CN114410730A - Salivary microorganism-based thyroid cancer molecular marker and application thereof - Google Patents

Salivary microorganism-based thyroid cancer molecular marker and application thereof Download PDF

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CN114410730A
CN114410730A CN202210078774.0A CN202210078774A CN114410730A CN 114410730 A CN114410730 A CN 114410730A CN 202210078774 A CN202210078774 A CN 202210078774A CN 114410730 A CN114410730 A CN 114410730A
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prevotella
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严江伟
李万婷
程凤
张君
刘海燕
李彩红
张晓梦
漆小琴
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Shanxi Medical University
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Abstract

The invention provides a thyroid cancer molecular marker based on salivary microorganisms and application thereof, belonging to the field of microbial markers. The thyroid cancer microbial molecular marker is obtained by screening the genus of the difference in saliva samples of thyroid cancer patients and healthy contrast persons, and the accuracy of the thyroid cancer microbial molecular marker can reach 85.5% through verification and centralized verification of a logistic two-classification model, so that the thyroid cancer microbial molecular marker is proved to have higher accuracy and sensitivity. The detection method provided by the invention is mainly based on saliva samples, saliva collection is a non-invasive operation, is simple and feasible, is suitable for wide application, and provides a new thought and method for early diagnosis and detection of thyroid cancer.

Description

Salivary microorganism-based thyroid cancer molecular marker and application thereof
Technical Field
The invention relates to the field of microbial markers, in particular to a salivary microorganism-based thyroid cancer molecular marker and application thereof.
Background
Thyroid cancer (thyroid carcinoma) is a common thyroid malignancy, accounting for about 1% of all malignancies, and includes four pathological types, papillary, follicular, undifferentiated, and medullary. Among them, papillary carcinomas with low malignancy and good prognosis are most common, and most thyroid carcinomas other than medullary carcinomas originate from follicular epithelial cells. The vast majority of thyroid cancers occur in one lateral thyroid gland lobe, often as a single tumor.
The etiology of thyroid cancer includes: (1) iodine deficiency results in decreased thyroid hormone synthesis, increased Thyroid Stimulating Hormone (TSH) levels, stimulation of hypertrophy of thyroid follicular hyperplasia, development of goiter, and appearance of thyroid hormone, resulting in an increased incidence of thyroid cancer, while a high iodine diet may increase the incidence of papillary thyroid cancer. (2) Radiation causes a decrease in thyroxine synthesis, leading to carcinogenesis. (3) Increased serum TSH levels induce nodular goiter, and stimulation with mutagens and TSH induces follicular thyroid carcinoma. (4) Abnormal sex hormone secretion may lead to thyroid cancer. (5) Dietary thyroid-producing substances cause thyroid cancer, such as food or drugs like cassava, cabbage, thiouracil, thiocyanate, etc. (6) Genetic factors: about 5-10% of patients with medullary thyroid carcinoma have a significant family history and are inherited as autosomal dominant genes. At present, methods such as B-ultrasonic, nuclear scanning, CT and magnetic resonance imaging, thyroid biopsy and blood examination are mainly adopted for detecting thyroid cancer. But it has limited effect on early diagnosis and detection of thyroid cancer.
In the oral cavity of the human body, a large number of microorganisms exist, and these microorganisms play an important role in digestion, immunity, and the like of the host. Among them, the species of human salivary microbial bacteria can be over 600, and studies have reported that clostridium in oral cavity can reach colorectal tumor through blood, thereby accelerating colorectal cancer development. For example, patent CN201811137118.3 discloses a microbial population for predicting oral cancer risk and its application, the detection process is non-invasive, and the safety is high. The patent CN202110544282.1 discloses an oral cavity microorganism gene marker for noninvasive diagnosis of patients infected by novel coronavirus, which has good discriminative ability, feasibility and applicability.
However, the research on the relationship between thyroid cancer and oral microorganisms has not been reported clearly. In addition, the collection of the excrement sample can be limited by time and place, the collection of the saliva sample is more convenient, and the self-collection of the saliva sample can be realized for many times at any time and any place. Saliva samples are easier to transport and store, and the economic cost is relatively low. Therefore, it is highly desirable to provide a salivary microorganism-based thyroid cancer molecular marker, which provides a reference basis for detection and diagnosis of thyroid cancer.
Disclosure of Invention
Aiming at the defects, the invention provides a thyroid cancer molecular marker based on salivary microorganisms and application thereof. According to the invention, by comparing the difference of salivary microorganisms between a thyroid gland patient and a healthy contrast person, a thyroid cancer microorganism molecular marker is discovered for the first time, and the method has better detection accuracy, namely sensitivity.
