The tsRNAs (tRFdb-3013a and tRFdb-3013b) Serve as Novel Biomarkers for Colon Adenocarcinomas

Background: Postoperative ileus is one of the most common complications after diverting loop ileostomy closure. Some reports have investigated the risk factors for postoperative complications or ileus after ileostomy closure; however, these studies did not evaluate the index surgery suciently. In this study, we evaluated the risk factors, including the details of the index surgery, for ileus after diverting ileostomy closure. Methods: This was a retrospective study of patients who underwent ileostomy closure following index surgery for rectal cancer. Patients who developed postoperative ileus (POI (+)) and patients who did not (POI (−)) after ileostomy closure were compared. Results: Fifty-nine patients were evaluated and were divided into two groups: POI (+) (n=9) and POI (−) (n=50), and the groups were compared. There were no signicant differences in the details of the index surgery, operative procedure, transanal total mesorectal excision, lateral lymph node dissection, operating time, or blood loss. The incidence of Clavien–Dindo grade ≥ III complications after the index surgery was signicantly higher in the POI (+) group. Conclusions: The incidence of Clavien–Dindo grade ≥ III complications after the index surgery may increase the risk of postoperative ileus after ileostomy closure. expression levels of ST3GAL1 protein and mRNA in colon adenocarcinoma cells. These results suggest tRFdb-3013a and tRFdb-3013b might directly target the 3′UTR of ST3GAL1 and regulate ST3GAL1 expression in colon adenocarcinomas. tRF:tRNA-derived NGS:next generation sequencing, TCGA:the Cancer Genome Atlas, GEO:Gene Expression Omnibus, DEG:differentially expressed tsRNA genes, logFC:log2 fold-change, GO:gene ontology, GSEA:gene set enrichment analysis, qRT-PCR:quantitative real-time polymerase chain reaction, CCK-8:Cell Counting Kit-8; ST3GAL1:ST3 beta-galactoside alpha-2,3-sialyltransferase 1; CRC:colorectal cancer; 3′UTR:3' untranslated region.


Introduction
The tsRNAs (tRNA-derived small RNAs) are novel class of small non-coding RNAs derived from transfer-RNAs (tRNAs), and that also known as tRNA-derived fragments (tRFs) [1]. Identi cation and characterization of tsRNAs are accelerated greatly in the last few years, due in large part to the emergence of next generation sequencing (NGS) systems-level approaches to molecular genetics [2]. The tsRNA fragments are produced through cleavage of mature or premature tRNAs at various positions, the fragments are short and range in length from 16 to 35 nucleotides [3]. A general classi cation of tsRNAs is based on their position in relation to the parental tRNA sequence, which can be classi ed into several distinct categories, including 5′-tRFs, 3′-tRFs, and internal tRFs, these tsRNAs originate precisely from the span of mature tRNAs [3,4]. Two remaining tsRNAs: 5′U-tRFs, which derived from the 5′ leader sequence of primary tRNA genes; and tRF-1 that comprise part of the 3′ trailer sequence [3]. Numerous researches suggest that the production of tRNAs into speci c small fragments is a regimented process, do not result from degradation [5]. These cleavages of tRNAs are context-dependent, including sex, ethnicity, disease, and tissue type of an individual. The dependencies increase an urgency to understand the regulatory roles of tsRNAs. Such efforts are gaining momentum, and comprise experimental and computational approaches [2,6].
Analysis of the datasets in the Cancer Genome Atlas (TCGA) have discriminated more than thousand microRNA isoforms [7], it is worth mentioning that many of the tsRNAs are sequenced at an abundance comparable to that of many abundant miRNAs. Subsequent efforts attempted to classify and tabulate the tsRNAs that were being discovered in some higher organisms into a curated repository, and structured the tRFdb database [8]. Several studies have linked the tsRNAs to different human cancers, including colorectal [9], liver, and breast cancer [10], etc. Growing evidences demonstrated that many tsRNAs were differentially expressed in the tissues of multiple cancer patients, these dysregulated tsRNA fragments might serve as potential candidates for diagnostic and prognostic biomarkers, and even contribute to various biological processes of cancer development and progression [11]. Colon adenocarcinoma (COAD) are the most common type of primary tumor originating in the intestinal tract [12]. According to cancer statistics, mortality due to colon adenocarcinomas is the highest among gastrointestinal tumors [13]. The pathogenesis of colon adenocarcinomas is extremely complicated, which involves the aberrant activation of proto-oncogenes and inactivation of anti-oncogenes [12,14]. However, it is not clear about whether the tsRNAs would serve as novel molecular biomarkers for diagnosis, prognosis and target therapy of colon adenocarcinomas.
In the present study, we aimed to identify the tsRNAs pro les in the sncRNA-seq datasets of human colon adenocarcinomas samples via a deterministic and exhaustive tsRNAs mining pipeline, then investigate the expression pattern and biological function of tRFdb-3013a and tRFdb-3013b within colon adenocarcinomas. Additionally, the relationship between tRFdb-3013a/b and its target genes are also studied in order to reveal the underlying mechanisms.

