A set of microbial markers for identifying adenoid hypertrophy and applications thereof

Abstract

This invention belongs to the field of biomedical technology, specifically relating to a set of microbial biomarkers for identifying adenoid hypertrophy and their applications. Based on the relationship between different microbial colonization patterns and the severity of adenoid hypertrophy, this invention utilizes abundance data from combinations of oral microbial biomarkers from different sample types to construct a model that can predict the severity of adenoid hypertrophy in patients. The predictive model constructed in this invention has an AUC value of 0.752, demonstrating good predictive performance. Combining model parameters and microbial characteristic information shows that the invented model is consistent with current clinical understanding, proving the reliability of the model.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a set of microbial biomarkers for identifying adenoid hypertrophy and their applications. Background Technology

[0002] Adenoid hypertrophy (AH) is one of the most common upper respiratory tract diseases in children. In severe cases, it can lead to complications such as airway obstruction, sleep apnea, and otitis media. Currently, the diagnosis of adenoid hypertrophy mainly relies on nasopharyngeal examination, nasal endoscopy, or imaging examinations (such as X-rays and CT scans). However, these examination methods have the following problems: (1) they require specialized equipment and operators; (2) some examinations are invasive and children have low cooperation; and (3) it is difficult to achieve large-scale screening and early warning.

[0003] In recent years, microbiome research has shown that the human gut microbiome is closely related to various disease states. The oral cavity, as one of the largest microbial habitats in the human body, may reflect the host's health status through changes in its microbial community composition. However, for adenoid hypertrophy, there is currently no mature predictive model construction method based on microbial biomarkers from different sample types (oral surface, adenoid tissue, nasal cavity), which cannot meet the needs of precise clinical diagnosis. Summary of the Invention

[0004] The first aspect of the present invention aims to provide the use of a substance for detecting combinations of microbial markers in the preparation of products for predicting adenoid hypertrophy.

[0005] The second aspect of the present invention is to provide a method for constructing a model for predicting adenoid hypertrophy.

[0006] A third aspect of the present invention is to provide a system for predicting adenoid hypertrophy.

[0007] To achieve the above-mentioned objectives of this invention, the technical solution adopted by this invention is as follows: A first aspect of the invention provides the use of a substance for detecting combinations of microbial markers in the preparation of products for predicting adenoid hypertrophy.

[0008] In some embodiments of the present invention, the combination of microbial markers includes: *Ralstonia* spp. ( Ralstonia ), Peptostreptococcus ( Peptostreptococcus ), Masseilles ( Massilia Listeria spp. Listeria ), Cartozobacter spp. Catonella ), *Streptococcus* genus ( Granulicatella ), genus Rombutz ( Romboutsia ), genus Turichiformis ( TuricibacterBifidobacterium spp. Bifidobacterium ), Peptococcus ( Peptococcus Clostridium genus 1 ( Clostridium_sensu_stricto_1 ), Eikenella spp. Eikenella Flavobacterium ( Flavobacterium ), Enterobacteriaceae ( Enterobacter ), Bacillus spp. ( Paenibacillus ), Marine bacteria ( Idiomarina Akkermania ( ) Akkermansia ), Amaryl cocci ( Amaricoccus ), genus Roselle ( Roseburia One or more of the following.

[0009] In some embodiments of the present invention, the microbial marker combination is *Rolstonia* spp. ( Ralstonia ), Peptostreptococcus ( Peptostreptococcus ), Masseilles ( Massilia Listeria spp. Listeria ), Cartozobacter spp. Catonella ), *Streptococcus* genus ( Granulicatella ), genus Rombutz ( Romboutsia ), genus Turichiformis ( Turicibacter Bifidobacterium spp. Bifidobacterium ), Peptococcus ( Peptococcus Clostridium genus 1 ( Clostridium_sensu_stricto_1 ), Eikenella spp. Eikenella Flavobacterium ( Flavobacterium ), Enterobacteriaceae ( Enterobacter ), Bacillus spp. ( Paenibacillus ), Marine bacteria ( Idiomarina Akkermania ( ) Akkermansia ), Amaryl cocci ( Amaricoccus ), genus Roselle ( Roseburia ).

[0010] In some embodiments of the present invention, the substance for detecting the combination of microbial biomarkers includes reagents for detecting the combination of microbial biomarkers at the protein or gene level.

[0011] In some embodiments of the present invention, the reagents for detecting combinations of microbial biomarkers at the protein level are selected from reagents of one or more detection methods from the group consisting of: chemiluminescence, immunofluorescence, protein chip, proteometry, immunohistochemistry, patch tracing based on labeling technology, Western blotting, and enzyme-linked immunosorbent assay (ELISA).

[0012] In some embodiments of the present invention, the reagents for detecting combinations of microbial biomarkers at the gene level are selected from reagents of one or more detection methods from the group consisting of: high-throughput sequencing, digital PCR, and quantitative real-time PCR.

