Pregnostic marker gene related to sepsis blood coagulation and application of prognostic marker gene in preparation of sepsis prognosis prediction and diagnosis product

Abstract

The invention discloses a sepsis blood coagulation related prognosis marker gene and application thereof in preparation of sepsis prognosis prediction and diagnosis products, and belongs to the technical field of biology. The sepsis blood coagulation related prognostic marker genes are composed of WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4, and a prognostic scoring system (a sepsis prognostic risk prediction model) is further established based on the six sepsis blood coagulation related prognostic marker genes. A time-dependent ROC curve and multivariable Cox regression analysis show that the discrimination capability of six gene prognosis scoring systems developed by the invention on the total survival rate (OS) is superior to that of other clinical factors (age and gender), and the area under a three-week curve is not inferior to the area within one or two weeks; therefore, the prediction scoring system has lasting and relatively independent discrimination capability on the prognosis of the sepsis patient.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular relates to a prognostic marker gene related to sepsis coagulation and an application thereof in preparing a sepsis prognosis prediction and diagnosis product. Background Art

[0002] Sepsis is a life-threatening disease, a syndrome of multiple organ dysfunction caused by a systemic inflammatory response of the host, often accompanied by high morbidity and high risk of death. Its mechanism involves pathogenic microorganisms, systemic inflammatory response, abnormal energy metabolism, immunosuppression, multiple target organs, and other aspects. Although we have made significant progress in medical technologies such as anti-infection treatment, fluid resuscitation, and multi-organ support, this cannot change the fact that patients with sepsis have a malignant progression and a high probability of death.

[0003] Sepsis-induced coagulopathy (SIC) is characterized by the unique feature of coagulation activation with excessive inhibition of fibrinolysis, which is recognized as an independent indicator of sepsis severity and mortality. Patients with sepsis are initially more likely to exhibit a prothrombotic state through extrinsic activation pathways, inhibition of anticoagulant pathways, and cytokine-induced amplification of coagulation. Paradoxical bleeding characteristics may occur in the later stages as the excess coagulation factors are cumulatively consumed and disseminated intravascular coagulation (DIC) is established. Anticoagulant or antithrombotic prophylactic therapy appears to benefit patients with sepsis. For example, thrombomodulin (TM), an endothelial anticoagulant cofactor that blocks the activity of proinflammatory cytokines, has been recommended as a protective therapy for patients with sepsis and SIC. Based on the recently published Surviving Sepsis Campaign guidelines, antithrombotic prophylaxis with low molecular weight heparin is strongly recommended for patients with sepsis or septic shock. In addition, in the latest Japanese Surviving Sepsis Campaign guidelines, anticoagulants are also recommended for the treatment of sepsis-associated DIC. Multiple clinical trials and large observational studies have reported that thrombosis is a common and fatal complication in hospitalized patients with COVID-19. Therefore, expert consensus and treatment guidelines also strongly recommend the use of antithrombotic prophylaxis. However, not all patients benefit from anticoagulants. Vincent JL et al. did not find that TM was beneficial for SIC patients in a phase III randomized controlled trial. Some COVID-19 patients who received thromboprophylaxis died from fatal bleeding, such as airway bleeding, gastrointestinal bleeding, or chest bleeding, indicating that patients with sepsis may be in a different coagulation state.

[0004] Recognizing that we are studying and treating a syndrome rather than a unique specific disease is part of studying and improving clinical problems. Precision medicine is urgently awaited in the field of sepsis. In other words, accurate diagnosis and treatment at the genomic level are necessary for future clinical practice. However, there is currently no set of coagulation-related genes that can predict sepsis prognosis and treatment responsiveness. Therefore, it is necessary to develop a set of sepsis prognostic markers and coagulation-related sepsis prognostic risk prediction systems to assess prognosis and guide treatment to meet current clinical needs. Summary of the invention

[0005] The purpose of the present invention is to provide a group of sepsis coagulation-related prognostic marker genes and their use in preparing sepsis prognosis prediction and diagnosis products, which can be used to evaluate sepsis prognosis and guide the selection of treatment plans.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a group of sepsis coagulation-related prognosis marker genes, wherein the sepsis coagulation-related prognosis marker genes consist of WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4.

[0008] The second aspect of the present invention provides a product for measuring the expression level of sepsis coagulation-related prognostic marker genes, including a product for detecting the mRNA expression levels of the six sepsis coagulation-related prognostic marker genes in sepsis peripheral blood.

[0009] Furthermore, the product includes: a probe and a primer for detecting the sepsis coagulation-related prognostic marker gene. The probe can combine with the target gene sequence through molecular hybridization to generate a hybridization signal, and the primer can amplify the target gene through a PCR-based technique.

[0010] The test sample in the present invention is peripheral blood from a patient with sepsis. The mRNA or DNA in the leukocytes is separated from the peripheral blood sample, and the expression levels of 6 sepsis coagulation-related prognostic marker genes are determined, and then the clinical outcome of the patient is predicted by calculating the prognostic score. The above 6 genes are WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4.