Identi cation pipeline
The data processing and ow chart for tsRNAs identi cation are shown in Fig. 1A. a conservative pipeline was implemented to get an accurate estimation of tsRNAs expression as described previously [15]. The adaptor trimming and quality ltering of raw FASTQ les on sncRNA-seq data were pre-processed via using Trim Galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), which is a wrapper tool for Cutadapt and FastQC method. Then, clean reads of small RNA sequencing were re-mapped to the reference human genome (GRCh37 /hg19) and the sequences of our tsRNAs annotation le by applying TopHat2 tool [19]. After alignment, only the mapped reads could be quanti ed to count the number of reads belonging to each of the candidate tsRNAs with HTSeq software [20], other ambiguous reads were removed for a more accurate and conservative analysis. Finally, the expression value of tsRNAs were calculated and normalized as reads per million (RPM) of total raw counts [21], and the tsRNAs with average expression level less than one log2 RPM were ltered to eliminate random degradation sequences. Data analysis was performed with R statistical software (https://www.r-project.org/).

Bioinformatic analysis and visualization
The RNA expression pro les of TCGA datasets were downloaded and prepared using TCGAbiolinks package of Bioconductor R [22]. The differential expression analysis between colon adenocarcinoma samples and non-tumor controls was performed with the Limma package [23]. An absolute log2 foldchange (|logFC|) more than one and P-value less than 0.05 were set as cut-off point. Hierarchical clustering of differentially expressed tsRNA genes was performed based on gene expression values of each sample to verifying the difference between colon adenocarcinomas and non-tumors [24]. Visualization of the identi ed tsRNAs including volcano plots and heat-map was performed with the ggplot2 [25] and pheatmap packages of R (https://cran.r-project.org/web/packages/pheatmap/), respectively. Survival analysis with Kaplan-Meier curves plus the log-rank test were performed and visualized via the R survival and survminer package [26]. Hierarchical cluster analysis was performed on tsRNAs expression and primary pathology characteristics, its corresponding heat-map was visualized by pheatmap package. Pearson correlation was estimated for each pair of tsRNA and candidate mRNA, its scatter diagram was plotted by ggplot2 and ggpubr package in R software [25]. Enrichment analyses were performed to provide biological insights concerning tRFdb-3013a/b-related genes, the molecular signatures database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/) were used as functional gene sets for gene set enrichment analysis (GSEA) [27]. Gene ontology (GO), KEGG pathway, and GSEA analysis were performed and visualized via clusterPro ler and enrichplot package (https://yulabsmu.github.io/clusterPro ler-book/) [28]. The computational binding interactions between tsRNA and target genes were predicted via tRFtarget database with RNAhybrid and IntaRNA tools [29]. The interaction relationships of top 50 targets and tRFdb-3013a/b was organized as a network using Cytoscape (v3.7.2).

RNA extraction and qRT-PCR analysis
Total RNA was extracted from tissues or cultured cells using the TRIzol reagent (Invitrogen, USA). One microgram total RNA of each sample was reverse transcribed into cDNA under standard conditions by using PrimeScript RT reagent Kit with gDNA Eraser (Takara, Japan). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed with the SYBR® Premix DimerEraser™ (Takara, Japan) on the LightCycler® 480 system (Roche Diagnostics, Switzerland). The Bulge-Loop miRNA qRT-PCR Stater Kit (Ribobio, China) with speci c stem-loop RT primers was used to quantify the expression of tsRNAs, following the manufacturer's protocol. RNU6 (U6) was used as the internal control for tsRNAs template normalization and ACTB (β-actin) for mRNA template normalization. Relative quanti cation of gene expression was calculated by the comparative cycle-threshold (CT) (2-ΔΔCT) method [24]. Stem-loop primers for tRFdb-3013a, tRFdb-3013b, and RNU6 were designed and synthesized by Ribobio Biotech (Guangzhou, China). The general primers for ST3GAL1 and ACTB were designed and synthesized by Sangon Biotech (Shanghai, China).

