CN113684242A - Lymph node microbial flora-based head and neck cancer prognosis biomarker and application thereof - Google Patents
Lymph node microbial flora-based head and neck cancer prognosis biomarker and application thereof Download PDFInfo
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Abstract
The invention provides a lymph node microbial flora-based head and neck cancer prognosis biomarker and application thereof, and belongs to the technical field of disease prognosis and molecular biology. The invention proves that different species exist in flora species of metastatic lymph nodes and non-metastatic lymph nodes in head and neck cancer patients by performing high-throughput sequencing on 16S ribosomal RNA genes of the lymph node microbiota of the head and neck cancer patients, and further, the microbial flora interrelation between the metastatic lymph nodes and the non-metastatic lymph nodes of the HNC patients is also different on the genus level through a microbial flora symbiotic relationship network.
Description
Technical Field
The invention belongs to the technical field of disease prognosis and molecular biology, and particularly relates to a lymph node microbial flora-based head and neck cancer prognosis biomarker and application thereof.
Background
The information in this background section is only for enhancement of understanding of the general background of the invention and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art that is already known to a person of ordinary skill in the art.
Head and Neck Cancer (HNC) refers to malignant tumors of the Head and Neck, including oral cavity, oropharynx, nasopharynx, larynx and other parts, and about 65 million new cases of Head and Neck Cancer occur every year worldwide, which are one of the more common malignant tumors and seriously threaten the life health and safety of human beings. The cause of head and neck cancer is not completely clear in the scientific community at present, and is considered to be closely related to two factors, namely external environment and individual heredity, wherein the external environment mainly induces human papilloma virus, alcohol, tobacco and the like, and the individual heredity comprises gene mutation, susceptibility gene, family disease history and the like.
Although there are many treatment regimens for head and neck cancer, the 5-year survival rate is only 60%, and the main causes of death include recurrence and metastasis of local tumors. Therefore, it is very valuable to develop research on the mechanism of head and neck carcinogenesis. However, the current research on the mechanism of the occurrence and development mechanism of head and neck cancer mainly focuses on the aspect of individual genetics, and the research on the flora related to the head and neck cancer is not deep.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a lymph node microbial flora-based head and neck cancer prognosis biomarker and application thereof. The invention discovers that the prognosis condition of the head and neck cancer patient is closely related to the lymph node microbial community for the first time, so that the lymph node microbial community can be used as a prognosis biomarker of the head and neck cancer and is used for prognosis evaluation of the head and neck cancer.
Specifically, the invention relates to the following technical scheme:
in a first aspect of the invention, there is provided a biomarker for prognostic assessment of head and neck cancer, the biomarker comprising any one or more selected from the group consisting of:
firmicutes, Bacilli, caulobacteriales, caulobacteriaceae, orazaceae, moraxellaceae, Gammaproteobacteria, unclassified Gammaproteobacteria, lactariiformes, Lactobacillus acidophilus, Lactobacillus, anaerobacter, anaerobactereobacillus, anaerobactereobactereobaeae, Lactobacillus, and Proteobacteria.
Specifically, the microbial flora is selected from lymph nodes of patients with head and neck cancer.
The prognostic assessment of head and neck cancer comprises assessment of the survival of a patient with head and neck cancer.
Wherein the survival of the head and neck cancer patient comprises Overall Survival (OS) and three-year survival.
The invention analyzes and identifies 19 different species in the flora species of metastatic lymph nodes and non-metastatic lymph nodes of HNC patients by adopting an LEfSe method. The predominant flora in the negative lymph nodes of HNC patients include Firmicutes, Bacilli, Bacillales, Bacillaceae, Bacillus, and villobacteriaceae; in contrast, 13 dominant bacterial groups were found in metastatic lymph nodes of HNC patients, including the order of the Tobobacterium (Caulobacter), the family of the Tobobacterium (Caulobacter), the species of Oesophagostomum (Moraxella-oslorensis), the class of the Gamma-Proteobacteria (Gamma-Proteobacteria), the unclassified class of the Gamma-Proteobacteria (unidentified _ Gamma-Proteobacteria), the genus of the Aquifex (Enhydrolacter), the class of the anaerobic Corynebacteria (Anaroleae), the order of the anaerobic Corynebacteriales (Anaroleae), the family of the anaerobic Corynebacteriaceae (Lactobacillus), the genus of Lactobacillus (Lactobacillus), the phylum of the Chloroflexi (Chloroflexi), and the phylum of Proteobacteria (Proteobacteria).
