tsRNAs sequencing reveals tRNA-Gly (GCC)-derived small RNAs as colorectal cancer biomarker

tsRNAs sequencing reveals tRNA-Gly (GCC)-derived small RNAs as colorectal cancer biomarker | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article tsRNAs sequencing reveals tRNA-Gly (GCC)-derived small RNAs as colorectal cancer biomarker Yu Wan, Yuting Jiao, Anrui Liu, Yushan Lai, Wenfeng Luo, Xiaoying Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5794498/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) is one of the most common gastrointestinal tumors and the second leading cause of malignancy-related death worldwide. Novel biomarkers with high sensitivity and specificity are necessary to improve the diagnosis of colorectal cancer (CRC) in terms of early diagnosis and prognosis. In this study, we obtained tsRNAs expression profiles from formalin-fixed and paraffin-embedded (FFPE) clinical tissue samples to identify novel tsRNAs with potential biomarker properties in colorectal cancer. The expression profiles of colorectal cancer tsRNAs were successfully constructed, 612 up-regulated and 439 down-regulated tsRNAs were identified in the tumor group. tRNA-Gly (GCC)-derived i-tRF-Gly-GCC and 5′-tRF-Gly-GCC were highly expressed in CRC tissues compared to the paraneoplastic tissues. The same results were found in serum from colorectal cancer patients compared to serum from healthy volunteers. Both tsRNAs were highly expressed in CRC tissues and the AUC in ROC analysis was greater than 0.7, which has clinical diagnostic value. WGCNA analysis showed that the target genes of the two tsRNAs were closely related to CRC, and the expression of the target genes was significantly decreased in the cancer groups of the COAD and READ datasets. We also performed validation experiments in HCT-116 cells, and the results confirmed that i-tRF-Gly-GCC and 5′-tRF-Gly-GCC significantly enhanced cell proliferation and migration. In conclusion, we identified and characterized two tsRNAs (i-tRF-Gly-GCC and 5′-tRF-Gly-GCC) as the biomarkers for the diagnosis of colorectal cancer. colorectal cancer tsRNAs biomarkers early diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colorectal Cancer (CRC) is one of the most common gastrointestinal tract tumors and is the third most common malignancy and the second leading cause of malignancy-related deaths worldwide [ 1 – 3 ]. According to the Global Cancer Statistics 2020 report published by the International Agency for Research on Cancer (IARC), in 2020, the total number of colorectal cancer incidence (1,932,000) and the number of deaths from colorectal cancer (286,000) will occur globally [ 3 ]. The high incidence and mortality rates of colorectal cancer make it imperative to take measures to enhance prevention and treatment to reduce its threat to people's health. In recent years, tRNA-derived small RNAs (tsRNAs) have received much attention as novel biomarkers [ 4 – 6 ]. tsRNAs are produced in specific cells or tissues or under certain conditions such as stress and hypoxia by the action of specific nucleic acid enzymes, for example, Dicer and angiogenin (ANG) on mature tRNAs (mat-tRNA) or tRNA precursor (pre-tRNA) by specific nucleases such as Dicer and angiogenin (ANG) [ 7 ], and produced by particular shearing, they are a class of non-coding RNAs that are capable of regulating gene expression at the transcriptional and translational levels [ 8 ]. Numerous studies have demonstrated the role of tsRNAs in different pathological states, especially in autoimmune diseases [ 9 ], neurodegenerative diseases [ 10 ], and a variety of malignant tumors [ 11 , 12 ], such as colorectal cancer [ 5 , 13 , 14 ], breast cancer [ 15 , 16 ], B-cell lymphoma [ 17 ], prostate cancer [ 16 ], and hepatocellular carcinoma [ 5 , 13 , 14 , 18 ], all of which can fulfill their roles as biomarkers to fulfill their roles. In addition, tsRNAs play a crucial role in the pathogenesis of benign digestive system disorders including liver damage and pancreatitis, showing promising clinical potential [ 19 , 20 ]. Furthermore, tsRNAs have the potential to serve as drug-resistance targets in cancer therapy, providing new research directions for clinical treatment [ 21 , 22 ]. Weighted Gene Co-expression Network Analysis (WGCNA) is a correlation network-based systems biology method that utilizes the expression correlation coefficients between genes to calculate their high co-expression relationships, and then to group genes with similar expression patterns into the same module [ 23 ]. The aim is to use the expression correlation coefficient to calculate the co-expression relationship between genes, and effectively identify the sets of genes that show highly synergistic changes at the expression level. Subsequently, the modules highly correlated with clinical phenotypes are screened. Possible biomarkers or therapeutic targets are identified through the internal connectivity of the gene sets and the correlation with the phenotypes, which is a novel approach to exploring the relationship between numerous genes and clinical phenotypes. Besides, WGCNA has been used to identify biomarkers, such as bladder cancer and renal cell carcinoma [ 24 , 25 ]. In this study, we identified tsRNAs with potential biomarker properties in colorectal cancer by the tsRNAs sequencing method. To ensure the accuracy of the sequencing results as well as the detectability of the non-invasive assay, the sequencing samples and the clinical serum samples were further validated by using RT-qPCR technology. Subsequently, miRanda and TargetScan were used to probe the mRNA target genes that the tsRNAs bind to and function on, and the biological functions of the modules where the target genes are located were analyzed in combination with WGCNA [ 26 , 27 ]. The target genes of tsRNAs and the effects of tsRNAs on the proliferation and migration were verified in cultured cells. Our results identified novel tsRNAs with potential biomarker properties in colorectal cancer. Materials and methods Human samples collection Clinical samples for this study were obtained from the Department of Gastroenterology, Panyu Central Hospital, Guangzhou Medical University, and were approved by the Ethics Committee. Formalin-fixed paraffin-embedded mass tissues and corresponding paracancerous tissues from 12 colorectal cancer patients were included for tsRNAs sequencing. To ensure the accuracy of the sequencing results and the detectability of the noninvasive assay [ 28 ], we further collected serum samples from 10 colorectal cancer patients and 10 healthy physical examination volunteers for RT-qPCR validation. Ethical review approval number: PYRC-2024-262-01. The colorectal cancer patients were all diagnosed with colorectal cancer by endoscopy, had cancer for the first time and primary colorectal cancer, and could not have received any form of treatment at the start of the study, including surgery, chemotherapy, radiotherapy, or any other type of treatment. Healthy volunteers with no abnormalities throughout the bowel on endoscopy in the past year and no serious physiologic or pathologic abnormalities in the past year. tsRNAs sequencing Deparaffinization of paraffin-embedded FFPE tissue samples and total RNA was extracted with TRIzol reagent (#15596026, Invitrogen). Sample integrity and concentration were examined by agarose gel electrophoresis and NanoDrop One (Thermo Fisher Scientific, MA, USA). Using the rtStar™ tRF&tiRNA Pretreatment Kit (#AS-FS-005, Arraystar) to remove the 3'-carbamoyl and 3'-cP termini, phosphorylation of the 5'-OH terminus, and demethylation of m 1 A, m 1 G, and m 3 C demethylation to prepare RNA samples suitable for tsRNAs sequencing library construction[ 29 ]. The extracted total RNA samples were ligated one by one with small RNA junctions at the 3' and 5' ends, and the cDNA was synthesized and amplified using proprietary reverse transcription primers and amplification primers (Illumina, CA, USA). Approximately 134–160 bp of PCR amplified fragments were extracted and purified from PAGE gels to ensure the purity and quality of the final PCR amplified fragments obtained. The concentration and purity of the tsRNAs library was determined using an Agilent 2100 Bioanalyzer (Agilent, CA, USA). The library was denatured and diluted to a volume of 1.3 mL at a concentration of 1.8 pM. The diluted library samples were added to the NextSeq 500/550 V2 Kit (#FC-404-2005, Illumina) and sequenced on an Illumina NextSeq 500 system by following the instructions provided by the manufacturer. tsRNAs sequencing service Provided by Aksomics (China). Bioinformatics Analysis The raw data (Raw sequencing data) was in FASTQ format and firstly, the raw data was quality controlled using FastQC (v 0.11.7) and the corresponding quality scores were generated. The quality-checked and standardized data were then processed by Cutadapt (v 1.17), which included removing splice sequences and filtering out fragments less than 14 nts or more than 40 nts in length to obtain trimmed data, and the length distributions of reads were counted. Trimmed data were in FASTA format and were aligned to mature tRNA sequences using Bowtie (v 1.2.2), with the maximum number of mismatched bases allowed to be set to 1. For Reads that failed to match mature tRNA sequences, they were aligned to the tRNA precursor reference sequences using Bowtie and again, the number of mismatched bases allowed was set to 1. number of bases was set to 1. The abundance of tRNAs was assessed by Counts (number of sequenced Reads matched to the genome) and normalized by Counts Per Million (CPM). If the CPM values of all samples were lower than 20, the corresponding tsRNAs were filtered and the expression profiles of tsRNAs were finally obtained. The edgeR software was used to analyze the differential expression of tsRNAs, and the tsRNAs with significant differences in expression levels between the two groups of samples were screened out based on the statistical criteria of Fold Change (FC) greater than 1.5 and P-value less than 0.05. Target gene prediction and enrichment pathway analyses This study used two tools, miRanda and TargetScan, were used for the prediction of target genes of tsRNAs [ 26 , 27 ]. miRanda mainly focuses on the evolutionary conservatism of the binding region of miRNAs to target genes and the thermodynamic stability of miRNAs and mRNA double-stranded structures. targetScan is based on the principle of sequence complementarity, and it can be used to screen the potential target genes of miRNAs based on the criteria of thermodynamic stability by identifying the conserved sequences in comparison with the target genes 3' untranslated region (UTR) comparison of conserved seed match sequences and further screening potential target genes for miRNAs based on the criteria of thermodynamic stability. In this study, miRanda parameters were miranda score ≥ 140, miranda energy ≤ -10, and TargetScan parameters were context plus score ≤ -0.3. The ClusterProfiler program package analyzes the GO function of target genes and enriches the KEGG pathway to associate potential target genes with known biological pathways [ 30 – 32 ]. Quantitative real-time PCR In this study, the stem-loop method was chosen to design primers for tsRNAs, and RT-qPCR for tsRNAs was completed according to the miRNA 1st Strand cDNA Synthesis Kit (by stem-loop) (#MR101, Vazyme) and miRNA Universal SYBR qPCR Master Mix (#MQ101, Vazyme) kit