Marker combination and application thereof in predicting chronic atrophic gastritis

Abstract

The invention discloses a marker combination and application thereof in prediction of chronic atrophic gastritis, and particularly relates to application in prediction of damp-heat associated syndromes. According to the present invention, the marker combination comprises the intestinal flora and the feces metabolite, the intestinal flora comprises the actinomycetes, the actinomycetes comprise the actinomycetes and the sactinomycetes, the actinomycetes comprise the actinomycetes and the sactinomycetes, the feces metabolite comprises the azelaic acid, the palmitoleic acid and the pentadecanoic acid, the intestinal flora comprises the intestinal flora, the intestinal flora comprises the intestinal flora, the intestinal flora comprises the intestinal flora, the intestinal flora comprises the intestinal flora, the intestinal flora comprises the intestinal flora and the feces metabolite, and the intestinal flora comprises the intestinal flora and the feces metabolite. A classification model formed by combining the markers shows high specificity and sensitivity, and different syndromes of chronic atrophic gastritis can be effectively distinguished. Through analysis of an excrement sample, the invention provides a non-invasive diagnosis method, and the compliance and diagnosis accuracy of a patient are improved. Compared with traditional gastroscopy, the method has the advantages that the medical cost is reduced, and the diagnosis feasibility is improved. The method not only can be used for diagnosing chronic atrophic gastritis, but also can be popularized to early screening and prevention of gastric cancer.

Description

Technical Field

[0001] The present invention belongs to the field of biological detection, and relates to a biomarker combination and its application in predicting chronic atrophic gastritis, especially its application in predicting the syndrome of dampness-heat; particularly in the field of medical diagnostic technology, it involves using multi-omics data and machine learning algorithms to identify and diagnose biomarkers for different syndromes of chronic atrophic gastritis. Background Art

[0002] Chronic atrophic gastritis is a precancerous lesion of gastric cancer. In traditional Chinese medicine theory, the syndrome of dampness-heat is a common syndrome in patients with chronic atrophic gastritis and is closely related to the development and progression of gastric cancer. However, there is currently a lack of objective diagnostic criteria and non-invasive biomarkers to identify patients with chronic atrophic gastritis accompanied by the syndrome of dampness-heat in the spleen and stomach, which limits the prevention and management of the gastric cancer process.

[0003] The existing technology mainly relies on mucosal biopsy under gastroscopy as the gold standard for the diagnosis of chronic atrophic gastritis, but this method may cause esophageal injury and psychological pressure to patients, and cannot distinguish patients with chronic atrophic gastritis accompanied by the syndrome of dampness-heat. Traditional Chinese medicine syndrome differentiation mainly relies on the experience of doctors and lacks objective diagnostic criteria. With the rapid development of high-throughput analysis technology and metabolomics technology, data on fecal metabolites or gut microbiota have achieved good diagnostic effects in the diagnosis of chronic atrophic gastritis, but their key role in the diagnosis of the syndrome of dampness-heat or the syndrome of deficiency-cold in chronic atrophic gastritis is still unclear.

[0004] There have been numerous studies on using metabolomics and microbiomics data to identify disease biomarkers. For example, Huang et al. identified 6 metabolites associated with a reduced risk of early gastric cancer through non-targeted metabolomics analysis, and 3 of them were related to the progression of intestinal metaplasia (IM). [1,2] Yu et al. found that 15 metabolites increased in gastric cancer patients, while creatine and threonic acid decreased, indicating that oxidative stress and disorders of amino acid and fatty acid metabolism may be involved in the pathological process of gastric cancer. [3] In microbiome research, Cui et al. used metagenomic sequencing to find that Campylobacter concisus was associated with the development of gastric pre-cancerous lesions (GPLs), which was detected in the tongue coating and gastric juice of gastritis patients, indicating that it may be used as a non-invasive biomarker for long-term disease monitoring. [1,4]

[0005] ​These studies have provided methods for identifying biomarkers from a single aspect (either metabolite aspect or gut microbiota aspect), but they have failed to comprehensively consider the interaction between the microbiota and host metabolites in the damp-heat syndrome or deficiency-cold syndrome of chronic atrophic gastritis, and have the following drawbacks / deficiencies: insufficient diagnostic specificity and sensitivity; lack of non-invasive biomarkers, resulting in poor patient compliance; high cost; limited application fields; limited data integration and analysis capabilities; performance issues with the stability of bacterial species and metabolites. Summary of the Invention

[0006] To overcome the deficiencies of the above-mentioned prior art solutions, the present invention provides a biomarker combination and its application in predicting chronic atrophic gastritis, especially in predicting cases with damp-heat syndrome. This method has high specificity and sensitivity, can accurately identify patients with chronic atrophic gastritis accompanied by damp-heat syndrome and deficiency-cold syndrome, reduce misdiagnosis and missed diagnosis, and thus allow for earlier intervention and treatment. The non-invasive diagnostic method of the present invention can improve patient comfort and compliance, while reducing medical costs and increasing the popularity of diagnostic techniques, enabling more patients to receive advanced diagnostic services. At the same time, the present invention provides a more comprehensive understanding of the disease mechanism, identifies new biomarkers, enhances the depth and breadth of diagnosis, helps improve the effectiveness of gastric cancer prevention, reduces the incidence and mortality of gastric cancer, improves the consistency and credibility of traditional Chinese medicine diagnosis, and promotes the modernization and internationalization of traditional Chinese medicine diagnostic methods.

[0007] By combining multi-omics data and the random forest algorithm, the present invention not only identifies metabolites and gut microbiota in feces, but also reveals their interaction with the symptoms of chronic atrophic gastritis, especially in patients with damp-heat syndrome, thus providing a new, non-invasive diagnostic method for chronic atrophic gastritis.

[0008] By combining multi-omics data and machine learning algorithms, the present invention aims to overcome these drawbacks and provide a new, non-invasive diagnostic method for chronic atrophic gastritis to improve the diagnostic specificity, sensitivity, and patient compliance.

