C. Second, we incubated the washed beads with Argonaute-2 antibody to produce the beads-antibody complex and used the Normal Rabbit IgG antibody as the negative control. Third, 100 µL supernatant of the RIP lysate was removed and added to each beads-antibody complex in RIP Immunoprecipitation Buffer (10 µL of the supernatant was stored at − 80 °C as Input). All the tubes were incubated and rotated overnight at 4 °C. Finally, RNA purification and RT-PCR were manipulated to test the binding tRF-Val-CAC-016.

Immunohistochemistry (IHC) and immunofluorescence staining
Tissue sections were deparaffinized and dehydrated, and then the tissues were blocked with serum and placed in the 37 °C incubator for half an hour. Next, We added the primary antibodies to the slides and incubated them overnight in the 4 °C incubator. Analogously, we then added the secondary antibodies to the sections and set them at 37 °C for half an hour. Afterward, we stained these tissue sections with DAB substrate and hematoxylin successively and dehydrated them with ethanol. Images were captured with a microscope. In terms of immunofluorescence staining, cells were rinsed and rewarmed with PBS for 20 min. We then applied 0.5% TritonX-100 to permeabilize the sections at RT for 20 min. Subsequently, serum blocking and antibody incubation were performed successively. Nuclei were stained with DAPI. We collected images using a fluorescence microscope.

Subcutaneous xenograft experiments
Eighteen five-week-old Balb/c female nude mice were purchased from the Shanghai Experimental Animal Center of the Chinese Academic of Sciences (Shanghai, China) and were divided into tRF-Val-CAC-016 agomir group, tRF-Val-CAC-016 negative control (NC) group, and normal saline (NS) group randomly and evenly. These reagents were purchased from Ribobio (Ribobio, Guangzhou, China). Approximately 1.2 × 10 6 processed NCI-N87 cells were then subcutaneously injected into the right flanks of these mice. Tumor sizes and weights were measured every 3 days. After 39 days of rearing, all mice were sacrificed, and all tumors were resected. Tumor volumes were calculated with this formula: 1/2 × (length × width 2 ). All animal experiments complied with the National Research Council's Guide for the Care and Use of Laboratory Animals. The care of animals was in accordance with the institution guidelines of Jiangsu Cancer Hospital.

Immunoblotting
Gel-electrophoresis (10% SDS-PAGE) for extracted proteins was conducted, and the proteins were then transferred to a polyvinylidene difluoride membrane. Next, we blocked the membranes with 1 × Tris-buffered saline (TBS) containing 0.1% Tween 20 and 5% BSA or 5% skim milk for one hour at RT. Then, we incubated the membranes with specific primary antibodies overnight at 4 °C and the secondary antibodies for two hours at RT. After three washes, the membranes were visualized with the chemiluminescence (ECL) kit (Millipore, Bedford, MA, USA). Basic information on the antibodies used in this research is presented in Additional file 1: Table S1.

Statistical Analysis
We utilized GraphPad Prism Version 4 program (Graph-Pad Software Inc., San Diego, CA) and SPSS 19.0 (IBM, Chicago, USA) to deal with the data. All numeric values were presented as the mean ± SD. The significant data was determined using ANOVA followed by Dunnett's test or Student's unpaired t-test. P < 0.05 was taken as statistically significant.

tRFs and tiRNAs sequencing profiles of GC tissues
We performed tsRNAs sequencing to distinguish the differentially expressed tsRNAs (DETs) and not differentially expressed tsRNAs (NDETs), and tRF-Val-CAC-016 was finally selected based on the profiles. Subsequently, we applied the hierarchical clustering to classify the DETs, a total of 69 up-regulated and 42 down-regulated DETs were presented with hierarchical clustering heatmap (Fig. 1a). We used the volcano plot to exhibit the most discrepant DETs, which indicated that five downregulated and six up-regulated tsRNAs were presented in the plot based on the foldchange (FC) of the profiles (log 2 FC ≥ 3 or log 2 FC ≤ -3, p < 0.05) (Fig. 1b). Obviously, we selected tRF-Val-CAC-016 for further research considering the feasibility and statistical significance. Meanwhile, we accomplished the heatmap of the correlation coefficient to elaborate on the similarity of the GC samples using the R gplots package (Fig. 1c). The distribution and frequency of tsRNA subtypes were presented

