Comprehensive Assessment of serum 3′-tRF Arg as a novel diagnostic biomarker for gastric cancer

Comprehensive Assessment of serum 3′-tRF Arg as a novel diagnostic biomarker for gastric cancer | 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 Comprehensive Assessment of serum 3′-tRF Arg as a novel diagnostic biomarker for gastric cancer Rui Ding, Yang Li, Yu Zhang, Xun Li, Xinliang Gu, Xianjuan Shen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4272899/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 Background : Gastric cancer (GC) is one of the malignant tumors with the highest mortality rates worldwide, yet there is a lack of diagnostic markers with high sensitivity in the clinic. tRNA-derived small RNAs (tsRNAs) are a novel type of non-coding small RNAs characterized by their abundance in body fluids and specific biological functions. In this study, we focused on the potential of tsRNAs as biomarkers for the diagnosis of GC. Methods : Differential expression of tsRNAs was screened by high-throughput sequencing, and Quantitative real-time PCR verified the expression of 3′-tRF Arg in GC serum and tissues. The methodological evaluation of 3′-tRF Arg was confirmed using Sanger sequencing and agarose gel electrophoresis. The correlation between the expression levels of 3′-tRF Arg and clinical pathological parameters was analyzed using Chi-square tests. The diagnostic value was assessed through the receiver operating characteristic curve, and the impact of 3′-tRF Arg expression on survival was evaluated using Kaplan–Meier survival analysis. Result : 3′-tRF Arg was found underexpressed in GC tissues and serum with good stability. The differential expression of serum 3′-tRF Arg could identify GC patients and show significant correlations with clinical pathological features. Furthermore, the receiver operating characteristic curve indicated that 3′-tRF Arg possesses a higher diagnostic value than conventional biomarkers, particularly in the early diagnosis of GC. Conclusions : 3′-tRF Arg is significantly underexpressed in the serum of GC patients and can serve as a biomarker of high sensitivity. It possesses superior diagnostic efficacy compared to traditional markers and is valuable for monitoring tumor development and prognosis. tRNA-derived small RNAs 3′-tRFArg Gastric cancer Biomarker Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Gastric cancer (GC) is a common malignant tumor of the digestive tract worldwide. Helicobacter pylori infection and unhealthy dietary habits, such as smoking, high salt diet, and high intake of processed meats, are key factors contributing to the high incidence of GC (Thrift et al. 2023). Early GC symptoms resemble those of benign gastric diseases such as gastritis and gastric ulcers, which can be easily overlooked, resulting in patients often being diagnosed at an advanced stage, missing the opportunity for cure (Dassen et al. 2010; Shen et al. 2013). It is generally accepted that upper gastrointestinal endoscopy is the gold standard for diagnosing GC, however this invasive procedure has the potential to cause discomfort to patients. The outcome of the diagnosis is also dependent on the operation and diagnostic level of endoscopist (Park et al. 2014). As a non-invasive diagnostic tool, tumor marker detection has attracted much attention. However, traditional biomarkers such as carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), and carbohydrate antigen 724 (CA724) have limited sensitivity in diagnosing GC (Li et al. 2011). In light of this, novel diagnostic markers with high sensitivity are of great clinical value for the detection and treatment of GC at an early stage. Non-coding RNA (ncRNA) is a type of RNA that does not encode proteins and constitutes the largest component of the human transcriptome (Hanly et al. 2018). ncRNAs come in a wide variety, including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), Piwi-interacting RNAs (piRNAs), and microRNAs (miRNAs) (Li et al. 2014). Several mechanisms have been implicated in the biological role of these ncRNAs in cancer such as DNA methylation, RNA silencing, and mRNA translation regulation (Li et al. 2021). Chen et al. found that hsa-circ-0000711 is upregulated in liver cancer cells, promoting liver cancer cell proliferation and inhibiting apoptosis by targeting has-miR-103a-3p. This suggests that the elevated expression level of hsa-circ-0000711 is positively correlated with the progression of liver cancer and potentially serves as a diagnostic biomarker and therapeutic target for liver cancer (Chen et al. 2020). Shi et al. reported that the expression level of lncRNA HOTAIR is significantly higher in stage II, III, and IV breast cancer than in stage I, suggesting its potential as a diagnostic and prognostic marker for breast cancer (Shi et al. 2020). However, there are a number of limitations associated with these diagnostic markers. Therefore, we are attempting to find a more suitable biomarker. tRNA-derived small RNAs (tsRNAs) are a novel class of non-coding small RNA, generated from tRNA or tRNA precursors at specific cleavage sites rather than being random degradation products of tRNA. They are produced through precise regulation (Kumar et al. 2014). tsRNAs can be classified into two types based on different cleavage sites: (1) tRNA-derived fragments (tRFs), which are produced by Dicer enzyme cleavage of the D-loop or T-loop of mature tRNAs. (2) tRNA halves (tiRNAs), which are generated by endonucleases such as angiogenin and RNase L, cleaving the anticodon loop (Liu et al. 2021). tsRNAs participate in regulating various stages of gene expression, including transcriptional gene silencing, post-transcriptional gene silencing, rRNA regulation, translational regulation, and reverse transcriptional regulation, and are associated with critical cellular processes such as self-renewal, differentiation, and proliferation (Zhang et al. 2023). At the post-transcriptional gene level, tsRNAs function similarly to miRNAs and influence disease progression by targeting messenger RNAs (mRNAs) to regulate their stability (Lai et al. 2023). The tRNA-derived tRF3008A inhibits Colorectal cancer (CRC) metastasis and progression by binding to the Argonaute (AGO) protein and decreasing the stability of the oncogenic transcript FOXK1 in CRC cells, suggesting that active tRFs may be useful as biomarkers and therapeutic targets for CRC (Han et al. 2022). Goodarzi et al. found that both normal breast epithelial cells and breast cancer cells induce i-tRF expression under hypoxic conditions. Hypoxia-induced i-tRF competitively binds to YB-1, leading to the degradation of the oncogenic transcript by depriving it of YB-1 protection (Goodarzi et al. 2015). Additionally, tsRNAs can regulate protein translation by affecting ribosome biogenesis. It has been reported that LeuCAG3′tsRNA can bind to the coding region of mRNA for ribosomal protein RPS28, altering the secondary structure of the ribosomal protein and enhancing its translation (Kim et al. 2017). tsRNAs exist in abundance and with high stability in body fluids and are involved in a wide range of pathological processes. They exhibit a strong ability to discriminate between cancer patients and healthy individuals, providing a new avenue for the clinical development of non-invasive biomarkers with high specificity (Gu et al. 2022; Zhang et al. 2022). The possibility of using tsRNAs as cancer biomarkers has been demonstrated in several studies. Jin et al. found that tRF-Pro-AGG-004 and tRF-Leu-CG-002 may be potential biomarkers for pancreatic cancer, and that their combined diagnosis exhibits certain disease specificity (Jin et al. 2021). In CRC, 5′-tRF-GlyGCC contributes to the diagnosis and prognosis of CRC and may also serve as a therapeutic target for the disease (Wu et al. 2021). In conclusion, tsRNAs provide significant clinical value in our search for a possible biomarker for GC. In this study, 3′-tRF Arg was evaluated as a potential tumor biomarker for GC. Compared to healthy controls, both the serum and tissue expression levels of 3′-tRF Arg were decreased in GC patients. Furthermore, its expression levels were negatively correlated with tumor grade and neural/vascular invasion, which showed good diagnostic performance in distinguishing GC patients from healthy individuals. 3′-tRF Arg exhibited good stability in clinical applications and was not easily disturbed by environmental interference. Based on receiver operating characteristic (ROC) analysis, 3′-tRF Arg was found to have greater diagnostic efficacy when compared with CEA, CA199, and CA724, with the highest efficiency achieved through combined diagnosis. Additionally, it effectively monitored postoperative conditions in GC patients and played a dynamic monitoring role. Therefore, 3′-tRF Arg may be used as a valuable tumor diagnostic biomarker. 2. Materials and methods 2.1 Clinical specimens According to the ethical guidelines of the World Medical Association, serum samples were collected from 129 GC patients, 120 healthy donors, 52 gastritis patients, and 40 postoperative GC patients from the Affiliated Hospital of Nantong University. A total of 20 sets of GC tissues and paracancerous tissues were obtained from the Department of Pathology, immediately frozen in liquid nitrogen, and then transferred to a -80°C refrigerator for long-term storage. All GC tissues were diagnosed by two or more pathologists and staged according to the 8th edition of the World Health Organization TNM classification. None of the patients mentioned above received adjuvant chemotherapy, targeted therapy, or radiotherapy. The informed consent forms were signed according to ethical standards. This project was approved by the Ethics Committee at the Affiliated Hospital of Nantong University. 2.2 Total RNA extraction and complementary DNA (cDNA) synthesis Total RNA extraction from serum samples of GC patients was extracted using the Total RNA Purification Kit and Spin Column Separation Kit (BioTeke, Wuxi, Jiangsu, China). Total RNA from tissue samples was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Subsequently, the extracted total RNA was reverse transcribed into cDNA using the Revert Aid RT Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA). The reverse transcription reaction was conducted in a 10 µl reaction system, incubated at 42°C for 60 minutes, followed by inactivation at 70°C for 5 minutes. All procedures were performed according to the instructions of the manufacturer. 2.3 Quantitative real-time polymerase chain reaction (qRT-PCR) The qRT-PCR reaction was performed on a QuantStudio 5 instrument (Thermo Fisher Scientific, Waltham, MA, USA). The total reaction volume was 20 µL, comprising 10 µL of ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, Jiangsu, China), 5 µL of cDNA, 1 µL of primers, and 3 µL of nuclease-free water. Primers included forward and reverse primers for 3′-tRF Arg and RNU6B, all of which were manufactured by RiboBio (RiboBio, Guangzhou, Guangdong, China). RNU6B was used as a reference gene to normalize 3′-tRF Arg expression. The relative expression level was calculated using the 2 -ΔΔCt method. ΔΔCt was calculated as follows: ΔΔCt=ΔCt tumor[Ct (target)−Ct (reference)] −ΔCt control[Ct (target)−Ct (reference)]. 2.4 Room temperature placement and repeated freeze-thaw experiments A total of 20 serum samples were randomly mixed and left at room temperature (25°C) for 0, 6, 12, 18 and 24 hours. The mixed serum was freeze-thawed 0, 1, 3, 5, and 10 times at -80°C and room temperature, followed by RNA extraction and detection of 3′-tRF Arg expression. 2.5 Gradient dilution assay RNA was extracted from 20 randomly mixed serum samples, and total RNA was reverse transcribed into cDNA. The obtained cDNA was then diluted to 10, 10 2 , 10 3 , and 10 4 times to detect the expression of 3′-tRF Arg . 2.6 Statistical analyses SPSS Statistics Version 20.0 (IBM SPSS Statistics, Chicago, IL, USA) and GraphPad Prism 8.0 (GraphPad Software, San Jose, California, USA) were used for data analysis in this study. 3′-tRF Arg expression in each group is presented as mean ± standard deviation (SD). All research data first underwent normality testing using GraphPad Prism 8.0 to exclude the possibility of normal distribution. The Mann–Whitney U test was employed to compare two independent groups, while the Kruskal–Wallis H test was used to compare multiple independent groups. The Wilcoxon signed-rank test was utilized to analyze the difference in expression levels of 3′-tRF Arg in preoperative and postoperative serum samples from GC patients. A chi-square test was conducted to assess the correlation between 3′-tRF Arg and pathological parameters, and Kaplan–Meier curves were employed for survival data evaluation. The Area Under the Curve (AUC) was analyzed to evaluate the diagnostic performance of serum 3′-tRF Arg for GC. The cutoff value of 3′-tRF Arg was determined using Youden index, and the reference ranges of CEA, CA199, and CA724 were obtained from the Affiliated Hospital of Nantong University. The difference was considered statistically significant at a P-value <0.05. 