Plasma exosome mRNA biomarker and application thereof in construction of gastric cancer diagnosis model

Abstract

The application provides a plasma exosome mRNA biomarker and application thereof in construction of a gastric cancer diagnosis model, and relates to the technical field of biological medicine.The application first discovers that plasma exosome mRNA MORC3 is significantly enriched in plasma of gastric cancer patients, indicating that the plasma exosome mRNA MORC3 is expected to be used as a biomarker for gastric cancer diagnosis and as a target gene for treatment of gastric cancer.We take the plasma exosome mRNA MORC3 as a target marker to construct a diagnosis model and a corresponding diagnosis system for gastric cancer.The diagnosis model provided by the application shows high sensitivity and specificity in distinguishing gastric cancer patients from healthy people, displays good diagnosis efficiency, and can effectively identify gastric cancer patients, thereby providing an effective auxiliary tool for clinical diagnosis.The application provides a new choice for a molecular marker for gastric cancer diagnosis, overcomes the low sensitivity of existing cancer markers when used for gastric cancer diagnosis, and provides a new method for early screening and diagnosis of gastric cancer.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a plasma exosome mRNA biomarker and its application in constructing a gastric cancer diagnostic model. Background Technology

[0002] Gastric cancer (GC) is a malignant tumor with high incidence and mortality rates in my country. Currently, clinical detection still relies on conventional histopathological methods and imaging techniques such as X-ray, magnetic resonance imaging, and ultrasound, which have certain limitations in early diagnosis and detection of recurrence and metastasis. In vitro diagnostic markers for gastric cancer mainly include serum CEA and CA199, but these have shortcomings such as low specificity, low early-stage positive rates, and inability to dynamically monitor tumor progression and treatment efficacy. Furthermore, gastric cancer exhibits strong heterogeneity, severely impacting patient treatment outcomes and prognosis. Therefore, exploring new early screening strategies and novel biomarkers is urgently needed.

[0003] Exosomes (Exo) are small vesicles with a diameter of 30 nm to 150 nm, capable of carrying various biomolecules, including proteins, RNA, and lipids. The contents of tumor cell-derived exosomes exhibit good consistency with tumor cells themselves, serving not only as tumor detection markers but also reflecting the functional state of tumor cells. Therefore, they can be used for early tumor screening and are excellent biomarkers for the in vitro diagnosis of gastric cancer. However, traditional exosome extraction and identification procedures are cumbersome, and rapid and sensitive methods for detecting exosome mRNA are lacking. Furthermore, while it has been proven that exosome mRNA can influence the tumor microenvironment, patient treatment outcomes, and prognosis, currently, there is a lack of identified gastric cancer-specific exosome mRNAs. Therefore, there is an urgent need to develop an in vitro diagnostic system based on gastric cancer-specific exosome mRNA. This paper proposes a non-invasive biomarker, diagnostic model, and system for the early diagnosis of gastric cancer. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a plasma exosome mRNA biomarker and its application in constructing a gastric cancer diagnostic model.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a biomarker for early diagnosis and / or postoperative MRD detection and diagnosis of gastric cancer, wherein the biomarker is plasma exosome mRNA MORC3.

[0009] Specifically, the exosomal mRNA MORC3 has the ID 23515 in NCBI Gene, and its sequence is as follows:

[0010] MORC3-Forward: GCCTCAGCGCGATAATGCT SEQ ID NO.1

[0011] MORC3-Reverse:ACCATTCCCATTGTCGGTGAA SEQ ID NO.2

[0012] Secondly, the present invention provides the application of a reagent for detecting the carrying capacity of the biomarker MORC3 in the preparation of products for early diagnosis of gastric cancer and / or postoperative MRD detection and diagnosis.

[0013] Specifically, the test sample for the product is plasma exosomes extracted from isolated plasma.

[0014] Specifically, the reagent is used to detect MORC3 loading using RT-qPCR technology.

[0015] Specifically, the reagent contains a specific amplification probe designed for MORC3.

[0016] Thirdly, this invention provides a diagnostic model for early diagnosis and / or postoperative MRD detection of gastric cancer. The model establishes a Logistic binary regression diagnostic model using the biomarker MORC3 as the target marker. The specific formula is logistic(GC) = 0.642 × MORC3 - ​​1.560.

