Related application of CHRNA9 gene in prediction of psoriasis

Abstract

The invention discloses related application of a CHRNA9 gene in prediction of psoriasis, and relates to the field of biology. According to the application, the increase of the expression level of the CHRNA9 gene is related to occurrence, progress and / or prognosis of the psoriasis, and the CHRNA9 gene can be used as an independent predictive factor, so that the survival time of a psoriasis patient can be prolonged.

Description

Technical Field

[0001] The present invention relates to the biological field, and in particular to the application of CHRNA9 gene in the prediction of psoriasis. Background Art

[0002] Psoriasis is a recurrent, chronic skin disease. In mild cases, it only manifests as local erythema and scaling. In severe cases, it may cause damage to multiple system organs such as joints, nails, etc., thus affecting daily life, work and study. The pathogenesis of psoriasis was initially believed to be due to excessive proliferation of keratinocytes, resulting in abnormal differentiation and impaired barrier function, because the clinical manifestations of the disease are mainly manifested as skin changes. However, since the 1970s, more and more studies have found the involvement of immune cells in psoriasis, including T lymphocytes and macrophages. Subsequently, people turned the pathogenesis of psoriasis to immune-related diseases. The pathogenesis involved in psoriasis is extremely complex and has not yet been fully elucidated. The key genes involved in the occurrence and development of psoriasis are still largely unknown. There is a lack of means to effectively predict psoriasis.

[0003] In view of this, the present invention is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide relevant applications of CHRNA9 gene in the prediction of psoriasis.

[0005] The present invention is achieved in that:

[0006] In a first aspect, an embodiment of the present invention provides the use of a reagent for detecting the expression level of CHRNA9 gene in the preparation of a product for the prediction of psoriasis.

[0007] In a second aspect, an embodiment of the present invention provides a method for training a prediction model, which includes:

[0008] Obtaining the detection results of the CHRNA9 gene expression levels of the training samples and the corresponding annotation results; the annotation results include any one or more labels representing whether the sample suffers from psoriasis, the degree of progression of psoriasis, and the prognostic risk of psoriasis;

[0009] Input the obtained detection results into a pre-built machine learning model to obtain a prediction result of the sample;

[0010] According to the labeling results and prediction results, the parameters of the machine learning model are updated to obtain the prediction model.

[0011] In a third aspect, an embodiment of the present invention provides a prediction device, comprising:

[0012] An acquisition module is used to obtain the detection result of the CHRNA9 gene expression level of the sample to be tested;

[0013] The prediction module is used to input the detection result of the sample to be tested into the prediction model trained by the training method described in the above embodiment to obtain the prediction result of the sample to be tested.

[0014] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store a program, and when the program is executed by the processor, the processor implements a method for predicting psoriasis;

[0015] The method for predicting psoriasis comprises the following steps: obtaining the detection result of the CHRNA9 gene expression level of the sample to be tested; inputting the detection result of the sample to be tested into the prediction model trained by the training method described in the above embodiment to obtain the prediction result of the sample to be tested.

[0016] In a fifth aspect, an embodiment of the present invention provides a computer-readable medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for predicting psoriasis described in the aforementioned embodiment is implemented.

[0017] The present invention has the following beneficial effects:

[0018] The present application found that increased expression levels of the CHRNA9 gene are associated with the occurrence, progression and / or prognosis of psoriasis, and can be used as an independent predictive factor to help prolong the survival time of psoriasis patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 (A) Volcano plot of differentially expressed genes in GSE14905; (B) Volcano plot of differentially expressed genes in GSE114286; (C) Volcano plot of differentially expressed genes in GSE121212; (D) Venn diagram of differentially expressed genes in the three GEO databases and the ion channel gene set;

[0021] Figure 2 (A) Chrnα9 expression levels in patients and normal controls in GSE14905; (B) Chrnα9 expression levels in patients and normal controls in GSE114286; (C) Chrnα9 expression levels in patients and normal controls in GSE121212;

