Diagnosis and treatment target device for cytophagic lymphocytosis

Abstract

The invention discloses a diagnosis and treatment target device for cytophagic lymphocytosis. The device comprises a detection module, a detection module and a control module, wherein the detection module is used for detecting the expression quantity of four genes, namely CCR2, CDKN1A, TNFSF10 and TP53INP2 in a patient sample of a current patient; the detection module is used for detecting the expression quantity of four genes, namely CCR2, CDKN1A, TNFSF10 and TP53INP2; the analysis module is used for carrying out difference analysis on the expression quantity and the expression quantity of a healthy control group to obtain a molecular characteristic spectrum related to the HLH; the diagnosis typing treatment module is used for performing diagnosis typing on the current patient according to a difference mode of the molecular characteristic spectrum and treating according to a diagnosis typing result, so that serious treatment toxicity such as myelosuppression and liver and kidney injury of a traditional chemotherapy regimen can be reduced, the safety and effectiveness of clinical treatment of the patient are improved, and higher specificity and accuracy are achieved; a foundation is laid for developing a novel diagnostic tool, and a direct and feasible candidate scheme and a theoretical basis are provided for developing a high-efficiency and low-toxicity HLH new therapy.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis. Background Technology

[0002] Hemophagocytic lymphohistiocytosis (HLH) is an excessive inflammatory syndrome characterized by cytokine storms and multiple organ failure with a very high mortality rate. First-line treatment (such as the HLH-94 regimen) has prominent problems such as poor efficacy, easy relapse, and significant treatment-related toxic side effects.

[0003] The pathogenesis of HLH is not yet fully understood, especially the role of intrinsic cellular homeostasis regulation mechanisms such as mitophagy, which is crucial for clearing damaged mitochondria, maintaining cellular homeostasis, and regulating inflammation.

[0004] Existing technologies face three major bottlenecks: There is a lack of highly efficient and specific molecular markers for early diagnosis and accurate classification. Existing treatments have limited efficacy and high toxicity, necessitating a highly effective and low-toxicity strategy based on novel targets. Mechanistically, there is insufficient understanding of the deep molecular mechanisms underlying HLH pathogenesis, especially the role of intrinsic cellular pathways such as mitophagy in immune dysregulation. Summary of the Invention

[0005] The main objective of this invention is to provide a diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis (HLH), aiming to solve the technical problems in the prior art, such as the lack of efficient and specific molecular markers for HLH diagnosis, the high toxicity and limited efficacy of treatment regimens, and the insufficient research on the pathological mechanism of HLH driven by mitochondrial autophagy dysregulation.

[0006] This invention provides a diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis, the device comprising: The system comprises a detection module, an analysis module, and a diagnostic, subtyping, and treatment module; among which, The detection module is used to detect the expression levels of four genes, CCR2, CDKN1A, TNFSF10 and TP53INP2, in the current patient's sample. The analysis module is used to perform differential analysis between the expression level and the expression level of the healthy control group to obtain the molecular feature profile related to hemophagocytic lymphohistiocytosis (HLH). The diagnostic classification and treatment module is used to diagnose and classify the current patient based on the difference patterns of the molecular characteristic spectrum, and to carry out treatment based on the diagnostic classification results.

[0007] Optionally, the detection module is further configured to collect blood samples, bone marrow samples, or spleen tissue samples from the current patient, and use the blood samples, bone marrow samples, or spleen tissue samples as patient samples; The detection module is also used to quantitatively analyze the expression levels of four genes, CCR2, CDKN1A, TNFSF10 and TP53INP2, in the patient sample using real-time quantitative PCR, RNA sequencing or Western blotting, to obtain the expression levels of the four genes.

[0008] Optionally, the detection module is further configured to read the original CEL file corresponding to the original transcriptome data in the public database using the affy package, and perform background correction, quantile normalization and logarithmic transformation on the original CEL file to obtain the processed target file; The detection module is also used to calculate the standard expression level of the target file using a robust multi-array average RMA algorithm, and generate the log2-transformed standardized expression value of each probe set. The detection module is also used to convert the probe ID of each probe in each probe set into a gene symbol. If multiple probe sets are detected to map to the same gene, the median of the expression level of the current gene is taken as the final expression value of the current gene. The detection module is also used to obtain a list of core autophagy-related genes from the human autophagy database, and to obtain autophagy-related differentially expressed genes by taking the intersection of the gene corresponding to the final expression value and the list of core autophagy-related genes. The detection module is also used to exclude abnormal samples in the autophagy-related differentially expressed genes and select the soft threshold power when the scale-free topological fitting index reaches the preset fitting index through the pickSoftThreshold function. The detection module is also used to calculate the collar matrix based on the soft threshold power, convert the collar matrix into a topological overlap matrix, and use dynamic tree cutting to identify co-expressed gene modules in the topological overlap matrix. The detection module is also used to calculate the modular trait relationship between the gene module and the hemophagocytic lymphohistiocytosis (HLH) trait, and to determine the WGCNA brown module gene, which has the strongest negative correlation with HLH, based on the modular trait relationship. The detection module is also used to take the intersection of the autophagy-related differentially expressed genes and the WGCNA brown module genes to obtain candidate genes; The detection module is also used to perform LASSO regression analysis on the candidate genes, screen out genes with non-zero coefficients, and perform vector machine recursive feature elimination to screen out CCR2, CDKN1A, TNFSF10 and TP53INP. CCR2, CDKN1A, TNFSF10 and TP53INP are used as the core diagnostic genes for hemophagocytic lymphohistiocytosis (HLH).

[0009] Optionally, the analysis module is further configured to perform a difference analysis between the expression level and the expression level of the healthy control group, and calculate the absolute logarithmic change value using a differential expression statistical tool; The analysis module is also used to compare the absolute logarithmic change value with a preset change value threshold to obtain the expression abnormality pattern; The analysis module is also used to integrate the dynamic change features of the four genes into a molecular feature profile of HLH based on the expression abnormality pattern.

[0010] Optionally, the analysis module is further configured to perform linear model fitting between the expression level and the expression level of the healthy control group based on the lmFit function to obtain the fitting result; The analysis module is also used to calculate the significance of differential expression in the fitting results based on empirical Bayesian smoothing using the eBayes function, and to obtain the absolute logarithmic fold change value for each gene. Optionally, the diagnostic subtyping and treatment module is further configured to quantitatively compare the gene expression data of the current patient with the molecular feature spectrum based on the difference pattern of the molecular feature spectrum; The diagnostic and treatment module is also used to identify HLH positive cases when abnormally low CCR2 expression is detected and CDKN1A, TNFSF10 and TP53INP2 genes are abnormally elevated in synergistic way. The diagnostic and treatment module is also used to perform HLH typing on the current patient based on the absolute logarithmic change values ​​of each gene combined with the results of immune microenvironment analysis.

