Biomarker for identifying sepsis and non-infectious systemic inflammation response and application thereof
By detecting the gene transcription or protein levels of CD177, IL18, MMP8, and S100A8, and combining this with a machine learning model, the problem of early and accurate diagnosis of sepsis and differentiation between sepsis and non-infectious systemic inflammatory response has been solved, achieving rapid and reliable diagnosis and differentiation.
Patent Information
- Application Number
- CN202511006927.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-22
- Publication Date
- 2025-12-12
AI Technical Summary
Current technologies struggle to diagnose sepsis accurately in its early stages and differentiate it from non-infectious systemic inflammatory responses. Traditional methods are time-consuming and prone to misdiagnosis, and there is a lack of effective biomarkers in clinical practice to distinguish between the two.
Four biomarkers, CD177, IL18, MMP8, and S100A8, were used. The gene transcription or protein levels of these biomarkers were detected by methods such as quantitative PCR, high-throughput sequencing, BCA protein quantification, immunofluorescence, and Western blotting. A machine learning model was then constructed for diagnosis and differentiation.
It enables rapid and reliable diagnosis of sepsis and differentiation between sepsis and non-infectious systemic inflammatory response, overcoming the limitations of traditional methods and providing an important means for early intervention and treatment.
Smart Images
Figure CN121109570A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of biomarkers, in particular to a marker for diagnosing sepsis and distinguishing sepsis from non-infectious systemic inflammatory response. BACKGROUND
[0002] Sepsis is a systemic inflammatory response syndrome caused by infection, usually manifested as high fever, rapid heart rate, shortness of breath and other symptoms. Its development is rapid and can cause multiple organ failure, which can be life-threatening. Early and accurate diagnosis of sepsis is crucial for developing effective treatment plans. However, traditional diagnostic methods rely heavily on pathogen culture, which not only takes time, usually several days to obtain results, but in some cases can produce false negatives or false positives, leading to missed or misdiagnosed sepsis in clinical practice, missing the best treatment opportunity.
[0003] In addition, non-infectious systemic inflammatory response can also exhibit similar clinical symptoms to sepsis, such as acute respiratory distress, shock, etc., which poses a great challenge to doctors in differential diagnosis. Non-infectious systemic inflammatory response can be caused by a variety of factors, including trauma, surgery, allergic reactions, etc., making it even more complex to accurately distinguish between the two states in a clinical setting.
[0004] In recent years, biomarkers have gradually gained attention as a potential tool for rapid diagnosis. However, there have been no reports in the field of markers for distinguishing between sepsis and non-infectious systemic inflammatory response. SUMMARY
[0005] To solve the above problems, the present application provides a marker composition that can be used for the diagnosis of sepsis and the distinction between sepsis and non-infectious systemic inflammatory response. Specifically,
[0006] The first aspect of the present application provides the use of a reagent for detecting a marker combination in the preparation of a product for diagnosing sepsis, wherein the marker combination is selected from one or more of CD177, IL18, MMP8 and S100A8.
[0007] The second aspect of the present application provides the use of a reagent for detecting a marker combination in the preparation of a product for distinguishing between sepsis and non-infectious systemic inflammatory response, wherein the marker combination is selected from one or more of CD177, IL18, MMP8 and S100A8.
[0008] In some embodiments, the marker combination consists of CD177, IL18, MMP8 and S100A8.
[0009] In some embodiments, the reagent detects the gene transcription level of the marker by a combination of one or several methods selected from the group consisting of quantitative PCR and high-throughput sequencing.
[0010] In some embodiments, the reagent detects the protein level of the marker by a combination of one or several methods selected from the group consisting of BCA protein quantification, immunofluorescence, Western blot, proteomics.
[0011] In some embodiments, the reagent detects the gene transcription level of the marker by a combination of one or several methods selected from the group consisting of quantitative PCR and high-throughput sequencing.
[0012] In some embodiments, the reagent detects the protein level of the marker by a combination of one or several methods selected from the group consisting of BCA protein quantification, immunofluorescence, Western blot, proteomics.
[0013] In some embodiments, the reagent detects the protein level of the marker by a combination of one or several methods selected from the group consisting of BCA protein quantification, immunofluorescence, Western blot, proteomics.
[0014] In some embodiments, the instruction book records the standard for diagnosing sepsis according to the detection level of the marker combination in the subject's tissue.
[0015] In some embodiments, the instruction book records the standard for diagnosing sepsis according to the detection level of the marker combination in the subject's tissue.
[0016] In some embodiments, the instruction book records the standard for diagnosing sepsis according to the detection level of the marker combination in the subject's tissue.
[0017] In some embodiments, the reagent detects the gene transcription level of the marker by a combination of one or several methods selected from the group consisting of quantitative PCR and high-throughput sequencing. In some embodiments, the reagent detects the protein level of the marker by a combination of one or several methods selected from the group consisting of BCA protein quantification, immunofluorescence, Western blot, proteomics; in some embodiments, the instruction book records the standard for distinguishing sepsis and non-infectious systemic inflammatory response according to the detection level of the marker combination in the subject's tissue.
