TsRNA combined marker for auxiliary diagnosis of gastric cancer and application thereof

Abstract

The application discloses a tsRNA combined marker for auxiliary diagnosis of gastric cancer and application thereof, and belongs to the technical field of diagnostic markers. The tsRNA combined marker comprises i-tRF-AspGTC and 3'tRF-ThrAGT. The application discloses a tsRNA combined marker for auxiliary diagnosis of gastric cancer and application thereof, the tsRNA combined marker has high gastric cancer specificity, the diagnostic efficiency is significantly better than that of a traditional marker, the diagnosis efficiency of gastric cancer is significantly improved, the possibility of misdiagnosis is reduced, and accurate identification of a key window period of gastric cancer is realized.

Description

Technical Field

[0001] This invention relates to the field of diagnostic biomarker technology, and in particular to a tsRNA combined biomarker for the auxiliary diagnosis of gastric cancer and its application. Background Technology

[0002] Gastric cancer is one of the malignant tumors with high incidence and mortality rates worldwide. The prognosis of gastric cancer is closely related to the stage of the disease at diagnosis. Early-stage gastric cancer, with standardized treatment, can achieve a high long-term survival rate, while the prognosis of advanced-stage gastric cancer is relatively poor. Therefore, achieving early diagnosis of gastric cancer is of great clinical significance for improving patient prognosis and reducing disease mortality. Currently, upper gastrointestinal endoscopy combined with biopsy pathology is the clinical gold standard for diagnosing gastric cancer and precancerous lesions. Endoscopy allows direct visualization of the gastric mucosa morphology and allows for biopsy of suspicious lesions for pathological analysis to clarify the nature of the lesions. However, endoscopy is an invasive procedure requiring specialized equipment and skilled endoscopists, resulting in high costs and potential discomfort and procedural risks for patients. Existing biomarkers used for routine detection, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9), have insufficient sensitivity and specificity for reliable diagnosis of early gastric cancer. This results in many patients being diagnosed only when obvious symptoms appear, by which time the disease has often progressed to the intermediate or advanced stage, missing the best time for treatment.

[0003] Transfer RNA-derived small RNAs (tsRNAs) are a class of small non-coding RNA molecules produced by specific cleavage of precursor or mature transfer RNA (tRNA), and are widely distributed in various tissues and body fluids of the human body. Studies have shown that tsRNAs have good stability in body fluids such as serum, and differential expression exists in various malignant tumors. Some tsRNA molecules are associated with the occurrence and development of gastric cancer, and changes in the expression of specific tsRNAs can be detected in the serum of gastric cancer patients. However, currently reported research on tsRNA biomarkers mainly focuses on the exploration of single molecules, and systematic research and validation are still lacking in aspects such as gastric cancer specificity, early diagnostic efficacy, and the ability to differentiate early gastric cancer from precancerous lesions. Summary of the Invention

[0004] The purpose of this invention is to provide a tsRNA combined biomarker for the auxiliary diagnosis of gastric cancer and its application. This tsRNA combined biomarker has high gastric cancer specificity, significantly better diagnostic efficacy than traditional biomarkers, and excellent sensitivity and specificity for the diagnosis of early gastric cancer.

[0005] To achieve the above objectives, the present invention provides the following solution: A tsRNA combined biomarker for the adjuvant diagnosis of gastric cancer, wherein the tsRNA combined biomarker comprises i-tRF-AspGTC and 3'tRF-ThrAGT.

[0006] Preferably, the nucleotide sequence of the i-tRF-AspGTC is shown in SEQ ID NO.1.

[0007] Preferably, the nucleotide sequence of the 3'tRF-ThrAGT is shown in SEQ ID NO.2.

[0008] This invention also provides the application of the aforementioned tsRNA combined biomarker in the preparation of products for the auxiliary diagnosis of gastric cancer.

[0009] Preferably, the product includes a reagent kit, a chip, or a reagent.

[0010] Preferably, the product is used for the early diagnosis of gastric cancer, monitoring the efficacy of gastric cancer surgery, or tumor recurrence.

[0011] The present invention also provides a kit for assisting in the diagnosis of gastric cancer, the kit comprising a first detection reagent for detecting the expression level of i-tRF-AspGTC and a second detection reagent for detecting the expression level of 3'tRF-ThrAGT.

[0012] Preferably, the first detection reagent contains a primer pair for specifically amplifying i-tRF-AspGTC, and the second detection reagent contains a primer pair for specifically amplifying 3'tRF-ThrAGT.

[0013] Preferably, the primer pair for specifically amplifying i-tRF-AspGTC includes an upstream primer F1 and a downstream primer R1, wherein the nucleotide sequence of F1 is shown in SEQ ID NO.3 and the nucleotide sequence of R1 is shown in SEQ ID NO.4.

[0014] Preferably, the primer pair for specifically amplifying 3'tRF-ThrAGT includes an upstream primer F2 and a downstream primer R2, wherein the nucleotide sequence of F2 is shown in SEQ ID NO.5 and the nucleotide sequence of R2 is shown in SEQ ID NO.6.