[0013] In some embodiments of the present invention, the product includes a test kit, a test chip, or a test strip.

[0014] In some embodiments of the present invention, the test sample for the product is adenoid tissue.

[0015] In some embodiments of the present invention, the test subjects of the product include children or adults.

[0016] A second aspect of the present invention provides a method for constructing a model for predicting adenoid hypertrophy, comprising the following steps: Models were constructed using the content of combinations of microbial biomarkers.

[0017] The microbial biomarker combination is the microbial biomarker combination described in the first aspect of this invention.

[0018] In some embodiments of the present invention, the model construction algorithm includes at least one of logistic regression, linear discriminant analysis, support vector machine, random forest, and recursive partitioning tree.

[0019] A third aspect of the invention provides a system for predicting adenoid hypertrophy, the system comprising a computational device for predicting adenoid hypertrophy based on the detection results of the content of a combination of microbial markers.

[0020] In some embodiments of the present invention, the detection results include protein level results, DNA or RNA level results.

[0021] In some embodiments of the present invention, the system further includes any one or more of the following: 1) Detection result collection device, also known as detection result input device, can specifically be one or more of the following: mouse, keyboard, touch screen display, one or more buttons, one or more switches, one or more triggers, etc. 2) Diagnostic result output device, also known as diagnostic result display device, can specifically be one or more of the following: liquid crystal display (LCD), light-emitting diode (LED) display, plasma display, projection display, touch screen display, etc. 3) Diagnostic result sending device, which can send the results of distinguishing whether the subject is in a strong or weak risk group to an information communication terminal device that can be viewed by the patient or medical staff.

[0022] The beneficial effects of this invention are: This invention, based on the relationship between different microbial colonization patterns and the severity of adenoid hypertrophy, utilizes abundance data of oral microbial biomarker combinations from different sample types to construct a model that can predict the severity of adenoid hypertrophy in patients. The predictive model constructed in this invention has an AUC value of 0.752, demonstrating good predictive performance. Combining model parameters and microbial characteristic information shows that the invented model is consistent with current clinical understanding, proving the reliability of the model. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein: Figure 1 The area under the curve (AUC) and 95% confidence intervals of the three models for predicting the severity of adenoid hypertrophy are shown in gray; red: AS samples; blue: AT samples; green: NS samples.

[0024] Figure 2 The AT model feature contribution distribution (SHAP value) shows the contribution of different microbial genera to the final predictive ability. Detailed Implementation

[0025] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0026] Example 1: Sample Collection This invention recruited a total of 276 children from the Guangzhou Women and Children's Medical Center affiliated with Guangzhou Medical University, including 231 patients and 45 healthy controls.

[0027] Inclusion criteria were: 1. Adenoid hypertrophy grade I-IV with inflammation; 2. Symptoms lasting at least 1 year; 3. Age 1-14 years; 4. On the waiting list for adenoidectomy and / or tonsillectomy. A total of 134 males and 97 females were included in the study, with a mean age of 76.9 ± 3.6 months.

[0028] Exclusion criteria were as follows: 1. Previous adenoidectomy / adenoidectomy; 2. Obstruction caused by structural lesions (such as posterior nasal atresia / septum or significant nasal septum deviation) or chronic sinusitis with nasal polyps; 3. Systemic diseases, including rheumatic diseases, autoimmune diseases, craniofacial malformation syndromes, or neuromuscular pathologies; 4. Concurrent infectious processes (purulent and non-purulent manifestations); 5. Severe asthma; 6. Morbid obesity (BMI ≥ 40 kg / m²); 7. Use of antibiotics within 15 days prior to enrollment; 8. Use of glucocorticoids within 30 days prior to the start of the study; 9. Informed consent could not be obtained.

[0029] The severity of adenoid hypertrophy is classified into four grades according to the Adenoid Hypertrophy Score (AHscore): Grade I: Tonsils are confined to the tonsillar fossa, with only a few lymphoid tissues visible behind the anterior palatine arch; Grade II: Tonsils extend significantly beyond the anterior palatine arch and are visible behind it; Grade III: Tonsils grow towards the midline, occupying three-quarters of the oropharyngeal cavity; Grade IV: Tonsils completely obstruct the airway. Grades I, II, and III are defined as mild, while Grade IV is defined as severe.

[0030] This study has been approved by the Institutional Review Committee of Guangzhou Women and Children's Medical Center, affiliated with Guangzhou Medical University (No.: 2022285B00). Written informed consent was obtained from the legal guardians / close relatives of all children participating in this study, authorizing the publication of any potentially identifiable images or data from this study.

