Circulating snoRNA biomarker for gastric cancer diagnosis and application of circulating snoRNA biomarker

Abstract

The circulating snoRNA biomarker comprises the following six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94, the differential expression of the circulating snoRNA biomarker in plasma is remarkable, and the circulating snoRNA biomarker is suitable for early screening, diagnosis and prognosis evaluation of gastric cancer. The circulating snoRNA is applied to a detection reagent for gastric cancer diagnosis, and has the advantages of non-invasiveness and convenience in detection.

Description

Technical Field

[0001] The present invention relates to the field of bioinformation technology, and in particular to a circulating snoRNA biomarker for gastric cancer diagnosis and its application. Background Art

[0002] Gastric cancer, a malignant tumor that develops in the gastric mucosal epithelium, is one of the most common cancers worldwide. Its incidence and mortality rates remain high in my country, posing a serious threat to the lives and health of residents. Screening, early diagnosis, and treatment for individuals at high risk of gastric cancer can effectively reduce its incidence and mortality.

[0003] Early-stage gastric cancer often presents with no obvious symptoms. As the disease progresses, symptoms such as upper abdominal discomfort, dull pain, loss of appetite, nausea, vomiting, and melena may appear. The traditional model for gastric cancer screening and diagnosis involves serological screening, followed by a gold-standard endoscopic biopsy for positive initial screening results. While traditional pathological examinations offer a certain degree of accuracy, they are invasive and inconvenient for patients, even leading to complications and sequelae such as bleeding and infection. Compared to pathological examinations, the development of blood biomarkers has facilitated the screening and diagnosis of gastric cancer, providing more stable results and high patient compliance. The widespread use of existing tests for pepsinogen, gastrin 17, HP, and tumor markers demonstrates the potential of biomarkers in the screening and diagnosis of gastric cancer. Therefore, the search for more effective, accurate, and sensitive non-invasive clinical biomarkers for the early screening and diagnosis of gastric cancer is urgently needed.

[0004] SnoRNAs are noncoding RNAs ranging from 60 to 300 nucleotides in length. They are primarily found in the nucleolus but can also be secreted into plasma to exert their effects. SnoRNAs are closely associated with epigenetic regulation, genomic stability, and the development and progression of various tumors. They are relatively stable and easily detected in body fluids. Therefore, snoRNAs have the potential to serve as biomarkers. Currently, further research is needed on the role of snoRNAs in gastric cancer. A better understanding of the role of snoRNAs in the development and progression of gastric cancer will allow the discovery of new biomarkers for early gastric cancer screening and diagnosis, which is crucial for early screening and diagnosis of gastric cancer patients. Summary of the Invention

[0005] The present invention aims to provide a circulating snoRNA biomarker for gastric cancer diagnosis and its application. The circulating snoRNA biomarker provided by the present invention is used for gastric cancer diagnosis and has the advantages of being non-invasive and easy to detect.

[0006] The technical solution of the present invention is a circulating snoRNA biomarker for gastric cancer diagnosis, wherein the circulating snoRNA biomarker includes the following six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94.

[0007] The above-mentioned circulating snoRNA biomarker for gastric cancer diagnosis, the SNORA7B has a nucleotide sequence as shown in SEQ ID No: 1, the SNORD41 has a nucleotide sequence as shown in SEQ ID No: 2, the SNORA74A has a nucleotide sequence as shown in SEQ ID No: 3, the SNORA79B has a nucleotide sequence as shown in SEQ ID No: 4, the SNORD83A has a nucleotide sequence as shown in SEQ ID No: 5, and the SNORD94 has a nucleotide sequence as shown in SEQ ID No: 6.

[0008] The aforementioned circulating snoRNA biomarker for gastric cancer diagnosis is a circulating biomarker in human plasma and tissues.

[0009] The use of the aforementioned circulating snoRNA biomarkers in the preparation of detection reagents for gastric cancer diagnosis.

[0010] In the above application, the detection reagent uses SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94 as detection objects; the detection reagent is a kit and / or a high-throughput chip.