3. Results 3.1 Screening of 3′-tRF Arg in GC Through high-throughput sequencing of GC tissues and their matched paracancerous tissues, tsRNAs differentially expressed in GC were identified. Based on the sequencing results, we screened three low-expressed tsRNAs and validated them in the serum of 24 GC patients by qRT-PCR and found that only 3′-tRF Arg exhibited significant differences (Fig. 1a). Subsequently, we collected 20 pairs of GC tissues and their adjacent paracancerous tissues. Consistently, the expression level of 3′-tRF Arg was significantly lower in GC tissues compared to matched paracancerous tissues (Fig. 1b). Furthermore, correlation analysis of GC tissues and corresponding patient serum samples revealed that patients with lower serum levels of 3′-tRF Arg also exhibited lower expression in paired GC tissues (Fig. 1c). Therefore, we selected 3′-tRF Arg for an in-depth study. Fig. 1 Expression of tsRNAs in GC and screening of 3′-tRF Arg . a Relative expression of three low-expressed tsRNAs in the serum of 24 GC patients; b Expression levels of 3′-tRF Arg in 20 pairs of GC tissues and their adjacent non-cancerous tissues; c Pearson correlation analysis of the expression levels of 3′-tRF Arg in 20 pairs of GC tissues and corresponding patient serum samples. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 3.2 3′-tRF Arg is a sort of tRFs As shown in the human genome build (GRCh37/hg19) in the UCSC Genome Browser database (http://genome-asia.ucsc.edu/biomarker.html), 3′-tRF Arg is located on chromosome 7, with coordinates ranging from 139,025,486 to 139,025,518 (Fig. 2a). Based on the basic information in MINTbase v2.0 (http://cm.jefferson.edu/MINTbase/), 3′-tRFArg is identified as a 33bp 3'-tRF fragment (CAGGGATTGTGGGTTCGAGTCCCATCTGGGGTGCCA) (Fig. 2b), with the cleavage site located on the anticodon stem of tRNA-Arg-CCT-4-1 (http://gtrnadb.ucsc.edu/genomes/eukaryota/Hsapi19/genes/tRNA-Arg-CCT-4-1.html) (Fig. 2c). Accordingly, we named it 3′-tRF Arg (Lyons et al. 2016). Upon agarose gel electrophoresis, we observed a clear, single band of approximately 75 bp, which confirmed the integrity and accuracy of the qRT-PCR product (Fig. 2d). Meanwhile, Sanger sequencing of the product was consistent with the designed sequence (Fig. 2e). Fig. 2 3′-tRF Arg is a sort of tRFs. a UCSC Genome Browser database showed that 3′-tRF Arg is located on chromosome 7, with coordinates of 139,025,486-139,025,518; b According to MINTbase v2.0, 3′-tRF Arg is a 3'-tRF (5'-CAGGGATTGTGGGTTCGAGTCCCATCTGGGGTGCCA-3'); c The cleavage site of 3′-tRF Arg is located on the Anticodon stem of tRNA-Arg-CCT-4-1; d Agarose gel electrophoresis showed that the RT-qPCR product of 3′-tRF Arg has a single band of approximately 75bp; e Sanger sequencing of the qRT-PCR product confirmed its consistency with the designed sequence 3.3 Methodological evaluation of 3′-tRF Arg We analyzed the molecular properties of 3′-tRF Arg in order to determine whether the method for estimating the expression level is suitable for clinical use. Firstly, the stability of 3′-tRF Arg was tested using mixed serum samples, showing good performance with a coefficient of variation (CV) of 1.85 in the intra-assay and 2.47 in the inter-assay (Table 1). Subsequently, the mixed serum samples were left at room temperature for 0, 6, 12, 18, and 24 hours with repeated freeze-thaw cycles (0, 1, 3, 5, and 10 times). Despite the change in external conditions, there was no significant difference between the expression levels of 3′-tRF Arg (P > 0.05), which demonstrated its stability and good resistance to interference (Fig. 3a, b). Gradient dilution experiments showed that 3′-tRF Arg exhibited good linearity, ensuring the reproducibility of the measurements (Fig. 3c, d). The qRT-PCR results revealed smooth amplification curves and single-peak melting curves for 3′-tRF Arg (Fig. 3e, f ). Fig. 3 Methodological evaluation of 3′-tRF Arg . a, b Room temperature placement and repeated freeze-thaw experiments showed no significant change in the expression level of 3′-tRF Arg ; c, d Gradient dilution assays demonstrated good linearity; e, f 3′-tRF Arg exhibited smooth amplification curves and single-peak melting curves, with the red line representing 3′-tRF Arg and the blue line representing RNU6B Table 1 The intra-assay CV and the inter-assay CV of 3′-tRF Arg 3′-tRF Arg U6 Intra assay CV, % 1.85 2.08 Inter assay CV, % 2.47 2.82 Abbreviations: CV, coefficient of variation 3.4 Clinical role and prognostic value of the serum 3′-tRF Arg expression A serum sample analysis of 129 GC patients, 52 gastritis patients, and 120 healthy donors explored the utility of 3′-tRF Arg as a GC biomarker in clinical practice. The results showed that the expression level of 3′-tRF Arg in the serum of GC patients was considerably lower than that in healthy donors and gastritis patients (P < 0.05), while there was no significant difference in expression level between gastritis patients and healthy donors (Fig. 4a). To further explore the correlation between the expression level of 3′-tRF Arg and clinicopathological features, the 129 GC patients were split into two groups based on the median expression level: a relatively high expression group (expression level >0.494479171, n=64) and a relatively low expression group (expression level ≤0.494479171, n=65). The analysis of the correlation of serum 3′-tRF Arg expression and clinicopathological parameters was conducted using Chi-square tests (Table 2). The results showed that the expression level of 3′-tRF Arg was significantly associated with tumor differentiation, T stage, lymph node metastasis, TNM stage, and neural/vascular invasion (Fig. 4b-e), with no significant differences observed in terms of gender, age, tumor size, Lauren classification, C-erbB-2, and MMR. Different TNM stages significantly differed in 3′-tRF Arg expression levels in the serum of GC patients: patients with stage I-IV GC showed significantly lower expression levels than healthy donors, with the lowest expression levels observed in stage III-IV patients, followed by stage I-II patients (Fig. 4f). As shown in Fig. 4d, patients with neural/vascular invasion have a lower serum concentration of 3′-tRF Arg than those without invasion, suggesting that 3′-tRF Arg could facilitate the diagnosis of malignant progression. By following 40 GC patients after surgery, we investigated the correlation between serum expression levels and prognosis. Our findings indicated that serum expression levels had increased notably after surgery, approaching normal levels (Fig. 4g). According to a Kaplan-Meier analysis, patients with low expression showed a significantly worse prognosis than those with high expression (P < 0.05) (Fig. 4h). The findings suggest that 3′-tRF Arg may be useful as a diagnostic biomarker for GC, assisting in the clinical dynamic monitoring of tumor progression Fig. 4 Clinical value and prognostic effect of serum 3′-tRF Arg in GC. a Expression levels of 3′-tRF Arg in serum samples from GC patients (n=129), gastritis patients (n=52), and healthy donors (n=120); b Expression levels of 3′-tRF Arg in serum samples from GC patients with high differentiation (n=59) and low differentiation (n=70); c Expression levels of 3′-tRF Arg in serum samples from GC patients at different stages of tumor invasion depth and healthy donors (T1–T2: n=81, T3–T4: n=48, healthy donors: n=120); d Expression levels of 3′-tRF Arg in serum samples from GC patients with (n=66) or without (n=63) neural/vascular invasion; e Expression levels of 3′-tRF Arg in serum samples from GC patients with (n=98) or without (n=31) lymph node metastasis; f Expression levels of 3′-tRF Arg in serum samples from GC patients at stages I–II (n=76), stages III–IV (n=53), and healthy donors (n=120); g Changes in serum 3′-tRF Arg expression levels before and after surgery in 40 GC patients; h Kaplan-Meier curve analysis of the relationship between 3′-tRF Arg expression levels and survival rates in GC patients. *P<0.05 **P<0.01 ***P<0.001 ****P<0.0001 Table 2 Clinical pathological analysis of 3′-tRF Arg Parameter No. of patients 3′-tRF Arg (low) 3′-tRF Arg (high) P-value Sex male 85 43 42 0.758 female 44 21 23 Age （ year ） ＜60 33 12 21 0.078 ≥60 96 52 44 Tumor size ＜5 91 46 45 0.742 ≥5 38 18 20 Differentiation grade Well-moderate 59 21 38 0.003 Poor-undifferentiation 70 43 21 T stage T1-T2 81 31 50 0.001 T3-T4 48 33 15 Lymph node status Positive 98 56 42 0.002 Negative 31 8 23 TNM stage Ⅰ-Ⅱ 76 25 51 <0.001 Ⅲ-Ⅳ 53 39 14 Nerve/vascular invasion Positive 66 42 24 0.001 Negative 63 22 41 Intestinal type 60 32 28 Lauren classifcation Mixed type 45 20 25 0.665 Difuse type 24 12 12 C-erbB-2 Positive 22 11 11 0.968 Negative 107 53 54 MMR dMMR 119 57 62 0.179 pMMR 10 7 3 Abbreviations: MLH1, PMS2, MSH2, and MSH6 were all positive for pMMR (normal expression), and 1 or more negative for dMMR (deletion) 3.5 Evaluation of the diagnostic efficacy of serum 3′-tRF Arg for GC Given the limitations of commonly used GC diagnostic markers such as CEA, CA199, and CA724, we explored the diagnostic value of 3′-tRF Arg as a potential biomarker for GC. An analysis of ROC curves was performed to evaluate the expression levels of 3′-tRF Arg , CEA, CA199, and CA724 in 129 GC patients and 120 healthy individuals., comprehensively analyzing the diagnostic efficacy of each biomarker. 3′-tRF Arg had an AUC of 0.808 (95% confidence interval (CI) 0.752-0.863), which was higher than that of CEA (0.747, 95% CI 0.686–0.808), CA199 (0.683, 95% CI 0.616–0.750), and CA724 (0.759, 95% CI 0.699–0.818) (Fig. 5a). Subsequently, 3′-tRF Arg was combined with CEA, CA199, and CA724 for diagnosis and with all three and four biomarkers together. Based on Fig. 4b, combined diagnosis had a higher AUC than any single biomarker. When the four markers were combined, the AUC reached the highest value (0.856) (95% CI:0.808-0.903) (Fig. 5c). A combination and a single diagnostic model were investigated for their ability to differentiate GC patients from healthy donors based on the sensitivity (SEN), overall accuracy (ACCU), positive predictive value (PPV), and negative predictive value (NPV). With a cutoff point of 1.093635 and a Youden index of 0.566, 3′-tRF Arg showed higher SEN (79%), ACCU (81%), PPV (83%), and NPV (79%) compared to CEA, CA199, and CA724 (Table 3). These analyses indicate that 3′-tRF Arg may be useful as a biomarker for GC, and its diagnostic efficacy can be enhanced in combination with other tumor markers. The lack of highly sensitive biomarkers in the clinic often leads to GC patients being diagnosed at an advanced stage, missing the opportunity for an early cure. For the evaluation, we collected information from 76 early-stage GC patients (stage I and II) and 120 healthy donors. The ROC curve showed that the AUC of 3′-tRF Arg was 0.785 (95% CI 0.718–0.852), superior to that of CEA (0.744, 95% CI 0.675–0.814), CA199 (0.663, 95% CI 0.582–0.745), and CA724 (0.763, 95% CI 0.694–0.832) (Fig. 5d). Moreover, with a cut-off point of 1.093635 and a Youden index of 0.549, 3′-tRF Arg had SEN of 72%, ACCU of 79%, PPV of 72%, and NPV of 83%, which were all higher than those of CEA, CA199, and CA724 (Table 4). The combined diagnosis had a higher AUC than any single biomarker in identifying early-stage GC patients from healthy donors (Fig. 5e). When all four biomarkers were combined, the AUC reached the highest value of 0.839 (95% CI 0.782–0.895) (Fig. 5f). In our analysis of serum 3′-tRF Arg levels, we found differences between patients with GC and those with gastritis. In light of the fact that the symptoms of GC are similar to gastritis in the early stages, the ability of serum 3′-tRF Arg to distinguish between early-stage GC patients and gastritis patients is of great significance. We performed ROC analysis of the expression levels of serum 3′-tRF Arg and conventional biomarkers in 129 patients with GC and 50 patients with gastritis. The AUC of 3′-tRF Arg was 0.784 (95% CI 0.713–0.855), higher than that of CEA (0.659, 95% CI 0.576–0.742), CA199 (0.660, 95% CI 0.576–0.743), and CA724 (0.734, 95% CI 0.658–0.810) (Fig. 5g). The AUC increased when 3′-tRF Arg was diagnosed in combination with other markers (Fig. 5h). The AUC reached a maximum value of 0.858 when the four biomarkers were combined (Fig. 5i), and the SEN increased to 96% (Table 5). These findings indicate that serum 3′-tRF Arg expression levels can differentiate between GC patients and gastritis patients, and its diagnostic value is further enhanced when used in conjunction with other tumor markers. Fig. 5 Diagnostic value of serum 3′-tRF Arg for GC. a-c Diagnostic efficacy of 3′-tRF Arg , CEA, CA199, and CA724 in distinguishing GC patients from healthy donors; d-f Diagnostic value of 3′-tRF Arg , CEA, CA199, and CA724 in distinguishing early-stage GC patients from healthy donors as determined by ROC analysis; g-i Ability of 3′-tRF Arg , CEA, CA199, and CA724 to distinguish between GC patients and gastritis patients as determined by ROC analysis. *P＜0.05**P＜0.01***P＜0.001****P<0.0001 Table 3 Diagnostic performance of 3′-tRF Arg , CEA, CA199, and CA724 in distinguishing GC patients from healthy controls SEN SPE ACCU PPV NPV 3′-tRF Arg 0.79(102/129) 0.83(99/120) 0.81(201/249) 0.83(102/123) 0.79(99/126) CEA 0.52(67/129) 0.82(98/120) 0.66(165/249) 0.75(67/89) 0.61(98/160) CA199 0.43(56/129) 0.88(105/120) 0.65(161/249) 0.79(56/71) 0.59(105/178) CA724 0.51(66/129) 0.86(103/120) 0.68(169/249) 0.80(66/83) 0.62(103/166) 3′-tRF Arg +CEA 0.90(116/129) 0.67(80/120) 0.79(196/249) 0.74(116/156) 0.86(80/93) 3′-tRF Arg +CA199 0.85(110/129) 0.72(86/120) 0.79(196/249) 0.76(110/144) 0.82(86/105) 3′-tRF Arg +CA724 0.91(117/129) 0.69(83/120) 0.80(200/249) 0.76(117/154) 0.87(83/95) 3′-tRF Arg +CEA+CA199 0.93(120/129) 0.59(71/120) 0.77(191/249) 0.71(120/169) 0.89(71/80) 3′-tRF Arg +CEA+CA724 0.95(122/129) 0.55(66/120) 0.76(188/249) 0.69(122/176) 0.90(66/73) 3′-tRF Arg +CA199+CA724 0.92(119/129) 0.61(73/120) 0.77(192/249) 0.72(119/166) 0.88(73/83) 3′-tRF Arg +CEA+CA199+CA724 0.96(124/129) 0.49(59/120) 0.73(183/249) 0.67(124/185) 0.92(59/64) Abbreviations: SEN, sensitivity; SPE, specificity; ACCU, overall accuracy; PPV, positive predictive value; NPV, negative predictive