Circulating snoRNA biomarkers for gastric cancer diagnosis and their applications

Abstract

The application discloses a circulating snoRNA biomarker for gastric cancer diagnosis and application, wherein the circulating snoRNA biomarker comprises six specific snoRNAs, namely, SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94, which are significantly different in plasma expression and are 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 convenient detection.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, and in particular to a circulating snoRNA biomarker for the diagnosis of gastric cancer and its application. Background Technology

[0002] Stomach cancer is a malignant tumor that occurs in the epithelial lining of the stomach. It is one of the most common cancers worldwide and poses a serious threat to people's lives and health. Screening, early diagnosis, and early treatment for high-risk groups can effectively reduce the incidence and mortality of stomach cancer.

[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 screening and diagnosis model for gastric cancer involves serological screening, followed by endoscopic biopsy, the gold standard method, for confirmation. While traditional pathological examination has a certain degree of accuracy, it is an invasive procedure that causes considerable inconvenience to patients and can even lead to complications and sequelae such as bleeding and infection. Compared to pathological examination, the development of blood biomarkers has facilitated the screening and diagnosis of gastric cancer, providing more stable results and higher patient compliance. The widespread use of existing tests for pepsinogen, gastrin-17, hepatitis B surface antigen (HP), and tumor markers demonstrates the potential of biomarkers in the screening and diagnosis of gastric cancer. Therefore, exploring more effective, accurate, and sensitive non-invasive clinical biomarkers is urgently needed for the early screening and diagnosis of gastric cancer.

[0004] snoRNAs are non-coding RNAs ranging from 60 to 300 nt in length, primarily found in the nucleolus, and can also be secreted into plasma to exert their functions. snoRNAs are closely related to epigenetic regulation, maintenance of genome stability, and the occurrence and development of various tumors. They are relatively stable in body fluids and easily detectable. Therefore, snoRNAs have the potential to serve as biomarkers. Currently, further research is needed regarding the role of snoRNAs in gastric cancer patients. A better understanding of the role of snoRNAs in the development and progression of gastric cancer can help identify novel biomarkers for early gastric cancer screening and diagnosis, which is crucial for the early screening and diagnosis of gastric cancer patients. Summary of the Invention

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

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

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

[0008] The aforementioned circulating snoRNA biomarkers for gastric cancer diagnosis, wherein the snoRNAs are circulating biomarkers in human plasma and tissues.

[0009] The aforementioned application of circulating snoRNA biomarkers in the preparation of diagnostic reagents for gastric cancer.

[0010] In the above applications, the detection reagents use SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94 in combination as the detection targets; the detection reagents are kits and / or high-throughput chips.

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

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

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

[0014] In the aforementioned applications, the kit contains standards or positive controls of SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94 to calibrate test results and improve test accuracy.

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

[0016] Compared with existing technologies, this invention uses circulating snoRNA biomarkers for gastric cancer diagnosis. Compared with traditional serological markers (such as pepsinogen and gastrin-17), these circulating snoRNA biomarkers can more sensitively reflect changes at the molecular level in the early stages of gastric cancer, effectively improving the detection rate of early gastric cancer. Furthermore, through comprehensive analysis of multiple indicators, the risk of false positives or false negatives from single markers is reduced, significantly improving the accuracy and reliability of diagnostic results and providing a more comprehensive basis for clinical decision-making. In addition, this invention uses blood testing, which is convenient, rapid, minimally invasive, easy to use, and stable. It avoids the risk of physical harm to patients caused by multiple tissue biopsies performed via gastroscopy to obtain tumor tissue for pathological identification, greatly reducing testing costs. This invention provides novel, highly sensitive, and specific biomarkers for the diagnosis and prognosis of gastric cancer, and has high clinical application and promotion value. Attached Figure Description

[0017] Figure 1 This is a flowchart of the steps in Embodiment 1 of the present invention;

[0018] Figure 2 This is a diagram showing the expression of six snoRNAs in plasma;

[0019] Figure 3 This is a survival curve of circulating snoRNA biomarkers in plasma as biomarkers;

[0020] Figure 4 This is the ROC curve of circulating snoRNA biomarkers in plasma as biomarkers;

[0021] Figure 5 This refers to the expression of circulating snoRNA biomarkers as biomarkers in gastric cancer tissues;

[0022] Figure 6 This is a ROC curve analysis of the circulating snoRNA biomarkers obtained from gastric cancer patient tissues. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0024] Example 1: Analysis based on plasma snoRNA expression profile.