[0015] The present invention discloses the following technical effects: This invention discloses a tsRNA combined biomarker for the auxiliary diagnosis of gastric cancer and its application. This tsRNA combined biomarker comprises two tsRNA molecules, i-tRF-AspGTC and 3'tRF-ThrAGT, which are stably and highly expressed in the serum of gastric cancer patients. It has been validated for differentiation from various digestive tract tumors, including esophageal cancer, colorectal cancer, liver cancer, and pancreatic cancer, demonstrating high specificity for gastric cancer and avoiding false positives caused by interference from other digestive tract tumors. Furthermore, the diagnostic efficacy of this tsRNA combined biomarker is significantly superior to traditional biomarkers, significantly improving the diagnostic efficiency of gastric cancer, reducing the possibility of misdiagnosis, and achieving accurate identification of the critical window period for gastric cancer development. This tsRNA combined biomarker exhibits good stability, meets the needs of routine clinical sample processing, and has the potential for dynamic monitoring of treatment response, laying the foundation for the clinical translation of non-invasive liquid biopsy. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 The results of small RNA microarray analysis and STEM (Short Time-series Expression Miner) analysis of the expression trends of different tsRNAs are shown in the figure. Figure 2 Heatmaps showing differentially expressed tsRNAs for small RNA microarray analysis and STEM analysis; Figure 3 This is a schematic diagram comparing the relative expression levels of highly expressed molecules in the gastric cancer group and the healthy control group. Figure 3 In this context, A represents the expression of i-tRF-AspGTC. Figure 3 In this context, B represents the expression of 3'tRF-mtPheGAA. Figure 3 C in the figure represents the expression of 3'tRF-ThrAGT. Figure 3 In this context, D represents the expression of i-tRF-GlyGCC; Figure 4 This is a schematic diagram comparing the expression of highly expressed molecules in gastric cancer, other gastrointestinal tumors, and healthy individuals. Figure 4 In this context, A represents the expression of i-tRF-AspGTC. Figure 4 In this context, B represents the expression of 3'tRF-mtPheGAA. Figure 4 C in the figure represents the expression of 3'tRF-ThrAGT. Figure 4In this context, D represents the expression of i-tRF-GlyGCC; Figure 5 The figure shows the results of specificity analysis of i-tRF-AspGTC, 3'tRF-ThrAGT, and cel-miR-39-3p primers. Figure 5 In the figure, A represents the melting curve of i-tRF-AspGTC qRT-PCR. Figure 5 In the image, B represents the agarose gel electrophoresis image of i-tRF-AspGTC and U6. Figure 5 C in the figure represents the melting curve of 3'tRF-ThrAGT qRT-PCR; Figure 5 D in the image represents the agarose gel electrophoresis results of 3'tRF-ThrAGT and U6; Figure 6 This is a schematic diagram illustrating the Sanger sequencing validation results of the i-tRF-AspGTC and 3'tRF-ThrAGT amplification products. Figure 6 In this context, A represents the i-tRF-AspGTC sequencing result. Figure 6 B in the sequence represents the 3'tRF-ThrAGT sequencing result; Figure 7 The figure shows the expression of i-tRF-AspGTC and 3'tRF-ThrAGT in gastric cancer and the results of ROC analysis. Figure 7 In the figure, A represents the expression of i-tRF-AspGTC in gastric cancer and controls. Figure 7 In the figure, B represents the expression of 3'tRF-ThrAGT in gastric cancer and controls. Figure 7 In the figure, C represents the ROC curve used to differentiate gastric cancer from controls by i-tRF-AspGTC, CEA, CA19-9, and CA72-4. Figure 7 D in the figure represents the ROC curves used to differentiate gastric cancer from controls by 3'tRF-ThrAGT, CEA, CA19-9, and CA72-4. Figure 7 E in the figure represents the ROC curves of the combined diagnostic model of i-tRF-AspGTC and 3'tRF-ThrAGT, CEA, CA19-9 and CA72-4 in differentiating gastric cancer from controls. Figure 8 This is a statistical chart showing the expression of i-tRF-AspGTC, 3'tRF-ThrAGT, and traditional biomarkers in gastric cancer patients, precancerous lesions, gastritis patients, and healthy individuals. Figure 8 In this context, A represents the expression of CEA. Figure 8 In the text, B represents the expression of CA19-9. Figure 8 In the text, C represents the expression of CA72-4. Figure 8 In this context, D represents the expression of i-tRF-AspGTC. Figure 8E in the figure represents the expression of 3'tRF-ThrAGT; Figure 9 ROC curves for i-tRF-AspGTC and 3'tRF-ThrAGT in the early diagnosis of gastric cancer, including single-molecule, combined diagnostic models, and traditional biomarkers, as well as calibration curves for the combined diagnostic model, are presented. Figure 9 In the figure, A represents the ROC curve used to differentiate early gastric cancer from controls using i-tRF-AspGTC, CEA, CA19-9, and CA72-4. Figure 9 B in the figure represents the ROC curve used to differentiate early gastric cancer from controls using 3'tRF-ThrAGT, CEA, CA19-9, and CA72-4. Figure 9 In the figure, C represents the ROC curve for the combined diagnosis of i-tRF-AspGTC and 3'tRF-ThrAGT, and for differentiating early gastric cancer from controls using CEA, CA19-9, and CA72-4. Figure 9 D in the figure represents the calibration curve for the combined diagnostic model of i-tRF-AspGTC and 3'tRF-ThrAGT to distinguish early gastric cancer from the control. Figure 10 The diagnostic value of i-tRF-AspGTC and 3'tRF-ThrAGT as single molecules, combined diagnostic models, and traditional biomarkers in identifying early gastric cancer and precancerous lesions, as well as the calibration curve of the combined diagnostic model, were studied. Figure 10 In the figure, A represents the ROC curve used to differentiate early gastric cancer from precancerous lesions by i-tRF-AspGTC, CEA, CA19-9, and CA72-4. Figure 10 In the figure, B represents the ROC curve used to differentiate early gastric cancer from precancerous lesions by 3'tRF-ThrAGT, CEA, CA19-9, and CA72-4. Figure 10 In the figure, C represents the ROC curve for the combined diagnosis of i-tRF-AspGTC and 3'tRF-ThrAGT, and for CEA, CA19-9, and CA72-4 to differentiate early gastric cancer from precancerous lesions. Figure 10 D in the figure represents the calibration curve for the combined diagnostic model of i-tRF-AspGTC and 3'tRF-ThrAGT to distinguish between early gastric cancer and precancerous lesions. Figure 11 Schematic diagrams of receiver operating characteristic (ROC) curves and calibration curves of the combined diagnostic model for differentiating advanced gastric cancer from controls using single molecules of i-tRF-AspGTC and 3'tRF-ThrAGT, combined diagnostic models, and traditional biomarkers. Figure 11 In the figure, A represents the ROC curve used to differentiate advanced gastric cancer from the control group by i-tRF-AspGTC, CEA, CA19-9, and CA72-4. Figure 11In the figure, B represents the ROC curve used to differentiate advanced gastric cancer from the control group by 3'tRF-ThrAGT, CEA, CA19-9, and CA72-4. Figure 11 In the figure, C represents the ROC curve for the combined diagnosis of i-tRF-AspGTC and 3'tRF-ThrAGT, and for differentiating advanced gastric cancer from the control group using CEA, CA19-9, and CA72-4. Figure 11 D in the figure represents the calibration curve for the combined diagnostic model of i-tRF-AspGTC and 3'tRF-ThrAGT to distinguish advanced gastric cancer from the control group. Figure 12 The diagnostic value of i-tRF-AspGTC and 3'tRF-ThrAGT as single molecules, combined diagnostic models, and traditional biomarkers in identifying advanced gastric cancer and precancerous lesions, as well as the calibration curve of the combined diagnostic model, were studied. Figure 12 In the figure, A represents the ROC curve used to differentiate advanced gastric cancer from precancerous lesions by i-tRF-AspGTC, CEA, CA19-9, and CA72-4. Figure 12 In the figure, B represents the ROC curve used to differentiate advanced gastric cancer from precancerous lesions by 3'tRF-ThrAGT, CEA, CA19-9, and CA72-4. Figure 12 In the figure, C represents the ROC curve for the combined diagnosis of i-tRF-AspGTC and 3'tRF-ThrAGT, and for CEA, CA19-9, and CA72-4 to differentiate advanced gastric cancer from precancerous lesions. Figure 12 D in the figure represents the calibration curve for the combined diagnostic model of i-tRF-AspGTC and 3'tRF-ThrAGT to distinguish between advanced gastric cancer and precancerous lesions. Figure 13 This is a schematic diagram comparing the relative expression levels of i-tRF-AspGTC and 3'tRF-ThrAGT in the serum of gastric cancer patients before and after surgery. Figure 13 In the figure, A represents the expression of i-tRF-AspGTC before and after surgery in gastric cancer patients. Figure 13 B in the figure represents the expression of 3'tRF-ThrAGT before and after surgery in gastric cancer patients; Figure 14 This is a schematic diagram showing the results of stability verification of serum i-tRF-AspGTC and 3'tRF-ThrAGT after being stored at room temperature for different times. Figure 14 In the figure, A represents the room temperature stability results of i-tRF-AspGTC. Figure 14 B in the figure represents the room temperature stability results of 3'tRF-ThrAGT. Detailed Implementation