[0022] Figure 3 (A) Subject curve of GSE14905; (B) Subject curve of GSE114286; (C) Subject curve of GSE121212;

[0023] Figure 4 (A) Pearson correlation analysis between CHRNα9 gene and inflammatory gene set in GSE14905; (B) Pearson correlation analysis between CHRNα9 gene and inflammatory gene set in GSE114286; (C) Pearson correlation analysis between CHRNα9 gene and inflammatory gene set in GSE121212;

[0024] Figure 5 (A) Mouse back skin; (B) HE staining section of mouse back;

[0025] Figure 6 (A) Cell viability of HACAT cells after treatment with different concentrations of bupropion for 48 hours; (B) Cell viability of HACAT cells after treatment with different concentrations of bupropion for 72 hours; (C) Cell live / dead staining of HACAT cells after treatment with different concentrations of bupropion for 48 hours; (D) Cell live / dead staining of HACAT cells after treatment with different concentrations of bupropion for 72 hours. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be described clearly and completely below. If the specific conditions are not specified in the embodiments, they are carried out according to conventional conditions or conditions recommended by the manufacturer. If the manufacturer of the reagents or instruments used is not specified, they are all conventional products that can be purchased commercially.

[0027] On the one hand, an embodiment of the present invention provides the use of a reagent for detecting the expression level of CHRNA9 gene in the preparation of a product for the prediction of psoriasis.

[0028] CHRNA9, also known as "Acetylcholine Receptor Alpha 9", "nAChRα9", "chrnα9", is a subunit of the neuron-type nAChR family. It is mainly expressed in some sensory neurons, especially hair cells in the inner ear. Hair cells are sensory cells in the inner ear that are responsible for transducing sound signals.

[0029] In some embodiments, the expression level includes the mRNA expression level and / or protein expression level of the CHRNA9 gene.

[0030] In some embodiments, the psoriasis prediction includes: predicting any one or more of whether the subject suffers from psoriasis, the degree of progression of psoriasis, and the prognostic risk of psoriasis.

[0031] In some embodiments, predicting the prognostic risk of psoriasis in a subject comprises: predicting the survival period of a lung adenocarcinoma patient and / or predicting the 1-year, 3-year and / or 5-year survival rate of the patient.

[0032] In some embodiments, the product includes: any one of a reagent, a chip, and a kit.

[0033] On the other hand, an embodiment of the present invention provides the use of an inhibitor of CHRNA9 gene expression level or activity of its encoded protein in the preparation of a product for preventing, treating or assisting in the treatment of psoriasis.

[0034] In some embodiments, the inhibitor includes bupropion, which is a drug mainly used as an antidepressant and smoking cessation drug, and can also be used as a second-line medicine for the treatment of attention deficit hyperactivity disorder in combination with central nervous system stimulants, or as an alternative to central nervous system stimulants. Bupropion is a targeted inhibitor of acetylcholine receptors.

[0035] On the other hand, an embodiment of the present invention provides a prediction model training method, which includes:

[0036] Obtaining the detection results of the CHRNA9 gene expression levels of the training samples and the corresponding annotation results; the annotation results include any one or more labels representing whether the sample suffers from psoriasis, the degree of progression of psoriasis, and the prognostic risk of psoriasis;

[0037] Input the obtained detection results into a pre-built machine learning model to obtain a prediction result of the sample;

[0038] According to the labeling results and prediction results, the parameters of the machine learning model are updated to obtain the prediction model.

[0039] In some embodiments, the label may be a character or a string. The content of the prediction result corresponds to the content of the annotation result.

[0040] It is understandable that the categories and number of training samples can be routinely selected by those skilled in the art, and the number of total training samples and training samples of various categories (e.g., healthy people and patients) can be ≥ any value or range between any two of 10, 50, 100, 200, 300, 400 and 500. The training samples can include a positive group (e.g., good prognosis) and a negative group (e.g., poor prognosis).