[0018] A fifth aspect of the present invention provides a system for diagnosing sepsis, comprising the following modules: a data input module for inputting data on gene transcription levels or protein levels in a combination of biomarkers for a subject, wherein the biomarker combination is selected from one or more of CD177, IL18, MMP8, and S100A8; a data storage module for storing data on gene transcription levels or protein levels in the biomarker combination in a population sample, as well as information on whether each sample originates from a patient with sepsis; and an analysis module connected to the data input module and the data storage module, wherein the analysis module constructs a machine learning model using the stored data on gene transcription levels or protein levels in the biomarker combination in the population sample and information on whether each sample originates from a patient with sepsis, and determines whether the subject has sepsis based on the machine learning model.
[0019] A sixth aspect of the present invention provides a system for distinguishing between sepsis and non-infectious systemic inflammatory response, comprising the following modules: a data input module for inputting data on gene transcription levels or protein levels in a combination of biomarkers for a subject, wherein the biomarker combination is selected from one or more of CD177, IL18, MMP8, and S100A8; a data storage module for storing data on gene transcription levels or protein levels in the biomarker combination in a population sample and information on whether each sample originates from a patient with sepsis or a non-infectious systemic inflammatory response; and an analysis module connected to the data input module and the data storage module, wherein a machine learning model is constructed using the gene transcription levels or protein levels in the biomarker combination in the population sample stored in the data storage module and information on whether each sample originates from a patient with sepsis or a non-infectious systemic inflammatory response, and the machine learning model is used to determine whether the subject has sepsis or a non-infectious systemic inflammatory response.
[0020] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0021] 1) The four biomarkers CD177, IL18, MMP8 and S100A8 proposed in this invention have the ability to quickly determine the risk of sepsis by detecting changes in their concentration in peripheral plasma.
[0022] 2) The markers of the present invention can also be used to differentiate between sepsis and non-infectious systemic inflammatory responses.
[0023] 3) By performing quantitative analysis on these four biomarkers, the limitations of traditional methods can be effectively overcome, providing a more reliable and rapid diagnostic tool for clinical use, thus playing an important role in early intervention and treatment. Attached Figure Description
[0024] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:
[0025] Figure 1 Display the ROC curves for CD177, IL18, MMP8, and S100A8.
[0026] Figure 2 Display the AUC of CD177, IL18, MMP8, and S100A8.
[0027] Figure 3 Display the cutoff values for CD177, IL18, MMP8, and S100A8. Detailed Implementation
[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0029] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
[0030] Example 1: Screening and Validation of Biomarkers
[0031] For case screening, the research team selected 102 sepsis patients and 40 healthy controls as the discovery cohort, and used DIA non-target proteomics for initial screening. Subsequently, in validation cohort 1, TMT proteomics technology was used to analyze samples from 40 sepsis patients and 20 healthy controls to verify the reliability of the preliminary results. Furthermore, in validation cohort 2, the research team used ELISA to quantitatively detect biomarker levels in 30 sepsis patients, 10 septic shock patients, 30 healthy controls, and 10 non-infectious SIRS (systemic inflammatory response syndrome) patients.
[0032] Case screening:
[0033] The patient's SIRS score was assessed, and a score of 2 or higher was considered SIRS.
[0034] The SIRS assessment criteria are as follows:
[0035] ① Body temperature >38℃ or <36℃;
[0036] ② Heart rate > 90 beats / min;
[0037] ③ Breathing rate > 20 breaths / minute;
[0038] ④ White blood cell count >12×10⁹ / L or <4×10⁹ / L.
[0039] The SOFA score of patients in the infection group was calculated, and patients who met the diagnosis were included in the sepsis group according to the International Consensus on the Definition of Sepsis and Septic Shock, Third Edition (hereinafter referred to as "Sepsis 3.0").
[0040] Non-infectious SIRS patients meet the SIRS scoring criteria but have no pathogen infection.
[0041] The method for ELISA quantitative detection is as follows:
[0042] Enzyme-linked immunosorbent assay (ELISA) is a qualitative and quantitative detection method that utilizes the specificity of antigen-antibody binding in an immune reaction. The experiment employs a "sandwich method," where capture antibodies are coated onto an ELISA plate to capture target proteins in both the sample and standards. The biotinylated detection antibody binds to the target protein, forming an immune complex, which further binds to the SABC complex. After adding TMB chromogenic buffer, a blue color appears in the reaction wells if target protein is present; this turns yellow upon the addition of stop buffer. The OD value is measured at 450 nm using an ELISA reader. The target protein concentration is directly proportional to the OD value, and a standard curve is plotted to calculate the target protein concentration in the sample.
[0043] For plasma samples, select the appropriate anticoagulant according to the kit requirements, and collect the supernatant by centrifugation.
[0044] All reagents need to be preheated and diluted in advance, and working solutions for biotinylated antibodies and SABC complexes should be prepared.
[0045] Add the sample and standard, react at 37°C for 50 minutes, and wash 3 times.
[0046] Add biotinylated detection antibody, react at 37°C for 50 minutes, and wash 3 times.
[0047] Add the SABC complex, react at 37°C for 30 minutes, and wash three times.
