Diagnostic system for chronic fibrotic liver disease

Abstract

The purpose of the present invention is to achieve quantitative and reliable diagnosis of liver fibrosis. Provided is a diagnostic system for chronic fibrotic liver disease, used for diagnosing or assisting in the diagnosis of a degree of chronic liver fibrosis. A diagnostic result is obtained by means of detecting tertiary lymphoid structures in a patient's liver. The system comprises: an image forming apparatus, used for imaging a stained sample of liver tissue of a patient, to obtain a sample image; and an image analysis apparatus, used for analyzing the sample image, to obtain an analysis result of tertiary lymphoid structures in the patient's liver, and further deriving a diagnostic result based on the analysis result of the tertiary lymphoid structures.

Description

A diagnostic system for chronic liver fibrosis Technical Field

[0001] This invention belongs to the field of medical devices, specifically relating to a diagnostic system for chronic liver fibrosis. Background Technology

[0002] Liver fibrosis is a pathological change present in most chronic liver diseases. Various pathogenic factors, including viral, alcoholic, drug-induced, metabolic, autoimmune, and cholestatic factors, can lead to hepatocyte damage or death, activating multiple mechanisms such as post-injury repair, chronic inflammation, and abnormal connective tissue proliferation, thus initiating the process of liver fibrosis. This process results in diffuse excessive deposition and abnormal distribution of the extracellular matrix (i.e., collagen, glycoproteins, and proteoglycans), representing the liver's pathological repair response to chronic injury. It is a crucial step in the progression of various chronic liver diseases to cirrhosis and an important factor affecting the prognosis of chronic liver diseases. If the damaging factors persist for a long period, fibrosis may further develop into cirrhosis and even malignant liver tumors.

[0003] Clinically, liver fibrosis can be diagnosed through serological diagnostic models, conventional ultrasound, CT, MRI, and non-invasive examinations such as elastography and magnetic resonance elastography. However, the assessment of liver fibrosis based on a single blood indicator has limited value; conventional ultrasound, CT, and MRI often lack characteristic findings in early liver fibrosis, thus having limited significance for early diagnosis; elastography and magnetic resonance elastography have become promising non-invasive methods for diagnosing and assessing liver fibrosis, but a unified set of liver elastography values ​​for different etiologies of liver fibrosis has not yet been established, and their clinical application value needs further evaluation.

[0004] For advanced liver fibrosis and even cirrhosis, liver biopsy remains the "gold standard" for diagnosis. Liver biopsy involves puncturing the liver to obtain a tissue sample, followed by microscopic pathological examination and evaluation. It is currently the most accurate and reliable diagnostic method for liver fibrosis and a crucial basis for assessing inflammatory activity, the degree of fibrosis, and determining the efficacy of medications. Commonly used liver inflammation grading and fibrosis staging systems include the Scheuer, Metavir, and Ishak scoring systems. However, these systems have limitations: the scores represent only a semi-quantitative assessment of a specific lesion type, not an absolute quantification. Furthermore, the definitions of different degrees of damage, such as interface hepatitis, portal inflammation, and hepatocellular injury, are not linearly correlated, thus failing to provide the most direct diagnostic and assessment value.

[0005] In other words, there is currently no specific and effective diagnostic method for liver fibrosis in clinical practice. Therefore, how to effectively utilize liver biopsy tissue to achieve quantitative and reliable diagnosis of liver fibrosis is of great significance in the early drug development, treatment, and intervention for chronic liver fibrosis.

[0006] Tertiary lymphoid structures (TLS), also known as third lymphoid organs or ectopic lymphoid structures, are organized aggregates of immune cells that develop after birth in non-lymphoid tissues under non-physiological conditions. They are generally found in autoimmune diseases, chronic infections, and chronic inflammatory tissues caused by tumors. TLS mainly includes B cells, follicular dendritic cells (FDCs), T cells, fibroblasts (FRCs), stromal cells, dendritic cells, neutrophils, macrophages, and endothelial cells. Among them, follicular dendritic cells (FDCs) are usually located in the center of B cells, expressing various receptors and promoting antigen presentation to B cells to express highly active antibodies, while endothelial cells are located at the periphery of TLS. The formation and maturation of TLS is a multi-step, dynamic process. In this process, pro-inflammatory signals derived from immune cells act as inducing factors, and activated fibroblasts act as organizers by establishing and maintaining lymphocyte structures or producing key chemokines, recruiting various cells to appropriate locations. These cells, through extensive intercellular interactions, eventually differentiate and mature, and exert immune effects. Mature TLS includes B cell regions surrounded by T cells and germinal centers. It is an important site for the interaction between tissue cells and immune cells and is closely related to the prognosis and immunotherapy response of various cancer patients.

