An autophagy biomarker related to depression, a risk assessment model and its application

Abstract

An autophagy biomarker related to depression, a risk assessment model and its application. Through multi-dimensional screening of large sample data and machine learning algorithms, the present invention screens out a group of autophagy-related biomarkers for the auxiliary diagnosis of depression, including the following genes: GABARAPL2, RB1CC1, FOS, ULK1, and the differential expression thereof is confirmed in the cortical brain tissue of chronically stressed depression-like mice. The present invention also provides a risk assessment model for the auxiliary diagnosis of depression. A logistic regression model is constructed with the said biomarkers as features, the score formula and risk threshold for the risk assessment of depression are determined, and the prediction performance of the model is evaluated in various aspects. The said prediction model has good prediction accuracy and calibration, can efficiently and conveniently achieve the early diagnosis of depression, and improve the accuracy of depression treatment.

Description

Technical Field

[0001] The present invention belongs to the technical field of bioinformatics, and particularly relates to an autophagy biomarker related to depression, a risk assessment model and its application. Background Art

[0002] Major depressive disorder (MDD) is a common mental disorder characterized by high prevalence and high suicide rate, and is one of the five major causes of disability globally. It is expected to rank first in the global disease burden list by 2030. According to the WHO's Global Mental Health Assessment published in 2019, the proportion of global MDD patients reached 4.4% in 2015, approximately 322 million people. Currently, the most widely used diagnostic criteria for MDD worldwide are the Diagnostic and Statistical Manual of Mental Disorders (DSM5) and the International Classification of Diseases (ICD11) criteria. These diagnostic criteria rely on clinicians' subjective judgments of symptomatology and always lack objective markers for disease diagnosis.

[0003] Autophagy is a process of cellular self - degradation that can protect cells from damage by preventing the accumulation of toxic proteins and damaged organelles, and is an important mechanism for cell survival and maintaining homeostasis. Many studies have shown that autophagy genes are involved in the occurrence of various phenotypes and diseases such as neurodegenerative diseases. On the one hand, stable autophagy is crucial for maintaining the functions and activities of neuronal cells themselves and regulating synaptic homeostasis. At the same time, autophagy also affects the functions and homeostasis of neurons by influencing the release of regulatory factors from glial cells. On the other hand, the "inflammatory hypothesis" and "neurotrophic hypothesis" are important pathogenic mechanisms of depression. Research reports that autophagy, as an emergency - activated cellular degradation pathway, has a very close bidirectional regulatory relationship with inflammatory responses and the production of inflammasomes. Moreover, the autophagy signaling pathway can regulate the release of brain - derived neurotrophic factor (BDNF), and various antidepressant drugs also have the effect of activating the autophagy pathway to regulate autophagy. More and more evidence shows that autophagy disorders are involved in the occurrence and development of depression through multiple mechanisms and pathways.

[0004] Early diagnosis and appropriate treatment are important means to reduce the incidence and mortality of depression. However, due to the complex etiology, large symptom differences and low recognition rate of high - risk individuals of depression, the clinical judgment based on personal medical history lacks reliability. Therefore, there is an urgent need for an objective and reliable diagnostic method. Summary of the Invention

[0005] Technical problems to be solved: In view of the problems in the prior art that the clinical judgment based on personal medical history in the early screening and diagnosis of depression lacks reliability and objectivity, the present invention provides an autophagy biomarker related to depression, a risk assessment model and its application, which can provide new methods and ideas for the early screening and diagnosis of depression, and lay a foundation for early intervention and treatment to timely correct the dangerous behaviors of patients to avoid the occurrence and further aggravation of depression.

[0006] Technical solution: In a first aspect, the present invention provides an autophagy biomarker related to depression, and the biomarker includes at least one of the following genes: GABARAPL2, RB1CC1, FOS, and ULK1.

[0007] Preferably, the biomarker includes the following genes: GABARAPL2, RB1CC1, FOS, and ULK1.