[0011] The third aspect of the present invention provides a group of the sepsis coagulation-related prognostic marker genes and the expression level measurement products of the sepsis coagulation-related marker genes for use in establishing a model for predicting the prognosis risk of sepsis / a model for predicting the coagulation state of sepsis, establishing a sepsis prognosis risk prediction system / a sepsis coagulation state prediction system, or preparing a sepsis prognosis prediction diagnostic product / a sepsis coagulation state prediction diagnostic product.

[0012] In a fourth aspect of the present invention, a sepsis prognosis risk prediction model / sepsis coagulation state prediction model is provided, and the calculation formula of the prediction model is as follows:

[0013]

[0014] The gene i is the sepsis coagulation-related prognostic marker gene: WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4. The higher the score, the greater the risk of death of the patient, and the higher the score, the greater the risk of hypercoagulable state of the patient.

[0015] The expression level of the gene i is the mRNA expression level of the sepsis coagulation-related prognostic marker gene.

[0016] The coefficient of the gene i is obtained by performing COX multivariate regression analysis on the mRNA expression level of the sepsis coagulation-related prognostic marker gene.

[0017] Since different samples and different detection methods obtain different mRNA expression levels of each gene, the coefficients of the measured gene i will be different.

[0018] Furthermore, the method for determining the mRNA expression level of the sepsis coagulation-related prognostic marker gene includes any one of PCR, gene chip, second-generation high-throughput sequencing, etc., or a combination of at least two of them.

[0019] In a fifth aspect of the present invention, a prediction model is provided for use in the preparation of a product for predicting the prognosis risk of sepsis / a product for predicting the coagulation risk of sepsis, wherein the input variable of the prediction model is the mRNA level of the sepsis coagulation-related prognosis marker gene, and the prediction score of the prognosis risk of sepsis / the coagulation risk of sepsis can be obtained by calculation.

[0020] In a sixth aspect of the present invention, a sepsis prognosis risk prediction system / sepsis coagulation risk prediction system is provided, the system comprising: a processor and a memory; the memory is coupled to the processor, the memory stores instructions, and when the instructions are executed by the processor, the following steps are used: the mRNA expression level of sepsis coagulation-related prognostic marker genes is input into the prediction model to obtain a sepsis prognosis risk / sepsis coagulation risk prediction score.

[0021] In a seventh aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are used: the expression level of the mRNA of the sepsis coagulation-related prognostic marker gene is input into the prediction model as described above, and the sepsis prognostic risk / sepsis coagulation risk prediction score is obtained by calculation.

[0022] In an eighth aspect, the present invention provides a sepsis prognosis prediction and diagnosis product / sepsis coagulation state prediction and diagnosis product, comprising:

[0023] Products for measuring the mRNA levels of sepsis coagulation-related prognostic marker genes;

[0024] and the sepsis prognosis risk prediction system / sepsis coagulation risk prediction system or the computer-readable storage medium.

[0025] The present invention has at least the following advantages and beneficial effects: the present invention selects the most comprehensive and unbiased sepsis genome data to date, adopts a multi-step bioinformatics strategy, and develops a group of sepsis coagulation-related prognostic marker genes. Time-dependent ROC curve and multivariate Cox regression analysis show that the 6-gene prognostic scoring system developed by the present invention has significantly better discriminant ability for overall survival (OS) than other clinical factors (such as age and gender), and the area under the curve at 3 weeks is not inferior to its area at 1 or 2 weeks, indicating that the prognostic scoring system has independent and more lasting discriminant power for sepsis prognosis. The 6-gene prognostic scoring system of the present invention was verified in multiple independent clinical cases, and the results showed that the scoring system can accurately predict the prognosis of sepsis patients, which is highly consistent with the prognostic results of actual clinical cases. Compared with traditional prognostic evaluation methods, the gene scoring system of the present invention is easy to operate and the detection process is fast, which greatly shortens the time of prognostic evaluation and improves clinical work efficiency. This scoring system can help doctors diagnose different stages of sepsis, understand the prognosis of different sepsis patients, and dynamically monitor the treatment process, adjust treatment strategies at any time, and improve the flexibility and effectiveness of treatment. This system can also be used to assist in predicting the response of sepsis patients to therapeutic interventions, determine whether patients benefit from anti-inflammatory, anti-infective, immune or other treatments, select treatment options, avoid over-medicalization, and achieve the goal of personalized medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 : Differentially expressed genes associated with coagulopathy in sepsis. (A) The differentially expressed genes between sepsis patients and healthy controls were plotted as a heat map. (B) Venn diagram showing the differentially expressed genes included in GSE65682 and genes affecting coagulation.

[0027] Figure 2: Consensus clustering of coagulation-related DEGs in sepsis. (A) Heat map of differentially expressed genes related to coagulation between sepsis patients and healthy controls. (B) Cluster analysis of differentially expressed genes between patient and control groups. (C) UMAP clustering diagram based on coagulation marker gene set. (D) Kaplan-Meier curves of 28-day overall survival of high-coagulation and low-coagulation cluster groups. (E) Biological process functional enrichment analysis of DEGs between the two coagulation clusters. (F) Venn diagram showing highly differentially expressed genes between cluster A and cluster B. (G) Venn diagram showing differentially expressed genes and coagulation-related genes contained in the two coagulation clusters. (H) GSVA analyzed the biological pathways of the two coagulation subtypes (red represents activation of biological pathways, and blue represents inhibition of biological pathways).