[0018] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0019] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0020] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0021] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0022] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0023] The primers used in the following examples were synthesized by Huzhou Jian Biotechnology Co., Ltd., the reverse transcription kit was purchased from Thermo Fisher Scientific, USA, the ChamQ Universal SYBR qPCR Master Mix was purchased from Nanjing Novizan Biotechnology Co., Ltd., the rapid total RNA extraction kit was purchased from Beijing Biotech Biotechnology Co., Ltd., and the cel-miR-39-3p external reference standard and primers were purchased from Huzhou Jian Biotechnology Co., Ltd.

[0024] Example 1 Sample Collection: Serum samples were collected from 20 individuals at the Affiliated Hospital of Nantong University, including 5 cases of early-stage gastric cancer, 5 cases of advanced-stage gastric cancer, 5 cases of precancerous lesions, and 5 healthy individuals. All participants signed informed consent forms, and the study protocol was approved by the ethics committee (approval number: 2023-L166). Serum samples were collected in the morning on an empty stomach (5 mL). Within 2 hours of collection, the samples were centrifuged at 3000×g for 15 min at room temperature to separate the supernatant serum. The supernatant serum was then centrifuged at 12000×g for 10 min to remove cell debris. The samples were aliquoted into 200 μL tubes and stored at -80℃.

[0025] Small RNA microarray analysis: Total RNA was extracted from the 20 serum samples using TRIzol reagent and hybridized into a human tsRNA chip according to the manufacturer's instructions. The tsRNA library was constructed after scanning with a microarray scanner. The signal intensities detected by multiple probes corresponding to different tsRNAs were corrected and normalized, and the individual expression levels of each tsRNA were calculated to analyze the differential expression profiles of tsRNAs.

[0026] STEM analysis screening: STEM analysis was used to screen groups based on a fold change >1.5 and... P A value <0.05 was used as a threshold to screen and statistically analyze differentially expressed tsRNAs, and the top 20 candidate molecules with high and low expression were obtained by sorting them by fold change.

[0027] Table 1 shows the detailed information of the 20 differentially expressed tsRNA candidate molecules obtained through screening. The results of small RNA microarray analysis and STEM analysis for tsRNA screening are as follows: Figure 1 and Figure 2 As shown.

[0028] Table 1. Detailed information on the 20 differentially expressed tsRNA candidate molecules obtained through screening. Based on the expression trends of different tsRNAs ( Figure 1 The upregulated and downregulated molecule sets were screened out, and then the top 20 candidate molecules with high and low expression were further screened out according to the fold difference (Table 1). Figure 2 The differences in expression of the above candidate molecules are visually demonstrated, clearly showing the differences in expression patterns of different molecules in the sample.

[0029] Preliminary qRT-PCR validation: The above 20 candidate molecules were validated by qRT-PCR. The total RNA was extracted from serum using a rapid blood RNA extraction kit, following the instructions. Cel-miR-39-3p was added as an external reference standard.

[0030] Reverse transcription reaction system: After calculating the required volume of each reagent according to the reverse transcription system, the samples were added to the instrument. The reverse transcription amplification instrument program was set to 42℃ (60min), 70℃ (5min), 4℃ (∞). The reaction system is shown in Table 2 below.