[0008] Use of the above-mentioned autophagy biomarker related to depression in the preparation of a reagent or kit for diagnosing the occurrence of depression.

[0009] Use of the above-mentioned autophagy biomarker related to depression as a target for rescreening drugs for preventing or treating depression.

[0010] In a second aspect, the present invention provides a method for screening the above-mentioned depression biomarker related to autophagy, and the steps are as follows:

[0011] Step 1. Data collection and processing: Obtain the peripheral blood gene microarray datasets of depression patients and healthy controls, construct the original dataset, the original dataset includes the discovery cohort dataset and the independent validation dataset, and perform data preprocessing to obtain the input dataset based on the discovery cohort dataset;

[0012] Step 2. Differential gene analysis: Perform differential gene analysis on the input dataset. Based on the linear regression model, use weighted least squares to calculate the difference in gene expression between depressed patients and healthy controls, and perform the Bayesian method to correct the problem of multiple testing. The loci with adjusted P value < 0.05 and |log2 Fold change| > 1 are used as differential gene loci, and the corresponding differential gene names are obtained through annotation to obtain the differential gene dataset;

[0013] Step 3. Weighted gene co-expression network analysis: Based on the input dataset, construct a co-expression network, merge the modules with a difference less than 0.25, calculate the Pearson correlation coefficient between each module and the depressive phenotype, select the module with a Pearson correlation coefficient greater than 0.8, and use gene significance = 0.2 and module membership = 0.5 as thresholds to screen out the genes most related to depression;

[0014] Step 4: Take the intersection of the genes obtained in Steps 2 and 3, and perform clustering, dimensionality reduction analysis, and screening to obtain autophagy-related biomarkers for diagnosing depression. The methods of clustering, dimensionality reduction, and screening include the following steps:

[0015] (1) Use the cytoHubba plugin of the cytoscape software to perform the maximum clique centrality topological analysis algorithm on the genes in the intersection, analyze gene relevance, calculate the number of the maximum cliques to which the nodes belong to evaluate the centrality of the nodes, and select the top 30 genes as candidate biomarkers;

[0016] (2) Randomly divide the input data set of the candidate biomarkers into a training set and a test set. Perform the LASSO regression algorithm and the support vector machine recursive feature elimination algorithm on the training set to establish a depression diagnosis model respectively, and apply the established model to the test set and the independent validation data set to evaluate the model results, fit the optimal model, and obtain the screened genes;

[0017] (3) Take the intersection of the genes screened by the two algorithms in step (2) and screen out the genes related to the autophagy pathway to obtain a corresponding list of important genes as autophagy-related biomarkers for diagnosing depression.

[0018] In a third aspect, the present invention provides a risk assessment model for autophagy biomarkers related to depression. The risk assessment model uses the above autophagy biomarkers for judgment, and includes a data acquisition unit, a risk index calculation unit, and a risk judgment unit. The data acquisition unit includes: obtaining gene expression level data of the above autophagy-related biomarkers for auxiliary diagnosis of depression in a biological sample of a subject; the risk index calculation unit includes: based on the gene expression level obtained by the data acquisition unit, calculating the risk score of the subject suffering from depression through the autophagy-related biomarker risk assessment model for auxiliary diagnosis of depression. The risk score calculation formula is:

[0019]

[0020] Where X1, X2, X3, and X4 are respectively the gene expression level data of the biomarkers GABARAPL2, RB1CC1, ULK1, and FOS, β1, β2, β3, and β4 are respectively the weight coefficients corresponding to the biomarkers, β1 = 2.1128, β2 = -0.6776, β3 = 0.8163, β4 = -1.7358, and the intercept α = -7.8361;

[0021] The risk judgment unit includes: based on the calculated depression risk score, if the risk score < the model definition threshold, it is determined as low risk; if the risk score ≥ the model definition threshold, it is determined as high risk, and the model threshold is 0.656.

[0022] Preferably, the biological sample includes a blood sample.