[0011] In the aforementioned application, the kit comprises reagents that specifically recognize SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94.

[0012] In the aforementioned application, the reagents that specifically recognize SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94 are reverse transcription primers.

[0013] In the aforementioned application, the forward primer and reverse primer of SNORA7B are shown as SEQ ID No: 7 and SEQ ID No: 8; the forward primer and reverse primer of SNORD41 are shown as SEQ ID No: 9 and SEQ ID No: 10; the forward primer and reverse primer of SNORA74A are shown as SEQ ID No: 11 and SEQ ID No: 12; the forward primer and reverse primer of SNORA79B are shown as SEQ ID No: 13 and SEQ ID No: 14; the forward primer and reverse primer of SNORD83A are shown as SEQ ID No: 15 and SEQ ID No: 16; the forward primer and reverse primer of SNORD94 are shown as SEQ ID No: 17 and SEQ ID No: 18.

[0014] In the aforementioned application, the kit contains standards or positive controls of SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94 for calibrating the test results and improving the accuracy of the test.

[0015] In the aforementioned application, the positive control is an artificially synthesized RNA fragment containing the sequences of SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94.

[0016] Compared to existing technologies, the present invention utilizes circulating snoRNA biomarkers for gastric cancer diagnosis. Compared to traditional serological markers (such as pepsinogen and gastrin 17), this circulating snoRNA biomarker can more sensitively reflect changes in early molecular levels of gastric cancer, effectively improving the detection rate of early gastric cancer. Furthermore, through comprehensive multi-indicator analysis, the risk of false positives or false negatives associated with single markers is reduced, significantly improving the accuracy and reliability of diagnostic results and providing a more comprehensive basis for clinical decision-making. Furthermore, the present invention utilizes a blood test, which is convenient, rapid, minimally invasive, easy to use, and highly stable. This avoids the risk of multiple endoscopic biopsies to obtain tumor tissue for pathological identification, which can cause physical damage to the patient and significantly reduces testing costs. This invention provides novel, highly sensitive, and specific biomarkers for the diagnosis and prognosis of gastric cancer, with high clinical application and potential for widespread adoption. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of Example 1 of the present invention; Figure 2 is the expression of six snoRNAs in plasma; Figure 3is a survival curve plot of circulating snoRNA biomarkers in plasma as biomarkers; Figure 4 is the ROC curve plot of circulating snoRNA biomarkers in plasma as biomarkers; Figure 5 is the expression of circulating snoRNA biomarkers as biomarkers in gastric cancer tissues; Figure 6 This is a ROC curve analysis of the circulating snoRNA biomarkers obtained from gastric cancer patient tissues. DETAILED DESCRIPTION

[0018] The present invention will be further described below with reference to the accompanying drawings and examples, but they are not intended to limit the present invention.

[0019] Example 1: Analysis based on plasma snoRNA expression profile.

[0020] This example is divided into four parts: collecting plasma samples, extracting plasma small RNA with Qiagen miRNeasy plasma kit, and sequencing small RNA transcriptomes. The process of this example is as follows: Figure 1 shown.

[0021] (1) Collect plasma samples; 5 ml of whole blood was collected from 200 gastric cancer patients and 100 healthy controls and placed in blood collection tubes containing EDTA anticoagulant. After collection, the tubes were repeatedly inverted to thoroughly mix the EDTA anticoagulant with the blood. The blood was centrifuged at 3000 rpm and 4°C for 10 minutes. The supernatant was plasma. 2 ml of plasma was collected and placed in EP tubes and stored in a refrigerator at -80°C.