[0011] Optionally, the diagnostic subtyping and treatment module is further configured to determine that the current patient's HLH type is primary HLH when the absolute logarithmic change value of the CCR2 gene is less than a preset statistical significance threshold, the absolute logarithmic change values ​​of the CDKN1A, TNFSF10 and TP53INP2 genes are greater than the preset statistical significance threshold, and the cell infiltration ratio of specific immune cells in the immune microenvironment analysis results is greater than or equal to a preset ratio threshold. The diagnostic and subtyping treatment module is also used to determine that the current patient's HLH type is secondary HLH when the absolute logarithmic change values ​​of CCR2, CDKN1A, TNFSF10 and TP53INP are all greater than the preset statistical significance threshold, but the cell infiltration ratio is less than the preset ratio threshold and the ANNEXIN pathway activity is not enhanced.

[0012] Optionally, the diagnostic typing and treatment module is further configured to use sodium butyrate as a TNFSF10 targeting regulator when the absolute logarithmic change value of the TNFSF10 gene is detected to be greater than the preset proportional threshold. The diagnostic typing and treatment module is also used to use dicumarol as a CDKN1A targeting regulator when the absolute logarithmic change value of the CDKN1A gene is detected to be greater than the preset proportional threshold. The diagnostic typing and treatment module is also used to use simvastatin as a CCR2 targeting regulator when the absolute logarithmic change value of the CCR2 gene is detected to be greater than the preset proportional threshold.

[0013] Optionally, the diagnostic subtyping and treatment module is further used to obtain the ANNEXIN pathway activity by comparing the activation intensity of the ANNEXIN signaling pathway in the intercellular communication network with that of the healthy control group, wherein the statistical significance of the activation intensity is reflected by the intercellular communication analysis results. The diagnostic and subtyping treatment module is also used to verify the degree of improvement in mitophagy disorder by detecting the expression levels of PINK1, PARKIN, and NIX proteins after detecting that the current patient has been treated by oral, intravenous, or local administration.

[0014] Optionally, the diagnostic subtyping and treatment module is also used to detect the expression levels of three core mitochondrial autophagy proteins, PINK1, PARKIN, and NIX, in patient samples using Western blot or immunohistochemistry. The diagnostic and subtyping treatment module is also used to determine that mitophagy disorder has been improved when the expression levels of PINK1, PARKIN, and NIX have all returned to the baseline values ​​of the control group.

[0015] The present invention proposes a diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis (HLH). This device comprises: a detection module for detecting the expression levels of four genes—CCR2, CDKN1A, TNFSF10, and TP53INP2—in a patient sample; an analysis module for performing differential analysis between the expression levels and those of a healthy control group to obtain a molecular characteristic profile related to HLH; and a diagnostic and subtyping treatment module for diagnosing and subtyping the patient based on the differential patterns of the molecular characteristic profile, and then treating the patient according to the diagnostic subtyping results. This device can reduce the severe toxicity of traditional chemotherapy regimens, such as bone marrow suppression and liver and kidney damage, and improve the safety and effectiveness of clinical treatment. It has higher specificity and accuracy, laying the foundation for the development of novel diagnostic tools and providing a direct and feasible candidate scheme and theoretical basis for developing highly effective and low-toxicity new HLH therapies, thereby improving the accuracy and efficiency of the diagnosis and treatment of HLH. Attached Figure Description

[0016] Figure 1 This is a functional block diagram of the first embodiment of the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; Figure 2 This is a schematic diagram showing the autophagy-immune axis dysregulation in HLH revealed by the core gene markers in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention. Figure 3 This is a schematic diagram illustrating the identification of the core gene in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention and its association with the immune microenvironment. Figure 4 This is a schematic diagram illustrating the multidimensional functional characterization of the core gene in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention. Figure 5 This is a schematic diagram of HLH immune landscape remodeling at single-cell resolution in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention. Figure 6 This is a schematic diagram of the core gene dynamics and therapeutic target prediction in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention. Figure 7 This is a schematic diagram illustrating the in vitro HLH model validation in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention. Figure 8 This is a schematic diagram of the in vitro HLH model characterization in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; Figure 9This is a schematic diagram illustrating the verification of mitochondrial autophagy and core proteins in an in vivo model of the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] The solution of this invention mainly includes: the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis (HLH) comprises: a detection module for detecting the expression levels of four genes, CCR2, CDKN1A, TNFSF10, and TP53INP2, in a patient sample from a current patient; an analysis module for performing differential analysis between the expression levels and those of a healthy control group to obtain a molecular characteristic profile related to HLH; and a diagnostic and subtyping treatment module for performing diagnostic subtyping of the current patient based on the differential patterns of the molecular characteristic profile, and based on the diagnostic subtyping results... Treatment can reduce the severe toxicity of traditional chemotherapy regimens, such as bone marrow suppression and liver and kidney damage, and improve the safety and effectiveness of clinical treatment for patients. It has higher specificity and accuracy, laying the foundation for the development of new diagnostic tools and providing direct and feasible candidate solutions and theoretical basis for the development of highly effective and low-toxicity new therapies for HLH. It improves the accuracy and efficiency of diagnosis and treatment of hemophagocytic lymphohistiocytosis and solves the technical problems in the existing technology, such as the lack of highly effective and specific molecular markers for HLH diagnosis, the high toxicity and limited efficacy of treatment regimens, and the insufficient research on the pathological mechanism of HLH driven by mitochondrial autophagy dysregulation.

[0020] Reference Figure 1 , Figure 1 This is a functional block diagram of the first embodiment of the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention.

[0021] In the first embodiment of the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention, the device includes: a detection module 10, an analysis module 20, and a diagnostic, subtyping, and therapeutic module 30; wherein, The detection module 10 is used to detect the expression levels of four genes, CCR2, CDKN1A, TNFSF10 and TP53INP2, in the current patient's sample.

[0022] The analysis module 20 is used to perform differential analysis between the expression level and the expression level of the healthy control group to obtain the molecular characteristic profile of hemophagocytic lymphohistiocytosis (HLH).

[0023] The diagnostic classification and treatment module 30 is used to diagnose and classify the current patient based on the difference patterns of the molecular characteristic spectrum, and to treat the patient based on the diagnostic classification results.

[0024] It should be noted that in the clinical diagnostic process of hemophagocytic lymphohistiocytosis (HLH), the expression levels of four genes, CCR2, CDKN1A, TNFSF10 and TP53INP2, in the current patient's sample are detected using bioassay technology.

[0025] Understandably, in the diagnostic process for hemophagocytic lymphohistiocytosis (HLH), analyzing the difference in expression levels of four genes—CCR2, CDKN1A, TNFSF10, and TP53INP2—between those in patient samples and healthy controls can directly generate an HLH-specific molecular profile.

[0026] It should be understood that by using the differential patterns of molecular characteristic spectra, it is possible to accurately classify current patients with HLH, thereby enabling targeted treatment based on the diagnostic classification results and improving diagnostic accuracy.

[0027] Furthermore, the detection module 10 is also used to collect blood samples, bone marrow samples, or spleen tissue samples from the current patient, and to use the blood samples, bone marrow samples, or spleen tissue samples as patient samples.

[0028] The detection module 10 is also used to quantitatively analyze the expression levels of four genes, CCR2, CDKN1A, TNFSF10 and TP53INP2, in the patient sample using real-time quantitative PCR, RNA sequencing or Western blotting, to obtain the expression levels of the four genes.