Instructions to complete RT-qPCR against tsRNAs, selecting U6 as the internal reference gene. Reverse transcription primer sequences and RT-qPCR primer sequences are provided (Supplementary Table S1 ; Supplementary Table S2). RT-qPCR of mRNA was accomplished using HiScript III All-in-one RT SuperMix Perfect for qPCR (#R333, Vazyme) and ChamQ SYBR qPCR Master Mix (#Q311, Vazyme) kits, and GAPDH was selected as the internal reference gene.RNA primer sequences were provided (Supplementary Table S3). Weighted Gene Co-expression Network Analysis A weighted gene co-expression network analysis (WGCNA) approach was used to probe deeply into the gene expression patterns of the COAD and READ datasets in the TCGA database ( https://tcga-data.nci.nih.gov/tcga/ ). The FPKM of the mRNA data from the two datasets were fused, and the data were subjected to principal component analysis (PCA) via factoextra (v1.0.7) to filter outlier data as well as genes with lower expression levels. Batch effect correction was performed on the downscaled data using the ComBat function in the sva package (v3.48.0) to eliminate potential differences between batches. Soft thresholding β was determined by using the pickSoftThreshold function from the WGCNA package (v1.72-5) for constructing the weighted gene network. The TOM matrix was generated by using the TOMsimilarityFromExpr function in the WGCNA package, and the 1-TOM was obtained as the Dis-similarity Matrix (disTOM). Using the hclust function, a systematic clustering tree between genes was drawn. Subsequently, the genes were divided into modules of different colors by the cutreeDynamic function of the dynamicTreeCut package (v1.63-1), with minClusterSize set to 30. The eigenvector values of each module were computed using the mergeCloseModules function, and those modules with similar eigenvector values were combined, the cutHeight is set to 0.25. Cell culture and transfection Human colorectal adenocarcinoma cells HCT-116 were a gift from Sun Yat-sen University School of Medicine. They were cultured in 5% CO2, 37°C, and 70–80% humidity using DMEM medium containing a mixture of 10% fetal bovine serum and 1% penicillin-streptomycin (P/S double antibody). i-tRF-Gly-GCC and 5′-tRF-Gly-GCC mimics and NC were all synthesized by Jimma Bio and the sequences were provided (Supplementary Table S4). Cell transfection was performed using Lipofectamine 2000 Reagent (Invitrogen, USA) according to the manufacturer's instructions. Cell counting kit-8 proliferation assay Enhanced Cell Counting Kit-8 (#C0042, Beyotime) was used for the detection of cell proliferation ability. After cell counting of the transfected cells, 1000 cells were added to each well while ensuring a constant volume of 100 µL per well.To minimize experimental errors due to evaporation during the incubation process, it was necessary to add 100 µL of PBS solution to the outermost ring of wells in the 96-well plate. According to the manufacturer's instructions, after incubation until the cells were well adhered to the wall, 10 µL of enhanced CCK-8 solution was added to each well, and a set of zeroing wells was set up in which 100 µL of complete medium and enhanced CCK-8 solution were added, but no cells were added. After continuing incubation for 4 h in a cell culture incubator, absorbance was measured at 450 nm using an enzyme meter and OD values were recorded. Wound healing assay Use a marker to draw five parallel horizontal lines on the bottom of the six-well plate. Cells were spread and transfected in the six-well plate according to the cell transfection step. Among the transfected cells, cells with good growth status and whose growth reached about 90% were screened for subsequent experiments. Using a 200 µL pipette tip, a line was drawn in each well in a direction perpendicular to the horizontal line. After the line was drawn, each well needed to be washed twice with PBS to guarantee that the cell scratch area was washed clean and minimize suspended cells so as not to affect the photographic field of view. After that, 2 mL of low serum medium was added to each well to reduce the effect of cell proliferation. Observations were made under the microscope and recorded for photographing, at 0 hr. Subsequently, after 24 hours and 48 hours, the same washing and fluid change were performed. Photographic recordings were performed using a microscope (Olympus, Japan). Statistical analyses All data were expressed as the mean ± SD. analyses were performed using GraphPad Prism 8.0 and the language R. Statistical analyses used in this study included independent samples t-tests, ANOVA. * P < 0.05; ** P < 0.01; *** P < 0.001. Results Expression profile of tsRNAs in colorectal cancer FFPE tissues To screen out expression data with significant differences, the counts per million (CPM) values in the sequencing data of tsRNAs were analyzed by ANOVA. Subsequently, data with P-value less than 0.05 were selected as input data for principal component analysis, and the PCA results were visualized as 3D scatter plots using the scatterplot3d package in R (Fig. 1 A). To identify tsRNAs with changed expression patterns in colorectal cancer, differential expression analysis of tsRNAs was performed using edgeR software. The results of the differential analysis in the form of volcano plots (Fig. 1 B), in which the number of up-regulated tsRNAs in the Tumor group compared to the Normol group amounted to 612, and the total number of down-regulated tsRNAs was 439. In the Tumor group, 599 tsRNAs showed specific expression; in the Normal group, the number of specifically expressed tsRNAs was 893, and the number of co-expressed tsRNAs in these two groups reached 2,285 (Fig. 1 C). Based on the K-means clustering algorithm, the data were sorted and grouped according to the expression levels (i.e., CPM values) of the tsRNAs, thus demonstrating the similarities and differences between the samples more intuitively (Fig. 1 D). These results indicated that the expression levels of some tsRNAs were significantly changed in colorectal cancer, which might be closely related to the occurrence and development of colorectal cancer. The trimmed data were statistically analyzed for reads length distribution (Fig. 1 E). tsRNAs in the colorectal cancer FFPE tissue samples sequenced in this study were mainly distributed in the length intervals of 21–23 nts and 27–29 nts, and there was a similarity in the Reads length distribution of the tsRNAs in the colorectal cancer mass group and the para cancer control group. The tRNA-derived small RNAs (tsRNAs) were categorized into groups based on their shear sites and lengths, mainly including tRFs and tiRNAs. tRFs can be further classified into the following subtypes: tRF-1, tRF-2, tRF-3a, tRF-3b, tRF-5a, tRF-5b, tRF-5c. tiRNAs include tRNA-3 and tiRNA-5. The pie chart shows the subtype distribution of tRNAs (Fig. 1 F). tRNA isodecoders are a special class of tRNA molecules that have different anticodons (Anticodon) and carry the same amino acid. Their diversity and abundance were revealed by analyzing the isoforms of tsRNAs produced by different isodecoders (Fig. 1 G). (A) Principal component analysis. (B) Volcano diagrams for variance analysis. (C) Group-specific expression analysis. (D) Between-group differences in tsRNAs isoforms. (E) Reads length distribution statistics. (F) Distribution of tsRNAs subtypes. (G) Distribution of tsRNAs isoforms in tRNA isodecoders. Target gene prediction and enrichment analysis There were some tsRNAs with significantly higher expression in the Tumor group than in the Normal group, which were considered potential tumor markers and might be closely related to tumorigenesis and progression. Screening for tsRNAs with higher expression in both groups, we chose tsRNAs with a length greater than 17 nts as candidates, because longer tsRNAs have higher specificity and stability, and thus are more conducive to the identification and application of tumor markers. ROC analysis of tsRNAs provides a more comprehensive understanding of their predictive ability and accuracy in tumor detection, providing a strong basis for further screening. After the screening process described above, tsRNAs with high expression, appropriate length, and good predictive performance were selected as candidate colorectal cancer biomarkers. We used miRanda and TargetScan for target gene prediction of the candidate colorectal cancer biomarkers, took the intersection of the analysis results of the two, and performed GO and KEGG enrichment analysis (Fig. 2 A-D). The enrichment results showed that the target genes of both i-tRF-Gly-GCC and 5′-tRF-Gly-GCC were enriched in pathways closely related to cancer. The potential target genes of i-tRF-Gly-GCC were mainly involved in the processes of cell attachment, neurotransmission, and transcriptional deregulation in cancer; and those of 5′-tRF-Gly-GCC were mainly involved in the processes of organ development, maintenance of normal physiological functions, viral oncogenesis, and cell adhesion. i-tRF-Gly-GCC,5′-tRF-Gly-GCC as colorectal cancer biomarkers were both highly expressed in FFPE tissues and serum To ensure the accuracy of the sequencing results, RT-qPCR experiments were performed on seven sets of RNA samples remaining after sequencing was completed in this study, and the relative amounts of target genes were calculated using the standard curve method to assess their expression levels in the samples. The two-dimensional structures of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC are illustrated (Fig. 2 E). i-tRF-Gly-GCC and 5′-tRF-Gly-GCC are both tRNA-Gly (GCC) derived. i-tRF-Gly-GCC and 5′-tRF-Gly-GCC are both tRNA-Gly (GCC) derived. tRF-Gly-GCC and 5′-tRF-Gly-GCC are both tRNA-Gly(GCC)-derived. i-tRF-Gly-GCC sequence is 5′-ATGGGGTGGTTCAGTGGGTAGAATTC-3′, and 5′-tRF-Gly-GCC sequence is 5′- GCATGGGTGGTTCAGTGGTAGAATTC-3′. After RT-qPCR validation, the P -value of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC was less than 0.05, and the expression levels were consistent with the sequencing results (Fig. 2 F) and had clinical diagnostic value (Fig. 2 G). To ensure the accuracy and wide applicability of the results, this study further collected serum samples from 10 patients who were diagnosed with colorectal cancer and admitted to the Gastroenterology Center of Panyu Central Hospital affiliated with Guangzhou Medical University, as well as 10 healthy volunteers recruited by the Health Management Center. The expression levels of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC in the sera of colorectal cancer patients could be accurately measured by RT-qPCR experiments (Fig. 2 H). This will provide a solid foundation for subsequent studies and help to gain a deeper understanding of the roles of these two tsRNAs in colorectal carcinogenesis and progression, as well as their value as potential noninvasive detection biomarkers. (A, B) GO and KEGG enrichment of the i-tRF-Gly-GCC. (C, D) GO and KEGG enrichment of the 5′-tRF-Gly-GCC. (E) 2D structure of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC. (F) RT-qPCR validation of sequencing samples(n = 7). (G) ROC analysis of of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC. (H) RT-qPCR of clinical serum samples (n = 10). Identification of target genes and tsRNA correlations To explore the biological functions of the target genes of tsRNAs, we performed functional analysis for the COAD and READ datasets of the