Method for diagnosing lymphoma via bacterial metagenomic analysis
11708615 · 2023-07-25
Assignee
Inventors
Cpc classification
International classification
Abstract
Provided is a method of diagnosing lymphoma by analyzing an increase or decrease in contents of specific bacterial extracellular vesicles by performing bacterial metagenomic analysis using normal individual and subject-derived samples, wherein a lymphoma risk group may be diagnosed and predicted early to delay the time of onset or prevent the onset of lymphoma with proper cure, and after onset, early diagnosis may be performed, thereby reducing the incidence of lymphoma and increasing a therapeutic effect.
Claims
1. A method of providing information for diagnosing lymphoma, the method comprising: (a) isolating bacteria-derived extracellular vesicles from blood samples obtained from a subject who is suspected of being at risk of developing lymphoma and from a normal individual; (b) extracting DNAs from the bacteria-derived extracellular vesicles isolated from the samples; (c) detecting the content of extracellular vesicles in the samples by performing polymerase chain reaction (PCR) on the extracted DNA using a pair of primers comprising SEQ ID NO: 1 and SEQ ID NO: 2, and then performing metagenomic sequencing the product of the PCR to determine content of bacteria-derived extracellular vesicles in each sample; (d) selecting strains exhibiting a p-value less than 0.05 between the two groups in a t-test and a mean value difference of two-fold or more between two groups; and (e) forming a diagnostic model for lymphoma consisting of the extracellular vesicles derived from: (i) the genus Cupriavidus, the genus Deinococcus, the genus Clostridium, the genus Dialister, the genus Faecalibacterium, the genus Lactobacillus, and the genus Citrobacter, and (ii) the genus Micrococcus, the genus Corynebacterium, the genus Propionibacterium, the genus Anaerococcus, the genus Porphyromonas, the genus Prevotella, the genus Veillonella, the genus Rothia, the genus Actinomyces, the genus Haemophilus, the genus Peptoniphilus, the genus Capnocytophaga, the genus Lautropia, the genus Granulicatella, the genus Finegoldia, the genus Neisseria, the genus Selenomonas, and the genus Alcanivorax wherein in the diagnostic model, an increase in the content of the extracellular vesicles derived from the 18 genera in (ii) by two-fold or more and a decrease in the content of the extracellular vesicles derived from the 7 genera in (i) by two-fold or more in the sample from the subject, in comparison with the content of extracellular vesicles in the sample from the normal individual indicate a risk of developing lymphoma.
2. A method of providing information for diagnosing lymphoma, the method comprising: (a) isolating bacteria-derived extracellular vesicles from a blood sample obtained from a subject who is suspected of being at risk of developing lymphoma and from a normal individual group; (b) extracting DNAs from the bacteria-derived extracellular vesicles isolated from the samples; (c) detecting the content of extracellular vesicles in the samples by performing polymerase chain reaction (PCR) on the extracted DNA using a pair of primers comprising SEQ ID NO: 1 and SEQ ID NO: 2, and then performing metagenomic sequencing of the product of the PCR to determine content of bacteria-derived extracellular vesicles in each sample; (d) selecting strains exhibiting a p-value less than 0.05 between the two groups in a t-test and a mean value difference of two-fold or more between two groups; and (e) forming a diagnostic model for lymphoma consisting of the extracellular vesicles derived from the genus Finegoldia, the genus Alcanivorax, the genus Corynebacterium, and the genus Propionibacterium, wherein Area Under the Curve (AUC) value of the four extracellular vesicles is 0.81 or higher, calculated by logistic regression analysis, and wherein the increase of the content of the four extracellular vesicles by two-fold or more, in comparison with the content of the extracellular vesicles in the sample from the normal individual, indicate a risk of developing lymphoma.
Description
DESCRIPTION OF DRAWINGS
(1)
(2)
(3)
(4)
(5)
(6)
BEST MODE
(7) The present invention relates to a method of diagnosing lymphoma through bacterial metagenomic analysis. The inventors of the present invention extracted genes from bacteria-derived extracellular vesicles using a normal individual and a subject-derived sample, performed metagenomic analysis thereon, and identified bacteria-derived extracellular vesicles capable of acting as a causative factor of lymphoma.