[0007] The most in-depth research on TLS (Thin-Terminal Lesion) is in solid tumors, where its regulation of adaptive immunity is considered central to anti-tumor immunity and a promising target for solid tumors and immunotherapy. As producers of tumor-specific antibodies, TLS-resident B cells may suppress tumor-specific immunity through mechanisms such as IL-10 secretion. Furthermore, depending on the antibody isotype produced and the type of immune cells present, tumor-specific antibodies can also suppress tumor-specific immune responses through signals emitted by inhibitory Fc receptors. While inducing TLS formation or enhancing TLS function may improve anti-tumor immune function, such interventions may also simultaneously enhance autoreactive T and B cell responses in other tissue sites, inducing autoimmune toxicity.

[0008] Chronic liver fibrosis is not a single disease, but rather a result of multiple chronic causes. However, to date, there is no research elucidating or application of how the formation and functional activation of TLS affect the progression of chronic liver fibrosis. Summary of the Invention

[0009] The inventors of this application discovered that persistent intrahepatic inflammation remains the initiating factor driving the formation of fibrosis, and is a key driving force for the progression of fibrosis to cirrhosis and even hepatocellular carcinoma. Furthermore, chronic liver fibrosis is not a single disease, but may be caused by multiple different chronic etiologies. The inventors further discovered that TLS may promote hepatic inflammation, exacerbate pathological fibrosis, and promote liver function impairment in chronic liver fibrosis caused by different etiologies, suggesting the potential value of TLS in the assessment and diagnosis of chronic liver fibrosis.

[0010] Based on the above findings, in order to achieve quantitative and reliable diagnosis of liver fibrosis, this invention provides a diagnostic system for chronic liver fibrosis caused by different etiologies, used for diagnosing or assisting in the diagnosis of the degree of chronic liver fibrosis. The system is characterized by obtaining diagnostic results by detecting the tertiary lymphoid structures in the patient's liver, comprising: an image forming device for imaging a stained sample of the patient's liver tissue to obtain a sample image; and an analysis device for analyzing the sample image to obtain the analysis results of the tertiary lymphoid structures in the patient's liver, and further obtaining a diagnostic result based on the analysis results of the tertiary lymphoid structures.

[0011] The diagnostic system for chronic liver fibrosis provided by the present invention may also have the following technical features, wherein the results of the tertiary lymphoid structure analysis include the number of tertiary lymphoid structures and the maturity of the tertiary lymphoid structures.

[0012] Furthermore, the diagnostic system for chronic liver fibrosis provided by the present invention may also have the following technical features: the liver tissue staining sample includes H&E staining sample and multicolor immunofluorescence staining sample; the image forming device includes a slice scanner for scanning the H&E staining sample and a fluorescence scanner for imaging the multicolor immunofluorescence staining sample; the number of tertiary lymphoid structures is obtained by analyzing the sample image corresponding to the H&E staining sample; and the maturity of the tertiary lymphoid structures is obtained by analyzing the sample image corresponding to the multicolor immunofluorescence staining sample.

[0013] Furthermore, the maturity of the tertiary lymphoid structure in this invention can be characterized by the number of secondary lymphoid follicle-type tertiary lymphoid structures.

[0014] In addition, the multicolor immunofluorescence staining samples in this invention may also include fluorescent staining samples of T cell CD3, B cell CD20 and CD23, follicular cell CD21, high endothelial vein PNAd, dendritic cell CD208 and cell nucleus DAPI. The sample image corresponding to the multicolor immunofluorescence staining sample is obtained by superimposing the imaging images corresponding to each fluorescent staining sample.

[0015] In addition, the diagnostic system for chronic liver fibrosis of the present invention may further include: a gene detection device for detecting a characteristic tertiary lymphoid structure gene set and obtaining a gene detection score based on the detection results; and an analysis device for further combining the gene detection score with the tertiary lymphoid structure analysis results to obtain a diagnostic result.