[0041] In some embodiments, the present invention has no particular restrictions on the type of machine learning model, and can be selected from algorithm models conventionally used in the art for prognosis prediction, immunotherapy resistance prediction, and drug treatment effect prediction. Optionally, the machine learning model can be selected from any one of: logistic regression, SVM, Random Forest, Gradient Boosting Classifier (GBC), KNN, decision tree, and naive Bayes.

[0042] On the other hand, an embodiment of the present invention provides a prediction device, comprising:

[0043] An acquisition module is used to obtain the detection result of the CHRNA9 gene expression level of the sample to be tested;

[0044] The prediction module is used to input the detection result of the sample to be tested into the prediction model trained by the training method described in any of the above embodiments to obtain the prediction result of the sample to be tested.

[0045] The modules described in the embodiments of the present invention may be stored in a memory in the form of software or firmware or solidified in the operating system (OS) of the electronic device provided by the present application, and may be executed by a processor in the electronic device. At the same time, the data and program codes required to execute the above modules may be stored in the memory.

[0046] On the other hand, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store a program, and when the program is executed by the processor, the processor implements a method for predicting psoriasis;

[0047] The method for predicting psoriasis comprises the following steps: obtaining the detection result of the CHRNA9 gene expression level of the sample to be tested; inputting the detection result of the sample to be tested into the prediction model trained by the training method described in any of the aforementioned embodiments to obtain the prediction result of the sample to be tested.

[0048] An electronic device may include a memory, a processor, a bus, and a communication interface, wherein the memory, the processor, and the communication interface are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via one or more buses or signal lines.

[0049] The memory can be, but is not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), etc.

[0050] The processor can be an integrated circuit chip with signal processing capabilities. The processor 120 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0051] The electronic device can be a server, a cloud platform, a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (PDA), a wearable electronic device, a virtual reality device, etc. Therefore, the embodiments of the present application do not limit the type of electronic device.

[0052] In addition, an embodiment of the present invention provides a computer-readable medium having a computer program stored thereon. When the computer program is executed by a processor, the method for predicting psoriasis described in any of the aforementioned embodiments is implemented.

[0053] In some embodiments, the computer-readable medium may be a general storage medium, such as a mobile disk, a hard disk, etc.

[0054] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0055] Example 1

[0056] 1. Screening of differentially expressed genes

[0057] The three sample sets of GSE14905 (n=82), GSE121212 (n=66), and GSE114286 (n=27) in the GEO (gene expression and RNA modification database managed by the National Center for Bioinformation, USA) database were used to screen for differentially expressed genes between the patient group and the normal group, and then the intersection with the ion channel gene set was taken to screen out the acetylcholine receptor gene CHRNA9. The results are shown in Figure 1 .

[0058] Clinical and pathological characteristics of different patients were analyzed according to the expression level of CHRNA9. Figure 2 .

[0059] 2. Construct a prediction model for predicting psoriasis and use the ROC curve to evaluate the accuracy of the CHRNA9 gene in predicting psoriasis.

[0060] 2.1. The construction of the prediction model includes:

[0061] Obtaining the detection results of the CHRNA9 gene expression levels of the training samples and the corresponding annotation results; the annotation results include any one or more labels representing whether the sample suffers from psoriasis, the degree of progression of psoriasis, and the prognostic risk of psoriasis;

[0062] Input the obtained detection results into a pre-built machine learning model to obtain a prediction result of the sample;

[0063] According to the labeling results and prediction results, the parameters of the machine learning model are updated to obtain the prediction model.

[0064] The sample size of the training samples was 82 (GSE14905), including 21 CTRL (non-psoriasis patient group) and 61 PSO (psoriasis patient group), and the machine learning model was SVM.

[0065] 2.2. Use the validation set to verify the effectiveness of the trained prediction model.