In order to achieve the above object, the technical solution of the present invention is as follows:
in one aspect, the invention provides a microbial molecular marker for salivary thyroid cancer.
Specifically, the molecular markers include Proteus (Prevotella _7), Porphyromonas (Porphyromonas), Lactobacillus (Lactobacillales), Prevotella (Alloprovella), Porphyromonas (Selenomonas _3), Comamonas (Comamonas), Clostridium (Fusobarium), Rothia (Rothia), Corynebacterium (Corynebacterium), Campylobacter (Campylobacter), Streptococcus (Streptococcus), Gemenella (Gemelalla), Ruminococcus (Ruminococcaceae _ UCG-014), Mycoplasma (Leptotrichia) and Haemophilus (Haemophilus).
Further specifically, the relative abundance of the genera Prevotella (Prevotella _7), Porphyromonas (Porphyromonas), Lactobacillales (Lactobacillales), plausibilis (Alloprevotella), lunallomonas (Selenomonas _3), Comamonas (Comamonas) is remarkably increased; the relative abundance of the genus Clostridium (Fusobacterium), the genus Roxburgh (Rothia), the genus Corynebacterium (Corynebacterium), the genus Campylobacter (Campylobacter), the genus Streptococcus (Streptococcus), the genus Gemelalla (Gemelalla), the genus Ruminococcus (Ruminococcus _ UCG-014), the genus cilium (Leptotriia) and the genus Haemophilus (Haemophilus) was significantly reduced.
More specifically, the molecular marker is used for detecting the thyroid cancer risk, and the thyroid cancer risk is obtained by calculating the relative abundance of the genus:
the formula of the model is as follows:
Z＝2.4905400-0.8970850X1+4.8303135X2-0.3630332X3-1.6945272X4+0.2513430X5+0.3843337X6-0.4107600X7+0.4504628X8-0.7942644X9-0.2010625X10-0.3727216X11-0.2103645X12-1.2745899X13+0.9434769X14+1.3181818X15；
wherein, X1、X2、X3… to X15Respectively, the relative abundance of the genera Fusobacterium, Prevotella _7, Rothia, Corynebacterium, Porphyromonas, Lactobacillus, Haemophilus, Alloprovella, Camphylobacter, Streptococcus, Gemella, Ruminococcaceae _ UCG-014, Leptotrichia, Selenomonas _3, and Comamonas;
the second formula of the model is as follows:
Figure BDA0003485215880000031
substituting the obtained Z value into a formula II, wherein e represents a natural constant, and-Z is an index of the natural constant, and obtaining a y value, if y is more than 0.5, the patient is predicted to be thyroid cancer, and if y is less than or equal to 0.5, the patient is predicted to be a healthy control.
In another aspect, the invention provides an application of the above molecular marker in preparing a tool for detecting thyroid cancer.
In another aspect, the invention provides the use of the reagent for detecting the above molecular marker in the preparation of a tool for detecting thyroid cancer.
Specifically, the reagent for detecting the molecular marker comprises a primer for amplifying the region 16S V3-V4 of the molecular marker.
More specifically, the primers are 341F and 806R, the sequence of 341F is shown as SEQ ID NO. 1, and the sequence of 806R is shown as SEQ ID NO. 2.
In still another aspect, the present invention provides a tool for detecting thyroid cancer, which comprises a reagent for detecting the above molecular marker.
Specifically, the tool is an independent reagent or a kit.
Specifically, the reagent for detecting the molecular marker comprises a primer for amplifying the region 16S V3-V4 of the molecular marker.
More specifically, the primers are 341F and 806R, the sequence of 341F is shown as SEQ ID NO. 1, and the sequence of 806R is shown as SEQ ID NO. 2.
In another aspect, the present invention provides a method for building the above molecular marker model, the method comprising the following steps:
(1) extracting the total genomic DNA of saliva samples of thyroid cancer and healthy controls respectively;
(2) constructing a gene library of a salivary flora 16SV3-V4 region of the total genomic DNA extracted in the step (1);
(3) sequencing the gene library constructed in the step (2);
(4) and (4) processing the sequencing data in the step (3) to obtain a molecular marker model.
Specifically, the construction of the gene library of the salivary flora 16SV3-V4 region in the step (2) comprises the following steps: carrying out PCR amplification by using a specific primer; purifying PCR products; obtaining a library; purifying the library; and (5) performing quality inspection on the library.
Specifically, the data processing step in the step (4) is to splice sequencing data, filter to remove redundant sequences, perform OTU clustering and species annotation, analyze species abundance and species diversity in saliva samples of thyroid cancer and healthy controls, and screen to obtain the molecular markers.