[0009] The core technical solution of this patent solves the problems existing in the diagnosis of damp-heat syndrome or deficiency-cold syndrome of chronic atrophic gastritis in the prior art by integrating multi-omics data and applying the random forest algorithm. The following are the key technical features and innovations of this patent:

[0010] (1) Multi-omics data integration: Combining gut microbiota and metabolome data in fecal samples provides a comprehensive bioinformatics analysis platform. Through 16S rRNA sequencing and metabolomics analysis, the changes in gut microbiota and metabolites are comprehensively evaluated. Integrating microbiota and metabolite data reveals their direct connections with damp-heat syndrome or deficiency-cold syndrome. (2) Application of random forest algorithm: Using the random forest algorithm to analyze multi-omics data to identify the most diagnostically valuable biomarkers. The high accuracy and robustness of the algorithm reduce the risk of overfitting and improve the generalization ability of the model. The random forest algorithm is applied for the first time in the diagnosis of traditional Chinese medicine syndromes, improving the objectivity and accuracy of diagnosis. (3) Non-invasive diagnostic method: Identifying biomarkers by analyzing fecal samples, avoiding the invasiveness of traditional gastroscopy. It improves patient comfort and compliance, reduces medical costs and risks. It provides a new non-invasive method for the early diagnosis and screening of chronic atrophic gastritis. (4) Network analysis: Constructing the network relationship between microbiota, metabolites and damp-heat syndrome, revealing their roles in the disease process. Through network analysis, the interactions and influences between microbiota and metabolites in disease development are revealed. It provides a new biological perspective for understanding traditional Chinese medicine syndromes, enhancing the depth and breadth of diagnosis. (5) Identification and verification of biomarkers: Through the random forest algorithm and network analysis, fecal metabolites and microbiota related to damp-heat syndrome are identified and verified. The identified biomarkers have high diagnostic accuracy and are verified through an independent sample set. It provides a new set of biomarkers with high diagnostic potential, providing a new direction for the diagnosis and research of chronic atrophic gastritis.

[0011] Compared with the prior art, the technical solution of this patent provides a more comprehensive, accurate and non-invasive diagnostic method for chronic atrophic gastritis. By integrating gut microbiota and metabolome data and using advanced data analysis techniques, the present invention not only improves the specificity and sensitivity of diagnosis, but also reduces medical costs and patient discomfort, while providing a new scientific basis for the objectification and standardization of traditional Chinese medicine syndromes.

[0012] In the first aspect of the present invention, a biomarker combination is claimed, which includes gut microbiota, and the gut microbiota includes the genus Actinomyces.

[0013] In a preferred embodiment, the genus Actinomyces includes s__uncultured_Actinomyces_sp. and s__Actinomyces_bouchesdurhonensis.

[0014] The Taxonomy ID of the s__Actinomyces_bouchesdurhonensis described in the present invention is: 1852361, NCBI: txid1852361, and it is registered in the following website:

[0015] https: / / www.ncbi.nlm.nih.gov / Taxonomy / Browser / wwwtax.cgi? id=1852361 The NCBI Taxonomy ID of the s_uncultured_Actinomyces_sp. described in the present invention:

[0016] 249061, and it is registered in the following website:

[0017] https: / / www.ncbi.nlm.nih.gov / datasets / taxonomy / 249061 / .

[0018] In a preferred embodiment, the prefix "s" of the s_uncultured_Actinomyces_sp. and s__Actinomyces_bouchesdurhonensis described in the present invention represents a bacterial species.

[0019] In a preferred embodiment, the marker combination further includes metabolites, and the metabolites include one or more of azelaic acid, palmitoleic acid, and pentadecanoic acid.

[0020] In a preferred embodiment, the metabolite is a fecal metabolite.

[0021] In a preferred embodiment, the intestinal flora is obtained from feces.

[0022] The second aspect of the present invention provides an application of the marker combination as described in the first aspect in the preparation of a drug for predicting and / or diagnosing chronic atrophic gastritis.

[0023] In a preferred embodiment, the chronic atrophic gastritis is accompanied by damp-heat syndrome or deficiency-cold syndrome.

[0024] CAG patients with damp-heat syndrome (SSDHS-CAG) refers to the syndrome of internal damp-heat and spleen dysfunction. It is mostly caused by exogenous damp-heat evil; or spleen deficiency, dampness blocking the middle, dampness stagnation and heat; or excessive consumption of fatty and greasy food, excessive drinking, dampness and heat, and internal accumulation of spleen and stomach. The main clinical manifestations are epigastric distension or pain; bitter taste in the mouth, bad breath, or nausea and vomiting, or epigastric burning, or sticky and loose stools. The tongue is red, the tongue coating is thick or greasy, and the pulse is slippery and rapid. This syndrome is often seen in the CAG with intestinal metaplasia stage.

[0025] CAG patients with asthenia-cold syndrome (SSDCS-CAG) refers to the syndrome of spleen yang deficiency and failure to warm and transport. It is mostly due to the further development of spleen qi deficiency; or due to excessive consumption of raw and cold food, direct external cold, excessive use of bitter and cold food, which damages spleen yang; or insufficient kidney yang, the decline of life gate fire, and the failure of fire to generate earth, resulting in spleen yang deficiency, failure of warming and transporting, internal generation of cold, failure of water and grain transport, and failure of water and moisture to transform. The main manifestations are fullness or dull pain in the stomach, the stomach likes to be pressed or warmed; poor appetite, or loose stools, or fatigue, or shortness of breath and laziness, or abdominal distension after eating. The tongue is pale and the pulse is thin and weak. This syndrome is more common in patients with a long course of disease and is related to reduced gastric acid secretion and reduced gastric mucosal defense mechanism.

[0026] In a preferred embodiment, when the drug is used to predict and / or diagnose chronic atrophic gastritis with damp-heat syndrome or chronic atrophic gastritis with damp-heat syndrome, the subject with significantly increased intestinal flora level is suffering from chronic atrophic gastritis with damp-heat syndrome.