[0023] Fourthly, the present invention provides a method for constructing a risk assessment model of an autophagy biomarker related to depression as described above. The construction method includes the following steps:

[0024] (1) Using the full-sample gene expression matrix of the biomarker as the original data set, the original data set includes a discovery cohort data set and an independent validation data set, and performing data preprocessing on the original data set to obtain an input data set based on the discovery cohort data set. Randomly divide the input data set into a training set and a test set, use the biomarker as a feature, and construct a logistic regression model with whether suffering from depression as the outcome.

[0025] (2) The logistic regression model estimates the regression coefficients by fitting the model sample data of the training set, thereby establishing the relationship between the independent variable and the target variable, and determining the score formula for depression risk assessment. Where P is the risk score, and the depression risk assessment score formula is:

[0026]

[0027] Where α is the intercept, and β1, β2, β3, β4 are the weight coefficients of GABARAPL2, RB1CC1, ULK1, and FOS respectively. β1 = 2.1128, β2 = -0.6776, β3 = 0.8163, β4 = -1.7358, and the intercept α = -7.8361;

[0028] Perform performance evaluation on the risk assessment model of the autophagy-related biomarker for auxiliary diagnosis of depression. Input the test set and the independent validation data set into the constructed risk assessment model, and use discrimination and calibration as performance measurement criteria. Discrimination includes the concordance index and AUC, and calibration includes the goodness-of-fit test. Combine the accuracy rate to verify the prediction performance of the model;

[0029] Discrimination refers to the ability of the model to correctly distinguish between individuals with high risk and low risk of the outcome, that is, the ability of the model to correctly classify whether the research event occurs. A discrimination greater than 0.75 is considered high accuracy; calibration refers to the degree of consistency between the predicted risk of the prediction model and the actual risk, and is an important dimension to measure the prediction accuracy of the model. A goodness-of-fit test P greater than 0.05 indicates good calibration of the model. The highest accuracy rate of the depression auxiliary diagnosis model constructed by the present invention can reach 85%, AUC = 93.6%, and the concordance index

[0030] = 0.94, goodness-of-fit test P = 0.15.

[0031] (3) Determine the model threshold. The calculation method of the model threshold is to evaluate through sensitivity, specificity and Youden index. The Youden index measures the ability of the classifier to maximize the true negative rate while maintaining a high true positive rate. The maximum value of the Youden index corresponds to the maximum diagnostic critical point of this method. The defined threshold of the model is the maximum value of the Youden index, and the calculation formula is:

[0032]

[0033] Youden index = sensitivity + specificity - 1;

[0034] In the formula, TP represents true positive, that is, the number of people diagnosed as patients by the diagnostic model among the patients diagnosed by the gold standard; FN represents false negative, that is, the number of people diagnosed as non-patients by the diagnostic model among the patients diagnosed by the gold standard; FP represents false positive, that is, the number of people diagnosed as patients by the diagnostic model among the non-patients diagnosed by the gold standard; TN represents true negative, that is, the number of people diagnosed as non-patients by the diagnostic model among the non-patients diagnosed by the gold standard.

[0035] In the fifth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored; when the computer program is executed by a processor, the method for constructing a risk assessment model of the autophagy biomarker related to depression as described above is realized.

[0036] In the sixth aspect, the present invention provides an information data processing terminal for realizing the method for constructing a risk assessment model of the autophagy biomarker related to depression as described above.

[0037] Beneficial effects:

[0038] 1. The present invention uses weighted gene co-expression network analysis, maximum clique centrality topology algorithm, and machine learning algorithms such as LASSO and support vector machine recursive feature elimination to perform multi-level analysis on the gene transcription level data of large samples of depressive patients and normal controls, which can process high-dimensional gene expression data and improve the effects of feature selection and model training;

[0039] 2. The present invention develops a risk prediction model for the auxiliary diagnosis of depression based on gene expression levels, and conducts multiple repeated experiments. The AUC of the model is stable above 0.75, and the highest can reach 0.95, which has high accuracy and stability and improves the reliability of early diagnosis of depression;

[0040] 3. The biological sample used in the present invention is peripheral blood, which is easy to collect and obtain. Therefore, the technical solution of the present invention is more easily promoted.