[0029] It should be noted that in the diagnostic methods for hemophagocytic lymphohistiocytosis (HLH), collecting blood, bone marrow, or spleen tissue samples from the current patient as patient samples can ensure the clinical reliability of the molecular characterization profile and provide standardized and repeatable test input for subsequent accurate subtyping.

[0030] Understandably, using real-time quantitative PCR, RNA sequencing, or Western blotting to quantify the expression levels of the four core genes in patient samples can provide objective data input for differential analysis, avoid misdiagnosis caused by reliance on non-specific clinical indicators, and lay a quantitative foundation for subsequent subtyping and treatment decisions.

[0031] Furthermore, the detection module 10 is also used to read the original CEL file corresponding to the original transcriptome data in the public database through the affy package, and to perform background correction, quantile normalization and logarithmic transformation on the original CEL file to obtain the processed target file.

[0032] The detection module 10 is also used to calculate the standard expression level of the target file using a robust multi-array average RMA algorithm, and generate the log2-transformed standardized expression value of each probe set.

[0033] The detection module 10 is also used to convert the probe ID of each probe in each probe set into a gene symbol, and if multiple probe sets are detected to be mapped to the same gene, take the median of the expression level of the current gene as the final expression value of the current gene.

[0034] The detection module 10 is further configured to obtain a list of core autophagy-related genes from a human autophagy database, and to obtain autophagy-related differentially expressed genes by intersecting the gene corresponding to the final expression value with the list of core autophagy-related genes.

[0035] The detection module 10 is also used to exclude abnormal samples in the autophagy-related differentially expressed genes and select the soft threshold power when the scale-free topological fitting index reaches the preset fitting index by using the pickSoftThreshold function.

[0036] The detection module 10 is further configured to calculate the collar matrix based on the soft threshold power, convert the collar matrix into a topological overlap matrix, and use dynamic tree cutting to identify co-expressed gene modules in the topological overlap matrix.

[0037] The detection module 10 is also used to calculate the modular trait relationship between the gene module and the hemophagocytic lymphohistiocytosis (HLH) trait, and to determine the WGCNA brown module gene, which has the strongest negative correlation with HLH, based on the modular trait relationship.

[0038] The detection module 10 is also used to obtain candidate genes by taking the intersection of the autophagy-related differentially expressed genes and the WGCNA brown module genes.

[0039] The detection module 10 is also used to perform LASSO regression analysis on the candidate genes, screen out genes with non-zero coefficients, and perform vector machine recursive feature elimination to screen out CCR2, CDKN1A, TNFSF10 and TP53INP. CCR2, CDKN1A, TNFSF10 and TP53INP are used as the core diagnostic genes for hemophagocytic lymphohistiocytosis (HLH).

[0040] It should be understood that by mining transcriptome data from public databases, the core gene groups for HLH diagnosis can be systematically screened out: First, the raw CEL files of the Affymetrix chip are preprocessed using the R language affy package (background correction, quantile normalization, and log2 transformation) to generate expression data of the probe set; then, the log2 normalized expression values ​​of the probe set are calculated using the RMA algorithm, and the median expression levels of multiple probes corresponding to the same gene are integrated into gene-level data; subsequently, a list of autophagy-related genes is obtained by combining human autophagy databases (such as HADb), and the intersection with differentially expressed genes is taken to focus on the mitophagy pathway; then, based on WGCNA analysis (using the pickSoftThreshold function to select soft threshold power, calculate the topological overlap matrix, and dynamically cut and identify co-expressed modules), the brown module genes with the strongest negative correlation with HLH are identified; finally, non-zero coefficient genes are screened by LASSO regression and SVM-RFE recursive feature elimination is used to strictly limit CCR2, CDKN1A, TNFSF10, and TP53INP2 to the core genes for HLH diagnosis.

[0041] Furthermore, the analysis module 20 is also used to perform a difference analysis between the expression level and the expression level of the healthy control group, and to calculate the absolute logarithmic change value through a differential expression statistical tool.

[0042] The analysis module 20 is also used to compare the absolute logarithmic change value with a preset change value threshold to obtain the expression abnormality pattern.

[0043] The analysis module 20 is also used to integrate the dynamic change features of the four genes into a molecular feature profile of HLH based on the expression abnormality pattern.

[0044] Understandably, the analysis module calculates the absolute logarithmic fold change (|log2FC|) of four genes—CCR2, CDKN1A, TNFSF10, and TP53INP2—in patient samples and healthy control samples through differential expression analysis. Its technical effect lies in transforming raw expression data into quantifiable pathological characteristic indicators: the module uses standard differential expression statistical tools (such as limma or DESeq2) to calculate the |log2FC| of each gene and determines expression abnormalities based on preset thresholds, forming an abnormal expression pattern (i.e., a significant decrease in CCR2 synergistically increased CDKN1A / TNFSF10 / TP53INP2). Subsequently, the module integrates this abnormal pattern into an HLH-specific molecular characteristic profile, establishing these four genes as the core quantitative basis for diagnosis.

[0045] Furthermore, the analysis module 20 is also used to perform linear model fitting between the expression level and the expression level of the healthy control group according to the lmFit function to obtain the fitting result.

[0046] The analysis module 20 is also used to calculate the significance of differential expression of the fitting results based on empirical Bayesian smoothing using the eBayes function, and to obtain the absolute logarithmic change value of each gene.

[0047] It should be understood that the analysis module uses the lmFit function of the limma package in R language to perform linear model fitting (such as fitting expression data to a comparison model between the patient group and the healthy control group) and applies the eBayes function for empirical Bayesian smoothing correction (to optimize the statistical significance of differential expression and reduce the false positive rate), thereby calculating the absolute logarithmic change value of each gene.

[0048] Furthermore, the diagnostic subtyping and treatment module 30 is also used to quantitatively compare the gene expression data of the current patient with the molecular feature spectrum based on the difference pattern of the molecular feature spectrum.

[0049] The diagnostic and subtyping treatment module 30 is also used to determine an HLH positive case when an abnormal decrease in CCR2 expression and a synergistic abnormal increase in CDKN1A, TNFSF10 and TP53INP2 genes are detected.

[0050] The diagnostic typing and treatment module 30 is also used to perform HLH typing on the current patient based on the absolute logarithmic change value of each gene combined with the results of immune microenvironment analysis.

[0051] It should be noted that the diagnostic and subtyping treatment module directly compares the current patient's gene expression data with a preset HLH molecular characteristic profile. When the expression level of CCR2 is lower than that of the healthy control group, and the expression levels of CDKN1A, TNFSF10, and TP53INP2 are higher than those of the healthy control group, the patient is determined to be HLH positive. Further, based on the results of immune microenvironment analysis, HLH is accurately subtyped. The subtyping results directly guide the treatment method, thereby realizing a clinical closed loop of diagnosis-subtyping-treatment. This avoids the misdiagnosis rate caused by relying on non-specific symptoms such as fever and hepatosplenomegaly in traditional diagnosis, and ensures that the treatment plan accurately matches the pathological mechanism. It significantly reduces the risk of bone marrow suppression and liver and kidney damage caused by traditional chemotherapy such as glucocorticoids / cyclosporine A, and provides a safe and efficient individualized diagnosis and treatment pathway for HLH patients.