[0027] In a preferred embodiment, when the drug is used to predict and / or diagnose chronic atrophic gastritis with damp-heat syndrome and chronic atrophic gastritis with damp-heat syndrome, subjects whose palmitoleic acid and pentadecanoic acid levels are significantly increased are suffering from chronic atrophic gastritis with damp-heat syndrome; subjects whose azelaic acid levels are significantly decreased are suffering from chronic atrophic gastritis with damp-heat syndrome.

[0028] In a preferred embodiment, when the drug is used to predict and / or diagnose damp-heat syndrome or deficiency-cold syndrome, the subject with significantly reduced intestinal flora level is a subject with chronic atrophic gastritis with deficiency-cold syndrome.

[0029] In a preferred embodiment, when the drug is used to predict and / or diagnose chronic atrophic gastritis with damp-heat syndrome or with deficiency-cold syndrome, subjects whose palmitoleic acid and pentadecanoic acid levels are significantly reduced are suffering from chronic atrophic gastritis with deficiency-cold syndrome; subjects whose azelaic acid levels are significantly increased are suffering from chronic atrophic gastritis with deficiency-cold syndrome.

[0030] The above-mentioned "significantly increased" or "significantly decreased" represents the statistical significance level, such as the p-value in hypothesis testing: when the p-value is less than the preset significance level (such as p < 0.001, p < 0.01, p < 0.05), the result is considered to have statistical significance. The above-mentioned significance level can also represent the statistical result of the confidence interval. If the confidence interval does not contain the value of the null hypothesis (such as zero), the result is statistically significant. For example, when the 95% confidence interval does not contain 0, it is equivalent to a significance level of p < 0.05. The above-mentioned significance level is usually graded using the significance level and marked with symbols to represent different degrees of strictness, such as "*" representing 5% significance (p < 0.05) and "**" representing 1% significance (p < 0.01).

[0031] Unless otherwise specified, in the present invention, when predicting or diagnosing Damp-Heat syndrome, the "significantly increased" refers to a significant increase compared to the metabolite level or gut microbiota level in healthy subjects or subjects with Chronic Atrophic Gastritis with Deficiency-Cold syndrome.

[0032] Unless otherwise specified, in the present invention, when predicting or diagnosing Damp-Heat syndrome, the "significantly decreased" refers to a significant increase compared to the metabolite level or gut microbiota level in healthy subjects or subjects with Chronic Atrophic Gastritis with Deficiency-Cold syndrome.

[0033] Unless otherwise specified, in the present invention, when predicting or diagnosing Deficiency-Cold syndrome, the "significantly increased" refers to a significant increase compared to the metabolite level or gut microbiota level in healthy subjects or subjects with Chronic Atrophic Gastritis with Damp-Heat syndrome.

[0034] Unless otherwise specified, in the present invention, when predicting or diagnosing Deficiency-Cold syndrome, the "significantly decreased" refers to a significant increase compared to the metabolite level or gut microbiota level in healthy subjects or subjects with Chronic Atrophic Gastritis with Damp-Heat syndrome.

[0035] The third aspect of the present invention also provides the use of a reagent for determining a biomarker combination in the preparation of a kit for judging the syndrome of Chronic Atrophic Gastritis in a subject, wherein the syndrome of Chronic Atrophic Gastritis in the subject is Chronic Atrophic Gastritis with Damp-Heat syndrome or with Deficiency-Cold syndrome, and the biomarker combination is as described in the first method of the present invention.

[0036] The "syndrome" in the present invention refers to the TCM syndrome, which is a generalization of the pathophysiological reaction state at a certain stage in the development of a disease, including multiple aspects such as etiology, disease nature, disease location, the relationship between healthy qi and pathogenic factors, disease trend, and prognosis. The syndrome is obtained through the comprehensive diagnosis of inspection, auscultation and olfaction, inquiry, and palpation, and can reveal the essence and pathological changes of the disease.

[0037] In a preferred embodiment, the gut microbiota and metabolites are obtained from the fecal sample of the subject.

[0038] In a preferred embodiment, after obtaining the intestinal flora and metabolites, the kit determines the syndromes of chronic atrophic gastritis in the subject, including: subjects with a significantly increased level of the intestinal flora have chronic atrophic gastritis with damp-heat syndrome.

[0039] In a preferred embodiment, after obtaining the intestinal flora and metabolites, the kit determines the syndromes of chronic atrophic gastritis in the subject, including: subjects with a significantly increased level of palmitoleic acid and pentadecanoic acid have chronic atrophic gastritis with damp-heat syndrome; subjects with a significantly decreased level of azelaic acid have chronic atrophic gastritis with damp-heat syndrome.

[0040] In a preferred embodiment, the reagent is a reagent for determining the level of the intestinal flora or a reagent for determining the metabolite level.

[0041] In a specific embodiment, the reagent is a reagent for 16S rRNA V3-V4 region sequencing, metagenomic shotgun sequencing, and / or UPLC-MS / MS analysis.

[0042] The fourth aspect of the present invention further provides a reagent for detecting a biomarker combination, where the biomarker combination is as described in the first aspect of the present invention, and the reagent is defined as the reagent in the third aspect of the present invention.

[0043] The fifth aspect of the present invention further provides a diagnostic kit for determining the syndromes of chronic atrophic gastritis in a subject, which includes the reagent and a control as described in the fourth aspect of the present invention. The syndromes of chronic atrophic gastritis are accompanied by damp-heat syndrome or deficiency-cold syndrome, and the kit has the functions of the kit in the application described in the third aspect of the present invention.

[0044] Another aspect of the present invention further provides a diagnostic system for chronic atrophic gastritis syndromes. The diagnostic system includes a detection module and an analysis and judgment module; the detection module detects the level of the biomarker combination in the feces of the subject and transmits the level data to the analysis and judgment module; the analysis and judgment module judges the level data of the biomarker combination and outputs a diagnostic result that the subject has chronic atrophic gastritis with damp-heat syndrome or chronic atrophic gastritis with deficiency-cold syndrome; where the biomarker combination is as described in the first aspect of the present invention.

