A marker for non-alcoholic fatty liver disease and its application

Abstract

This proposal discloses the field of medical testing technology, and particularly relates to a marker for non-alcoholic fatty liver disease and its application. The sequences of the markers are shown in SEQ ID No. 1, SEQ ID No. 2, SEQ ID No. 3, and SEQ ID No. 4. Any of the markers shown can be used to prepare a preparation or detection tool for treating or assisting in the treatment of non-alcoholic fatty liver disease. It can quickly determine whether a patient is at risk of developing non-alcoholic fatty liver disease or whether non-alcoholic fatty liver disease has already occurred, allowing for timely diagnosis or treatment. Furthermore, compared to existing detection methods, the detection reagents or tools prepared using the markers are less expensive and more efficient, and will not adversely affect the patient during the detection process.

Description

Technical Field

[0001] The present invention belongs to the field of medical testing technology, and particularly relates to a marker for non-alcoholic fatty liver disease and an application thereof. Background Art

[0002] With the global prevalence of obesity and type 2 diabetes, the prevalence of non-alcoholic fatty liver disease (NAFLD) is also increasing proportionally. NAFLD has become a major public health problem facing the world and has become the second most common liver disease (the first is viral hepatitis). NAFLD is a clinicopathological syndrome characterized by diffuse macrovesicular fat in hepatocytes. A subtype of NAFLD can be described as non-alcoholic steatohepatitis (NASH), a potentially progressive liver disease that can lead to cirrhosis, hepatocellular carcinoma, and death. NAFLD is also associated with extrahepatic manifestations such as chronic kidney disease, cardiovascular disease, and sleep apnea.

[0003] It's undeniable that NAFLD can be a silent disease before the onset of characteristic symptoms and imaging changes. During this long, subclinical stage, NASH may have already developed and become irreversible. The primary means of preclinical diagnosis of non-alcoholic fatty liver disease are liver biopsy and imaging studies (ultrasound and computed tomography). However, these methods have significant drawbacks. While liver biopsy is the gold standard for diagnosing non-alcoholic fatty liver disease, it is also invasive, with drawbacks such as high cost and poor patient compliance. Liver biopsy is more suitable for detecting diseases such as liver cancer. Ultrasound, while less expensive, is subject to the influence of the operating physician and lacks quantitative assessment of fatty liver disease. While computed tomography can eliminate operator influence and quantify fatty liver content, it uses radiation, which can have certain health risks, and also has drawbacks such as high cost. These two diagnostic methods share the following key weaknesses in diagnosing non-alcoholic fatty liver disease: 1) They are expensive and difficult for patients to accept. 2) Both invasive examinations and CT scans can adversely affect patients' health. 3) Invasive examinations and ultrasound require highly experienced and skilled physicians. 4) Patients can only be examined sequentially, preventing large-scale testing, which wastes time. This patented method, however, allows for faster, simpler, and more cost-effective procedures with minimal harm, significantly improving efficiency.

[0004] Therefore, the development of new diagnostic biomarkers reflecting hepatocellular damage is of great significance for the timely diagnosis and treatment of NAFLD. Summary of the Invention

[0005] The present invention aims to address the deficiencies in the existing technology and provide a new diagnostic biomarker reflecting liver cell damage, which is of great significance for the timely diagnosis and treatment of NAFLD.

[0006] The markers of non-alcoholic fatty liver disease in this protocol include any one of FOSB, GPAT3, RNF43, and RGCC.

[0007] Among them, the nucleic acid sequence of the marker FOSB is shown as SEQ ID No.1; the nucleic acid sequence of the marker GPAT3 is shown as SEQ ID No.2; the nucleic acid sequence of the marker RNF43 is shown as SEQ ID No.3; and the nucleic acid sequence of the marker RGCC is shown as SEQ ID No.4.

[0008] Any of the above markers can be used to prepare preparations or detection tools for treating or assisting in the treatment of non-alcoholic fatty liver disease.

[0009] The method for using any of the above markers in a specific application includes the following steps:

[0010] (1) The blood samples of the subjects were used as the experimental group, and the levels of any one of FOSB, GPAT3, RNF43, and RGCC in the blood samples of the subjects were detected;

[0011] (2) using normal blood samples as a control group, and obtaining the levels of any one of FOSB, GPAT3, RNF43, and RGCC in normal blood samples;

[0012] (3) If the RNF43 level in the experimental group is higher than that in the control group, or the level of any one of FOSB, GPAT3, and RGCC in the experimental group is lower than that of the corresponding marker in the control group, it is determined that there is a risk of non-alcoholic fatty liver disease or non-alcoholic fatty liver disease has occurred.