[0017] Specifically, MORC3 in the above formula refers to the amount of MORC3 carried in plasma exosomes.

[0018] Fourthly, the present invention provides a diagnostic system for early diagnosis and / or postoperative MRD detection of gastric cancer, specifically comprising:

[0019] (1) A module for quantitatively detecting the amount of MORC3 carried in a sample;

[0020] (2) Logistic binary regression calculation module: logistic(GC)=0.642×MORC3-1.560, where MORC3 refers to the amount of MORC3 carried in plasma exosomes;

[0021] (3) Result judgment module: When logistic(GC)>0.5, the diagnosis result is tumor group; when logistic(GC)<0.5, the diagnosis result is non-tumor group.

[0022] (III) Beneficial Effects

[0023] This invention is the first to discover that plasma exosomal mRNA MORC3 is significantly enriched in the plasma of gastric cancer patients, suggesting that plasma exosomal mRNA MORC3 holds promise as a biomarker for gastric cancer diagnosis and a target gene for gastric cancer treatment. Furthermore, we constructed a diagnostic model for gastric cancer using plasma exosomal mRNA MORC3 as the target biomarker and provided a system for gastric cancer diagnosis. The diagnostic model provided by this invention exhibits high sensitivity and specificity in distinguishing between gastric cancer patients and healthy individuals, demonstrating good diagnostic efficacy and effectively identifying gastric cancer patients, providing an effective auxiliary tool for clinical diagnosis. This invention provides a new option for molecular biomarkers for gastric cancer diagnosis, overcoming the low sensitivity of existing cancer biomarkers when used in gastric cancer segments. This invention provides a new method for the early screening and diagnosis of gastric cancer. Attached Figure Description

[0024] Figure 1 The discovery set sample contains a volcano plot of mRNA differential expression between the GC group and the HC group. The X-axis represents log2 (FoldChange), reflecting the change in gene expression fold; the Y-axis represents -log10 (p-value), reflecting the significance level of differential expression; red dots represent differentially expressed genes that are significantly upregulated (the first 10 genes are specially marked), and blue dots represent differentially expressed genes that are significantly downregulated.

[0025] Figure 2 Differences in exosome mRNA expression between the GC and HC groups in the screening set samples: A: IRF1 gene was significantly upregulated in the GC group (p<0.0001); B: RBM39 gene showed no significant difference in the GC group (p=0.1348); C: ZEB1 gene showed no statistically significant difference in the GC group (p=0.0663); D: MORC3 gene was significantly upregulated in the GC group (p<0.0001); E: FLNA gene was significantly upregulated in the GC group (p<0.0001).

[0026] Figure 3 Differential expression of target genes between the GC and HC groups in the training set samples; A: MORC3 gene was significantly upregulated in the GC group (p<0.0001); B: IRF1 gene was significantly upregulated in the GC group (p<0.0001).

[0027] Figure 4 To verify the differential expression of target genes between the GC and HC groups in the validation set; A: MORC3 gene was significantly upregulated in the GC group (p<0.0001); B: IRF1 gene was significantly upregulated in the GC group (p<0.0001).

[0028] Figure 5 ROC curves for exosomal mRNA IRF1(A) and MORC3(B) genes in the GC and HC groups of the screening set samples.

[0029] Figure 6 ROC curves for the exosomal mRNAs MORC3(A) and IRF1(B) genes in the GC and HC groups of the training set samples.

[0030] Figure 7 ROC analysis of the joint diagnostic model based on MORC3 and IRF1 in the training set.

[0031] Figure 8 ROC analysis on the validation set based on MORC3, IRF1 single-factor diagnostic models and MORC3+IRF1 combined factor diagnostic model. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise specified, the reagents, methods, and equipment used in the present invention are conventional reagents, methods, and equipment in this technical field. Unless otherwise specified, the reagents and materials used in the following embodiments are all commercially available.

[0033] Example 1

[0034] Screening and validation of gastric cancer biomarkers based on plasma exosomal mRNA

[0035] 1 Experimental Sample

[0036] Peripheral blood plasma exosome specimens were obtained from the First Affiliated Hospital of Henan University of Traditional Chinese Medicine between November 2023 and February 2025, including 137 patients with gastric cancer (GC) and 126 healthy controls (HC). Sample collection and use were approved by the Ethics Committee of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine. Subjects or patients in each group were randomly assigned to the discovery set, screening set, training set, and validation set; the specific sample sizes are shown in Table 1.