value Table 4 Diagnostic performance of 3′-tRF Arg , CEA, CA199, and CA724 in distinguishing early-stage GC patients from healthy controls SEN SPE ACCU PPV NPV 3′-tRF Arg 0.72(55/76) 0.83(99/120) 0.79(154/196) 0.72(55/76) 0.83(99/120) CEA 0.50(38/76) 0.82(98/120) 0.69(136/196) 0.63(38/60) 0.72(98/136) CA199 0.37(28/76) 0.88(105/120) 0.68 133/196 0.65 28/43 0.69(105/153) CA724 0.46(35/76) 0.86(103/120) 0.70(138/196) 0.67(35/52) 0.72(103/144) 3′-tRF Arg +CEA 0.87(66/76) 0.67(80/120) 0.74(146/196) 0.62(66/106) 0.89(80/90) 3′-tRF Arg +CA199 0.80(61/76) 0.72(86/120) 0.75(147/196) 0.64(61/95) 0.85(86/101) 3′-tRF Arg +CA724 0.86(65/76) 0.69(83/120) 0.76(148/196) 0.64(65/102) 0.88(83/94) 3′-tRF Arg +CEA+CA199 0.91(69/76) 0.59(71/120) 0.71(140/196) 0.58(69/118) 0.91(71/78) 3′-tRF Arg +CEA+CA724 0.92(70/76) 0.55(66/120) 0.69(136/196) 0.56(70/124) 0.92(66/72) 3′-tRF Arg +CA199+CA724 0.88(67/76) 0.61(73/120) 0.71(140/196) 0.59(67/114) 0.89(73/82) 3′-tRF Arg +CEA+CA199+CA724 0.95(72/76) 0.49(59/120) 0.67(131/196) 0.54(72/133) 0.94(59/63) Abbreviations: SEN, sensitivity; SPE, specificity; ACCU, overall accuracy; PPV, positive predictive value; NPV, negative predictive value Table 5 Diagnostic performance of 3′-tRF Arg , CEA, CA199, and CA724 in distinguishing GC patients from gastritis patients SEN SPE ACCU PPV NPV 3′-tRF Arg 0.80(103/129) 0.67(35/52) 0.76(138/181) 0.86(103/120) 0.57(35/61) CEA 0.52(67/129) 0.79(41/52) 0.60(108/181) 0.86(67/78) 0.40(41/103) CA199 0.43(56/129) 0.88(46/52) 0.56(102/181) 0.90(56/62) 0.39(46/119) CA724 0.51(66/129) 0.79(41/52) 0.59(107/181) 0.86(66/77) 0.39(41/104) 3′-tRF Arg +CEA 0.91(117/129) 0.54(28/52) 0.80(145/181) 0.83(117/141) 0.70(28/40) 3′-tRF Arg +CA199 0.85(110/129) 0.58(30/52) 0.77(140/181) 0.83(110/132) 0.61(30/49) 3′-tRF Arg +CA724 0.91(118/129) 0.54(28/52) 0.81(146/181) 0.83(118/142) 0.72(28/39) 3′-tRF Arg +CEA+CA199 0.93(120/129) 0.46(24/52) 0.80(144/181) 0.81(120/148) 0.73(24/33) 3′-tRF Arg +CEA+CA724 0.95(123/129) 0.42(22/52) 0.80(145/181) 0.80(123/153) 0.79(22/28) 3′-tRF Arg +CA199+CA724 0.92(119/129) 0.44(23/52) 0.78(142/181) 0.80(119/148) 0.70(23/33) 3′-tRF Arg +CEA+CA199+CA724 0.96(124/129) 0.35(18/52) 0.78(142/181) 0.78(124/158) 0.78(18/23) Abbreviations: SEN, sensitivity; SPE, specificity; ACCU, overall accuracy; PPV, positive predictive value; NPV, negative predictive value 3.6 Prediction of downstream target genes of 3′-tRF Arg The downstream target genes of 3′-tRF Arg were predicted. Utilizing bioinformatics databases, we conducted an analysis to anticipate the binding of 3′-tRF Arg with its target genes. As illustrated in Fig. 6a, an overlap of 537 potential target genes was observed between the miRanda and TargetScan prediction tools, suggesting a high likelihood of interaction with 3′-tRF Arg . An analysis of the connection network identified 80 target genes associated with 3′-tRF Arg (Fig. 6b). Subsequent enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways highlighted significant enrichment in amino sugar and nucleotide sugar metabolism, the Hippo signaling pathway, and central carbon metabolism in cancer (Fig. 6c). Gene Ontology (GO) functional enrichment analysis of these target genes indicated potential involvement in energy metabolism, transcriptional regulation, and cellular signal transduction (Fig. 6d). Further exploration is essential to elucidate the underlying regulatory mechanisms of 3′-tRF Arg in GC. Fig. 6 Prediction of the downstream regulation mechanism of 3′-tRF Arg . a Venn diagram evaluating the overlapping genes predicted by the miRanda and TargetScan databases; b Potential target genes of 3′-tRF Arg ; c Enrichment analysis of potential target genes in the Kyoto Encyclopedia of Genes and Genomes; d Functional enrichment analysis of potential target genes in Gene Ontology. 4. Discussion As one of the most severe malignancies worldwide, GC is often diagnosed at a late stage due to a lack of specific diagnostic markers (Joshi et al. 2021). Despite the wide application of various treatment modalities for GC, including traditional radiotherapy and chemotherapy, molecular targeting, and immunotherapy, the five-year survival rate for advanced GC remains only 6% (Alsina et al. 2023). The identification of highly sensitive biomarkers for GC early detection is therefore urgently needed. tsRNAs have attracted our attention with the advent of high-throughput sequencing technologies. They are not randomly degraded products but are generated through precise biological processes, playing important roles in stress response, signal transduction, and gene expression (Shen et al. 2018; Zhu et al. 2019). tsRNAs can be classified into two categories: tiRNAs and tRFs, with specific molecular size, nucleotide composition, and physiological functions (Anderson et al. 2014; Pliatsika et al. 2016; Zheng et al. 2016). Under oxidative stress conditions such as hypoxia, the expression levels of some tsRNAs are significantly upregulated (Lee et al. 2009). For instance, Tao et al. found that the hypoxic environment generated by the sustained rapid growth of cancer cells stimulates the expression of HIF1α. HIF1α, in turn, targets the angiogenin promoter, promoting transcription and consequently increasing the levels of 5'tiRNA-His-GTG. This specific tiRNA then targets and inhibits the expression of LATS2, resulting in the suppression of the Hippo signaling pathway, which promotes CRC progression, indicating that 5'tiRNA-His-GTG plays a significant role in CRC progression (Tao et al. 2021). Additionally, the 3'-tRF found in B-cell lymphoma cell lines was also reported to inhibit the mRNA levels of the single-stranded DNA binding protein RPA1, which in turn inhibits endogenous RPA1 expression, inhibiting cell proliferation and regulating DNA damage response (Maute et al. 2013). All of the above studies have validated the critical role of tsRNAs in tumorigenesis but have not explored and evaluated the potential of tsRNAs as tumor biomarkers. tsRNAs are highly enriched and stably present in biological fluids, with abundances that are sometimes even higher than those of miRNAs (Dhahbi et al. 2013; Schageman et al. 2013; Olvedy et al. 2016). They are widely involved in pathological processes, demonstrating their potential as biomarkers. The 3′-tRF Arg , which is significantly underexpressed in GC, was selected for further investigation. We analyzed serum samples from 129 GC patients, 52 gastritis patients, and 120 healthy individuals and found that 3′-tRF Arg was differentially expressed in patients with GC, gastritis patients, and healthy donors. We also found significant correlations between 3′-tRF Arg expression and clinical pathological features such as tumor differentiation, lymph node metastasis, and TNM stage. 3′-tRF Arg had the highest SEN (0.79) in the combined diagnosis of GC by 3′-tRF Arg , CEA, CA199, and CA724. The highest AUC value (0.856) was obtained by combining the four markers. Furthermore, our study comprehensively analyzed the potential of 3′-tRF Arg as a GC biomarker. Through analysis of postoperative expression levels and survival curves of GC patients, we found that 3′-tRF Arg can dynamically monitor the postoperative status of GC patients, paving the way for the development of tsRNA-based biomarkers. A significant inverse correlation was observed between 3′-tRF Arg expression level and lymph node metastatic status and tumor grade in our study, indicating its clinical utility in the diagnosis of GC. The absence of early diagnostic biomarkers in clinical practice leads to the diagnosis of GC often occurring later in its progression, with a poorer prognosis (Necula et al. 2019). Therefore, we estimated the diagnostic performance of 3′-tRF Arg in the early stages (stage I and II). As expected, both the SEN and the AUC of 3′-tRF Arg were higher than those of common tumor markers, underlining its utility in the early diagnosis of GC. This provides a promising opportunity to develop non-invasive diagnostic biomarkers as well as improve the prognosis of patients. However, the current experiment still has some limitations: there was a limited number of participants in this experiment and the samples collected are geographically restricted, we need more samples to verify the effectiveness of the clinical application. A series of validations are required for the formal application of 3′-tRF Arg as a diagnostic marker in the clinic. Although we observed low expression of serum 3′-tRF Arg in GC, the mechanism of its oncogenic role in GC remains unclear. Previous studies have suggested that tsRNAs may either promote or inhibit tumor initiation and progression according to different internal mechanisms. For instance, tsRNAs can inhibit disease progression by mediating the binding of AGO proteins to transcriptional targets and inhibiting their expression in a miRNA-like manner (Shao et al. 2017; Kuscu et al. 2018). For example, Huang found that tRF/miR-1280, which binds to AGO proteins, can target the 3′UTR region of JAG2 and induce its degradation, inhibiting the Notch signaling pathways and thus suppressing CRC development (Huang et al. 2017). Additionally, there is also evidence that some tsRNAs may be able to influence disease progression and onset through a protein sponge effect (Krishna et al. 2019). 5′-tiRNA-Gln derived from hepatocellular carcinoma functions by binding EIF4A1, which negatively regulates the translation of related proteins through the intramolecular G-quadruplex structure and inhibits the proliferation and metastasis of hepatocellular carcinoma cells (Wu et al. 2023). tRFs are classified as 5′-tRF and 3′-tRF, which are generated from the 5′ and 3′ ends of mature tRNAs (Kumar et al. 2016). Meta-analysis of small RNA data has shown that 3′-tRFs are primarily distributed in the cytoplasm, while 5′-tRFs, although generated in the cytoplasm, may be transported to the nucleus through RNA-like transport mechanisms. Analysis of the CLASH data of Helwak et al. revealed that 3′-tRF has the potential to interact with AGO proteins to regulate gene expression through mechanisms similar to miRNAs (Helwak et al. 2013). 3′-tRF Arg is a type of 3′-tRF, and we speculate that it may have a similar function to miRNAs, directly interacting with the mRNA binding, inhibiting the expression of complementary targets and participating in the regulation of GC progression. This is the first report elucidating the potential of 3′-tRF Arg for the diagnosis and postoperative monitoring of GC. However, the specific mechanisms underlying the association of 3′-tRF Arg with GC occurrence remain unclear, and further investigation and validation are required in future studies. 5. Conclusion In summary, we have demonstrated that serum 3′-tRF Arg holds promise as a new biomarker for GC, exhibiting high diagnostic efficacy even in the early stages of the disease. Furthermore, our findings suggest that 3′-tRF Arg in tumor tissue could serve as a valuable biomarker for predicting postoperative survival time in patients. We intend to further investigate the underlying mechanisms of 3′-tRF Arg in GC in future studies. Abbreviations GC: Gastric cancer; tsRNAs: tRNA-derived small RNAs; CEA: carcinoembryonic antigen; CA199: Carbohydrate antigen199; CA724: Carbohydrate antigen724; ncRNA: Non-coding RNA; lncRNAs: long non-coding RNAs; circRNAs: circular RNAs; piRNAs: Piwi-interacting RNAs; miRNAs: microRNAs; tRFs: tRNA-derived fragments; tiRNAs: tRNA halves; Colorectal cancer: CRC; AGO: Argonaute; mRNAs: messenger RNAs; ROC: receiver operating characteristic; cDNA: complementary DNA; qRT-PCR: Quantitative real-time polymerase chain reaction; SD: Standard Deviation; AUC: Area Under the Curve; CV: coefficient of variation; CI: confidence interval; SEN: sensitivity; ACCU: the overall accuracy; PPV: positive predictive value; NPV: negative predictive value. Declarations Acknowledgments We appreciate all the patients who participated in this study and all those who contributed to it. Author contributions Rui Ding and Yang Li performed study design, material preparation, data collection and analysis. Rui Ding written the first draft of the manuscript, Yu Zhang took part in the experiment, Shaoqing Ju provided resources and guidance for the paper, and all authors read and approved this manuscript. Funding This project was supported by grants from the National Natural Science Foundation of China (No. 82072363, No.82272411), Jiangsu Provincial Medical Key Discipline (Laboratory) (ZDXK202240), Science and Technology Project of Jiangsu Province (BE2023741) and Foundation of Jiangsu Province Research Hospital (YJXYY202204-XKB16). Availability of data and materials Data are available upon reasonable request. The data used in the current study are available from the corresponding author on reasonable request. Consent for publication The informed consent obtained from study participants. Competing interests The authors declare that they have no competing interests. References Alsina M, Arrazubi V, Diez M et al. 