[0031] After subtracting the excluded individuals, a total of 152 patients were identified. Three types of samples were collected from each patient and 16S rRNA sequencing was performed. These data were used for the methodological construction of this invention: adenoid surface sample AS (Adenoid Swab); adenoid tissue sample AT (Adenoid Tissue); and nasal swab sample NS (Nasal Swab).

[0032] The data collection method is as follows: AS sample: Wipe the surface of the adenoids with a sterile swab.

[0033] AT sample: A partial adenoid tissue was obtained through biopsy.

[0034] NS sample: Collected from the nasopharynx using a sterile nasal swab.

[0035] The data from these 152 patients were divided into 106 groups as the training dataset and 46 groups as independent validation datasets.

[0036] Example 2 Identification and Model Construction of Core Microbial Markers For 106 training set samples, LefSe analysis was used to select species with significant differences in distribution among the three groups, which were then used as feature variables for constructing the prediction model. The specific method is as follows: 1. DNA extraction and sequencing Genomic DNA was extracted from the samples, and a template-free negative control group (n = 5) was set up to exclude potential background DNA contamination. Total microbial genomic DNA was extracted using the Ezup oral swab genomic DNA extraction kit provided by Sangon Biotech, following the instructions.

[0037] The V3–V4 hypervariable region of bacterial 16S rRNA was amplified using universal primers: Forward primer 341F: CCTAGGGNGGCWGCAG (SEQ ID NO: 1); Reverse primer 805R: GACTACHVGGGTATCTAATCC (SEQ ID NO: 2).

[0038] The amplified 16S ribosomal RNA gene fragment was sequenced using the Illumina NovaSeq high-throughput sequencing platform, generating a 250 bp paired-end sequence readout.

[0039] 2. Raw16S rRNA sequencing data processing, noise reduction, and species annotation 1) The raw sequencing data first undergoes quality control processing: PhiX sequences are removed, and paired-end reads with Q values ​​≥ 20 are spliced ​​using QIIME2 software and the EasyAmplicon tool. Subsequently, the DADA2 method is used for denoising, which identifies unique sequences by removing sequence duplicates (equivalent to 100% similarity clustering). Each denoised, non-redundant sequence is called an ASV (Amplicon Sequence Variant), equivalent to a representative sequence of an OTU (Operational Taxonomic Unit).

[0040] 2) On the QIIME2 platform, a pre-trained Naive Bayes classifier from the classify-sklearn algorithm was used to annotate each ASV. Based on the ASV annotation results and the feature tables of each sample, taxonomic abundance tables were constructed at the kingdom, phylum, class, order, family, genus, and species levels. In subsequent analysis, genus-level taxa that cannot colonize humans and kitome-related microorganisms were removed. The abundance of each taxa was then analyzed; low-abundance species were excluded, and taxa with an abundance of at least 1% in at least 15% of the samples were retained.

[0041] 3. Feature Filtering 1) LefSe analysis: Intergroup difference analysis was performed using LefSe software; Setting the LDA score threshold to >2 and the P-value to <0.05 - filters out 116 genera with significant differences.

[0042] 2) Univariate regression analysis: Construct a generalized linear model for each different species; Species with a p-value < 0.2 were selected as the final feature.

[0043] 4. Specific markers (based on the SILVA classification database): The results of differential markers in different samples are shown in Table 1.

[0044] Table 1

[0045] 5. Predictive Model Construction This embodiment uses the Elastic Net Regression model to construct a binary classification prediction model. Elastic Net Regression combines the advantages of L1 regularization (Lasso) and L2 regularization (Ridge), ensuring model stability and having the ability to filter features when processing high-dimensional data.

[0046] The specific construction steps are as follows: Step 1: Data Acquisition and Preprocessing 1) Collect oral microbial samples from the subjects (AS, AT, and NS types); 2) Microbial community data were obtained using 16S rRNA sequencing technology; 3) Obtain relative abundance data of samples at the genus level; 4) Record the subject's gender and age information at the same time.

[0047] Step 2: Feature Filtering 1) Use LefSe analysis to screen for species with significant differences in distribution among different sample types; 2) Screening criteria: LDA score > 2, P-value < 0.05; 3) Construct a univariate generalized linear model for each selected species; 4) Select microbial genera that can differentiate the degree of AH (P-value < 0.2).

[0048] Step 3: Model Building 1) Use a resilient network regression model; 2) Parameter optimization was performed using 10-fold cross-validation; 3) Determine the final prediction model based on the minimum λ value; 4) Construct three independent binary regression models for the AS, AT, and NS classes of samples respectively.

[0049] Example 3: Validation of the marker model The predictive models for the AS, AT, and NS classes of samples constructed in Example 2 were used to validate the biomarker effects on the validation set. The 95% confidence interval of the AUC of the receiver operating characteristic (ROC) curve was estimated by repeatedly estimating the bootstrap method 2000 times.