[0037] The following conditions must be met for the inclusion of the sample: (1) the healthy controls and the gastric cancer patients are age-matched; (2) the patients are newly diagnosed with gastric cancer by imaging, serological examination and pathological examination; (3) the plasma samples are collected before the operation, are well preserved and the patients have not undergone surgery and radiotherapy or chemotherapy.

[0038] Table 1 Sample Grouping and Quantity

[0039] Discovery Collection Filter set training set Validation set Total sample size GC group 5 20 52 60 137 HC group 4 20 50 52 126

[0040] 2 Sample Processing

[0041] 2.1 Plasma sample collection and plasma exosome extraction

[0042] Whole blood samples were collected from gastric cancer patients and healthy individuals at the Department of Laboratory Medicine, First Affiliated Hospital of Henan University of Traditional Chinese Medicine. The samples were centrifuged at 3000g for 10 minutes at 4℃. The supernatant was aliquoted into two 1.5mL centrifuge tubes. One tube was frozen at -80℃, and the other tube was used to enrich exosomes.

[0043] (1) Extract exosomes by PEG precipitation at 10000g / 15 minutes at 4℃ (to remove large vesicles and cell debris). Take 500μL of supernatant into a 1.5mL centrifuge tube, add 500μL of 16% PEG (2×) to the plasma at a ratio of 1:1, to a final PEG concentration of 8%, and mix by pipetting (2-3 times is sufficient, do not shake to mix);

[0044] (2) After incubation at 4℃ for 60 minutes, use 3000g at 4℃ for 60 minutes. Discard the supernatant; the precipitate at the bottom is the exosome.

[0045] (3) Ultrafiltration: Resuspend the bottom precipitate with 400 μL of 1×PBS (1×PBS needs to be pre-cooled at 4°C); rinse the inner tube of the ultrafiltration tube with 100 μL of 1×PBS (pre-exposed to UV light);

[0046] (4) Then add 400 μL of excretory weight suspension from the previous step into the ultrafiltration tube (total 500 μL); concentrate the sample from 500 μL to about 150 μL at 4℃ and 14000xg for 15 minutes;

[0047] (5) Recover the concentrate, The μLtra-0.5 device is inverted in a clean microcentrifuge tube and centrifuged at 1000xg for 5 minutes at 4°C to transfer the concentrated sample from the filter tube to the centrifuge tube.

[0048] 2.2 Total RNA extraction from exosomes

[0049] (1) Add Vizol to the exosome suspension at a ratio of 1:5, for example: add 500 μL of Vizol to 100 μL of exosomes;

[0050] (2) Vigorously shake to mix thoroughly, and let stand at room temperature for 5 minutes (this step is to allow the protein to be fully separated from the nucleic acid, and ultimately keep the solution homogeneous);

[0051] (3) Centrifuge briefly, add chloroform equal to the initial volume of exosome suspension, for example: add 100 μL of chloroform to 100 μL of exosomes, shake vigorously for 15 seconds (this step is to ensure the full formation of the subsequent separation phase), and let stand at room temperature for 2-3 minutes.

[0052] (4) Centrifuge at 12000g at 4℃ for 15 minutes. After low-temperature centrifugation, the solution can be seen to separate into layers, with RNA in the upper colorless aqueous phase. Transfer the upper layer solution to a new EP tube.

[0053] (5) Add 2.5 times the volume of anhydrous ethanol to obtain the aqueous solution, for example, add 500L of anhydrous ethanol to 200μL of aqueous solution, mix well with a pipette and centrifuge.

[0054] (6) Freeze at -40℃ for 1 hour, then centrifuge at 4℃ for 10 minutes at 12000g.

[0055] (7) Discard all supernatant (including liquid on the tube wall if possible), being careful not to touch the precipitate. Wash with 500 μL of 75% anhydrous ethanol and centrifuge at 12000g at 4℃ for 10 minutes.

[0056] (8) Repeat the previous step once;

[0057] (9) Remove all liquid, place a piece of paper under the EP tube, open and lay flat. Let stand at room temperature for 2-5 minutes to allow the ethanol to evaporate;

[0058] (10) Dissolve the RNA in 10 μL of RNase-free ddH2O;

[0059] (11) The dissolved RNA solution needs to be stored at -80℃ or quickly converted to cDNA and stored at -20℃ for subsequent experiments.