(8) Therefore, the present invention provides a method of providing information for diagnosing lymphoma, the method comprising:
(9) (a) extracting DNAs from extracellular vesicles isolated from normal individual and subject samples;
(10) (b) performing polymerase chain reaction (PCR) on the extracted DNA using a pair of primers comprising SEQ ID NO: 1 and SEQ ID NO: 2; and
(11) (c) comparing an increase or decrease in content of bacteria-derived extracellular vesicles of the subject-derived sample with that of a normal individual-derived sample through sequencing of a product of the PCR.
(12) The term “lymphoma diagnosis” as used herein refers to determining whether a patient has a risk for lymphoma, whether the risk for lymphoma is relatively high, or whether lymphoma has already occurred. The method of the present invention may be used to delay the onset of lymphoma through special and appropriate care for a specific patient, which is a patient having a high risk for lymphoma or prevent the onset of lymphoma. In addition, the method may be clinically used to determine treatment by selecting the most appropriate treatment method through early diagnosis of lymphoma.
(13) The term “metagenome” as used herein refers to the total of genomes including all viruses, bacteria, fungi, and the like in isolated regions such as soil, the intestines of animals, and the like, and is mainly used as a concept of genomes that explains identification of many microorganisms at once using a sequencer to analyze non-cultured microorganisms. In particular, a metagenome does not refer to a genome of one species, but refers to a mixture of genomes, including genomes of all species of an environmental unit. This term originates from the view that, when defining one species in a process in which biology is advanced into omics, various species as well as existing one species functionally interact with each other to form a complete species. Technically, it is the subject of techniques that analyzes all DNAs and RNAs regardless of species using rapid sequencing to identify all species in one environment and verify interactions and metabolism. In the present invention, bacterial metagenomic analysis is performed using bacteria-derived extracellular vesicles isolated from, for example, blood.
(14) In the present invention, the normal individual and subject sample may be blood or urine, and the blood may be preferably whole blood, serum, plasma, or blood mononuclear cells, but the present invention is not limited thereto.
(15) In an embodiment of the present invention, metagenomic analysis is performed on the bacteria-derived extracellular vesicles, and bacteria-derived extracellular vesicles capable of acting as a cause of the onset of lymphoma were actually identified by analysis at phylum, class, order, family, and genus levels.
(16) More particularly, in one embodiment of the present invention, as a result of performing bacterial metagenomic analysis on extracellular vesicles present in subject-derived blood samples at a phylum level, the content of extracellular vesicles derived from bacteria belonging to the phylum Cyanobacteria, the phylum Thermi, and the phylum Euryarchaeota was significantly different between lymphoma patients and normal individuals (see Example 4).
(17) More particularly, in one embodiment of the present invention, as a result of performing bacterial metagenomic analysis on extracellular vesicles present in subject-derived blood samples at a class level, the content of extracellular vesicles derived from bacteria belonging to the class Deinococci, the class Chloroplast, and the class Betaproteobacteria was significantly different between lymphoma patients and normal individuals (see Example 4).
(18) More particularly, in one embodiment of the present invention, as a result of performing bacterial metagenomic analysis on extracellular vesicles present in subject-derived blood samples at an order level, the content of extracellular vesicles derived from bacteria belonging to the order Deinococcales, the order Rickettsiales, the order Streptophyta, the order Rhizobiales, the order Oceanospirillales, the order Pasteurellales, and the order Neisseriales was significantly different between lymphoma patients and normal individuals (see Example 4).
(19) More particularly, in one embodiment of the present invention, as a result of performing bacterial metagenomic analysis on extracellular vesicles present in subject-derived blood samples at a family level, the content of extracellular vesicles derived from bacteria belonging to the family Erythrobacteraceae, the family Rhodospirillaceae, the family Deinococcaceae, the family Nocardioidaceae, the family Oxalobacteraceae, the family mitochondria, the family Lactobacillaceae, the family Ruminococcaceae, the family Halomonadaceae, the family Micrococcaceae, the family Corynebacteriaceae, the family Propionibacteriaceae, the family Prevotellaceae, the family Burkholderiaceae, the family Actinomycetaceae, the family Tissierellaceae, the family Pasteurellaceae, the family Carnobacteriaceae, the family Neisseriaceae, and the family Alcanivoracaceae was significantly different between lymphoma patients and normal individuals (see Example 4).