[0025] This embodiment consists of four parts: collecting plasma samples, extracting small RNA from plasma using the Qiagen miRNeasy plasma kit, and small RNA transcriptome sequencing. The procedure of this embodiment is as follows: Figure 1 As shown.

[0026] (1) Collect plasma samples;

[0027] Five 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 blood collection tubes were repeatedly inverted to ensure thorough mixing of the EDTA anticoagulant with the blood. The blood was centrifuged at 3000 rpm and 4°C for 10 minutes, and the supernatant was the plasma. Two ml of plasma was placed in an EP tube and stored at -80°C.

[0028] (2) Small RNA was extracted from plasma using the Qiagen miRNeasy plasma kit;

[0029] 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℃) 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 12000g for 15 min at 4℃. After centrifugation, transfer the upper aqueous phase to a new EP tube, add 1.5 times the volume of 100% ethanol, and mix thoroughly by inverting. Pipette 700 μL of the liquid into an RNeasy MinElute ssnon column, centrifuge at 8000g for 15 s at room temperature, and discard the waste liquid in the collection tube. Repeat the above steps once with the remaining liquid. Add 700 μl of Buffer RWT to the RNeasy MinElute ssnon column, centrifuge at 8000g for 15 s, and discard the waste liquid in the collection tube. Pipette 500 μl of RPE buffer into the RNeasy MinElute ssnon column, centrifuge at 8000g for 15 s, and discard the waste liquid in the collection tube. Add 500 μl of 80% ethanol to the RNeasy MinElute ssnon column, centrifuge at 8000g for 2 min, and discard the waste liquid in the collection tube. Transfer the RNeasy MinElute ssnon column to a new 2 ml EP tube, open the screw cap, centrifuge at maximum speed for 5 min, and discard the waste liquid and collection tube. Transfer the RNeasy MinElute ssnon column to a new 1.5 ml collection tube.

[0030] Small RNA elution: Add 15 μL of RNase-free water to the middle of the filter membrane, gently cover the tube, let stand at room temperature for 2 min, centrifuge at maximum speed for 1 min, and the RNA separated is at the bottom of the tube.

[0031] RNA concentration and integrity assessment: 1 μL was used for Aglient 2100 RNA snoco microarray analysis; peak values ​​were generally below 200 nt. Sequencing libraries were constructed using only high-quality RNA samples (RIN ≥ 7, > 50 ng / μL, OD260 / 280 between 1.8 and 2.2).

[0032] (3) Small RNA transcriptome sequencing;

[0033] Small RNA quantification: Small RNA samples used for library construction are first quantified using a library quantification kit. 1 μg is used as the starting material to generate the sequencing library.

[0034] Connector sequence: Connect the connector sequence at the 3' and 5' ends respectively.

[0035] cDNA synthesis: Under the action of MMLV-derived PrimeScript reverse transcriptase (RT), one-stranded cDNA is synthesized by reverse transcoding using random primers and RNA ligated after the adapter as a template. Then, two-stranded synthesis is performed to form a stable double-stranded structure.

[0036] Library enrichment: PCR amplification using sequencing primers (11-12 cycles) to enrich the library concentration.

[0037] Library purification: Based on the length distribution characteristics of small RNAs, the target fragments were recovered by gel excision (6% Novex TBE PAGE gel, 1.0 mm, 10 wells).