Meanwhile, by utilizing ROC and Cox analysis, the flora difference characteristics (diversity index values and relative abundance of different species) have good prediction effect on three-year survival time and total survival time of head and neck cancer patients, so that the population difference characteristics can be used as a prognosis biomarker.
In a second aspect of the present invention, there is provided a use of the above biomarker for preparing a product for prognosis evaluation of head and neck cancer.
In a third aspect of the invention, there is provided a product comprising a substance for detecting the above biomarker, for use in the prognostic assessment of a patient with head and neck cancer.
The prognostic assessment includes predictive assessment of the survival of a head and neck cancer patient.
Wherein the survival of the head and neck cancer patient comprises Overall Survival (OS) and three-year survival.
The products include, but are not limited to, reagents, devices and/or equipment for detecting the (relative) abundance and/or amount (OTU number) of biomarkers in a sample to be tested.
The relative abundance information of the biomarkers is obtained by using a sequencing method, and further comprises the following steps: isolating a nucleic acid sample from the sample (lymph node) of a head and neck cancer patient, constructing a DNA library based on the nucleic acid sample obtained, sequencing the DNA library so as to obtain a sequencing result, and comparing the sequencing result with a reference gene set based on the sequencing result to determine relative abundance information of the biomarker.
The product may be a kit.
In a fourth aspect of the invention, the biomarker and the product are provided for use in preparing a system for assessing risk of head and neck cancer prognosis.
The head and neck cancer prognostic risk assessment system can be used for predictive assessment of survival of head and neck patients, including Overall Survival (OS) and three-year survival.
Therefore, in a fifth aspect of the present invention, there is provided a head and neck cancer prognosis risk assessment system, comprising an analysis unit and an assessment unit;
wherein the analysis unit is configured to obtain data of patient-related risk factors;
the evaluation unit outputs the prognosis state of the patient according to the acquired data of the risk factors.
Wherein the patient-associated risk factors include the alpha diversity index of the lymph node flora and the biomarkers described above.
Wherein the alpha diversity index of the lymph node flora includes but is not limited to Observed species, Simpson, Chao1, Ace and PD white tree;
in particular, the biomarkers include, but are not limited to, Proteobacteria (Proteobacteria), Gamma-Proteobacteria (Gamma-Proteobacteria), unclassified Gamma-Proteobacteria (unidentified _ Gamma-Proteobacteria), Aquifex (Enhydrosbacter) and Ostemola (Moraxella-oslorensis), Bacteria (Bacillus), Bacillales (Bacillus), Bacillaceae (Bacillus) and Bacillus (Bacillus).
In particular, when performing a three-year survival assessment of a patient with head and neck cancer, the alpha diversity index of the lymph node flora includes, but is not limited to, Observed species, Chao1, Ace, and PD whole tree;
the biomarkers include, but are not limited to, Proteobacteria (Proteobacteria), Gammaproteobacteria (Gamma), unclassified Gammaproteobacteria (unidentified _ Gamma), Aquifex (Enhydrosbacter), and Oersura (Moraxella-oslorensis).
When performing overall survival assessment for head and neck cancer patients, the alpha diversity index for the lymph node flora includes, but is not limited to, Simpson, Chao1, and Ace.
The biomarkers include, but are not limited to, Bacilli (Bacilli), Bacillales (Bacillales), Bacillaceae (Bacillaceae), Bacilli (Bacillus), unclassified gammophytes (unidentified _ Gammaproteobacteria), aquaticus (aquabacter), and oras terrestris (Moraxella-ostreae).
In a sixth aspect of the present invention, there is provided a method for prognostic risk assessment of a patient with head and neck cancer, the method comprising assessing using the above biomarker and/or the above prognostic risk assessment system for head and neck cancer.
The prognostic risk assessment includes predictive assessment of the survival of head and neck cancer patients, including Overall Survival (OS) and three-year survival.
