Intestinal microbial marker for distinguishing biliary duct cell carcinoma patient from healthy person and application of intestinal microbial marker

Abstract

The invention belongs to the technical field of biological medicines, and particularly relates to an intestinal microbial marker for distinguishing a bile duct cell carcinoma patient from a healthy person and application of the intestinal microbial marker. The invention provides an enteric microbiological marker for distinguishing a bile duct cell carcinoma patient from a healthy person. The enteric microbiological marker consists of 23 enteric microbiological strains. The intestinal microbial marker disclosed by the invention realizes good distinguishing ability in cholangiocellular carcinoma and healthy people. And the feasibility and applicability of the distinguishing model are verified in cross-regional samples in China. In addition, the distinguishing model achieves good disease specificity in a common data set queue of 11 diseases from the world.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to an intestinal microbial marker for distinguishing cholangiocarcinoma patients from healthy people and an application thereof. Background Art

[0002] Cholangiocarcinoma (CCA) is the second most common primary liver cancer, accounting for up to 20%. The incidence and mortality of cholangiocarcinoma have been on the rise in recent years. CCA is sporadic, and there are regional differences in risk factors. In Southeast Asia, common risk factors include liver flukes and chronic typhoid carriers and chronic bile duct stones, as well as common bile duct cysts and Caroli's disease (intrahepatic bile duct cysts). In Western countries, the most important risk factor is primary sclerosing cholangitis, and there are other risk factors, including radiation, hepatitis B virus or hepatitis C virus infection and cirrhosis. However, most patients diagnosed with CCA have no obvious predisposing factors. Due to the lack of effective diagnostic methods and specific clinical symptoms in early CCA patients, most CCA patients are diagnosed in the late stage. Surgical resection is considered the only possible cure, but only 20-30% of CCA patients can undergo this surgery, and the postoperative recurrence rate is still high (49%-64%). The prognosis of CCA patients is still poor. Therefore, finding new potential therapeutic targets and establishing non-invasive diagnostic tools are of great significance to improving the prognosis of CCA patients.

[0003] The intestinal flora has been shown to be involved in the occurrence and development of various hepatobiliary diseases. The "biliary-enteric circulation" and "liver-intestine" axes are closely related to hepatobiliary and intestinal functions. The liver transports bile salts and antimicrobial molecules (IgA and angiogenin) to the intestine through the bile duct to control bacterial overgrowth and maintain intestinal microbial balance. Bile salts, as important signaling molecules, regulate hepatic bile acid synthesis, sugar metabolism, lipid metabolism, dietary energy utilization and other processes through receptors such as FXR and GPBAR1. In addition, intestinal products such as host or microbial metabolites, microbial-associated molecular patterns, etc. can be transported to the liver through the portal vein to affect liver function. In addition, liver metabolites transported to the intestine through the capillary system can affect the intestinal barrier. Therefore, intestinal microbial dysbiosis can promote the occurrence and development of liver diseases. On the other hand, studies have shown that intestinal microbial dysbiosis is a common feature of different independent risk factors for CCA (primary sclerosing cholangitis, liver fluke infection, gallstones, etc.). Therefore, revealing the role of intestinal microbiota in the occurrence and development of CCA is of great clinical significance for the precise prevention and treatment of CCA.

[0004] Cholangiocarcinoma is difficult to diagnose due to its anatomical location, growth pattern, and lack of clear diagnostic criteria. Although new diagnostic biomarkers are frequently discovered and reported, there are still many limitations. Liver biopsy is the gold standard for diagnosing cholangiocarcinoma, but it is an invasive procedure and is sometimes not possible. Therefore, there is an urgent need to find new, non-invasive diagnostic biomarkers for cholangiocarcinoma. The gut microbiome is used as a diagnostic biomarker for many diseases, including colorectal cancer, type 2 diabetes, and autoimmune hepatitis. In our previous study, we constructed a diagnostic model for hepatocellular carcinoma based on 30 gut microbial markers and achieved cross-regional validation in different regions of China. Although some small sample studies have reported diagnostic models for cholangiocarcinoma based on the gut microbiome, these models have certain limitations. A reliable microbial diagnostic model based on large-scale gut microbiome data from multiple regions is a major scientific issue that needs to be addressed. Summary of the invention

[0005] The present invention provides an intestinal microbial marker for distinguishing cholangiocarcinoma patients from healthy people, which is composed of 23 intestinal microbial species.