TCGA database. After importing the datasets, the data were preprocessed to obtain the mRNA expression matrix containing the gene expression information of all the qualified samples. We performed PCA analysis before and after batch correction (Fig. 3 A). The data distribution was more compact and consistent after the correction. Taking the truncation threshold as 0.85, a soft threshold of 9 was jointly determined based on the average of the neighbor-joining functions of all genes in the gene module (Fig. 3 B). The disTOM matrix was passed through the hclust function to draw a systematic clustering tree among genes. We show the module gene clustering dendrogram, divided modules, and merged modules in turn (Fig. 3 C). The merged gene modules have 33 gene modules except for the meaningless grey. To deeply explore the correlation between each gene module and the clinical phenotype, Pearson's correlation coefficient of each gene in the two sets of samples was first calculated by t-test. Subsequently, the P-value was calculated by hypothesis testing to assess the statistical significance of the association between the modules and the clinical phenotypes. Subsequently, we performed a correlation analysis between modules and clinical phenotypes (Fig. 3 D). Modules with a high correlation of tumorigenesis risk were explored in the Tumor group, and the absolute value of the correlation coefficient exceeding 0.3 was used as the benchmark for screening. darkred module (Cor = 0.59, P-value = 9e-65), lightyellow module (Cor = 0.43, P-value = 5e-31), darkorange module (Cor = 0.4, P-value = 2e-27), plum1 module (Cor = 0.38, P-value = 5e-24), yellowgreen module (Cor = 0.36, P-value = 9e-22), greenyellow module (Cor = 0.36, P-value = 7e-22 ), grey60 module (Cor = -0.9, P-value = 7e-245), midnightblue module (Cor = -0.56, P-value = 2e-55), darkorange2 module (Cor = -0.52, P-value = 5e-48), darkolivegreen module (Cor = -0.34, P-value = 5e-19), darkgrey module (Cor = -0.3, P-value = 4e-15). Based on this data, the two modules most associated with colorectal cancer risk were selected: grey60 (Cor = -0.9) and darkred module (Cor = 0.59), as well as two colorectal cancer risk modules strongly associated with cancer: midnightblue module (Cor = − 0.56) and darkgrey module (Cor = -0.3) were analyzed in detail. (A) PCA before and after batch correction. (B) Soft threshold filtering. (C) Gene clustering tree and gene module division map. (D) Correlation analysis between modules and clinical phenotypes. Identification of gene modules and analysis of enriched pathways in different gene modules In grey60 (Cor = -0.9) module, genes are mainly enriched in pathways for multiple metabolic processes, enzyme activities, ABC transporters, etc (Fig. 4 A). These pathways play important roles in normal cellular functions and metabolic processes. The genes in darkred module (Cor = 0.59) were mainly enriched in ribosomal processes. These processes play key roles in cell growth, division and gene expression regulation (Fig. 4 B). For the colorectal cancer risk module, which is closely related to cancer, the genes within the midnightblue (Cor = -0.56) module were mainly enriched in cancer-related pathways such as neuron-associated, MAPK signaling pathway, Wnt signaling pathway, proteoglycan in cancer, and Hippo signaling pathway (Fig. 4 C). These pathways have important effects on proliferation, survival and metastasis of cancer cells. Finally, the genes within the darkgrey (Cor = -0.3) module were mainly enriched in cancer-related pathways such as immune-related, cytokine-related, pathways of cancer, MAPK signaling pathway, transcriptional dysregulation in cancer, NF-kappa B signaling pathway, proteoglycans in cancer, and other cancer-related pathways (Fig. 4 D). These pathways play key roles in the function of the immune system, inflammatory responses, and the growth and invasion of cancer cells. (A) Grey60 module GO and KEGG enrichment analysis. (B) Darkred module GO and KEGG enrichment analysis. (C) Midnightblue module GO and KEGG enrichment analysis. (D) Darkgrey module GO and KEGG enrichment analysis. i-tRF-Gly-GCC promotes colorectal cancer progression by targeting the RAC2 gene and 5′-tRF-Gly-GCC by targeting the ARNT2 gene To delve into the role of tsRNAs in cancer development, genes associated with the cancer pathway (hsa05200), which play a key role in cancer onset and progression, were first extracted from the KEGG database. Then, these genes were compared with the target genes of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC and intersections were obtained. The i-tRF-Gly-GCC and 5′-tRF-Gly-GCC were mimics overexpressed in HCT-116 cells, respectively, over RT-qPCR to determine the transfection efficiency, and the mimics transfection efficiencies of both tsRNAs were high as expected (Fig. 5 A). RT-qPCR was used to compare the changes in the expression levels of potential target genes in cells overexpressing the tsRNAs mimics (Fig. 5 B), the experimental results revealed that the expression levels of the target genes of i-tRF-Gly-GCC, RAC2 , and VHL , and the target genes of 5′-tRF-Gly-GCC, ARNT2 , and GADD45B , were significantly decreased in the mimics overexpressing cells. with significantly decreased expression in the mimics overexpressing cells. This suggests that i-tRF-Gly-GCC may act by regulating the expression of RAC2 and VHL , and 5′-tRF-Gly-GCC may exert its biological function by regulating ARNT2 and GADD45B . According to the results of WGCNA, special attention was paid to the above four potential target genes: RAC2 , VHL , ARNT2 , and GADD45B . Specifically, the RAC2 gene was in the darkgrey gene module, with a correlation coefficient of -0.3; the VHL gene was located in the lightyellow gene module, with a correlation coefficient of The correlation coefficient of the ARNT2 gene is -0.56 in the midnightblue gene module, and finally, GADD45B gene is in the steelblue gene module, with a correlation coefficient of 0.081. Given the weak correlation between the steelblue gene module and the clinical phenotypes, we will focus on the correlation coefficient of the GADD45B gene in the subsequent study. In subsequent studies, we will focus on the analysis of RAC2 , VHL , and ARNT2 . Based on the expression Count files of COAD and READ datasets in TCGA, the three genes, RAC2 , VHL , and ARNT2 , were statistically analyzed by independent samples t-test, and it was found that the expression of the RAC2 gene and ARNT2 gene was significantly reduced in colorectal cancer patients (Fig. 5 C). This phenomenon suggests that these two genes may play a crucial role in the development and progression of colorectal cancer. However, for the VHL gene, no significant difference was observed in the two data sets. The metabolic activity of the cells was measured by CCK-8 assay, thus indirectly reflecting the cell proliferation. The experimental results showed (Fig. 5 D) that overexpression of both i-tRF-Gly-GCC and 5′-tRF-Gly-GCC significantly enhanced the proliferation of colorectal cancer cells HCT-116. A cell scratch assay was used to observe the effects of overexpression of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC on the migratory ability of colorectal cancer cells HCT-116 (Fig. 5 E). The results indicate that overexpression of these two molecules can significantly enhance the migratory ability of colorectal cancer cells. Based on the above data analysis and experimental results, a hypothesis was proposed that i-tRF-Gly-GCC further promotes the growth and migration of colorectal cancer cells by targeting the RAC2 gene, and 5′-tRF-Gly-GCC further promotes the growth and migration of colorectal cancer cells by targeting the ARNT2 gene, and we show a schematic of its binding site (Fig. 5 F-G). This finding reveals the critical roles of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC in colorectal carcinogenesis and progression and provides an important theoretical basis for future therapeutic strategies targeting these two tsRNAs. (A) Transfection efficiency of mimics overexpressing tsRNAs. (B) Validation of target gene results by RT-qPCR after overexpression of tsRNAs. (C) Boxplot of gene expression in TCGA. (D) Overexpression of tsRNAs promotes proliferation of HCT-116 cells. (E) Overexpression of tsRNAs promotes HCT-116 cell migration. (F) i-tRF-Gly-GCC binding site to target gene RAC2 . (G) 5′-tRF-Gly-GCC binding site to target gene ARNT2 Discussion The incidence of colorectal cancer is gradually increasing in younger people, and among adults under 50 years of age, both men and women worldwide [ 33 – 35 ]. The incidence of colorectal cancer is grim, and its causes are very complex, including a combination of sex factors, genetic factors, environmental factors, lifestyle, and other factors [ 36 – 39 ]. Therefore, there is an urgent need for a biomarker that can be detected noninvasively and with high stability to achieve early screening, diagnosis, and treatment of colorectal cancer, thus improving patients' survival rate and quality of life [ 40 – 42 ]. Among many biomarkers, tRNA-derived small RNA (tsRNA) is an emerging biomarker that not only possesses the potential to serve as a target for drug resistance in cancer therapy but also has many other advantages [ 4 , 5 , 43 ]. For example, tsRNAs can be obtained in body fluids, are easy to obtain, can be detected noninvasively, are not easily degraded with modifications, and are highly stable [ 44 – 46 ]. Therefore, tsRNAs are expected to be important biomarkers and therapeutic targets for colorectal cancer diagnosis. However, the application of tsRNAs as biomarkers in colorectal cancer diagnosis and their regulatory relationship with colorectal cancer progression is still in its infancy. In this study, starting from high-throughput sequencing of tsRNAs, we successfully obtained tsRNAs with differential expression in colorectal cancer tissues and paracancerous tissues. to validate the reliability of the sequencing results, the sequencing samples were subjected to RT-qPCR verification. Meanwhile, to explore the possibility of using these tsRNAs as biomarkers for non-invasive detection in blood in the future in the clinic, RT-qPCR was performed on clinical serum samples [ 47 ]. Through the analysis of the above RT-qPCR results and screening by ROC analysis, two tRNA-Gly (GCC)-derived tsRNAs were finally identified, i-tRF-Gly-GCC and 5′-tRF-Gly-GCC. Subsequently, target gene studies were performed for i-tRF-Gly-GCC and 5′-tRF-Gly-GCC. Using the target gene prediction tools miRanda and TargetScan, target genes were predicted based on indicators such as evolutionary conservation of the binding region, thermodynamic stability of the double-stranded structure, and the principle of sequence complementarity. To further validate these potential target genes, i-tRF-Gly-GCC and 5′-tRF-Gly-GCC were overexpressed in human colorectal cancer cells HCT-116 using mimics transfection technology, and the changes in the expression of the target genes were examined using RT-qPCR, which led to the identification of potential mRNA targets of tsRNAs [ 48 – 50 ]. To comprehensively study the roles of target genes in colorectal cancer, we conducted a thorough analysis of the COAD and READ datasets from The Cancer Genome Atlas (TCGA) database. We discovered highly correlated gene sets that show significant alterations in colorectal cancer. To explore the connections between gene sets and