The beneficial technical effects of one or more technical schemes are as follows:
according to the technical scheme, the 16S ribosomal RNA (16SrRNA) genes of the lymph node microbiota of the head and neck cancer patient are subjected to high-throughput sequencing, so that different species exist in flora species of metastatic lymph nodes and non-metastatic lymph nodes of the head and neck cancer patient, furthermore, the microbial flora interrelation between the metastatic lymph nodes and the non-metastatic lymph nodes of the HNC patient is found to be different on the genus level through a microbial flora symbiotic relationship network, the result is that the characteristics of the microbiome of the metastatic lymph nodes and the non-metastatic lymph nodes of the HNC patient are found for the first time, and meanwhile, based on ROC and Cox analysis, the flora difference characteristics (diversity index values and the relative abundance of different species) have good prediction effects on the survival periods (three-year survival period and total survival period) of the head and neck cancer patient, so that the method has good practical application value.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of the microbial correlation between a metastatic lymph node of an HNC patient and a non-metastatic lymph node of an HNC patient in an embodiment of the present invention, wherein A is a diagram of the species abundance analysis of the microbes in the metastatic lymph node of the HNC patient and the non-metastatic lymph node of the HNC patient; b is a flora structure difference diagram of the metastatic lymph nodes of the HNC patient and the non-metastatic lymph nodes of the HNC patient; c is the distribution map of the metastatic lymph nodes of the HNC patient and the non-metastatic lymph nodes of the HNC patient on the portal level; d is a different species diagram of the metastatic lymph nodes of the HNC patient and the non-metastatic lymph nodes of the HNC patient; e is a microbial flora symbiotic relationship network diagram on the level of metastatic lymph nodes of HNC patients and non-metastatic lymph nodes of HNC patients.
FIG. 2 is a graph showing ROC analysis correlation of 3-year OS evaluation in examples of the present invention, wherein A is a graph showing ROC analysis based on the abundance index of lymph node flora in 3-year OS evaluation; b is a ROC analysis plot based on the relative abundance of microbial flora-differentiated species in the 3-year OS assessment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise. The experimental procedures, if specific conditions are not indicated in the following detailed description, are generally in accordance with conventional procedures and conditions of molecular biology within the skill of the art, which are fully explained in the literature. See, e.g., Sambrook et al, "molecular cloning: the techniques and conditions described in the laboratory Manual, or according to the manufacturer's recommendations.
The present invention is further illustrated by reference to specific examples, which are intended to be illustrative only and not limiting. If the experimental conditions not specified in the examples are specified, they are generally according to the conventional conditions, or according to the conditions recommended by the sales companies; materials, reagents and the like used in examples were commercially available unless otherwise specified.
The term "biomarker" refers to "a property that can be objectively detected and evaluated and that can be an indicator of a normal biological process, pathological process, or therapeutic intervention pharmacological response. For example, nucleic acid markers (also referred to as gene markers, e.g., DNA), protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers (species/genus markers) and functional markers (KO/OG markers) and the like. The meaning of the nucleic acid marker is not limited to the existing gene that can be expressed as a protein having biological activity, and includes any nucleic acid fragment, which may be DNA, RNA, modified DNA or RNA, unmodified DNA or RNA, and a collection of these. In the present invention, biomarkers may also be denoted by "lymph node markers" since the biomarkers found to be associated with head and neck cancer are selected from within the lymph nodes of a patient.
The biomarker can be used for batch analysis of lymph node samples of patients with head and neck cancer by using high-throughput sequencing. And (3) determining specific nucleic acid sequences relevant to the prognosis of the head and neck cancer patient based on the high-throughput sequencing data. For biomarkers the skilled person can also determine the presence of said species in the lymph node flora by conventional species identification means and biological activity test means. For example, species identification can be performed by performing 16 srna.
The terms "indicator" and "marker" are used interchangeably herein and refer to a sign or signal of a condition or to monitor a condition. Such "disorder" refers to a biological state of a cell, tissue or organ, or to a health and/or disease state of an individual. The indicator may be the presence or absence of molecules including, but not limited to, peptides, proteins, and nucleic acids, or may be a change in the level or pattern of expression of such molecules in a cell, or tissue, organ, or individual. The indicator can be a sign of the occurrence, development or presence of a disease in an individual or of further progression of such a disease. The indicator may also be a sign of the risk of developing a disease in the individual.
The term "up-regulation", "increase" or "increase" of the level of an indicator means that the level of such indicator is increased in a sample compared to a reference.
The term "down-regulation", "reduction" or "decrease" of the level of an indicator refers to a decrease in the level of such indicator in a sample compared to a reference.