[0022] (2) Qiagen miRNeasy plasma kit was used to extract plasma small RNA; Cell lysis and small RNA extraction: Add 1 ml of QIAzol Lysis Reagent to 200 μl of sample, vortex or invert to mix, and incubate at room temperature (15-25°C) for 5 min. Add 200 μl of chloroform, shake vigorously for 15 seconds, and incubate at room temperature for 2-3 min. After incubation, centrifuge at 12,000 g for 15 min at 4°C. After centrifugation, transfer the upper aqueous phase to a new EP tube, add 1.5 times the volume of 100% ethanol to the aqueous phase, and mix thoroughly by inverting. Pipette 700 μl of the solution onto an RNeasy MinElute ssnon column and centrifuge at 8,000 g for 15 seconds at room temperature. Discard the waste liquid in the collection tube. Repeat the above step for the remaining liquid. Add 700 μl of Buffer RWT to the RNeasy MinElute ssnon column and centrifuge at 8,000 g for 15 seconds. Discard the waste liquid in the collection tube. Pipette 500 μl of Buffer RPE into the RNeasy MinElutessnon column and centrifuge at 8000 g for 15 seconds. Discard the waste liquid in the collection tube. Add 500 μl of 80% ethanol to the RNeasy MinElutessnon column and centrifuge at 8000 g for 2 minutes. Discard the waste liquid in the collection tube. Place the RNeasy MinElutessnon column in a new 2 ml EP tube. Open the spin column cap, centrifuge at maximum speed, and centrifuge for 5 minutes. Discard the waste liquid and the collection tube. Place the RNeasy MinElutessnon column in a new 1.5 ml collection tube.

[0023] Elution of small RNA: Add 15 μL of RNase-free water to the middle of the filter membrane, gently cover the tube, let it stand at room temperature for 2 minutes, and centrifuge at maximum speed for 1 minute. The separated RNA is at the bottom of the tube.

[0024] RNA concentration and integrity assessment: 1 μL was analyzed on an Aglient 2100 RNA snoco chip. Peaks were generally below 200 nt. Only high-quality RNA samples (RIN ≥ 7, >50 ng / μL, OD260 / 280 between 1.8 and 2.2) were used to construct sequencing libraries.

[0025] (3) small RNA transcriptome sequencing; Small RNA quantification: Small RNA samples used for library construction were first quantified using a library quantification kit. 1 μg of starting material was used to generate sequencing libraries.

[0026] Connect the linker sequence: Connect the linker sequence at the 3' and 5' ends respectively.

[0027] cDNA synthesis: Using MMLV-derived PrimeScript reverse transcriptase (RT), random primers are used to reverse-synthesize single-strand cDNA using the linker-ligated RNA as a template, followed by second-strand synthesis to form a stable double-stranded structure.

[0028] Library enrichment: PCR amplification (11-12 cycles) was performed using sequencing primers to enrich the library concentration.

[0029] Library purification: Based on the length distribution characteristics of small RNAs, target fragments were recovered by gel cutting (6% Novex TBE PAGE gel, 1.0 mm, 10 wells).

[0030] Sequencing and data analysis: Quantification was performed using a Qubit 4.0 platform, and samples were mixed according to the data ratio. Bridge PCR amplification was performed on a cBot to generate clusters. Sequencing was performed on the Illumina NovaSeq 6000 platform.

[0031] (4) Bioinformatics analysis; Raw sequence data statistics: Illumina sequencing is a second-generation sequencing technology, generating billions of reads in a single run. This massive amount of data makes it impossible to analyze the quality of each read individually. Therefore, statistical methods are used to analyze the base distribution and quality fluctuation of all sequencing reads at each cycle, providing a high-level overview of the sequencing quality and library construction quality of the sample. Sequencing-related quality assessments are performed on each sample's raw sequencing data, including A / T / G / C base content distribution, base quality distribution, and base error rate distribution.

[0032] Quality control of raw sequencing data: The raw sequencing data contains sequencing adapter sequences or low-quality reads. To ensure the accuracy of subsequent bioinformatics analysis, the raw sequencing data is first filtered to obtain high-quality sequencing data to ensure the smooth progress of subsequent analysis. The specific steps and order are as follows: 1) Remove the 3' adapter sequence in the reads and remove reads without inserts due to reasons such as adapter self-ligation; 2) Cut the bases with low sequencing quality at the 3' end (quality value less than 20); 3) Remove reads containing unknown base N; 4) Remove reads that are too short (<18nt); 5) Remove reads that are too long (>32nt); After quality control, analyze the length of clean reads. Based on the characteristics of small RNA, select reads with a length of 18-32nt as usefμl reads for subsequent analysis.