[0016] The characteristic tertiary lymphoid structure gene set for chronic liver fibrosis caused by non-alcoholic steatohepatitis can include the following 12 characteristic genes: IGHG1, PTGDS, IGKC, IGHA1, JCHAIN, IGHM, VIM, MGP, CCL19, C7, S100A6, and IGHG3.

[0017] Furthermore, the analysis device can generate a TLS score based on the detection results of the gene set of the characteristic tertiary lymphoid structures in chronic liver fibrosis caused by non-alcoholic steatohepatitis. This TLS score is used to determine whether chronic liver fibrosis is diagnosed. The number of tertiary lymphoid structures and the maturity of the tertiary lymphoid structures are used to determine the progression of chronic liver fibrosis caused by different etiologies.

[0018] Invention Function and Effect

[0019] The diagnostic system for chronic liver fibrosis provided by this invention derives its diagnostic results by detecting the tertiary lymphoid structures in the liver. These tertiary lymphoid structures promote liver inflammation, exacerbate pathological fibrosis, and contribute to liver function impairment in chronic liver fibrosis caused by various etiologies. They are directly related to the development (degree of fibrosis) of chronic liver fibrosis. Therefore, the detection results of the tertiary lymphoid structures can reflect the degree of chronic liver fibrosis, thereby effectively and accurately detecting or assisting in the detection of chronic liver fibrosis.

[0020] Furthermore, since the number of tertiary lymphoid structures is determined by analyzing the sample images corresponding to H&E-stained samples, and the maturity of the tertiary lymphoid structures is determined by analyzing the sample images corresponding to multicolor immunofluorescence-stained samples, specifically the number of secondary lymphoid follicular tertiary lymphoid structures, and the number and maturity of tertiary lymphoid structures are basically linearly correlated with the degree of chronic liver fibrosis, the degree of chronic liver fibrosis can be quantitatively determined by the number and maturity of tertiary lymphoid structures. Therefore, the diagnostic system for chronic liver fibrosis of this application can also accurately achieve the quantitative detection of the degree of chronic liver fibrosis. Attached Figure Description

[0021] Figure 1 is a typical example of sample images corresponding to H&E sections of liver tissue samples from six types of chronic liver fibrosis diseases according to Embodiment 1 of the present invention.

[0022] Figure 2 is a graph showing the correlation between the number of TLS and the Ishak score in Embodiment 1 of the present invention.

[0023] Figure 3 is a graph showing the correlation analysis results between the number of TLS and the Ishak score in Embodiment 1 of the present invention.

[0024] Figure 4 is an imaging result of different immunofluorescence staining of a tissue sample in Example 2 of the present invention.

[0025] Figure 5 is a multicolor immunofluorescence stained sample image formed by superimposing the imaging results of different immunofluorescence staining in Figure 4.

[0026] Figure 6 is a graph showing the correlation analysis results between the number of SFL-TLS and the Ishak score in Embodiment 2 of the present invention.

[0027] Figure 7 is a graph showing the GSEA analysis results of transcriptome sequencing public datasets of liver tissue specimens from chronic liver fibrosis caused by different etiologies in Example 4 of the present invention.

[0028] Figure 8 is a ROC curve analysis diagram of the chronic liver fibrosis disease group and the healthy control group based on the TLS score in Example 4 of the present invention.

[0029] Figure 9 is a schematic diagram of the working principle of the chronic liver fibrosis disease diagnostic system of Embodiment 4 of the present invention. Detailed Implementation

[0030] The following examples illustrate specific embodiments of the present invention. In the examples described below, reagents and materials not otherwise specified were obtained through conventional commercial channels, and unspecified operating steps and parameters refer to existing techniques in the art.

[0031] Example 1: Correlation analysis between the number of TLS and chronic liver fibrosis

[0032] This embodiment uses liver tissue samples collected clinically from patients with chronic liver fibrosis to perform a correlation analysis between the number of liver tissue samples (TLS) and chronic liver fibrosis, and uses the Ishak score as a diagnostic criterion for comparison. The specific process is as follows:

[0033] Liver tissue samples were collected from six different etiologies of chronic liver fibrosis: hepatitis B (n=5), alcoholic (n=5), drug-induced (n=5), non-alcoholic fatty liver (n=5), autoimmune liver (n=5), and cholestatic liver (n=5), totaling 30 samples. Each sample was fixed with 4% paraformaldehyde, dehydrated, and embedded in paraffin. Hematoxylin and eosin (H&E) staining was performed on the tissue sections, and images of the H&E sections were obtained using a NanoZoomer S360 digital slide scanner. The images were analyzed, and the number of TLS (transmissible fibrosis tissue) in each section was counted (per 500 mm²). 2 (The area is measured). Then, the number of TLS in each sample is compared with the corresponding patient's Ishak score.