[0045] In a preferred embodiment, the analysis and judgment module judges the level data of the biomarker combination, including: subjects with a significantly increased level of the intestinal flora have chronic atrophic gastritis with damp-heat syndrome.

[0046] In a preferred embodiment, the analysis and judgment module determines that the horizontal data of the biomarker combination includes: subjects with a significant increase in the levels of palmitoleic acid and pentadecanoic acid are patients with chronic atrophic gastritis with damp-heat syndrome; subjects with a significant decrease in the level of azelaic acid are patients with chronic atrophic gastritis with damp-heat syndrome.

[0047] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the functions of the diagnostic system as described in the above aspects of the present invention.

[0048] On the other hand, the present invention also provides an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor is used to execute the computer program to implement the functions of the diagnostic system as described in the above aspects of the present invention.

[0049] The beneficial effects of the present invention are as follows:

[0050] The classification model of the present invention shows high specificity and sensitivity, and can effectively distinguish different syndromes of patients with chronic atrophic gastritis, especially the damp-heat syndrome. Through the analysis of fecal samples, the present invention provides a non-invasive diagnostic method, which improves the compliance of patients. Compared with the traditional gastroscopy examination, the method of the present invention reduces the medical cost and improves the feasibility and accuracy of diagnosis. The method of the present invention can not only be used for the diagnosis of chronic atrophic gastritis, but also be extended to the early screening and prevention of gastric cancer. Description of the Drawings

[0051] Figure 1 It is a structural diagram of the research design and population. 185 fecal samples were collected from Shanghai (61 CAG patients with SSDHS, 45 CAG patients with SSDCS, and 79 healthy controls). Fecal metabolites were obtained from the A sample set (20 healthy controls, 20 CAG patients with SSDCS, and 26 CAG patients with SSDHS) for characterization. Intestinal microbiota groups and fecal metabolite potential biomarkers were identified as diagnostic biomarkers for SDHS-CAG in the A sample set through a random forest model. The diagnostic efficacy was verified in the B sample set (33 healthy controls, 20 CAG patients with SSDCS, and 21 CAG patients with SSDHS). The diagnostic efficacy was verified in the C sample set (26 healthy controls, 5 CAG patients with SSDCS, and 14 CAG patients with SSDHS).

[0052] Figure 2Alpha and beta diversity analysis of fecal microbiota in Sample Set A. (A) Rarefaction curves based on the Sobs index. The estimated OTU richness in all samples was basically close to saturation. (B) Venn diagram showing the overlap between groups, indicating that there were 410 OTUs common to all three groups, while the SSDHS-CAG group had 48 unique OTUs. (C) Compared with the healthy control group, the Sobs, Shannon, Ace, and Chao of CAG patients in SSDHS were significantly decreased, while Simpson was significantly increased (p values were 0.004, 0.003, 0.001, 0.004, and 0.004, respectively). (D) Microbiota PLS-DA analysis was performed among SSDHS-CAG, SSDCS-CAG, and healthy control groups (PERMANOVA, p = 0.004).

[0053] Figure 3 Network between CAG patients and SSDHS and SSDCS, based on the network of direct interactions between 266 genera or 146 metabolites and 15 SSDHS / SSDCS symptoms (signs). Nodes highlighted in red represent symptoms (signs) of SSDHS; nodes highlighted in blue represent symptoms (signs) of SSDCS; nodes highlighted in purple represent 16 differentially expressed metabolites and 1 differentially expressed genus; nodes highlighted in orange represent other metabolites and genera.

[0054] Figure 4 Classification model based on gut microbiota. (A) Importance of g_Actinomyces in Sample Set A. (B) Evaluation of g_Actinomyces using the ROC curve in Sample Set A. (C) Results of evaluating g_Actinomyces using the ROC curve in Sample Set B. (D) Importance results of s__uncultured_Actinomyces_sp. and s__Actinomyces_bouchesdurhonensis in Sample Set A. (E) Results of evaluating s__uncultured_Actinomyces_sp. and s__Actinomyces_bouchesdurhonensis using the ROC curve in Sample Set A.

[0055] Figure 5 Classification model based on fecal metabolites. (A) Importance of 16 fecal metabolites arranged in descending order in Sample Set A. (B) Results of evaluation using the ROC curve in Sample Set A. (C) Results of evaluation using the ROC curve in Sample Set C.

[0056] Figure 6It is a classification model based on fecal metabolites and gut microbiota. (A) Importance arranged in descending order in Sample Set A. (B) Results evaluated using the ROC curve in Sample Set A.

[0057] Figure 7 It is a network based on the direct interactions between 5 potential biomarkers and 15 SSDHS / SSDCS symptoms (signs). The nodes highlighted in red refer to the symptoms (signs) of SSDHS; the nodes highlighted in blue refer to the symptoms (signs) of SSDCS; the nodes highlighted in pink refer to the 5 potential biomarkers.

[0058] Figure 8 It is the result of metagenomic sequencing analysis of gut microbiota from feces in Sample Set A. (A) Taxonomic annotation of three groups of gut microbiota reference gene catalogs. (B) LEfSe analysis. (C) LDA analysis.

[0059] Figure 9 It is the metabolite profile from feces in Sample Set A. (A) Composition ratios of metabolites in the SSDHS-CAG, SSDCS-CAG, and HC groups. (B) PLS-DA analysis of the targeted metabolome in the SSDHS-CAG, SSDCS-CAG, and HC groups. (C-D) Relative abundances of 16 fecal metabolites in the SSDHS-CAG, SSDCS-CAG, and HC groups. (E) Heatmap of 16 significantly different metabolites in the SSDHS-CAG, SSDCS-CAG, and HC groups. Detailed implementation manners

[0060] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. Based on the embodiments provided by the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0061] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in such a development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as the content disclosed in the present application being insufficient.