[0028] Figure 3 : Consensus clustering of immune-related differentially expressed genes in sepsis. (A) ssGSEA analysis based on the degree of immune cell infiltration. (B) UMAP clustering diagram based on immune score. (C) Kaplan-Meier plot of overall survival between high and low immune groups. (D) Heat map showing GSVA analysis of differentially expressed genes between two immune clusters. (E) Venn diagram showing highly differentially expressed genes between high and low immune groups.

[0029] Figure 4 : Identification of DEGs associated with coagulopathy-immune prognosis. (A) Venn diagram showing coagulopathy-related DEGs and immune-related DEGs. (B) Kaplan-Meier curves of 4-week overall survival of patients in the four groups. (C) Venn diagram showing highly differentially expressed genes between Group 1 and Group 2. (D) Scatter plot showing the difference in immune infiltration between Group 1 and Group 2. (E) Venn diagram showing coagulopathy-related DEGs, immune-related differentially expressed genes, and prognosis-related differentially expressed genes.

[0030] Figure 5:Construction of a prognostic prediction model for sepsis based on DEGs. (A) Forest plot of 18 DEGs with P < 0.05 in univariate Cox regression analysis. (B) LASSO coefficient spectrum of 18 coagulopathy-immune prognosis-related DEGs screened. (C) Three-fold cross validation of LASSOCox regression analysis. Error bars indicate SE. Dashed vertical lines indicate the best value. (D) Survival difference analysis between high-risk and low-risk patients. (E) ROC curve to evaluate the predictive value of the prognostic model for the 1-3 week prognosis of the discovery cohort patients. (F) Multivariate COX regression analysis of risk score and age and gender. (G) Prognostic model nomogram. (H) Calibration curve of the model predicting 1-3 week survival rate. (I) From top to bottom: distribution of patients with increased risk scores in the two groups; scatter plots showing the survival of patients with increased risk scores (red for death and light green for survival); heat map of differential expression of model genes.

[0031] Figure 6 :Construction of a coagulation status prediction model based on the prognostic model. (A) Coagulation status prediction model nomogram. (B) Coagulation status prediction model calibration curve. (C) ROC curve to evaluate the predictive value of 6 DEGs for coagulation status. (D) Gene feature importance score of training set. (E) Gene feature importance score of test set. (F) Confusion matrix.

[0032] Figure 7 :Differential expression analysis of the six DEGs involved in the prediction model in the control group and different venous thrombosis risk level groups (A) Differential expression analysis of CX3CR1 in different venous thrombosis risk level groups. (B) Differential expression analysis of DEFA4 in different venous thrombosis risk level groups. (C) Differential expression analysis of GIMAP4 in different venous thrombosis risk level groups. (D) Differential expression analysis of FGFBP2 in different venous thrombosis risk level groups. (E) Differential expression analysis of ELANE in different venous thrombosis risk level groups. (F) Differential expression analysis of WFDC1 in different venous thrombosis risk level groups.

[0033] Figure 8 :Differential analysis of model genes and validation of diagnostic efficacy in external independent cohorts. (A) Differential analysis of 6 DEGs in the GSE26378 dataset. (B) Differential analysis of 6 DEGs in the GSE13904 dataset. (C) ROC analysis of 6 DEGs in the GSE112099 dataset. (D) ROC analysis of 6 DEGs in the GSE26378 dataset. (E) Differential analysis of 6 DEGs in the GSE13904 dataset. (F) ROC analysis of 6 DEGs in the GSE112099 dataset.

[0034] Fig. 9: Validation of the diagnostic efficacy of the prognostic prediction model in clinical samples. (A) Analysis of survival differences between high-risk and low-risk patients with sepsis. (B) ROC curve to evaluate the predictive value of the prognostic prediction model for the prognosis of sepsis patients at 1, 2, and 3 weeks. (C) Box plot of differences in platelet count (PLT) in sepsis patients with different risks. (D) Box plot of differences in international normalized ratio (INR) in sepsis patients with different risks. (E) Box plot of differences in prothrombin time (PT) in sepsis patients with different risks. (F) Box plot of differences in D-dimer (DD) in sepsis patients with different risks. DETAILED DESCRIPTION

[0035] The present invention will be described in detail below in conjunction with specific implementations and examples, and the advantages and various effects of the present invention will be more clearly presented. It should be understood by those skilled in the art that these specific implementations and examples are used to illustrate the present invention, rather than to limit the present invention.

[0036] Throughout the specification, unless otherwise specifically stated, the terms used herein should be understood as meanings commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art to which the present invention belongs. In the event of a conflict, the present specification takes precedence.