[0034] Figure 1 is a typical example of sample images corresponding to H&E sections of liver tissue samples from six etiologies of chronic liver fibrosis in Embodiment 1 of the present invention.

[0035] As shown in Figure 1, TLS structures (indicated by arrows) appeared to varying degrees in the H&E slice images of liver tissue samples from six different etiologies of chronic liver fibrosis.

[0036] Figure 2 is a graph showing the correlation between the number of TLS and the Ishak score in Embodiment 1 of the present invention. In Figure 2, the vertical axis represents the number of TLS and the horizontal axis represents the Ishak score.

[0037] Figure 3 is a graph showing the correlation analysis results between the number of TLS and the Ishak score in Embodiment 1 of the present invention, which is the result graph after linear fitting of the data results in Figure 2.

[0038] As shown in Figures 2 and 3, in chronic liver fibrosis caused by different etiologies, TLS-like structures were formed in the liver tissue microenvironment of the corresponding patients, and the number of TLS in each liver tissue showed a significant positive correlation with the Ishak score, a fibrosis index (P<0.0001, r=0.8832).

[0039] Example 2: Correlation analysis between TLS maturity and chronic liver fibrosis

[0040] This embodiment uses liver tissue samples from clinically collected patients with chronic liver fibrosis to analyze the correlation between TLS maturity and chronic liver fibrosis, and uses the Ishak score as a diagnostic criterion for comparison. TLS maturity is characterized by the number of secondary lymphoid follicle-type TLS (SFL-TLS). The specific process is as follows:

[0041] Referring to Example 1 above, liver tissue samples were collected from six different etiologies of chronic liver fibrosis, including: hepatitis B (n=5), alcoholic (n=5), drug-induced (n=5), non-alcoholic fatty liver (n=5), autoimmune liver (n=5), and cholestatic liver (n=5), for a total of 30 samples.

[0042] Each sample was fixed with 4% paraformaldehyde, dehydrated, embedded in paraffin, and then sectioned. Multicolor immunofluorescence staining was performed on T cells (CD3), B cells (CD20 and CD23), follicular cells (CD21), high endothelial veins (PNAd), dendritic cells (CD208), and cell nuclei (DAPI) to obtain corresponding fluorescent stained samples. Then, Vectra multispectral imaging was used to image each fluorescent stained sample, and the imaging results were superimposed to obtain the corresponding sample images.

[0043] Based on sample images, the number of secondary lymphoid follicular TLS (SFL-TLS) in the liver tissue of each sample was analyzed, and then this number was compared with the Ishak score.

[0044] Figure 4 is an imaging result of different immunofluorescence staining of a tissue sample in Example 2 of the present invention, and Figure 5 is a multicolor immunofluorescence staining sample image formed by superimposing the imaging results of different immunofluorescence staining in Figure 4.

[0045] As shown in Figure 5, the structure of SFL-TLS is very obvious in the sample image obtained after multicolor immunofluorescence staining and imaging overlap (the part indicated by the arrow).

[0046] Figure 6 is a graph showing the correlation analysis results between the number of SFL-TLS and the Ishak score in Embodiment 2 of the present invention.

[0047] As shown in Figure 7, the number of SFL-TLS formed in the liver tissue of patients with chronic liver fibrosis caused by different etiologies varies, and there is a significant positive correlation between the number of SFL-TLS and the fibrosis index Ishak score (P<0.0001, r=0.8188).

[0048] Example 3: Diagnostic System for Chronic Liver Fibrosis and Its Clinical Application

[0049] As mentioned above, there is a significant positive correlation between the number of liver fibrotic tract markers (TLS) and the number of spontaneously generated liver fibrotic tract markers (SFL-TLS) in liver tissue and the Ishak score. Since the number of SFL-TLS characterizes the maturity of TLS, this significant positive correlation can also be considered to exist between the maturity of TLS and the overall maturity of TLS. The specific mechanism is that under various pathological conditions such as viral, alcoholic, drug-induced, metabolic, autoimmune, and cholestatic conditions, TLS form in the liver microenvironment and gradually mature. By regulating and activating humoral immune responses, they promote the progression of liver inflammation and pathological fibrosis. The number and maturity of TLS in liver tissue both indicate the degree of fibrosis progression. Therefore, TLS can serve as a highly specific and sensitive biomarker for the diagnosis and auxiliary diagnosis of chronic liver fibrosis caused by different etiologies.