clinical characteristics, we identified potential biomarkers and therapeutic targets, which significantly reduced expression in overexpressing cells, and in large dataset cohorts and modules linked to clinical outcomes, were pinpointed as possible targets for tsRNAs, on this basis, cell phenotyping experiments revealed that high expression of both i-tRF-Gly-GCC and 5'-tRF-Gly-GCC promotes cell proliferation and migration. The strength of this study is that it is the first time to clarify that tRNA-Gly (GCC)-derived i-tRF-Gly-GCC and 5′-tRF-Gly-GCC are both highly expressed in colorectal cancer mass tissues compared to paracancerous tissues and in colorectal cancer patient sera compared to the sera of healthy physical examination volunteers. This finding provides important clues for further investigation of the role of tsRNAs in colorectal cancer development. To screen the potential target genes of tsRNAs, multiple methods were used for comprehensive analysis including target gene prediction, verification of gene expression changes by RT-qPCR after overexpression of tsRNAs, gene expression analysis using the TCGA database, and association of the module in the WGCNA analysis. In future studies, it is necessary to expand the sample size and cover more regions and populations. Due to the complexity of the genesis of colorectal cancer, the present study did not investigate the changes in the downstream protein levels of tsRNAs after inhibiting their transcription by binding to mRNAs. In addition, tsRNAs may be produced under certain conditions such as stress and hypoxia, which was not clarified in this study and can be investigated in the future [ 51 , 52 ]. Future studies can also further elucidate the process of tsRNAs production and investigate how various environmental or disease conditions impact their expression levels [ 5 , 53 , 54 ]. Conclusion In summary, we found that i-tRF-Gly-GCC and 5′-tRF-Gly-GCC are highly expressed in colorectal cancer tissues and sera of colorectal cancer patients, our results suggest that they can be used as biomarkers and therapeutic targets for colorectal cancer. Specifically, i-tRF-Gly-GCC promotes colorectal cancer development by targeting the RAC2 gene, and 5′-tRF-Gly-GCC promotes colorectal cancer development by targeting the ARNT2 gene. This study is important for understanding the influence of tsRNAs on colorectal cancer development and provides a theoretical basis for utilizing i-tRF-Gly-GCC and 5′-tRF-Gly-GCC as biomarkers for the early diagnosis of colorectal cancer and the development of targeted therapeutic approaches against CRC. Declarations Supplementary Information The online version contains supplementary material available at Funding This work was supported by the National Natural Science Foundation of China (32070615, 81902093), Guangdong Provincial Natural Science Foundation (2022A1515010569), Guangzhou Science and Technology Project (2024A04J6265), Medical Science and Technology Research Foundation of Guangdong Province (A2024164), Science and Technology Program of Panyu central hospital (PY-2023-003), the Science and Technology Program of Guangzhou (202002020023). Authors′ contributions X. Wang, and Y. Wan proposed and designed the study. Y. Jiao, Y. Lai and A. Liu performed the experiments. W. Luo, X. Zhang and X. Lin provided the clinical samples. Y. Jiao wrote the manuscript. A. Liu, X. Wang, and Y. Wan revised the manuscript. All authors read and approved the final manuscript. Ethics approval and consent to participate The study received ethical approval from the Institutional Review Board (PYRC-2024-262-01) of The Affiliated Panyu Central Hospital of Guangzhou Medical University prior to its commencement. Consent for publication Not applicable. Acknowledgements We would like to acknowledge and thank all members who contributed to this study. Competing of interest The authors declare no competing interest. Data availability All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE272805 (reviewer token: enwlomaqtxefraj). References Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72:7–33 Biller LH, Schrag D (2021) Diagnosis and Treatment of Metastatic Colorectal Cancer: A Review. JAMA 325:669–685 Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB (2019) Colorectal cancer. 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BMC Genomics 14:298 Tuorto F, Liebers R, Musch T, Schaefer M, Hofmann S, Kellner S, Frye M, Helm M, Stoecklin G, Lyko F (2012) RNA cytosine methylation by Dnmt2 and NSun2 promotes tRNA stability and protein synthesis. Nat Struct Mol Biol 19:900–905 Shaukat A, Levin TR (2022) Current and future colorectal cancer screening strategies. Nat Rev Gastroenterol Hepatol 19:521–531 Lee YS, Shibata Y, Malhotra A, Dutta A (2009) A novel class of small RNAs: tRNA-derived RNA fragments (tRFs). Genes Dev 23:2639–2649 Akiyama Y, Kharel P, Abe T, Anderson P, Ivanov P (2020) Isolation and initial structure-functional characterization of endogenous tRNA-derived stress-induced RNAs. RNA Biol 17:1116–1124 Lyons SM, Gudanis D, Coyne SM, Gdaniec Z, Ivanov P (2017) Identification of functional tetramolecular RNA G-quadruplexes derived from transfer RNAs. 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Cell Death Dis 14:748 Supplementary Files Supplementaryfile.pdf Graphicalabstract.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5794498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406221368,"identity":"a4cbf466-7dd9-4d98-a852-6454b3fd070d","order_by":0,"name":"Yu Wan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYPACGx5+/gbStKTJSM44QJqWwzYGDQlEqjU4fvaYNO+e8zwGDAcYP3zMIUbLmbw0aZ5nt3nMmRuYJWduI0KL2YEcM2meA7d5LBsOsDHzEqXl/BuQlnM8BgcSiNVyA2zLARK02N94Y2w550Ayj+SMg83E+UWyP8fwxpsDdvb8/M0HP3wkRgsQsEhAaMYG4tQDAfMHopWOglEwCkbByAQAH7c07RwNFbcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0886-0921","institution":"Guangzhou Panyu Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wan","suffix":""},{"id":406221369,"identity":"d591bc2e-6bcd-4620-91e0-f3c6097131d8","order_by":1,"name":"Yuting Jiao","email":"","orcid":"","institution":"Guangzhou Panyu Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Jiao","suffix":""},{"id":406221370,"identity":"896d6324-563c-41fd-88fc-924ec76ad6f4","order_by":2,"name":"Anrui Liu","email":"","orcid":"","institution":"South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Anrui","middleName":"","lastName":"Liu","suffix":""},{"id":406221371,"identity":"615a1a64-a459-45f7-b13c-54b0192cc44f","order_by":3,"name":"Yushan Lai","email":"","orcid":"","institution":"South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yushan","middleName":"","lastName":"Lai","suffix":""},{"id":406221372,"identity":"135aa396-e9be-4692-a703-7478591cf2a0","order_by":4,"name":"Wenfeng Luo","email":"","orcid":"","institution":"Guangzhou Panyu Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenfeng","middleName":"","lastName":"Luo","suffix":""},{"id":406221373,"identity":"763cce4e-c8f9-49f2-a2db-f59159889d8f","order_by":5,"name":"Xiaoying Zhang","email":"","orcid":"","institution":"Guangzhou Panyu Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Zhang","suffix":""},{"id":406221374,"identity":"4659d2b3-6808-401f-8738-13bb81ddce6a","order_by":6,"name":"Xiaoling Lin","email":"","orcid":"","institution":"Guangzhou Panyu Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Lin","suffix":""},{"id":406221375,"identity":"d5cef1db-1671-42bf-a64e-bf56c64e5f0d","order_by":7,"name":"Xiaoyun Wang","email":"","orcid":"","institution":"Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences: Chinese Academy of Sciences Guangzhou Institutes of Biomedicine and Health","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-09 08:18:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5794498/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5794498/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75311891,"identity":"7b6e584a-ef53-4ef2-a581-d0c4589c286b","added_by":"auto","created_at":"2025-02-03 09:10:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":385552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequencing of tsRNAs for differential expression and isoform analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Principal component analysis. (B) Volcano diagrams for variance analysis. (C) \u0026nbsp;Group-specific expression analysis. (D) Between-group differences in tsRNAs isoforms. (E) Reads length distribution statistics. (F) Distribution of tsRNAs subtypes. (G) Distribution of tsRNAs isoforms in tRNA isodecoders.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/d77b85858d5199fb15a1060b.png"},{"id":75311892,"identity":"ef048c79-88fc-420b-9ed4-e0e9f100e943","added_by":"auto","created_at":"2025-02-03 09:10:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":463728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening of tsRNAs and target gene prediction.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) GO and KEGG enrichment of the i-tRF-Gly-GCC. (C, D) GO and KEGG enrichment of the 5′-tRF-Gly-GCC. (E) 2D structure of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC. (F) RT-qPCR validation of sequencing samples(n=7). (G) ROC analysis of of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC. (H) RT-qPCR of clinical serum samples (n = 10).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/fea4600716b806d48ba21874.png"},{"id":75312821,"identity":"ada9b45e-fc4f-449d-9580-f45e6b6f96a0","added_by":"auto","created_at":"2025-02-03 09:18:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":604532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWGCNA analysis on the COAD and READ datasets of the TCGA database.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PCA before and after batch correction. (B) Soft threshold filtering. (C) Gene clustering tree and gene module division map. (D) Correlation analysis between modules and clinical phenotypes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/67a8b844ae21a758316e6171.png"},{"id":75311906,"identity":"f8d98a4d-42f6-47aa-8b61-076422e4ddc3","added_by":"auto","created_at":"2025-02-03 09:10:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":598863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis of different modules.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Grey60 module GO and KEGG enrichment analysis. (B) Darkred module GO and KEGG enrichment analysis. (C) Midnightblue module GO and KEGG enrichment analysis. (D) Darkgrey module GO and KEGG enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/a9a7169b0bfb1b47486d1f1c.png"},{"id":75311903,"identity":"82568a9b-2d57-41b6-bc7e-32b433427243","added_by":"auto","created_at":"2025-02-03 09:10:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":448564,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of i-tRF-Gly-GCC and 5′-tRF-Gly-GCC on the biological behavior of CRC cells.