Protein extraction and western blot
Total protein was extracted from cultured cells using RIPA buffer (Beyotime, China) and the protein concentration was determined using a BCA Protein Assay Kit (Bio-Rad). The speci c primary antibody (anti-ST3GAL1 antibody diluted at 1:500, Santa Cruz, USA; anti-β-actin antibody diluted at 1:1000, Sigma-Aldrich, USA). The β-actin served as an endogenous protein for normalization. The protein bands were visualized using ChemiDocTM XRS + imaging system and Image Lab software (Bio-Rad, California, USA).

Cell proliferation, migration and invasion assays
Cell proliferation assays were performed with Cell Counting Kit-8 (CCK-8; Dojindo Laboratories, Japan), the absorbance (A) in each well was measured at 450 nm with a Microplate Reader (BioTek, VT, USA). Cell migration and invasion ability were tested via the scratch and Transwell assays. The images of scratch gap and invasion cells were recorded and photographed using a Leica microscope (Heidelberg, Germany).

Statistical analysis
The statistical analyses were performed with the R software (https://www.r-project.org/), GraphPad Prism version 8.0 (GraphPad Software, Inc., USA) was used for graphing and analysis. Data were exhibited as means ± standard deviation (SD). The χ2 test was used to compare the categorical variables. Regarding the numerical variables, statistical signi cance of differences between two groups was assessed using two-sided Student's t test; and comparisons of multiple groups were made by one-way analysis of variance (ANOVA). All experiments were performed in triplicate and P values less than 0.05 were considered a statistically signi cant difference.

Results
The identi cation of tsRNAs in colon adenocarcinomas datasets.
With computational approaches, we determined lots of tsRNAs and assessed their expression pro le within the sncRNA-seq (small non-coding RNA sequence) data of TCGA-COAD dataset, the summary of our identi cation pipeline is shown in Fig. 1A. After ltering, a total of 192 tsRNAs with available expression abundance were identi ed within COAD samples (Table S1), of which about thirty-two percent derived from the 3′ trailer (tRF-1) regions of primary tRNA genes, and thirty percent tsRNAs from the 3′ end (3'-tRFs) of mature tRNA (Fig. 1C). As for the chromosomes, more than thirty-ve percent of tsRNAs are originated from the human chr1 region, and nearly twenty percent originated from the chr6 region ( Fig. 1B).
Base on the differential expression analysis, we discriminated a total of forty-two differentially expressed tsRNA genes (DEGs), of which fteen were down-regulated and twenty-seven were up-regulated ( Fig. 2A) between colon adenocarcinomas and non-tumor controls. Hierarchical cluster analysis and heat-map of differentially expressed tsRNA genes were performed and exhibited in Fig. 2B. The expression patterns of tsRNAs were signi cantly distinguished between colon cancers and non-tumor groups. As shown in the blue dotted line frame of Fig. 2B, we identi ed several tsRNAs (including ts-43, ts-44, tRFdb-3013a and tRFdb-3013b) that derived from tRNA-His-GUG, which is a histidyl-transfer-RNA [30].
Signi cance of tRFdb-3013a and tRFdb-3013b within colon adenocarcinomas Among the identi ed tsRNA fragments, we are interested in the fragments derived from tRNA-His-GUG or tRNA-His-GTG gene. As shown in Fig. 3A, we have identi ed seven tsRNA fragments in the sncRNA-seq datasets of colon adenocarcinomas samples, there are two 3'-tRFs fragments (tRFdb-3013a and tRFdb-3013b) can re-map to the cleavage of 3′ end of several different mature tRNA-His-GUG ( Fig. 3A and 4A), and ve tRF-1 fragments (ts-30, ts-43, ts-44, ts-1 and ts-88) that can re-mapping to the down-stream regions (35 bases) of primary tRNA-His-GTG genes. To reveal the implication of these tsRNAs in colon adenocarcinomas, the expression pattens of above tsRNAs were analyzed in the samples of TCGA-COAD datasets. It showed that the expressions of four tsRNAs (ts-43, ts-44, tRFdb-3013a and tRFdb-3013b) were signi cantly decreased in colon adenocarcinoma samples compared to paired non-tumor controls ( Fig. 3 all P < 0.05), however, no signi cant difference for other tsRNAs was observed between colon adenocarcinomas and controls (both P > 0.05).
Next, we investigated the relationship between tsRNAs expression and overall survival of colon adenocarcinoma samples using Kaplan-Meier curve analysis with a log-rank comparison. Base on the median value of tsRNAs expression, colon adenocarcinoma samples were divided into low expression group and high expression group. As shown in Fig. 4B, the clinical survival of colon adenocarcinoma patients with low tRFdb-3013a expression was signi cantly worse than that of the high expression patients (P = 0.029); the survival of patients with low tRFdb-3013b expression was also worse than that of high expression patients (P = 0.026). These results suggested that down-regulated tRFdb-3013a and tRFdb-3013b (known simply as tRFdb-3013a/b) were associated with poor survival prognosis of colon adenocarcinoma patients.
The primary clinical and molecular pathology characteristics of colon adenocarcinoma patients were listed in Table S4. The correlations of tRFdb-3013a/b expression with pathology characteristics were analyzed using Chi-square test, and that were visualized by Hierarchical clustering heatmap. As shown in Fig. 5A-5B, the expression of tRFdb-3013a and tRFdb-3013b was signi cantly down-regulated in colon adenocarcinoma samples with four stages (both P < 0.05) compared to non-tumor controls. Moreover, tRFdb-3013a/b may tend to down-regulated in the patients with lymphatic or vascular invasion present (Fig. 5A). However, no signi cant relationship was found between tRFdb-3013a/b expression and other pathology characteristics including gender, grade, MLH1 silencing, methylation subtype, colon polyps and MSI status, etc. (all P > 0.05). These ndings implied that tRFdb-3013a and tRFdb-3013b may serve as novel biomarkers for diagnosis and prognosis of colon adenocarcinomas.