[0041] 4. The animal experiments of the present invention have demonstrated that in the cerebral cortex of depressive mice, the expression changes of the four biomarkers are consistent with those in the peripheral blood of depressive patients, further confirming the accuracy and stability of the present invention and having reliable diagnostic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram for screening autophagy-related biomarkers for the auxiliary diagnosis of depression in Example 1 of the present invention;

[0043] Figure 2 It is a schematic diagram for constructing a risk assessment model for the auxiliary diagnosis of depression based on the gene expression levels of biomarkers in Example 2 of the present invention;

[0044] Figure 3 It is the verification of the diagnostic efficacy of the risk assessment model for the auxiliary diagnosis of depression in Example 2. In the figure, (a) is the ROC curve of the auxiliary diagnosis model for depression, where AUC = 0.936; (b) is the result of the goodness-of-fit test of the model, P = 0.15; (c) is the concordance index = 0.94, and the accuracy rate is 85%;

[0045] Figure 4 It is a schematic diagram of the structure of the depression risk assessment system in Example 3 of the present invention;

[0046] Figure 5 It is a test result diagram of the establishment of a depression animal model in Example 4 of the present invention. In the figure, (a) is the sucrose preference test; (b) is the open field test; (c) is the tail suspension test; (d) is the forced swimming test;

[0047] Figure 6 It is a diagram of the expression level results of GABARAPL2, RB1CC1, ULK1, and FOS. In the figure, (a) is the distribution diagram of the gene expression levels of biomarkers in human peripheral blood in Example 2, and (b) is the test result diagram of the expression levels of GABARAPL2, RB1CC1, ULK1, and FOS in the cerebral cortex tissue of depressive model mice in Example 4. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention. The following embodiments are used to illustrate the present invention but cannot be used to limit the scope of the present invention.

[0049] Example 1

[0050] As shown Figure 1 in the figure, the present invention provides a group of autophagy-related biomarkers for the auxiliary diagnosis of depression, and specifically includes the following implementation steps:

[0051] 1. Data source: Collect and organize the data sets that meet the inclusion criteria in the GEO database and exclude the cases that do not meet the criteria (see Table 1 below for the exclusion criteria of irrelevant variables). The inclusion criteria are: (1) Use a case-control design; (2) Have no other diseases except depression; (3) Have not used drugs.

[0052] Table 1 Exclusion of Irrelevant Variables

[0053]

[0054] Construct an original data set, including a total of 6 data sets, namely GSE201332, GSE217811, GSE38206, GSE76826, GSE39653, and GSE98793. The included data sets and sample information are shown in Table 2 below. The expressions of the 6 data sets all come from human peripheral blood samples. Among them, GSE201332, GSE217811, GSE38206, GSE76826, and GSE39653 are used as discovery cohort data sets for biomarker screening, with a total of 75 gene microarray data sets of depression patients and 70 healthy controls; GSE98793 is used as an independent validation data set, including 64 healthy controls and 128 gene microarray data sets of depression patients.

[0055] Table 2 Data Sets and Sample Information Included in the Invention

[0056]

[0057] 2. Preprocessing of the original data set: ① Use the arrayQulitMetrics package in R language to evaluate the quality of the data set, and use the Robust Multi-Array Average expression measure (RMA) to standardize the data to make up for the influence of systematic deviation on the data; ② Discard the sites with missing values and the probes mapped to multiple positions on the genome; ③ Use the empirical Bayesian method to adjust the batch effect of the data and eliminate the batch influence caused by technical factors such as substrate chips and positions; ④ Filter out the genes with expression levels similar to the background to obtain an input data set based on the discovery cohort data set. The classification of the data sets in the present invention is as follows:

[0058] Table 3 Classification of Data Sets

[0059]

[0060] 3. Differential gene analysis: differential gene analysis was performed on the input data set. Based on the linear regression model, the weighted least squares method was used to calculate the difference in gene expression between depressed patients and healthy controls. The Bayesian method was used to correct the problem of multiple testing. The sites with adjusted P value < 0.05 and |log2 Fold change|> 1 were used as differential gene sites. A total of 2287 differential gene sites were obtained and the corresponding differential gene names were obtained through annotation to obtain the differential gene data set.