tRF-Val-CAC-016 was significantly low-expressed in GC tissues
We applied Gel-electrophoresis to verify the PCR product of tRF-Val-CAC-016, demonstrate the feasibility of PCR primers, and verify the authenticity of tsRNA sequencing (Fig. 2a). As shown in Fig. 2b, the length of tRF-Val-CAC-016 ranged between 50 and 100 bp, and we undertook the Sanger sequencing to verify the results. Then Fluorescent In Situ Hybridization (FISH) assay indicated that tRF-Val-CAC-016 is located in both nuclei and cytoplasm, but mainly in the cytoplasm (Fig. 2c). Afterward, the expression level of tRF-Val-CAC-016 was tested in GC cell lines, NCI-N87 and HGC-27 were finally selected (Fig. 2d). Finally, to confirm the efficiency of tRF-Val-CAC-016 mimics, we conducted the transfection, and the result was in line with our expectations (Fig. 2e). Analogously, the low expression level was confirmed in 40 pairs of GC tissues (Fig. 2f ). Meanwhile, the expression of tRF-Val-CAC-016 was significantly associated with tumor size and histology in the aspect of clinicopathological features (Table 1). Furthermore, the prognostic outcome of tRF-Val-CAC-016 was calculated after the follow-up for the patients after gastrectomies, but the result was not significant (Fig. 2g). The sequences of tRF-Val-CAC-016 and related primers are listed in Additional file 3: Table S2.

tRF-Val-CAC-016 suppressed the proliferation of GC
As shown in Fig. 3a-b, tRF-Val-CAC-016 significantly suppressed the proliferation of GC cells in CCK-8 assays, achieving a similar performance of oxaliplatin. Besides, tRF-Val-CAC-016 inhibitor promoted the proliferation of GC compared with the inhibitor control. The ethynyl-2′-deoxyuridine (EdU) assays demonstrated that tRF-Val-CAC-016 could suppress the cell replication activity of GC, but tRF-Val-CAC-016 inhibitor enhanced the replication activity (Fig. 3c-d). Meanwhile, we found that tRF-Val-CAC-016 could regulate the checkpoints of the cell cycle in GC. As presented in Fig. 3e-g, oxaliplatin mainly handled the G1 phase in GC. However, tRF-Val-CAC-016 adjusted the S phase significantly both in NCI-N87 and HGC-27. In colony formation assays, tRF-Val-CAC-016 could inhibit the viability of GC cells, slightly weaker than oxaliplatin. tRF-Val-CAC-016 inhibitor enhanced the ability of colony formation in GC cells (Fig. 3h-i).
Consistently, these phenomena were rigorously explained in the immunoblotting assays, as indicated in Fig. 3j-k, tRF-Val-CAC-016 mimics obviously declined the protein expression of CyclinD1, CyclinB, c-myc. tRF-Val-CAC-016 inhibitor increased the protein expression of CyclinD1, CyclinB, c-myc. On the other hand, oxaliplatin decreased the expression of CyclinD1, c-myc compared with the control.

Bioinformatics analysis
We applied miRanda and TargetScan databases to predict the target genes of tsRNAs. Based on the matching of mRNA and tsRNAs expression profile data, some biological site-sequence features of significance and scoring models with relative conservation were uncovered. Parameters such as structure score, energy and con-text_plus_score were used to optimize the selection of the target genes of tsRNAs [21,22]. Then target genes of the down-regulated or the up-regulated tsRNAs were then enriched in the GO and KEGG analysis. In the down-regulated group, GO analysis was presented in Fig. 4a-c, and we found that the MAPK signaling pathways were enriched significantly (Fig. 4d). In the upregulated group, GO analysis was shown in Fig. 4e-g, and we found that the Wnt signaling pathway was quite prominent (Fig. 4h). We then compared the bioinformatics data in the present study with GEO (Additional file 4: Fig. S2) and TCGA databases (Additional file 5: Fig. S3), which suggested that proliferation-related pathways were frequently enriched (Additional file 4: Fig. S2f and Additional file 5: Fig. S3f ), and the Calcium signaling pathway was uncovered (Additional file 5: Fig. S3i), consistent with the function of CACNA1d.