Biological insights of tRFdb-3013a/b on colon adenocarcinoma cells.
To further explore the effects of tRFdb-3013a/b on colon adenocarcinomas. The mimic or inhibitor of tRFdb-3013a/b was used to over-expressed or inhibit the tRFdb-3013a/b in cells. Cell proliferation of colon adenocarcinomas was determined by CCK-8 assays, it showed that cell proliferation was signi cantly suppressed in tRFdb-3013a mimic transfected cells compared to negative controls (both P < 0.05; Fig. 6A). Transwell and scratch assays were performed to test the effect of tRFdb-3013a on invasion and migration ability of colon adenocarcinoma cells. The results displayed that migratory rate of cells transfected with tRFdb-3013a mimic was signi cantly reduced in comparison with negative controls (Fig. 6B); the numbers of invasive cells with tRFdb-3013a mimic were signi cantly reduced and that cells with tRFdb-3013a inhibitor were increased compared with those of negative controls (Fig. 6C). These indicated that tRFdb-3013a may be effective to regulate the cell proliferation, cell migration and invasion of colon adenocarcinomas.
Moreover, some enrichment analyses were performed to provide biological insights concerning the role of tRFdb-3013a/b. TCGA-COAD mRNA expression pro ling data were used to nd some correlated genes of tRFdb-3013a/b via Spearman's correlation method. As shown in Fig. 7A, for gene ontology (GO) analysis, some tRFdb-3013a-related genes were enriched in the speci c GO terms, including extracellular matrix organization, collagen-containing extracellular matrix, and extracellular matrix structural constituent (EMILIN1, ADAT2, FBLN1). The dot plots (Fig. 7B) showed that tRFdb-3013a-related genes were enriched in several KEGG pathway, such as phagosome (C1R). In the GSEA analyses (Fig. 7C), a molecular signature (LMTK3 target genes) was found to associated with tRFdb-3013a, such as FBLN1 and ILVBL-AS1 are both the tRFdb-3013a correlated-genes. The enrichment analyses of tRFdb-3013b were also conducted and presented in supplemental material ( Figure S1). These data suggested that tRFdb-3013a/b may play an important role in tumor progression of colon adenocarcinomas.
The tRFdb-3013a/b target and regulate ST3GAL1 expression in colon adenocarcinomas.
By using bioinformatics database and tools, many potential targets of tRFdb-3013a/b were predicted and screened out. The interaction relationship of tRFdb-3013a/b and their target genes were presented in Fig. 8A. Among these targets, we picked out a molecular with strong binding score, ST3GAL1, which is a type membrane protein with sialyl-transferase activity [31]. The positions in 3' untranslated region (3′UTR) of ST3GAL1 mRNA contained a putative binding site of tRFdb-3013a/b (Fig. 8B). Dual-luciferase reporter assay was performed to validate their direct binding a nity, it showed that relative luciferase activity was reduced in the co-transfection of pmirRB-ST3GAL1 wild-type reporter and tRFdb-3013a/b mimic (both P < 0.05, Fig. 9B-left), but no signi cant difference in that mutant reporter. Moreover, the analysis of TCGA-COAD and GSE39582 datasets showed that ST3GAL1 was highly expressed in colon adenocarcinomas compare to non-tumor controls (Fig. 9A), as was mentioned above, tRFdb-3013a and tRFdb-3013b were decreased in colon adenocarcinomas (Fig. 5B). In addition, the cell experiments demonstrated that expression levels of ST3GAL1 mRNA and protein were down-regulated in colon adenocarcinoma cells transfected with tRFdb-3013a/b mimic (both P < 0.01, Fig. 9B-right and 9C). These results suggested tRFdb-3013a and tRFdb-3013b might directly target ST3GAL1 and regulate ST3GAL1 expression in colon adenocarcinomas.