[0038] Sequencing and data analysis: Quantitative analysis was performed using Qubit 4.0, and the data were mixed and fed into the machine according to the specified ratio; bridge PCR amplification was performed on cBot to generate clusters; sequencing was performed on the Illumina NovaSeq 6000 platform.

[0039] (4) Bioinformatics analysis section;

[0040] Raw sequence data statistics: Illumina sequencing is a second-generation sequencing technology, generating billions of reads in a single run. Such massive amounts of data make it impossible to analyze the quality of each individual read. Therefore, statistical methods are used to analyze the base distribution and quality fluctuations for each cycle of all sequencing reads. This provides a macroscopic and intuitive reflection of the sequencing quality and library construction quality of the sample. Sequencing-related quality assessments are performed on the raw sequencing data of each sample, including: statistical analysis of A / T / G / C base content distribution, statistical analysis of base quality distribution, and statistical analysis of base error rate distribution.

[0041] Raw sequencing data quality control: 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 3' adapter sequences from reads, and remove reads that do not contain inserted fragments due to adapter self-ligation or other reasons; 2) Cut the 3' end of low-quality bases (quality value less than 20); 3) Remove reads containing the unknown base N; 4) Remove reads that are too short (<18nt); 5) Remove reads that are too long (>32nt); After quality control, the length of clean reads is analyzed. Based on the characteristics of small RNA, reads with a length of 18-32nt are selected as usefμl reads for subsequent analysis.

[0042] Alignment with reference genome: Use Bowtie to align the quality-controlled usefμl reads with the specified reference genome (human genome), then use the local integrated database based on the R package "ensembldb" for gene annotation, and then select snoRNA genes.

[0043] Independent risk factors were screened based on plasma snoRNA expression.

[0044] After obtaining the plasma snoRNA expression matrix, univariate and multivariate Cox regression analyses were performed. Six snoRNAs were ultimately identified as independent risk factors for gastric cancer, which can serve as biomarkers for early screening and diagnosis of gastric cancer; these are termed circulating snoRNA biomarkers. The circulating snoRNA biomarkers include the following six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94. These snoRNAs can serve as circulating biomarkers in human plasma and tissues, among which:

[0045] The SNORA7B has the nucleotide sequence shown in SEQ ID No:1: GACCTCCTGGGATCGCATCTGGAGACTGCCTAGTATTCTGCCAGCTTCGGAAAGGGAGGGAAAGCAAGCCTGGCAGAGGCACCCATTCCATTCCCAGCTTGCTCCGTAGCTGGTGATTGGAAGACACTCTGCGACAGTG (SEQ ID No:1)

[0046] The SNORD41 has the nucleotide sequence shown in SEQ ID No:2: TGGGAAGTGATGACACCTGTGACTGTTGATGTGGAACTGATTTATCGCGTATTCGTACTGGCTGATCCTG (SEQ ID No:2)

[0047] The SNORA74A has the nucleotide sequence shown in SEQ ID No:3: TCCAGCGGTTGTCAGCTATCCAGGCTCATGTGGTGCCTGTGATGGTGTTACACTGTTGGAAGAGCAAACACTGTCTTTATTGAGGTTTGGCTCCAAGCACTGTTTTGGTGTTGTAGCTGAGTACCTTTGGGCAGTGTTTTGCACCTCTGAGAGTGGAATGACTCCTGTGGAGTTGATCCTAGTCTGGGTGCAAACAAT (SEQ ID No:3)

[0048] The SNORA79B has the nucleotide sequence shown in SEQ ID No:4: TGATGGCTGTTCCTCTCACTGCTTGAAGCCTTAGGCAGTGGGATTTTGATCCATCATATATCAAAAATGGCTTATCTTCACTCAGGGCACCATGAGGATGGGCTGGCTGTCCGTTAGTGCCTTCTGATTTTTGCGGAGTCAAACAATT (SEQ ID No:4)