[0006] Among them, 9 species were significantly enriched in the intestines of patients with cholangiocarcinoma:

[0007] 1. Clostridium symbiosum

[0008] 2. Hungatella hathewayi

[0009] 3. Flavonifractor plautii

[0010] 4. Prevotella stercorea

[0011] 5. Veillonella atypica

[0012] 6.Bacteroides thetaiotaomicron

[0013] 7. Clostridium citroniae

[0014] 8. Bacteroides vulgatus

[0015] 9. Veillonella parvula

[0016] Fourteen bacterial species were significantly reduced in the intestines of patients with cholangiocarcinoma:

[0017] 1. Bifidobacterium longum

[0018] 2.Bifidobacterium adolescentis

[0019] 3. Actinomyces oris

[0020] 4. Dorea formicigenerans

[0021] 5. Dorea longicatena

[0022] 6. Eubacterium hallii

[0023] 7. Romboutsia ilealis

[0024] 8. Coprococcus catus

[0025] 9. Blautia wexlerae

[0026] 10. Intestinibacter bartlettii

[0027] 11. Roseburia sp CAG 471

[0028] 12.Anaerostipes hadrus

[0029] 13.Collinsella aerofaciens

[0030] 14.Fusicatenibacter saccharivorans

[0031] The present invention provides the use of intestinal microbial markers composed of the above 23 intestinal microbial species in the preparation of a detection product for distinguishing cholangiocarcinoma from healthy people, and the use of a substance for detecting intestinal microbial markers of 23 intestinal microbial species in the preparation of a detection product for distinguishing cholangiocarcinoma from healthy people.

[0032] The product includes reagents, devices and / or equipment for detecting the relative abundance and / or quantity of biomarkers in a sample to be tested.

[0033] The feces of the subject is tested to determine whether the sample contains the microbial species and whether a microbial model can be established to distinguish between cholangiocarcinoma patients and healthy people.

[0034] By collecting fecal samples from the subjects, extracting total microbial DNA, completing metagenomic sequencing of the microbial DNA, and detecting whether the 23 microbial species described in claim 1 are present.

[0035] Furthermore, stool samples were collected from the subjects, total microbial DNA was extracted, and metagenomic sequencing of the stool flora was performed. Based on high-throughput sequencing data, a microbial differentiation model between cholangiocarcinoma and healthy people was established and verified.

[0036] Specifically include:

[0037] (1) Fecal samples were collected from the subjects (cholangiocarcinoma patients and healthy people), and the total microbial DNA in the fecal samples was extracted according to the standard DNA extraction method. The metagenomic sequencing of the intestinal flora was completed on the Illumina NovaSeq 6000 platform.

[0038] (2) Based on the high-throughput sequencing data of the discovery set (East China—Hangzhou), 23 optimal microbial markers for this model were identified based on a random forest model among 51 cases of cholangiocarcinoma and 105 healthy subjects through a five-fold cross-validation algorithm.

[0039] (3) Based on 23 microbial markers, the microbial discrimination model had a discrimination ability of 98.95% between 51 cholangiocarcinoma patients and 105 healthy subjects.

[0040] (4) In the validation set (East China—Hangzhou), the microbial discrimination model had a discrimination ability of 99.89% between 21 cholangiocarcinoma patients and 45 healthy controls.

[0041] (5) To further determine the universality of the model in China, in the cross-regional validation set (Central Plains - Zhengzhou), the microbial discrimination model had a discrimination ability of 84.32% between 70 cholangiocarcinoma patients and 84 healthy controls.

[0042] (6) In the cross-regional validation set (Northwest China-Xinjiang), the microbial discrimination model had a discrimination ability of 79.37% between 30 patients with cholangiocarcinoma and 37 healthy controls.

[0043] (7) To further determine the disease specificity of the model worldwide, data from 31 studies covering 6,847 samples of 11 disease states were downloaded from the public database of gut microbial metagenomics, including colorectal cancer (CRC, n = 628), Crohn's disease (CD, n = 404), type 2 diabetes (T2D, n = 382), ulcerative colitis (UC, n = 372), liver disease (LD, n = 239), atherosclerotic coronary artery disease (ACD, n = 214), pancreatic cancer (PC, n = 144), type 1 diabetes (T1D, n = 91), breast cancer (BRCA, n = 62), chronic pancreatitis (CP, n = 29), pancreatitis (n = 29), and healthy controls (CTR, n = 4253). The results showed that the average false positive rate of all samples in the public dataset was 0.19. Except for breast cancer (0.5), Crohn's disease (0.48), and atherosclerotic coronary artery disease (0.37), the false-positive rates of other diseases were low.