[0037] The overall idea of ​​the technical solution of the present invention is as follows:

[0038] The present invention obtains coagulation-related gene information from the Molecular Signatures Database (MSigDB version 6.0) GEO and KEGG (hsa04610, hsa04611) database, obtains clinical information and transcriptome data of sepsis patients from the GEO (Gene Expression Omnibus) database, and screens out 18 genes with significant correlation with coagulation, immunity and overall survival (OS) through Cluster clustering and GSVA grouping; LASSO regression is used to establish a survival model to determine 6 genes as a group of markers related to sepsis prognosis; and on this basis, a prognostic scoring system is established and its evaluation value and accuracy are verified. Through the GEO database and clinical cases, the discriminative ability of the 6 genes for sepsis is further verified.

[0039] The terms "probe" and "primer" used in this specification refer to oligonucleotides, preferably single-stranded deoxyribonucleotides, including naturally occurring dNMPs (dAMP, dGMP, dCMP and dTMP), deformed nucleotides or non-natural nucleotides, and may also contain ribonucleotides.

[0040] The probe and primer used in the present invention include hybridization nucleotide sequences complementary to the target position of the target nucleic acid. The term "complementary" means that the primer or probe is fully complementary to the target nucleic acid sequence for selective hybridization under hybridization conditions, and has the meaning of including substantially complementary and perfectly complementary, preferably fully complementary. The term "substantially complementary sequence" used in this specification not only includes completely identical sequences, but also includes sequences that can play a primer role on a specific target sequence and are partially inconsistent with the sequence as a comparison object. The sequence of the probe and primer does not need to have a sequence that is completely complementary to a part of the sequence of the template, as long as it has sufficient complementarity within the scope of being able to hybridize with the template to play its inherent role. Therefore, the probe and primer in the present invention do not need to have a sequence that is completely complementary to the above-mentioned nucleotide sequence as a template, as long as it has sufficient complementarity within the scope of being able to hybridize with the template to play its inherent role. The design of primers and probes is a skill mastered by those skilled in the art, for example, a primer design program (for example: PRIMER 3 program) can be used.

[0041] The determination of the gene mRNA level in the present invention can be carried out using methods known in the art, including but not limited to quantitative PCR, gene chip, and second-generation high-throughput sequencing technology.

[0042] Unless otherwise specified, various raw materials, reagents, instruments and equipment used in the embodiments of the present invention can be purchased from the market or prepared by existing methods.

[0043] The following will describe in detail a group of sepsis coagulation-related prognostic marker genes of the present invention and their application in preparing sepsis prognosis prediction and diagnosis products in conjunction with the embodiments.

[0044] Example 1. Method for obtaining and screening genes related to sepsis coagulation

[0045] Differential analysis of genes related to coagulation status in sepsis samples: Gene information related to coagulation was obtained from the Molecular Signatures Database (MSigDB version 6.0), GEO and KEGG (hsa04610, hsa04611) databases, and clinical information and transcriptome data of sepsis patients were obtained from the GEO (Gene Expression Omnibus) database. Differential gene analysis was performed using the R language limma package, and the ConsensusClusterPlus package was used to divide sepsis patients into two groups: high coagulation state and low coagulation state. Differential analysis was used to further evaluate the genes related to coagulation status in sepsis patients.

[0046] The specific steps include:

[0047] 1. A total of 1253 genes related to coagulation were obtained from the Molecular Signatures Database (MSigDB version 6.0) GEO and KEGG (hsa04610, hsa04611) databases (Table 1). A total of 2842 differentially expressed genes in GSE65682 were analyzed using the limma package, of which 223 genes were related to coagulation ( Figure 1 A, 1B).

[0048] Table 1: Detailed information of coagulation-related genes.

[0049]

[0050] 2. Screening of characteristic genes related to sepsis coagulation status

[0051] In GEO, 223 coagulation genes were clustered using the ConsensusClusterPlus package to divide sepsis patients into two groups ( Figure 2 B, 2C), survival analysis was used to determine the survival difference between the two groups ( Figure 2 D), and the coagulation status of the clustered groups was determined by GO enrichment analysis ( Figure 2 E), 279 differentially expressed genes related to coagulation status in sepsis after grouping were analyzed using the limma package ( Figure 2 F), of which 31 genes were related to blood coagulation ( Figure 2 G). Figure 2 H is the GSVA analysis of 31 differentially expressed genes.

[0052] 3. Screening of characteristic genes related to the immune status of sepsis

[0053] The sepsis patients in GSE65682 were divided into high immune status group and low immune status group by GSVA ( Figure 3 A, 3B), Kaplan-Meier analysis was used to determine the survival difference between patients under the two immune states ( Figure 3 C), 234 differentially expressed genes related to coagulation status in sepsis were analyzed by limma package after grouping ( Figure 3 E), of which 28 genes were related to blood coagulation ( Figure 4 A).

[0054] 4. Screening of characteristic genes related to both coagulation and immune status in sepsis

[0055] Sepsis patients in the GSE65682 dataset were divided into four groups according to their coagulation and immune status (Table 2).

