Host response marker for early diagnosis of severe septic shock
By conducting transcriptome sequencing analysis of clinical samples, genetic markers were screened out and diagnostic models were constructed, and the problem of insufficient sensitivity and specificity of diagnostic markers of septic shock in the prior art was solved, and efficient early diagnosis and critical condition evaluation were achieved.
Patent Information
- Application Number
- CN202411801895.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-09
- Publication Date
- 2025-05-13
Smart Images
Figure CN119979692A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of biomedical technology, and in particular to a host response marker for early diagnosis of severe septic shock. Background Art
[0002] Sepsis is a systemic inflammatory response syndrome caused by a disordered host infection response. The disease progresses rapidly and can cause shock and multiple organ dysfunction in patients in severe cases. It is a common disease in intensive care units with a high mortality rate, which seriously threatens the life and health of patients. Therefore, early identification and diagnosis of septic shock are of great significance for timely treatment of the disease and patient prognosis. However, the existing clinical biomarkers for sepsis or septic shock, such as procalcitonin and IL-6, have a narrow detection range, low sensitivity and specificity, and cannot comprehensively reflect the overall intensity and degree of the host infection response. Therefore, it is very important to find highly specific and sensitive diagnostic biomarkers for septic shock.
[0003] In recent years, biomarker screening based on host transcriptome sequencing is expected to provide a reliable source for the diagnosis of some diseases. Transcriptome sequencing (RNA-sequencing, RNA-seq) is the whole RNA sequence information of cells or tissues obtained by high-throughput sequencing technology, mainly including coding mRNA and non-coding RNA, which is widely used in the study of disease pathogenesis and the screening of related markers. With the rapid development of new high-throughput sequencing technology, RNA-seq is increasingly used in host response research. Through transcriptomic research, almost all the transcriptional information of a specific tissue or cell in a certain state can be obtained comprehensively and quickly, which can realize multi-faceted research on genes.
[0004] At present, some machine learning models for sepsis phenotypes have been reported, but there are few reports on classification models for more severe septic shock. In addition, the subjects of related studies have basically excluded immunosuppressed patients, and early identification of septic shock in such patients is crucial for diagnosis and treatment. Therefore, early diagnosis of septic shock has high clinical value. Summary of the invention
[0005] The purpose of the present invention is to provide a host response marker for early diagnosis of severe septic shock, which can be used to diagnose septic shock and has good specificity and sensitivity, as well as relatively stable diagnostic performance.
[0006] To this end, in a first aspect, the present invention provides a marker for diagnosing septic shock, comprising the following combination of RNA encodings or proteins encoded thereby: CHKA, SPRY1, NRTN, and KRTAP1-4.
[0007] The second aspect of the present invention provides use of the marker in preparing a product for diagnosing septic shock.
[0008] In some embodiments, the product includes a detection reagent for detecting the expression level of the marker; the expression level refers to the nucleic acid expression level and / or protein expression level of the marker.
[0009] In some embodiments, the detection reagent includes one or a combination of two or more selected from the following group: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker.
[0010] In some embodiments, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment, or a single-chain Fv fragment.
[0011] In some embodiments, the sample detected by the detection reagent is from a body fluid of a subject; for example, urine, blood, serum, plasma, saliva, lymph, cerebrospinal fluid, ascites, feces, bile, tissue fluid, etc.
[0012] In some embodiments, the sample is from the subject's blood.
[0013] In some embodiments, the subject is a mammal; eg, a human.
[0014] In some embodiments, the product is selected from the following group: reagents, test kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms.
[0015] In some embodiments, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.
[0016] A third aspect of the present invention provides a product for diagnosing septic shock, the product comprising a detection reagent, the detection reagent detecting the expression level of the marker described in the first aspect of the present invention.
[0017] In some embodiments, the detection reagent includes one or a combination of two or more selected from the following group: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker.
[0018] In some embodiments, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment, or a single-chain Fv fragment.
[0019] In some embodiments, the product is selected from the following group: reagents, kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms.
[0020] In some embodiments, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.
[0021] A fourth aspect of the present invention provides a method for training a model for diagnosing septic shock, comprising the following steps:
[0022] Step S1: obtaining expression level data of a marker in a sample; the marker is the marker described in the first aspect of the present invention;
[0023] Step S2: Using the expression level data of the marker obtained in step S1, the machine learning model is trained to obtain a model for diagnosing septic shock.
[0024] In some embodiments, the machine learning model is a binary logistic regression model.
[0025] In some embodiments, the step S2 further includes: validating the model for diagnosing septic shock.
[0026] In some embodiments, the model for diagnosing septic shock is validated by Bootstrap method and cross-validation method.
