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

Abstract

The invention 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 plasma exosome mRNA MORC3 is found to be remarkably enriched in the plasma of a gastric cancer patient for the first time, and it is indicated that the plasma exosome mRNA MORC3 is expected to serve as a biomarker for gastric cancer diagnosis and a target gene for gastric cancer treatment. A diagnosis model and a corresponding diagnosis system for gastric cancer are constructed by taking plasma exosome mRNA MORC3 as a target marker, and the diagnosis model provided by the invention shows relatively high sensitivity and specificity in the aspect of distinguishing gastric cancer patients from healthy people, shows relatively good diagnosis efficiency, can effectively identify the gastric cancer patients, and has a good application prospect. And an effective auxiliary tool is provided for clinical diagnosis. The invention provides a new choice for the molecular marker for gastric cancer diagnosis, overcomes the defect of low sensitivity when the existing cancer marker is used in the gastric cancer section, and provides a new method for early screening and diagnosis of gastric cancer.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a plasma exosome mRNA biomarker and its application in constructing a gastric cancer diagnostic model. Background Art

[0002] Gastric cancer (GC) is a malignant tumor with high morbidity and mortality in my country. Currently, clinical detection still relies on conventional histopathological methods and imaging detection technologies such as X-rays, magnetic resonance imaging, and ultrasound examinations, which have a certain lag in the early diagnosis of GC and the detection of recurrence and metastasis. The main in vitro diagnostic markers for GC are serum CEA and CA199, but they have shortcomings such as low specificity, low early tumor positivity rate, and inability to dynamically monitor tumor progression and treatment effects. At the same time, GC has strong heterogeneity, which seriously affects the treatment effect and prognosis of patients. Therefore, it is urgent to explore new early screening strategies and novel markers.

[0003] Exosomes (Exo) are small vesicles with a diameter of 30nm to 150nm that can carry a variety of biomacromolecules, including proteins, RNA, and lipids. The contents of tumor cell-derived exosomes are highly consistent with the tumor cells themselves. They can not only serve as tumor detection markers, but also reflect the functional status of the tumor cells themselves. Therefore, they can be used for early tumor screening and are good markers for in vitro diagnosis of gastric cancer. However, traditional exosome extraction and identification procedures are cumbersome, and there is a lack of rapid and sensitive methods for detecting exosomal mRNA. Secondly, it has been confirmed that exosomal mRNA can affect the tumor microenvironment, patient treatment effects, and prognosis, but there is currently a lack of identified gastric cancer-specific exosomal mRNA. Therefore, there is an urgent need to develop an in vitro diagnostic system based on gastric cancer-specific exosomal mRNA. Here, we propose a biomarker, diagnostic model, and system for non-invasive early diagnosis of gastric cancer. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the deficiencies of the existing technology, the present invention provides a plasma exosome mRNA biomarker and its application in constructing a gastric cancer diagnostic model.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

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

[0009] Specifically, the exosomal mRNA MORC3 has an ID of 23515 in NCBI Gene and a sequence of:

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

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

[0012] In a second aspect, the present invention provides the use of a reagent for detecting the loading amount of the biomarker MORC3 in the preparation of a product for early diagnosis of gastric cancer and / or postoperative MRD detection and diagnosis.

[0013] Specifically, the test sample of the product is plasma exosomes extracted from in vitro plasma.

[0014] Specifically, the reagent is a reagent for detecting the MORC3 carrying amount by RT-qPCR technology.

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

[0016] In a third aspect, the present invention provides a diagnostic model for early diagnosis of gastric cancer and / or postoperative MRD detection, wherein the model is a logistic binary regression diagnostic model established with the biomarker MORC3 as the target marker, and the model formula is specifically 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] In a fourth aspect, the present invention provides a diagnostic system for early diagnosis of gastric cancer and / or postoperative MRD detection, 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 the tumor group; when logistic (GC)<0.5, the diagnosis result is the non-tumor group.