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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-4272899","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291946733,"identity":"4d9c0397-a33e-415c-a0f1-4decea91bd1c","order_by":0,"name":"Rui Ding","email":"","orcid":"","institution":"Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Ding","suffix":""},{"id":291946735,"identity":"3b470179-4a62-4c61-9395-036324d179f0","order_by":1,"name":"Yang Li","email":"","orcid":"","institution":"Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":291946736,"identity":"fb85cecb-95c9-4a1d-8b66-980c9c0a5ff3","order_by":2,"name":"Yu Zhang","email":"","orcid":"","institution":"Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":291946737,"identity":"7e395c85-af1c-4178-a5ab-bb534c461a88","order_by":3,"name":"Xun Li","email":"","orcid":"","institution":"Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China","correspondingAuthor":false,"prefix":"","firstName":"Xun","middleName":"","lastName":"Li","suffix":""},{"id":291946738,"identity":"de7bf3ea-2613-4aaf-978b-1bf037578b89","order_by":4,"name":"Xinliang Gu","email":"","orcid":"","institution":"General Clinical Research Center, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China","correspondingAuthor":false,"prefix":"","firstName":"Xinliang","middleName":"","lastName":"Gu","suffix":""},{"id":291946739,"identity":"e8eacafc-f49e-4a23-8480-970177827766","order_by":5,"name":"Xianjuan Shen","email":"","orcid":"","institution":"Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China","correspondingAuthor":false,"prefix":"","firstName":"Xianjuan","middleName":"","lastName":"Shen","suffix":""},{"id":291946740,"identity":"5047fd49-a9fb-45b8-94d2-483cb6b7ae4d","order_by":6,"name":"Shaoqing Ju","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACxgYGxgcJPDYJYF5CAXFamA0eyKQlMLCBtBgQZxGb5AObwxAtDMRoYe4/vNkgIed8Hr98d+KHBwYM8vxiBwg4bEZa4YOEM7eLJdt4N0sAHWY4c3YCIS08xgaJPbcTNxzj3QDSkmBwm5CW/jNmEon/zoG0bP5BnJaGHDOJBJ4DIC3biLRlRlqxQQJPcuLMttxtFgkGEoT9Yth/eOPDHzx2if3MZzff/FFhI88vTUhLA2pcSOBXDgLyxEXfKBgFo2AUjGgAAKO3RdUiM9ceAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong 226001, China","correspondingAuthor":true,"prefix":"","firstName":"Shaoqing","middleName":"","lastName":"Ju","suffix":""}],"badges":[],"createdAt":"2024-04-16 03:36:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4272899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4272899/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55113969,"identity":"29d28df4-60ec-42e6-ad06-847747c08a04","added_by":"auto","created_at":"2024-04-22 19:16:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103740,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of tsRNAs in GC and screening of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e. \u003cstrong\u003ea\u003c/strong\u003e Relative expression of three low-expressed tsRNAs in the serum of 24 GC patients; \u003cstrong\u003eb\u003c/strong\u003e Expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in 20 pairs of GC tissues and their adjacent non-cancerous tissues; \u003cstrong\u003ec\u003c/strong\u003e Pearson correlation analysis of the expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in 20 pairs of GC tissues and corresponding patient serum samples. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272899/v1/9f64cbca07809c1dccb0e59a.jpg"},{"id":55113973,"identity":"f0de6488-92cf-4968-9d29-62a167ab182e","added_by":"auto","created_at":"2024-04-22 19:16:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210696,"visible":true,"origin":"","legend":"\u003cp\u003e3′-tRF\u003csup\u003eArg\u003c/sup\u003e is a sort of\u0026nbsp;tRFs. \u003cstrong\u003ea\u003c/strong\u003e UCSC Genome Browser database showed that 3′-tRF\u003csup\u003eArg\u003c/sup\u003e is located on chromosome 7, with coordinates of 139,025,486-139,025,518; \u003cstrong\u003eb\u003c/strong\u003e According to MINTbase v2.0, 3′-tRF\u003csup\u003eArg\u003c/sup\u003e is a 3'-tRF (5'-CAGGGATTGTGGGTTCGAGTCCCATCTGGGGTGCCA-3'); \u003cstrong\u003ec\u003c/strong\u003e The cleavage site of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e is located on the Anticodon stem of tRNA-Arg-CCT-4-1; \u003cstrong\u003ed\u003c/strong\u003e Agarose gel electrophoresis showed that the RT-qPCR product of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e has a single band of approximately 75bp; \u003cstrong\u003ee\u003c/strong\u003e Sanger sequencing of the qRT-PCR product confirmed its consistency with the designed sequence\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272899/v1/fd00766e0d38491d9cc9c31d.jpg"},{"id":55113972,"identity":"1462cf20-b21b-4cb6-bfcf-5cd8129748dc","added_by":"auto","created_at":"2024-04-22 19:16:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153705,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological evaluation of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e. \u003cstrong\u003ea, b\u003c/strong\u003e Room temperature placement and repeated freeze-thaw experiments showed no significant change in the expression level of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e; \u003cstrong\u003ec, d\u003c/strong\u003e Gradient dilution assays demonstrated good linearity; \u003cstrong\u003ee, f\u003c/strong\u003e 3′-tRF\u003csup\u003eArg\u003c/sup\u003e exhibited smooth amplification curves and single-peak melting curves, with the red line representing 3′-tRF\u003csup\u003eArg\u003c/sup\u003e and the blue line representing RNU6B\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272899/v1/0f345c047a0ecd8b98bb4930.jpg"},{"id":55113971,"identity":"5ab8464a-b40f-4bee-ae3a-5deb71cf6342","added_by":"auto","created_at":"2024-04-22 19:16:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":131046,"visible":true,"origin":"","legend":"\u003cp\u003eClinical value and prognostic effect of serum 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in GC. \u003cstrong\u003ea\u003c/strong\u003e Expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients (n=129), gastritis patients (n=52), and healthy donors (n=120); \u003cstrong\u003eb\u003c/strong\u003e Expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients with high differentiation (n=59) and low differentiation (n=70); \u003cstrong\u003ec\u003c/strong\u003e Expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients at different stages of tumor invasion depth and healthy donors (T1–T2: n=81, T3–T4: n=48, healthy donors: n=120); \u003cstrong\u003ed\u003c/strong\u003e Expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients with (n=66) or without (n=63) neural/vascular invasion; \u003cstrong\u003ee\u003c/strong\u003e Expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients with (n=98) or without (n=31) lymph node metastasis; \u003cstrong\u003ef\u003c/strong\u003e Expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients at stages I–II (n=76), stages III–IV (n=53), and healthy donors (n=120); \u003cstrong\u003eg\u003c/strong\u003e Changes in serum 3′-tRF\u003csup\u003eArg\u003c/sup\u003e expression levels before and after surgery in 40 GC patients; \u003cstrong\u003eh\u003c/strong\u003e Kaplan-Meier curve analysis of the relationship between 3′-tRF\u003csup\u003eArg\u003c/sup\u003e expression levels and survival rates in GC patients. *P\u0026lt;0.05 **P\u0026lt;0.01 ***P\u0026lt;0.001 ****P\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272899/v1/cd295ac8ee921f2b1a9bc457.jpg"},{"id":55113974,"identity":"2f40dd4b-a3a5-4b9e-a08f-d414d26eb12b","added_by":"auto","created_at":"2024-04-22 19:16:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":168225,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic value of serum 3′-tRF\u003csup\u003eArg\u003c/sup\u003e for GC. \u003cstrong\u003ea-c\u003c/strong\u003e Diagnostic efficacy of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in distinguishing GC patients from healthy donors; \u003cstrong\u003ed-f\u003c/strong\u003e Diagnostic value of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in distinguishing early-stage GC patients from healthy donors as determined by ROC analysis; \u003cstrong\u003eg-i\u003c/strong\u003e Ability of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 to distinguish between GC patients and gastritis patients as determined by ROC analysis. *P＜0.05**P＜0.01***P＜0.001****P\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272899/v1/ad90b946177b4b3a3df18fc1.jpg"},{"id":55113975,"identity":"0ed8a72c-2cab-4788-887e-ac621ede19ee","added_by":"auto","created_at":"2024-04-22 19:16:35","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":159902,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of the downstream regulation mechanism of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e. \u003cstrong\u003ea \u003c/strong\u003eVenn diagram evaluating the overlapping genes predicted by the miRanda and TargetScan databases; \u003cstrong\u003eb\u003c/strong\u003e Potential target genes of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e; \u003cstrong\u003ec\u003c/strong\u003e Enrichment analysis of potential target genes in the Kyoto Encyclopedia of Genes and Genomes; \u003cstrong\u003ed\u003c/strong\u003e Functional enrichment analysis of potential target genes in Gene Ontology.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4272899/v1/d2ca722fdc42f81eb23736a9.jpg"},{"id":55631439,"identity":"99d3bfe2-f299-436f-b2cf-c013f1878476","added_by":"auto","created_at":"2024-04-30 19:42:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1496662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4272899/v1/f4056b42-7a5d-488f-b6a6-b8c23dd5195b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Assessment of serum 3′-tRF Arg as a novel diagnostic biomarker for gastric cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) is a common malignant tumor of the digestive tract worldwide. Helicobacter pylori infection and unhealthy dietary habits, such as smoking, high salt diet, and high intake of processed meats, are key factors contributing to the high incidence of GC\u0026nbsp;(Thrift et al. 2023). Early GC symptoms resemble those of benign gastric diseases such as gastritis and gastric ulcers, which can be easily overlooked, resulting in patients often being diagnosed at an advanced stage, missing the opportunity for cure\u0026nbsp;(Dassen et al. 2010; Shen et al. 2013). It is generally accepted that upper gastrointestinal endoscopy is the gold standard for diagnosing GC, however this invasive procedure has the potential to cause discomfort to patients. The outcome of the diagnosis is also dependent on the operation and diagnostic level of endoscopist\u0026nbsp;(Park et al. 2014). As a non-invasive diagnostic tool, tumor marker detection has attracted much attention. However, traditional biomarkers such as carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), and carbohydrate antigen 724 (CA724) have limited sensitivity in diagnosing GC\u0026nbsp;(Li et al. 2011). In light of this, novel diagnostic markers with high sensitivity are of great clinical value for the detection and treatment of GC at an early stage.\u003c/p\u003e\n\u003cp\u003eNon-coding RNA (ncRNA) is a type of RNA that does not encode proteins and constitutes the largest component of the human transcriptome\u0026nbsp;(Hanly et al. 2018). ncRNAs come in a wide variety, including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), Piwi-interacting RNAs (piRNAs), and microRNAs (miRNAs)\u0026nbsp;(Li et al. 2014). Several mechanisms have been implicated in the biological role of these ncRNAs in cancer such as DNA methylation, RNA silencing, and mRNA translation regulation\u0026nbsp;(Li et al. 2021). Chen et al. found that hsa-circ-0000711 is upregulated in liver cancer cells, promoting liver cancer cell proliferation and inhibiting apoptosis by targeting has-miR-103a-3p. This suggests that the elevated expression level of hsa-circ-0000711 is positively correlated with the progression of liver cancer and potentially serves as a diagnostic biomarker and therapeutic target for liver cancer\u0026nbsp;(Chen et al. 2020). Shi et al. reported that the expression level of lncRNA HOTAIR is significantly higher in stage II, III, and IV breast cancer than in stage I, suggesting its potential as a diagnostic and prognostic marker for breast cancer\u0026nbsp;(Shi et al. 2020). However, there are a number of limitations associated with these diagnostic markers. Therefore, we are attempting to find a more suitable biomarker.\u003c/p\u003e\n\u003cp\u003etRNA-derived small RNAs (tsRNAs) are a novel class of non-coding small RNA, generated from tRNA or tRNA precursors at specific cleavage sites rather than being random degradation products of tRNA. They are produced through precise regulation\u0026nbsp;(Kumar et al. 2014). tsRNAs can be classified into two types based on different cleavage sites: (1) tRNA-derived fragments (tRFs), which are produced by Dicer enzyme cleavage of the D-loop or T-loop of mature tRNAs. (2) tRNA halves (tiRNAs), which are generated by endonucleases such as angiogenin and RNase L, cleaving the anticodon loop\u0026nbsp;(Liu et al. 2021). tsRNAs participate in regulating various stages of gene expression, including transcriptional gene silencing, post-transcriptional gene silencing, rRNA regulation, translational regulation, and reverse transcriptional regulation, and are associated with critical cellular processes such as self-renewal, differentiation, and proliferation\u0026nbsp;(Zhang et al. 2023). At the post-transcriptional gene level, tsRNAs function similarly to miRNAs and influence disease progression by targeting messenger RNAs (mRNAs) to regulate their stability\u0026nbsp;(Lai et al. 2023). The tRNA-derived tRF3008A inhibits Colorectal cancer (CRC) metastasis and progression by binding to the Argonaute (AGO) protein and decreasing the stability of the oncogenic transcript FOXK1 in CRC cells, suggesting that active tRFs may be useful as biomarkers and therapeutic targets for CRC\u0026nbsp;(Han et al. 2022). Goodarzi et al. found that both normal breast epithelial cells and breast cancer cells induce i-tRF expression under hypoxic conditions. Hypoxia-induced i-tRF competitively binds to YB-1, leading to the degradation of the oncogenic transcript by depriving it of YB-1 protection\u0026nbsp;(Goodarzi et al. 2015). Additionally, tsRNAs can regulate protein translation by affecting ribosome biogenesis. It has been reported that LeuCAG3\u0026prime;tsRNA can bind to the coding region of mRNA for ribosomal protein RPS28, altering the secondary structure of the ribosomal protein and enhancing its translation\u0026nbsp;(Kim et al. 2017).