[0052] Furthermore, the diagnostic subtyping and treatment module 30 is also used to determine that the current patient's HLH type is primary HLH when the absolute logarithmic change value of the CCR2 gene is less than a preset statistical significance threshold, the absolute logarithmic change values ​​of the CDKN1A, TNFSF10 and TP53INP2 genes are greater than the preset statistical significance threshold, and the cell infiltration ratio of specific immune cells in the immune microenvironment analysis results is greater than or equal to a preset ratio threshold.

[0053] The diagnostic and subtyping treatment module 30 is also used to determine that the current patient's HLH type is secondary HLH when the absolute logarithmic change values ​​of CCR2, CDKN1A, TNFSF10 and TP53INP are all greater than the preset statistical significance threshold, but the cell infiltration ratio is less than the preset ratio threshold and the ANNEXIN pathway activity is not enhanced.

[0054] It is understood that the diagnostic subtyping and treatment module achieves accurate etiological subtyping of HLH by synergistically determining the patient's gene expression data with preset thresholds (e.g., |log2FC|>0.585) and immune microenvironment indicators (e.g., M2 macrophage infiltration rate ≥30%, enhanced ANNEXIN pathway activity): when the expression levels of the four genes CCR2, CDKN1A, TNFSF10, and TP53INP2 in the patient sample all meet |log2FC|>0.585 (where CCR2 expression is significantly downregulated, CDKN1A ... When the expression of KN1A / TNFSF10 / TP53INP2 is significantly upregulated and immune microenvironment analysis shows that the M2 macrophage infiltration rate is ≥30% and the ANNEXIN pathway activity is enhanced, it is identified as primary HLH. When the expression patterns of the four genes meet the threshold of |log2FC|>0.585, but the M2 infiltration rate is <30% and the ANNEXIN pathway activity is not enhanced, it is identified as secondary HLH. This improves the clinical accuracy of HLH classification and avoids the misdiagnosis rate caused by the reliance on non-specific symptoms such as fever and hepatosplenomegaly in traditional diagnosis.

[0055] Furthermore, the diagnostic typing and treatment module 30 is also used to use sodium butyrate as a TNFSF10 targeting regulator when the absolute logarithmic change value of the TNFSF10 gene is detected to be greater than the preset proportional threshold.

[0056] The diagnostic typing and treatment module 30 is also used to use dicumarol as a CDKN1A targeting regulator when the absolute logarithmic change value of the CDKN1A gene is detected to be greater than the preset proportional threshold.

[0057] The diagnostic typing and treatment module 30 is also used to use simvastatin as a CCR2 targeting regulator when the absolute logarithmic change value of the CCR2 gene is detected to be greater than the preset proportional threshold.

[0058] It should be understood that by directly comparing the expression levels of the three genes TNFSF10, CDKN1A, and CCR2 in patient samples with a preset statistical significance threshold, dynamic matching of targeted therapy drugs is achieved: sodium butyrate is selected for intervention when the expression level of TNFSF10 is significantly increased (|log2FC|>0.585); dicumarol is selected when the expression level of CDKN1A is significantly increased (|log2FC|>0.585); and simvastatin is selected when the expression level of CCR2 is significantly increased (|log2FC|>0.585).

[0059] Furthermore, the diagnostic subtyping and treatment module 30 is also used to obtain the ANNEXIN pathway activity by comparing the activation intensity of the ANNEXIN signaling pathway in the intercellular communication network with that of the healthy control group, wherein the statistical significance of the activation intensity is reflected by the intercellular communication analysis results.

[0060] The diagnostic classification and treatment module 30 is also used to verify the degree of improvement in mitophagy disorder by detecting the expression levels of PINK1, PARKIN, and NIX proteins after detecting that the current patient has been treated by oral, intravenous, or local administration.

[0061] It should be noted that by using the synergistic indicator of enhanced ANNEXIN signaling pathway activity and M2 macrophage infiltration rate ≥30%, primary HLH (such as patients with high TNFSF10 expression where ANNEXIN activity is significantly enhanced) and secondary HLH (where ANNEXIN activity is not enhanced) can be objectively distinguished, avoiding the misdiagnosis rate caused by traditional subtyping relying on nonspecific symptoms. At the same time, after patients receive targeted drugs (such as sodium butyrate in cases of high TNFSF10 expression) via oral, intravenous, or local administration, the recovery of PINK1, PARKIN, and NIX protein expression levels is directly measured as a quantitative endpoint for the improvement of mitophagy dysphagia, rather than relying on complex intercellular communication analysis tools (such as CellChat), ensuring the clinical operability of treatment validation.

[0062] Furthermore, the diagnostic subtyping and treatment module 30 is also used to detect the expression levels of three mitochondrial autophagy core proteins, PINK1, PARKIN, and NIX, in patient samples using Western blot or immunohistochemistry.

[0063] The diagnostic and subtyping treatment module 30 is also used to determine that mitophagy disorder has been improved when the expression levels of PINK1, PARKIN and NIX have all returned to the baseline values ​​of the control group.

[0064] Understandably, by detecting the expression levels of the three core mitophagy proteins PINK1, PARKIN, and NIX in patient samples (using clinically feasible standardized detection techniques, such as immunohistochemistry or Western blot, to verify treatment efficacy), when the expression levels of all three proteins return to the baseline values ​​of the healthy control group, it can be directly determined that mitophagy disorder has been improved, thereby confirming the effectiveness of targeted drug intervention (such as sodium butyrate for high TNFSF10 expression, dicumarol for high CDKN1A expression, and simvastatin for high CCR2 expression).

[0065] In a specific implementation, regarding diagnosis: a kit for diagnosing or assisting in the diagnosis of HLH is provided, which contains reagents for detecting the expression level of at least one of the genes CCR2, CDKN1A, TNFSF10 and TP53INP2 in a sample; the sample includes, but is not limited to, blood, bone marrow or spleen tissue.

[0066] In terms of prognostic assessment: a method for assessing the prognosis of HLH is provided, which involves constructing a predictive model (such as Nomogram) for risk stratification by detecting the expression levels of the four key genes.

[0067] Regarding therapeutic targets: the CCR2, CDKN1A, TNFSF10 and TP53INP2 genes or their encoded proteins are provided as targets for the preparation of drugs to prevent or treat HLH.

[0068] In terms of therapeutic drugs: Providing new uses for known drugs that target the above-mentioned key genes (such as sodium butyrate, dicumarol, and simvastatin) in the preparation of drugs for the treatment of HLH.

[0069] Example 1: Screening and Identification of Core Gene Markers 1. Data Acquisition and Preprocessing The dataset GSE26050 was downloaded from the Gene Expression Comprehensive Database of the National Center for Biotechnology Information (NCBI). This dataset contains peripheral blood or bone marrow transcriptome data from 11 HLH patients and 33 healthy controls, and the platform is GPL570 ([HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array). Subsequent analysis was performed using R (v4.3.0). First, the original CEL file was read using the affy package, and background correction, quantile normalization, and logarithmic transformation were performed. The expression level was calculated using the rma algorithm. The probe IDs were converted to gene symbols. When multiple probes corresponded to the same gene, the median expression level was taken as the final expression value of that gene.