[0060] 2.3 Construction of exosome whole transcriptome sequencing library

[0061] (1) rRNA was removed using the VAHTS rRNA removal kit according to the instructions; DNase I (NEB, M0303) and RNaseOut (Invitrogen, 10777019) were used to remove DNase and RNase from the enriched RNA.

[0062] (2) RNA fragmentation was performed using RNA Fragmentation Reagents (Ambion, AM8740); RNA samples were purified using RNA Clean Beads (Vazyme, N412), and RNA concentration was detected using Qubits.

[0063] (3) Take 40 ng of RNA from each sample (the remaining RNA samples are frozen at -80℃ for subsequent studies). Use the [VAHTSUniversal V8 RNA-seq Library Prep Kit for Illumina] reagent and construct the RNA sequencing library according to the instructions. The main process includes: cDNA I strand synthesis, cDNA II strand synthesis, II strand synthesis product purification, A-Tailing addition, adapter addition, ligation product purification, cDNA library amplification, and library PCR product purification; Qubit is used to determine the concentration of dsDNA library, and 2100 is used to detect library quality.

[0064] 2.4 Exosome whole transcriptome sequencing and mRNA screening analysis

[0065] (1) Transcriptome sequencing was performed using the Illumina NovaSeq 6000 platform; quality control of RNA-Seq data was performed using FastQC; adapters were trimmed using cutadapt and low-quality bases were removed using Trimmomatic.

[0066] (2) Clean raw reads were mapped to the human genome (GRCh38) using hisat2 and Htseq-count with default parameters, with mapping performed using Ensembl 78 genome annotation; Htseq-count was selected to calculate the number of reads mapped to each Ensembl gene with the following parameters: -m union-s no; DESeq2 was selected to analyze gene expression by calculating the TPM of genes in each sample; differentially expressed genes were assigned cutoff limits |FoldChange|≥1.2 and P<0.05.

[0067] 3 Results

[0068] 3.1 Discovery Phase

[0069] Plasma exosomal mRNA from the discovery set samples was sequenced and analyzed using Illumina NovaSeq 6000 sequencing technology. Analysis of the sequencing results identified 1471 differentially expressed genes, of which 822 were upregulated and 649 were downregulated in the gastric cancer group compared to the healthy control group. Based on this, the following screening criteria were set: p < 0.05. Genes were ranked according to their log2 (Fold Change) value and the fold change in expression. The top 10 genes with the most significant differential expression were selected for further analysis. The results of the differential expression analysis of the selected genes are shown in Table 2 below. Figure 1 As shown.

[0070] Table 2. Significantly Increased Exosomal mRNA Expression

[0071]

[0072] Note: E represents 10 to the power of N.

[0073] 3.2 Screening Stage

[0074] Based on previous plasma exosome sequencing results, this study screened candidate genes according to the following criteria: ① genes with significantly differential expression; ② genes that could be detected in all sequencing samples and whose expression levels were significantly upregulated. In addition, we focused on exosomal mRNAs with high tumor relevance, namely IRF1, RBM39, ZEB1, MORC3, and FLNA. RT-qPCR was used to preliminarily detect the expression levels of these five candidate exosomal mRNAs in the screening set. Normality tests showed that the payloads of RBM39, FLNA, and ZEB1 all conformed to a normal distribution. Then, homogeneity of variance tests (F test p = 0.0526, < 0.0001, and 0.0486) showed that RBM39 had homogeneous variance. Therefore, an independent samples t-test was applied, yielding p = 0.1348. Figure 2 B), FLNA and ZEB1 have unequal variances. Using the Welch's test for unequal variances, p = 0.0010 and 0.0663 respectively were calculated. Figure 2 E and Figure 2 C); The payloads of IRF1 and MORC3 do not conform to a normal distribution. Using the Mann-Whitney U test, the p-values ​​for both IRF1 and MORC3 are less than 0.0001. Figure 2 A and Figure 2 D).