[0061] 4. Weighted gene co-expression network analysis: Based on the input data set, ① calculate the median difference of each gene in the data set, screen the top 500 genes and exclude missing values ​​and outliers; ② use R 2 =0.9 to construct a scale-free network, and β=6 was selected as the soft threshold according to scale independence and average connectivity; ③ Hierarchical clustering and dynamic shear tree were used to preliminarily construct network modules, and then modules with dissimilarity below 0.25 were merged to obtain 12 gene modules; ④ The Pierce correlation coefficient between each module and the depressive phenotype was calculated, and module genes with a Pearson correlation coefficient greater than 0.8 were selected: the correlation between turquoise module genes and MDD was =0.86, P<1e-200, and the correlation between brown module genes and MDD was =0.81, P≤4.5e-188; ⑤ According to gene significance (GS) =0.2 and module membership (MM) =0.5, a total of 2681 genes most related to depression were screened out from the two modules and the HUB genes of each module.

[0062] 5. Take the intersection of the genes obtained in steps 3 and 4 to obtain 302 genes that are highly correlated with depression.

[0063] 6. Maximum cluster centrality topology analysis algorithm analysis: The above 302 genes were analyzed using the cytoHubba plug-in of the cytoscape software to execute the maximum cluster centrality topology analysis algorithm, analyze the gene association, calculate the maximum number of clusters to which the node belongs to evaluate the centrality of the node, and select the top 30 genes as candidate biomarkers.

[0064] 7. Machine learning model construction: ① Stratified random sampling is performed on the input dataset of the candidate biomarkers at a ratio of 0.8:0.2 to divide it into a training set and a test set; ② LASSO algorithm: First, perform the LASSO regression algorithm of binomial on the training set, apply the constructed model to the test set for verification, draw the ROC curve and calculate the AUC for evaluation, repeat 1000 iterations, fit the model with the best effect, and detect the hub genes significantly related to depression; ③ SVM-RFE algorithm: At the same time, perform the support vector machine recursive feature elimination algorithm on the training set to extract features, fit the model and evaluate the performance of the data, verify the constructed model in the test set, perform 10-fold cross-validation to screen out the optimal subset, fit the optimal model, screen out the core genes, and improve the stability and accuracy of the model.

[0065] 8. Determine the autophagy-related biomarkers for the auxiliary diagnosis of depression: ① Take the intersection of the genes identified by the two algorithms in step 7 ② and ③ to obtain 7 of the most important depression-related genes; ② Screen the 7 genes among 803 autophagy-related genes to obtain 4 autophagy-related biomarkers for the diagnosis of depression. The ENTREZ IDs of the 4 autophagy-related biomarkers for the diagnosis of depression, GABARAPL2, RB1CC1, ULK1, and FOS, are as follows: GABARAPL2: Entrez Gene ID: 11345; RB1CC1: Entrez Gene ID: 9821; ULK1: Entrez Gene ID: 8408; FOS: Entrez Gene ID: 2353.

[0066] Example 2

[0067] As Figures 2 - 3 shown, based on the biomarkers screened in Example 1, construct a risk assessment model for the auxiliary diagnosis of depression and evaluate the accuracy of the assessment model, which specifically includes the following implementation steps;

[0068] 1. Stratified random sampling is performed on the input dataset of the 4 biomarkers (i.e., the input dataset in Example 1) at a ratio of 0.8:0.2 to divide it into a training set and a test set. Use the 4 biomarkers as features and whether suffering from depression as the outcome, and use python to construct a binary logistic regression model.