The term "kit" as used herein refers to a collection of the above-mentioned components, preferably provided separately or in a single container. The container also preferably contains instructions for carrying out the method of the invention. Examples of these components of the kit and methods of use thereof have been given in the present specification. Preferably, the kit comprises the above components in a ready-to-use formulation. Preferably, the kit may additionally comprise instructions, such as a user's manual for adjusting the components (e.g., the concentration of the detection agent) and for interpreting the results of any assay with respect to the diagnosis provided by the methods of the invention. In particular, such a manual may comprise information for assigning the amount of a determined gene product to a diagnostic type. Details are found elsewhere in this specification. Furthermore, such user manual may provide instructions on the correct use of the kit components for determining the amount of the respective biomarker. The user manual may be provided in paper or electronic form (e.g., stored on a CD or CD ROM). The invention also relates to the use of said kit in any method according to the invention.
In one exemplary embodiment of the invention, a biomarker for prognostic assessment of head and neck cancer is provided, the biomarker comprising any one or more selected from the group consisting of:
firmicutes, Bacilli, caulobacteriales, caulobacteriaceae, orazaceae, moraxellaceae, Gammaproteobacteria, unclassified Gammaproteobacteria, lactariiformes, Lactobacillus acidophilus, Lactobacillus, anaerobacter, anaerobactereobacillus, anaerobactereobactereobaeae, Lactobacillus, and Proteobacteria.
In still another embodiment of the present invention, the microbial population is selected from lymph nodes of a patient with head and neck cancer.
In yet another embodiment of the invention, the prognostic assessment comprises a predictive assessment of the survival of a head and neck cancer patient.
In yet another embodiment of the present invention, the survival of the head and neck cancer patient comprises Overall Survival (OS) and three-year survival.
The invention analyzes and identifies 19 different species in the flora species of metastatic lymph nodes and non-metastatic lymph nodes of HNC patients by adopting an LEfSe method. The predominant flora in the negative lymph nodes of HNC patients include Firmicutes, Bacilli, Bacillales, Bacillaceae, Bacillus, and villobacteriaceae; in contrast, 13 dominant bacterial groups were found in metastatic lymph nodes of HNC patients, including the order of the Tobobacterium (Caulobacter), the family of the Tobobacterium (Caulobacter), the species of Oesophagostomum (Moraxella-oslorensis), the class of the Gamma-Proteobacteria (Gamma-Proteobacteria), the unclassified class of the Gamma-Proteobacteria (unidentified _ Gamma-Proteobacteria), the genus of the Aquifex (Enhydrolacter), the class of the anaerobic Corynebacteria (Anaroleae), the order of the anaerobic Corynebacteriales (Anaroleae), the family of the anaerobic Corynebacteriaceae (Lactobacillus), the genus of Lactobacillus (Lactobacillus), the phylum of the Chloroflexi (Chloroflexi), and the phylum of Proteobacteria (Proteobacteria).
Meanwhile, by utilizing ROC analysis and Cox analysis, the flora difference characteristics (diversity index values and relative abundance of different species) have good prediction effect on three-year survival time and total survival time of head and neck cancer patients, so that the population difference characteristics can be used as a prognosis biomarker.
In another embodiment of the present invention, the application of the above biomarker in the preparation of a product for prognosis evaluation of head and neck cancer is provided.
In a further embodiment of the invention, a product is provided, comprising a substance for detecting the above biomarker, for use in the prognostic assessment of a patient with head and neck cancer.
The prognostic assessment includes predictive assessment of the survival of patients with head and neck cancer.
Wherein the survival of the head and neck cancer patient comprises Overall Survival (OS) and three-year survival.
The products include, but are not limited to, reagents, devices and/or equipment for detecting the (relative) abundance and/or amount (OTU number) of biomarkers in a sample to be tested.
The relative abundance information of the biomarkers is obtained by using a sequencing method, and further comprises the following steps: isolating a nucleic acid sample from the sample (lymph node) of a head and neck cancer patient, constructing a DNA library based on the nucleic acid sample obtained, sequencing the DNA library so as to obtain a sequencing result, and comparing the sequencing result with a reference gene set based on the sequencing result to determine relative abundance information of the biomarker.
The product may be a kit.
In another embodiment of the present invention, the biomarker and the product are used for preparing a system for assessing risk of head and neck cancer prognosis.
The head and neck cancer prognosis risk assessment system can be used for predicting and assessing the survival time of a head and neck cancer patient, including overall survival time (OS) and three-year survival time.