[0031] Table 2 Serum RNA reverse transcription reaction system After completion, the cDNA product was removed, diluted with 40 μL of DEPC water, and stored at -20℃.

[0032] qRT-PCR reaction system (total volume 20 μL): 10 μL of 2x universal SYBR Green rapid qPCR premix, 1 μL of upstream primer, 1 μL of downstream primer, 3 μL of RNase-free H2O, and 5 μL of diluted cDNA; qRT-PCR program: 95℃ for 10 min; 95℃ for 15 s, 60℃ for 30 s, and 72℃ for 30 s, for a total of 45 cycles; using cel-miR-39-3p as the external reference gene, 2... -△△Ct The method calculates the relative expression levels of each molecule.

[0033] qRT-PCR was performed on 20 candidate molecules (n=20 in the gastric cancer group and n=20 in the healthy control group). Molecules showing statistically significant differences were identified as follows: Figure 3 As shown.

[0034] Depend on Figure 3 It can be seen that the four molecules i-tRF-AspGTC, 3'tRF-mtPheGAA, 3'tRF-ThrAGT, and i-tRF-GlyGCC are stably highly expressed in the serum of gastric cancer patients. P <0.001).

[0035] Example 2 Identification and screening of gastric cancer-specific molecules: Sample collection: Serum samples were collected from 35 patients with gastric cancer, 23 patients with esophageal cancer, 35 patients with colorectal cancer, 35 patients with liver cancer, 14 patients with pancreatic cancer, and 35 healthy individuals from the Affiliated Hospital of Nantong University. Sample processing methods were the same as in Example 1.

[0036] qRT-PCR detection: Following the method described in Example 1, the relative expression levels of four molecules—i-tRF-AspGTC, 3'tRF-mtPheGAA, 3'tRF-ThrAGT, and i-tRF-GlyGCC—were detected in each of the above sample groups. cel-miR-39-3p was used as the external reference gene, and 2... -△△Ct The method was used to calculate the relative expression levels of each molecule. The results are as follows: Figure 4 As shown.

[0037] Depend on Figure 4 It can be seen that the expression levels of i-tRF-AspGTC and 3'tRF-ThrAGT in gastric cancer are significantly higher than those in esophageal cancer, colorectal cancer, liver cancer, pancreatic cancer, and healthy individuals. P <0.01), indicating significantly better gastric cancer specificity than 3'tRF-mtPheGAA and i-tRF-GlyGCC. 3'tRF-mtPheGAA and i-tRF-GlyGCC were also highly expressed in other gastrointestinal tumors, lacking sufficient specificity, and were therefore excluded. Finally, i-tRF-AspGTC and 3'tRF-ThrAGT were selected as candidate molecules with high gastric cancer specificity for further research.

[0038] Example 3 Specific primers were designed using Primer 5.0 software, referencing the tsRNA sequence information in GenBank. The external reference gene was cel-miR-39-3p. The nucleotide sequences of i-tRF-AspGTC and 3'tRF-ThrAGT are shown in SEQ ID NO.1 and SEQ ID NO.2, respectively. The nucleotide sequences of the upstream primer F1 of i-tRF-AspGTC are shown in SEQ ID NO.3 and SEQ ID NO.4, respectively. The nucleotide sequences of the upstream primer F2 of 3'tRF-ThrAGT are shown in SEQ ID NO.5 and SEQ ID NO.6, respectively. The nucleotide sequences of the upstream primer F3 of cel-miR-39-3p are shown in SEQ ID NO.7 and SEQ ID NO.8, respectively.

[0039] SEQ ID NO. 1: CGCCTGTCACGCGGGAGA.

[0040] SEQ ID NO. 2: CCCAGCGGTGCCTCCA.

[0041] SEQ ID NO. 3: TGGGGTAACTTAGCAGTTTTCAAT.

[0042] SEQ ID NO. 4: GGCAAGCAGTAATCTTACATGCAC.

[0043] SEQ ID NO. 5: CTCTGAAATGAACACTACCCAC.

[0044] SEQ ID NO. 6: CCATCAGCCTTCGGACA.

[0045] SEQ ID NO.7: GGCCGTCACCGGGTGTAAATC.

[0046] SEQ ID NO. 8: GTGCAGGGTCCGAGGT.

[0047] Melting curve analysis: Amplification was performed according to the qRT-PCR method described in Example 1, and melting curve analysis was performed after the qRT-PCR cycle was completed.

[0048] Agarose gel electrophoresis verification: Prepare a 2% agarose gel by dissolving 1.2g of agarose in TAE solution, heating until completely dissolved, shaking well, adding GoldenView dye while hot, mixing well, and slowly pouring into the electrophoresis tank. Place a comb in the tank, and remove it after the liquid has cooled and solidified. Mix the qRT-PCR amplification product with DNA buffer and add the sample. Finally, add 5μL of marker to one electrophoresis tank, pour in the TAE buffer, and close the tank according to the red and black electrode settings. Set the voltage program to 110V and the time to 40min, and observe the results under UV light. The results are as follows. Figure 5 As shown.

[0049] Depend on Figure 5 A and Figure 5 As shown in C, the qRT-PCR melting curves of i-tRF-AspGTC and 3'tRF-ThrAGT both exhibit a single-peak shape with no interfering peaks, indicating that the primers have good specificity and no non-specific amplification.

[0050] Depend on Figure 5 B and Figure 5 As shown in D, the amplification products of both molecules exhibit a clear single band, with a product size of approximately 75 bp, consistent with the expected target fragment size, further verifying the specificity of the amplification.

[0051] Sanger sequencing validation: The qRT-PCR amplification products were subjected to Sanger sequencing. The sequencing results were compared with the standard sequences of i-tRF-AspGTC and 3'tRF-ThrAGT in the MINTbase database. The results are as follows: Figure 6 As shown.

[0052] Depend on Figure 6 As can be seen, the sequencing results are completely consistent with the standard sequence in the database, confirming that the amplified product is the target molecule.