[0027] In some embodiments, the marker is screened by the following steps: extracting total RNA of the sample, constructing an lncRNA chain-specific transcriptome library and sequencing it to obtain transcriptome sequencing data; analyzing the transcriptome sequencing data and calculating the number of gene sequences (reads); then screening the differentially expressed genes (DEGs) of the comparison group of non-septic shock vs. septic shock through the R program package, and the screening criteria are P<0.05, |log2FoldChange|≥1 (difference fold); using the least absolute shrinkage and selection operator regression (LASSO) method to perform the first step feature screening on DEGs to obtain DEGs that can distinguish septic shock; performing random forest analysis on the features and calculating the importance (Importance); selecting features with Importance>2.0; using the variable stepwise regression method, screening the minimum feature combination to complete the screening of the marker combination; screening to obtain the marker described in the first aspect of the present invention.
[0028] In some embodiments, the sample includes samples from: non-septic shock infection patients and septic shock patients.
[0029] In some embodiments, the number of samples is at least 40, at least 60, or at least 100.
[0030] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:
[0031] (1) The present invention screens a group of gene markers by performing transcriptome sequencing analysis on clinical samples, and further constructs a disease diagnosis model based on this group of markers, which can be used to assist in the diagnosis of septic shock. The markers provided by the present invention are a combination of multiple genes and have more stable diagnostic performance than a single indicator.
[0032] (2) The present invention constructs a classification model for non-septic shock and septic shock, which can not only perform early diagnosis of septic shock, but also assess the critical condition of the patient's infection.
[0033] (3) The research subjects of the present invention are not limited to patients with normal immunity, but also include patients with abnormal or damaged immunity, so that the diagnostic model has a higher generalization ability. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only used for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. In the accompanying drawings:
[0035] Figure 1 : In the discovery cohort, the ROC curve and classification performance of the model provided by the present invention for diagnosing septic shock;
[0036] Figure 2 : In the discovery cohort, the Bootstrap method was used to verify the AUC distribution results of the model classification;
[0037] Figure 3 : In the validation cohort, the ROC curve and classification performance of the model provided by the present invention for diagnosing septic shock. DETAILED DESCRIPTION
[0038] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0039] Unless otherwise specified, the terms used herein have the meanings commonly understood by those skilled in the art.
[0040] In this article, the term "severe septic shock" has the same meaning as "septic shock", which is usually caused by the further development of sepsis. The term sepsis is a syndrome of organ dysfunction caused by the body's dysregulated response to infection, mainly manifested by symptoms such as chills, fever (or hypothermia), palpitations, shortness of breath, and changes in mental status. Sepsis can further develop into more severe septic shock, which can lead to organ dysfunction and circulatory disorders, with a high mortality rate.
[0041] Herein, a "subject" is a mammal. Mammals include, but are not limited to, primates (e.g., humans and non-human primates such as monkeys) or other mammals (e.g., cattle, sheep, cats, dogs, horses, rabbits, and rodents such as mice and rats). In particular, the subject is a human.
[0042] The relevant information of the markers involved in this article is as follows:
[0043] Table 1
[0044]
[0045]
[0046] Example 1 Case Collection
[0047] 1 mL of whole blood samples were collected from patients with suspected bloodstream infection at Peking University People's Hospital, and RNA preservation solution (ZYMO DNA / RNA shield) was added in a 1:1 ratio and then frozen at -80°C. The basic information, medical history, and medical records of the patients were reviewed and counted through the hospital's laboratory report and case reporting system. All information was desensitized, and the protocol was approved by the Ethics Committee of Peking University People's Hospital. According to the sepsis diagnostic criteria version 3.0, 31 patients with septic shock and 176 patients with non-septic shock infection were screened. Specific inclusion and exclusion criteria include:
[0048] Follow up suspected infected patients with fever (body temperature ≥ 37 degrees Celsius), previous history of infection, endotracheal intubation or mechanical ventilation, abnormal imaging or elevated inflammatory indicators. Inclusion criteria: positive results of pathogen detection and exclusion of contamination; whole blood samples within three days before and after the pathogen detection time; successful completion of RNA-seq and available data. Exclusion criteria: positive pathogen detection but suspected contamination; no qualified whole blood samples; unsuccessful completion of RNA-seq and no available data.
[0049] Example 2
[0050] The clinical samples of Example 1 were divided into a discovery cohort (training set) and a validation cohort (test set) using a stratified sampling method, wherein the ratio of cases in the training set to the test set was approximately 5: 5. The training set was used for feature screening and model construction, and the test set was used for model validation.
[0051] 1. Extraction of total RNA from whole blood samples: Take 2 mL of whole blood sample containing preservation solution from Example 1 and use ZYMO Quick-DNA / RNA TM Total RNA was extracted using Microprep Plus Kit. Nucleic acid quantification was performed using Vazyme Equalbit RNA HS Assay Kit and Thermo Fisher Scientific Qubit4.0. All RNA samples were frozen at -80°C until the construction of lncRNA strand-specific transcriptome libraries.