[0069] 2. The logistic regression model estimates the regression coefficients by fitting the model sample data of the training set, thereby establishing the relationship between the independent variable and the target variable, and determining the score formula for the depression risk assessment. The depression risk assessment score P formula is:

[0070]

[0071] Among them, β1, β2, β3, and β4 are the weight coefficients of GABARAPL2, RB1CC1, ULK1, and FOS respectively, where β1 = 2.1128, β2 = -0.6776, β3 = 0.8163, β4 = -1.7358, and the intercept α = -7.8361.

[0072] 3. Model performance evaluation. The test set and the independent validation dataset GSE98793 are input into the constructed risk assessment model. Using discrimination (concordance index and AUC) and calibration (goodness-of-fit test), etc. as performance metrics, combined with accuracy, to verify the prediction performance of the model (the results are shown in Figure 3 ).

[0073] Discrimination refers to the ability of the model to correctly distinguish between individuals at high risk and low risk of the outcome, that is, the ability of the model to correctly classify whether a research event occurs. A discrimination greater than 0.75 indicates high accuracy; calibration refers to the degree of consistency between the predicted risk of the prediction model and the actual risk, which is an important dimension to measure the prediction accuracy of the model. A goodness-of-fit test P greater than 0.05 indicates good calibration of the model.

[0074] The highest accuracy of the depression auxiliary diagnosis model constructed by the present invention can reach 85%, AUC = 93.6%, concordance index = 0.94, and goodness-of-fit test P = 0.15. It is significantly better than the single-gene diagnosis efficiency: ULK1 AUC = 0.708, FOS AUC = 0.636, GABARAPL2 AUC = 0.588, RB1CC1 AUC = 0.587.

[0075] 4. Determine the model threshold. The calculation method of the model threshold is to evaluate through sensitivity, specificity, and Youden index. The Youden index measures the ability of the classifier to maximize the true negative rate while maintaining a high true positive rate. The maximum value of the Youden index corresponds to the maximum diagnostic critical point of this method. The defined threshold of the model is the maximum value of the Youden index. The calculation formula is:

[0076]

[0077] Youden index = sensitivity + specificity - 1;

[0078] TP represents true positive, that is, the number of people diagnosed as patients by the diagnostic model among the patients confirmed by the gold standard; FN represents false negative, that is, the number of people diagnosed as non-patients by the diagnostic model among the patients confirmed by the gold standard; FP represents false positive, that is, the number of people diagnosed as patients by the diagnostic model among the non-patients confirmed by the gold standard; TN represents true negative, that is, the number of people diagnosed as non-patients by the diagnostic model among the non-patients confirmed by the gold standard.

[0079] In the present invention, a threshold value (0.656) corresponding to the maximum Youden index (0.869) is selected as the model critical value.

[0080] Example 3

[0081] As Figure 4 shown, the present invention provides a system (risk assessment model) for autophagy-related biomarker risk assessment for the auxiliary diagnosis of depression, comprising:

[0082] A: A data acquisition unit, which acquires the gene expression level data of autophagy-related biomarkers for the auxiliary diagnosis of depression in the biological samples of the subjects;

[0083] B: A risk index calculation unit, which can calculate the risk score of the subject suffering from depression through the autophagy-related biomarker risk assessment model for the auxiliary diagnosis of depression based on the gene expression level obtained by the data acquisition unit. The risk score calculation formula is:

[0084]

[0085] wherein X1, X2, X3, and X4 are respectively the gene expression level data of the biomarkers GABARAPL2, RB1CC1, ULK1, and FOS, and β1, β2, β3, and β4 are respectively the weight coefficients corresponding to the biomarkers. β1 = 2.1128, β2 = -0.6776, β3 = 0.8163, β4 = -1.7358, and the intercept α = -7.8361;

[0086] C: A risk judgment unit, which determines a low risk when the calculated depression risk score < the model definition threshold; determines a high risk when the risk score ≥ the model definition threshold, and the model cut-off value is 0.656.