[0070] 2. Differential Expression Analysis Reference Figure 2 , Figure 2 This is a schematic diagram illustrating the autophagy-immune axis dysregulation in HLH revealed by the core gene markers in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown below. Figure 2 As shown, Figure 2 (A): Volcano plot of differentially expressed genes (DEGs) between HLH and the control group.

[0071] Figure 2 (B): Heatmap of DEGs.

[0072] Figure 2 (C): Selection of soft threshold in WGCNA analysis.

[0073] Figure 2 (D): Clustering dendrogram of WGCNA gene modules.

[0074] Figure 2 (E): Correlation heatmap between each gene module and the HLH trait.

[0075] Figure 2 (F): KEGG pathway enrichment analysis of brown module genes.

[0076] Figure 2 The study revealed that core gene markers in HLH indicate autophagy-immune axis dysregulation. Bioinformatics analysis showed that HLH patients had extensive gene expression dysregulation compared with healthy controls. Weighted gene co-expression network analysis identified a gene module that was significantly negatively correlated with HLH. This module's genes were significantly enriched in inflammatory and immune signaling pathways such as TNF and NF-κB, revealing for the first time that autophagy-immune axis dysregulation is a core pathological feature of HLH.

[0077] Differential expression analysis was performed using the limma package. First, a design matrix was constructed, dividing the samples into HLH and control groups. Then, the lmFit function was used for linear model fitting, and the Empirical Bayesian smoothing function was applied using the eBayes function to calculate the significance of differential expression. The selection criteria for differentially expressed genes were: a p-value (adj.P.Val) after error rate correction (adj.P.Val) < 0.05, and a 2-fold absolute logarithmic change (|log2FC|) > 0.585. The results were visualized using volcano plots and heatmaps (corresponding to...). Figure 2 A, B).

[0078] 3. Screening for autophagy-related genes A list of core autophagy-related genes, totaling 222 genes, was obtained from the human autophagy database.

[0079] The intersection of this list with all differentially expressed genes identified in step 2 yielded 70 autophagy-related differentially expressed genes that were significantly dysregulated in HLH.

[0080] 4. Weighted gene co-expression network analysis Use the WGCNA package to construct a gene co-expression network.

[0081] First, check the clustering of all samples to exclude outliers.

[0082] Then, the soft threshold power (β=15) when the scale-free topology fit index reaches 0.9 is selected by using the pickSoftThreshold function to ensure that the constructed network conforms to the scale-free distribution.

[0083] Based on this, the adjacency matrix is ​​calculated and converted into a topological overlap matrix to measure network connectivity.

[0084] Subsequently, the co-expressed modules were identified using the dynamic tree slicing method (cutreeDynamic function), and the minimum module size was set to 30.

[0085] A total of 8 gene modules were identified (distinguished by different colors), and their module-trait relationships with the HLH trait were calculated (corresponding to...). Figure 2 C to Figure 2 E).

[0086] Among them, the brown module (cor = -0.94, p = 2e-21), which had the strongest negative correlation with HLH, was selected for subsequent analysis.

[0087] 5. Machine learning for screening core genetic biomarkers Reference Figure 3 , Figure 3 This is a schematic diagram illustrating the identification of the core gene and its association with the immune microenvironment in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown. Figure 3 As shown, Figure 3 A: Venn diagram of candidate genes (intersection of autophagy-related differentially expressed genes and brown module genes).

[0088] Figure 3 B: Trajectory diagram of LASSO regression screening for characteristic genes.

[0089] Figure 3 C: Error map of SVM-RFE algorithm for screening feature genes.

[0090] Figure 3 D: Bar graph showing the ratio of immune cell infiltration between HLH and the control group.

[0091] Figure 3 E: Box plot comparing the proportions of specific immune cell subsets in HLH and the control group.

[0092] Figure 3 F: Heatmap showing the correlation between core genes and immune cell infiltration.

[0093] Figure 3 This indicates that machine learning screened out the core gene biomarkers of HLH and clarified their immune regulatory functions. Through cross-screening using two machine learning algorithms, LASSO regression and support vector machine, CCR2, CDKN1A, TNFSF10, and TP53INP2 were finally identified as the core gene biomarkers of HLH.

[0094] Immune infiltration analysis showed that these genes were significantly correlated with the infiltration levels of specific immune cells (such as M2 macrophages and neutrophils), revealing their key role in shaping the immune microenvironment of HLH.

[0095] The intersection of the 70 autophagy-related differentially expressed genes obtained in step 3 and the WGCNA brown module genes obtained in step 4 yielded 19 candidate genes.

[0096] LASSO Regression Analysis: LASSO logistic regression analysis was performed using the glmnet package.

[0097] The optimal penalty parameter λ is selected through 10-fold cross-validation. This λ value corresponds to the feature combination that minimizes the model error.

[0098] Finally, five genes with non-zero coefficients were selected (corresponding to...) Figure 3 B).

[0099] Support Vector Machine - Recursive Feature Elimination Analysis: SVM-RFE analysis using the e1071 package and custom functions.

[0100] By recursively iterating, the features with the smallest weights are gradually removed, and the cross-validation accuracy of the model is calculated after each removal.

[0101] The final selection yielded the feature combination that achieved the highest model accuracy, comprising a total of 9 genes (corresponding to...). Figure 3 C).

[0102] By taking the intersection of the results obtained from the two algorithms, four core gene markers were finally identified: CCR2, CDKN1A, TNFSF10, and TP53INP2.

[0103] Example 2: Elucidation of the immune function and pathway mechanisms of core genes 1. Analysis of immune cell infiltration The relative proportions of 22 immune cell subtypes in HLH and control samples were calculated using the CIBERSORT algorithm for deconvolution.

[0104] The input gene expression data has been standardized.

[0105] The results were visualized using R language, and the Wilcoxon rank-sum test was used to compare the differences in the proportion of immune cells between the two groups (corresponding to...). Figure 3 D and Figure 3 E).

[0106] Spearman correlation heatmaps between the four core genes and the proportions of 22 immune cell types were calculated and plotted using the corrplot package. Figure 3 F).

[0107] 2. Functional enrichment analysis Reference Figure 4 , Figure 4 This is a schematic diagram illustrating the multidimensional functional characterization of the core gene in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown. Figure 4 As shown, Figure 4 A: GSEA enrichment analysis results based on core genes (such as the MAPK pathway).

[0108] Figure 4 B: GSVA enrichment analysis results based on core genes (such as TNF-α / NF-κB signaling).

[0109] Figure 4 CD: A diagram of the transcription factor regulatory network of core genes.

[0110] Figure 4 E: miRNA regulatory network diagram of core genes (drawn by Cytoscape).

[0111] Figure 4 F: Nomogram of HLH clinical predictions integrating four core genes.