(20) More particularly, in one embodiment of the present invention, as a result of performing bacterial metagenomic analysis on extracellular vesicles present in subject-derived blood samples at a genus level, the content of extracellular vesicles derived from bacteria belonging to the genus Cupriavidus, the genus Deinococcus, the genus Clostridium, the genus Dialister, the genus Faecalibacterium, the genus Lactobacillus, the genus Citrobacter, the genus Micrococcus, the genus Corynebacterium, the genus Propionibacterium, the genus Anaerococcus, the genus Porphyromonas, the genus Prevotella, the genus Veillonella, the genus Rothia, the genus Actinomyces, the genus Haemophilus, the genus Peptoniphilus, the genus Capnocytophaga, the genus Lautropia, the genus Granulicatella, the genus Finegoldia, the genus Neisseria, the genus Selenomonas, and the genus Alcanivorax was significantly different between lymphoma patients and normal individuals (see Example 4).
(21) Through the results of the examples, it was confirmed that distribution variables of the identified bacteria-derived extracellular vesicles could be usefully used for the prediction of the onset of lymphoma.
MODE OF THE INVENTION
(22) Hereinafter, the present invention will be described with reference to exemplary examples to aid in understanding of the present invention. However, these examples are provided only for illustrative purposes and are not intended to limit the scope of the present invention.
EXAMPLES
Example 1. Analysis of In Vivo Absorption, Distribution, and Excretion Patterns of Intestinal Bacteria and Bacteria-Derived Extracellular Vesicles
(23) To evaluate whether intestinal bacteria and bacteria-derived extracellular vesicles are systematically absorbed through the gastrointestinal tract, an experiment was conducted using the following method. More particularly, 50 μg of each of intestinal bacteria and the bacteria-derived extracellular vesicles (EVs), labeled with fluorescence, were orally administered to the gastrointestinal tracts of mice, and fluorescence was measured at 0 h, and after 5 min, 3 h, 6 h, and 12 h. As a result of observing the entire images of mice, as illustrated in
(24) After intestinal bacteria and intestinal bacteria-derived extracellular vesicles were systematically absorbed, to evaluate a pattern of invasion of intestinal bacteria and the bacteria-derived EVs into various organs in the human body after being systematically absorbed, 50 μg of each of the bacteria and bacteria-derived EVs, labeled with fluorescence, were administered using the same method as that used above, and then, at 12 h after administration, blood, the heart, the lungs, the liver, the kidneys, the spleen, adipose tissue, and muscle were extracted from each mouse. As a result of observing fluorescence in the extracted tissues, as illustrated in
Example 2. Vesicle Isolation and DNA Extraction from Blood
(25) To isolate extracellular vesicles and extract DNA, from blood, first, blood was added to a 10 ml tube and centrifuged at 3,500×g and 4° C. for 10 min to precipitate a suspension, and only a supernatant was collected, which was then placed in a new 10 ml tube. The collected supernatant was filtered using a 0.22 μm filter to remove bacteria and impurities, and then placed in centrifugal filters (50 kD) and centrifuged at 1500×g and 4° C. for 15 min to discard materials with a smaller size than 50 kD, and then concentrated to 10 ml. Once again, bacteria and impurities were removed therefrom using a 0.22 μm filter, and then the resulting concentrate was subjected to ultra-high speed centrifugation at 150,000×g and 4° C. for 3 hours by using a Type 90ti rotor to remove a supernatant, and the agglomerated pellet was dissolved with phosphate-buffered saline (PBS), thereby obtaining vesicles.
(26) 100 μl of the extracellular vesicles isolated from the blood according to the above-described method was boiled at 100° C. to allow the internal DNA to come out of the lipid and then cooled on ice. Next, the resulting vesicles were centrifuged at 10,000×g and 4° C. for 30 minutes to remove the remaining suspension, only the supernatant was collected, and then the amount of DNA extracted was quantified using a NanoDrop sprectrophotometer. In addition, to verify whether bacteria-derived DNA was present in the extracted DNA, PCR was performed using 16s rDNA primers shown in Table 1 below.