[0053] Example 4: Evaluation of single-molecule diagnostic efficacy in an internal validation cohort The efficacy of i-tRF-AspGTC and 3'tRF-ThrAGT alone for gastric cancer diagnosis was evaluated in an internal validation cohort and compared with conventional serum biomarkers.

[0054] In the internal cohort validation, 105 cases of gastric cancer and 121 controls (18 cases of precancerous lesions, 12 cases of gastritis, and 91 healthy individuals) were analyzed. The clinicopathological characteristics of each group are detailed in Table 3.

[0055] Table 3. Clinical and pathological characteristics of research subjects at each research stage The serum relative expression levels of candidate tsRNAs (i-tRF-AspGTC, 3'tRF-ThrAGT) were detected using qRT-PCR. Using the expression level of tsRNAs in the serum of healthy individuals as a reference, the relative fold increases of tsRNA expression in the serum of gastric cancer patients and patients with precancerous lesions were calculated to analyze the differences in expression under different disease states.

[0056] Commonly used serum biomarkers for gastric cancer (carcinoembryonic antigen CEA, carbohydrate antigen 19-9 CA19-9, and carbohydrate antigen 72-4 CA72-4) were selected as controls. Serum CEA and CA19-9 levels were quantitatively detected using the Beckman Coulter DxI 800 system (Beckman Coulter, USA), while CA72-4 levels were detected using the Abbott i2000 SR platform (Abbott Diagnostics, USA). Both detection platforms employed chemiluminescent enzyme immunoassay technology and used original reagents compatible with the instruments. The reference ranges for each biomarker are as follows: CEA < 5 ng / mL, CA19-9 < 27 U / mL, CA72-4 < 5.3 U / mL. Values ​​exceeding the upper limit of the reference range were considered positive.

[0057] Statistical analysis: Data processing software was used for statistical analysis. The Mann-Whitney U test was used to compare expression differences between the two groups. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC), sensitivity, specificity, and 95% confidence interval (95% CI) were calculated to evaluate the diagnostic efficacy of candidate tsRNAs and combined diagnostic models. The Youden index (Youden index = sensitivity + specificity - 1) was used to determine the optimal cutoff value, which was used to distinguish between positive and negative samples. P A value <0.05 was considered statistically significant. Results are as follows: Figure 7 As shown.

[0058] Depend on Figure 7 A and Figure 7As shown in B, the relative expression levels of i-tRF-AspGTC and 3'tRF-ThrAGT in the gastric cancer group were significantly higher than those in the control group, and the difference in expression between the two groups was statistically significant. P <0.05), both candidate molecules were specifically highly expressed in gastric cancer. Figure 7 C in the figure represents the ROC curve of i-tRF-AspGTC for diagnosing gastric cancer (Nantong cohort). The AUC of this molecule is 0.915, which is significantly greater than that of CEA, CA19-9 and CA72-4, indicating that this single molecule has a diagnostic efficacy superior to traditional biomarkers. Figure 7 The D in the figure represents the ROC curve of 3'tRF-ThrAGT. The AUC of this molecule is 0.849, which is also higher than that of the three traditional biomarkers. Furthermore, its sensitivity and specificity show good combination, further validating its potential as a diagnostic biomarker for gastric cancer.

[0059] Construction of the joint diagnostic model and verification of its overall diagnostic efficacy: A joint diagnostic model of i-tRF-AspGTC and 3'tRF-ThrAGT was constructed using binary logistic regression analysis, with "tsRNA expression level" as the independent variable and "disease state (gastric cancer / non-gastric cancer)" as the dependent variable, generating a joint diagnostic score. The diagnostic efficacy of the joint diagnostic model was compared with that of single tsRNA and traditional biomarkers using ROC curves. The model equation is as follows: Logit(P) = 1.243 × i-tRF-AspGTC expression level + 0.987 × 3'tRF-ThrAGT expression level - 3.721.

[0060] The ROC curve generated by the combined diagnostic model (incorporating i-tRF-AspGTC and 3'tRF-ThrAGT) constructed based on binary logistic regression analysis is shown below. Figure 7 E in the middle.

[0061] Depend on Figure 7 As shown in E, the AUC of the combined model is 0.929 (95% CI: 0.895-0.963), which is not only significantly better than each traditional biomarker (CEA, CA19-9, CA72-4), but also surpasses any single molecule. Figure 7 C and Figure 7 (D in the text). This indicates that the combined use of i-tRF-AspGTC and 3'tRF-ThrAGT molecules can further improve the diagnostic accuracy of gastric cancer, exhibiting a synergistic and complementary effect.

[0062] Example 5 Evaluation of the diagnostic efficacy of early gastric cancer in differential diagnosis compared with control groups: We analyzed the expression of traditional biomarkers and i-tRF-AspGTC and 3'tRF-ThrAGT in patients with gastric cancer, precancerous lesions, gastritis, and healthy individuals.

[0063] Multiple group comparisons: The Kruskal-Wallis H test was used to compare the expression differences of various markers (CEA, CA19-9, CA72-4, i-tRF-AspGTC, 3'tRF-ThrAGT) among the four groups: early gastric cancer group, precancerous lesion group, gastritis group, and healthy group. The Mann-Whitney U test was used for pairwise comparisons between groups. P A value <0.05 was considered statistically significant. The traditional biomarker detection method and the candidate tsRNA detection method were the same as in Example 4. Results are as follows... Figure 8 As shown.