Therefore, in a further embodiment of the present invention, there is provided a head and neck cancer prognosis risk assessment system, comprising an analysis unit and an assessment unit;
wherein the analysis unit is configured to obtain data of patient-related risk factors;
the evaluation unit outputs the prognosis state of the patient according to the acquired data of the risk factors.
Wherein the patient-associated risk factors include the alpha diversity index of the lymph node flora and the biomarkers described above.
Wherein the alpha diversity index of the lymph node flora includes but is not limited to Observed species, Simpson, Chao1, Ace and PD white tree;
in particular, the biomarkers include, but are not limited to, Proteobacteria (Proteobacteria), Gamma-Proteobacteria (Gamma-Proteobacteria), unclassified Gamma-Proteobacteria (unidentified _ Gamma-Proteobacteria), Aquifex (Enhydrosbacter) and Ostemola (Moraxella-oslorensis), Bacteria (Bacillus), Bacillales (Bacillus), Bacillaceae (Bacillus) and Bacillus (Bacillus).
In particular, when performing a three-year survival assessment of a patient with head and neck cancer, the alpha diversity index of the lymph node flora includes, but is not limited to, Observed species, Chao1, Ace, and PD whole tree;
the biomarkers include, but are not limited to, Proteobacteria (Proteobacteria), Gammaproteobacteria (Gamma), unclassified Gammaproteobacteria (unidentified _ Gamma), Aquifex (Enhydrosbacter), and Oersura (Moraxella-oslorensis).
When performing overall survival assessment for head and neck cancer patients, the alpha diversity index for the lymph node flora includes, but is not limited to, Simpson, Chao1, and Ace.
The biomarkers include, but are not limited to, Bacilli (Bacilli), Bacillales (Bacillales), Bacillaceae (Bacillaceae), Bacilli (Bacillus), unclassified gammophytes (unidentified _ Gammaproteobacteria), aquaticus (aquabacter), and oras terrestris (Moraxella-ostreae).
In yet another embodiment of the present invention, there is provided a method for prognostic risk assessment of a patient with head and neck cancer, the method comprising assessing using the above biomarker and/or the above prognostic risk assessment system for head and neck cancer.
The prognostic risk assessment refers to predictive assessment of the survival of head and neck cancer patients, including Overall Survival (OS) and three-year survival.
The invention is further illustrated by the following examples, which are not to be construed as limiting the invention thereto. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention.
Examples
Experimental methods
1. Clinical cohort
The study included head and neck cancer patients who were admitted to the qilu hospital of shandong university from 1 month to 2015 2006.
1.1 conditions are included:
age 20-76 years, unlimited in nature, with tumor resection and pathologically confirmed primary cases of head and neck cancer, with tumor sites including the oral cavity (tongue, floor of mouth, gums, buccal mucosa, hard palate, posterior molar region) and oropharynx (soft palate, tongue root and tonsil).
1.2 exclusion conditions:
the history of head and neck cancer and other tumors, and patients treated by operation, radiotherapy and chemotherapy.
1.3 protocol:
all subjects followed up to 2019 for 5 months or died.
2. Sequencing
DNA was extracted from paraffin-embedded lymph node tissue, 16S rRNA amplification was performed on V4 variable regions, purified and pooled before sequencing using Ion S5TMXL platform. And (3) exporting the crash q file by offline data, performing quality control by using Cutadaptt V1.9.1, reserving the sequence with the length of 200-400bp, and performing chimera filtration by using VSearch V2.9.0 to obtain effective data which can be used for subsequent analysis, namely Clean reads. OTUs (operational Taxomic units) were clustered at 97% Identity (Identity) using Uperase V7.0.1001 for the Effective Tags of the samples, followed by species annotation of representative sequences of OTUs according to the SILVA132 database.