(27) TABLE-US-00001 TABLE 1 SEQ ID Primer Sequence NO. 16S rDNA 16S_V3_F 5′-TCGTCGGCAGCGTC 1 AGATGTGTATAAGAG ACAGCCTACGGGNGG CWGCAG-3′ 16S_V4_R 5′-GTCTCGTGGGCTCG 2 GAGATGTGTATAAGA GACAGGACTACHVGG GTATCTAATCC-3′
Example 3. Metagenomic Analysis Using DNA Extracted from Blood
(28) DNA was extracted using the same method as that used in Example 2, and then PCR was performed thereon using 16S rDNA primers shown in Table 1 to amplify DNA, followed by sequencing (Illumina MiSeq sequencer). The results were output as standard flowgram format (SFF) files, and the SFF files were converted into sequence files (.fasta) and nucleotide quality score files using GS FLX software (v2.9), and then credit rating for reads was identified, and portions with a window (20 bps) average base call accuracy of less than 99% (Phred score<20) were removed. After removing the low-quality portions, only reads having a length of 300 bps or more were used (Sickle version 1.33), and, for operational taxonomy unit (OTU) analysis, clustering was performed using UCLUST and USEARCH according to sequence similarity. In particular, clustering was performed based on sequence similarity values of 94% for genus, 90% for family, 85% for order, 80% for class, and 75% for phylum, and phylum, class, order, family, and genus levels of each OTU were classified, and bacteria with a sequence similarity of 97% or more were analyzed (QIIME) using 16S DNA sequence databases (108,453 sequences) of BLASTN and GreenGenes.
Example 4. Lymphoma Diagnostic Model Based on Metagenomic Analysis of Bacteria-Derived EVs Isolated from Blood
(29) EVs were isolated from blood samples of 63 lymphoma patients and 53 normal individuals, the two groups matched in age and gender, and then metagenomic sequencing was performed thereon using the method of Example 3. For the development of a diagnostic model, first, a strain exhibiting a p value of less than 0.05 between two groups in a t-test and a difference of two-fold or more between two groups was selected, and then an area under curve (AUC), sensitivity, and specificity, which are diagnostic performance indexes, were calculated by logistic regression analysis.
(30) As a result of analyzing bacteria-derived EVs in blood at a phylum level, a diagnostic model developed using bacteria belonging to the phylum Cyanobacteria, the phylum Thermi, and the phylum Euryarchaeota as a biomarker exhibited significant diagnostic performance for lymphoma (see Table 2 and
(31) TABLE-US-00002 TABLE 2 Control Lymphoma t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity p_Cyanobacteria 0.0232 0.0369 0.0059 0.0142 0.0022 0.25 0.74 0.66 0.40 0.87 p_[Thermi] 0.0030 0.0059 0.0002 0.0008 0.0016 0.08 0.71 0.66 0.34 0.94 p_Euryarchaeota 0.0009 0.0022 0.0000 0.0002 0.0039 0.03 0.63 0.63 0.21 0.98
(32) As a result of analyzing bacteria-derived EVs in blood at a phylum level, a diagnostic model developed using bacteria belonging to the class Deinococci, the class Chloroplast, and the class Betaproteobacteria as a biomarker exhibited significant diagnostic performance for lymphoma (see Table 3 and
(33) TABLE-US-00003 TABLE 3 Control Lymphoma t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity c_Deinococci 0.0030 0.0059 0.0002 0.0008 0.0016 0.08 0.71 0.66 0.34 0.94 c_Chloroplast 0.0216 0.0365 0.0057 0.0142 0.0043 0.26 0.71 0.67 0.40 0.90 c_Betaproteobacteria 0.0408 0.0440 0.1045 0.0486 0.0000 2.56 0.91 0.88 0.87 0.89