Compared with the prior art, the invention has the advantages that:
(1) the thyroid cancer microbial molecular marker is obtained by screening the genus of the difference in saliva samples of thyroid cancer patients and healthy contrast persons, and the accuracy of the thyroid cancer microbial molecular marker can reach 85.5% through verification and centralized verification of a logistic two-classification model, so that the thyroid cancer microbial molecular marker is proved to have higher accuracy and sensitivity.
(2) The detection method is mainly based on saliva samples, saliva collection is non-invasive and non-invasive, simple and feasible, and the method is suitable for wide application.
(3) The salivary thyroid cancer microbial molecular marker provides a new idea and a new method for early diagnosis and detection of thyroid cancer.
Drawings
FIG. 1 is a schematic diagram of a model validation ROC curve.
Detailed Description
The present invention will be further illustrated in detail with reference to the following specific examples, which are not intended to limit the present invention but are merely illustrative thereof. The experimental methods used in the following examples are not specifically described, and the materials, reagents and the like used in the following examples are generally commercially available under the usual conditions without specific descriptions.
Example 1 establishment and verification of microbial molecular markers for salivary thyroid cancer
1. Sample collection
Saliva samples were collected from 144 thyroid cancer and 47 healthy controls, and a saliva collection tube was placed on the lower lip with the lips slightly open to collect approximately 1mL of saliva naturally secreted in an unstimulated state. The sample information is specifically shown in table 1 below.
TABLE 1
Figure BDA0003485215880000041
Figure BDA0003485215880000051
DNA extraction
DNA was extracted using DNeasy PowrSoil Kit with a saliva sample input of 600. mu.L, an eluent volume of 100. mu.L and an extracted DNA concentration ranging from 10 ng/. mu.L to 60 ng/. mu.L.
3. Library construction
Two rounds of PCR amplification were used to amplify the bacterial 16S V3-V4 region and to ligate the tag sequence and sequencing adapter. The upstream primer of PCR amplification is 341F-5 '-CCTACGGGNG-GCWGCAG-3' (SEQ ID NO:1), and the downstream primer is 806R-5 '-GGACTACHVGGGTWTCTAAT-3' (SEQ ID NO: 2).
The system for the first round of PCR amplification is shown in table 2 below:
TABLE 2
Reagent composition Volume (μ L)
2mM dNTPs 2.5
10×PCR Buffer 2.5
25mM MgSO4 1.5
KOD-Plus-Neo 0.5
RNase-Free ddH2O 11
341F 1
806R 1
DNA 5
Total 25
The first round of PCR amplification conditions are shown in table 3 below:
TABLE 3
Figure BDA0003485215880000052
The second round PCR amplification system is shown in table 4 below:
TABLE 4
Reagent composition Volume (μ L)
2mM dNTPs 5
10×PCR Buffer 5
25mM MgSO4 3
KOD-Plus-Neo 1
RNase-Free ddH2O 28
F(SEQ ID NO:3) 2
R(SEQ ID NO:4) 2
DNA 4
Total 50
The second round of PCR amplification conditions are shown in table 5 below:
TABLE 5
Figure BDA0003485215880000061
4. Library purification
And (3) purifying the library on a magnetic frame by using an AMPure XP magnetic bead reagent, wherein the input amount of the magnetic beads and the input amount of DNA are equal to 1: 1, magnetic beads and DNA binding time about 15min, using 80% ethanol purification of DNA twice, 80% ethanol aspiration to discard, finally adding 25 u L water DNA elution.
5. Library quantification
The purified library was quantified using a KAPA library quantification kit by diluting the library to 15 ng/. mu.L and 10000-fold diluting the library with 0.05% Tween under the QPCR cycling conditions shown in Table 6 below:
TABLE 6
Figure BDA0003485215880000062
6. Mixed libraries
The libraries after QPCR quantification were diluted to 6nM, 10nM, 20nM and 30nM by concentration and the same concentration of library was mixed in equal volumes and the mixed library was subjected to additional QPCR quantification.
7. Library dilution
The library is diluted to 4nmol/L, and the same volume is added into an EP tube to be mixed into one tube, the tube is fully shaken and centrifuged, 7 mu L of mixed library is taken and added into 3 mu L of balanced library to form a total library. Mixing the total library with equal volume of 0.2N NaOH for denaturation, and taking HT from Miseq Reagent V3 kit1The library was diluted in solution to 12pmol/L and 600. mu.L of the total library was added to well 17 of the sequencing kit for sequencing.