[0044] In addition, the present invention also provides a detection product for distinguishing cholangiocarcinoma and healthy people, and the product includes a substance for detecting the intestinal microbial markers according to claim 1.

[0045] The product includes reagents, devices and / or equipment for detecting the relative abundance and / or quantity of biomarkers in a sample to be tested.

[0046] The intestinal microbial markers of the present invention have achieved good discrimination ability between cholangiocarcinoma and healthy people. The feasibility and applicability of the discrimination model have also been verified in cross-regional samples in China. In addition, the discrimination model has achieved good disease specificity in public datasets of 11 diseases from around the world. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 .Study design and application of a gut microbiome model for distinguishing cholangiocarcinoma from healthy subjects.

[0048] Figure 2 .The best intestinal microbial gene markers were identified based on the random forest model using the five-fold cross-validation method.

[0049] Figure 3 .The discriminatory efficacy of the microbial species discrimination model was achieved in a cohort of 51 cholangiocarcinoma cases and 105 healthy subjects in the discovery set (East China—Hangzhou).

[0050] Figure 4 .The discriminatory efficacy of the microbial species discrimination model was achieved in a validation set (East China—Hangzhou) of 21 cases of cholangiocarcinoma and 45 healthy subjects.

[0051] Figure 5 .The discrimination efficiency achieved by the microbial species discrimination model in a cross-regional validation set (Central Plains - Zhengzhou) of 70 cases of cholangiocarcinoma and 84 healthy people.

[0052] Figure 6 .The discriminatory efficacy of the microbial species discrimination model was achieved in a cross-regional validation set (Northwest China-Xinjiang) of 30 cases of cholangiocarcinoma and 37 healthy people.

[0053] Figure 7 Model disease specificity achieved by the microbial species-discriminatory model in a worldwide external validation cohort downloaded from public databases. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with embodiments, but the protection content of the present invention is not limited to these embodiments.

[0055] The methods used in the following examples are all conventional methods unless otherwise specified. The materials or reagents required in the following examples are all obtained from public commercial channels unless otherwise specified.

[0056] The present invention collects fecal samples from the subjects, extracts total microbial DNA, and performs metagenomic sequencing of the intestinal flora. Based on high-throughput sequencing data, a microbial differentiation model between cholangiocarcinoma and healthy people is established in the training set and verified in the validation set.

[0057] The operation steps are as follows:

[0058] (1) According to the design principles of prospective clinical trials, the research design of the present invention is as follows Figure 1 The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University and the First Affiliated Hospital of Zhejiang University School of Medicine. All enrolled patients signed the informed consent for the study protocol and the informed consent for clinical sample collection.

[0059] (2) Each enrolled cholangiocarcinoma patient and healthy person provided a fresh stool sample. The researchers divided the sample into 10 portions of 500 μg each and immediately stored them in a -80°C refrigerator. The extraction method of fecal bacterial total DNA was carried out according to the instructions of the kit.

[0060] (3) Complete the amplification of fecal bacterial total DNA samples and the construction of DNA libraries, and complete metagenomic sequencing on the Illumina NovaSeq 6000 sequencing platform. All output sequences complete basic preprocessing and basic bioinformatics analysis.

[0061] (4) The raw reads were quality trimmed using fastp (v0.19.7) and filtered against the human genome (hg19) using Bowtie2 (v2.3.4). After quality trimming and filtering, an average of 76.5 million reads per sample were retained and used for downstream analysis.

[0062] (5) The Wilcoxon test method and linear discriminant analysis effect size (LEfSe) were used to identify differential microbial species between groups. (6) In the training set of the microbial discrimination model, including 51 patients with cholangiocarcinoma and 105 healthy people, the screened species abundance files were used to screen microbial gene markers in a random forest model (R software 3.4.1 and random forest package 4.6–12) using a five-fold cross-validation algorithm (except for setting "importance = TRUE", the software parameters were default). Five trials of five-fold cross-validation were used to obtain a cross-validation error curve, in which the minimum cross-validation error point was used as the cut-off value. The minimum cross-validation error value plus the standard deviation of the corresponding value was the cut-off value. A set of less than 23 species markers with an error rate less than the cut-off value was screened out, and the set with the minimum number of species was selected as the best set of microbial gene markers. Finally, the best 23 microbial species markers for this model were identified ( Figure 2 ).