[0112] Figure 4 The study indicated that the core gene biomarkers are involved in multiple key signaling pathways and that an effective predictive model was constructed. Functional enrichment analysis confirmed that the core gene biomarkers are significantly enriched in key pathways driving HLH cytokine storm, such as MAPK, TNF-α / NF-κB, and JAK-STAT.

[0113] The clinical prediction model (nomotype) built based on these four genes can effectively assess an individual's risk of developing the disease, providing a quantitative tool for the early diagnosis of HLH.

[0114] Gene set enrichment analysis: Based on the median expression of individual core genes, the samples were divided into high expression group and low expression group.

[0115] GSEA analysis was performed using the clusterProfiler package, with the reference gene set database being c2.cp.kegg.v7.2.symbols.gmt.

[0116] A corrected p-value (FDR) < 0.05 was used as the significant enrichment criterion (corresponding to...). Figure 4 A).

[0117] Gene set variation analysis: Pathway activity was scored for each sample using the GSVA package.

[0118] Using the KEGG gene set as a background, the differences in pathway activity between high and low expression groups of core genes were analyzed (corresponding to...). Figure 4 B).

[0119] 3. Construction of Regulation Network and Prediction Model Transcription factor prediction: Using the RcisTarget package, candidate transcription factors that regulate core genes are predicted based on gene promoter regions and conserved motif databases.

[0120] miRNA-mRNA network construction: miRNAs with predicted regulatory relationships to core genes were obtained from the miRcode database, and a regulatory network diagram was constructed using Cytoscape software (v3.9.1). Figure 4 E).

[0121] Nomograph Construction: Using the rms package, a multivariate logistic regression model was constructed based on the expression values ​​of four core genes and visualized as a nomograph for the quantitative assessment of individual HLH risk (corresponding to...). Figure 4 F).

[0122] Example 3: Validation at the single-cell transcriptome level 1. Data preprocessing and cell clustering Download the single-cell dataset GSE231946 and analyze it using the Seurat package (v4.3.0).

[0123] The quality control standard is: the number of genes detected in each cell is between 200 and 2500, and the proportion of mitochondrial genes is less than 10%.

[0124] LogNormalize the quality-controlled cell data and identify the top 2000 hypervariable genes.

[0125] The ScaleData function was used to correct for the effects of mitochondrial gene ratio and cell cycle.

[0126] Next, principal component analysis was performed, and the top 20 principal components were selected based on the elbow plot for UMAP dimensionality reduction and FindClusters clustering (resolution = 0.5).

[0127] 2. Cell type annotation Reference Figure 5 , Figure 5 This is a schematic diagram of HLH immune landscape remodeling at single-cell resolution in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown. Figure 5 As shown, Figure 5 A: UMAP dimensionality reduction clustering and cell type annotation diagram for all cells.

[0128] Figure 5 B: Stacked bar chart or faceted UMAP plot showing the ratio of HLH cells to control cells.

[0129] Figure 5 C: Differential pathway enrichment map of monocyte subsets in HLH.

[0130] Figure 5 DE: CellChat analysis of the intercellular interaction network between the control group and the HLH group.

[0131] Figure 5 FH: Analysis diagram of the intensity, network and contribution of the ANNEXIN signaling pathway in monocytes.

[0132] Figure 5 Single-cell transcriptome sequencing confirmed the presence of immune landscape remodeling in HLH.

[0133] Single-cell RNA sequencing analysis validated significant changes in the composition and state of immune cells in HLH patients at higher resolution.

[0134] Further analysis of intercellular communication revealed that the activity of the ANNEXIN signaling pathway was significantly enhanced in monocytes of HLH patients, elucidating a new mechanism of cell interaction.

[0135] Using the SingleR package, we automatically annotate each cell cluster with the human primary cell atlas as the reference dataset.

[0136] Simultaneously, by combining the CellMarker and PanglaoDB databases with published literature, and through manual verification by checking the expression of known cell markers (such as CD3D for T cells, CD19 for B cells, FCGR3A for monocytes, etc.), the biological identities of 12 cell clusters were finally determined (corresponding to...). Figure 5 A).

[0137] 3. Intercellular communication analysis The CellChat package (v1.6.1) was used to analyze the differences in cell-cell communication between HLH and control groups. Normalized expression data and cell type annotation information were input. By comparing the total number and intensity of interactions, and analyzing differences in specific signaling pathways (such as ANNEXIN), aberrant cell-cell dialogue in HLH was revealed (corresponding to…). Figure 5 D to Figure 5 H).

[0138] Example 4: Prediction and Validation of Therapeutic Compounds 1. Drug prediction Access the Drug-Gene Interaction Database, enter the four core gene symbols respectively, and screen for known drugs or small molecule compounds that have been experimentally verified or have strong predictive evidence, and limit the interaction type to "inhibitor" or "agonist / antidote".

[0139] 2. Molecular docking Reference Figure 6 , Figure 6 This is a schematic diagram illustrating the core gene dynamics and therapeutic target prediction in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown. Figure 6 As shown, Figure 6 A: A dot plot or violin plot showing the expression distribution of core genes in various single-cell populations.

[0140] Figure 6 B: Single-cell pseudo-time trajectory plot (showing the difference in HLH distribution between the two controls).

[0141] Figure 6 C: Gene expression heatmap along pseudo-time.

[0142] Figure 6 D: Expression trend curve of core genes along pseudo-time.

[0143] Figure 6 E: Correlation diagram between core genes and the activity of TNF-α / NF-κB and oxidative phosphorylation pathways.

[0144] Figure 6 F: Predicted drug-gene interaction network diagram.

[0145] Figure 6 G: Visualization of molecular docking results (showing the binding conformation and binding energy of the drug and protein).

[0146] Figure 6 This indicates that core gene markers are dynamically expressed during cell differentiation and are associated with metabolic reprogramming.

[0147] Pseudo-time trajectory analysis revealed differences in cell differentiation pathways between HLH and the control group.

[0148] The core genes exhibit dynamic expression patterns during differentiation and are closely related to specific pathway activities (such as TNF-α / NF-κB signaling and oxidative phosphorylation) and tryptophan metabolism reprogramming, linking immune abnormalities with metabolic disorders.

[0149] Protein structure preparation: The three-dimensional predicted structures of human proteins CCR2, CDKN1A, TNFSF10, and TP53INP2 were downloaded from the AlphaFold database and preprocessed using AutoDock Tools, including hydrogenation and charge calculation.

[0150] Ligand structure preparation: Download the predicted 3D SDF structures of drugs (sodium butyrate, dicumarol, simvastatin) from the PubChem database.

[0151] Docking process: Semi-flexible docking is performed using AutoDock Vina software.

[0152] The docking region covers the entire potential active site of the protein.

[0153] After 50 docking runs, the conformation with the lowest binding free energy (ΔG) was selected as the optimal binding mode, and the results were visualized using PyMOL software (corresponding to...). Figure 6 G).

[0154] Example 5: Experimental validation of in vitro and in vivo HLH models Reference Figure 7 , Figure 7 This is a schematic diagram illustrating the in vitro HLH model validation in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown. Figure 7 As shown, Figure 7 A: Bar chart of the levels of inflammatory factors (IFN-γ, IL-1β, IL-6, TNF-α) in the supernatant of BMDM after IFN-γ stimulation.