8. Screening for different genera
The bacterial flora is classified as: kingdom, phylum, class, order, family, genus and species. The 16S rDNA sequencing obtains biological results, and the results are more accurate when species are annotated to the genus level, so the method is carried out at the genus level of bacteria.
And (5) performing quality control on the sequencing by utilizing qiime software, wherein the sequencing quality is qualified when the cluster passing rate is more than 85%. Performing double-end sequence splicing on a double-end sequence of the offline data by using a seqprep method in qiime software; the vsearch method (https:// github. com/torogens/vsearch) removes redundant sequences (low abundance noise) and the filtering threshold is set at 8. Sequences with similarity greater than 97% are classified into 1 species, and compared with a silvera database (https:// www.arb-silvera. de /), species annotation is performed to obtain the genus information of each sample.
The edgeR package in the R software (version 4.1.1) was used to compare the genus difficile between 144 thyroid cancer patients and 47 healthy controls. FC, fold change, log2FC, indicates that the ratio of a certain target expression level between two groups is up-regulation or down-regulation, and log2FC is base 2 logarithm. CPM is counts per million, and logCPM is used for measuring the expression of a certain target. FDR, i.e., False Discovery Rate (False Discovery Rate), corrects for a significant difference in p-value. Selection of p <0.05 and FDR <0.05 as screening criteria.
And performing lasso regression by using a glmnet packet in the R, setting the random seed value as 66, selecting lambda.1se as a parameter in the fit function, and screening out 15 difficiles. Relative abundance refers to the ratio of the number of sequences measured by the bacterium to the total number of sequences measured in the sample. Thyroid cancer patients had significantly increased relative abundance of Prevotella (Prevotella _7), Porphyromonas (Porphyromonas), Lactobacillales (Lactobacillales), bacteroides (Alloprevotella), Selenomonas (Selenomonas _3), Comamonas (Comamonas), clostridium (Fusobacterium), rhodinium (Rothia), Corynebacterium (Corynebacterium), Campylobacter (Campylobacter), Streptococcus (Streptococcus), gemococcus (Gemella), ruminococcus (ruminococcae _ UCG-014), cilium (Leptotrichia), Haemophilus (Haemophilus) in saliva compared to healthy controls, and the results were significantly decreased as shown in table 7 below.
TABLE 7 genus Difference in saliva samples from thyroid cancer patients and healthy controls
Genus of genus Bacillus log2FC log2CPM P value FDR level
Fusobacterium -0.995 14.41 4.69E-11 1.52E-09 Reduce
Prevotella_7 1.191 10.652 8.23E-06 7.13E-05 Is raised
Rothia -1.141 13.088 4.05E-05 0.000277 Reduce
Corynebacterium -0.669 10.485 0.004855 0.01661 Reduce
Porphyromonas 0.554 14.757 0.032743 0.085132 Is raised
Lactobacillales 0.898 12.962 0.002569 0.009278 Is raised
Alloprevotella 0.978 13.403 0.000196 0.00091 Is raised
Campylobacter -0.553 12.308 0.008298 0.02697 Reduce
Streptococcus -1.458 12.365 2.15E-05 0.000164 Reduce
Gemella -1.291 12.505 3.28E-08 6.09E-07 Reduce
Ruminococcaceae_UCG-014 -0.61 11.556 0.016428 0.045438 Reduce
Leptotrichia -1.424 10.214 1.32E-06 1.32E-05 Reduce
Haemophilus -2.821 12.55 5.99E-18 7.79E-16 Reduce
Selenomonas_3 1.122 11.656 6.06E-05 0.000375 Is raised
Comamonas 0.992 10.262 0.010127 0.030617 Is raised
9. Model construction
The logistic regression binary classification model was constructed using the glm package in R, using the fit function with parameters set to lambda.1se, to obtain the regression coefficients and constant terms for the 15 Difference genera described above.
The formula of the model is as follows:
Z＝2.4905400-0.8970850X1+4.8303135X2-0.3630332X3-1.6945272X4+0.2513430X5+0.3843337X6-0.4107600X7+0.4504628X8-0.7942644X9-0.2010625X10-0.3727216X11-0.2103645X12-1.2745899X13+0.9434769X14+1.3181818X15；
wherein, X1、X2、X3… to X15Respectively, the relative abundance of the genera Fusobacterium, Prevotella _7, Rothia, Corynebacterium, Porphyromonas, Lactobacillus, Haemophilus, Alloprovella, Camphylobacter, Streptococcus, Gemella, Ruminococcaceae _ UCG-014, Leptotrichia, Selenomonas _3, and Comamonas.