[0033] Alignment with the reference genome: Bowtie was used to align the quality-controlled use fμl reads with the specified reference genome (human genome), and then a local integrated database based on the R package "ensembldb" was used for gene annotation, and snoRNA genes were selected.

[0034] Independent risk factors were screened based on plasma snoRNA expression.

[0035] After obtaining the plasma snoRNA expression matrix, univariate and multivariate Cox regression analyses were performed, ultimately identifying six snoRNAs that collectively represent independent risk factors for gastric cancer. These snoRNAs can be used as early screening and diagnostic markers for gastric cancer, termed circulating snoRNA biomarkers. These circulating snoRNA biomarkers include the following six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94. These snoRNAs can be used as circulating biomarkers in human plasma and tissues: The SNORA7B has a nucleotide sequence as shown in SEQ ID No: 1: GACCTCCTGGGATCGCATCTGGAGACTGCCTAGTATTCTGCCAGCTTCGGAAAGGGAGGGAAAGCAAGCCTGGCAGAGGCACCCATTCCATTCCCAGCTTGCTCCGTAGCTGGTGATTGGAAGACACTCTGCGACAGTG (SEQ ID No: 1) The SNORD41 has a nucleotide sequence as shown in SEQ ID No: 2: TGGGAAGTGATGACACCTGTGACTGTTGATGTGGAACTGATTTATCGCGTATTCGTACTGGCTGATCCTG (SEQ ID No: 2) The SNORA74A has a nucleotide sequence as shown in SEQ ID No: 3: TCCAGCGGTTGTCAGCTATCCAGGCTCATGTGGTGCCTGTGATGGTGTTACACTGTTGGAAGAGCAAACACTGTCTTTATTGAGGTTTGGCTCCAAGCACTGTTTTGGTGTTGTAGCTGAGTACCTTTGGGCAGTGTTTTGCACCTCTGAGAGTGGAATGACTCCTGTGGAGTTGATCCTAGTCTGGGTGCAAACAAT (SEQ ID No: 3) The SNORA79B has a nucleotide sequence as shown in SEQ ID No: 4: TGATGGCTGTTCCTCTCACTGCTTGAAGCCTTAGGCAGTGGGATTTTGATCCATCATATATCAAAAATGGCTTATCTTCACTCAGGGCACCATGAGGATGGGCTGGCTGTCCGTTAGTGCCTTCTGATTTTTGCGGAGTCAAACAATT (SEQ ID No: 4) The SNORD83A has a nucleotide sequence as shown in SEQ ID No: 5: GCTGTTCGTTGATGAGGCTCAGAGTGAGCGCTGGGTACAGCGCCCGAATCGGACAGTGTAGAACCATTCTCTACTGCCTTCCTTCTGAGAACAGC (SEQ ID No: 5) The SNORD94 has a nucleotide sequence as shown in SEQ ID No: 6: CAGGCTGTGATGATTGGCGCAGGGGTACGGACCTCAGCTGAGTCATGGGAGCTGAATGTATGTGTTTCTCCTTTGTCCTGCATGTGGCAGGCTGATGGGGAGCACTTACATGAGACTGTTGCCTCAATCTGAGCCTG (SEQ ID No: 6) A risk model was constructed based on the risk coefficient of each snoRNA and the expression level of each patient. Patients were then divided into high-risk and low-risk groups based on their risk position. Six snoRNA risk scores were then used as biomarkers for gastric cancer to plot patient survival curves (p<0.0001). Receiver-operating characteristic (ROC) curve analysis was also performed on the six snoRNA risk scores. Finally, survival curves were plotted for the entire dataset, with p-values ​​of all less than 0.0001.