[0063] Preferably, 9 bacterial species were significantly enriched in the intestines of patients with cholangiocarcinoma:

[0064] 1. Clostridium symbiosum

[0065] 2. Hungatella hathewayi

[0066] 3. Flavonifractor plautii

[0067] 4. Prevotella stercorea

[0068] 5. Veillonella atypica

[0069] 6.Bacteroides thetaiotaomicron

[0070] 7. Clostridium citroniae

[0071] 8. Bacteroides vulgatus

[0072] 9. Veillonella parvula

[0073] Fourteen bacterial species were significantly reduced in the intestines of patients with cholangiocarcinoma:

[0074] 1. Bifidobacterium longum

[0075] 2.Bifidobacterium adolescentis

[0076] 3. Actinomyces oris

[0077] 4. Dorea formicigenerans

[0078] 5. Dorea longicatena

[0079] 6. Eubacterium hallii

[0080] 7. Romboutsia ilealis

[0081] 8. Coprococcus catus

[0082] 9. Blautia wexlerae

[0083] 10. Intestinibacter bartlettii

[0084] 11. Roseburia sp CAG 471

[0085] 12.Anaerostipes hadrus

[0086] 13.Collinsella aerofaciens

[0087] 14.Fusicatenibacter saccharivorans

[0088] (7) The receiver operating curve (ROC) was calculated using the pROC tool in the R 3.3.0 package to evaluate the microbial discrimination model, and the area under the curve (AUC) was used to specify the effect size of the ROC.

[0089] (8) Based on the samples of the discovery set (East China—Hangzhou), the microbial discrimination model had a discrimination ability of 98.95% between 51 cases of cholangiocarcinoma and 105 healthy subjects ( Figure 3 ).

[0090] (9) Based on the samples of the validation set (East China—Hangzhou), the microbial discrimination model achieved a discrimination ability of 99.89% between 21 cholangiocarcinoma patients and 45 healthy controls ( Figure 4 ).

[0091] (10) In the cross-regional validation set (Zhongyuan region-Zhengzhou), the microbial discrimination model had a discrimination ability of 84.32% between 70 cholangiocarcinoma patients and 84 healthy controls ( Figure 5 ).

[0092] (11) In the cross-regional validation set (Northwest China-Xinjiang), the microbial discrimination model had a discrimination ability of 79.37% between 30 cholangiocarcinoma patients and 37 healthy controls ( Figure 6 ).

[0093] (12) Publicly available fecal metagenomic datasets were screened from published case-control or cohort studies in public databases, and data from 31 studies were obtained. Raw sequencing data were downloaded from the European Nucleotide Archive and processed and analyzed in the same way as above. Each external dataset was then evaluated using a trained CCA metagenomic classifier. The false positive rate (FPR) was used as the evaluation metric.

[0094] (13) Data from 31 studies covering 6,847 samples from 11 disease states were downloaded from the public database of gut microbial metagenomics, including colorectal cancer (CRC, n = 628), Crohn's disease (CD, n = 404), type 2 diabetes (T2D, n = 382), ulcerative colitis (UC, n = 372), liver disease (LD, n = 239), atherosclerotic coronary artery disease (ACD, n = 214), pancreatic cancer (PC, n = 144), type 1 diabetes (T1D, n = 91), breast cancer (BRCA, n = 62), chronic pancreatitis (CP, n = 29), pancreatitis (n = 29), and healthy controls (CTR, n = 4253). The results showed that the average false positive rate of all samples in the public dataset was 0.19. Except for breast cancer (0.5), Crohn's disease (0.48), and atherosclerotic coronary artery disease (0.37), the false positive rates of other diseases were low ( Figure 7 ).

[0095] Therefore, the microbial gene discrimination model of the present invention achieves good discrimination ability between cholangiocarcinoma and healthy people. The feasibility and applicability of the discrimination model were also verified in cross-regional samples within China. In addition, the discrimination model achieved good disease specificity in public dataset cohorts of 11 diseases from around the world.