[0049] The SNORD83A has the nucleotide sequence shown in SEQ ID No:5: GCTGTTCGTTGATGAGGCTCAGAGTGAGCGCTGGGTACAGCGCCCGAATCGGACAGTGTAGAACCATTCTCTACTGCCTTCCTTCTGAGAACAGC (SEQ ID No:5)

[0050] The SNORD94 has the nucleotide sequence shown in SEQ ID No:6: CAGGCTGTGATGATTGGCGCAGGGGTACGGACCTCAGCTGAGTCATGGGAGCTGAATGTATGTGTTTCTCCTTTGTCCTGCATGTGGCAGGCTGATGGGGAGCACTTACATGAGACTGTTGCCTCAATCTGAGCCTG (SEQ ID No:6)

[0051] A risk model was constructed based on the risk coefficient of each snoRNA and the expression level in patients. Patients were then divided into high-risk and low-risk groups according to their position in the risk spectrum. Survival curves were plotted for patients with gastric cancer using the six snoRNA risk scores as biomarkers (p < 0.0001). ROC curve analysis was also performed on the six snoRNA risk scores. Finally, survival curves were plotted on the total dataset, with p values ​​all less than 0.0001.

[0052] This invention included 36 patients with gastric cancer and paired adjacent normal tissue samples from the First Affiliated Hospital of Wenzhou Medical University. These patients had not received any treatment prior to surgery, such as radiotherapy or chemotherapy. Each tissue sample was stored in liquid nitrogen. Risk scores were calculated using a risk scoring formula derived from Cox regression analysis of snoRNAs in plasma. All tissue samples were then divided into high-risk and low-risk groups. Figure 2 The graph shows the expression of six snoRNAs in plasma. 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 is evident that adjacent normal tissue is mainly in the low-risk group, while gastric cancer tissue is mainly in the high-risk group. Figure 5 This study demonstrates the expression of circulating snoRNA biomarkers in gastric cancer tissues. Figure 5 The differences in risk scores between the two groups (non-tumor group and tumor group) are evident, allowing for a direct comparison of changes in risk scores among individuals in both groups. From Figure 5 It can be seen that there is a highly significant difference in risk scores between the tumor group and the non-tumor group. Figure 6 This paper presents ROC curve analysis of circulating snoRNA biomarkers obtained from gastric cancer patient tissues. ROC curve analysis can be used to evaluate the predictive diagnostic value of six snoRNAs for gastric cancer patients. Figure 6 As can be seen, the area under the ROC curve for tissue samples is 0.814, indicating that the combination of these 6 snoRNAs has good discriminatory ability between gastric cancer patients and adjacent tissues.

[0053] Example 2: Application of circulating snoRNA biomarkers in the preparation of diagnostic reagents for gastric cancer.

[0054] In the preparation of detection reagents, SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A, and SNORD94 can be used in combination as the detection target. Detection reagents can be kits or high-throughput chips.

[0055] In this embodiment, 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 capable of specifically binding to target snoRNAs. Specifically, these reagents can achieve the capture or detection of specific snoRNAs through intermolecular interactions, such as base pairing or antigen-antibody binding. As a preferred embodiment, these reagents can be immobilized on a solid support, such as the surface of microspheres or a chip, to facilitate subsequent separation or detection operations.

[0056] 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 domains can be developed. These reagents need to undergo rigorous specificity and sensitivity testing to ensure they do not cross-react with other non-target molecules. Furthermore, these reagents can be optimized for use on different detection platforms, such as real-time quantitative PCR, microarrays, or next-generation sequencing technologies.

[0057] In a preferred embodiment, the kit includes reverse transcription primers that specifically recognize the six snoRNAs mentioned above. The reverse transcription primers are used to reverse transcribe the target snoRNA into cDNA for subsequent PCR amplification and detection. The reverse transcription primers can be designed to bind complementary to specific sites on the target snoRNA, ensuring the specificity and efficiency of the reverse transcription. For example, the reverse transcription primers can contain sequences complementary to the 3' end or internal conserved regions of the target snoRNA.