[0062] References to "embodiments" in this application mean that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0063] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meaning as understood by those of ordinary skill in the technical field to which this application belongs. The words such as "a", "an", "one kind", "the" and the like involved in this application do not indicate a limitation in quantity and can represent a singular or plural number. The terms "include", "comprise", "have" and any variations thereof involved in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units (units) is not limited to the listed steps or units, but may further include steps or units not listed, or may further include other steps or units inherent to these processes, methods, products or devices. The "multiple" / "several" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0064] In the description of the present invention, the description with reference to terms such as "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0065] For the experimental methods without specific conditions noted in the following embodiments, they are carried out according to conventional methods and conditions, or according to the product instructions.

[0066] Example 1: Sample Collection and Processing

[0067] 1. Sample Source

[0068] The sample structure diagram is shown in Figure 1。Samples were collected from 185 participants in Shanghai. In this study, a total of four sample sets were used to analyze the gut microbiota and metabolite profiles in patients with chronic atrophic gastritis (CAG):

[0069] Sample set A: It included 66 participants, divided into a healthy control group (HC group, 20 people), CAG patients with damp-heat syndrome (SSDHS-CAG group, 26 people), and CAG patients with deficiency-cold syndrome (SSDCS-CAG group, 20 people). This sample set was used for the preliminary detection of fecal metabolites and gut genera, and for the identification of biomarkers and model construction.

[0070] Sample set B: It included 74 participants, also divided into HC group (33 people), SSDHS-CAG group (21 people), and SSDCS-CAG group (20 people). This sample set was used to further verify the diagnostic accuracy of gut microbiota through 16S rRNA sequencing. Specifically, it was the sequencing of the V3-V4 region of 16S rRNA.

[0071] Sample set C: It included 45 participants, divided into HC group (26 people), SSDHS-CAG group (14 people), and SSDCS-CAG group (5 people). This sample set was used to further verify the diagnostic accuracy of fecal metabolites through UPLC-MS / MS analysis.

[0072] Metagenomic sample set: Twenty-nine samples were selected from sample set A for metagenomic sequencing, divided into HC group (10 people), SSDHS-CAG group (10 people), and SSDCS-CAG group (9 people). This sample set was used to deeply analyze the species-level differences in gut microbiota and reveal the biological basis of SSDHS and SSDCS in CAG patients.

[0073] Sample processing: According to what was reported in previous studies [5] Fecal samples were collected. Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) and 16S rRNA sequencing technologies were used to analyze the metabolites and gut microbiota in fecal samples.

[0074] 2. Experimental methods

[0075] DNA extraction, 16S rRNA V3-V4 region sequencing and data processing

[0076] The extracted DNA samples were amplified to construct a DNA library, and the overhang sequences were sequenced according to the manufacturer's instructions. The primer pairs 338F and 806R were used on ABI The hypervariable regions V3-V4 of the bacterial 16S rRNA gene were amplified on a 9700 PCR thermal cycler (ABI, Madison, WI, USA). The PCR conditions are available online.

[0077] The data were analyzed using the Majorbio Cloud Platform (www.majorbio.com). For alpha diversity analysis, the sobs, Shannon, Simpson, ACE, and Chao indices were calculated. The differences in beta diversity were analyzed by partial least squares discriminant analysis (PLS-DA). The differences in the beta diversity of the microbiota among the SSDHS-CAG, SSDCS-CAG, and HC groups were evaluated using permutational multivariate analysis of variance (PERMANOVA) tests. Based on log10 LDA > 2.0, the linear discriminant analysis (LDA) effect size (LEFSe) method and the Kruskal-Wallis H test were used to analyze the significant differences among the bacterial genera in the three groups. The data are presented as the mean ± standard deviation (SD). P < 0.05 was considered statistically significant. All tests were two-sided.

[0078] Metagenomic shotgun sequencing and data analysis

[0079] Multiple spare intestinal samples from the HC (N = 10), SSDHS-CAG (N = 10), and SSDCS-CAG (N = 9) groups in the A sample set were analyzed by metagenomic shotgun sequencing. Total genomic DNA was extracted from the above samples using the FastPure fecal DNA extraction kit (magnetic beads) (MJYH, Shanghai, China). After evaluating the purity and concentration, the genomic DNA was pooled on an Illumina NovaSeq at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai) TMSequencing was performed using the NovaSeq X Series 25B kit on X Plus (Illumina Inc., San Diego, CA, USA). The data was analyzed on the Majorbio cloud platform (www.majorbio.com). Reads were aligned to the human genome by BWA (http: / / bio-bwa.sourceforge.net, version 0.7.17), and matches related to the reads and their paired reads were removed. Contigs with a length ≥ 300 bp were used as the final assembly results. Open reading frames (ORFs) in each assembled contig were predicted using Prodigal (https: / / github.com / hyangtpd / Prodigal, version 2.6.3) for each assembled contig, and ORFs with a length ≥ 100 bp were retrieved. Subsequently, the remaining reads were used for taxonomic analysis with default parameters.

[0080] Targeted fecal metabolomics analysis and data processing

[0081] All metabolites from feces in this study were detected by UPLC-MS / MS and the Q300 kit (Metabo-profile Biotechnology, Shanghai, China).

[0082] For data processing, the raw data files generated by UPLC-MS / MS were processed using MassLynx software (v4.1, Waters Corp., Milford, MA, USA), and peak integration, calibration, and quantification were performed for each metabolite. The calculated absolute metabolite concentrations were used for univariate and multivariate analyses. Statistical analysis was performed on the iMAP platform (v1.0; Metabo-Profile, Shanghai, China). β-diversity was calculated by PLS-DA. Before performing the heatmap analysis, the concentration values were transformed into z-scores using standardized z-score transformation. Univariate analysis was used to analyze the significant differences in fecal metabolites among the three groups. KEGG pathway analysis was used to enrich the defined biological system functions. Spearman rank correlation analysis was used to predict the correlations among fecal metabolites, gut microbiota, and symptoms. Cytoscape only included edges with p < 0.05. The data was expressed as mean ± standard deviation. All tests were two-tailed tests.