\u003c/p\u003e\n\u003cp\u003e(A) Transfection efficiency of mimics overexpressing tsRNAs. (B) Validation of target gene results by RT-qPCR after overexpression of tsRNAs. (C) Boxplot of gene expression in TCGA. (D) Overexpression of tsRNAs promotes proliferation of HCT-116 cells. (E) Overexpression of tsRNAs promotes HCT-116 cell migration. (F) i-tRF-Gly-GCC binding site to target gene \u003cem\u003eRAC2\u003c/em\u003e. (G) 5′-tRF-Gly-GCC binding site to target gene \u003cem\u003eARNT2\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/237d93c5d778bf4749650750.png"},{"id":85224877,"identity":"399b548f-dc2c-4955-ae83-fa5e389c6ea8","added_by":"auto","created_at":"2025-06-23 14:56:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3420005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/eae9ea9b-afbb-4e3d-adde-2f193b86663d.pdf"},{"id":75311893,"identity":"aacdd588-83e7-49fb-a660-72488bf36098","added_by":"auto","created_at":"2025-02-03 09:10:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":161857,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/de7e3c4a9a057dd740c987ec.pdf"},{"id":75311897,"identity":"2834e0ab-754d-417f-8ad8-b20264d762f3","added_by":"auto","created_at":"2025-02-03 09:10:03","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":229198,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5794498/v1/a99287dea095399798c3b5c0.pdf"}],"financialInterests":"","formattedTitle":"tsRNAs sequencing reveals tRNA-Gly (GCC)-derived small RNAs as colorectal cancer biomarker","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal Cancer (CRC) is one of the most common gastrointestinal tract tumors and is the third most common malignancy and the second leading cause of malignancy-related deaths worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the Global Cancer Statistics 2020 report published by the International Agency for Research on Cancer (IARC), in 2020, the total number of colorectal cancer incidence (1,932,000) and the number of deaths from colorectal cancer (286,000) will occur globally [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The high incidence and mortality rates of colorectal cancer make it imperative to take measures to enhance prevention and treatment to reduce its threat to people's health.\u003c/p\u003e \u003cp\u003eIn recent years, tRNA-derived small RNAs (tsRNAs) have received much attention as novel biomarkers [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. tsRNAs are produced in specific cells or tissues or under certain conditions such as stress and hypoxia by the action of specific nucleic acid enzymes, for example, Dicer and angiogenin (ANG) on mature tRNAs (mat-tRNA) or tRNA precursor (pre-tRNA) by specific nucleases such as Dicer and angiogenin (ANG) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and produced by particular shearing, they are a class of non-coding RNAs that are capable of regulating gene expression at the transcriptional and translational levels [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Numerous studies have demonstrated the role of tsRNAs in different pathological states, especially in autoimmune diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], neurodegenerative diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and a variety of malignant tumors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], such as colorectal cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], breast cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], B-cell lymphoma [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], prostate cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and hepatocellular carcinoma [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], all of which can fulfill their roles as biomarkers to fulfill their roles. In addition, tsRNAs play a crucial role in the pathogenesis of benign digestive system disorders including liver damage and pancreatitis, showing promising clinical potential [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, tsRNAs have the potential to serve as drug-resistance targets in cancer therapy, providing new research directions for clinical treatment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWeighted Gene Co-expression Network Analysis (WGCNA) is a correlation network-based systems biology method that utilizes the expression correlation coefficients between genes to calculate their high co-expression relationships, and then to group genes with similar expression patterns into the same module [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The aim is to use the expression correlation coefficient to calculate the co-expression relationship between genes, and effectively identify the sets of genes that show highly synergistic changes at the expression level. Subsequently, the modules highly correlated with clinical phenotypes are screened. Possible biomarkers or therapeutic targets are identified through the internal connectivity of the gene sets and the correlation with the phenotypes, which is a novel approach to exploring the relationship between numerous genes and clinical phenotypes. Besides, WGCNA has been used to identify biomarkers, such as bladder cancer and renal cell carcinoma [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we identified tsRNAs with potential biomarker properties in colorectal cancer by the tsRNAs sequencing method. To ensure the accuracy of the sequencing results as well as the detectability of the non-invasive assay, the sequencing samples and the clinical serum samples were further validated by using RT-qPCR technology. Subsequently, miRanda and TargetScan were used to probe the mRNA target genes that the tsRNAs bind to and function on, and the biological functions of the modules where the target genes are located were analyzed in combination with WGCNA [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The target genes of tsRNAs and the effects of tsRNAs on the proliferation and migration were verified in cultured cells. Our results identified novel tsRNAs with potential biomarker properties in colorectal cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman samples collection\u003c/h2\u003e \u003cp\u003e Clinical samples for this study were obtained from the Department of Gastroenterology, Panyu Central Hospital, Guangzhou Medical University, and were approved by the Ethics Committee. Formalin-fixed paraffin-embedded mass tissues and corresponding paracancerous tissues from 12 colorectal cancer patients were included for tsRNAs sequencing. To ensure the accuracy of the sequencing results and the detectability of the noninvasive assay [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we further collected serum samples from 10 colorectal cancer patients and 10 healthy physical examination volunteers for RT-qPCR validation. Ethical review approval number: PYRC-2024-262-01.\u003c/p\u003e \u003cp\u003eThe colorectal cancer patients were all diagnosed with colorectal cancer by endoscopy, had cancer for the first time and primary colorectal cancer, and could not have received any form of treatment at the start of the study, including surgery, chemotherapy, radiotherapy, or any other type of treatment. Healthy volunteers with no abnormalities throughout the bowel on endoscopy in the past year and no serious physiologic or pathologic abnormalities in the past year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003etsRNAs sequencing\u003c/h3\u003e\n\u003cp\u003eDeparaffinization of paraffin-embedded FFPE tissue samples and total RNA was extracted with TRIzol reagent (#15596026, Invitrogen). Sample integrity and concentration were examined by agarose gel electrophoresis and NanoDrop One (Thermo Fisher Scientific, MA, USA). Using the rtStar\u0026trade; tRF\u0026amp;tiRNA Pretreatment Kit (#AS-FS-005, Arraystar) to remove the 3'-carbamoyl and 3'-cP termini, phosphorylation of the 5'-OH terminus, and demethylation of m\u003csup\u003e1\u003c/sup\u003eA, m\u003csup\u003e1\u003c/sup\u003eG, and m\u003csup\u003e3\u003c/sup\u003eC demethylation to prepare RNA samples suitable for tsRNAs sequencing library construction[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The extracted total RNA samples were ligated one by one with small RNA junctions at the 3' and 5' ends, and the cDNA was synthesized and amplified using proprietary reverse transcription primers and amplification primers (Illumina, CA, USA). Approximately 134\u0026ndash;160 bp of PCR amplified fragments were extracted and purified from PAGE gels to ensure the purity and quality of the final PCR amplified fragments obtained. The concentration and purity of the tsRNAs library was determined using an Agilent 2100 Bioanalyzer (Agilent, CA, USA). The library was denatured and diluted to a volume of 1.3 mL at a concentration of 1.8 pM. The diluted library samples were added to the NextSeq 500/550 V2 Kit (#FC-404-2005, Illumina) and sequenced on an Illumina NextSeq 500 system by following the instructions provided by the manufacturer. tsRNAs sequencing service Provided by Aksomics (China).\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eThe raw data (Raw sequencing data) was in FASTQ format and firstly, the raw data was quality controlled using FastQC (v 0.11.7) and the corresponding quality scores were generated. The quality-checked and standardized data were then processed by Cutadapt (v 1.17), which included removing splice sequences and filtering out fragments less than 14 nts or more than 40 nts in length to obtain trimmed data, and the length distributions of reads were counted. Trimmed data were in FASTA format and were aligned to mature tRNA sequences using Bowtie (v 1.2.2), with the maximum number of mismatched bases allowed to be set to 1. For Reads that failed to match mature tRNA sequences, they were aligned to the tRNA precursor reference sequences using Bowtie and again, the number of mismatched bases allowed was set to 1. number of bases was set to 1. The abundance of tRNAs was assessed by Counts (number of sequenced Reads matched to the genome) and normalized by Counts Per Million (CPM). If the CPM values of all samples were lower than 20, the corresponding tsRNAs were filtered and the expression profiles of tsRNAs were finally obtained. The edgeR software was used to analyze the differential expression of tsRNAs, and the tsRNAs with significant differences in expression levels between the two groups of samples were screened out based on the statistical criteria of Fold Change (FC) greater than 1.5 and P-value less than 0.05.\u003c/p\u003e\n\u003ch3\u003eTarget gene prediction and enrichment pathway analyses\u003c/h3\u003e\n\u003cp\u003eThis study used two tools, miRanda and TargetScan, were used for the prediction of target genes of tsRNAs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. miRanda mainly focuses on the evolutionary conservatism of the binding region of miRNAs to target genes and the thermodynamic stability of miRNAs and mRNA double-stranded structures. targetScan is based on the principle of sequence complementarity, and it can be used to screen the potential target genes of miRNAs based on the criteria of thermodynamic stability by identifying the conserved sequences in comparison with the target genes 3' untranslated region (UTR) comparison of conserved seed match sequences and further screening potential target genes for miRNAs based on the criteria of thermodynamic stability. In this study, miRanda parameters were miranda score\u0026thinsp;\u0026ge;\u0026thinsp;140, miranda energy \u0026le; -10, and TargetScan parameters were context plus score \u0026le; -0.3. The ClusterProfiler program package analyzes the GO function of target genes and enriches the KEGG pathway to associate potential target genes with known biological pathways [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eQuantitative real-time PCR\u003c/h3\u003e\n\u003cp\u003eIn this study, the stem-loop method was chosen to design primers for tsRNAs, and RT-qPCR for tsRNAs was completed according to the miRNA 1st Strand cDNA Synthesis Kit (by stem-loop) (#MR101, Vazyme) and miRNA Universal SYBR qPCR Master Mix (#MQ101, Vazyme) kit Instructions to complete RT-qPCR against tsRNAs, selecting U6 as the internal reference gene. Reverse transcription primer sequences and RT-qPCR primer sequences are provided (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Supplementary Table S2). RT-qPCR of mRNA was accomplished using HiScript III All-in-one RT SuperMix Perfect for qPCR (#R333, Vazyme) and ChamQ SYBR qPCR Master Mix (#Q311, Vazyme) kits, and GAPDH was selected as the internal reference gene.RNA primer sequences were provided (Supplementary Table S3).