[0056] Kaplan-Meier analysis was used to determine the survival differences among patients in the four conditions ( Figure 4 B), using the limma package to perform differential analysis on Group 1 and Group 2, where the survival differences were most obvious, 426 differentially expressed genes were obtained ( Figure 4 C). Figure 4 D is the difference analysis of immune cell infiltration between group 1 and group 2 after grouping according to immune status. By taking the intersection of the differential genes obtained in step 2 and step 3, a total of 27 characteristic genes related to both coagulation status and immune status were obtained ( Figure 4 E).

[0057] Table 2: Detailed information about the grouping

[0058]

[0059] 5. Verification of characteristic genes screened

[0060] Through univariate COX regression analysis, 18 genes with P < 0.05 were screened out from 27 genes ( Figure 5 A). LASSOCox regression was used to screen the best variables from these 18 significant candidate variables, and finally 6 best variables were retained ( Figure 5 B, C). Survival analysis showed that the prognosis of the high-risk group was significantly worse than that of the low-risk group ( Figure 5 D), and showed an area under the ROC curve (AUC) close to 0.7 ( Figure 5 E), the P value was smaller than that of clinical factors age and gender ( Figure 5 F) showed that the model had good predictive value for the prognosis of patients with sepsis. In addition, a diagnostic curve for predicting 1-3 week overall survival (OS) was constructed using the Cox regression model ( Figure 5G). The calibration curve also demonstrated good consistency of the predicted results ( Figure 5 H). Expression profiles of the six best differentially expressed genes between the high-risk group and the low-risk group in the heat map and risk curves. Figure 5 Shown in I.

[0061] 6. Characteristic gene coagulation status prediction model

[0062] The coagulation status prediction model was constructed by characteristic genes. The dataset GSE65682 was randomly divided into training set and test set in a ratio of 7:3 to construct the patient coagulation status prediction model. Figure 6 A is the prediction model nomogram, from which we can see that gene ELANE contributes the most. Figure 6 B is the correction curve of the prediction model. The AUC value of the model prediction effect is 0.979, which means that the model has a good prediction effect ( Figure 6 C). Use the Feature Imoprtance function to rank the importance of genes in the training set and test set ( Figure 6 D, 6E). Figure 6 F is the confusion matrix. It can be seen intuitively that the prediction accuracy in the high coagulation group is 92%, and the prediction accuracy in the low coagulation group is 90%, and the error rates are 8% and 10%, respectively.

[0063] 7. Validation of external datasets of characteristic genes

[0064] The venous thromboembolism risk dataset and other sepsis datasets were obtained from the GEO database (Table 3). The expression differences of characteristic genes were analyzed using the limma package. The results showed that the sepsis-related coagulation genes CX3CR1, DEFA4, and GIMAP4 were significantly different in different venous thromboembolism risk groups (P<0.05, Figure 7 AC). However, there was no significant difference in the expression of FGFBP2, ELANE, and WFDC1 ( Figure 7 DF). In addition, the established prediction method was validated in three external datasets (“GSE13904”, “GSE26378”, and “GSE112099”). In the “GSE13904” and “GSE26378” cohorts, WFDC1, ELANE, and DEFA4 risk factors were upregulated, while FGFBP2, CX3CR1, and GIMAP4 were downregulated in sepsis samples ( Figure 8 A, B), which is consistent with the previous conclusion. The AUC value is >0.7 in the first two data sets ( Figure 8D, E), indicating that the prediction model has good sensitivity for the prognosis of sepsis patients. However, when the model was applied to a urine molecular validation sepsis cohort, the expression levels of WFDC1, GIMAP4, ELANE, and DEFA4 were opposite to those in blood, and the diagnostic value of these genes in urine was weak ( Figure 8 C, F). In other sepsis datasets, six specific sepsis prognostic genes WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE, and DEFA4 were differentially expressed to varying degrees and had good diagnostic efficacy.

[0065] Table 3: Dataset details

[0066]

[0067]

[0068] Example 2. Sepsis prognosis risk prediction model and its establishment method

[0069] 1. Sepsis prognosis risk prediction model

[0070] Based on the results of Example 1, the present invention developed a sepsis-6 gene prognostic scoring model. By applying Lasso regression to identify the characteristic genes of patient prognosis, a scoring model consisting of 6 specific sepsis prognostic genes was finally determined, including: WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4.

[0071] The above sepsis prognostic scoring model uses the prediction score to calculate the patient's survival probability. The scoring model is defined as a linear combination of gene expression based on the Cox risk function coefficient. The calculation formula is as follows:

[0072]

[0073] Since different samples and different detection methods obtain different mRNA levels, the coefficients of gene i measured will be different.

[0074] In an embodiment of the present invention, the mRNA expression data in peripheral blood leukocytes of patients with sepsis were detected by high-throughput sequencing of the GEO database, and the coagulation-related genes in the Molecular Signatures Database and the KEGG database were combined to finally screen the 6 characteristic genes through Cluster clustering grouping and LASSO regression model, and a sepsis prognosis risk prediction model (survival model) and a sepsis patient coagulation state diagnosis model were constructed. The coefficient of gene i in the formula is shown in Table 4, and the expression level of gene i is the mRNA expression level of the sepsis prognosis marker gene.

[0075] Table 4: Typical discriminant function coefficients of survival models

[0076]

[0077] The above formula can also be used to predict the coagulation state. The coefficient of gene i in the formula is shown in Table 5.