[0050] Based on this, this embodiment proposes a diagnostic system for chronic liver fibrosis, comprising: an image forming device for imaging a patient's stained liver tissue sample (including H&E stained samples and multicolor immunofluorescence stained samples) to obtain sample images; and an analysis device comprising a slice scanner for scanning H&E stained samples and a fluorescence scanner for imaging multicolor immunofluorescence stained samples, wherein the number of liver fibrosis tracts (TLS) is determined based on the H&E stained samples, and the number of liver fibrosis-transferable tracts (SFL-TLS) is determined based on the multicolor immunofluorescence stained samples as a characterization of TLS maturity, and the degree of liver fibrosis is further determined based on the number of TLS and the number of SFL-TLS.

[0051] A higher number of TLS (transient liver fibrosis) detected indicates a more severe progression of chronic liver fibrosis, which can be diagnosed or aided in the diagnosis. Conversely, a lower number of TLS indicates a milder progression of chronic liver fibrosis. For example, by referencing the correlation between these two scores and the Ishak score, their average can be directly calculated as a quantitative score, which can clinically replace indicators such as the Ishak score to characterize the degree of liver fibrosis.

[0052] The specific clinical implementation process is as follows:

[0053] After ruling out contraindications for liver biopsy, ultrasound-guided percutaneous liver biopsy is selected. It is recommended to use a 16G biopsy needle, and the liver tissue sample should be at least 1.5 cm long. The patient should be in a supine or left lateral decubitus position with the right side of the body close to the edge of the bed, and the right arm raised and bent behind the head. Strict aseptic operation is required. The operator should wear a mask, cap, and sterile gloves. The skin at the puncture site should be routinely disinfected and a sterile drape should be laid. The skin, muscle, and liver capsule at the puncture site should be anesthetized layer by layer with 2% lidocaine. The patient should be instructed to breathe calmly. The operator holds the puncture needle and inserts it into the liver parenchyma under ultrasound guidance. The needle is fired after reaching the target position. After the sample is collected, the coaxial needle is withdrawn while the patient holds their breath. The puncture site is disinfected, covered and secured with sterile gauze, and a multi-headed abdominal binder is tightly applied to the ribs and upper abdomen for at least 2 hours.

[0054] Complete the pathology form, including information such as name, age, gender, department, hospital number, location, quantity, time of excision, and operation time; flush the liver tissue sample from the needle with normal saline into a 1.8 mL specimen tube, immediately remove a portion of the tissue and store it in a container containing 10% neutral formalin, with the fixative solution being at least 5 times the sample volume, and fixation time being 4-6 hours; immediately store the remaining tissue in liquid nitrogen and transport it to the laboratory within 24 hours for subsequent related operations.

[0055] For each sample, the following procedures were performed: numbering, registration, sampling, dehydration, paraffin infiltration, embedding, sectioning, staining, and mounting in the dark. The H&E staining and multicolor immunofluorescence staining steps were performed according to Examples 1 and 2 above. Microscopic diagnosis was performed using a scanner. The NanoZoomer S360 digital slide scanner and Vectra multispectral imager were used to perform imaging scans and TLS detection for H&E staining and multicolor immunofluorescence staining (staining reagents included CD3, CD20, CD23, CD21, PNAd, CD208, and DAPI, etc.). Image analysis and recognition methods were used to statistically analyze the number and maturity (i.e., SFL-TLS number) of TLS in the tissue sections.

[0056] The image analysis and recognition method can be a machine learning-based image target recognition and analysis method, such as a neural network model. First, a large number of TLS specimen images (H&E stained images or multicolor immunofluorescence stained images) are used as a training set to train the model, enabling it to recognize TLS in H&E stained images or SFL-TLS in multicolor immunofluorescence stained images. Then, the number of TLS in H&E stained images or SFL-TLS in multicolor immunofluorescence stained images is counted to obtain the number of TLS in H&E stained images or the number of SFL-TLS in multicolor immunofluorescence stained images. The specific training and other operations of this image analysis and recognition method can all use existing technologies and will not be elaborated further here.