[0058] As a preferred implementation, the length of the reverse transcription primer can be between 18 and 25 nucleotides to balance specificity and binding efficiency. Furthermore, the reverse transcription primer may also contain modified nucleotides, such as locked nucleic acids (LNAs) or 2'-O-methyl modifications, to improve primer stability and binding ability.

[0059] Specifically, the reverse transcription primers for SNORA7B are as follows:

[0060] Forward primer:

[0061] 5'-CTGGGATCGCATCTGGAGAC-3'(SEQ ID No:7)

[0062] Reverse primer:

[0063] 5'-AGCTGGGAATGGAATGGGTG-3'(SEQ ID No:8)

[0064] The reverse transcription primers for SNORD41 are shown below:

[0065] Forward primer:

[0066] 5'-AAGTGATGACACCTGTGACTGT-3'(SEQ ID No:9)

[0067] Reverse primer:

[0068] 5'-GGATCAGCCAGTACGAATACGC-3'(SEQ ID No:10)

[0069] The reverse transcription primers for SNORA74A are shown below:

[0070] Forward primer:

[0071] 5'-TGGTGCCTGTGATGGTGTTA-3'(SEQ ID No:11)

[0072] Reverse primer:

[0073] 5'-CCAAAACAGTGCTTGGAGCC-3'(SEQ ID No:12)

[0074] The reverse transcription primers for SNORA79B are shown below:

[0075] Forward primer:

[0076] 5'-ATGGCTGTCCTCTCACTGC-3'(SEQ ID No:13)

[0077] Reverse primer:

[0078] 5'-CATGGTGCCCTGAGTGAAGAT-3'(SEQ ID No:14)

[0079] The reverse transcription primers for SNORD83A are shown below:

[0080] Forward primer:

[0081] 5'-GAGTGAGCCTGGGTACAG-3'(SEQ ID No:15)

[0082] Reverse primer:

[0083] 5'-GCTGTTCTCAGAAGGAAGGCA-3'(SEQ ID No:16)

[0084] The reverse transcription primers for SNORD94 are shown below:

[0085] Forward primer:

[0086] 5'-GATGATTGGCGCAGGGGTA-3'(SEQ ID No:17)

[0087] Reverse primer:

[0088] 5'-TGTAAGTGCTCCCCATCAGC-3' (SEQ ID No: 18);

[0089] 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. Primer design needs to consider factors such as length, GC content, and melting temperature to ensure amplification specificity and efficiency. As a preferred implementation, primer length can be controlled at 18-25 bases, GC content maintained at 40%-60%, and melting temperature between 55-65℃. Furthermore, primer design should avoid the formation of secondary structures or primer dimers. For example, primer specificity can be verified by BLAST alignment to avoid binding to non-target sequences. Thus, the designed primers can accurately identify and amplify the target snoRNA sequence. These optimized primer sequences can specifically recognize and bind to the corresponding snoRNA, ensuring the efficiency and accuracy of the reverse transcription process.

[0090] Furthermore, the kit may also include standards or positive controls for the aforementioned six snoRNAs to calibrate test results and improve accuracy. Positive controls can be synthetically produced RNA fragments containing these snoRNA sequences. The concentration range is set at 10. 3 Up to 10 6The reagents are available in copies / µL to cover diverse detection needs. The introduction of standards or positive controls effectively addresses false negatives or false positives caused by sample differences or operational fluctuations during testing. For example, standards can be set with gradient concentrations to cover the range of snoRNA expression that may occur in clinical samples, thereby establishing a standard curve for quantitative detection. Positive controls, as an important component of the test kit, are used to calibrate test results and improve accuracy. The positive control RNA fragment is synthesized through in vitro transcription; its sequence is completely identical to the target snoRNA, but specific mutation sites are introduced to distinguish it from endogenous snoRNA, thus avoiding 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 purification steps ensure product purity. The synthesized RNA fragment can be quantitatively diluted to prepare standards of different concentrations for establishing standard curves or as a quality control reference. Furthermore, standards and positive controls can be pre-packaged with reverse transcription primers in the kit to verify primer efficacy and reaction condition stability through a simultaneous detection system.