[0036] This study included 36 gastric cancer patients and paired adjacent tissue samples from the First Affiliated Hospital of Wenzhou Medical University. These patients did not receive any prior treatment, such as radiotherapy or chemotherapy, before surgery. Each tissue sample was stored in liquid nitrogen. A risk score was calculated using a Cox regression analysis of snoRNA in plasma. All tissue samples were then divided into high-risk and low-risk groups. Figure 2 The expression of 6 snoRNAs in plasma is shown. Figure 3 Survival curves of circulating snoRNA biomarkers in plasma as biomarkers are shown. Figure 4 The ROC curve of circulating snoRNA biomarkers in plasma is shown. It can be seen that patients with adjacent tissues are mainly in the low-risk group, while patients with gastric cancer tissue are mainly in the high-risk group. Figure 5 The expression of circulating snoRNA biomarkers as biomarkers in gastric cancer tissues was demonstrated. Figure 5 The difference in risk scores between the two groups of data (non-tumor group and tumor group) can be seen, and the changes in risk scores of individuals in the two groups of data can be compared intuitively. Figure 5 It can be seen that there are highly significant differences in risk scores between the tumor group and the non-tumor group. Figure 6 The ROC curve analysis of the circulating snoRNA biomarkers obtained from gastric cancer patient tissues is shown. The ROC curve analysis can be used to evaluate the predictive diagnostic value of the six snoRNAs for gastric cancer patients. Figure 6 As can be seen from the figure, the area under the ROC curve of tissue samples is 0.814, indicating that the combination of these six snoRNAs has a good ability to distinguish between gastric cancer patients and adjacent tissues.

[0037] Example 2: Application of circulating snoRNA biomarkers in the preparation of detection reagents for gastric cancer diagnosis.

[0038] In the preparation of the detection reagent, SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94 can be combined as detection targets. The detection reagent can be a kit or a high-throughput chip.

[0039] In this example, the kit contains reagents that specifically recognize SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94. These reagents can be nucleic acid probes, antibodies, or other molecular recognition elements that specifically bind to the target snoRNA. Specifically, these reagents can capture or detect specific snoRNAs through intermolecular interactions, such as base pairing or antigen-antibody binding. In a preferred embodiment, these reagents can be immobilized on a solid support, such as a microsphere or chip surface, to facilitate subsequent separation or detection.

[0040] Specifically, the design of specific recognition reagents needs to consider the sequence characteristics and secondary structure of the target snoRNA. For example, for SNORA7B, nucleic acid probes complementary to its conserved regions can be designed; for SNORD41, antibodies capable of recognizing its specific structural domains can be developed. These reagents need to undergo rigorous specificity and sensitivity testing to ensure that they do not cross-react with other non-target molecules. Furthermore, these reagents can be optimized for different detection platforms, such as real-time fluorescence quantitative PCR, microarrays, or next-generation sequencing technologies.

[0041] As a preferred embodiment, the kit includes reverse transcription primers that specifically recognize the six aforementioned snoRNAs. These reverse transcription primers are used to reverse transcribe the target snoRNA into cDNA for subsequent PCR amplification and detection. These reverse transcription primers can be designed to bind complementary to specific sites within the target snoRNA, ensuring specificity and efficiency of reverse transcription. For example, the reverse transcription primers can contain sequences complementary to the 3' end or an internal conserved region of the target snoRNA.

[0042] As a preferred embodiment, the length of the reverse transcription primer can be between 18 and 25 nucleotides to balance specificity and binding efficiency. In addition, the reverse transcription primer can also contain modified nucleotides, such as locked nucleic acid (LNA) or 2'-O-methyl modification, to improve primer stability and binding ability.