[0057] Example 4: Diagnostic system for chronic liver fibrosis combined with gene detection and its clinical application

[0058] This embodiment utilizes liver tissue from chronic liver fibrosis caused by non-alcoholic steatohepatitis (NAHST) and combines spatial transcriptome sequencing technology to analyze and screen a characteristic set of TLS genes specific to NHAST. Simultaneously, it uses public datasets of liver transcriptome sequencing from other chronic liver fibrosis diseases caused by different etiologies to further explore the characteristic TLS gene set related to NHAST and its application in diagnosis.

[0059] The screening and analysis process for characteristic genes is as follows:

[0060] Spatial transcriptome sequencing was performed on liver tissue samples from four patients with chronic liver fibrosis caused by non-alcoholic steatohepatitis (NAH). Thrombocytoma (TLS) and non-TLS regions were identified using Hematoxylin and eosin (H&E) and multicolor immunofluorescence staining. Spots in the TLS and non-TLS regions of the liver tissues from the four NHA-related chronic liver fibrosis patients were extracted for differential gene analysis, resulting in four sets of differentially expressed genes characteristic of the TLS region. The top 25 genes from each differentially expressed gene set were extracted and cross-analyzed, ultimately identifying 12 TLS genes highly associated with chronic liver fibrosis, namely the 12 characteristic TLS genes of chronic liver fibrosis, as follows:

[0061] IGHG1, PTGDS, IGKC, IGHA1, JCHAIN, IGHM, VIM, MGP, CCL19, S100A6 and IGHG3.

[0062] The sequences of the F and R primers in the 12 primer pairs corresponding to the 12 characteristic genes mentioned above are shown in SEQ ID NO.1-SEQ ID NO.24, respectively, as detailed in Table 1 below:

[0063] Table 1. Primer sequences for characteristic genes

[0064] Based on the above 12 characteristic genes, this embodiment constructs a characteristic TLS gene set for chronic liver fibrosis caused by non-alcoholic steatohepatitis (NAHH), and constructs a scoring system based on the detection results of this gene set (i.e., the expression level results of each gene). The scoring method is as follows:

[0065] First, gene expression profiles from transcriptome sequencing data of liver tissue samples were used. Gene Set Variation Analysis (GSVA) ​​(DOI:10.18129 / B9.bioc.GSVA, version 1.40.1) was employed, and a predefined gene set (a characteristic TLS gene set for chronic liver fibrosis caused by non-alcoholic steatohepatitis) was used to evaluate the enrichment score of the gene set. The minimum gene set was set to 5, and the maximum gene set was set to 5000. The enrichment score of each sample in each gene set was calculated, and the enrichment score (i.e., TLS score) matrix was finally obtained.

[0066] This embodiment also validates the above gene set and scoring system using public transcriptome sequencing datasets of other chronic liver fibrosis diseases caused by different etiologies, as follows:

[0067] Eleven public transcriptome sequencing datasets were collected from liver tissues and hepatocellular carcinoma tissues of six different etiologies of chronic liver fibrosis, including: non-alcoholic fatty liver disease (NAFLD, GSE151158, n=61), non-alcoholic steatohepatitis (NASH, GSE49541, n=104), diet-induced non-alcoholic steatohepatitis mouse model (NASH, GSE207856, n=21), pediatric biliary atresia (BA, GSE46960, n=95), carbon tetrachloride-induced chronic liver fibrosis mouse model (GSE152329, n=198), and chronic-onset fibrosis. The datasets included chronic liver failure (ACLF, GSE38941, n=27), hepatitis B virus (HBV, GSE83148, n=122), alcoholic hepatitis (AH, GSE28619, n=22), childhood autoimmune hepatitis (PAH, GSE206364, n=24), chronic cholestatic liver disease (CLD, GSE206364, n=10), and hepatocellular carcinoma (HCC, GSE14520, n=488). Based on the TLS gene set consisting of the above 12 genes, the datasets were subjected to the GSVA analysis described above to obtain the corresponding TLS scores.

[0068] Figure 7 is a graph showing the GSVA analysis results of the liver tissue specimen transcriptome sequencing public dataset in Example 4 of the present invention.