[0064] Depend on Figure 8 As shown in A, the CEA level was significantly higher in the early gastric cancer group compared to the healthy group. P = 0.0059); but the early gastric cancer group and the precancerous lesion group ( P = 0.9773) and the early gastric cancer group and the gastritis group ( P There was no statistically significant difference between the values ​​of CEA (e.g., 0.9957). This indicates that while serum CEA can distinguish between early-stage gastric cancer and healthy individuals, it cannot effectively differentiate between early-stage gastric cancer and precancerous lesions or benign gastritis. Figure 8 As shown in B, there was a significant difference between the early gastric cancer group and the healthy group. P = 0.0015); but the early gastric cancer group and the precancerous lesion group ( P = 0.6313) and the gastritis group ( P = 0.3210) showed no significant difference. This indicates that CA19-9 also lacks the ability to differentiate between early gastric cancer and precancerous lesions or benign gastritis. Figure 8 As shown in C, there is a statistically significant difference between the early gastric cancer group and the healthy group. P = 0.0243); however, the early gastric cancer group was significantly different from the precancerous lesion group (p=0.9902) and the gastritis group (p=0.0243). P = 0.9617) showed no significant difference. Therefore, the three traditional serum biomarkers can only distinguish early gastric cancer from healthy individuals, and are ineffective in distinguishing early gastric cancer from precancerous lesions or gastritis. Figure 8 As shown in D, the expression level in the early gastric cancer group was significantly higher than that in the healthy group. P <0.05), higher than the gastritis group ( P <0.05 and higher than the precancerous lesion group ( P <0.05). By Figure 8 As can be seen from E, the relative expression level in the early gastric cancer group was significantly higher than that in the healthy group. P<0.05), higher than the gastritis group ( P <0.05 and higher than the precancerous lesion group ( P <0.0001). This indicates that i-tRF-AspGTC and 3'tRF-ThrAGT molecules have the ability to distinguish early gastric cancer from benign or precancerous lesions, which is superior to traditional markers.

[0065] To further evaluate the diagnostic value of the two candidate molecules (i-tRF-AspGTC and 3'tRF-ThrAGT) for early gastric cancer, ROC curves were plotted for single molecules, the combined diagnostic model, and traditional serum biomarkers (CEA, CA19-9, CA72-4) in the Nantong cohort. A calibration curve for the combined model was also plotted to assess predictive consistency. The results are as follows: Figure 9 As shown in Table 4.

[0066] Table 4. Comparison of the diagnostic efficacy of i-tRF-AspGTC and 3'tRF-ThrAGT as single molecules, combined diagnostic models, and traditional biomarkers for early gastric cancer. Figure 9 In the table, A represents the ROC curve for i-tRF-AspGTC in diagnosing early gastric cancer. The AUC corresponding to this molecule is 0.927 (95% CI: 0.889-0.965), significantly higher than that of traditional markers CEA (AUC = 0.681), CA19-9 (AUC = 0.610), and CA72-4 (AUC = 0.620). Table 4 shows that at the optimal cutoff value of 2.272, i-tRF-AspGTC has a sensitivity of 82.6% and a specificity of 89.4%. These results indicate that this single molecule has excellent diagnostic ability for early gastric cancer, significantly superior to existing conventional serum markers. Figure 9 B in the table represents the ROC curve of 3'tRF-ThrAGT for diagnosing early gastric cancer. The AUC of this molecule was 0.861 (95% CI: 0.805-0.916), which is also higher than the three traditional biomarkers. At a cutoff value of 2.011, the sensitivity was 84.1% and the specificity was 74.5% (Table 4). Although its AUC was slightly lower than i-tRF-AspGTC, its sensitivity was superior, suggesting that this molecule has good detection capability in early gastric cancer. Figure 9 As shown in Table C, the combined model exhibits an AUC as high as 0.932 (95% CI: 0.895-0.968), which is not only higher than any single molecule but also significantly superior to CEA, CA19-9, and CA72-4. Table 4 shows that the combined model achieves a sensitivity of 92.8% and a specificity of 79.8%, demonstrating the best overall performance. These results indicate that the combined application of two molecules can significantly improve the diagnostic accuracy and sensitivity of early gastric cancer.

[0067] To verify whether the combined diagnostic model can be applied clinically, a model fit test was performed: the Hosmer-Lemeshow test was used to evaluate the goodness of fit of the combined diagnostic model. P A value greater than 0.05 indicates that there is no significant difference between the model's predicted values ​​and the actual observed values, and the model fits well. At the same time, a calibration curve is plotted, with "Predicted Probability" as the horizontal axis and "Actual Probability" as the vertical axis. The model calibration effect is intuitively evaluated by the degree of closeness of the curve to the diagonal (ideal fit line). The diagonal (45° line) represents the perfect prediction under ideal conditions.

[0068] Depend on Figure 9 As shown in D, the nonparametric curve (dashed line) in the graph closely follows the diagonal, indicating a good consistency between the model's predicted probabilities and the actual observations. Further Hosmer-Lemeshow testing yields... P = 0.502 (0.05), indicating no significant deviation between the model's predicted values ​​and the actual observed values, suggesting a good model fit and reliable calibration. This result supports the high predictive accuracy and stability of the combined diagnostic model in clinical applications.

[0069] Example 6 Diagnostic efficacy assessment for differentiating early gastric cancer from precancerous lesions: To evaluate the efficacy of i-tRF-AspGTC, 3'tRF-ThrAGT, and the combined diagnostic model in differentiating early gastric cancer from precancerous lesions, and to compare them with traditional biomarkers.

[0070] Seventy cases of early gastric cancer (EGC) and 30 cases of precancerous lesions (21 cases of chronic atrophic gastritis and 9 cases of intestinal metaplasia) were extracted from the internal validation cohort and analyzed separately.

[0071] The relative expression levels of i-tRF-AspGTC and 3'tRF-ThrAGT, as well as the levels of CEA, CA19-9, and CA72-4, were detected in each sample according to the above experimental methods. The diagnostic efficacy comparison between early gastric cancer and precancerous lesions is shown in Table 5. ROC curves and calibration curves of the combined diagnostic model are shown below. Figure 10 As shown.