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Gene Co-expression Network Analysis\u003c/h2\u003e \u003cp\u003eA weighted gene co-expression network analysis (WGCNA) approach was used to probe deeply into the gene expression patterns of the COAD and READ datasets in the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/tcga/\u003c/span\u003e\u003cspan address=\"https://tcga-data.nci.nih.gov/tcga/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The FPKM of the mRNA data from the two datasets were fused, and the data were subjected to principal component analysis (PCA) via factoextra (v1.0.7) to filter outlier data as well as genes with lower expression levels. Batch effect correction was performed on the downscaled data using the ComBat function in the sva package (v3.48.0) to eliminate potential differences between batches. Soft thresholding β was determined by using the pickSoftThreshold function from the WGCNA package (v1.72-5) for constructing the weighted gene network. The TOM matrix was generated by using the TOMsimilarityFromExpr function in the WGCNA package, and the 1-TOM was obtained as the Dis-similarity Matrix (disTOM). Using the hclust function, a systematic clustering tree between genes was drawn. Subsequently, the genes were divided into modules of different colors by the cutreeDynamic function of the dynamicTreeCut package (v1.63-1), with minClusterSize set to 30. The eigenvector values of each module were computed using the mergeCloseModules function, and those modules with similar eigenvector values were combined, the cutHeight is set to 0.25.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell culture and transfection\u003c/h3\u003e\n\u003cp\u003eHuman colorectal adenocarcinoma cells HCT-116 were a gift from Sun Yat-sen University School of Medicine. They were cultured in 5% CO2, 37\u0026deg;C, and 70\u0026ndash;80% humidity using DMEM medium containing a mixture of 10% fetal bovine serum and 1% penicillin-streptomycin (P/S double antibody). i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC mimics and NC were all synthesized by Jimma Bio and the sequences were provided (Supplementary Table S4). Cell transfection was performed using Lipofectamine 2000 Reagent (Invitrogen, USA) according to the manufacturer's instructions.\u003c/p\u003e\n\u003ch3\u003eCell counting kit-8 proliferation assay\u003c/h3\u003e\n\u003cp\u003eEnhanced Cell Counting Kit-8 (#C0042, Beyotime) was used for the detection of cell proliferation ability. After cell counting of the transfected cells, 1000 cells were added to each well while ensuring a constant volume of 100 \u0026micro;L per well.To minimize experimental errors due to evaporation during the incubation process, it was necessary to add 100 \u0026micro;L of PBS solution to the outermost ring of wells in the 96-well plate. According to the manufacturer's instructions, after incubation until the cells were well adhered to the wall, 10 \u0026micro;L of enhanced CCK-8 solution was added to each well, and a set of zeroing wells was set up in which 100 \u0026micro;L of complete medium and enhanced CCK-8 solution were added, but no cells were added. After continuing incubation for 4 h in a cell culture incubator, absorbance was measured at 450 nm using an enzyme meter and OD values were recorded.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWound healing assay\u003c/h2\u003e \u003cp\u003eUse a marker to draw five parallel horizontal lines on the bottom of the six-well plate. Cells were spread and transfected in the six-well plate according to the cell transfection step. Among the transfected cells, cells with good growth status and whose growth reached about 90% were screened for subsequent experiments. Using a 200 \u0026micro;L pipette tip, a line was drawn in each well in a direction perpendicular to the horizontal line. After the line was drawn, each well needed to be washed twice with PBS to guarantee that the cell scratch area was washed clean and minimize suspended cells so as not to affect the photographic field of view. After that, 2 mL of low serum medium was added to each well to reduce the effect of cell proliferation. Observations were made under the microscope and recorded for photographing, at 0 hr. Subsequently, after 24 hours and 48 hours, the same washing and fluid change were performed. Photographic recordings were performed using a microscope (Olympus, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll data were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. analyses were performed using GraphPad Prism 8.0 and the language R. Statistical analyses used in this study included independent samples t-tests, ANOVA. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExpression profile of tsRNAs in colorectal cancer FFPE tissues\u003c/h2\u003e \u003cp\u003eTo screen out expression data with significant differences, the counts per million (CPM) values in the sequencing data of tsRNAs were analyzed by ANOVA. Subsequently, data with P-value less than 0.05 were selected as input data for principal component analysis, and the PCA results were visualized as 3D scatter plots using the scatterplot3d package in R (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). To identify tsRNAs with changed expression patterns in colorectal cancer, differential expression analysis of tsRNAs was performed using edgeR software. The results of the differential analysis in the form of volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), in which the number of up-regulated tsRNAs in the Tumor group compared to the Normol group amounted to 612, and the total number of down-regulated tsRNAs was 439. In the Tumor group, 599 tsRNAs showed specific expression; in the Normal group, the number of specifically expressed tsRNAs was 893, and the number of co-expressed tsRNAs in these two groups reached 2,285 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Based on the K-means clustering algorithm, the data were sorted and grouped according to the expression levels (i.e., CPM values) of the tsRNAs, thus demonstrating the similarities and differences between the samples more intuitively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). These results indicated that the expression levels of some tsRNAs were significantly changed in colorectal cancer, which might be closely related to the occurrence and development of colorectal cancer. The trimmed data were statistically analyzed for reads length distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). tsRNAs in the colorectal cancer FFPE tissue samples sequenced in this study were mainly distributed in the length intervals of 21\u0026ndash;23 nts and 27\u0026ndash;29 nts, and there was a similarity in the Reads length distribution of the tsRNAs in the colorectal cancer mass group and the para cancer control group.\u003c/p\u003e \u003cp\u003eThe tRNA-derived small RNAs (tsRNAs) were categorized into groups based on their shear sites and lengths, mainly including tRFs and tiRNAs. tRFs can be further classified into the following subtypes: tRF-1, tRF-2, tRF-3a, tRF-3b, tRF-5a, tRF-5b, tRF-5c. tiRNAs include tRNA-3 and tiRNA-5. The pie chart shows the subtype distribution of tRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). tRNA isodecoders are a special class of tRNA molecules that have different anticodons (Anticodon) and carry the same amino acid. Their diversity and abundance were revealed by analyzing the isoforms of tsRNAs produced by different isodecoders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) Principal component analysis. (B) Volcano diagrams for variance analysis. (C) Group-specific expression analysis. (D) Between-group differences in tsRNAs isoforms. (E) Reads length distribution statistics. (F) Distribution of tsRNAs subtypes. (G) Distribution of tsRNAs isoforms in tRNA isodecoders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTarget gene prediction and enrichment analysis\u003c/h2\u003e \u003cp\u003eThere were some tsRNAs with significantly higher expression in the Tumor group than in the Normal group, which were considered potential tumor markers and might be closely related to tumorigenesis and progression. Screening for tsRNAs with higher expression in both groups, we chose tsRNAs with a length greater than 17 nts as candidates, because longer tsRNAs have higher specificity and stability, and thus are more conducive to the identification and application of tumor markers. ROC analysis of tsRNAs provides a more comprehensive understanding of their predictive ability and accuracy in tumor detection, providing a strong basis for further screening. After the screening process described above, tsRNAs with high expression, appropriate length, and good predictive performance were selected as candidate colorectal cancer biomarkers.\u003c/p\u003e \u003cp\u003eWe used miRanda and TargetScan for target gene prediction of the candidate colorectal cancer biomarkers, took the intersection of the analysis results of the two, and performed GO and KEGG enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D). The enrichment results showed that the target genes of both i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC were enriched in pathways closely related to cancer. The potential target genes of i-tRF-Gly-GCC were mainly involved in the processes of cell attachment, neurotransmission, and transcriptional deregulation in cancer; and those of 5\u0026prime;-tRF-Gly-GCC were mainly involved in the processes of organ development, maintenance of normal physiological functions, viral oncogenesis, and cell adhesion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ei-tRF-Gly-GCC,5\u0026prime;-tRF-Gly-GCC as colorectal cancer biomarkers were both highly expressed in FFPE tissues and serum\u003c/h2\u003e \u003cp\u003eTo ensure the accuracy of the sequencing results, RT-qPCR experiments were performed on seven sets of RNA samples remaining after sequencing was completed in this study, and the relative amounts of target genes were calculated using the standard curve method to assess their expression levels in the samples. The two-dimensional structures of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC are illustrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC are both tRNA-Gly (GCC) derived. i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC are both tRNA-Gly (GCC) derived. tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC are both tRNA-Gly(GCC)-derived. i-tRF-Gly-GCC sequence is 5\u0026prime;-ATGGGGTGGTTCAGTGGGTAGAATTC-3\u0026prime;, and 5\u0026prime;-tRF-Gly-GCC sequence is 5\u0026prime;- GCATGGGTGGTTCAGTGGTAGAATTC-3\u0026prime;. After RT-qPCR validation, the \u003cem\u003eP\u003c/em\u003e-value of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC was less than 