Discussion
The tsRNAs are novel class of small non-coding RNAs derived from transfer-RNAs (tRNAs), also known as tRNA-derived fragments (tRFs) [1]. Identi cation and characterization of tsRNAs are accelerated greatly in the last few years, due in large part to the emergence of next generation sequencing (NGS) systems-level approaches to molecular genetics [2]. In the present study, we rstly identi ed some tsRNA fragments within the sncRNA-seq datasets of colon adenocarcinoma samples, most of tsRNAs were derived from the 3′ end (3'-tRFs) of mature tRNA or 3′ trailer (tRF-1) regions of primary tRNA genes. Furthermore, a total of forty-two differentially expressed tsRNA genes were discriminated by bioinformatics analysis, of which fteen were down-regulated and twenty-seven were up-regulated between colon adenocarcinomas and non-tumor controls. Among them, we picked out seven tsRNA fragments derived from tRNA-His-GUG, which is a histidyl-transfer-RNA [30]. There are two 3'-tRFs fragments (tRFdb-3013a, tRFdb-3013b) can re-map to the cleavage of 3′ end of mature tRNA-His-GUG, the expressions of tRFdb-3013a and tRFdb-3013b were signi cantly decreased in colon adenocarcinomas compared to non-tumor controls. Kaplan-Meier curve analyses demonstrated that clinical survival of colon adenocarcinoma patients with low tRFdb-3013a/b expressions were signi cantly worse than that of the high expression patients, it suggested that down-regulated tRFdb-3013a and tRFdb-3013b were associated with poor survival prognosis of colon adenocarcinoma patients. Further analyses revealed that tRFdb-3013a/b expression may tend to down-regulated in the patients with lymphatic or vascular invasion present. These ndings implied that tRFdb-3013a and tRFdb-3013b serve as novel biomarkers for diagnosis and prognosis of colon adenocarcinomas.
Several studies have linked the tsRNA fragments to different human cancers, including colorectal [9], liver, and breast cancer [10], etc. Growing evidences demonstrated that many tsRNAs were differentially expressed in the tissues or plasma of multiple cancer patients, these dysregulated tsRNA fragments might serve as potential candidates for diagnostic and prognostic biomarkers, and even contribute to various biological processes of cancer development and progression [11]. For example, Tao et.al. have revealed the expression pro le of tsRNAs in human colorectal cancer (CRC) specimens via sequencing analyses, and identi ed a speci c tsRNA derived from the internal of mature tRNA-His-GTG, 5'tiRNA-His-GTG (its base sequence is GCCGTGATCGTATAGTGGTTAGTACTCTGCGTTGT), which is up-regulated in CRC tissues [9]. Then, in vitro and in vivo experiments have revealed that inhibition of 5'tiRNA-His-GTG suppressed cancer cell proliferation and colony formation, induced cell apoptosis, and affected tumor growth in nude mice, these indicate that 5'tiRNA-His-GTG may act an oncogenic role in the progression of colorectal cancers [9]. In breast cancer, Mo et.al. have determined the differential expressed tsRNAs between tumor tissues with matched non-tumor controls, and found a tsRNA AS-tDR-001430 was signi cantly decreased in tissues and serum from breast cancer patients [32]. Moreover, lower expression of AS-tDR-001430 is associated with higher stage progression and lymph node metastasis in breast cancers. AS-tDR-001430 is a 5'-half fragment derived from the internal of mature tRNA-Val-CAC and mapped to chromosome 6p22. In breast cancer cells, overexpression of this tsRNA suppress cells malignant activities, these suggest that tsRNA AS-tDR-001430 may serve as a potential tumor-suppressor in breast cancer [32]. In present study, certain cyto-biology assays were performed to provide biological insights concerning the role of tRFdb-3013a on colon adenocarcinoma cells. It demonstrated that tRFdb-3013a over-expression suppressed cell proliferation, cell migration and invasion of colon adenocarcinomas in vitro. Moreover, the enrichment analyses showed tRFdb-3013a was associated with speci c GO terms, such as extracellular matrix organization, collagen-containing extracellular matrix, and extracellular matrix structural constituent (EMILIN1, ADAT2, FBLN1); the tRFdb-3013a-related genes may refer to phagosome (C1R), as well as the GSEA molecular signature genes (FBLN1 and ILVBL-AS1). These results indicated that down-regulated tRFdb-3013a might act as a suppressive role in cell migration and invasion of colon adenocarcinomas via speci c signaling pathway.
Though the biological functions of tsRNAs are complex and require further elucidation, current knowledge of their function have been a key focus on cancer research. Previous researches have revealed that tsRNA fragments could bind to argonaute (AGO) proteins and play a critical role in gene silencing by directly targeting the 3' untranslated regions (3'UTRs) of some mRNAs in a manner similar to miRNAs [2]. For example, Goodarzi et.al. have identi ed the new class of tsRNAs derived from tRNA(Glu), tRNA(Asp), tRNA(Gly), and tRNA(Tyr) through hypoxic stress induction, this tsRNA fragments can bind and displace the 3'UTRs of YBX1 gene, which encoded a RNA-binding protein, then suppress the stability of multiple oncogenic transcripts such as EIF4EBP1 and AKT1, results in tumor-suppressive and metastasissuppressive activity in breast cancers [10]. Furthermore, in colorectal cancer cells, the upregulated 5'tiRNA-His-GTG is found to directly target LATS2, which is a key component of the hippo signaling pathway, and inhibits cell proliferation and promotes cell apoptosis by regulating YAP protein activity [9]. Our bioinformatics analyses displayed the interaction relationship of tRFdb-3013a/b and their predicted target genes, and found tRFdb-3013a/b could bind the 3'UTR of ST3GAL1, which is highly expressed in colon adenocarcinomas. ST3GAL1 is a membrane protein with sialyl-transferase activity, which has been reported to promote tumor metastasis and invasion by previous study [31]. Dual-luciferase reporter assay showed that tRFdb-3013a/b could directly target 3′UTR of ST3GAL1 and reduce its relative luciferase activity. In addition, over-expression of tRFdb-3013a/b could down-regulated the expression levels of ST3GAL1 protein and mRNA in colon adenocarcinoma cells. These results suggest tRFdb-3013a and tRFdb-3013b might directly target the 3′UTR of ST3GAL1 and regulate ST3GAL1 expression in colon adenocarcinomas.