CACNA1d was verified up-regulated in GC tissues and was selected as the potential target of tRF-Val-CAC-016
We introduced the TCGA and GEO databases to predict the possible target to conduct further research. As shown in Fig. 5a, the Venn diagram took the overlap of MAPK components and the predicted target genes of tRF-Val-CAC-016. Then we analyzed the GEO database (GSE65801) and TCGA-STAD database and found that CACNA1d, PLA2G4A and TNF in GSE65801 (Fig. 5b), CACNA1d and PLA2G4A in TCGA-STAD were significantly up-regulated (Fig. 5c). Furthermore, we then applied the Kaplan-Meier plotter website and discovered that CACNA1d, TNF, TGFBR1, PDGFC, GADD45B were significantly and oppositely related to the prognosis of GC (Fig. 5d-l). Analysis above reminded us the vital role of CACNA1d as the possible downstream target of tRF-Val-CAC-016. Subsequently, we obtained the tissue microarray (TMA) with 90 pairs of GC specimens, including detailed follow-up data (Fig. 5m). IHC results were presented in Fig. 5n. Through the analysis of the follow-up data, we found that the expression of CAC-NA1d was not significantly related to the prognosis of (See figure on next page.) GC (p = 0.1805) (Fig. 5o). The representative IHC images of GC and NATs are shown in Fig. 5p. However, the protein levels of CACNA1d in GC tissues were up-regulated compared with corresponding NATs (Fig. 5q). Hence, we selected CACNA1d as the target gene based on the intersection of the comprehensive analysis. To confirm the expression and function of CACNA1d, we purchased siR-NAs for CACNA1d and selected si-CACNA1d-1 as the better inhibitory effect compared with si-CACNA1d-2 and si-CACNA1d-3 (Fig. 5r). On the contrary, pcDNA-CACNA1d could significantly enhance the expression of CACNA1d (Fig. 5s). Moreover, the tRF-Val-CAC-016 inhibitor was able to reverse the suppressive function of si-CACNA1d on GC cells to some extent (Fig. 5t).
Analogously, the effect of pcDNA-CACNA1d on GC was partly relieved by tRF-Val-CAC-016 mimics (Fig. 5u). The sequences of CACNA1d primers and siRNAs are listed in Additional file 3: Table S2.

tRF-Val-CAC-016 was immunoprecipitated by Argonaute-2 and could modulate the proliferation of GC by targeting CACNA1d
The sequencing profile has elucidated the possible binding relation between tRF-Val-CAC-016 and CACNA1d mRNA (Fig. 6a), and the tRF-Val-CAC-016 mimics could significantly reduce the expression of CACNA1d in RT-PCR (Fig. 6b). The protein level of CACNA1d was decreased by tRF-Val-CAC-016 mimics and promoted by tRF-Val-CAC-016 inhibitor (Fig. 6c). Subsequently, we found that WT-CACNA1d-3′UTR plus tRF-Val-CAC-016 mimics group could significantly reduce the luciferase ratio compared with other groups in the Dual-luciferase reporter assay (Fig. 6d). Then we introduced RIP (RNA-binding protein immunoprecipitation) assay and found that tRF-Val-CAC-016 was significantly immunoprecipitated by Argonaute-2 compared with the IgG group (Fig. 6e). Gel-electrophoresis further verified the PCR product of RIP assays (Fig. 6f ). Furthermore, the immunoblotting confirmed the integrity of the process to wash the magnetic beads (Fig. 6g).

CACNA1d strengthened the proliferation of GC and was modulated by tRF-Val-CAC-016
Rescue assays were undertaken to elucidate the connection between tRF-Val-CAC-016 and CACNA1d further. In the CCK-8 assays, si-CACNA1d could significantly decline the proliferation of GC cells, which was partially recovered by tRF-Val-CAC-016 inhibitor ( Fig. 7a-b). In the EdU assays, the tRF-Val-CAC-016 inhibitor could rescue the inhibitory effect of si-CAC-NA1d in terms of cell replication activity (Fig. 7c-d).