\u003c/p\u003e\n\u003cp\u003etsRNAs exist in abundance and with high stability in body fluids and are involved in a wide range of pathological processes. They exhibit a strong ability to discriminate between cancer patients and healthy individuals, providing a new avenue for the clinical development of non-invasive biomarkers with high specificity\u0026nbsp;(Gu et al. 2022; Zhang et al. 2022). The possibility of using tsRNAs as cancer biomarkers has been demonstrated in several studies. Jin et al. found that tRF-Pro-AGG-004 and tRF-Leu-CG-002 may be potential biomarkers for pancreatic cancer, and that their combined diagnosis exhibits certain disease specificity\u0026nbsp;(Jin et al. 2021). In CRC, 5\u0026prime;-tRF-GlyGCC contributes to the diagnosis and prognosis of CRC and may also serve as a therapeutic target for the disease\u0026nbsp;(Wu et al. 2021). In conclusion, tsRNAs provide significant clinical value in our search for a possible biomarker for GC.\u003c/p\u003e\n\u003cp\u003eIn this study, 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was evaluated as a potential tumor biomarker for GC. Compared to healthy controls, both the serum and tissue expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e were decreased in GC patients. Furthermore, its expression levels were negatively correlated with tumor grade and neural/vascular invasion, which showed good diagnostic performance in distinguishing GC patients from healthy individuals. 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e exhibited good stability in clinical applications and was not easily disturbed by environmental interference. Based on receiver operating characteristic (ROC) analysis, 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was found to have greater diagnostic efficacy when compared with CEA, CA199, and CA724, with the highest efficiency achieved through combined diagnosis. Additionally, it effectively monitored postoperative conditions in GC patients and played a dynamic monitoring role. Therefore, 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e may be used as a valuable tumor diagnostic biomarker.\u003c/p\u003e"},{"header":"2.\tMaterials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Clinical specimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the ethical guidelines of the World Medical Association, serum samples were collected from 129 GC patients, 120 healthy donors, 52 gastritis patients, and 40 postoperative GC patients from the Affiliated Hospital of Nantong University. A total of 20 sets of GC tissues and paracancerous tissues were obtained from the Department of Pathology, immediately frozen in liquid nitrogen, and then transferred to a -80\u0026deg;C refrigerator for long-term storage. All GC tissues were diagnosed by two or more pathologists and staged according to the 8th edition of the World Health Organization TNM classification.\u0026nbsp;None of the patients mentioned above received adjuvant chemotherapy, targeted therapy, or radiotherapy. The informed consent forms were signed according to ethical standards. This project was approved by the Ethics Committee at the Affiliated Hospital of Nantong University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Total RNA extraction and complementary DNA (cDNA) synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA extraction from serum samples of GC patients was extracted using the Total RNA Purification Kit and Spin Column Separation Kit (BioTeke, Wuxi, Jiangsu, China). Total RNA from tissue samples was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Subsequently, the extracted total RNA was reverse transcribed into cDNA using the Revert Aid RT Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA). The reverse transcription reaction was conducted in a 10 \u0026micro;l reaction system, incubated at 42\u0026deg;C for 60 minutes, followed by inactivation at 70\u0026deg;C for 5 minutes. All procedures were performed according to the instructions of the manufacturer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Quantitative real-time polymerase chain reaction (qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe qRT-PCR reaction was performed on a QuantStudio 5 instrument (Thermo Fisher Scientific, Waltham, MA, USA). The total reaction volume was 20 \u0026micro;L, comprising 10 \u0026micro;L of ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, Jiangsu, China), 5 \u0026micro;L of cDNA, 1 \u0026micro;L of primers, and 3 \u0026micro;L of nuclease-free water. Primers included forward and reverse primers for 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e and RNU6B, all of which were manufactured by RiboBio (RiboBio, Guangzhou, Guangdong, China). RNU6B was used as a reference gene to normalize 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression. The relative expression level was calculated using the 2\u003csup\u003e-\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method.\u003c/p\u003e\n\u003cp\u003e\u0026Delta;\u0026Delta;Ct was calculated as follows:\u003c/p\u003e\n\u003cp\u003e\u0026Delta;\u0026Delta;Ct=\u0026Delta;Ct\u003csub\u003etumor[Ct (target)\u0026minus;Ct (reference)]\u003c/sub\u003e\u0026minus;\u0026Delta;Ct\u003csub\u003econtrol[Ct (target)\u0026minus;Ct (reference)].\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Room temperature placement and repeated freeze-thaw experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 20 serum samples were randomly mixed and left at room temperature (25\u0026deg;C) for 0, 6, 12, 18 and 24 hours. The mixed serum was freeze-thawed 0, 1, 3, 5, and 10 times at -80\u0026deg;C and room temperature, followed by RNA extraction and detection of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Gradient dilution assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA was extracted from 20 randomly mixed serum samples, and total RNA was reverse transcribed into cDNA. The obtained cDNA was then diluted to 10, 10\u003csup\u003e2\u003c/sup\u003e, 10\u003csup\u003e3\u003c/sup\u003e, and 10\u003csup\u003e4\u003c/sup\u003e times to detect the expression of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS Statistics Version 20.0 (IBM SPSS Statistics, Chicago, IL, USA) and GraphPad Prism 8.0 (GraphPad Software, San Jose, California, USA) were used for data analysis in this study. 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression in each group\u0026nbsp;is presented as mean \u0026plusmn; standard deviation (SD). All research data first underwent normality testing using GraphPad Prism 8.0 to exclude the possibility of normal distribution. The Mann\u0026ndash;Whitney U test was employed to compare two independent groups, while the Kruskal\u0026ndash;Wallis H test was used to compare multiple independent groups. The Wilcoxon signed-rank test was utilized to analyze the difference in expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in preoperative and postoperative serum samples from GC patients. A chi-square test was conducted to assess the correlation between 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e and pathological parameters, and Kaplan\u0026ndash;Meier curves were employed for survival data evaluation. The Area Under the Curve (AUC) was analyzed to evaluate the diagnostic performance of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e for GC. The cutoff value of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was determined using Youden index, and the reference ranges of CEA, CA199, and CA724 were obtained from the Affiliated Hospital of Nantong University. The difference was considered statistically significant at a P-value \u0026lt;0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Screening of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in GC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough high-throughput sequencing of GC tissues and their matched paracancerous tissues, tsRNAs differentially expressed in GC were identified. Based on the sequencing results, we screened three low-expressed tsRNAs and validated them in the serum of 24 GC patients by qRT-PCR and found that only 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e exhibited significant differences (Fig. 1a). Subsequently, we collected 20 pairs of GC tissues and their adjacent paracancerous tissues. Consistently, the expression level of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was significantly lower in GC tissues compared to matched paracancerous tissues (Fig. 1b). Furthermore, correlation analysis of GC tissues and corresponding patient serum samples revealed that patients with lower serum levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e also exhibited lower expression in paired GC tissues (Fig. 1c). Therefore, we selected 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e for an in-depth study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 1\u003c/strong\u003e Expression of tsRNAs in GC and screening of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e. \u003cstrong\u003ea\u003c/strong\u003e Relative expression of three low-expressed tsRNAs in the serum of 24 GC patients; \u003cstrong\u003eb\u003c/strong\u003e Expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in 20 pairs of GC tissues and their adjacent non-cancerous tissues; \u003cstrong\u003ec\u003c/strong\u003e Pearson correlation analysis of the expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in 20 pairs of GC tissues and corresponding patient serum samples. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e is a sort of tRFs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in the human genome build (GRCh37/hg19) in the UCSC Genome Browser database (http://genome-asia.ucsc.edu/biomarker.html), 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e is located on chromosome 7, with coordinates ranging from 139,025,486 to 139,025,518 (Fig. 2a). Based on the basic information in MINTbase v2.0 (http://cm.jefferson.edu/MINTbase/), 3\u0026prime;-tRFArg is identified as a 33bp 3\u0026apos;-tRF fragment (CAGGGATTGTGGGTTCGAGTCCCATCTGGGGTGCCA) (Fig. 2b), with the cleavage site located on the anticodon stem of tRNA-Arg-CCT-4-1 (http://gtrnadb.ucsc.edu/genomes/eukaryota/Hsapi19/genes/tRNA-Arg-CCT-4-1.html) (Fig. 2c). Accordingly, we named it 3\u0026prime;-tRF\u003csup\u003eArg\u0026nbsp;\u003c/sup\u003e(Lyons et al. 2016). Upon agarose gel electrophoresis, we observed a clear, single band of approximately 75 bp, which confirmed the integrity and accuracy of the qRT-PCR product (Fig. 2d). Meanwhile, Sanger sequencing of the product was consistent with the designed sequence (Fig. 2e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 2\u003c/strong\u003e 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e is a sort of tRFs. \u003cstrong\u003ea\u003c/strong\u003e UCSC Genome Browser database showed that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e is located on chromosome 7, with coordinates of 139,025,486-139,025,518; \u003cstrong\u003eb\u003c/strong\u003e According to MINTbase v2.0, 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e is a 3\u0026apos;-tRF (5\u0026apos;-CAGGGATTGTGGGTTCGAGTCCCATCTGGGGTGCCA-3\u0026apos;); \u003cstrong\u003ec\u003c/strong\u003e The cleavage site of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e is located on the Anticodon stem of tRNA-Arg-CCT-4-1; \u003cstrong\u003ed\u003c/strong\u003e Agarose gel electrophoresis showed that the RT-qPCR product of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e has a single band of approximately 75bp; \u003cstrong\u003ee\u003c/strong\u003e Sanger sequencing of the qRT-PCR product confirmed its consistency with the designed sequence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Methodological evaluation of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the molecular properties of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in order to determine whether the method for estimating the expression level is suitable for clinical use. Firstly, the stability of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was tested using mixed serum samples, showing good performance with a coefficient of variation (CV) of 1.85 in the intra-assay and 2.47 in the inter-assay (Table 1). Subsequently, the mixed serum samples were left at room temperature for 0, 6, 12, 18, and 24 hours with repeated freeze-thaw cycles (0, 1, 3, 5, and 10 times). Despite the change in external conditions, there was no significant difference between the expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e (P \u0026gt; 0.05), which demonstrated its stability and good resistance to interference (Fig. 3a, b). Gradient dilution experiments showed that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e exhibited good linearity, ensuring the reproducibility of the measurements (Fig. 3c, d). The qRT-PCR results revealed smooth amplification curves and single-peak melting curves for 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e (Fig. 3e, f\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.