[0013] The beneficial technical effects of the present invention are as follows: Testing has demonstrated that any one of the markers FOSB, GPAT3, RNF43, and RGCC can effectively distinguish between non-alcoholic fatty liver disease samples and normal samples. When used to prepare a detection reagent or tool, it can quickly determine whether a patient is at risk for non-alcoholic fatty liver disease or has already developed non-alcoholic fatty liver disease, facilitating timely diagnosis or treatment. Furthermore, compared to existing detection methods, detection reagents or tools prepared using these markers are less expensive and more efficient, pose no adverse effects to patients during the testing process, and require less stringent procedures from medical personnel, facilitating convenient clinical use. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1for the differences between NAFLD and healthy specimens;

[0015] Figure 2 The results of GO analysis of cellular components in 334 DEGs by ClusterProfile are shown;

[0016] Figure 3 The results of GO analysis of molecular functions of 334 DEGs by ClusterProfile are shown;

[0017] Figure 4 The results of GO analysis of biological processes in 334 DEGs by ClusterProfile are shown;

[0018] Figure 5 The results of KEGG analysis of 334 DEGs were performed by ClusterProfile;

[0019] Figure 6 This is a diagram of the tuning feature screening of the LASSO model in the selection of candidate diagnostic markers for NAFLD;

[0020] Figure 7 This is the biomarker screening diagram based on the SVM-RFE algorithm;

[0021] Figure 8 Venn diagram showing the four diagnostic biomarkers common to LASSO and SVM-RFE;

[0022] Figure 9 The expression of FOSB, GPAT3, RGCC, and RNF43 in NAFLD;

[0023] Figure 10 The diagnostic significance of FOSB, GPAT3, RGCC, and RNF43 in NAFLD;

[0024] Figure 11 The combined ROC test results of FOSB, GPAT3, RGCC, and RNF43 are shown;

[0025] Figure 12 The diagnostic results of the markers FOSB, GPAT3, RNF43 and RGCC in animal models and clinical trials of non-alcoholic fatty liver disease. DETAILED DESCRIPTION

[0026] The following is further described in detail through specific implementation methods:

[0027] Example

[0028] 1. Process for determining the markers of the present invention

[0029] 1. This study retrospectively studied data from 19 NAFLD patients and 24 controls from the GEO dataset (GSE55235). After removing the batch effect, the DEGs in the metadata were analyzed using the Limma package. Figure 1 As shown, a total of 334 DEGs were identified: 223 genes were significantly up-regulated and 111 genes were significantly down-regulated.

[0030] 2. Study the biological functions of 334 DEGs in NAFLD

[0031] The genes of the 334 identified DEGs were subjected to GO analysis and KEGG analysis using the ClusterProfile R package. The results showed that the 334 DEGs were mainly involved in chemical reactions, negative regulation of cellular processes, response to organic matter, cell response to chemical stimuli, response to external stimuli, cell death, programmed cell death, signal receptor binding, sequence-specific DNA binding, DNA binding in transcriptional regulatory regions, plasma membrane part, plasma membrane intrinsic components, and plasma membrane overall components, such as Figures 2 to 4 At the same time, KEGG analysis showed that pathways such as cytokine-cytokine receptor interaction, IL-17 signaling pathway, TNF signaling pathway and transcriptional misregulation in cancer were significantly enriched, as shown in Figure 2. Figure 5 shown.

[0032] 3. Using two different algorithms to select potential biomarkers

[0033] These DEGs were identified by LASSO regression algorithm, and finally 12 variables were screened as diagnostic markers of NAFLD, such as Figure 6 As shown. Through the SVM-RFE algorithm, a subset of 8 features was determined in the differential analysis object, as shown Figure 7 Finally, four overlapping features between these four algorithms were selected, namely the markers FOSB, GPAT3, RNF43 and RGCC described in the present invention, as shown in FIG. Figure 8 The above four genes may be key genes involved in the progression of NAFLD.

[0034] 2. Verify the diagnostic value of the markers FOSB, GPAT3, RNF43, and RGCC in non-alcoholic fatty liver disease

[0035] like Figure 9 As shown in Figure 3, the expression levels of FOSB, GPAT3, and RGCC were significantly downregulated in NAFLD samples, while the expression level of RNF43 was significantly upregulated in NAFLD samples. To further explore the diagnostic value of FOSB, GPAT3, RGCC, and RNF43, ROC analysis was also performed. Figure 10As shown in Figure 2, it was found that FOSB, AUC = 0:974, GPAT3, AUC = 0:983, RGCC, AUC = 0:985, and RNF43, AUC = 0:958, four genes showed strong differences in screening NAFLD samples from normal samples. Then, the four genes were used for joint ROC analysis, and the results were as follows: Figure 11 As shown, the combined analysis of the four genes was found to be more highly correlated with non-alcoholic fatty liver disease, with an AUC of 0:997.