3. Microbial flora analysis
The microbial community Diversity within the samples (Alpha Diversity) and between the samples (Beta Diversity) were analyzed using QIIME V.1.9.1. Differences in the microbiota diversity index shannon, simpson, chao1, ACE, goods _ coverage and PD _ white _ tree of metastatic and non-metastatic lymph node tissue were compared by the Wilcox test with a p value of less than 0.05 as a significant statistical difference. The Bray-Curtis distances reflecting Beta diversity were calculated using phylogenetic relationships between OTUs, and Wilcox's test was performed by Principal coordinate Analysis (PCoA), Principal Co-ordinates Analysis, with p values less than 0.05 as significant statistical differences. Linear Differential Analysis (LDA) effect size (LEfSe) was used to look for differential species between metastatic and non-metastatic lymph nodes and LDA values were reflected by histograms and phylogenetic distributions by cladogram. A LDA value greater than 4.0 and a p value less than 0.05 was taken as a significant statistical difference. After species correlation coefficient matrixes are obtained through Spearman's rank correlation test calculation on relative abundance of all sample genus levels, connections with correlation coefficient <0.6 and node abundance less than 0.01% are removed, and a Microbial flora co-occurrence network diagram (Microbial co-occurrence network) is obtained.
ROC analysis
The differential characteristics of the microbial flora described above, including diversity index and differential species, were analyzed for the prediction of total survival over three years, and table 1 shows the area under the curve (AUC), P-value, sensitivity and specificity, as well as the corresponding Youden index and cut-off.
Cox survival assay
The above threshold values were used to classify the diversity index values and the relative abundance values of the different species into two groups, high and low, and the relationship between them and the overall survival was analyzed, see table 2. Age, tumor size, lymph node metastasis, differentiation and p16 status were corrected in the multifactorial Cox analysis.
Results of the experiment
We compared the alpha and beta diversity of the flora in 38 lymph node tissues with metastasis and 29 lymph node tissues without metastasis.
1. We found that the index Chao1, which represents the abundance of species in the sample, was statistically significant, i.e., the abundance of species in the metastatic lymph nodes of HNC patients was significantly higher than that in HNC non-metastatic lymph nodes (fig. 1A).
2. Based on the beta diversity of the Bray-Curtis distance, i.e., the differences between HNC samples, which are statistically significant, we found that the flora structure of the metastatic and non-metastatic lymph nodes of HNC patients are significantly different (FIG. 1B).
3. We found that the bacterial population species of the metastatic lymph nodes and non-metastatic lymph nodes of HNC patients have similar species distribution at the phylum level (fig. 1C), but we analyzed and identified 19 different species in the bacterial population species of the metastatic lymph nodes and non-metastatic lymph nodes of HNC patients using the LEfSe method (fig. 1D). The predominant flora in the negative lymph nodes of HNC patients include Firmicutes, Bacilli, Bacillales, Bacillaceae, Bacillus, and villobacteriaceae; in contrast, 13 dominant bacterial groups were found in metastatic lymph nodes of HNC patients, including the order of the Tobobacterium (Caulobacter), the family of the Tobobacterium (Caulobacter), the species of Oesophagostomum (Moraxella-oslorensis), the class of the Gamma-Proteobacteria (Gamma-Proteobacteria), the unclassified class of the Gamma-Proteobacteria (unidentified _ Gamma-Proteobacteria), the genus of the Aquifex (Enhydrolacter), the class of the anaerobic Corynebacteria (Anaroleae), the order of the anaerobic Corynebacteriales (Anaroleae), the family of the anaerobic Corynebacteriaceae (Lactobacillus), the genus of Lactobacillus (Lactobacillus), the phylum of the Chloroflexi (Chloroflexi), and the phylum of Proteobacteria (Proteobacteria).
4. By means of the microbial flora symbiosis network, we found that the flora interrelation of the species of the metastatic lymph nodes and the non-metastatic lymph nodes of the HNC patients also differed at the genus level, and the result is that the characteristics of the microbiome of the metastatic lymph nodes and the non-metastatic lymph nodes of the HNC patients are found for the first time (FIG. 1E).
5. Using ROC analysis, we analyzed the prediction of the above flora difference characteristics (diversity index values and relative abundance of different species) on total survival for three years, and the area under the curve AUC and its 95% CI, P values, etc. are shown in Table 1 and FIG. 2.
(1) ROC analysis of lymph node microbial signatures in three years OS assessment: we found that differences in the lymph node flora abundance index, i.e., the alpha diversity index, Observed species, Chao1, Ace and PDwhole tree, and Proteobacteria, Gamma-Proteobacteria, unclassified Gamma-Proteobacteria, Enterobacteria and Oerskola species (Moraxella-oslorensis) of HNC patients were predictive for three-year OS of HNC patients, and the P values were statistically different.