[0066] The sample size of the validation set is 93 cases, of which 66 cases are from GSE121212 (39 cases of CTRL + 27 cases of PSO) and 27 cases are from GSE114286 (9 cases of CTRL + 18 cases of PSO).

[0067] The ROC curve results for evaluating the accuracy of the prediction model are shown in Figure 3, it can be seen that the area under the curve (AUC) values ​​of the 1-year, 3-year, and 5-year risk scores are all greater than 90%, indicating that the CHRNA9 gene may have a good predictive ability for the long-term prognosis of psoriasis.

[0068] 3. Correlation analysis of inflammatory gene sets

[0069] Psoriasis is an immune-mediated inflammatory skin disease, and its pathogenesis is related to the infiltration of inflammatory cells and inflammatory factors. Therefore, this example studies the relationship between RiskScore (immune risk score, the prediction model constructed in the above steps is used to calculate the RiskScore of each sample in the data set) and known inhibitory immune checkpoints.

[0070] Seven large gene clusters related to the immune system were selected as markers of immune status, including hematopoietic cell kinase (HCK), lymphocyte-specific kinase (LCK), major histocompatibility complex (MHC), signal transducer and activator of transcription 1 / 2 (STAT1 / 2), interferon, and IgG. Figure 4 As shown, RiskScore was positively correlated with most inflammatory markers.

[0071] Example 2: Verify the effect of CHRNA9 inhibitor bupropion on IMQ-induced psoriasis model.

[0072] Animal experiments

[0073] Experimental animals: All animal experiments were conducted in accordance with the Animal Care Guidelines. Female BALB / c mice aged 4-6 weeks and weighing 18-21 g were selected as experimental animals. A total of 30 mice were randomly divided into 6 groups (5 mice in each group): blank group, model group, positive drug group, low concentration group, medium concentration group, and high concentration group.

[0074] According to the average body weight of mice in the positive drug group, 1 mg / kg of methotrexate was weighed (mouse weight kg. methotrexate mg and the same applies to the following), methotrexate was dissolved in 25 μl of DMSO solution, and then 475 μl of sterile PBS solution was added to prepare a 1 mg / kg methotrexate solution.

[0075] 5 mg / kg bupropion was weighed according to the average body weight of mice in the low-concentration group, and bupropion was dissolved in 500 μl of sterile PBS solution to prepare a 5 mg / kg bupropion solution.

[0076] According to the average body weight of mice in the medium concentration group, 10 mg / kg bupropion was weighed and dissolved in 500 μl of sterile PBS solution to prepare a 10 mg / kg bupropion solution.

[0077] 20 mg / kg bupropion was weighed according to the average body weight of mice in the high-concentration group, and bupropion was dissolved in 500 μl of sterile PBS solution to prepare a 20 mg / kg bupropion solution.

[0078] In the first week, the mice were familiarized with the environment and raised normally. In the second week, the back of each mouse was depilated. The mice in the blank group were smeared with sterile PBS on the back skin every day. The mice in the model group, positive drug group, low concentration group, medium concentration group, and high concentration group were smeared with 5% IMQ (imiquimod) ointment 62.5 mg on the back skin every afternoon. The mice in the positive drug group were gavaged with 1 mg / kg methotrexate solution every morning, the low concentration group was gavaged with 5 mg / kg bupropion solution every morning, the medium concentration group was gavaged with 10 mg / kg bupropion solution every morning, and the high concentration group was gavaged with 20 mg / kg bupropion solution every morning, for ten days, once a day.

[0079] Mouse experimental results Figure 5 As shown, the thickness of mouse skin from thick to thin is model group, low concentration group, medium concentration group, positive drug group and high concentration group, indicating that the high concentration group of bupropion may have the effect of eliminating psoriasis.

[0080] Example 3: Verify the effect of the CHRNA9 inhibitor bupropion on the proliferation of human immortalized keratinocytes.

[0081] Cell experiments.