The second formula of the model is as follows:
Figure BDA0003485215880000081
substituting the obtained Z value into a formula II, wherein e represents a natural constant, and-Z is an index of the natural constant, and obtaining a y value, if y is more than 0.5, the patient is predicted to be thyroid cancer, and if y is less than or equal to 0.5, the patient is predicted to be a healthy control.
10. Model validation
And adopting AUC area under the ROC curve as an index for evaluating the performance of the model. Abundance information of 44 thyroid cancer patients and 15 healthy control genera was selected as a validation set and substituted into the model constructed above, and the AUC value was 85.5%, i.e., the accuracy of the model in the validation set was 85.5%, as shown in the ROC curve of fig. 1.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
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Claims (10)

1. A microbial molecular marker for salivary thyroid cancer, which is characterized in that: the molecular marker comprises Proteus, Porphyromonas, Lactobacillus, Prevotella, Oenomonas, Comamonas, Clostridium, Rogomyces, Corynebacterium, Campylobacter, Streptococcus, twin coccus, rumenococcus, cilium and Haemophilus.
2. The molecular marker of claim 1, wherein: the relative abundance of the genera prevotella, porphyromonas, lactobacillales, Prevotella, Oenomonas and Comamonas is obviously increased; the relative abundance of the clostridium, the roche, the corynebacterium, the campylobacter, the streptococcus, the twinborn coccus, the ruminococcus, the cilium and the haemophilus is obviously reduced.
3. The molecular marker of claim 2, wherein: the molecular marker is used for detecting the risk of thyroid cancer.
4. A molecular marker according to claim 3, characterized in that: the thyroid cancer risk is obtained by calculating the relative abundance of the genus:
the formula of the model is as follows:
Z＝2.4905400-0.8970850X1+4.8303135X2-0.3630332X3-1.6945272X4+0.2513430X5+0.3843337X6-0.4107600X7+0.4504628X8-0.7942644X9-0.2010625X10-0.3727216X11-0.2103645X12-1.2745899X13+0.9434769X14+1.3181818X15；
wherein, X1、X2、X3… to X15Respectively, the relative abundance of the genera Fusobacterium, Prevotella _7, Rothia, Corynebacterium, Porphyromonas, Lactobacillus, Haemophilus, Alloprovella, Camphylobacter, Streptococcus, Gemella, Ruminococcaceae _ UCG-014, Leptotrichia, Selenomonas _3, and Comamonas;
the second formula of the model is as follows:
Figure FDA0003485215870000011
substituting the Z value obtained by the calculation of the formula I into a formula II, wherein e represents a natural constant, and-Z is an index of the natural constant, and obtaining a y value, if y is more than 0.5, the thyroid cancer patient is predicted, and if y is less than or equal to 0.5, the thyroid cancer patient is predicted to be a healthy contrast person.
5. Use of a molecular marker according to any of claims 1 to 4 for the preparation of a tool for the detection of thyroid cancer.
6. Use of a reagent for detecting a marker according to any one of claims 1 to 4 in the preparation of a tool for detecting thyroid cancer.
7. A thyroid cancer detection tool, comprising: the means comprises reagents for detecting the molecular marker of any one of claims 1 to 4.
8. The tool of claim 7, wherein: the reagent for detecting the molecular marker of any one of claims 1 to 4 comprises a primer for amplifying the region 16S V3-V4 of the molecular marker.
9. The tool of claim 8, wherein: the primers are 341F and 806R, the sequence of 341F is shown as SEQ ID NO. 1, and the sequence of 806R is shown as SEQ ID NO. 2.
10. A method for establishing a molecular marker model according to any one of claims 1 to 4, wherein: the method comprises the following steps:
(1) extracting the total genomic DNA of saliva samples of thyroid cancer and healthy controls respectively;
(2) constructing a gene library of a salivary flora 16SV3-V4 region of the total genomic DNA extracted in the step (1);
(3) sequencing the gene library constructed in the step (2);
(4) and (4) processing the sequencing data in the step (3) to obtain a molecular marker model.
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Publication number Priority date Publication date Assignee Title
US20110275065A1 (en) * 2010-05-07 2011-11-10 Ranju Ralhan Methods and compositions for the diagnosis and treatment of thyroid cancer
CN104007165A (en) * 2014-05-29 2014-08-27 深圳市第二人民医院 Saliva proteome decision-making tree diagnosis model for screening thyroid cancer, and construction method of tree
CN110878358A (en) * 2019-12-19 2020-03-13 上海宝藤生物医药科技股份有限公司 Thyroid cancer markers and application thereof
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