[0083] Example 2: Metabolite and gut microbiota analysis

[0084] Gut microbiota analysis:

[0085] The diversity and composition of fecal-derived gut microbiota in all fecal samples were detected by 16S rRNA sequencing. In the A sample set, rarefaction curve analysis showed that the operational taxonomic unit (OTU) richness of all three groups reached saturation ( Figure 2 of A). A total of 3,106,192 reads with an average length of 412 were identified from 66 fecal samples, and these reads could be classified into 792 bacterial OTUs. Among these 792 OTUs, 410 OTUs were shared by SSDHS-CAG, SSDCS-CAG, and HC, while the SSDHS-CAG, SSDCS-CAG, and HC groups had 48, 72, and 72 unique OTUs, respectively ( Figure 2 of B). The α-diversity was estimated using the Sobs, Shannon, Simpson, Ace, and Chao indices. Analysis of the V3-V4 region of the gut microbiota showed that compared with the HC group, the Sobs, Shannon, Ace, and Chao indices of the SSDHS-CAG group were significantly lower, while the Simpson index was higher ( Figure 2 of C). However, there were no significant differences in the Sobs, Shannon, Simpson, Ace, and Chao indices between the CAG-SSDHS and CAG-SSDCS groups ( Figure 2 of C).

[0086] In this example, the PLS-DA method was used to reflect the differences in gut microbiota among the CAG-SSDHS, CAG-SSDCS, and HC groups and to study the aggregation trend within the same group and the separation trend between different groups. As Figure 2 shown in D, there was an obvious separation trend in gut microbiota among the SSDHS-CAG, SSDCS-CAG, and HC groups (F model: 1.85, p = 0.004). The gut microbiota in the SSDHS-CAG group was mainly distributed in the lower right quadrant, while the microbiota in the SSDCS-CAG group was mainly distributed in the upper right and upper left quadrants. The microbiota in the HC group was mainly distributed in the lower left quadrant.

[0087] Twenty-nine of the 66 participants who underwent 16S rRNA gene sequencing (10 healthy control group HC, 10 SSDHS-CAG group, 9 SSDCS-CAG group) were selected for metagenomic shotgun sequencing of gut microbiota. Clustering at 95% nucleotide sequence identity generated a non-redundant microbial gene catalog of 2,247,271 with an average length of 717.35 bp ( Figure 8 of A). After data analysis, bacteria, viruses, archaea, and eukaryotes were found in fecal samples ( Figure 8A). Bacteria were the most abundant microorganisms in all samples, accounting for 96.41%. Finally, 130 phyla, 250 classes, 447 orders, 834 families, 2,494 genera, and 11,727 bacterial species were detected in the fecal samples. The gut microbiota was mainly composed of Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria, which was consistent with the results of 16S rRNA gene sequencing.

[0088] LEfSe was further used to distinguish the gut microbiota differences among the SSDHS-CAG group, SSDCS-CAG group, and HC group in sample set A. At the species level, compared with the SSDCS-CAG group and HC group, 53 gut bacterial species were significantly increased in the feces of the SSDHS-CAG group ( Figure 8 B and C). Specifically, among the genus Actinomyces, s_Actinomyces_bouchesdurhonensis and s_uncultured_Actinomyces_sp. were significantly increased in the feces of the SSDHS-CAG group ( Figure 8 B and C).

[0089] Metabolite analysis:

[0090] UPLC-MS / MS technology was used to detect the metabolites in the fecal samples. A total of 146 metabolites belonging to 16 categories were identified in the C sample sets of the SSDHS-CAG, SSDCS-CAG, and HC groups (see Figure 9 A), including 31 amino acids, 27 bile acids, 24 fatty acids, 16 organic acids, etc. As Figure 9 shown in B, the results of PLS-DA analysis indicated a clear separation among the SSDHS-CAG, SSDCS-CAG, and HC groups. Univariate analysis was used to determine the metabolite differences among the SSDHS-CAG, SSDCS-CAG, and HC groups. As Figure 9As shown in C and D, compared with the SSDCS-CAG and HC groups, there were 16 significantly different fecal metabolites in the SSDHS-CAG group, including four organic acids (alpha-ketoisovaleric acid, benzoic acid, 3-methyl-2-oxopentanoic acid, ketoleucine), three fatty acids (azelaic acid, palmitoleic acid, pentadecanoic acid), two amino acids (sarcosine, gamma-aminobutyric acid), two SCFAs (butyric acid, acetic acid), one phenol (4-hydroxyphenylpyruvic acid), one indole (indoleacrylic acid), one bile acid (LCA-3S), one phenylpropanoid (cinnamic acid), and one benzene ring compound (phenylpyruvic acid). Compared with the fecal metabolites of the SSDCS-CAG and HC groups, 9 fecal-derived metabolites (azelaic acid, alpha-ketoisovaleric acid, benzoic acid, 3-methyl-2-oxopentanoic acid, ketoisoleucine, phenylpyruvic acid, 4-hydroxyphenylpyruvic acid, indoleacrylic acid, cinnamic acid) were downregulated in the SSDHS-CAG group; 7 fecal metabolites (sarcosine, GABA, palmitoleic acid, pentadecanoic acid, acetic acid, LCA-3S, butyric acid) were upregulated in the SSDHS-CAG group (p<0.05, Figure 9 of E).

[0091] Example 3: Network analysis

[0092] Network construction: A network was constructed based on the direct interactions between 266 gut microbiota, 146 metabolites, and 15 symptoms of damp-heat syndrome / cold-deficiency syndrome, as Figure 3 shown. The network contained 143 nodes and 470 edges. The nodes represented genera, metabolites, or symptoms (signs), and the edges represented the interactions between genera / metabolites and symptoms (signs).

[0093] Network analysis results: 16 differentially expressed metabolites (azelaic acid, α-ketoglutaric acid, benzoic acid, 3-methyl-2-oxovaleric acid, ketoleucine, phenylpyruvic acid, 4-hydroxyphenylpyruvic acid, indoleacetic acid, cinnamic acid, serine, γ-aminobutyric acid, palmitoleic acid, pentadecanoic acid, acetic acid, LCA-3S, butyric acid) and 1 differentially expressed genus (g_Actinomyces) showed high correlations with symptoms. Through network topology feature analysis, these metabolites and genus had high degrees, betweenness centrality, and closeness centrality in the network, indicating their importance in topological structure and greater impact on network imbalance compared to other nodes.