[0069] In Figure 7, A and J correspond to different datasets, and each vertical axis represents the TLS score. In A, the horizontal axis HD represents healthy patients, and NAFLD represents non-alcoholic liver disease; in B, the horizontal axis NASH-mild represents mild non-alcoholic steatohepatitis, and NASH-severe represents severe non-alcoholic steatohepatitis; in C, the horizontal axis HD represents healthy patients, NAFLD represents non-alcoholic liver disease, and NASH represents non-alcoholic steatohepatitis; in D, the horizontal axis NC represents healthy controls, and BA represents biliary atresia; in E, the horizontal axis Vehicle represents the control group, and CCL4 represents the carbon tetrachloride experiment. Group F; the horizontal axis HD represents healthy patients, HBV-ACLF represents patients with chronic hepatitis B positive and acute-on-chronic liver failure; the horizontal axis HD in G represents healthy patients, HBV represents patients with chronic hepatitis B; the horizontal axis HD in G represents healthy patients, AH represents patients with alcoholic hepatitis; the horizontal axis HD in I represents healthy patients, ACLD represents adult cholestatic liver disease, PCLD represents pediatric cholestatic liver disease, ALGS represents patients with Alagille syndrome, PAH represents pediatric autoimmune hepatitis; the horizontal axis Normal in J represents normal controls, HCC represents hepatocellular carcinoma.

[0070] Figure 8 is a ROC curve analysis diagram of the chronic liver fibrosis disease group and the healthy control group based on the TLS score in Example 4 of the present invention.

[0071] As shown in Figures 7 and 8, the TLS scores of chronic liver fibrosis disease groups induced by various etiologies were significantly higher than those of the healthy control group, while the TLS scores of hepatocellular carcinoma groups were significantly lower than those of the normal control group.

[0072] Table 2 below shows the results of TLS scoring for six chronic liver fibrosis diseases based on the TLS gene set.

[0073] Table 2. TLS score results for diagnosing chronic liver fibrosis.

[0074] As shown in Table 2 above, the correlation between the TLS score and chronic liver fibrosis can be represented by the OR value in Table 2, or by the coefficients β and p-values ​​in Table 2, among which the OR value is the most intuitive and obvious. For example, the OR value of NASH (GSE49541) is as high as 231,636,343.462, which is a very high correlation. Therefore, the degree of liver fibrosis in NASH can be determined by the TLS score alone. Similarly, the OR value of PAH (GSE206364) is as high as 9,540,555.0893; and the OR value of CLD (GSE206364) reaches 7,509,628.248.

[0075] As mentioned above, the TLS score, calculated based on a gene set consisting of 12 characteristic genes, is highly correlated with the degree of liver fibrosis. Therefore, this TLS score alone can achieve a basic diagnostic or auxiliary diagnostic effect. Thus, by further combining gene testing with Example 3, the diagnostic result of the degree of fibrosis in chronic liver fibrosis can be accurately obtained.

[0076] Figure 9 is a schematic diagram of the working principle of the chronic liver fibrosis disease diagnostic system of Embodiment 4 of the present invention.

[0077] As shown in Figure 9, in this embodiment, after obtaining liver tissue samples using the liver biopsy procedure of Example 3, a portion of the liver biopsy sample can be taken for gene detection while performing H&E staining and multicolor immunofluorescence staining, as follows:

[0078] RNA was extracted and reverse transcribed from liver biopsy tissue: The specimen tube was removed from liquid nitrogen, ensuring that the amount of tissue used did not exceed 30 mg. After adding magnetic beads, the tissue was centrifuged at 4°C for 1 minute using an OMINI magnetic bead tissue homogenizer. After discarding the magnetic beads, 30 μL of RNA was extracted using a Mini Kit (Qiagen). The concentration of the obtained RNA was measured using a NanoDrop2000 ultra-micro spectrophotometer.

[0079] A suitable amount of RNA was taken for transcriptome sequencing analysis. Using the GSVA tool, the analysis was performed based on the TLS gene set consisting of the aforementioned 12 characteristic genes to obtain the TLS score.