Inhibitor of MAPK signaling pathway (p38 MAPK-IN) significantly reversed the enhancement of tRF-Val-CAC-016 inhibitor on GC proliferation
To demonstrate the role of the MAPK signaling pathway, we took p38 MAPK-IN to rescue the enhancement of tRF-Val-CAC-016 inhibitor on GC proliferation. It indicated that p38 MAPK-IN was able to block the pathway conduction partly as expected, which was manifested in the aspect of the deterioration to the influence of tRF-Val-CAC-016 inhibitor on GC. As shown in inhibitor promoted the cell replication activity, and p38 MAPK-IN could also decline this activity (Fig. 9c-d).
The results of a considerable amount of studies were consistent with our present study regarding the role of tsRNAs as a regulator in the aspect of tumor progression [7,[31][32][33][34][35][36][37][38][39]. Farina et al. found that RUNX1 could maintain the mammary epithelium by repressing ts-112 [40]. Kim et al. discovered that LeuCAG3′tsRNA might maintain ribosomal protein S28 (RPS28) levels after translation initiation to explain why some mRNA levels do not necessarily correlate with protein levels [41]. Wang et al. identified the novel tsRNA-06018 as a critical regulator in hMSC adipogenesis [42]. Generally speaking, a considerable amount of studies mainly focused on the post-transcriptional regulation to influence the translation of specific proteins. However, many researchers also found the critical function of tsRNAs as the biomarkers in diagnosing diseases [5,43,44]. Meanwhile, many tsRNAs were found related to several signaling pathways to facilitate the modulation of certain tumors' progression. For instance, Wang et al. reported that tiRNA Tyr−GTA could modulate the peroxisome proliferator-activated receptor signaling pathway [45]. Furthermore, tRF/miR-1280 was discovered to maintain the function of cancer stem cell-like cells of CRC by inhibiting the Notch signaling pathway [26]. Balatti et al. applied CRISPR technology to generate ts-101 and ts-46 KO stable cell lines from HEK293 cells, then Affymetrix gene-expression profiling was performed to screen the differentially expressed genes. Finally, they discovered that tsRNAs could regulate the chromatin configuration and the epigenetic control of gene expression. In addition, several signaling pathways related to cell transformation and cancer development, such as PDGF signaling, mTOR signaling, and PTEN signaling, were also found to be modulated [46]. Our study demonstrated the critical role of tRF-Val-CAC-016 in the activation of the MAPK signaling pathway, which was also noticeable in the field of tsRNAs research.
In terms of the present research, we selected tRF-Val-CAC-016 as the research target. Interestingly, tRF-Val-CAC-016 was significantly low-expressed in GC cell lines (NCI-N87 and HGC-27) and tissues compared with GES-1 and NATs, respectively. However, the expression levels of GC and NATs were not grouped consistently as the heterogeneity of tumors. The expression of tumors may be closely related to the differentiation, histology, location, and even sampling methods. Besides, individual differences also need to be fully recognized.
Therefore, a slight inconsistency in expression levels is acceptable. Subsequently, by the overexpression of tRF-Val-CAC-016, we observed that tRF-Val-CAC-016 could suppress the proliferation of GC cells. Through the RIP assay and Dual-luciferase reporter assay, we rigorously confirmed the bioinformatic results concerning the binding relation between tRF-Val-CAC-016 and CAC-NA1d. As CACNA1d is a member of the MAPK signaling pathway, we assumed that tRF-Val-CAC-016 might modulate the GC proliferation via tRF-Val-CAC-016/ CACNA1d/ MAPK signaling pathways axis. Through the rescue assays, we confirmed the regulative relationships among tRF-Val-CAC-016, CACNA1d, and MAPK signaling pathway. We conducted the subcutaneous xenograft experiments to verify the function of tRF-Val-CAC-016 in vivo further. Subsequently, the results of immunoblotting assays related to the MAPK signaling pathway were consistent with our hypothesis that tRF-Val-CAC-016 modulates the canonical MAPK signaling pathways by targeting CACNA1d to influence the proliferation of gastric carcinoma. Hence, these findings uncovered a remarkable role of tRF-Val-CAC-016 as a tumor inhibitor in GC.
This study preliminarily explored the mechanism that tRF-Val-CAC-016 participated in to regulate the proliferation of GC. However, there still exist a few deficiencies in this research. For instance, the detailed mechanism of tRNAs splicing to produce tsRNAs was not investigated by us thoroughly and the regulatory network of the significant tsRNAs is not analyzed comprehensively. The crosstalking among the tsRNAs needs to be discovered in further research.

Conclusions
In summary, this research illustrated the function of tRF-Val-CAC-016 in GC for the first time. The discussion for the mechanism tRF-Val-CAC-016 was involved in was relatively comprehensive to some extent. This study showed that tRF-Val-CAC-016 was down-regulated significantly in GC, and the expression was related to the histology and tumor size in terms of the clinicopathological analysis. Besides, tRF-Val-CAC-016 modulates the transduction of CACNA1d-mediated MAPK signaling pathways to suppress the proliferation of gastric carcinoma. Taken together, tRF-Val-CAC-016 could be a potential diagnostic biomarker and a new therapeutic target for GC.