\u003c/strong\u003e \u003cstrong\u003e3\u003c/strong\u003e Methodological evaluation of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e. \u003cstrong\u003ea, b\u003c/strong\u003e Room temperature placement and repeated freeze-thaw experiments showed no significant change in the expression level of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e; \u003cstrong\u003ec, d\u003c/strong\u003e Gradient dilution assays demonstrated good linearity; \u003cstrong\u003ee, f\u003c/strong\u003e 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e exhibited smooth amplification curves and single-peak melting curves, with the red line representing 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e and the blue line representing RNU6B\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e The intra-assay CV and the inter-assay CV of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eU6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIntra assay CV, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInter assay CV, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CV, coefficient of variation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Clinical role and prognostic value of the serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA serum sample analysis of 129 GC patients, 52 gastritis patients, and 120 healthy donors explored the utility of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e as a GC biomarker in clinical practice. The results showed that the expression level of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in the serum of GC patients was considerably lower than that in healthy donors and gastritis patients (P \u0026lt; 0.05), while there was no significant difference in expression level between gastritis patients and healthy donors (Fig. 4a). To further explore the correlation between the expression level of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e and clinicopathological features, the 129 GC patients were split into two groups based on the median expression level: a relatively high expression group (expression level \u0026gt;0.494479171, n=64) and a relatively low expression group (expression level \u0026le;0.494479171, n=65). The analysis of the correlation of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression and clinicopathological parameters was conducted using Chi-square tests (Table 2). The results showed that the expression level of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was significantly associated with tumor differentiation, T stage, lymph node metastasis, TNM stage, and neural/vascular invasion (Fig. 4b-e), with no significant differences observed in terms of gender, age, tumor size, Lauren classification, C-erbB-2, and MMR. Different TNM stages significantly differed in 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression levels in the serum of GC patients: patients with stage I-IV GC showed significantly lower expression levels than healthy donors, with the lowest expression levels observed in stage III-IV patients, followed by stage I-II patients (Fig. 4f). As shown in Fig. 4d, patients with neural/vascular invasion have a lower serum concentration of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e than those without invasion, suggesting that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e could facilitate the diagnosis of malignant progression. By following 40 GC patients after surgery, we investigated the correlation between serum expression levels and prognosis. Our findings indicated that serum expression levels had increased notably after surgery, approaching normal levels (Fig. 4g). According to a Kaplan-Meier analysis, patients with low expression showed a significantly worse prognosis than those with high expression (P \u0026lt; 0.05) (Fig. 4h). The findings suggest that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e may be useful as a diagnostic biomarker for GC, assisting in the clinical dynamic monitoring of tumor progression\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.\u003c/strong\u003e \u003cstrong\u003e4\u003c/strong\u003e Clinical value and prognostic effect of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in GC. \u003cstrong\u003ea\u003c/strong\u003e Expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients (n=129), gastritis patients (n=52), and healthy donors (n=120); \u003cstrong\u003eb\u003c/strong\u003e Expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients with high differentiation (n=59) and low differentiation (n=70); \u003cstrong\u003ec\u003c/strong\u003e Expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients at different stages of tumor invasion depth and healthy donors (T1\u0026ndash;T2: n=81, T3\u0026ndash;T4: n=48, healthy donors: n=120); \u003cstrong\u003ed\u003c/strong\u003e Expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients with (n=66) or without (n=63) neural/vascular invasion; \u003cstrong\u003ee\u003c/strong\u003e Expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients with (n=98) or without (n=31) lymph node metastasis; \u003cstrong\u003ef\u003c/strong\u003e Expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in serum samples from GC patients at stages I\u0026ndash;II (n=76), stages III\u0026ndash;IV (n=53), and healthy donors (n=120); \u003cstrong\u003eg\u003c/strong\u003e Changes in serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression levels before and after surgery in 40 GC patients; \u003cstrong\u003eh\u003c/strong\u003e Kaplan-Meier curve analysis of the relationship between 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression levels and survival rates in GC patients. *P\u0026lt;0.05 **P\u0026lt;0.01 ***P\u0026lt;0.001 ****P\u0026lt;0.0001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Clinical pathological analysis of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e(low)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e(high)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eyear\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e＜60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e＜5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.742\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003e\u0026ge;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferentiation grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eWell-moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003ePoor-undifferentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eT1-T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eT3-T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eⅠ-Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eⅢ-Ⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNerve/vascular invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.136363636363637%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.727272727272727%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.045454545454547%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eIntestinal type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLauren classifcation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eMixed type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eDifuse type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-erbB-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003edMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003epMMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MLH1, PMS2, MSH2, and MSH6 were all positive for pMMR (normal expression), and 1 or more negative for dMMR (deletion)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Evaluation of the diagnostic efficacy of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e for GC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the limitations of commonly used GC diagnostic markers such as CEA, CA199, and CA724, we explored the diagnostic value of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e as a potential biomarker for GC. An analysis of ROC curves was performed to evaluate the expression levels of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in 129 GC patients and 120 healthy individuals., comprehensively analyzing the diagnostic efficacy of each biomarker. 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e had an AUC of 0.808 (95% confidence interval (CI) 0.752-0.863), which was higher than that of CEA (0.747, 95% CI 0.686\u0026ndash;0.808), CA199 (0.683, 95% CI 0.616\u0026ndash;0.750), and CA724 (0.759, 95% CI 0.699\u0026ndash;0.818) (Fig. 5a). Subsequently, 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was combined with CEA, CA199, and CA724 for diagnosis and with all three and four biomarkers together. Based on Fig. 4b, combined diagnosis had a higher AUC than any single biomarker. When the four markers were combined, the AUC reached the highest value (0.856) (95% CI:0.808-0.903) (Fig. 5c). A combination and a single diagnostic model were investigated for their ability to differentiate GC patients from healthy donors based on the sensitivity (SEN), overall accuracy (ACCU), positive predictive value (PPV), and negative predictive value (NPV). With a cutoff point of 1.093635 and a Youden index of 0.566, 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e showed higher SEN (79%), ACCU (81%), PPV (83%), and NPV (79%) compared to CEA, CA199, and CA724 (Table 3). These analyses indicate that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e may be useful as a biomarker for GC, and its diagnostic efficacy can be enhanced in combination with other tumor markers.\u003c/p\u003e\n\u003cp\u003eThe lack of highly sensitive biomarkers in the clinic often leads to GC patients being diagnosed at an advanced stage, missing the opportunity for an early cure. For the evaluation, we collected information from 76 early-stage GC patients (stage I and II) and 120 healthy donors. The ROC curve showed that the AUC of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was 0.785 (95% CI 0.718\u0026ndash;0.852), superior to that of CEA (0.744, 95% CI 0.675\u0026ndash;0.814), CA199 (0.663, 95% CI 0.582\u0026ndash;0.745), and CA724 (0.763, 95% CI 0.694\u0026ndash;0.832) (Fig. 5d). Moreover, with a cut-off point of 1.093635 and a Youden index of 0.549, 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e had SEN of 72%, ACCU of 79%, PPV of 72%, and NPV of 83%, which were all higher than those of CEA, CA199, and CA724 (Table 4). The combined diagnosis had a higher AUC than any single biomarker in identifying early-stage GC patients from healthy donors (Fig. 5e). When all four biomarkers were combined, the AUC reached the highest value of 0.839 (95% CI 0.782\u0026ndash;0.895) (Fig. 5f).\u003c/p\u003e\n\u003cp\u003eIn our analysis of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e levels, we found differences between patients with GC and those with gastritis. In light of the fact that the symptoms of GC are similar to gastritis in the early stages, the ability of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e to distinguish between early-stage GC patients and gastritis patients is of great significance. We performed ROC analysis of the expression levels of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e and conventional biomarkers in 129 patients with GC and 50 patients with gastritis. The AUC of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was 0.784 (95% CI 0.713\u0026ndash;0.855), higher than that of CEA (0.659, 95% CI 0.576\u0026ndash;0.742), CA199 (0.660, 95% CI 0.576\u0026ndash;0.743), and CA724 (0.734, 95% CI 0.658\u0026ndash;0.810) (Fig. 5g). The AUC increased when 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was diagnosed in combination with other markers (Fig. 5h). The AUC reached a maximum value of 0.858 when the four biomarkers were combined (Fig. 5i), and the SEN increased to 96% (Table 5). These findings indicate that serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression levels can differentiate between GC patients and gastritis patients, and its diagnostic value is further enhanced when used in conjunction with other tumor markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.