[0094] Example 4: Construction of random forest model and verification of diagnostic accuracy

[0095] Use the random forest algorithm to analyze metabolite and microbiota data and construct a classification model for damp-heat syndrome - chronic atrophic gastritis.

[0096] Feature selection was performed through the iMAP platform (v1.0; Metabo-Profile, Shanghai, China). The importance of fecal metabolites and microbiota was calculated using the random forest (hereinafter also referred to as RF) model and arranged in descending order. The diagnostic efficacy of potential biomarkers was evaluated through the ROC curve, and the area under the curve (AUC) was used as the evaluation index. An AUC greater than 0.7 indicated that these biomarkers had predictive value. The screened fecal metabolite and microbiota biomarkers were combined into a new classifier, and the ROC value of this classifier was calculated simultaneously.

[0097] Three fecal metabolites (azelaic acid, palmitoleic acid, and pentadecanoic acid) and two bacterial species (s__uncultured_Actinomyces_sp. and s__Actinomyces_bouchesdurhonensis) were identified as candidate biomarkers by the random forest algorithm.

[0098] Use the ROC curve to evaluate the diagnostic accuracy of potential biomarkers.

[0099] In the A sample set, the importance score of the genus Actinomyces (g_Actinomyces) was 26.478( Figure 4A), ROC curve analysis showed that the model based on Actinomyces (g_Actinomyces) had AUC values of 0.876 (SP = 0.8, SE = 0.885) and 0.745 (SP = 0.6, SE = 0.846) when differentiating SSDHS-CAG from healthy controls (HC) and SSDHS-CAG from patients with deficiency-cold syndrome (SSDCS-CAG) ( Figure 4 B). Meanwhile, in the B sample set, the AUC values were 0.756 (SSDHS-CAG vs. HC) and 0.688 (SSDHS-CAG vs. SSDCS-CAG) ( Figure 4 C).

[0100] Furthermore, it was found that at the species level, the importance scores of s_uncultured_Actinomyces_sp. and s_Actinomyces_bouchesdurhonensis under Actinomyces were 10.050 and 8.610 respectively ( Figure 4 D). The classification model based on s__uncultured_Actinomyces_sp. and s__Actinomyces_bouchesdurhonensis had an AUC value of 0.9 between the damp-heat syndrome group of chronic atrophic gastritis and healthy controls, and an AUC value of 0.8 between the damp-heat syndrome group of chronic atrophic gastritis and the deficiency-cold syndrome group ( Figure 4 E).

[0101] Similarly, random forest was used for feature selection. As shown in A of Figure 5 , three fecal metabolites were identified as key biomarkers: azelaic acid (importance score 7.905), palmitoleic acid (importance score 3.900), and pentadecanoic acid (importance score 3.240). On the A sample set, ROC curve analysis showed that the model based on azelaic acid, palmitoleic acid, and pentadecanoic acid had AUC values of 0.987 and 0.923 when differentiating SSDHS-CAG from healthy controls (HC) and SSDHS-CAG from patients with deficiency-cold syndrome (SSDCS-CAG) respectively ( Figure 5 B). In the C sample set, the AUC values were 0.898 (SSDHS-CAG vs. HC) and 1 (SSDHS-CAG vs. SSDCS-CAG) ( Figure 5 C).

[0102] Further combine the five biomarkers obtained from the above analysis and use RF to analyze their importance. As shown in A of Figure 6 , the importance scores from high to low are azelaic acid (5.766), palmitoleic acid (4.493), s__uncultured_Actinomyces_sp. (3.435), s__Actinomyces_bouchesdurhonensis (2.564), and pentadecanoic acid (2.404). On the A sample set, further ROC curve analysis was performed. The results showed that when the classifier model formed based on the above five biomarker combinations was used to distinguish SSDHS-CAG from the healthy control group (HC) and SSDHS-CAG from patients with deficiency-cold syndrome (SSDCS-CAG), the AUC values were 0.97 and 0.9, respectively ( Figure 6 B of

[0103] The above results indicate that the classifier formed by azelaic acid, palmitoleic acid, s__uncultured_Actinomyces_sp., s__Actinomyces_bouchesdurhonensis, and pentadecanoic acid can effectively distinguish SSDHS-CAG from the healthy control group and SSDHS-CAG from SSDCS-CAG.

[0104] Example 5: Construct a correlation network

[0105] Further combined with clinical symptom (sign) characteristics, the diagnostic value of potential biomarkers based on the above criteria was explored by analyzing the association between potential biomarkers and SSDHS / SSDCS symptoms (signs). A network was constructed based on the direct interactions between 5 potential biomarkers and 15 SSDHS / SSDCS symptoms (signs). The results are shown in Figure 7As shown in the figure, the network consists of 15 nodes and 19 edges (nodes represent strains, metabolites, or symptoms / signs, and edges represent the interactions between strains / metabolites and symptoms / signs). The results show that the upregulated biomarkers (palmitoleic acid, pentadecanoic acid, s__uncultured_Actinomyces_sp., s__Actinomyces_bouchesdurhonensis) are positively correlated with the SSDHS symptoms (signs) (yellow greasy tongue coating, bitter taste in the mouth, red tongue), and negatively correlated with the SSDCS symptoms (signs) (diarrhea, fear of cold, cold limbs, listlessness) (Table 1). In contrast, the downregulated biomarker azelaic acid is negatively correlated with the SSDHS symptoms (signs) (yellow greasy tongue coating, bitter taste in the mouth, red tongue, unsmooth defecation), and positively correlated with the SSDCS symptoms (signs) (listlessness, fear of cold, dull pain in the stomach, pale tongue) (Table 1).