(34) As a result of analyzing bacteria-derived EVs in blood at a phylum level, a diagnostic model developed using bacteria belonging to the order Deinococcales, the order Rickettsiales, the order Streptophyta, the order Rhizobiales, the order Oceanospirillales, the order Pasteurellales, and the order Neisseriales as a biomarker exhibited significant diagnostic performance for lymphoma (see Table 4 and
(35) TABLE-US-00004 TABLE 4 Control Lymphoma t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity o_Deinococcales 0.0026 0.0059 0.0002 0.0008 0.0054 0.09 0.66 0.65 0.30 0.94 o_Rickettsiales 0.0017 0.0033 0.0003 0.0014 0.0048 0.19 0.65 0.63 0.26 0.94 o_Streptophyta 0.0211 0.0364 0.0057 0.0142 0.0055 0.27 0.70 0.66 0.38 0.90 o_Rhizobiales 0.0125 0.0161 0.0040 0.0062 0.0007 0.32 0.73 0.66 0.45 0.84 o_Oceanospirillales 0.0079 0.0101 0.0176 0.0130 0.0000 2.24 0.75 0.68 0.66 0.70 o_Pasteurellales 0.0050 0.0059 0.0240 0.0245 0.0000 4.80 0.77 0.71 0.77 0.65 o_Neisseriales 0.0170 0.0447 0.0899 0.0486 0.0000 5.29 0.94 0.90 0.89 0.90
(36) As a result of analyzing bacteria-derived EVs in blood at a phylum level, a diagnostic model developed using bacteria belonging to the family Erythrobacteraceae, the family Rhodospirillaceae, the family Deinococcaceae, the family Nocardioidaceae, the family Oxalobacteraceae, the family mitochondria, the family Lactobacillaceae, the family Ruminococcaceae, the family Halomonadaceae, the family Micrococcaceae, the family Corynebacteriaceae, the family Propionibacteriaceae, the family Prevotellaceae, the family Burkholderiaceae, the family Actinomycetaceae, the family Tissierellaceae, the family Pasteurellaceae, the family Carnobacteriaceae, the family Neisseriaceae, and the family Alcanivoracaceae as a biomarker exhibited significant diagnostic performance for lymphoma (see Table 5 and
(37) TABLE-US-00005 TABLE 5 Control Lymphoma t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity f_Erythrobacteraceae 0.0038 0.0069 0.0000 0.0001 0.0002 0.00 0.71 0.71 0.38 0.98 f_Rhodospirillaceae 0.0011 0.0023 0.0001 0.0004 0.0036 0.07 0.64 0.64 0.25 0.97 f_Deinococcaceae 0.0026 0.0059 0.0002 0.0008 0.0056 0.09 0.66 0.64 0.28 0.94 f_Nocardioidaceae 0.0021 0.0037 0.0002 0.0012 0.0008 0.11 0.70 0.65 0.30 0.94 f_Oxalobacteraceae 0.0094 0.0143 0.0013 0.0022 0.0002 0.14 0.73 0.66 0.43 0.84 f_mitochondria 0.0017 0.0032 0.0003 0.0014 0.0055 0.19 0.64 0.63 0.26 0.94 f_Lactobacillaceae 0.0450 0.0527 0.0126 0.0129 0.0001 0.28 0.77 0.72 0.57 0.86 f_Ruminococcaceae 0.0678 0.0531 0.0221 0.0228 0.0000 0.33 0.78 0.73 0.55 0.89 f_Halomonadaceae 0.0075 0.0099 0.0030 0.0055 0.0050 0.40 0.64 0.67 0.42 0.89 f_Micrococcaceae 0.0095 0.0094 0.0195 0.0147 0.0000 2.04 0.73 0.62 0.62 0.62 f_Corynebacteriaceae 0.0602 0.1119 0.1262 0.0852 0.0007 2.09 0.81 0.78 0.74 0.83 f_Propionibacteriaceae 0.0276 0.0296 0.0607 0.0318 0.0000 2.20 0.82 0.78 0.79 0.76 f_Prevotellaceae 0.0120 0.0187 0.0363 0.0412 0.0001 3.02 0.73 0.67 0.75 0.60 f_Burkholderiaceae 0.0013 0.0026 0.0043 0.0067 0.0019 3.19 0.65 0.57 0.68 0.48 f_Actinomycetaceae 0.0017 0.0027 0.0062 0.0076 0.0000 3.65 0.71 0.61 0.68 0.56 f_[Tissierellaceae] 0.0059 0.0113 0.0270 0.0230 0.0000 4.59 0.85 0.78 0.79 0.76 f_Pasteurellaceae 0.0050 0.0059 0.0240 0.0245 0.0000 4.81 0.77 0.71 0.77 0.65 f_Carnobacteriaceae 0.0007 0.0018 0.0037 0.0060 0.0003 5.12 0.63 0.61 0.83 0.43 f_Neisseriaceae 0.0170 0.0447 0.0899 0.0486 0.0000 5.29 0.94 0.90 0.89 0.90 f_Alcanivoracaceae 0.0004 0.0014 0.0146 0.0117 0.0000 39.30 0.93 0.91 0.94 0.87