\u003c/strong\u003e \u003cstrong\u003e5\u003c/strong\u003e Diagnostic value of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e for GC. \u003cstrong\u003ea-c\u003c/strong\u003e Diagnostic efficacy of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in distinguishing GC patients from healthy donors; \u003cstrong\u003ed-f\u003c/strong\u003e Diagnostic value of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in distinguishing early-stage GC patients from healthy donors as determined by ROC analysis; \u003cstrong\u003eg-i\u003c/strong\u003e Ability of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 to distinguish between GC patients and gastritis patients as determined by ROC analysis. *P＜0.05**P＜0.01***P＜0.001****P\u0026lt;0.0001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Diagnostic performance of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in distinguishing GC patients from healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eSEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eSPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eACCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.79(102/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.83(99/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.81(201/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.83(102/123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.79(99/126)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.52(67/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.82(98/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.66(165/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.75(67/89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.61(98/160)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eCA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.43(56/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.88(105/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.65(161/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.79(56/71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.59(105/178)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eCA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.51(66/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.86(103/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.68(169/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.80(66/83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.62(103/166)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.90(116/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.67(80/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.79(196/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.74(116/156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.86(80/93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.85(110/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.72(86/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.79(196/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.76(110/144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.82(86/105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.91(117/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.69(83/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.80(200/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.76(117/154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.87(83/95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.93(120/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.59(71/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.77(191/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.71(120/169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.89(71/80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.95(122/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.55(66/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.76(188/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.69(122/176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.90(66/73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA199+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.92(119/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.61(73/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.77(192/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.72(119/166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.88(73/83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA199+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.96(124/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.49(59/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.73(183/249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.67(124/185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.92(59/64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SEN, sensitivity; SPE, specificity; ACCU, overall accuracy; PPV, positive predictive value; NPV, negative predictive value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Diagnostic performance of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in distinguishing early-stage GC patients from healthy controls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eSEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eACCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.72(55/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.83(99/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.79(154/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.72(55/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.83(99/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.50(38/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.82(98/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.69(136/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.63(38/60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.72(98/136)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003eCA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.37(28/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.88(105/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.68 133/196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.65 28/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.69(105/153)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003eCA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.46(35/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.86(103/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.70(138/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.67(35/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.72(103/144)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.87(66/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.67(80/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.74(146/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.62(66/106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.89(80/90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.80(61/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.72(86/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.75(147/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.64(61/95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.85(86/101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.86(65/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.69(83/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.76(148/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.64(65/102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.88(83/94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.91(69/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.59(71/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.71(140/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.58(69/118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.91(71/78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.92(70/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.55(66/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.69(136/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.56(70/124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.92(66/72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA199+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.88(67/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.61(73/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.71(140/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.59(67/114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.89(73/82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA199+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.95(72/76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.49(59/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.67(131/196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.54(72/133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.94(59/63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SEN, sensitivity; SPE, specificity; ACCU, overall accuracy; PPV, positive predictive value; NPV, negative predictive value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Diagnostic performance of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724 in distinguishing GC patients from gastritis patients\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eSPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eACCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.80(103/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.67(35/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.76(138/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.86(103/120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.57(35/61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.52(67/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.79(41/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.60(108/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.86(67/78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.40(41/103)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003eCA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.43(56/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.88(46/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.56(102/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.90(56/62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.39(46/119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003eCA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.51(66/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.79(41/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.59(107/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.86(66/77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.39(41/104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.91(117/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.54(28/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.80(145/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.83(117/141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.70(28/40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.85(110/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.58(30/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.77(140/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.83(110/132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.61(30/49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.91(118/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.54(28/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.81(146/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.83(118/142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.72(28/39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.93(120/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.46(24/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.80(144/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.81(120/148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.73(24/33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.95(123/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.42(22/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.80(145/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.80(123/153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.79(22/28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CA199+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.92(119/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.44(23/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.78(142/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.80(119/148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.70(23/33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\"\u003e\n \u003cp\u003e3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e+CEA+CA199+CA724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.96(124/129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.35(18/52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.78(142/181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.78(124/158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.78(18/23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: SEN, sensitivity; SPE, specificity; ACCU, overall accuracy; PPV, positive predictive value; NPV, negative predictive value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Prediction of downstream target genes of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe downstream target genes of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e were predicted. Utilizing bioinformatics databases, we conducted an analysis to anticipate the binding of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e with its target genes. As illustrated in Fig. 6a, an overlap of 537 potential target genes was observed between the miRanda and TargetScan prediction tools, suggesting a high likelihood of interaction with 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e. An analysis of the connection network identified 80 target genes associated with 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e (Fig. 6b). Subsequent enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways highlighted significant enrichment in amino sugar and nucleotide sugar metabolism, the Hippo signaling pathway, and central carbon metabolism in cancer (Fig. 6c). Gene Ontology (GO) functional enrichment analysis of these target genes indicated potential involvement in energy metabolism, transcriptional regulation, and cellular signal transduction (Fig. 6d). Further exploration is essential to elucidate the underlying regulatory mechanisms of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in GC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 6\u003c/strong\u003e Prediction of the downstream regulation mechanism of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e. \u003cstrong\u003ea\u0026nbsp;\u003c/strong\u003eVenn diagram evaluating the overlapping genes predicted by the miRanda and TargetScan databases; \u003cstrong\u003eb\u003c/strong\u003e Potential target genes of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e; \u003cstrong\u003ec\u003c/strong\u003e Enrichment analysis of potential target genes in the Kyoto Encyclopedia of Genes and Genomes; \u003cstrong\u003ed\u003c/strong\u003e Functional enrichment analysis of potential target genes in Gene Ontology.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs one of the most severe malignancies worldwide, GC is often diagnosed at a late stage due to a lack of specific diagnostic markers\u0026nbsp;(Joshi et al. 2021). Despite the wide application of various treatment modalities for GC, including traditional radiotherapy and chemotherapy, molecular targeting, and immunotherapy, the five-year survival rate for advanced GC remains only 6%\u0026nbsp;(Alsina et al. 2023). The identification of highly sensitive biomarkers for GC early detection is therefore urgently needed.