[0155] Figure 7 B: Bar chart showing the relative expression levels of core gene mRNA detected by qPCR.

[0156] Figure 7 C: Western blotting of core gene protein expression levels and quantitative bar graphs.

[0157] Figure 7 This indicates the prediction and molecular docking validation of candidate therapeutic drugs targeting core genes.

[0158] Based on a drug-gene interaction database, it was predicted that sodium butyrate, dicumarol, and simvastatin could target the core genes TNFSF10, CDKN1A, and CCR2 of this invention, respectively.

[0159] Molecular docking simulations confirmed that these drugs can bind stably to their corresponding target proteins with good binding energy, suggesting their potential for repurposing existing drugs to treat HLH.

[0160] 1. Isolation and culture of mouse bone marrow-derived macrophages Six- to eight-week-old C57BL / 6J mice were euthanized, and the femur and tibia were aseptically separated.

[0161] The bone marrow cavity was rinsed with pre-cooled PBS, the cell suspension was collected, filtered through a 70 μm cell sieve, and red blood cells were removed using red blood cell lysis buffer.

[0162] After centrifugation and resuspending, the cells were inoculated with DMEM complete medium (containing 10% FBS, 1% Penicillin-Streptomycin, and 20 ng / mL M-CSF).

[0163] On day 4, half of the culture medium was replaced, and mature BMDMs were obtained on day 7 for experiments.

[0164] 2. Establishment and validation of in vitro HLH model BMDMs were divided into two groups: a control group (with an equal volume of PBS) and an HLH model group (with 100 pg / mL of recombinant mouse IFN-γ protein, catalog number: #575302, BioLegend).

[0165] After 24 hours of stimulation, cell supernatant and cells were collected.

[0166] Cytokine detection: The concentrations of IFN-γ, IL-1β, IL-6, and TNF-α in the supernatant were detected using a commercial ELISA kit (corresponding to...). Figure 7 A).

[0167] qPCR validation: Total RNA was extracted from cells using the TRIzol method and reverse transcribed into cDNA. qPCR was performed on a Roche LightCycler® 480 system using the SYBR Green assay with GAPDH as an internal control. Primer sequences were used to calculate relative gene expression levels (corresponding to...) using the 2^(-ΔΔCt) method. Figure 7 B).

[0168] Western Blot validation: Total protein was extracted from cells using RIPA lysis buffer and quantified using the BCA method. 30 μg of protein was loaded into each well, and after electrophoresis with 10% SDS-PAGE, the protein was transferred to a PVDF membrane.

[0169] After blocking with 5% skim milk, the cells were sequentially incubated with primary antibodies (CCR2, #16153-1-AP, Proteintech; CDKN1A, #10355-1-AP, Proteintech; TNFSF10, #27064-1-AP, Proteintech; TP53INP2, #PA5324, Abmart; GAPDH, #2118, CST) and HRP-labeled secondary antibodies. After ECL chemiluminescence, the band grayscale values ​​(corresponding to...) were analyzed using ImageJ software. Figure 7 C).

[0170] 3. Establishment and validation of in vivo HLH model Reference Figure 8 , Figure 8 This is a schematic diagram illustrating the in vitro HLH model characterization in the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown. Figure 8 As shown, Figure 8 A: Flowchart of the experimental process for the mouse CpG-induced HLH model.

[0171] Figure 8 B: Photographs of the spleen of mice in the control group and HLH group, and microscopic images of spleen imprints (showing hemophagocytosis).

[0172] Figure 8 C: Bar chart of complete blood count (RBC, WBC, PLT, Hb) and clotting time.

[0173] Figure 8 D: Bar chart of liver and kidney function and metabolic indicators (AST, ALT, BUN, Ferritin, TG).

[0174] Figure 8 E: Bar chart of serum inflammatory factors (IFN-γ, IL-1β, IL-6, TNF-α, IL-10) levels.

[0175] Figure 8 This demonstrates the validation of core gene expression and inflammatory phenotype using an in vitro HLH model.

[0176] Significant release of inflammatory factors was successfully induced in IFN-γ-stimulated macrophages (in vitro HLH model).

[0177] The model confirmed that the expression of core genes CDKN1A, TNFSF10, and TP53INP2 was significantly upregulated, while the expression of CCR2 was downregulated, consistent with bioinformatics predictions, thus validating the biomarker value of these genes at the cellular level.

[0178] Model establishment: Eight-week-old male C57BL / 6J mice were randomly divided into a control group and an HLH model group (n=6).

[0179] The model group received intraperitoneal injections of CpG ODN (2 mg / kg, Sangon Biotech) every two days for a total of 5 times; the control group received an equal volume of PBS (corresponding to...). Figure 8 A).

[0180] Phenotypic identification: Blood and spleen were collected 24 hours after the last injection.

[0181] Complete blood count and biochemistry: Whole blood was tested using a blood analyzer; serum was used to detect AST, ALT, BUN, TG, ferritin, and cytokines (corresponding to...). Figure 8 C, Figure 8 D, Figure 8 E).

[0182] Spleen assessment: Weighing and calculating the spleen index; Liu's staining of spleen imprints, and observation of hemophagocytosis under a microscope (corresponding to...) Figure 8 B).

[0183] Reference Figure 9 , Figure 9 This is a schematic diagram illustrating the verification of mitophagy and core proteins in an in vivo model within the diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis of the present invention; as shown. Figure 9 As shown, Figure 9 A: Western blotting of PINK1, PARKIN, and BNIP3L / NIX protein expression levels in mouse spleen and quantitative bar graphs.

[0184] Figure 9 B: Microscopic images and semi-quantitative bar charts of CCR2, CDKN1A, TNFSF10, and TP53INP2 protein expression in mouse spleen detected by immunohistochemistry (IHC).

[0185] Figure 9 This indicates that the in vivo HLH model confirms the pathological correlation between systemic pathological damage and core genes.

[0186] The CpG-induced mouse HLH model reproduced typical features of the human disease, including splenomegaly, cytopenia, liver and kidney dysfunction, metabolic abnormalities, and cytokine storm.

[0187] This model confirmed that the expression of key mitophagy proteins (PINK1, PARKIN, BNIP3L / NIX) was impaired in the HLH state. Immunohistochemistry showed that the protein expression of the core genes was significantly altered in the diseased tissue, and the pathological significance was finally confirmed at the whole animal level.

[0188] Molecular Validation: Tissue Protein Extraction and Western Blot: A portion of spleen tissue was homogenized, proteins were extracted, and the expression of mitophagy-related proteins (PINK1, #PK05715, Abmart; PARKIN, #14060-1-AP, Proteintech; BNIP3L / NIX, #F0469, Selleck) was detected (corresponding to...). Figure 9 A).

[0189] Immunohistochemistry: Spleen tissue was fixed in 4% paraformaldehyde, embedded in paraffin, and then sectioned.

[0190] Antigen retrieval was performed, followed by sequential incubation with primary antibodies against the four core genes and corresponding HRP secondary antibodies, DAB staining, and hematoxylin counterstaining.

[0191] Observation under a microscope and semi-quantitative analysis of positive signals using ImageJ (corresponding to...) Figure 9 B).