(38) As a result of analyzing bacteria-derived EVs in blood at a phylum level, a diagnostic model developed using bacteria belonging to the genus Cupriavidus, the genus Deinococcus, the genus Clostridium, the genus Dialister, the genus Faecalibacterium, the genus Lactobacillus, the genus Citrobacter, the genus Micrococcus, the genus Corynebacterium, the genus Propionibacterium, the genus Anaerococcus, the genus Porphyromonas, the genus Prevotella, the genus Veillonella, the genus Rothia, the genus Actinomyces, the genus Haemophilus, the genus Peptoniphilus, the genus Capnocytophaga, the genus Lautropia, the genus Granulicatella, the genus Finegoldia, the genus Neisseria, the genus Selenomonas, and the genus Alcanivorax as a biomarker exhibited significant diagnostic performance for lymphoma (see Table 6 and
(39) TABLE-US-00006 TABLE 6 Control Lymphoma t-test Taxon Mean SD Mean SD p-value Ratio AUC Accuracy sensitivity specificity g_Cupriavidus 0.0048 0.0105 0.0003 0.0011 0.0032 0.05 0.68 0.67 0.36 0.94 g_Deinococcus 0.0026 0.0059 0.0002 0.0008 0.0056 0.09 0.66 0.64 0.28 0.94 g_Clostridium 0.0064 0.0120 0.0007 0.0021 0.0013 0.11 0.68 0.66 0.38 0.90 g_Dialister 0.0037 0.0061 0.0006 0.0015 0.0007 0.16 0.68 0.65 0.38 0.87 g_Faecalibacterium 0.0074 0.0100 0.0014 0.0025 0.0001 0.18 0.69 0.70 0.45 0.90 g_Lactobacillus 0.0441 0.0527 0.0126 0.0129 0.0001 0.29 0.77 0.73 0.58 0.86 g_Citrobacter 0.0043 0.0058 0.0013 0.0033 0.0014 0.30 0.70 0.65 0.40 0.86 g_Micrococcus 0.0029 0.0045 0.0059 0.0049 0.0010 2.04 0.71 0.63 0.64 0.62 g_Corynebacterium 0.0602 0.1119 0.1262 0.0852 0.0007 2.09 0.81 0.78 0.74 0.83 g_Propionibacterium 0.0275 0.0295 0.0607 0.0318 0.0000 2.21 0.82 0.78 0.79 0.76 g_Anaerococcus 0.0035 0.0080 0.0092 0.0098 0.0011 2.60 0.77 0.75 0.83 0.68 g_Porphyromonas 0.0017 0.0040 0.0044 0.0063 0.0064 2.61 0.63 0.61 0.79 0.46 g_Prevotella 0.0120 0.0187 0.0363 0.0412 0.0001 3.02 0.73 0.67 0.75 0.60 g_Veillonella 0.0066 0.0145 0.0202 0.0214 0.0001 3.07 0.78 0.73 0.81 0.67 g_Rothia 0.0028 0.0040 0.0113 0.0122 0.0000 4.08 0.73 0.70 0.79 0.62 g_Actinomyces 0.0013 0.0023 0.0061 0.0076 0.0000 4.58 0.72 0.65 0.74 0.57 g_Haemophilus 0.0047 0.0059 0.0219 0.0232 0.0000 4.68 0.77 0.72 0.79 0.67 g_Peptoniphilus 0.0010 0.0034 0.0048 0.0093 0.0040 4.75 0.70 0.67 0.85 0.52 g_Capnocytophaga 0.0005 0.0014 0.0022 0.0039 0.0015 4.81 0.61 0.55 0.81 0.33 g_Lautropia 0.0007 0.0020 0.0037 0.0059 0.0005 4.94 0.67 0.62 0.81 0.46 g_Granulicatella 0.0007 0.0018 0.0037 0.0059 0.0003 5.36 0.64 0.62 0.83 0.44 g_Finegoldia 0.0012 0.0026 0.0128 0.0178 0.0000 10.61 0.83 0.76 0.85 0.68 g_Neisseria 0.0012 0.0019 0.0130 0.0141 0.0000 10.79 0.79 0.75 0.85 0.67 g_Selenomonas 0.0001 0.0005 0.0012 0.0032 0.0066 13.94 0.66 0.59 0.55 0.62 g_Alcanivorax 0.0004 0.0014 0.0146 0.0117 0.0000 39.30 0.93 0.91 0.94 0.87
(40) The above description of the present invention is provided only for illustrative purposes, and it will be understood by one of ordinary skill in the art to which the present invention pertains that the invention may be embodied in various modified forms without departing from the spirit or essential characteristics thereof. Thus, the embodiments described herein should be considered in an illustrative sense only and not for the purpose of limitation.
INDUSTRIAL APPLICABILITY
(41) The method of providing information for diagnosing lymphoma through a bacterial metagenomic analysis according to the present invention may be used for predicting the risk of lymphoma onset and diagnosing lymphoma by performing a bacterial metagenomic analysis using normal individual-derived and subject-derived samples to analyze an increase or decrease in the content of specific bacteria-derived extracellular vesicles.