[0082] 1. CCK8 detection of cell activity

[0083] Human immortalized epidermal cells HACAT were used to verify the toxic side effects of bupropion on cells. The experimental method is as follows:

[0084] The cells in good condition and in the logarithmic growth phase were digested and counted, and seeded into a 96-well plate at a density of 2000 cells / well. Bupropion was added to the wells so that the working concentrations were 6.25 μM, 12.5 μM, 25 μM, 50 μM, 100 μM, and 200 μM, respectively, and the same volume of culture medium was added to the control wells.

[0085] At 24h and 72h after the addition of bupropion, a 96-well plate was taken at each time point, 10 μL of CCK8 solution was added to each well, mixed, and incubated in a cell culture incubator in the dark for 1h. Then, the OD value at 450nm was measured using a spectrophotometer, and the cell activity was calculated using GraphPad. The results are shown in Figure 5 As shown. It can be seen that bupropion has good cell safety.

[0086] 2.Calcein-AM / PI

[0087] Calcein-AM / PI staining reagent was used to perform cell live-dead staining experiments to evaluate the survival of human immortalized keratinocytes after the addition of bupropion. Calcein-AM can interact with living cells to produce green fluorescence, which can be used for viability testing and short-term labeling of living cells. PI can pass through the cell membrane of dead cells to reach the cell nucleus, interact with the DNA in the cell to produce red fluorescence, and can be used to stain dead cells. Calcein-AM and PI staining agents were used in combination to perform dual fluorescent staining of live and dead cells. The results of the two cell experiments indicate that bupropion treats psoriasis not by inhibiting the proliferation of (HaCat human immortalized keratinocytes) (keratinocytes), but may be affected by other factors such as the inhibition of the secretion of inflammatory factors. The results are shown in Figure 6 .

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Application of a reagent for detecting the expression level of CHRNA9 gene in the preparation of a product for the prediction of psoriasis.

2. The use according to claim 1, characterized in that: The expression level includes the mRNA expression level and / or protein expression level of the CHRNA9 gene.

3. The use according to claim 1, characterized in that: The psoriasis prediction includes: predicting any one or more of whether the subject suffers from psoriasis, the degree of progression of psoriasis, and the prognostic risk of psoriasis.

4. The use according to any one of claims 1 to 3, characterized in that: The product includes: any one of a reagent, a chip and a kit.

5. Use of inhibitors of CHRNA9 gene expression level or activity of its encoded protein in the preparation of products for the prevention, treatment or auxiliary treatment of psoriasis.

6. The use according to claim 5, characterized in that: Such inhibitors include bupropion.

7. A method for training a prediction model, characterized in that: It includes: Obtain the detection results of the CHRNA9 gene expression level of the training sample and its corresponding annotation results; The annotation result includes any one or more labels representing whether the sample suffers from psoriasis, the degree of progression of psoriasis, and the prognostic risk of psoriasis; Input the obtained detection results into a pre-built machine learning model to obtain a prediction result of the sample; According to the labeling results and prediction results, the parameters of the machine learning model are updated to obtain the prediction model.

8. A prediction device, characterized in that: It includes: An acquisition module is used to obtain the detection result of the CHRNA9 gene expression level of the sample to be tested; The prediction module is used to input the detection result of the sample to be tested into the prediction model trained by the training method described in claim 7 to obtain the prediction result of the sample to be tested.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory is used to store a program, and when the program is executed by the processor, the processor implements a method for predicting psoriasis; The method for predicting psoriasis comprises the following steps: obtaining the test result of the CHRNA9 gene expression level of the sample to be tested; The detection result of the sample to be tested is input into the prediction model trained by the training method described in claim 7 to obtain the prediction result of the sample to be tested.

10. A computer-readable medium, characterized in that The computer readable medium stores a computer program, and when the computer program is executed by a processor, the method for predicting psoriasis described in claim 9 is implemented.

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