[0087] Example 4 Animal Model Verification

[0088] A depression animal model is established, and behavioral tests (sucrose preference, open field, tail suspension, forced swimming) are used to evaluate the depression animal model. The results are as Figure 5 shown. The results in the figure show that the sucrose preference of the mice decreases, indicating that they show symptoms of anhedonia. The immobility time in the tail suspension and forced swimming decreases, showing a phenotype of despair. The residence time in the central area of the open field decreases, showing anxiety behavior. In summary, the mouse depression-like model is successfully established.

[0089] The primer sequences of GABARAPL2, RB1CC1, ULK1, and FOS are designed and synthesized. The cortical brain tissues of depression mice (CUMS) and normal mice (Control) are taken, and the relative mRNA expression levels of the biomarkers are detected by RT-qPCR. The primer sequences are as follows:

[0090] GABARAPL2 - F: 5'-GGAACCTGTCCTCTGGGAATC-3' (SEQ ID No.1)

[0091] GABARAPL2 - R: 5'-CAGAACTATGCATCAGCCCCT-3' (SEQ ID No.2)

[0092] RB1CC1 - F: 5'-ACGTGGCAAAGAACTTAGGG-3' (SEQ ID No.3)

[0093] RB1CC1 - R: 5'-ACGTGGCAAAGAACTTAGGG-3' (SEQ ID No.4)

[0094] ULK1 - F: 5'-GTCGACACCGCGAGAAGC-3' (SEQ ID No.5)

[0095] ULK1 - R: 5'-GAAGTCATACAGCGCCACGA-3' (SEQ ID No.6)

[0096] FOS - F: 5'-GGGAATGGTGAAGACCGTGTCA-3' (SEQ ID No.7)

[0097] FOS - R: 5'-GCAGCCATCTTATTCCGTTCCC-3' (SEQ ID No.8)

[0098] GAPDH - F: 5'-GGTTGTCTCCTGCGACTTCA-3' (SEQ ID No.9)

[0099] GAPDH - R: 5'-TGGTCCAGGGTTTCTTACTCC-3' (SEQ ID No.10)

[0100] The detection method is as follows:

[0101] 1. Total RNA extraction from mouse cortical brain tissue

[0102] Take 100 mg of mouse cortical brain tissue, let it stand at room temperature for half an hour, then add 1 mL of Trizol and vortex for 30 seconds, and let it stand at room temperature for 3 minutes; (2) Add 200 μL of chloroform, shake vigorously for 15 seconds and then let it stand at room temperature for 15 minutes; (3) Centrifuge at 12000 g at 4 °C for 15 minutes; (4) Carefully pipette the upper clear aqueous phase and transfer it to a 1.5 mL nuclease-free centrifuge tube, add 1.5 times the volume of absolute ethanol, and gently mix by inverting up and down; (5) Isopropanol precipitation: Add 500 μL of isopropanol, mix by inverting up and down 10 times, and let it stand at -20 °C for 20 minutes; Centrifuge at 12000 g × 10 minutes at 4 °C. (6) Ethanol washing: Discard the supernatant, gently add 1.5 mL of 75% ethanol, gently mix by inverting, and centrifuge at 7500 g × 5 minutes at 4 °C. (7) Discard the supernatant, dry the precipitate and dissolve: Try to aspirate the supernatant as much as possible, dry at room temperature for 5 - 10 minutes, and dissolve with 50 μL of DEPC treated H2O. (8) Use a One Drop spectrophotometer to measure the RNA concentration, and proceed to the next experiment or store it at -80 °C in the refrigerator for later use.

[0103] 2. RNA Reverse Transcription

[0104] Use the HiScript Q RT SuperMix for qPCR Kit (R123 - 01, Vazyme) reverse transcription kit

[0105] a) Genomic DNA removal: Total RNA: 200 ng; 4×gDNAwiper Mix 4 μL; DNase / RNase-free ddH2O Up to 16 μL; Gently pipette and mix, 42 °C, 2 minutes;

[0106] b) Reverse transcription reaction, the first reaction solution is 16 μL; 5×HiScript III qRT SuperMix 4 μL; Gently pipette and mix, and then run the following program in a PCR instrument: 37 °C for 15 minutes; 85 °C for 5 seconds; The obtained product can be directly used for qPCR reaction or stored at -20 °C in the refrigerator.