[0091] Therefore, a dual quality control system is achieved through a built-in reference system: before detection, standards are used to calibrate the instrument's sensitivity and linear range; during detection, a positive control monitors technical deviations throughout the RNA extraction, reverse transcription, and amplification process. Compared with existing technologies, its advantage lies in transforming post-event quality control into dynamic calibration throughout the entire process, significantly reducing detection fluctuations caused by batch-to-batch reagent differences or operational errors. The technical principle is that by adding exogenously sourced standard substances of known concentrations, a benchmark reference can be provided for the absolute quantification of target snoRNAs in the sample, while the synthetic positive control verifies the detection system's ability to capture low-abundance snoRNAs by simulating a real detection environment. Therefore, the present invention effectively solves the technical challenge of poor reproducibility in circulating snoRNA detection due to low biomarker content and complex sample matrices. By introducing an artificially synthesized positive control, the problem of false negatives or false positives caused by sample differences or operational errors during detection is solved. 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 quantitative accuracy 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.

[0092] In summary, this invention achieves highly efficient detection of gastric cancer by jointly detecting six specific circulating snoRNAs. The multi-indicator joint detection mode improves the sensitivity and specificity of the detection; the use of standards or positive controls effectively calibrates the test results, improving accuracy; and the kit and high-throughput chip format facilitate clinical application and meet the needs of large-scale screening. This technical solution addresses the problems of high invasiveness and poor patient compliance in existing gastric cancer screening methods, providing a non-invasive and easy-to-operate detection method.

[0093] Therefore, this invention provides a non-invasive detection method for gastric cancer diagnosis by detecting the expression level of circulating snoRNA biomarkers. Compared with traditional pathological examination, this method has the advantages of simple operation and high patient compliance. Furthermore, since snoRNA is relatively stable in body fluids and easily detected, this method also features stable results and high accuracy. Therefore, this invention provides an effective alternative to address the inconvenience and risks associated with invasive examinations in gastric cancer diagnosis.

[0094] The specific embodiments of the present invention have been described above. Those skilled in the art will understand that various changes, modifications, substitutions, and additions can be made to these embodiments, methodologies, and models without departing from the principles and spirit of the present invention, and these changes, modifications, substitutions, and additions should also be considered within the scope of protection of the present invention.

Claims

1. A circulating snoRNA biomarker for gastric cancer diagnosis, characterized in that: The circulating snoRNA biomarker comprises six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94.

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

6.

3. The circulating snoRNA biomarker for gastric cancer diagnosis according to claim 1, characterized by: The snoRNA is a circulating biomarker of human plasma and tissue.

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

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

6. Use according to claim 5, characterized in that: The kit comprises reagents specifically recognizing SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94.

7. Use according to claim 6, characterized in that: The reagent specifically recognizing SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94 is a reverse transcription primer.

8. Use according to claim 7, characterized in that: The forward primer and the reverse primer of SNORA7B are shown as SEQ ID No: 7 and SEQ ID No: 8; the forward primer and the reverse primer of SNORD41 are shown as SEQ ID No: 9 and SEQ ID No: 10; the forward primer and the reverse primer of SNORA74A are shown as SEQ ID No: 11 and SEQ ID No: 12; the forward primer and the reverse primer of SNORA79B are shown as SEQ ID No: 13 and SEQ ID No: 14; the forward primer and the reverse primer of SNORD83A are shown as SEQ ID No: 15 and SEQ ID No: 16; and the forward primer and the 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 comprises a standard or a positive control of SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94, which is used for calibrating the detection result and improving the accuracy of detection.

10. 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. The positive control is an artificially synthesized RNA fragment containing SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94 sequences.

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