TABLE 1 ROC analysis of three-year Total survival based on lymph node microbial signatures
| AUC(95％CI) | P value | Youden Index | Cutoff Value | Specificity(％) | Sensitivity(％) | |
| Observed_species | 0.634(0.487-0.780) | 0.036 | 0.298 | 1021.500 | 46.5 | 83.3 |
| Shannon | 0.534(0.388-0.680) | 0.327 | 0.160 | 6.938 | 53.5 | 62.5 |
| Simpson | 0.523(0.380-0.667) | 0.380 | 0.178 | 0.960 | 51.2 | 66.7 |
| Chao1 | 0.672(0.527-0.816) | 0.010 | 0.392 | 2698.032 | 76.7 | 62.5 |
| Ace | 0.685(0.540-0.830) | 0.006 | 0.449 | 2114.289 | 90.7 | 54.2 |
| Goods_coverage | 0.681(0.542-0.820) | 0.993 | 0.392 | 0.982 | 76.7 | 62.5 |
| PD_whole_tree | 0.631(0.484-0.778) | 0.039 | 0.249 | 146.265 | 79.1 | 45.8 |
| Muribaculaceae | 0.607(0.467-0.746) | 0.925 | 0.238 | 0.017 | 48.8 | 75.0 |
| Chloroflexi | 0.569(0.415-0.724) | 0.177 | 0.217 | 0.012 | 88.4 | 33.3 |
| Anaerolineae | 0.611(0.463-0.759) | 0.068 | 0.249 | 0.003 | 79.1 | 45.8 |
| Anaerolineales | 0.615(0.467-0.763) | 0.060 | 0.254 | 0.002 | 83.7 | 41.7 |
| Anaerolineaceae | 0.615(0.467-0.763) | 0.060 | 0.254 | 0.002 | 83.7 | 41.7 |
| Firmicutes | 0.686(0.550-0.822) | 0.994 | 0.424 | 0.292 | 67.4 | 75.0 |
| Bacilli | 0.775(0.657-0.893) | 1.000 | 0.461 | 0.129 | 62.8 | 83.3 |
| Bacillales | 0.749(0.631-0.866) | 1.000 | 0.466 | 0.034 | 67.4 | 79.2 |
| Bacillaceae | 0.752(0.636-0.869) | 1.000 | 0.438 | 0.032 | 60.5 | 83.3 |
| Bacillus | 0.757(0.641-0.872) | 1.000 | 0.438 | 0.029 | 60.5 | 83.3 |
| Lactobacillaceae | 0.561(0.414-0.708) | 0.797 | 0.192 | 0.030 | 44.2 | 75.0 |
| Lactobacillus | 0.578(0.431-0.726) | 0.857 | 0.203 | 0.003 | 95.3 | 25.0 |
| Proteobacteria | 0.631(0.491-0.771) | 0.039 | 0.231 | 0.455 | 81.4 | 41.7 |
| Caulobacterales | 0.609(0.466-0.753) | 0.071 | 0.239 | 0.005 | 69.8 | 54.2 |
| Caulobacteraceae | 0.603(0.459-0.747) | 0.083 | 0.216 | 0.005 | 67.4 | 54.2 |
| Gammaproteobacteria | 0.644(0.502-0.786) | 0.026 | 0.350 | 0.113 | 55.8 | 79.2 |
| unidentified_Gammaproteobacteria | 0.669(0.522-0.816) | 0.012 | 0.365 | 0.065 | 90.7 | 45.8 |
| Enhydrobacter | 0.623(0.470-0.775) | 0.050 | 0.300 | 0.017 | 88.4 | 41.7 |
| Moraxella_osloensis | 0.627(0.474-0.781) | 0.043 | 0.324 | 0.015 | 90.7 | 41.7 |
(2) Cox regression of lymph node microbial signatures and overall survival OS: according to the cutoff value corresponding to the Youden index, dividing the index value and the abundance value of the different species into a high group and a low group, and performing Cox regression survival analysis. The single and multifactorial Cox analyses are shown in table 2, wherein after correcting the effects of age, tumor size, lymph node metastasis, tumor differentiation and p16 status on overall survival, we found that the alpha diversity indices Simpson, Chao1 and Ace of the lymph node flora of HNC patients, as well as the levels of Bacilli (Bacilli), Bacillales (Bacillales), Bacillaceae (Bacillaceae), Bacilli (Bacilli), unclassified gamma-proteobacteria (unidentified _ Gammaproteobacteria), aquatics (endohydrosbacter) and osrema (Moraxella-oslensis) correlate with overall survival OS of HNC patients and are novel markers predicted by the OS of HNC patients.