[0036] 3. Animal Experiments

[0037] (I) Twelve 8-week-old, specific pathogen-free male C57BL / 6 mice weighing approximately 20 g were collected and housed in a temperature of 22 ± 2°C, humidity of 50 ± 5%, and a 12-h light / dark cycle with free access to food and water. All animal procedures were approved by the Animal Care and Use Committee of Tianjin University of Traditional Chinese Medicine. Before the start of the experiment, the mice were adaptively fed for 7 days and then randomly divided into two groups: a normal group (fed with a standard diet for 12 weeks) and a NAFLD model group (fed with a NAFLD / NASH high-fat diet and fructose-containing drinking water for 12 weeks). After 12 weeks, the mice were sacrificed and their liver samples were collected.

[0038] (ii) Total RNA was extracted from NAFLD and normal samples using TRIZOL (Invitrogen). cDNA was first synthesized using the extracted RNA with random primers and a reverse transcription kit (Takara, PRC).

[0039] Reverse transcription was performed at 37°C for 15 minutes and then at 85°C for 5 seconds. qRT-PCR was then performed on an ABI 7500 instrument using the Power SYBR Green kit (Takara, PRC). Relative mRNA levels were calculated using the 2-ΔΔCt method. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control for data normalization.

[0040] The primers for FOSB, GPAT3, RGCC, RNF43 and GAPDH are shown in Table 1.

[0041] Table 1: Quantitative RT-PCR primer sequences for determination of mRNA levels

[0042]

[0043]

[0044] Animal experiment results

[0045] The blood samples of mice in the NAFLD model group were used as the experimental group, and the blood samples of mice in the normal group were used as the control group. When the markers FOSB, GPAT3, RGCC and RNF43 described in the present invention were used to determine non-alcoholic fatty liver disease, Figure 12 As shown in (A, B, C, and D), the RNF43 level in the experimental group was higher than that in the control group, and the FOSB, GPAT3, and RGCC levels in the experimental group were lower than those in the control group. Figure 9 The results of machine learning analysis software are consistent.

[0046] IV. Clinical Trials

[0047] (1) Case collection

[0048] Blood samples from patients with nonalcoholic fatty liver disease and healthy controls were collected from Tianjin Second People's Hospital. All participants provided written informed consent, and ethical approval was obtained from the Ethics Committee of Tianjin Second People's Hospital. Diagnosis of nonalcoholic fatty liver disease was based on the diagnostic criteria for nonalcoholic fatty liver disease established by the Chinese Medical Association's Hepatology Branch. A total of 12 subjects were included in the study: 6 healthy controls and 6 with nonalcoholic fatty liver disease. Specific blood biochemical profiles are as follows (Table 2). Blood samples from patients and healthy controls were collected and tested using RT-PCR.

[0049] Table 2. Patient information and biochemical parameters.

[0050]

[0051] (ii) Total RNA was extracted from NAFLD and normal samples using TRIZOL (Invitrogen). cDNA was first synthesized using the extracted RNA with random primers and a reverse transcription kit (Takara, PRC).

[0052] Reverse transcription was performed at 37°C for 15 minutes and then at 85°C for 5 seconds. qRT-PCR was then performed on an ABI 7500 instrument using the Power SYBR Green kit (Takara, PRC). Relative mRNA levels were calculated using the 2-ΔΔCt method. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control for data normalization.

[0053] The primers for FOSB, GPAT3, RGCC, RNF43 and GAPDH are shown in Table 3.

[0054] Table 3: Quantitative RT-PCR primer sequences for determination of mRNA levels

[0055]

[0056] Clinical trial results

[0057] Blood samples from NAFLD patients were used as the experimental group, and blood samples from healthy subjects were used as the control group. When the markers FOSB, GPAT3, RGCC, and RNF43 described in the present invention were used to determine non-alcoholic fatty liver disease, Figure 12 As shown in (E, F, G, H), the RNF43 level in the experimental group was higher than that in the control group, and the FOSB, GPAT3, and RGCC levels in the experimental group were lower than those in the control group. Figure 9 The results of machine learning analysis software are consistent.

Claims

1. Use of a reagent for detecting four genes including FOSB, GPAT3, RNF43 and RGCC in the preparation of a preparation or detection tool for diagnosing non-alcoholic fatty liver disease.

2. The use according to claim 1, wherein: The nucleic acid sequence of FOSB is shown in SEQ ID No.

1.

3. The use according to claim 1, wherein: The nucleic acid sequence of GPAT3 is shown in SEQ ID No.

2.

4. The use according to claim 1, wherein: The nucleic acid sequence of RNF43 is shown in SEQ ID No.

3.

5. The use according to claim 1, characterized in that: The nucleic acid sequence of RGCC is shown in SEQ ID No. 4.

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