[0106] Table 1 Relationship between clinical symptoms (signs) of CAG patients and potential diagnostic markers

[0107]

[0108] References

[0109] [1]ZHANG Q, YANG M, ZHANG P, et al. Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods. Cancer BiolMed, 2023, 21(4): 312 - 330.

[0110] [2]HUANG S, GUO Y, LI Z W, et al. Identification and Validation of PlasmaMetabolomic Signatures in Precancerous Gastric Lesions That Progress toCancer. JAMAnetwork open, 2021, 4(6): e2114186.

[0111] [3]YU L, AA J, XU J, et al. Metabolomic phenotype of gastric cancer and precancerous stages based on gas chromatography time-of-flight mass spectrometry. J Gastroenterol Hepatol, 2011, 26(8): 1290 - 1297.

[0112] [4]CUI J, CUI H, YANG M, et al. Tongue coating microbiome as a potential biomarker for gastritis including precancerous cascade. Protein Cell, 2019, 10(7): 496 - 509.

[0113] [5]GAI X, QIAN P, GUO B, et al. Heptadecanoic acid and pentadecanoic acid crosstalk with fecal-derived gut microbiota are potential non-invasive biomarkers for chronic atrophic gastritis. Front Cell Infect Microbiol, 2022, 12: 1064737.

[0114] Through the above embodiments, the present invention has successfully solved the problems existing in the prior art and demonstrated excellent technical effects. By integrating and analyzing multi-omics data and applying the random forest algorithm, the present invention provides a new, non-invasive diagnostic method for chronic atrophic gastritis, with high specificity, sensitivity, and patient compliance, while reducing medical costs and broadening the application fields.

Claims

1. A marker combination, comprising an intestinal flora, wherein the intestinal flora comprises Actinomyces (g_Actinomyces); preferably, the Actinomyces comprises s__uncultured_Actinomyces_sp. and s__Actinomyces_bouchesdurhonensis.

2. The marker combination according to claim 1, characterized in that It further includes fecal metabolites, and the metabolites include one or more of azelaic acid, palmitoleic acid, and pentadecanoic acid.

3. Use of the marker combination as claimed in claim 1 or 2 in the preparation of a drug for predicting and / or diagnosing chronic atrophic gastritis.

4. The use according to claim 3, characterized in that The chronic atrophic gastritis is accompanied by damp-heat syndrome or deficiency-cold syndrome.

5. The use according to claim 4, characterized in that When the drug is used to predict and / or diagnose chronic atrophic gastritis with damp-heat syndrome or with deficiency-cold syndrome, the subject whose intestinal flora level is significantly increased is a subject with chronic atrophic gastritis with damp-heat syndrome; and / or, the subject whose palmitoleic acid (palmitoleic acid) and pentadecanoic acid (pentadecanoic acid) levels are significantly increased is a subject with chronic atrophic gastritis with damp-heat syndrome; the subject whose azelaic acid (azelaic acid) level is significantly decreased is a subject with chronic atrophic gastritis with damp-heat syndrome.

6. Use of a reagent for determining a combination of markers in the preparation of a kit for determining the syndrome of chronic atrophic gastritis in a subject, wherein: The chronic atrophic gastritis syndrome of the subject is chronic atrophic gastritis with damp-heat syndrome or with deficiency-cold syndrome, and the marker combination is as defined in claim 1 or 2.

7. The use according to claim 6, characterized in that By obtaining the intestinal flora and metabolites from stool samples of the subjects; Preferably, after obtaining the intestinal flora and metabolites, the kit determines the symptoms of chronic atrophic gastritis in the subject including: the subject whose intestinal flora level is significantly increased is suffering from chronic atrophic gastritis with damp-heat syndrome; and / or, the subject whose palmitoleic acid (palmitoleic acid) and pentadecanoic acid (pentadecanoic acid) levels are significantly increased is suffering from chronic atrophic gastritis with damp-heat syndrome; the subject whose azelaic acid (azelaic acid) level is significantly decreased is suffering from chronic atrophic gastritis with damp-heat syndrome.

8. The use according to claim 6 or 7, characterized in that The reagent is a reagent for determining the level of intestinal flora, or a reagent for determining the level of metabolites; for example, the reagent is a reagent for 16SrRNA V3-V4 region sequencing, metagenomic shotgun sequencing and / or UPLC-MS / MS analysis.

9. A reagent for detecting a marker combination, characterized in that: The marker combination is as defined in claim 1 or 2, and the reagent is as defined in the use according to any one of claims 6-8.

10. A diagnostic kit for judging the syndrome of chronic atrophic gastritis in a subject, comprising the reagent and reference substance as claimed in claim 9, wherein the syndrome of chronic atrophic gastritis is syndrome with damp-heat or syndrome with deficiency-cold, and the kit has the functions of the kit in the application as claimed in any one of claims 6 to 8.

11. A diagnostic system for chronic atrophic gastritis syndrome, characterized in that: The diagnostic system includes a detection module and an analysis and judgment module; the detection module detects the level of the marker combination in the subject's feces and transmits the level data to the analysis and judgment module; the analysis and judgment module judges the level data of the marker combination and outputs a diagnosis result: the subject has chronic atrophic gastritis with damp-heat syndrome or chronic atrophic gastritis with deficiency-cold syndrome; wherein the marker combination is as defined in claim 1 or 2.

12. The diagnostic system according to claim 11, characterized in that The analysis and judgment module judges the level data of the marker combination including: the subjects whose intestinal flora levels are significantly increased are suffering from chronic atrophic gastritis with damp-heat syndrome; and / or, the subjects whose palmitoleic acid (palmitoleic acid) and pentadecanoic acid (pentadecanoic acid) levels are significantly increased are suffering from chronic atrophic gastritis with damp-heat syndrome; the subjects whose azelaic acid (azelaic acid) levels are significantly decreased are suffering from chronic atrophic gastritis with damp-heat syndrome.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the functions of the diagnostic system according to claim 11 or 12 can be realized.

14. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: The processor is configured to execute the computer program to implement the functions of the diagnostic system according to claim 11 or 12.

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