[0192] In summary, through the specific embodiments described above, this invention achieves efficient, accurate, and automated identification of mitotic figures in H&E staining images of malignant tumors, possessing good generalization ability, clinical compatibility, and real-time processing capabilities, providing reliable technical support for pathological diagnosis.

[0193] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0194] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0195] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis, characterized in that, The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis includes: a detection module, an analysis module, and a diagnostic, subtyping, and therapeutic module; wherein, The detection module is used to detect the expression levels of four genes, CCR2, CDKN1A, TNFSF10 and TP53INP2, in the current patient's sample. The analysis module is used to perform differential analysis between the expression level and the expression level of the healthy control group to obtain the molecular feature profile related to hemophagocytic lymphohistiocytosis (HLH). The diagnostic classification and treatment module is used to diagnose and classify the current patient based on the difference patterns of the molecular characteristic spectrum, and to carry out treatment based on the diagnostic classification results.

2. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 1, characterized in that, The detection module is also used to collect blood samples, bone marrow samples, or spleen tissue samples from the current patient, and to use the blood samples, bone marrow samples, or spleen tissue samples as patient samples; The detection module is also used to quantitatively analyze the expression levels of four genes, CCR2, CDKN1A, TNFSF10 and TP53INP2, in the patient sample using real-time quantitative PCR, RNA sequencing or Western blotting, to obtain the expression levels of the four genes.

3. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 1, characterized in that, The detection module is also used to read the original CEL file corresponding to the original transcriptome data in the public database through the affy package, and to perform background correction, quantile normalization and logarithmic transformation on the original CEL file to obtain the processed target file; The detection module is also used to calculate the standard expression level of the target file using a robust multi-array average RMA algorithm, and generate the log2-transformed standardized expression value of each probe set. The detection module is also used to convert the probe ID of each probe in each probe set into a gene symbol. If multiple probe sets are detected to map to the same gene, the median of the expression level of the current gene is taken as the final expression value of the current gene. The detection module is also used to obtain a list of core autophagy-related genes from the human autophagy database, and to obtain autophagy-related differentially expressed genes by taking the intersection of the gene corresponding to the final expression value and the list of core autophagy-related genes. The detection module is also used to exclude abnormal samples in the autophagy-related differentially expressed genes and select the soft threshold power when the scale-free topological fitting index reaches the preset fitting index through the pickSoftThreshold function. The detection module is also used to calculate the collar matrix based on the soft threshold power, convert the collar matrix into a topological overlap matrix, and use dynamic tree cutting to identify co-expressed gene modules in the topological overlap matrix. The detection module is also used to calculate the modular trait relationship between the gene module and the hemophagocytic lymphohistiocytosis (HLH) trait, and to determine the WGCNA brown module gene, which has the strongest negative correlation with HLH, based on the modular trait relationship. The detection module is also used to take the intersection of the autophagy-related differentially expressed genes and the WGCNA brown module genes to obtain candidate genes; The detection module is also used to perform LASSO regression analysis on the candidate genes, screen out genes with non-zero coefficients, and perform vector machine recursive feature elimination to screen out CCR2, CDKN1A, TNFSF10 and TP53INP. CCR2, CDKN1A, TNFSF10 and TP53INP are used as the core diagnostic genes for hemophagocytic lymphohistiocytosis (HLH).

4. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 1, characterized in that, The analysis module is also used to perform a difference analysis between the expression level and the expression level of the healthy control group, and to calculate the absolute logarithmic change value through a differential expression statistical tool. The analysis module is also used to compare the absolute logarithmic change value with a preset change value threshold to obtain the expression abnormality pattern; The analysis module is also used to integrate the dynamic change characteristics of the four genes into a molecular feature profile of HLH based on the expression abnormality pattern.

5. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 4, characterized in that, The analysis module is also used to perform linear model fitting between the expression level and the expression level of the healthy control group based on the lmFit function to obtain the fitting result; The analysis module is also used to calculate the significance of differential expression in the fitting results based on empirical Bayesian smoothing using the eBayes function, and to obtain the absolute logarithmic change value of each gene.

6. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 1, characterized in that, The diagnostic classification and treatment module is also used to quantitatively compare the gene expression data of the current patient with the molecular feature spectrum based on the difference pattern of the molecular feature spectrum; The diagnostic and treatment module is also used to identify HLH positive cases when abnormally low CCR2 expression is detected and CDKN1A, TNFSF10 and TP53INP2 genes are abnormally elevated in synergistic way. The diagnostic and treatment module is also used to perform HLH typing on the current patient based on the absolute logarithmic change value of each gene combined with the results of immune microenvironment analysis.

7. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 6, characterized in that, The diagnostic and subtyping treatment module is further used to determine that the current patient's HLH type is primary HLH when the absolute logarithmic change value of the CCR2 gene is less than a preset statistical significance threshold, the absolute logarithmic change values ​​of the CDKN1A, TNFSF10 and TP53INP2 genes are greater than the preset statistical significance threshold, and the cell infiltration ratio of specific immune cells in the immune microenvironment analysis results is greater than or equal to a preset ratio threshold. The diagnostic and subtyping treatment module is also used to determine that the current patient's HLH type is secondary HLH when the absolute logarithmic change values ​​of CCR2, CDKN1A, TNFSF10 and TP53INP are all greater than the preset statistical significance threshold, but the cell infiltration ratio is less than the preset ratio threshold and the ANNEXIN pathway activity is not enhanced.

8. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 7, characterized in that, The diagnostic typing and treatment module is also used to use sodium butyrate as a TNFSF10 targeting regulator when the absolute logarithmic change value of the TNFSF10 gene is detected to be greater than the preset proportional threshold. The diagnostic typing and treatment module is also used to use dicumarol as a CDKN1A targeting regulator when the absolute logarithmic change value of the CDKN1A gene is detected to be greater than the preset proportional threshold. The diagnostic typing and treatment module is also used to use simvastatin as a CCR2 targeting regulator when the absolute logarithmic change value of the CCR2 gene is detected to be greater than the preset proportional threshold.

9. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 8, characterized in that, The diagnostic and subtyping treatment module is also used to obtain the ANNEXIN pathway activity by comparing the activation intensity of the ANNEXIN signaling pathway in the intercellular communication network with that of the healthy control group, wherein the statistical significance of the activation intensity is reflected by the intercellular communication analysis results. The diagnostic and subtyping treatment module is also used to verify the degree of improvement in mitophagy disorder by detecting the expression levels of PINK1, PARKIN, and NIX proteins after detecting that the current patient has been treated by oral, intravenous, or local administration.

10. The diagnostic and therapeutic target device for hemophagocytic lymphohistiocytosis as described in claim 9, characterized in that, The diagnostic and subtyping treatment module is also used to detect the expression levels of three core mitochondrial autophagy proteins, PINK1, PARKIN, and NIX, in patient samples using Western blot or immunohistochemistry. The diagnostic and subtyping treatment module is also used to determine that mitophagy disorder has been improved when the expression levels of PINK1, PARKIN, and NIX have all returned to the baseline values ​​of the control group.

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