[0050] The ROC curve results are as follows: Figure 1 As shown, the AUCs of the AS, NS, and AT sample models were 0.626, 0.632, and 0.752, respectively. The AT model's 95% confidence interval covered 0.6 to 0.895. Based on the Youden index, the optimal threshold for the model's predicted value (-0.324) was selected, resulting in a validation set sensitivity of 0.94, a specificity of 0.52, and an F1-score of 0.67, thus validating the model's stability.

[0051] SHAP analysis results are as follows Figure 2 As shown, in the AT sample model, *Roseburia* (g__Roseburia) has a negative SHAP value, which is associated with mild symptoms. This genus can produce short-chain fatty acids such as butyrate, which have anti-inflammatory properties. *Flavobacterium* (g__Flavobacterium) and *Massilia* (g__Massilia) have positive SHAP values, which are associated with severe symptoms. The distribution of SHAP values ​​among different genera is consistent with the physiological characteristics of microbial anti-inflammatory / pathogenic effects. Combined with model parameters and microbial characteristic information, this indicates that the invented model is consistent with current clinical understanding, thus improving the credibility of the invented model.

[0052] The specific parameters of the AT model are shown in Table 2.

[0053]

[0054] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof can be combined with each other unless otherwise specified.

Claims

1. Application of substances containing combinations of microbial markers in the preparation of products for predicting adenoid hypertrophy; The combination of microbial biomarkers includes: genus Roldstone Ralstonia ), Peptostreptococcus ( Peptostreptococcus ), Masseilles ( Massilia Listeria spp. Listeria ), Cartozobacter spp. Catonella ), *Streptococcus* spp. Granulicatella ), genus Rombutz ( Romboutsia ), genus Turichiformis ( Turicibacter Bifidobacterium spp. Bifidobacterium ), Peptococcus ( Peptococcus Clostridium genus 1 ( Clostridium_sensu_ stricto_1 ), Eikenella spp. Eikenella Flavobacterium ( Flavobacterium ), Enterobacteriaceae ( Enterobacter ), Bacillus spp. ( Paenibacillus ), Marine bacteria ( Idiomarina Akkermania ( ) Akkermansia ), Amaryl cocci ( Amaricoccus ), genus Roselle ( Roseburia One or more of the following.

2. The application according to claim 1, characterized in that: The microbial marker combination is of the genus *Rolstonia* (… Ralstonia ), Peptostreptococcus ( Peptostreptococcus ), Masseilles ( Massilia Listeria spp. Listeria ), Cartozobacter spp. Catonella ), *Streptococcus* spp. Granulicatella ), genus Rombutz ( Romboutsia ), genus Turichiformis ( Turicibacter Bifidobacterium spp. Bifidobacterium ), Peptococcus ( Peptococcus Clostridium genus 1 ( Clostridium_sensu_stricto_1 ), Eikenella spp. Eikenella Flavobacterium ( Flavobacterium ), Enterobacteriaceae ( Enterobacter ), Bacillus spp. ( Paenibacillus ), Marine bacteria ( Idiomarina Akkermania ( ) Akkermansia ), Amaryl cocci ( Amaricoccus ), genus Roselle ( Roseburia ).

3. The application according to claim 1, characterized in that: The substances used to detect combinations of microbial biomarkers include reagents for detecting combinations of microbial biomarkers at the protein or gene level.

4. The application according to claim 3, characterized in that: The reagents for detecting combinations of microbial biomarkers at the protein level are selected from reagents of one or more of the following detection methods: chemiluminescence, immunofluorescence, protein chip, proteometry, immunohistochemistry, labeling-based plaque tracing, Western blotting, and enzyme-linked immunosorbent assay (ELISA). The reagents for detecting combinations of microbial biomarkers at the gene level are selected from reagents of one or more detection methods from the group consisting of: high-throughput sequencing, digital PCR, and quantitative real-time PCR.

5. The application according to claim 1, characterized in that: The products include test kits, test chips, or test strips.

6. The application according to claim 1, characterized in that: The test sample for the product was adenoid tissue.

7. A method for constructing a model for predicting adenoid hypertrophy, comprising the following steps: Models were constructed using the content of combinations of microbial biomarkers; The microbial biomarker combination is the microbial biomarker combination according to any one of claims 1 to 6.

8. The construction method according to claim 7, characterized in that: The model construction algorithm includes at least one of logistic regression, linear discriminant analysis, support vector machine, random forest, and recursive partitioning tree.

9. A system for predicting adenoid hypertrophy, the system comprising a computational device for predicting adenoid hypertrophy based on the detection results of the content of a combination of microbial markers.

10. The system according to claim 9, characterized in that: The system further includes one or more of b1) to b3): b1) Device for collecting test results; b2) Diagnostic result output device; b3) Diagnostic result transmission device.

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