0.05, and the expression levels were consistent with the sequencing results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) and had clinical diagnostic value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eTo ensure the accuracy and wide applicability of the results, this study further collected serum samples from 10 patients who were diagnosed with colorectal cancer and admitted to the Gastroenterology Center of Panyu Central Hospital affiliated with Guangzhou Medical University, as well as 10 healthy volunteers recruited by the Health Management Center. The expression levels of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC in the sera of colorectal cancer patients could be accurately measured by RT-qPCR experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). This will provide a solid foundation for subsequent studies and help to gain a deeper understanding of the roles of these two tsRNAs in colorectal carcinogenesis and progression, as well as their value as potential noninvasive detection biomarkers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A, B) GO and KEGG enrichment of the i-tRF-Gly-GCC. (C, D) GO and KEGG enrichment of the 5\u0026prime;-tRF-Gly-GCC. (E) 2D structure of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC. (F) RT-qPCR validation of sequencing samples(n\u0026thinsp;=\u0026thinsp;7). (G) ROC analysis of of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC. (H) RT-qPCR of clinical serum samples (n\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of target genes and tsRNA correlations\u003c/h2\u003e \u003cp\u003eTo explore the biological functions of the target genes of tsRNAs, we performed functional analysis for the COAD and READ datasets of the TCGA database. After importing the datasets, the data were preprocessed to obtain the mRNA expression matrix containing the gene expression information of all the qualified samples. We performed PCA analysis before and after batch correction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The data distribution was more compact and consistent after the correction. Taking the truncation threshold as 0.85, a soft threshold of 9 was jointly determined based on the average of the neighbor-joining functions of all genes in the gene module (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The disTOM matrix was passed through the hclust function to draw a systematic clustering tree among genes. We show the module gene clustering dendrogram, divided modules, and merged modules in turn (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The merged gene modules have 33 gene modules except for the meaningless grey.\u003c/p\u003e \u003cp\u003eTo deeply explore the correlation between each gene module and the clinical phenotype, Pearson's correlation coefficient of each gene in the two sets of samples was first calculated by t-test. Subsequently, the P-value was calculated by hypothesis testing to assess the statistical significance of the association between the modules and the clinical phenotypes. Subsequently, we performed a correlation analysis between modules and clinical phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Modules with a high correlation of tumorigenesis risk were explored in the Tumor group, and the absolute value of the correlation coefficient exceeding 0.3 was used as the benchmark for screening. darkred module (Cor\u0026thinsp;=\u0026thinsp;0.59, P-value\u0026thinsp;=\u0026thinsp;9e-65), lightyellow module (Cor\u0026thinsp;=\u0026thinsp;0.43, P-value\u0026thinsp;=\u0026thinsp;5e-31), darkorange module (Cor\u0026thinsp;=\u0026thinsp;0.4, P-value\u0026thinsp;=\u0026thinsp;2e-27), plum1 module (Cor\u0026thinsp;=\u0026thinsp;0.38, P-value\u0026thinsp;=\u0026thinsp;5e-24), yellowgreen module (Cor\u0026thinsp;=\u0026thinsp;0.36, P-value\u0026thinsp;=\u0026thinsp;9e-22), greenyellow module (Cor\u0026thinsp;=\u0026thinsp;0.36, P-value\u0026thinsp;=\u0026thinsp;7e-22 ), grey60 module (Cor = -0.9, P-value\u0026thinsp;=\u0026thinsp;7e-245), midnightblue module (Cor = -0.56, P-value\u0026thinsp;=\u0026thinsp;2e-55), darkorange2 module (Cor = -0.52, P-value\u0026thinsp;=\u0026thinsp;5e-48), darkolivegreen module (Cor = -0.34, P-value\u0026thinsp;=\u0026thinsp;5e-19), darkgrey module (Cor = -0.3, P-value\u0026thinsp;=\u0026thinsp;4e-15). Based on this data, the two modules most associated with colorectal cancer risk were selected: grey60 (Cor = -0.9) and darkred module (Cor\u0026thinsp;=\u0026thinsp;0.59), as well as two colorectal cancer risk modules strongly associated with cancer: midnightblue module (Cor\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.56) and darkgrey module (Cor = -0.3) were analyzed in detail.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) PCA before and after batch correction. (B) Soft threshold filtering. (C) Gene clustering tree and gene module division map. (D) Correlation analysis between modules and clinical phenotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of gene modules and analysis of enriched pathways in different gene modules\u003c/h2\u003e \u003cp\u003eIn grey60 (Cor = -0.9) module, genes are mainly enriched in pathways for multiple metabolic processes, enzyme activities, ABC transporters, etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These pathways play important roles in normal cellular functions and metabolic processes. The genes in darkred module (Cor\u0026thinsp;=\u0026thinsp;0.59) were mainly enriched in ribosomal processes. These processes play key roles in cell growth, division and gene expression regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFor the colorectal cancer risk module, which is closely related to cancer, the genes within the midnightblue (Cor = -0.56) module were mainly enriched in cancer-related pathways such as neuron-associated, MAPK signaling pathway, Wnt signaling pathway, proteoglycan in cancer, and Hippo signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These pathways have important effects on proliferation, survival and metastasis of cancer cells. Finally, the genes within the darkgrey (Cor = -0.3) module were mainly enriched in cancer-related pathways such as immune-related, cytokine-related, pathways of cancer, MAPK signaling pathway, transcriptional dysregulation in cancer, NF-kappa B signaling pathway, proteoglycans in cancer, and other cancer-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). These pathways play key roles in the function of the immune system, inflammatory responses, and the growth and invasion of cancer cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) Grey60 module GO and KEGG enrichment analysis. (B) Darkred module GO and KEGG enrichment analysis. (C) Midnightblue module GO and KEGG enrichment analysis. (D) Darkgrey module GO and KEGG enrichment analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003ei-tRF-Gly-GCC promotes colorectal cancer progression by targeting the\u003c/b\u003e \u003cb\u003eRAC2\u003c/b\u003e \u003cb\u003egene and 5\u0026prime;-tRF-Gly-GCC by targeting the\u003c/b\u003e \u003cb\u003eARNT2\u003c/b\u003e \u003cb\u003egene\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo delve into the role of tsRNAs in cancer development, genes associated with the cancer pathway (hsa05200), which play a key role in cancer onset and progression, were first extracted from the KEGG database. Then, these genes were compared with the target genes of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC and intersections were obtained. The i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC were mimics overexpressed in HCT-116 cells, respectively, over RT-qPCR to determine the transfection efficiency, and the mimics transfection efficiencies of both tsRNAs were high as expected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). RT-qPCR was used to compare the changes in the expression levels of potential target genes in cells overexpressing the tsRNAs mimics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), the experimental results revealed that the expression levels of the target genes of i-tRF-Gly-GCC, \u003cem\u003eRAC2\u003c/em\u003e, and \u003cem\u003eVHL\u003c/em\u003e, and the target genes of 5\u0026prime;-tRF-Gly-GCC, \u003cem\u003eARNT2\u003c/em\u003e, and \u003cem\u003eGADD45B\u003c/em\u003e, were significantly decreased in the mimics overexpressing cells. with significantly decreased expression in the mimics overexpressing cells. This suggests that i-tRF-Gly-GCC may act by regulating the expression of \u003cem\u003eRAC2\u003c/em\u003e and \u003cem\u003eVHL\u003c/em\u003e, and 5\u0026prime;-tRF-Gly-GCC may exert its biological function by regulating \u003cem\u003eARNT2\u003c/em\u003e and \u003cem\u003eGADD45B\u003c/em\u003e. According to the results of WGCNA, special attention was paid to the above four potential target genes: \u003cem\u003eRAC2\u003c/em\u003e, \u003cem\u003eVHL\u003c/em\u003e, \u003cem\u003eARNT2\u003c/em\u003e, and \u003cem\u003eGADD45B\u003c/em\u003e. Specifically, the \u003cem\u003eRAC2\u003c/em\u003e gene was in the darkgrey gene module, with a correlation coefficient of -0.3; the \u003cem\u003eVHL\u003c/em\u003e gene was located in the lightyellow gene module, with a correlation coefficient of The correlation coefficient of the \u003cem\u003eARNT2\u003c/em\u003e gene is -0.56 in the midnightblue gene module, and finally, \u003cem\u003eGADD45B\u003c/em\u003e gene is in the steelblue gene module, with a correlation coefficient of 0.081. Given the weak correlation between the steelblue gene module and the clinical phenotypes, we will focus on the correlation coefficient of the \u003cem\u003eGADD45B\u003c/em\u003e gene in the subsequent study. In subsequent studies, we will focus on the analysis of \u003cem\u003eRAC2\u003c/em\u003e, \u003cem\u003eVHL\u003c/em\u003e, and \u003cem\u003eARNT2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eBased on the expression Count files of COAD and READ datasets in TCGA, the three genes, \u003cem\u003eRAC2\u003c/em\u003e, \u003cem\u003eVHL\u003c/em\u003e, and \u003cem\u003eARNT2\u003c/em\u003e, were statistically analyzed by independent samples t-test, and it was found that the expression of the \u003cem\u003eRAC2\u003c/em\u003e gene and \u003cem\u003eARNT2\u003c/em\u003e gene was significantly reduced in colorectal cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). This phenomenon suggests that these two genes may play a crucial role in the development and progression of colorectal cancer. However, for the \u003cem\u003eVHL\u003c/em\u003e gene, no significant difference was observed in the two data sets.\u003c/p\u003e \u003cp\u003eThe metabolic activity of the cells was measured by CCK-8 assay, thus indirectly reflecting the cell proliferation. The experimental results showed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) that overexpression of both i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC significantly enhanced the proliferation of colorectal cancer cells HCT-116. A cell scratch assay was used to observe the effects of overexpression of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC on the migratory ability of colorectal cancer cells HCT-116 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The results indicate that overexpression of these two molecules can significantly enhance the migratory ability of colorectal cancer cells.