Conclusion
Taken together, our investigation identi ed the tsRNAs pro les, especially several tsRNA fragments derived from tRNA-His-GUG, within the sncRNA-seq datasets of colon adenocarcinoma samples. Among them, tRFdb-3013a and tRFdb-3013b were signi cantly down-regulated in colon adenocarcinomas, the expressions of tRFdb-3013a/b were associated with clinical survival prognosis of colon adenocarcinoma patients. Over-expression of tRFdb-3013a suppressed cell proliferation, cell migration and invasion of colon adenocarcinomas. Down-regulated tRFdb-3013a/b might act as tumor-suppressive role in colon adenocarcinomas via speci c signaling pathway. Additionally, tRFdb-3013a/b could directly target the 3′UTR of ST3GAL1 and regulate ST3GAL1 expression in colon adenocarcinomas. These implied that tRFdb-3013a and tRFdb-3013b may play important role in tumor progression of colon adenocarcinomas, and could be explored as novel diagnostic and prognostic biomarkers for colon adenocarcinomas.

Declarations
Ethics approval and consent to participate This study was approved by the Ethics Committee of Tokushima University (approval number. 3215-1), and the study was conducted in accordance with the provisions of the Declaration of Helsinki. All patients provided informed consent for the use of their data.

Not applicable
Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 31. Wu X, Zhao J, Ruan Y, Sun L, Xu C, Jiang H. Sialyltransferase ST3GAL1 promotes cell migration, invasion, and TGF-beta1-induced EMT and confers paclitaxel resistance in ovarian cancer.     The effects of tRFdb-3013a on cell proliferation, cell migration and invasion of colon adenocarcinomas. (A) CCK-8 assays were performed to measure the cell proliferation of colon adenocarcinoma SW480 cell.

Supplementary Files
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