[0022] (3) Beneficial effects

[0023] The present invention discovered for the first time that plasma exosomal mRNA MORC3 is significantly enriched in the plasma of gastric cancer patients, suggesting that plasma exosomal mRNA MORC3 is promising as a biomarker for gastric cancer diagnosis and as a target gene for gastric cancer treatment. Furthermore, we constructed a diagnostic model for gastric cancer using plasma exosomal mRNA MORC3 as a target marker and provided a system for gastric cancer diagnosis. The diagnostic model provided by the present invention exhibited high sensitivity and specificity in distinguishing gastric cancer patients from healthy subjects, demonstrated good diagnostic efficacy, and was able to effectively identify gastric cancer patients, providing an effective auxiliary tool for clinical diagnosis. The present invention provides a new option for molecular markers for gastric cancer diagnosis, overcoming the shortcomings of existing cancer markers with low sensitivity when used for gastric cancer. The present invention provides a new method for early screening and diagnosis of gastric cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Volcano plot of the differential mRNA expression between the GC and HC groups in the discovery set samples; 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 significantly upregulated differentially expressed genes (the top 10 genes are specially marked), and blue dots represent significantly downregulated differentially expressed genes.

[0025] Figure 2 Differences in exosomal mRNA expression between the GC group and the HC group in the screening set samples; A: The IRF1 gene was significantly upregulated in the GC group (p<0.0001); B: There was no significant difference in the RBM39 gene in the GC group (p=0.1348); C: The difference in the ZEB1 gene in the GC group did not reach statistical significance (p=0.0663); D: The MORC3 gene was significantly upregulated in the GC group (p<0.0001); E: The FLNA gene was significantly upregulated in the GC group (p<0.0001).

[0026] Figure 3 Differential expression of target genes in the training set samples between the GC group and the HC group; 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 Figure 3 shows the differential expression of target genes in the validation set samples between the GC group and the HC group; 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 of exosomal mRNA IRF1 (A) and MORC3 (B) genes in the GC and HC groups in the screening set samples.

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

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

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

[0032] For making the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is a part of embodiment of the present invention, rather than whole embodiment. Based on the embodiment in the present invention, those of ordinary skill in the art, without making the every other embodiment obtained under creative work premise, all belong to the scope of protection of the present invention. Unless otherwise stated, the reagent, method and equipment used in the present invention are conventional reagents, methods and equipment in the art. Unless otherwise stated, the reagents and materials used in the following examples are commercially available.

[0033] Example 1

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

[0035] 1 Experimental sample

[0036] Peripheral blood plasma exosome samples were collected from 137 gastric cancer (GC) patients and 126 healthy controls (HC) at the First Affiliated Hospital of Henan University of Traditional Chinese Medicine between November 2023 and February 2025. Sample collection and use were approved by the Ethics Committee of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine. Each group of subjects or patients were randomly divided into discovery, screening, training, and validation sets. The specific sample sizes are shown in Table 1.

[0037] The samples included in the study must meet the following conditions: (1) the age of the healthy controls must match that of the gastric cancer patients; (2) the patients must be newly diagnosed with gastric cancer by imaging, serological examination, and pathology; (3) the plasma samples must be collected before surgery, well-preserved, and the patients must not have undergone surgery, chemotherapy, or other treatments.

[0038] Table 1 Sample groups and numbers

[0039] Discovery Set Filter Set training set Validation set Total number of samples 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 from gastric cancer patients and healthy subjects were collected in the laboratory of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine. The samples were centrifuged at 3000g and 4°C for 10 min. The supernatant was divided into two 1.5 mL centrifuge tubes, one of which was frozen at -80°C and the other was used to enrich exosomes in time.

[0043] (1) Extract exosomes based on PEG precipitation, 10,000 g / 15 min at 4°C (to remove large vesicles and cell debris). Take 500 μL of supernatant and add 500 μL of 16% PEG (2×) to the plasma in a 1.5 mL centrifuge tube at a ratio of 1:1, with a final PEG concentration of 8%. Mix thoroughly by pipetting (2-3 times, do not shake to mix).