[0078] Table 5: Typical discriminant function coefficients of coagulation status prediction model

[0079]

[0080] 2. Each patient’s prognostic score can be used to assess their overall survival and risk of death

[0081] (1) First, the samples are sorted according to their prognostic scores, and the optimal dichotomous cutoff value of the prognostic score is found based on the scores. The patients are divided into two groups: high-risk and low-risk (prognostic) according to the upper and lower cutoff values.

[0082] The absolute value and score boundaries of the prediction score may vary depending on the different technology platforms and need to be determined separately.

[0083] (2) Kaplan-Meier analysis and Log-Rank test were performed on 760 patients with sepsis in two groups; the difference in overall survival (OS) between the two groups was determined, and a survival model for sepsis patients was established for analysis.

[0084] Specifically: In this example, based on the mRNA expression data in peripheral blood leukocytes of 760 patients with sepsis, the coagulation-related genes in the Molecular Signatures Database and the KEGG database were combined and finally the six characteristic genes were screened out through Cluster clustering and LASSO regression model ( Figure 5B, C), and constructed a sepsis prognostic risk prediction model and a sepsis coagulation status diagnosis model. According to the above risk score formula, the sepsis patient prognostic risk score and coagulation risk score were obtained according to the coefficients in Tables 4 and 5 above. The risk scores were sorted, and the best dichotomy cutoff value of the prognostic score (0.120646257) and the best dichotomy cutoff value of the coagulation score (-0.701) were found according to the scoring method. The patients were divided into two groups (prognosis) of high risk and low risk according to the upper and lower cutoff values. The difference in overall survival (OS) between the high-risk and low-risk groups of 760 patients with sepsis was analyzed by Kaplan-Meier analysis and Log-Rank test ( Figure 5 D) After scoring the patients according to the scoring model, the time-dependent ROC curves of 1 week, 2 weeks and 3 weeks are fitted, as shown in Figure 5 As shown in E, the areas under the curve for 1 week, 2 weeks, and 3 weeks were all greater than 0.68; a risk model was constructed for the patients based on this scoring model ( Figure 5 I) can well distinguish low-risk and high-risk patients, indicating that the diagnostic efficacy of the scoring model is relatively good and can maintain this diagnostic efficacy over a long period of time. These results strongly verify the ability of this group of 6-gene markers and the prognostic scoring model to distinguish patients with different prognoses, and can well distinguish patients with good or poor prognosis. The results of the diagnosis of coagulation function in sepsis patients by the coagulation state diagnostic model are as follows Figure 6 As shown in Figure 2, the ROC curve shows that the diagnostic model has a good diagnostic value for the diagnosis of sepsis coagulation status (AUC>0.9, Figure 6 C); The confusion matrix also shows intuitively that the prediction accuracy of the high coagulation group is 92%, the prediction accuracy of the low coagulation group is 90%, and the error rates are 8% and 10%, respectively ( Figure 6 F). In order to further determine the diagnostic efficiency of the prognostic model and the diagnostic model, the present invention collected peripheral blood leukocytes from sepsis patients in Zhongnan Hospital of Wuhan University, and tested the efficacy of the diagnostic model and the prognostic model using the same transcriptome sequencing method and the same experimental conditions. The results are as follows: Fig. 9 As shown in Figure 2, the prognostic model can significantly distinguish the survival difference between high-risk patients and low-risk patients (P<0.005, Fig. 9 A); and it has good diagnostic value for survival differences in patients with sepsis, and its 1-week and 2-week time-dependent ROC curve areas are both greater than 0.8 ( Fig. 9 B). There are significant differences in platelet count (PLT), clotting time (PT) and D-dimer (DD) between sepsis patients with high coagulation risk and sepsis patients with low coagulation risk. The coagulation diagnostic model can also better distinguish sepsis patients with different coagulation risks ( Fig. 9 C, E, F).

[0085] 3. Comparison of the efficacy of the 6-gene prognostic scoring model of the present invention with other clinical factors (such as age and gender) in evaluating the prognosis of patients with sepsis

[0086] The results are as follows Figure 5 As shown in G, Figure 5 G shows that the time-dependent ROC curve and multivariate Cox regression analysis show that the 6-gene prognostic scoring model developed in the present invention has a significantly better ability to discriminate overall survival (OS) than other clinical factors (such as age, gender, Figure 5 F, 5G), and the area under the curve at 3 weeks was not inferior to that at 1 or 2 weeks ( Figure 5 E), indicating that this prognostic scoring model has independent and lasting discriminant power for the prognosis of sepsis.

[0087] Example 3: Sepsis prognosis risk prediction system

[0088] This embodiment provides a sepsis prognosis risk prediction system, the system comprising:

[0089] A processor and a memory, the memory being coupled to the processor, the memory storing instructions which, when executed by the processor, use the following steps:

[0090] The mRNA expression levels of the sepsis prognostic marker genes were input into the prediction model described in Example 2 to obtain the sepsis prognostic risk prediction score.