Claims

1. An intestinal microbial marker for distinguishing cholangiocarcinoma patients from healthy people, consisting of the following 23 intestinal microbial species: Clostridium symbiosum, Hungatella hathewayi, Flavonifractorplautii, Prevotellastercorea, Veillonella atypica, Bacteroides thetaiotaomicron, Clostridium citoniae, Bacteroidesvulgatus, Veillonella parvula, Bifidobacteriumlongum, Bifidobacterium adolescentis, Actinomyces oris, Dorea formicigenerans, Dorea longicatena, Eubacterium hallii, Romboutsiailealis, Coprococcus catus, Blautia wexlerae, Intestinibacter bartlettii, Roseburia sp CAG 471, Anaerostipeshadrus, Collinsella aerofaciens, Fusicatenibacter saccharivorans.

2. The intestinal microbial marker according to claim 1, characterized in that: The following 9 bacterial species were significantly enriched in the intestines of patients with cholangiocarcinoma: Clostridium symbiosum, Hungatella hathewayi, Flavonifractorplautii, Prevotella stercorea, Veillonella atypica, Bacteroides thetaiotaomicron, Clostridium citroniae, Bacteroides vulgatus, Veillonella parvula; the following 14 bacterial species were significantly reduced in the intestines of patients with cholangiocarcinoma: Bifidobacterium longum, Bifidobacterium adolescentis, Actinomyces oris, Doreaformicigenerans, Dorealongicatena, Eubacterium hallii, Romboutsia ilealis, Coprococcus catus, Blautiawexlerae, Intestinibacter bartlettii, Roseburia sp CAG 471, Anaerostipes hadrus, Collinsella aerofaciens, Fusicatenibacter saccharivorans.

3. Use of a substance for detecting the intestinal microbial marker of claim 1 in preparing a detection product for distinguishing cholangiocarcinoma from healthy people.

4. The use according to claim 3, characterized in that: The product includes reagents, devices and / or equipment for detecting the relative abundance and / or quantity of biomarkers in a sample to be tested.

5. The use according to claim 3, characterized in that: The product detects feces of a subject to be tested.

6. The use according to claim 3, characterized in that: By collecting fecal samples from the subjects, extracting total microbial DNA, completing metagenomic sequencing of the microbial DNA, and detecting whether the 23 microbial species described in claim 1 are present, and whether they are significantly enriched or significantly reduced.

7. The use according to claim 3, characterized in that: By collecting fecal samples from the subjects, total microbial DNA was extracted, and metagenomic sequencing of the intestinal flora was performed; based on high-throughput sequencing data, a microbial differentiation model between cholangiocarcinoma patients and healthy people was established in the cohort and verified.

8. The use according to claim 7, characterized in that: The steps of establishing and validating the microbial differentiation model between cholangiocarcinoma patients and healthy people specifically include: (1) Fecal samples were collected from subjects, including patients with cholangiocarcinoma and healthy people. The total microbial DNA in the fecal samples was extracted according to the standard DNA extraction method, and the metagenomic sequencing of the intestinal flora was completed on the Illumina NovaSeq 6000 platform; (2) Based on the discovery set high-throughput sequencing data, 23 optimal microbial markers were identified for the model based on a random forest model between 51 cholangiocarcinoma patients and 105 healthy subjects through a five-fold cross-validation algorithm; (3) Based on 23 microbial markers, the microbial discrimination model had a discrimination ability of 98.95% between 51 cholangiocarcinoma patients and 105 healthy subjects; (4) In the validation set, the microbial discrimination model had a discrimination ability of 99.89% between 21 patients with cholangiocarcinoma and 45 healthy controls; (5) To further determine the universality of the model in China, in the cross-regional validation set, the microbial discrimination model had a discrimination ability of 84.32% between 70 cholangiocarcinoma patients and 84 healthy controls; (6) In the cross-regional validation set, the microbial discrimination model had a discrimination ability of 79.37% between 30 cholangiocarcinoma patients and 37 healthy controls; (7) In order to further determine the disease specificity of the model worldwide, data from 31 studies covering 6,847 samples of 11 disease states were downloaded from the public database of intestinal microbial metagenomics, including colorectal cancer, Crohn's disease, type 2 diabetes, ulcerative colitis, liver disease, atherosclerotic coronary artery disease, pancreatic cancer, type 1 diabetes, breast cancer, chronic pancreatitis, pancreatitis, and healthy controls; the results showed that in the public dataset, the average false positive rate of all samples was 0.

19.

9. A detection product for distinguishing cholangiocarcinoma from healthy people, characterized in that: The product comprises a substance for detecting the intestinal microbial marker according to claim 1.

10. The product according to claim 9, characterized in that: The product includes reagents, devices and / or equipment for detecting the relative abundance and / or quantity of biomarkers in a sample to be tested.

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