[0107] 3. qPCR

[0108] Use Universal SYBR qPCR Master Mix (Q511, Vazyme) kit:

[0109] a) Reaction system:

[0110] 2×AceQ qPCR SYBR Green Master Mix 10 μL;

[0111] Forward Primer (10 μM) 0.4 μL;

[0112] Reverse Primer (10 μM) 0.4 μL;

[0113] Template DNA 2 μL;

[0114] DNase / RNase free ddH2O Up to 20 μL.

[0115] b) Reaction conditions:

[0116] Pre-denaturation: 95°C for 5 min

[0117] Cyclic reaction (45 cycles): 95°C for 10 s; 66°C for 30 s

[0118] Melting curve: 95°C for 15 s; 60°C for 1 min; 95°C for 15 s.

[0119] 4. Data processing

[0120] Obtain the qPCR off-machine data, use the relative content of the target gene mRNA with GAPDH as the internal reference control to calculate ΔCT, then use the normal control group as the reference for the depression group to calculate ΔΔCT, and finally use the method of 2 -ΔΔCT for normalization analysis.

[0121] The results are shown as Figure 6 (b). Compared with normal mice, the expressions of GABARAPL2, RB1CC1, ULK1, and FOS are significantly increased in the cortical tissues of the depression model mice, which is consistent with the changes in the expression levels of the four biomarker genes in the peripheral blood of depressive patients in the discovery dataset ( Figure 6 (a)). In the figure, MDD represents depressive patients, CUMS represents depressive mice, and control represents normal control.

[0122] In summary, the present invention provides an autophagy-related biomarker and risk assessment model for the auxiliary diagnosis of depression constructed at the gene expression level through multi-dimensional screening of large sample data and machine learning algorithms, and constructs a technical solution based on the expression levels of autophagy-related differential genes as biomarkers for the diagnosis of depression. This solution has high accuracy and stability, and has important application value for the early diagnosis and treatment of depression.

[0123] The above adjustment parameters of the present invention are only examples for explaining the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes and modifications can be made on the basis of the above description. It is impossible to enumerate all the implementation manners here. Any obvious changes or modifications derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. Use of a reagent for detecting autophagy biomarkers related to depression in the preparation of a kit for diagnosing the occurrence of depression, characterized in that, The autophagy biomarkers related to depression are GABARAPL2, RB1CC1, FOS, and ULK1.

2. A risk assessment system for autophagy biomarkers related to depression, characterized in that, The risk assessment system uses the autophagy biomarkers related to depression as described in claim 1 for judgment, and includes a data acquisition unit, a risk index calculation unit, and a risk judgment unit. The data acquisition unit includes: acquiring the gene expression level data of the autophagy biomarkers related to depression as described in claim 1 in the biological sample of the subject; the risk index calculation unit includes: based on the gene expression level obtained by the data acquisition unit, calculating the risk score of the subject suffering from depression through the autophagy biomarker risk assessment model for auxiliary diagnosis of depression. The risk score calculation formula is: Risk score P = , Where X1, X2, X3, X4 are the gene expression level data of the biomarkers GABARAPL2, RB1CC1, ULK1, and FOS respectively, β1, β2, β3, β4 are the weight coefficients corresponding to the biomarkers respectively, β1 = 2.1128, β2 = -0.6776, β3 = 0.8163, β4 = -1.7358, and the intercept α = -7.8361; The risk judgment unit includes: according to the calculated risk score, if the risk score < the model-defined threshold, it is determined as low risk; if the risk score ≥ the model-defined threshold, it is determined as high risk. The model-defined threshold is 0.

656.

3. The risk assessment system for an autophagy biomarker related to depression according to claim 2, wherein The biological sample includes a blood sample.

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