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

doi:10.21203/rs.3.rs-4272899/v1

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

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.

2.1 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, 10², 10³, and 10⁴ 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.

3.1 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	0.758
Age（year）	＜60	33	12	21	0.078
	≥60	96	52	44	0.078
Tumor size	＜5	91	46	45	0.742
	≥5	38	18	20	0.742
Differentiation grade	Well-moderate	59	21	38	0.003
	Poor-undifferentiation	70	43	21	0.003
T stage	T1-T2	81	31	50	0.001
	T3-T4	48	33	15	0.001
Lymph node status	Positive	98	56	42	0.002
	Negative	31	8	23	0.002
TNM stage	Ⅰ-Ⅱ	76	25	51	<0.001
	Ⅲ-Ⅳ	53	39	14	<0.001
Nerve/vascular invasion	Positive	66	42	24	0.001
	Negative	63	22	41	0.001
	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.

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.

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.

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.

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.

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No competing interests reported.

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

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

3. Results

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1