[0044] (2) After incubation at 4°C for 60 minutes, centrifuge at 3000g / 4°C for 60 minutes. Discard the supernatant and the precipitate at the bottom is the exosomes.

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

[0046] (4) Then, 400 μL of the exosome resuspension from the previous step was added to the ultrafiltration tube (a total of 500 μL); the sample was concentrated from 500 μL to approximately 150 μL at 14,000 × g for 15 minutes at 4°C.

[0047] (5) Recover the concentrated solution and Transfer the concentrated sample from the filter tube to a clean microcentrifuge tube by inverting the μLtra-0.5 device and centrifuging at 1000 x g for 5 minutes at 4°C.

[0048] 2.2 Exosome total RNA extraction

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

[0050] (2) Vigorously shake to mix thoroughly and let it 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) Instantaneous centrifugation, add an equal amount of chloroform to the exosome suspension, for example: 100 μL of exosome added to 100 μL of chloroform, shake vigorously for 15 s (the purpose of this step is to form the subsequent separation phase fully), stand at room temperature for 2-3 minutes;

[0052] (4) 12000g 4℃ centrifugation for 15 minutes, after low-temperature centrifugation, the solution can be layered, and the RNA is in the upper layer of colorless water phase, and the upper solution is transferred to a new EP tube;

[0053] (5) Add 2.5 times the volume of anhydrous ethanol to the aqueous solution, for example, 200 μL of aqueous solution added to 500L of anhydrous ethanol, mix up and down with a pipette and centrifuge;

[0054] (6) -40℃ freezing for 1 hour, 12000g, 4℃ centrifugation for 10 minutes;

[0055] (7) Discard all supernatant (as much as possible including the liquid on the tube wall), be careful not to touch the precipitate, wash with 500 μL of 75% anhydrous ethanol, 12000g 4℃ centrifugation for 10 minutes;

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

[0057] (9) Remove all liquid, pad a paper EP tube and open it flat. Stand still at room temperature for 2-5 minutes to evaporate the ethanol;

[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 into cDNA stored at -20℃, and immediately proceed to the subsequent experiment.

[0060] 2.3 Exosome whole transcriptome sequencing library construction

[0061] (1) Use VAHTS rRNA removal kit to remove rRNA according to the instructions; use DNase I (NEB, M0303) and RnaseOut (Invitrogen, 10777019) to remove DNAse and RNAse in the enriched RNA;

[0062] (2) RNA Fragmentation Reagents (Ambion, AM8740) for RNA fragmentation treatment; use RNA Clean Beads magnetic beads (Vazyme, N412) to purify the RNA sample, and Qubits to detect the RNA concentration.

[0063] (3) Each sample takes 40 ng of RNA (the rest of the RNA sample is stored at -80°C for subsequent studies), uses

VAHTS Universal V8 RNA-seq Library Prep Kit for Illumina

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

[0065] (1) Illumina NovaSeq 6000 platform is used for transcriptome sequencing; FastQC is used for quality control of RNA-Seq data; cutadapt is used for adapter trimming, and Trimmomatic is used for low-quality base removal;

[0066] (2) Use hisat2 and Htseq-count with default parameters to map clean raw reads to human genome (GRCh38) with Ensembl 78 genome annotation; select Htseq-count to calculate the number of reads mapped to each Ensembl gene, parameters as follows: -m union-s no; select DESeq2 to analyze gene expression by calculating the TPM of each gene in each sample; differential expression genes are assigned as cut-off |FoldChange|≥1.2 and P<0.05.