[0052] 2. Construction and sequencing of lncRNA strand-specific transcriptome library: (1) Use Vazyme UltraClean Ribo-off rRNA Depletion Kit (Human / Mouse / Rat) to remove ribosomal RNA (rRNA) from total RNA; (2) Use Vazyme UltraClean Universal V8 RNA-seq Library Prep Kit for Illumina to complete the construction of RNA sequencing library after rRNA removal; (3) Use Vazyme Equalbit DNA HS Assay Kit and Thermo Fisher Scientific Qubit4.0 to complete library quantification; (4) Use Illumina Hiseq platform for sequencing, paired-end sequencing, sequencing read length of 150bp, and obtain 12G data volume for each sample, which is the original transcriptome data.
[0053] 3. Bioinformatics analysis: The raw transcriptome data was subjected to connector removal, quality control, and filtering of low-quality sequences (reads) to obtain RNA-seq clean data. Bowtie2 software was used to align the GRCh38 human reference genome, and FeatureCounts was used to calculate the number of sequences for each gene. The measured gene reads were standardized and normalized. The R package EdgeR was used to screen differentially expressed genes (DEGs) between septic shock and non-septic shock, with the screening criteria of P < 0.05 and |log2FoldChange| ≥ 1 (difference fold).
[0054] 4. Machine learning feature screening and model construction: (1) The least absolute shrinkage and selection operator regression (LASSO) method was used to perform the first step of feature screening on DEGs to obtain DEGs that can distinguish septic shock; the screened features were subjected to random forest analysis and the importance was calculated. Features with Importance>2.0 were selected to complete the second step of feature screening; the minimum feature combination was screened using the stepwise regression method to complete the final screening. The final number of combined features was 4, namely CHKA, SPRY1, NRTN, and KRTAP1-4. (2) The diagnostic model was constructed using the final feature combination and binary logistic regression model algorithm, and the model performance was verified using the Bootstrap method and cross-validation method. The model function verification results are shown in the figure. Figures 1 to 3 shown.
[0055] According to the validation results, the model provided by the present invention has good specificity and sensitivity.
[0056] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.
Claims
1. A marker for diagnosing septic shock, characterized in that: Includes the following group of coding RNA combinations or proteins encoded by them: CHKA, SPRY1, NRTN, KRTAP1-4.
2. Use of the marker according to claim 1 in the preparation of a product for diagnosing septic shock.
3. The use according to claim 2, characterized in that The product includes a detection reagent for detecting the expression level of the marker; Preferably, the expression level refers to the nucleic acid expression level and / or protein expression level of the marker.
4. The use according to claim 3, characterized in that The detection reagent comprises one or a combination of two or more selected from the following groups: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker; Preferably, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment or a single-chain Fv fragment; Preferably, the sample detected by the detection reagent is from the body fluid of the subject; Preferably, the body fluid is selected from urine, blood, serum, plasma, saliva, lymph, cerebrospinal fluid, ascites, feces, bile, tissue fluid.
5. The use according to any one of claims 2 to 4, characterized in that: The product is selected from the group consisting of reagents, test kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms; Preferably, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.
6. A product for diagnosing septic shock, characterized in that: The product comprises a detection reagent, which detects the expression level of the marker of claim 1.
7. The product according to claim 6, characterized in that The detection reagent comprises one or a combination of two or more selected from the following groups: a mass spectrometry identification reagent, an antibody, a probe, or a primer that specifically binds to the marker; Preferably, the antibody is a full-length antibody, a Fab fragment, a Fab' fragment, a F(ab')2 fragment, a double-chain Fv fragment or a single-chain Fv fragment; Preferably, the product is selected from the group consisting of reagents, test kits, test strips, gene chips, high-throughput sequencing platforms, antibody chips, and instrument platforms; Preferably, the instrument platform comprises a measurement module for measuring the expression level of the marker in the sample to be tested.
8. A training method for a model for diagnosing septic shock, characterized in that: The following steps are involved: Step S1: obtaining expression level data of a marker in a sample; the marker is the marker according to claim 1; Step S2: Using the expression level data of the marker obtained in step S1, the machine learning model is trained to obtain a model for diagnosing septic shock.
9. The training method according to claim 8, characterized in that: The machine learning model is a binary logistic regression model; Preferably, after step S2, the method further includes: verifying the model for diagnosing septic shock; Preferably, the model for diagnosing septic shock is validated by Bootstrap method and cross-validation method.
10. The training method according to claim 8 or 9, characterized in that: The samples include samples from the following subjects: patients with non-septic shock infection and patients with septic shock; Preferably, the number of samples is at least 40, at least 60 or at least 100.