[0091] Embodiment 4: Computer readable storage medium

[0092] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in Embodiment 2 and / or the method in Embodiment 3 are implemented.

[0093] Of course, the computer executable instructions of the storage medium containing computer executable instructions provided in this embodiment are not limited to the operations of the method described above, and can also execute related operations in the method provided in any embodiment of the present invention.

[0094] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0095] It is worth noting that in the above embodiments, the various units and modules included are only divided according to functional logic, but are not limited to the above divisions, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0096] Application Example 1: Predicting the prognosis of clinical sepsis patients

[0097] Peripheral blood leukocytes of sepsis patients admitted to the clinic were collected and RNA was extracted. Then transcriptome sequencing was used to detect the expression levels of six genes, WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4. The expression levels of the six genes were respectively input into the prognostic scoring formula established by the present invention:

[0098]

[0099] After calculating the patient's prediction score, the doctor predicts the patient's prognosis based on the score, such as the 2-week survival rate. We have currently established a model through a retrospective study and successfully validated it in the GEO dataset. By calculating the prognostic scores of all patients in the dataset and dividing the patients into high-risk and low-risk groups based on their scores, the Kaplan-Meier analysis showed significant differences between the "high-risk" and "low-risk" prognostic patient groups. The results showed that patients with high prognostic scores had significantly shorter OS than those with low scores (p<0.05) ( Figure 5 D) Confirm that the 6-gene prognostic scoring system can reproducibly predict the prognosis of patients with sepsis.

[0100] Application Example 2: Predicting the prognosis and coagulation risk of clinical sepsis patients

[0101] Sample collection: Blood samples from 158 sepsis patients and healthy volunteers were collected from Zhongnan Hospital of Wuhan University, including 143 sepsis patients and 15 healthy volunteers. These samples were screened according to the following inclusion / exclusion criteria.

[0102] Inclusion criteria: From August 2020 to December 2023, the study collected patients diagnosed with sepsis in the Department of Critical Care Medicine of Wuhan University Zhongnan Hospital, with an age range of 18-80 years old, fully informed and signed informed consent; and selected healthy volunteers who underwent physical examinations in Wuhan University Zhongnan Hospital during the same period as the control group, with an age range of 18-80 years old; sepsis followed Sepsis-3.0 diagnosis; all participants were fully informed and informed consent, and the privacy and safety of patients were ensured during diagnosis and treatment. This study has been approved by the Ethics Committee of Wuhan University Zhongnan Hospital (No. 2019141).

[0103] Exclusion criteria: patients with missing data; patients diagnosed with sepsis for more than 24 hours before admission to the ICU; patients with malignant tumors or transplant surgery; pregnant patients; patients with metabolic diseases; patients with infectious diseases; patients with blood and immune system diseases; patients with chronic liver and kidney diseases and long-term use of glucocorticoids and immunosuppressive drugs; patients who are alcoholics and smokers; and patients whose family members refuse to sign informed consent.

[0104] The original information of relevant gene expression of the samples has been uploaded to the National GeneBank Sequence Archive System (https: / / db.cngb.org / cnsa / , project number: CNP0004962).

[0105] Experimental methods: Gene expression analysis was performed on the collected blood samples. Peripheral blood was collected from patients on the first day after the diagnosis of sepsis. Peripheral blood leukocytes were isolated using a leukocyte separation kit, and total RNA was subsequently extracted. After a comprehensive assessment of RNA integrity and quality, RNA with an RNA integrity number (RIN) ≥ 7.0 and a 28S / 18S ratio > 1.0 was selected for library construction using the TruSeqStranded mRNA Library Prep Kit (Illumina) and sequencing using the NovaSeq 6000 (Illumina). The samples were sequenced on the machine, and the image files were converted by the software provided by the sequencing platform to generate FASTQ raw data (Raw Data), i.e., offline data. The data was filtered by the following process: 1) fastp (0.22.0) was used to remove sequences with adapters at the 3' end; 2) Reads with an average quality score lower than Q20 were removed. All subsequent analyses were high-quality analyses based on Clean data. The reference genome and gene model annotation files were downloaded directly from the genome website. HISAT2 (v2.1.0) was used to construct the index of the reference genome, and paired-end clean reads were aligned to the reference genome using HISAT2. HTSeq (v0.9.1) was used to count the ReadCount values ​​aligned to each gene as the original expression of the gene. In order to make the gene expression levels of different genes and different samples comparable, FPKM was used to normalize the expression.