[0043] Specifically, the reverse transcription primers for SNORA7B are as follows: Forward primer: 5'-CTGGGATCGCATCTGGAGAC-3'(SEQ ID No:7) Reverse primer: 5'-AGCTGGGAATGGAATGGGTG-3'(SEQ ID No:8) The reverse transcription primers for SNORD41 are as follows: Forward primer: 5'-AAGTGATGACACCTGTGACTGT-3'(SEQ ID No:9) Reverse primer: 5'-GGATCAGCCAGTACGAATACGC-3'(SEQ ID No:10) The reverse transcription primers for SNORA74A are as follows: Forward primer: 5'-TGGTGCCTGTGATGGTGTTA-3'(SEQ ID No:11) Reverse primer: 5'-CCAAAACAGTGCTTGGAGCC-3'(SEQ ID No:12) The reverse transcription primers for SNORA79B are as follows: Forward primer: 5'-ATGGCTGTCCTCTCACTGC-3'(SEQ ID No:13) Reverse primer: 5'-CATGGTGCCCTGAGTGAAGAT-3'(SEQ ID No:14) The reverse transcription primers for SNORD83A are as follows: Forward primer: 5'-GAGTGAGCCTGGGTACAG-3'(SEQ ID No:15) Reverse primer: 5'-GCTGTTCTCAGAAGGAAGGCA-3'(SEQ ID No:16) The reverse transcription primers for SNORD94 are as follows: Forward primer: 5'-GATGATTGGCGCAGGGGTA-3'(SEQ ID No:17) Reverse primer: 5'-TGTAAGTGCTCCCCATCAGC-3' (SEQ ID No: 18); Forward and reverse primers are specific DNA fragments used for PCR amplification. The forward primer is complementary to the 5' end of the target sequence, and the reverse primer is complementary to the 3' end of the target sequence. Primer design requires consideration of factors such as length, GC content, and melting temperature to ensure specificity and efficiency of amplification. As a preferred embodiment, primer length can be controlled between 18-25 bases, with a GC content maintained at 40%-60%, and a melting temperature between 55-65°C. Furthermore, primer design must avoid the formation of secondary structures or primer dimers. For example, primer specificity can be verified through BLAST comparison to avoid binding to non-target sequences. This ensures that the designed primers accurately recognize and amplify the target snoRNA sequence. These primer sequences are optimized to specifically recognize and bind to the corresponding snoRNA, ensuring efficient and accurate reverse transcription.

[0044] Furthermore, the kit may also contain the above six snoRNA standards or positive controls to calibrate the test results and improve the accuracy of the test. Among them, the positive control can be an artificially synthesized RNA fragment containing these snoRNA sequences. The concentration range is set to 10 3 to 10 6 copies / µL to cover diverse testing needs. The inclusion of standards or positive controls can effectively mitigate false negatives or positives caused by sample variability or operational fluctuations during testing. For example, standards can be prepared using a gradient concentration to cover the range of snoRNA expression that may be present in clinical samples, thereby establishing a standard curve for quantitative detection. Positive controls are a crucial component of test kits, used to calibrate test results and improve accuracy. Positive control RNA fragments are synthesized via in vitro transcription. Their sequence is identical to the target snoRNA, but specific mutations are introduced to distinguish them from endogenous snoRNAs and prevent cross-contamination. The synthesis process must strictly adhere to the known sequence of the target snoRNA (e.g., SEQ ID No: 1 to SEQ ID No: 6), and product purity is ensured through purification steps. The synthesized RNA fragments can be prepared into standards of varying concentrations through quantitative dilution for use in establishing standard curves or as quality control references. Furthermore, standards and positive controls can be pre-installed in the kit along with reverse transcription primers, allowing for verification of primer efficacy and reaction stability using a simultaneous detection system.

[0045] This integrated reference system achieves dual quality control: before testing, standards are used to calibrate instrument sensitivity and linear range; during testing, positive controls monitor technical variations throughout the entire RNA extraction, reverse transcription, and amplification process. Compared to existing technologies, this approach offers the advantage of transforming post-test quality control into a dynamic calibration throughout the entire process, significantly reducing assay fluctuations caused by reagent batch variability or operational errors. The technical principle is that exogenously adding a standard substance of known concentration provides a baseline for the absolute quantification of target snoRNAs in a sample, while a synthetic positive control simulates a realistic testing environment to verify the detection system's ability to capture low-abundance snoRNAs. Therefore, the present invention effectively addresses the technical challenge of poor reproducibility in circulating snoRNA detection, often caused by low biomarker levels and complex sample matrices. The introduction of a synthetic positive control mitigates the risk of false negatives or false positives during testing due to sample variability or operational errors. Since the sequence and concentration of the positive control are known, the sensitivity and specificity of the detection system can be effectively calibrated, thereby improving the accuracy of quantitative analysis of circulating snoRNA biomarkers. Compared with traditional methods that rely on natural samples as controls, artificially synthesized positive controls have the advantages of high batch stability and strong reproducibility, further ensuring the reliability of test results.