\u003c/p\u003e \u003cp\u003eBased on the above data analysis and experimental results, a hypothesis was proposed that i-tRF-Gly-GCC further promotes the growth and migration of colorectal cancer cells by targeting the \u003cem\u003eRAC2\u003c/em\u003e gene, and 5\u0026prime;-tRF-Gly-GCC further promotes the growth and migration of colorectal cancer cells by targeting the \u003cem\u003eARNT2\u003c/em\u003e gene, and we show a schematic of its binding site (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-G). This finding reveals the critical roles of i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC in colorectal carcinogenesis and progression and provides an important theoretical basis for future therapeutic strategies targeting these two tsRNAs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(A) Transfection efficiency of mimics overexpressing tsRNAs. (B) Validation of target gene results by RT-qPCR after overexpression of tsRNAs. (C) Boxplot of gene expression in TCGA. (D) Overexpression of tsRNAs promotes proliferation of HCT-116 cells. (E) Overexpression of tsRNAs promotes HCT-116 cell migration. (F) i-tRF-Gly-GCC binding site to target gene \u003cem\u003eRAC2\u003c/em\u003e. (G) 5\u0026prime;-tRF-Gly-GCC binding site to target gene \u003cem\u003eARNT2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe incidence of colorectal cancer is gradually increasing in younger people, and among adults under 50 years of age, both men and women worldwide [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The incidence of colorectal cancer is grim, and its causes are very complex, including a combination of sex factors, genetic factors, environmental factors, lifestyle, and other factors [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, there is an urgent need for a biomarker that can be detected noninvasively and with high stability to achieve early screening, diagnosis, and treatment of colorectal cancer, thus improving patients' survival rate and quality of life [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong many biomarkers, tRNA-derived small RNA (tsRNA) is an emerging biomarker that not only possesses the potential to serve as a target for drug resistance in cancer therapy but also has many other advantages [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For example, tsRNAs can be obtained in body fluids, are easy to obtain, can be detected noninvasively, are not easily degraded with modifications, and are highly stable [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Therefore, tsRNAs are expected to be important biomarkers and therapeutic targets for colorectal cancer diagnosis. However, the application of tsRNAs as biomarkers in colorectal cancer diagnosis and their regulatory relationship with colorectal cancer progression is still in its infancy.\u003c/p\u003e \u003cp\u003eIn this study, starting from high-throughput sequencing of tsRNAs, we successfully obtained tsRNAs with differential expression in colorectal cancer tissues and paracancerous tissues. to validate the reliability of the sequencing results, the sequencing samples were subjected to RT-qPCR verification. Meanwhile, to explore the possibility of using these tsRNAs as biomarkers for non-invasive detection in blood in the future in the clinic, RT-qPCR was performed on clinical serum samples [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Through the analysis of the above RT-qPCR results and screening by ROC analysis, two tRNA-Gly (GCC)-derived tsRNAs were finally identified, i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC.\u003c/p\u003e \u003cp\u003eSubsequently, target gene studies were performed for i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC. Using the target gene prediction tools miRanda and TargetScan, target genes were predicted based on indicators such as evolutionary conservation of the binding region, thermodynamic stability of the double-stranded structure, and the principle of sequence complementarity. To further validate these potential target genes, i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC were overexpressed in human colorectal cancer cells HCT-116 using mimics transfection technology, and the changes in the expression of the target genes were examined using RT-qPCR, which led to the identification of potential mRNA targets of tsRNAs [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo comprehensively study the roles of target genes in colorectal cancer, we conducted a thorough analysis of the COAD and READ datasets from The Cancer Genome Atlas (TCGA) database. We discovered highly correlated gene sets that show significant alterations in colorectal cancer. To explore the connections between gene sets and clinical characteristics, we identified potential biomarkers and therapeutic targets, which significantly reduced expression in overexpressing cells, and in large dataset cohorts and modules linked to clinical outcomes, were pinpointed as possible targets for tsRNAs, on this basis, cell phenotyping experiments revealed that high expression of both i-tRF-Gly-GCC and 5'-tRF-Gly-GCC promotes cell proliferation and migration.\u003c/p\u003e \u003cp\u003eThe strength of this study is that it is the first time to clarify that tRNA-Gly (GCC)-derived i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC are both highly expressed in colorectal cancer mass tissues compared to paracancerous tissues and in colorectal cancer patient sera compared to the sera of healthy physical examination volunteers. This finding provides important clues for further investigation of the role of tsRNAs in colorectal cancer development. To screen the potential target genes of tsRNAs, multiple methods were used for comprehensive analysis including target gene prediction, verification of gene expression changes by RT-qPCR after overexpression of tsRNAs, gene expression analysis using the TCGA database, and association of the module in the WGCNA analysis.\u003c/p\u003e \u003cp\u003eIn future studies, it is necessary to expand the sample size and cover more regions and populations. Due to the complexity of the genesis of colorectal cancer, the present study did not investigate the changes in the downstream protein levels of tsRNAs after inhibiting their transcription by binding to mRNAs. In addition, tsRNAs may be produced under certain conditions such as stress and hypoxia, which was not clarified in this study and can be investigated in the future [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Future studies can also further elucidate the process of tsRNAs production and investigate how various environmental or disease conditions impact their expression levels [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we found that i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC are highly expressed in colorectal cancer tissues and sera of colorectal cancer patients, our results suggest that they can be used as biomarkers and therapeutic targets for colorectal cancer. Specifically, i-tRF-Gly-GCC promotes colorectal cancer development by targeting the \u003cem\u003eRAC2\u003c/em\u003e gene, and 5\u0026prime;-tRF-Gly-GCC promotes colorectal cancer development by targeting the \u003cem\u003eARNT2\u003c/em\u003e gene. This study is important for understanding the influence of tsRNAs on colorectal cancer development and provides a theoretical basis for utilizing i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC as biomarkers for the early diagnosis of colorectal cancer and the development of targeted therapeutic approaches against CRC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eSupplementary Information\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32070615, 81902093), Guangdong Provincial Natural Science Foundation (2022A1515010569), Guangzhou Science and Technology Project (2024A04J6265), Medical Science and Technology Research Foundation of Guangdong Province (A2024164), Science and Technology Program of Panyu central hospital (PY-2023-003), the Science and Technology Program of Guangzhou (202002020023).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026prime;\u0026nbsp;contributions\u003c/p\u003e\n\u003cp\u003eX. Wang, and Y. Wan proposed and designed the study. Y. Jiao, Y. Lai and A. Liu performed the experiments. W. Luo, X. Zhang and X. Lin provided the clinical samples. Y. Jiao wrote the manuscript. A. Liu, X. Wang, and Y. Wan revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the Institutional Review Board (PYRC-2024-262-01) of The Affiliated Panyu Central Hospital of Guangzhou Medical University prior to its commencement.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge and thank all members who contributed to this study.\u003c/p\u003e\n\u003cp\u003eCompeting of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE272805 (reviewer token: enwlomaqtxefraj).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. 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Cell Death Dis 14:748\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"colorectal cancer, tsRNAs, biomarkers, early diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-5794498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5794498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) is one of the most common gastrointestinal tumors and the second leading cause of malignancy-related death worldwide. Novel biomarkers with high sensitivity and specificity are necessary to improve the diagnosis of colorectal cancer (CRC) in terms of early diagnosis and prognosis. In this study, we obtained tsRNAs expression profiles from formalin-fixed and paraffin-embedded (FFPE) clinical tissue samples to identify novel tsRNAs with potential biomarker properties in colorectal cancer. The expression profiles of colorectal cancer tsRNAs were successfully constructed, 612 up-regulated and 439 down-regulated tsRNAs were identified in the tumor group. tRNA-Gly (GCC)-derived i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC were highly expressed in CRC tissues compared to the paraneoplastic tissues. The same results were found in serum from colorectal cancer patients compared to serum from healthy volunteers. Both tsRNAs were highly expressed in CRC tissues and the AUC in ROC analysis was greater than 0.7, which has clinical diagnostic value. WGCNA analysis showed that the target genes of the two tsRNAs were closely related to CRC, and the expression of the target genes was significantly decreased in the cancer groups of the COAD and READ datasets. We also performed validation experiments in HCT-116 cells, and the results confirmed that i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC significantly enhanced cell proliferation and migration. In conclusion, we identified and characterized two tsRNAs (i-tRF-Gly-GCC and 5\u0026prime;-tRF-Gly-GCC) as the biomarkers for the diagnosis of colorectal cancer.\u003c/p\u003e","manuscriptTitle":"tsRNAs sequencing reveals tRNA-Gly (GCC)-derived small RNAs as colorectal cancer biomarker","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 09:09:54","doi":"10.21203/rs.3.rs-5794498/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d50f1d36-c50e-47f6-bae2-147d763dc7cd","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T14:48:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 09:09:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5794498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5794498","identity":"rs-5794498","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}