[0075] 3.3 Verification Phase

[0076] Based on the differentially expressed exosomal mRNAs IRF1 and MORC3 obtained during the screening phase, RT-qPCR was used to detect the expression in the training and validation sets, and the relative payload was calculated. The results are as follows: Figure 3 and Figure 4 As shown in the figure. Normality test revealed that the relative carrying capacity of MORC3 and IRF1 did not conform to a normal distribution. The Mann-Whitney U test showed that the p values ​​for both MORC3 and IRF1 were less than 0.0001, indicating that the expression levels of MORC3 and IRF1 in the gastric cancer group were higher than those in the healthy control group, and the difference was statistically significant.

[0077] These results indicate that exosomal mRNAs IRF1 and MORC3 have great potential as biomarkers to distinguish between healthy individuals and those with gastric cancer.

[0078] Example 2

[0079] Construction of gastric cancer diagnostic model and diagnostic system

[0080] First, using the selected set as a sample, ROC curves were plotted using IBM SPSS Statistics 27.0 based on the relative carrying capacity of exosomal mRNAs IRF1 and MORC3 to evaluate the diagnostic efficacy of IRF1 and MORC3 in distinguishing between gastric cancer patients and healthy individuals. The results are as follows: Figure 5 As shown in Table 2, the area under the curve (AUC) of IRF1 was 0.910 (95% confidence interval: 0.812 to 1.000), with a sensitivity of 85.0% and a specificity of 90.0%. The AUC of MORC3 was 0.962 (95% confidence interval: 0.909 to 1.000), with a sensitivity of 90.0% and a specificity of 95.0%. An AUC value close to 1.0 generally indicates good diagnostic performance. These results suggest that IRF1 and MORC3 have good diagnostic efficacy for gastric cancer and hold promise for further diagnostic model development.

[0081] Table 2. Diagnostic efficacy of exosomal mRNA in the gastric cancer group and the healthy control group.

[0082]

[0083] Furthermore, we performed binary logistic regression analysis on the relative carrying capacity of exosomal mRNAs IRF1 and MORC3 in the training set samples using BMSPSS Statistics 27.0 software, constructing binary logistic regression models: logistic(GC) = 0.624 × MORC3 - ​​2.108; logistic(GC) = 0.555 × IRF1 - 1.542. The diagnostic efficacy of these two models was evaluated, and the results are as follows: Figure 6As shown in Table 3, the area under the ROC curve (AUC) of the MORC3 gene was 0.868 (95% confidence interval = 0.799 to 0.937). The optimal cutoff value for this model on the training set was 0.5, meaning that when logistic(GC) > 0.5, the diagnosis was in the tumor group; when logistic(GC) < 0.5, the diagnosis was in the non-tumor group. The sensitivity and specificity of the diagnosis were 88.5% and 72.0%, respectively. The AUC of the IRF1 gene was 0.782 (95% confidence interval = 0.692 to 0.873). The optimal cutoff value for this model on the training set was 0.5, meaning that when logistic(GC) > 0.5, the diagnosis was in the tumor group; when logistic(GC) < 0.5, the diagnosis was in the non-tumor group. The sensitivity and specificity of the diagnosis were 90.4% and 58.0%, respectively. These results indicate that both MORC3 and IRF1 have good diagnostic efficacy for gastric cancer.

[0084] We further constructed a binary logistic regression model using MORC3+IRF1 as the target biomarker, logistic(GC) = 0.575×MORC3 + 0.480×IRF1 - 3.379, and evaluated the diagnostic efficacy of this model. The results are as follows: Figure 7 As shown in Table 3, the AUC was 0.892 (95% confidence interval = 0.827 to 0.957, sensitivity = 80.8%, specificity = 86.0%). The optimal cutoff value for the model on the training set was 0.5, meaning that when logistic(GC) > 0.5, the diagnosis was in the tumor group; when logistic(GC) < 0.5, the diagnosis was in the non-tumor group. In this case, the positive predictive value was 84.0%, the negative predictive value was 80.8%, and the Youden index was 0.668. This indicates that the model performs well in balancing sensitivity and specificity and can effectively distinguish between positive and negative cases.

[0085] Table 3. Diagnostic efficacy of exosomal mRNA in the gastric cancer group and healthy control group in the training session.