[0080] Then, the detection results from Example 3 are combined with the TLS scoring results for comprehensive analysis to arrive at a diagnosis. During diagnosis, the TLS score can be used to determine whether a diagnosis is confirmed, and the degree of chronic liver fibrosis can be further determined based on the number of TLS and the number of SFL-TLS. The specific diagnostic method is as follows:

[0081] For patients clinically suspected of NAFLD, a TLS score higher than 0.91115 in liver biopsy tissue is considered a definitive diagnosis; for patients clinically suspected of NASH, a TLS score higher than -0.94578 in liver biopsy tissue is considered a definitive diagnosis; for patients clinically suspected of HBV, a TLS score higher than -2.9642 in liver biopsy tissue is considered a definitive diagnosis; for patients clinically suspected of AH, a TLS score higher than 2.1658 in liver biopsy tissue is considered a definitive diagnosis; for patients clinically suspected of PAH, a TLS score higher than -2.1466 in liver biopsy tissue is considered a definitive diagnosis; for patients clinically suspected of CLD, a TLS score higher than 0.26227 in liver biopsy tissue is considered a definitive diagnosis. For patients clinically suspected of chronic liver fibrosis, a TLS score higher than 0.91115 in liver biopsy tissue per 500 mm of tissue is considered a definitive diagnosis. 2When the number of SFL-TLS in the area is 0, liver fibrosis can be ruled out; when the number of SFL-TLS is 1-2, mild fibrosis should be considered; when the number of SFL-TLS is 3-4, severe fibrosis should be considered; when the number of SFL-TLS is greater than 5, cirrhosis should be considered.

Claims

1. A diagnostic system for chronic liver fibrosis, used for diagnosing or assisting in the diagnosis of the degree of chronic liver fibrosis. Its features are, The diagnostic result is obtained by detecting the tertiary lymphoid structures in the patient's liver, including: an image forming device for imaging a stained sample of the patient's liver tissue to obtain a sample image; and an analysis device for analyzing the sample image to obtain the analysis results of the tertiary lymphoid structures in the patient's liver, and the diagnostic result is obtained based at least on the analysis results of the tertiary lymphoid structures, wherein the analysis results of the tertiary lymphoid structures include the number of the tertiary lymphoid structures and the maturity of the tertiary lymphoid structures.

2. The diagnostic system for chronic liver fibrosis according to claim 1, characterized in that: in, The liver tissue staining samples include H&E staining samples and multicolor immunofluorescence staining samples. The image forming apparatus includes a slice scanner for scanning the H&E staining samples and a fluorescence scanner for imaging the multicolor immunofluorescence staining samples. The number of tertiary lymphoid structures is determined by analyzing the sample images corresponding to the H&E stained samples, and the maturity of the tertiary lymphoid structures is determined by analyzing the sample images corresponding to the multicolor immunofluorescence stained samples.

3. The diagnostic system for chronic liver fibrosis according to claim 2, characterized in that: in, The maturity of the tertiary lymphoid structures is characterized by the number of secondary lymphoid follicle-type tertiary lymphoid structures.

4. The diagnostic system for chronic liver fibrosis according to claim 3, characterized in that: in, The multicolor immunofluorescence staining samples include fluorescent staining samples of T cell CD3, B cell CD20 and CD23, follicular cell CD21, high endothelial vein PNAd, dendritic cell CD208, and cell nucleus DAPI. The sample image corresponding to the multicolor immunofluorescence staining sample is obtained by superimposing the imaging images corresponding to each of the fluorescent staining samples.

5. The diagnostic system for chronic liver fibrosis according to claim 1, characterized in that, Also includes: A gene detection device is used to detect the gene set of the three-level lymphoid structure in chronic liver fibrosis caused by non-alcoholic steatohepatitis. The analytical device, based on the results of the analysis of the three-level lymphoid structures, combined with the detection results of the gene set of the characteristic three-level lymphoid structures in chronic liver fibrosis caused by non-alcoholic steatohepatitis, arrives at the diagnostic result.

6. The diagnostic system for chronic liver fibrosis according to claim 5, characterized in that: in, The characteristic tertiary lymphoid structure gene set of chronic liver fibrosis caused by non-alcoholic steatohepatitis consists of the following 12 characteristic genes: IGHG1, PTGDS, IGKC, IGHA1, JCHAIN, IGHM, VIM, MGP, CCL19, C7, S100A6, and IGHG3.

7. The diagnostic system for chronic liver fibrosis according to claim 5 or 6, characterized in that: in, The diagnostic results include whether a diagnosis has been made and the extent of the disease progression. The analytical device generates a TLS score based on the detection results of the gene set of the characteristic tertiary lymphoid structures in chronic liver fibrosis caused by non-alcoholic steatohepatitis. The TLS score is used to determine whether a diagnosis has been made. The number of tertiary lymphoid structures and the maturity of the tertiary lymphoid structures are used to determine the degree of progression.

Images