[0067] 3 Results

[0068] 3.1 Discovery phase

[0069] Based on Illumina NovaSeq 6000 sequencing technology, the mRNA of plasma exosomes in the discovery set samples was sequenced and analyzed. Through the analysis of the sequencing results, a total of 1471 differential genes were obtained, of which 822 genes were up-regulated and 649 genes were down-regulated in the gastric cancer group compared with the healthy control group. Based on this, the screening criteria are set as follows: p<0.05, and according to the log2(Fold Change) value, the expression difference fold is sorted, and the top 10 most significant differential expression genes are selected for further analysis. The gene expression difference analysis results are shown in Table 2 and Figure 1 .

[0070] Table 2 Exosome mRNA with significantly increased expression

[0071]

[0072] Note: E represents the Nth power of 10

[0073] 3.2 Screening stage

[0074] Based on the previous plasma exosome sequencing results, this study screened candidate genes according to the following conditions: ① genes with significant differential expression; ② genes whose expression was detected in all sequenced samples and whose expression levels were significantly upregulated. In addition, we also focused on exosomal mRNAs that are highly correlated with tumors, namely IRF1, RBM39, ZEB1, MORC3 and FLNA. RT-qPCR technology was used to preliminarily detect the expression levels of these five candidate exosomal mRNAs in the screening set samples. The normal distribution test found that the carrying amounts of RBM39, FLNA, and ZEB1 all conformed to the normal distribution. Then, the variance homogeneity test F test p = 0.0526, <0.0001 and 0.0486 showed that RBM39 had homogeneous variance, so the independent sample t test was applied, and p = 0.1348 ( Figure 2 B), FLNA and ZEB1 had different variances, and the Welch's test was used to calculate the p values ​​of 0.0010 and 0.0663, respectively. Figure 2 E and Figure 2 C); The loading levels of IRF1 and MORC3 did not conform to the normal distribution. The Mann-Whitney U test was used to calculate that the p values ​​of IRF1 and MORC3 were both less than 0.0001 ( Figure 2 A and Figure 2 D).

[0075] 3.3 Verification Phase

[0076] Based on the differentially expressed exosomal mRNA IRF1 and MORC3 obtained in the screening stage, RT-qPCR technology was used to detect the training set samples and the validation set samples, and the relative carrying amount was calculated. The results are as follows Figure 3 and Figure 4 The normality test found that the relative loading levels of MORC3 and IRF1 did not conform to the normal distribution. The Mann-Whitney U test showed that the p values ​​for MORC3 and IRF1 were both 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 differences were statistically significant.

[0077] The above results indicate that exosomal mRNA IRF1 and MORC3 have great potential as biomarkers for distinguishing healthy subjects from gastric cancer patients.

[0078] Example 2

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

[0080] First, the screening set was used as a sample, and the relative loading amounts of exosomal mRNA IRF1 and MORC3 were analyzed by drawing ROC curves using IBM SPSS Statistics 27.0 to evaluate the diagnostic efficacy of IRF1 and MORC3 in distinguishing gastric cancer patients from healthy controls. Figure 5 As shown in Table 2, the area under the curve (AUC) for 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 for 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 are promising for the development of further diagnostic models.

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

[0082]

[0083] Furthermore, we conducted binary logistic regression analysis on the relative loading amounts of exosomal mRNA IRF1 and MORC3 in the training set samples using BMSPSS Statistics 27.0 software, and constructed a binary logistic regression model: logistic (GC) = 0.624 × MORC3 - ​​2.108; logistic (GC) = 0.555 × IRF1 - 1.542. We also evaluated the diagnostic efficacy of the above two models. The results are as follows: Figure 6As shown in Table 3, the area under the receiver operating characteristic (ROC) curve (AUC) for the MORC3 gene was 0.868 (95% confidence interval = 0.799 to 0.937). The optimal cutoff value for this model in 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 sensitivity was 88.5% and the specificity was 72.0%. The AUC for the IRF1 gene was 0.782 (95% confidence interval = 0.692 to 0.873). The optimal cutoff value for this model in 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 sensitivity was 90.4% and the specificity was 58.0%. These results indicate that both MORC3 and IRF1 have good diagnostic efficacy for gastric cancer.