[0072] Table 5. Comparison of diagnostic efficacy between early gastric cancer and precancerous lesions Depend on Figure 9As shown in A, the AUC of the i-tRF-AspGTC molecule is 0.875 (95% CI: 0.805-0.945), significantly higher than that of traditional markers CEA (AUC=0.512), CA19-9 (AUC=0.653), and CA72-4 (AUC=0.595). Table 5 shows that at the optimal cutoff value of 1.680, its sensitivity reaches 92.8%, and its specificity is 66.7%. In comparison, CEA (… P >0.05) and CA72-4 ( P AUCs >0.05 were not statistically significant, although CA19-9 showed a statistically significant difference ( P The value was <0.05), but the AUC was only 0.653, indicating low diagnostic value. The results show that i-tRF-AspGTC can effectively distinguish early gastric cancer from precancerous lesions, while traditional markers are largely unable to identify this transition process. Figure 10 As shown in section B, the AUC of the 3'tRF-ThrAGT molecule is 0.843 (95% CI: 0.768–0.918), which is also superior to all traditional biomarkers. At a cutoff value of 3.638, the sensitivity is 60.0%, and the specificity is as high as 96.7%. Although the sensitivity is lower than i-tRF-AspGTC, its extremely high specificity suggests that this molecule has a unique advantage in excluding precancerous lesions. Figure 10 As shown in C, the combined model achieved an AUC of 0.952 (95% CI: 0.915–0.990), which is not only higher than that of any single molecule but also significantly better than traditional biomarkers. Table 5 shows that the combined model had a sensitivity of 79.7% and a specificity of 97.0%, exhibiting the best overall performance. These results indicate that the combined application of two molecules can effectively differentiate early gastric cancer from precancerous lesions, overcoming the limitations of traditional serum biomarkers in this type of identification.

[0073] Figure 10 In the figure, D represents the calibration curve of the joint diagnostic model. The nonparametric curve (dashed line) closely follows the diagonal, indicating a good agreement between the model's predicted probabilities and actual observations. The Hosmer-Lemeshow test result is... P = 1.994 (>0.05), indicating that there is no significant deviation between the model's predicted values ​​and the actual observed values, the model fits well, and the calibration is reliable. This further supports the ability of the combined diagnostic model to stably and accurately predict the differentiation between early gastric cancer and precancerous lesions in clinical practice.

[0074] Example 7 Evaluation of the diagnostic efficacy of advanced gastric cancer in differential diagnosis compared with control groups: To evaluate the efficacy of i-tRF-AspGTC, 3'tRF-ThrAGT, and the combined diagnostic model in differentiating advanced gastric cancer from precancerous lesions, and to compare them with traditional biomarkers.

[0075] An internal validation cohort of 35 patients with advanced gastric cancer was compared with 121 controls (including precancerous lesions and healthy individuals). ROC curves and calibration curves for the combined diagnostic model were plotted and compared with traditional serum biomarkers. The results are shown in Table 6. Figure 11 As shown.

[0076] Table 6. Comparison of the diagnostic efficacy of i-tRF-AspGTC and 3'tRF-ThrAGT as single molecules, combined diagnostic models, and traditional biomarkers for advanced gastric cancer. Depend on Figure 10 As shown in A, the AUC of the i-tRF-AspGTC molecule is 0.892 (95% CI: 0.831–0.954), significantly higher than that of traditional markers CEA (AUC = 0.741), CA19-9 (AUC = 0.593), and CA72-4 (AUC = 0.574). Table 6 shows that at the optimal cutoff value of 1.537, its sensitivity is as high as 91.4%, and its specificity is 74.5%. In comparison, CA19-9 (… P >0.05) and CA72-4 ( P The AUC >0.05 was not statistically significant, while the CEA showed a statistically significant difference ( P (<0.05) but the AUC was only 0.741, limiting its diagnostic value. The results indicate that i-tRF-AspGTC has excellent diagnostic ability for advanced gastric cancer, significantly superior to traditional biomarkers. Figure 11 As shown in section B, the AUC of the 3'tRF-ThrAGT molecule is 0.826 (95% CI: 0.747–0.905), which is also higher than all conventional biomarkers. At a cutoff of 2.091, the sensitivity is 77.1% and the specificity is 75.5%. Although its AUC is slightly lower than i-tRF-AspGTC, it still demonstrates good diagnostic efficacy, and its specificity is comparable to i-tRF-AspGTC. Figure 10 As shown in C, the AUC of the joint model is 0.894 (95% CI: 0.833–0.955), which is similar to the AUC of i-tRF-AspGTC alone (0.892), but the sensitivity is further improved to 94.3% (Table 6), and the specificity is 72.3%. Compared with the three traditional biomarkers, the joint model has significantly better AUC, sensitivity, and specificity.

[0077] Depend on Figure 11As can be seen, the nonparametric curve (dashed line) in the figure closely matches the diagonal, indicating a good consistency between the model's predicted probabilities and the actual observations. The Hosmer-Lemeshow test result is... P = 0.181 (>0.05), indicating that there is no significant deviation between the model's predicted values ​​and the actual observed values, the model fits well, and the calibration is reliable. This further supports the high predictive stability and accuracy of the combined diagnostic model in the clinical diagnosis of advanced gastric cancer.

[0078] Example 8 Evaluation of diagnostic efficacy in differentiating advanced gastric cancer from precancerous lesions: To evaluate the efficacy of i-tRF-AspGTC, 3'tRF-ThrAGT, and the combined diagnostic model in differentiating advanced gastric cancer from precancerous lesions, and to compare them with traditional biomarkers.

[0079] Thirty-five patients with advanced gastric cancer and 30 patients with precancerous lesions (chronic atrophic gastritis, intestinal metaplasia) were analyzed separately. ROC curves and calibration curves of the combined diagnostic model were plotted and compared with traditional serum biomarkers (CEA, CA19-9, CA72-4). The results are shown in Table 7. Figure 12 As shown.