\u003c/p\u003e\n\u003cp\u003etsRNAs have attracted our attention with the advent of high-throughput sequencing technologies. They are not randomly degraded products but are generated through precise biological processes, playing important roles in stress response, signal transduction, and gene expression\u0026nbsp;(Shen et al. 2018; Zhu et al. 2019). tsRNAs can be classified into two categories: tiRNAs and tRFs, with specific molecular size, nucleotide composition, and physiological functions\u0026nbsp;(Anderson et al. 2014; Pliatsika et al. 2016; Zheng et al. 2016). Under oxidative stress conditions such as hypoxia, the expression levels of some tsRNAs are significantly upregulated\u0026nbsp;(Lee et al. 2009). For instance, Tao et al. found that the hypoxic environment generated by the sustained rapid growth of cancer cells stimulates the expression of HIF1\u0026alpha;. HIF1\u0026alpha;, in turn, targets the angiogenin promoter, promoting transcription and consequently increasing the levels of 5\u0026apos;tiRNA-His-GTG. This specific tiRNA then targets and inhibits the expression of LATS2, resulting in the suppression of the Hippo signaling pathway, which promotes CRC progression, indicating that 5\u0026apos;tiRNA-His-GTG plays a significant role in CRC progression\u0026nbsp;(Tao et al. 2021). Additionally, the 3\u0026apos;-tRF found in B-cell lymphoma cell lines was also reported to inhibit the mRNA levels of the single-stranded DNA binding protein RPA1, which in turn inhibits endogenous RPA1 expression, inhibiting cell proliferation and regulating DNA damage response\u0026nbsp;(Maute et al. 2013). All of the above studies have validated the critical role of tsRNAs in tumorigenesis but have not explored and evaluated the potential of tsRNAs as tumor biomarkers. tsRNAs are highly enriched and stably present in biological fluids, with abundances that are sometimes even higher than those of miRNAs\u0026nbsp;(Dhahbi et al. 2013; Schageman et al. 2013; Olvedy et al. 2016). They are widely involved in pathological processes, demonstrating their potential as biomarkers.\u003c/p\u003e\n\u003cp\u003eThe 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, which is significantly underexpressed in GC, was selected for further investigation. We analyzed serum samples from 129 GC patients, 52 gastritis patients, and 120 healthy individuals and found that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e was differentially expressed in patients with GC, gastritis patients, and healthy donors. We also found significant correlations between 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression and clinical pathological features such as tumor differentiation, lymph node metastasis, and TNM stage. 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e had the highest SEN (0.79) in the combined diagnosis of GC by 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e, CEA, CA199, and CA724. The highest AUC value (0.856) was obtained by combining the four markers. Furthermore, our study comprehensively analyzed the potential of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e as a GC biomarker. Through analysis of postoperative expression levels and survival curves of GC patients, we found that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e can dynamically monitor the postoperative status of GC patients, paving the way for the development of tsRNA-based biomarkers.\u003c/p\u003e\n\u003cp\u003eA significant inverse correlation was observed between 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e expression level and lymph node metastatic status and tumor grade in our study, indicating its clinical utility in the diagnosis of GC. The absence of early diagnostic biomarkers in clinical practice leads to the diagnosis of GC often occurring later in its progression, with a poorer prognosis\u0026nbsp;(Necula et al. 2019). Therefore, we estimated the diagnostic performance of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in the early stages (stage I and II). As expected, both the SEN and the AUC of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e were higher than those of common tumor markers, underlining its utility in the early diagnosis of GC. This provides a promising opportunity to develop non-invasive diagnostic biomarkers as well as improve the prognosis of patients. However, the current experiment still has some limitations: there was a limited number of participants in this experiment and the samples collected are geographically restricted, we need more samples to verify the effectiveness of the clinical application. A series of validations are required for the formal application of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e as a diagnostic marker in the clinic.\u003c/p\u003e\n\u003cp\u003eAlthough we observed low expression of serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in GC, the mechanism of its oncogenic role in GC remains unclear. Previous studies have suggested that tsRNAs may either promote or inhibit tumor initiation and progression according to different internal mechanisms. For instance, tsRNAs can inhibit disease progression by mediating the binding of AGO proteins to transcriptional targets and inhibiting their expression in a miRNA-like manner\u0026nbsp;(Shao et al. 2017; Kuscu et al. 2018). For example, Huang found that tRF/miR-1280, which binds to AGO proteins, can target the 3\u0026prime;UTR region of JAG2 and induce its degradation, inhibiting the Notch signaling pathways and thus suppressing CRC development\u0026nbsp;(Huang et al. 2017). Additionally, there is also evidence that some tsRNAs may be able to influence disease progression and onset through a protein sponge effect\u0026nbsp;(Krishna et al. 2019). 5\u0026prime;-tiRNA-Gln derived from hepatocellular carcinoma functions by binding EIF4A1, which negatively regulates the translation of related proteins through the intramolecular G-quadruplex structure and inhibits the proliferation and metastasis of hepatocellular carcinoma cells\u0026nbsp;(Wu et al. 2023). tRFs are classified as 5\u0026prime;-tRF and 3\u0026prime;-tRF, which are generated from the 5\u0026prime; and 3\u0026prime; ends of mature tRNAs\u0026nbsp;(Kumar et al. 2016). Meta-analysis of small RNA data has shown that 3\u0026prime;-tRFs are primarily distributed in the cytoplasm, while 5\u0026prime;-tRFs, although generated in the cytoplasm, may be transported to the nucleus through RNA-like transport mechanisms. Analysis of the CLASH data of Helwak et al. revealed that 3\u0026prime;-tRF has the potential to interact with AGO proteins to regulate gene expression through mechanisms similar to miRNAs\u0026nbsp;(Helwak et al. 2013). 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e is a type of 3\u0026prime;-tRF, and we speculate that it may have a similar function to miRNAs, directly interacting with the mRNA binding, inhibiting the expression of complementary targets and participating in the regulation of GC progression.\u003c/p\u003e\n\u003cp\u003eThis is the first report elucidating the potential of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e for the diagnosis and postoperative monitoring of GC. However, the specific mechanisms underlying the association of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e with GC occurrence remain unclear, and further investigation and validation are required in future studies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, we have demonstrated that serum 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e holds promise as a new biomarker for GC, exhibiting high diagnostic efficacy even in the early stages of the disease. Furthermore, our findings suggest that 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in tumor tissue could serve as a valuable biomarker for predicting postoperative survival time in patients. We intend to further investigate the underlying mechanisms of 3\u0026prime;-tRF\u003csup\u003eArg\u003c/sup\u003e in GC in future studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGC: Gastric cancer;\u0026nbsp;tsRNAs: tRNA-derived small RNAs;\u0026nbsp;CEA: carcinoembryonic antigen; CA199: Carbohydrate antigen199; CA724: Carbohydrate antigen724;\u0026nbsp;ncRNA: Non-coding RNA; lncRNAs: long non-coding RNAs;\u0026nbsp;circRNAs: circular RNAs;\u0026nbsp;piRNAs: Piwi-interacting RNAs;\u0026nbsp;miRNAs: microRNAs;\u0026nbsp;tRFs: tRNA-derived fragments; tiRNAs: tRNA halves;\u0026nbsp;Colorectal cancer: CRC; AGO: Argonaute; mRNAs:\u0026nbsp;messenger RNAs; ROC: receiver operating characteristic;\u0026nbsp;cDNA: complementary DNA; qRT-PCR: Quantitative real-time polymerase chain reaction; SD: Standard Deviation;\u0026nbsp;AUC: Area Under the Curve; CV: coefficient of variation; CI: confidence interval;\u0026nbsp;SEN: sensitivity;\u0026nbsp;ACCU: the overall accuracy; PPV: positive predictive value; NPV: negative predictive value.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate all the patients who participated in this study and all those who contributed to it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRui Ding and Yang Li performed study design, material preparation, data collection and analysis. Rui Ding written the first draft of the manuscript, Yu Zhang took part in the experiment, Shaoqing Ju provided resources and guidance for the paper, and all authors read and approved this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by grants from the National Natural Science Foundation of China (No. 82072363, No.82272411), Jiangsu Provincial Medical Key Discipline (Laboratory) (ZDXK202240),\u0026nbsp;Science and Technology Project of Jiangsu Province (BE2023741) and Foundation of Jiangsu Province Research Hospital (YJXYY202204-XKB16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request. The data used in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe informed consent obtained from study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlsina M, Arrazubi V, Diez M et al. (2023) Current developments in gastric cancer: from molecular profiling to treatment strategy. Nature Reviews Gastroenterology \u0026amp; Hepatology 20:155-170.https://doi.org/10.1038/s41575-022-00703-w\u003c/li\u003e\n\u003cli\u003eAnderson P \u0026amp; Ivanov P (2014) tRNA fragments in human health and disease. 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Cancer Letters 452:31-37.https://doi.org/10.1016/j.canlet.2019.03.012\u003c/li\u003e\n\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":false,"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":"tRNA-derived small RNAs, 3′-tRFArg, Gastric cancer, Biomarker, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4272899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4272899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Gastric cancer (GC) is one of the malignant tumors with the highest mortality rates worldwide, yet there is a lack of diagnostic markers with high sensitivity in the clinic. tRNA-derived small RNAs (tsRNAs) are a novel type of non-coding small RNAs characterized by their abundance in body fluids and specific biological functions. In this study, we focused on the potential of tsRNAs as biomarkers for the diagnosis of GC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Differential expression of tsRNAs was screened by high-throughput sequencing, and Quantitative real-time PCR verified the expression of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e in GC serum and tissues. The methodological evaluation of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e was confirmed using Sanger sequencing and agarose gel electrophoresis. The correlation between the expression levels of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e and clinical pathological parameters was analyzed using Chi-square tests. The diagnostic value was assessed through the receiver operating characteristic curve, and the impact of 3′-tRF\u003csup\u003eArg\u003c/sup\u003e expression on survival was evaluated using Kaplan–Meier survival analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e: 3′-tRF\u003csup\u003eArg\u003c/sup\u003e was found underexpressed in GC tissues and serum with good stability. The differential expression of serum 3′-tRF\u003csup\u003eArg\u003c/sup\u003e could identify GC patients and show significant correlations with clinical pathological features. Furthermore, the receiver operating characteristic curve indicated that 3′-tRF\u003csup\u003eArg\u003c/sup\u003e possesses a higher diagnostic value than conventional biomarkers, particularly in the early diagnosis of GC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: 3′-tRF\u003csup\u003eArg\u003c/sup\u003e is significantly underexpressed in the serum of GC patients and can serve as a biomarker of high sensitivity. It possesses superior diagnostic efficacy compared to traditional markers and is valuable for monitoring tumor development and prognosis.\u003c/p\u003e","manuscriptTitle":"Comprehensive Assessment of serum 3′-tRF Arg as a novel diagnostic biomarker for gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 19:16:30","doi":"10.21203/rs.3.rs-4272899/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":"947e013d-985b-416c-95a4-e2419313c527","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-24T07:51:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 19:16:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4272899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4272899","identity":"rs-4272899","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}