[0106] Data analysis: The obtained data were statistically analyzed to verify the efficacy of the six genes in the prognosis diagnosis of sepsis. The standardized data were used according to the above risk score formula and the typical discriminant function coefficients of the survival model and coagulation status prediction model in Tables 4 and 5 to obtain the prognostic risk score and coagulation risk score of each sample. According to the best dichotomy cutoff value of the prognostic score (0.120646257) and the best dichotomy cutoff value of the coagulation score (-0.701), the patients were divided into two groups (prognosis, coagulation) high risk and low risk. The Kaplan-Meier analysis and Log-Rank test were used to compare the differences in overall survival (OS) between the high-risk and low-risk groups of 143 patients with sepsis ( Fig. 9 A). After scoring the patients according to the scoring model, the time-dependent ROC curves of 1 week, 2 weeks and 3 weeks were fitted, as shown in Fig. 9As shown in Figure B, the area under the curve at 1 week and 2 weeks was greater than 0.8; the scoring model can distinguish the survival difference between low-risk and high-risk patients, and the ROC curve also shows that the diagnostic efficiency of the scoring model is good and can maintain this diagnostic efficiency for a long time. Similarly, the coagulation risk model was used to divide patients into high-coagulation risk sepsis patients and low-coagulation risk sepsis patients. The results showed that the two groups of patients had significant differences in platelet count (PLT), clotting time (PT) and D-dimer (DD). The coagulation diagnostic model can clearly distinguish sepsis patients with different coagulation risks ( Fig. 9 C, E, F).

[0107] Application Example 3: Predicting whether clinical sepsis patients need anticoagulation and immunotherapy

[0108] Patients with sepsis are initially more likely to exhibit a prothrombotic state through exogenous activation pathways, inhibition of anticoagulant pathways, and cytokine-induced coagulation amplification. With the cumulative consumption of excess coagulation factors and the establishment of disseminated intravascular coagulation (DIC), abnormal bleeding characteristics will appear in the later stage. Anticoagulant or antithrombotic prophylactic treatment seems to be beneficial for patients with sepsis. Since not all patients with sepsis can benefit from anticoagulation and immunotherapy, in order to reduce ineffective or excessive treatment, reduce medical costs, and treat some sepsis patients in a targeted manner, the present invention predicts whether clinical sepsis patients need auxiliary anticoagulation and immunotherapy through the following scheme: peripheral blood leukocytes are collected from sepsis patients admitted to the clinic and RNA is extracted. Then the expression levels of the 6-marker genes WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4 are quantitatively detected. The expression levels of the 6-marker genes are input into the prognostic scoring formula established by the present invention:

[0109]

[0110] After calculating the patient's predicted score, the doctor will consider whether the patient should receive anticoagulation and immunotherapy based on the score. For patients whose predicted scores indicate a poor prognosis, the doctor can recommend anticoagulation and immunotherapy for the patient.

[0111] Finally, it should be noted that the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements that are inherent to such process, method, article, or apparatus.

[0112] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0113] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the embodiments of the present invention and their equivalents, the embodiments of the present invention are also intended to include these modifications and variations.

Claims

1. A sepsis coagulation-related prognostic marker gene, characterized in that: The sepsis coagulation-related prognostic marker genes consist of six genes: WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4.

2. A product for measuring the expression level of sepsis coagulation-related prognostic marker genes, characterized in that: Comprising a product for detecting the mRNA expression levels of the six genes described in claim 1.

3. The product for measuring the expression level of sepsis coagulation-related prognostic marker genes according to claim 2, characterized in that: The product comprises: probes and primers for detecting the six genes described in claim 1.

4. Use of the marker gene according to claim 1 or the assay product according to claims 2-3, characterized in that: The application includes at least one of the following: Application in establishing models for predicting the prognostic risk of sepsis / predicting the coagulation status of sepsis; Application in establishing a risk prediction system for sepsis prognosis / sepsis coagulation status prediction system; Application in the preparation of sepsis prognosis prediction diagnostic products / sepsis coagulation status prediction diagnostic products.

5. A sepsis prognosis risk prediction model / sepsis coagulation state prediction model, characterized in that: The calculation formula of the prediction model is as follows: The gene i is the sepsis coagulation-related prognostic marker gene described in claim 1: WFDC1, FGFBP2, CX3CR1, GIMAP4, ELANE and DEFA4; The expression level of the gene i is the mRNA expression level of the gene; The coefficient of the gene i is obtained by performing COX multivariate regression analysis on the mRNA expression level of the gene.

6. The prediction model according to claim 5, characterized in that The method for determining the mRNA expression level of the gene includes any one of PCR, gene chip, and second-generation high-throughput sequencing, or a combination of at least two of them.

7. Use of the prediction model according to claim 5 or 6 in preparing a product for predicting sepsis risk / a product for predicting sepsis coagulation risk.

8. A sepsis prognosis risk prediction system / sepsis coagulation risk prediction system, characterized in that: The system comprises: A processor and a memory, the memory being coupled to the processor, the memory storing instructions which, when executed by the processor, use the following steps: The mRNA level of the gene described in claim 1 is input into the prediction model described in claim 5 or 6 to obtain a sepsis prognostic risk / sepsis coagulation risk prediction score.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by a processor, uses the following steps: The mRNA level of the gene described in claim 1 is input into the prediction model described in claim 5 or 6, and the sepsis prognosis risk / sepsis coagulation risk prediction score is obtained through calculation.

10. A sepsis prognosis prediction and diagnosis product / sepsis coagulation state prediction and diagnosis product, characterized in that: include: The product for measuring the mRNA expression level of sepsis coagulation-related prognostic marker genes according to claim 2 or 3; The sepsis prognosis risk prediction system / sepsis coagulation risk prediction system of claim 8 or the computer-readable storage medium of claim 9.

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