[0046] In summary, the present invention achieves efficient gastric cancer diagnosis through the combined detection of six specific circulating snoRNAs. This multi-indicator combined detection model improves detection sensitivity and specificity. The inclusion of standards or positive controls effectively calibrates test results, enhancing accuracy. The kit and high-throughput chip format facilitate clinical application and meet the needs of large-scale screening. This technical solution addresses the invasive nature and poor patient compliance of existing gastric cancer screening methods, providing a non-invasive, easy-to-use detection method.

[0047] Thus, the present invention provides a non-invasive method for gastric cancer diagnosis by detecting the expression levels of circulating snoRNA biomarkers. Compared with traditional pathological examinations, this method offers advantages such as ease of use and high patient compliance. Furthermore, because snoRNA is relatively stable and easily detected in body fluids, this method also offers stable results and high accuracy. Therefore, the present invention provides an effective alternative to the inconvenience and risks associated with invasive testing in gastric cancer diagnosis.

[0048] The above describes the specific embodiments of the present invention. Those skilled in the art will understand that various changes, modifications, replacements and supplements can be made to these embodiments, methodologies and models without departing from the principles and purpose of the present invention. These changes, modifications, replacements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. A circulating snoRNA biomarker for gastric cancer diagnosis, characterized by: The circulating snoRNA biomarkers include the following six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94.

2. The circulating snoRNA biomarker for gastric cancer diagnosis according to claim 1, characterized in that: The SNORA7B has the nucleotide sequence shown as SEQ ID No: 1, the SNORD41 has the nucleotide sequence shown as SEQ ID No: 2, the SNORA74A has the nucleotide sequence shown as SEQ ID No: 3, the SNORA79B has the nucleotide sequence shown as SEQ ID No: 4, the SNORD83A has the nucleotide sequence shown as SEQ ID No: 5, and the SNORD94 has the nucleotide sequence shown as SEQ ID No:

6.

3. The circulating snoRNA biomarker for gastric cancer diagnosis according to claim 1, characterized in that: The snoRNA is a circulating biomarker in human plasma and tissues.

4. Use of the circulating snoRNA biomarker according to any one of claims 1 to 3 in the preparation of a detection reagent for gastric cancer diagnosis.

5. The use according to claim 4, characterized in that: The detection reagent uses SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94 as detection objects; the detection reagent is a kit and / or a high-throughput chip.

6. The use according to claim 5, characterized in that: The kit includes reagents that specifically recognize SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94.

7. The use according to claim 6, characterized in that: The reagents that specifically recognize SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94 are reverse transcription primers.

8. The use according to claim 7, characterized in that: The forward primer and reverse primer of SNORA7B are shown as SEQ ID No: 7 and SEQ ID No: 8; the forward primer and reverse primer of SNORD41 are shown as SEQ ID No: 9 and SEQ ID No: 10; the forward primer and reverse primer of SNORA74A are shown as SEQ ID No: 11 and SEQ ID No: 12; the forward primer and reverse primer of SNORA79B are shown as SEQ ID No: 13 and SEQ ID No: 14; the forward primer and reverse primer of SNORD83A are shown as SEQ ID No: 15 and SEQ ID No: 16; the forward primer and reverse primer of SNORD94 are shown as SEQ ID No: 17 and SEQ ID No:

18.

9. The use according to claim 5, characterized in that: The kit contains standards or positive controls of SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94 for calibrating the test results and improving the accuracy of the test.

10. The use according to claim 9, characterized in that: The positive control is an artificially synthesized RNA fragment containing SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94 sequences.

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