[0084] We further constructed a binary logistic regression model with MORC3+IRF1 as target markers, logistic(GC)=0.575×MORC3+0.480×IRF1-3.379, and evaluated the diagnostic efficacy of the 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 this model in the training set was 0.5. That is, 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 of the diagnosis was 84.0%, the negative predictive value was 80.8%, and the Youden index was 0.668. This indicates that the model performed well in balancing sensitivity and specificity and was able to effectively distinguish between positive and negative cases.

[0085] Table 3 Diagnostic efficacy of exosome mRNA in gastric cancer group and healthy control group in training set

[0086]

[0087] Based on the MORC3 and IRF1 single factor diagnostic model and MORC3+IRF1 combined factor diagnostic model constructed above, the MORC3 and IRF1 carrying amounts of plasma exosome mRNA in the validation set healthy group and gastric cancer patient group were applied to the formula to calculate the logistic (GC) value corresponding to each data point, and the P=1 / 1+e -logistic(GC) The formula converts each data point into a risk score, thereby further evaluating the effectiveness of MORC3, IRF1 single-factor models and the MORC3+IRF1 combined factor diagnostic model in distinguishing healthy people from 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 of the model in the validation set was 0.5, that is, when logistic (GC)>0.5, the diagnosis result was the tumor group; when logistic (GC)<0.5, the diagnosis result was the 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 of the model in the validation set was 0.5, that is, when logistic (GC) > 0.5, the diagnosis result was the tumor group; when logistic (GC) < 0.5, the diagnosis result was the non-tumor group. At this time, the positive predictive value of the diagnosis 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 of this model in the validation set was 0.5, that is, when logistic (GC)>0.5, the diagnosis result was the tumor group; when logistic (GC)<0.5, the diagnosis result was the non-tumor group. At this time, the positive predictive value of the diagnosis was 76.7%, the negative predictive value was 73.1%, and the Youden index was 0.609. The above results are relatively consistent with the results of the training set, indicating that the predictive ability of the MORC3 and IRF1 single factor diagnostic model and the MORC3+IRF1 combined factor diagnostic model provided by the present invention on new data is consistent with the degree of fit on the training set.

[0091] Table 4 Diagnostic model validation in gastric cancer group and healthy control group in validation set

[0092]

[0093] In summary, the MORC3, IRF1 single factor diagnostic model and MORC3+IRF1 combined factor diagnostic model constructed by the present application can effectively identify the potential patterns in the data and apply them to the data that has not been seen. This consistency verifies the good generalization ability of the model, which proves that it can not only make good predictions on the original training data, but also adapt to different data distribution and sample characteristics, maintain stable and accurate prediction performance. Therefore, the single factor diagnostic model and the combined factor diagnostic model based on MORC3 and IRF1 show high sensitivity in distinguishing gastric cancer patients from healthy people, show good diagnostic efficiency, can identify gastric cancer patients, thereby providing an effective auxiliary tool for clinical diagnosis, and at the same time, it is shown that MORC3 and IRF1 are expected to be target genes for treating gastric cancer.

[0094] The above examples are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A biomarker for early diagnosis of gastric cancer, characterized in that: The biomarker is plasma exosomal mRNA MORC3.

2. Use of a reagent for detecting the loading amount of the biomarker MORC3 according to claim 1 in a product for early diagnosis of gastric cancer and / or postoperative MRD detection and diagnosis.

3. The product according to claim 2, characterized in that: The reagent is a reagent for detecting the MORC3 carrying amount through RT-qPCR technology.

4. The product according to claim 2, characterized in that: The test sample of the product is plasma exosomes extracted from in vitro plasma.

5. 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 model formula is logistic (GC) = 0.642 × MORC3-1.560, where MORC3 refers to the amount of MORC3 carried in plasma exosomes.

6. 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 the tumor group; when logistic (GC)<0.5, the diagnosis result is the non-tumor group.

Images