[0080] Table 7. Comparison of diagnostic efficacy between advanced gastric cancer and precancerous lesions. Depend on Figure 12 As shown in A, the AUC of i-tRF-AspGTC is 0.835 (95% CI: 0.740–0.931), significantly higher than that of traditional markers CEA (AUC = 0.582), CA19-9 (AUC = 0.638), and CA72-4 (AUC = 0.573). Table 7 shows that at the optimal cutoff value of 1.537, its sensitivity reaches 91.4%, and its specificity is 63.3%. In comparison, CEA (… P >0.05) and CA72-4 ( P The AUC >0.05 was not statistically significant, although CA19-9 showed a statistically significant difference ( P <0.05) but the AUC was only 0.638, indicating low diagnostic value. The results show that i-tRF-AspGTC can efficiently identify advanced gastric cancer and precancerous lesions, with its high sensitivity being a particularly prominent advantage. Figure 12As shown in section B, the AUC of the 3'tRF-ThrAGT molecule is 0.784 (95% CI: 0.674–0.894), which is also superior to all traditional biomarkers. At a cutoff value of 3.638, the sensitivity is 51.4%, and the specificity is as high as 96.7%. Although the sensitivity is relatively low, its extremely high specificity suggests that this molecule has unique value in excluding precancerous lesions and diagnosing advanced gastric cancer. Figure 12 As shown in C, the AUC of the combined model reached 0.882 (95% CI: 0.803-0.961), higher than that of any single molecule and significantly better than traditional biomarkers (all <0.638). Table 7 shows that the combined model had a sensitivity of 65.7% and a specificity of 96.7%, exhibiting the best overall performance. These results indicate that the combined application of two molecules can effectively differentiate advanced gastric cancer from precancerous lesions, compensating for the severe limitations of traditional serum biomarkers in this type of identification (CEA and CA72-4 were not statistically significant).

[0081] Figure 12 In the figure, D represents the calibration curve of the joint diagnostic model. The nonparametric curve (dashed line) closely follows the diagonal, indicating a good agreement between the model's predicted probabilities and actual observations. The Hosmer-Lemeshow test result is... P = 0.939 (>0.05), indicating that there is no significant deviation between the model's predicted values ​​and the actual observed values, the model fits well, and the calibration is reliable. This further supports the high predictive stability and accuracy of the combined diagnostic model in the clinical application of distinguishing advanced gastric cancer from precancerous lesions.

[0082] Example 9 By detecting changes in the expression of i-tRF-AspGTC and 3'tRF-ThrAGT in the serum of gastric cancer patients before and after surgery, we can assess their potential value in monitoring the efficacy of gastric cancer surgery.

[0083] Matched serum samples were collected from 18 patients with pathologically confirmed gastric cancer. Serum samples were collected 1-3 days before surgery (preoperative) and 5 days after surgery (postoperative). Sample processing was the same as in Example 1. Detection methods were the same as in the above examples. Results are as follows: Figure 13 As shown.

[0084] Depend on Figure 13 It can be seen that in the serum of a gastric cancer patient, the relative expression levels of i-tRF-AspGTC and 3'tRF-ThrAGT were significantly lower 5 days after surgery than before surgery. P <0.05 indicates that the expression level of the target molecule decreased significantly after surgery, providing potential value for postoperative monitoring.

[0085] Example 10 The stability of i-tRF-AspGTC and 3'tRF-ThrAGT in serum after being stored at room temperature for different times was evaluated to provide a reference for routine clinical sample processing. After serum samples were stored at room temperature for 0 h, 2 h, 4 h, 8 h, and 16 h, RNA was extracted and analyzed by qRT-PCR. Differences in relative expression levels and coefficient of variation (CT) values ​​were statistically analyzed. Results are as follows: Figure 14 As shown.

[0086] Depend on Figure 14 It can be seen that after serum samples were placed at room temperature for 0h, 2h, 4h, 8h, and 16h, there was no significant difference in the relative expression levels of i-tRF-AspGTC and 3'tRF-ThrAGT. P All values ​​were >0.05, and the coefficient of variation (CV) of the CT value was <5%, indicating that the target molecule has good stability at room temperature and can meet the routine processing requirements of clinical samples.

[0087] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A tsRNA combined biomarker for the adjuvant diagnosis of gastric cancer, characterized in that, The tsRNA co-markers include i-tRF-AspGTC and 3'tRF-ThrAGT.

2. The tsRNA co-marker according to claim 1, characterized in that, The nucleotide sequence of i-tRF-AspGTC is shown in SEQ ID NO.

1.

3. The tsRNA co-marker according to claim 1, characterized in that, The nucleotide sequence of the 3'tRF-ThrAGT is shown in SEQ ID NO.

2.

4. The application of the tsRNA combined biomarker as described in claim 1 in the preparation of products for the auxiliary diagnosis of gastric cancer.

5. The application according to claim 4, characterized in that, The products include reagent kits, chips, or reagents.

6. The application according to claim 5, characterized in that, The product is used for the early diagnosis of gastric cancer, monitoring the effectiveness of gastric cancer surgery, or tumor recurrence.

7. A reagent kit for assisting in the diagnosis of gastric cancer, characterized in that, The kit contains a first detection reagent for detecting the expression level of i-tRF-AspGTC and a second detection reagent for detecting the expression level of 3'tRF-ThrAGT.

8. The reagent kit according to claim 7, characterized in that, The first detection reagent contains a primer pair for specifically amplifying i-tRF-AspGTC, and the second detection reagent contains a primer pair for specifically amplifying 3'tRF-ThrAGT.

9. The reagent kit according to claim 8, characterized in that, The primer pair for specifically amplifying i-tRF-AspGTC includes an upstream primer F1 and a downstream primer R1, wherein the nucleotide sequence of F1 is shown in SEQ ID NO.3 and the nucleotide sequence of R1 is shown in SEQ ID NO.

4.

10. The reagent kit according to claim 8, characterized in that, The primer pair for specifically amplifying 3'tRF-ThrAGT includes an upstream primer F2 and a downstream primer R2, the nucleotide sequence of which is shown in SEQ ID NO.5 and the nucleotide sequence of which is shown in SEQ ID NO.6.

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