[0048] Add TMB colorimetric solution, react at 37°C for 10-20 minutes, and finally add stop solution and take the reading.
[0049] Result judgment and calculation:
[0050] When calculating the OD value, the blank well value needs to be subtracted, and a standard curve is plotted with the standard concentration on the x-axis and the OD value on the y-axis. The corresponding content is then calculated based on the sample OD value.
[0051] Figure 1Display the ROC curves for CD177, IL18, MMP8, and S100A8. Figure 2 Display the AUC of CD177, IL18, MMP8, and S100A8. Figure 3 Display the cutoff values for CD177, IL18, MMP8, and S100A8. (Source: [Insert Source Here]) Figures 1-3 The results show that CD177, IL18, MMP8, and S100A8, alone or in combination, can be accurately used for the diagnosis of sepsis.
[0052] Subsequent studies have shown that CD177, IL18, MMP8, and S100A8, alone or in combination, can also be accurately used to differentiate between sepsis and non-infectious systemic inflammatory responses.
Claims
1. The application of reagents for detecting biomarker combinations in the preparation of products for the diagnosis of sepsis, wherein, The combination of markers is selected from one or more of CD177, IL18, MMP8 and S100A8.
2. The application of reagents for detecting biomarker combinations in the preparation of products that differentiate between sepsis and non-infectious systemic inflammatory responses, wherein, The combination of markers is selected from one or more of CD177, IL18, MMP8 and S100A8.
3. The application according to claim 1 or 2, wherein the combination of markers consists of CD177, IL18, MMP8 and S100A8.
4. The application according to any one of claims 1-3, wherein, The reagents are used to detect the gene transcription level of the biomarker using a combination of one or more methods, including quantitative PCR and high-throughput sequencing.
5. The application according to any one of claims 1-3, wherein, The reagent is used to detect the protein level of the biomarker using one or a combination of methods including BCA protein quantification, immunofluorescence, Western blotting, and proteomics.
6. A reagent kit for the diagnosis of sepsis, characterized in that, The kit includes reagents for detecting a combination of biomarkers in the tissues of a subject, and optionally, instructions for use, wherein the combination of biomarkers is selected from one or more of CD177, IL18, MMP8, and S100A8; preferably, the reagents detect the gene transcription level of the biomarkers by a combination of one or more methods selected from quantitative PCR and high-throughput sequencing, and / or the reagents detect the protein level of the biomarkers by a combination of one or more methods selected from BCA protein quantification, immunofluorescence, Western blotting, and proteomics; preferably, the instructions for use describe the criteria for diagnosing sepsis based on the detection level of the biomarker combination in the tissues of a subject.
7. The kit according to claim 5, wherein, The cutoff values for the CD177 marker, IL18 marker, MMP8 marker, and S100A8 marker described in the description are 5.92 ng / ml, 332.32 ng / ml, 4733.70 ng / ml, and 1007.90 ng / ml, respectively.
8. A kit for differentiating between sepsis and non-infectious systemic inflammatory responses, characterized in that, The kit includes reagents for detecting a combination of biomarkers in the tissues of a subject, and optionally, instructions for use, wherein the biomarker combination is selected from one or more of CD177, IL18, MMP8, and S100A8; preferably, the reagents detect the gene transcription level of the biomarkers by a combination of one or more methods selected from quantitative PCR and high-throughput sequencing, and / or the reagents detect the protein level of the biomarkers by a combination of one or more methods selected from BCA protein quantification, immunofluorescence, Western blotting, and proteomics; preferably, the instructions for use describe criteria for distinguishing between sepsis and non-infectious systemic inflammatory responses based on the detection level of the biomarker combination in the subject's tissues.
9. A system for diagnosing sepsis, characterized in that, Includes the following modules: The data input module is used to input data on gene transcription level or protein level in the subject biomarker combination, wherein the biomarker combination is selected from one or more of CD177, IL18, MMP8 and S100A8; The data storage module is used to store data on gene transcription or protein levels in the biomarker combinations in the population sample, as well as information on whether each sample originated from a patient with sepsis. The analysis module is connected to the data input module and the data storage module respectively. It uses the gene transcription level or protein level data of the biomarker combination in the stored population sample stored in the data storage module and the information on whether each sample comes from a sepsis patient to construct a machine learning model, and determines whether the subject has sepsis based on the machine learning model.
10. A system for differentiating between sepsis and insensitive systemic inflammatory responses, characterized in that, Includes the following modules: The data input module is used to input data on gene transcription level or protein level in the subject biomarker combination, wherein the biomarker combination is selected from one or more of CD177, IL18, MMP8 and S100A8; The data storage module is used to store data on gene transcription or protein levels in the biomarker combinations in the population sample, as well as information on whether each sample is from a patient with sepsis or a non-infectious systemic inflammatory response. The analysis module is connected to the data input module and the data storage module respectively. It uses the gene transcription level or protein level data of the biomarker combination in the stored population sample stored in the data storage module and the information that each sample comes from patients with sepsis or non-infectious systemic inflammatory response to construct a machine learning model, and determines whether the subject has sepsis or non-infectious systemic inflammatory response based on the machine learning model.