TABLE 2 Cox regression of lymph node microbial signatures and Total survival OS
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A biomarker for prognostic assessment of head and neck cancer, the biomarker comprising any one or more selected from the group consisting of:
firmicutes, Bacilli, caulobacteriales, caulobacteriaceae, orazaceae, moraxellaceae, Gammaproteobacteria, unclassified Gammaproteobacteria, lactariiformes, Lactobacillus acidophilus, Lactobacillus, anaerobacter, anaerobactereobacillus, anaerobactereobactereobaeae, Lactobacillus, and Proteobacteria.
2. The biomarker of claim 1, selected from lymph nodes of patients with head and neck cancer.
3. The biomarker of claim 1, wherein the prognostic assessment includes predictive assessment of the survival of a head and neck cancer patient;
preferably, the survival of the head and neck cancer patient includes Overall Survival (OS) and three-year survival.
4. Use of a substance for detecting a biomarker according to any of claims 1 to 3 for the preparation of a product for the prognostic assessment of head and neck cancer.
5. A product comprising a substance for detecting a biomarker according to any of claims 1 to 3, for use in the prognostic assessment of a patient with head and neck cancer;
preferably, the prognostic assessment comprises predictive assessment of the survival of a head and neck cancer patient;
preferably, the survival of the head and neck cancer patient comprises Overall Survival (OS) and three-year survival;
preferably, the product comprises reagents, devices and/or means for detecting the (relative) abundance and/or amount (number of OTUs) of a biomarker in a sample to be tested;
preferably, the product is a kit.
6. Use of the biomarker according to any one of claims 1 to 3, the product according to claim 5 for the preparation of a system for the prognostic risk assessment of head and neck cancer; preferably, the head and neck cancer prognosis risk assessment system is used for predictive assessment of head and neck cancer survival, including Overall Survival (OS) and three-year survival.
7. A head and neck cancer prognostic risk assessment system, comprising an analysis unit and an assessment unit;
wherein the analysis unit is configured to obtain data of patient-related risk factors;
the evaluation unit outputs the prognosis state of the patient according to the acquired data of the risk factors;
preferably, said patient-associated risk factors comprise the alpha diversity index of the lymph node flora and a biomarker according to any of claims 1 to 3.
8. The system for prognostic risk assessment for head and neck cancer according to claim 7, wherein the alpha diversity index for lymph node flora comprises underneath specific, Simpson, Chao1, Ace and PD white tree;
the biomarkers include, but are not limited to, Proteobacteria (Proteobacteria), Gamma-Proteobacteria (Gamma), unclassified Gamma-Proteobacteria (unidentified _ Gamma), Aquifex (Enhydrosbacter), and Ostemora (Moraxella-osloresis), Bacteria (Bacillus), Bacillales (Bacillales), Bacillaceae (Bacillus), and Bacillus (Bacillus).
9. The system for prognostic risk assessment for head and neck cancer according to claim 8, wherein the index of alpha diversity for the lymph node flora includes Observed species, Chao1, Ace and PD whole tree when performing a three-year survival assessment for a patient with head and neck cancer;
the biomarkers include, but are not limited to, Proteobacteria (Proteobacteria), Gammaproteobacteria (Gamma), unclassified Gammaproteobacteria (unidentified _ Gamma), Aquifex (Enhydrostat), and Oersura (Moraxella-oslorensis); or the like, or, alternatively,
when performing an overall survival assessment for head and neck cancer patients, the alpha diversity index for the lymph node flora includes, but is not limited to, Simpson, Chao1, and Ace;
the biomarkers include, but are not limited to, Bacilli (Bacilli), Bacillales (Bacillales), Bacillaceae (Bacillaceae), Bacilli (Bacillus), unclassified gammophytes (unidentified _ Gammaproteobacteria), aquaticus (aquabacter), and oras terrestris (Moraxella-ostreae).
10. A method for prognostic risk assessment in a patient with head and neck cancer, said method comprising assessing using a biomarker according to any one of claims 1 to 3 and/or a system for prognostic risk assessment of head and neck cancer according to any one of claims 7 to 9;
the prognostic risk assessment includes predictive assessment of the survival of the patient's head and neck cancer, including Overall Survival (OS) and three-year survival.
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