[0086]

[0087] Based on the constructed MORC3 and IRF1 single-factor diagnostic models and MORC3+IRF1 combined factor diagnostic models, the MORC3 and IRF1 carrying capacities of plasma exosomal mRNA in the validation set of healthy individuals and gastric cancer patients were applied to the formula to calculate the logistic (GC) value for each data point. The result was calculated using P = 1 / (1+e^(-1 / 2)) -logistic(GC) The formula converts each data point into a risk score, thereby further evaluating the effectiveness of the MORC3, IRF1 single-factor model and the MORC3+IRF1 combined factor diagnostic model in distinguishing between healthy individuals and gastric cancer patients.

[0088] The results are as follows Figure 8 As shown in Table 4, the area under the ROC curve (AUC) of the MORC3 gene was 0.839 (95% confidence interval = 0.764 to 0.915, sensitivity = 86.7%, specificity = 75.0%). The optimal cutoff value for this model on the validation set was 0.5, that is, when logistic(GC) > 0.5, the diagnosis result was tumor group; when logistic(GC) < 0.5, the diagnosis result was non-tumor group. At this time, the positive predictive value of the diagnosis was 71.7%, the negative predictive value was 84.6%, and the Youden index was 0.617.

[0089] The AUC of the IRF1 gene was 0.783 (95% confidence interval = 0.692 to 0.873, sensitivity = 78.3%, specificity = 71.2%). The optimal cutoff value for the model on the validation set was 0.5, meaning that when logistic(GC) > 0.5, the diagnosis was in the tumor group; when logistic(GC) < 0.5, the diagnosis was in the non-tumor group. At this point, the positive predictive value was 68.3%, the negative predictive value was 76.9%, and the Youden index was 0.562.

[0090] The AUC of the MORC3+IRF1 combined factor diagnostic model was 0.858 (95% confidence interval = 0.789 to 0.927, sensitivity = 91.7%, specificity = 69.2%). The optimal cutoff value for this model on the validation set was 0.5, meaning that when logistic(GC) > 0.5, the diagnosis was tumor group; when logistic(GC) < 0.5, the diagnosis was non-tumor group. In this case, the positive predictive value was 76.7%, the negative predictive value was 73.1%, and the Youden index was 0.609. These results are relatively consistent with the results on the training set, indicating that the predictive ability of the MORC3 / IRF1 single-factor diagnostic model and the MORC3+IRF1 combined factor diagnostic model provided by this invention on new data is consistent with their good fit on the training set.

[0091] Table 4. Validation of the diagnostic model between the gastric cancer group and the healthy control group.

[0092]

[0093] In summary, the MORC3 and IRF1 single-factor diagnostic models and the MORC3+IRF1 combined factor diagnostic model constructed in this invention can effectively identify latent patterns in data and apply them to unseen data. This consistency verifies the model's good generalization ability, proving that it can not only make good predictions on the original training data, but also adapt to different data distributions and sample characteristics, maintaining stable and accurate predictive performance. Therefore, the MORC3 and IRF1-based single-factor and combined factor diagnostic models exhibit high sensitivity in distinguishing between gastric cancer patients and healthy individuals, demonstrating good diagnostic efficacy and the ability to identify gastric cancer patients, thus providing an effective auxiliary tool for clinical diagnosis. Furthermore, it suggests that MORC3 and IRF1 hold promise as target genes for the treatment of gastric cancer.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Application of reagents for detecting plasma exosome mRNA MORC3 loading in the preparation of products for early diagnosis of gastric cancer.

2. The application according to claim 1, characterized in that, The reagent is used to detect MORC3 loading using RT-qPCR technology.

3. The application according to claim 1, characterized in that, The test sample for the product is plasma exosomes extracted from isolated plasma.

4. A diagnostic model for early diagnosis of gastric cancer, characterized in that, The model established a Logistic binary regression diagnostic model with the biomarker MORC3 as the target marker. The specific model formula is logistic(GC) = 0.642 × MORC3 - ​​1.560, where MORC3 refers to the amount of MORC3 carried in plasma exosomes.

5. A diagnostic system for early diagnosis of gastric cancer, characterized in that, include: (1) A module for quantitatively detecting the amount of MORC3 carried in a sample; (2) Logistic binary regression calculation module: logistic(GC)=0.642× MORC3-1.560, where MORC3 refers to the amount of MORC3 carried in plasma exosomes; (3) Result judgment module: When logistic(GC)>0.5, the diagnosis result is tumor group; when logistic(GC)<0.5, the diagnosis result is non-tumor group.

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