Depression-related biomarker and use thereof

Abstract

The application relates to the field of molecular biology technology, and particularly relates to a biomarker related to depression and application thereof. The biomarker is SNORA73. The biomarker is based on the previous research finding that the non-coding RNA detection result of a chronic unpredictable mild stress group in an animal model shows that SNORA73 is significantly down-regulated, which indicates that SNORA73 can be used as a candidate core biomarker related to depression. The relative expression amount of SNORA73 is verified by qRT-PCR to be significantly lower than that of a normal control group. Finally, the exosomes of the ex vivo peripheral blood of clinical samples are extracted, and it is confirmed that the expression amount of SNORA73 in the clinical samples is significantly lower than that of the normal control group, and is negatively correlated with the depression degree. It is indicated that SNORA73 is directly related to depression, which fills the blank of the prior art that SNORA73 is related to depression.

Description

Technical Field

[0001] This application relates to the field of molecular biotechnology, and in particular to a biomarker related to depression and its application. Background Technology

[0002] Depression, a common mental disorder, is showing an increasing incidence rate year by year. It not only causes core symptoms such as depressed mood, loss of interest, and lack of energy, but is also frequently accompanied by sleep disturbances, cognitive impairment, and somatization symptoms. This severely impacts patients' work capacity, quality of life, and social functioning, placing a heavy emotional and economic burden on their families. It also results in a significant loss of the workforce, creating a serious public health problem and exacerbating the burden of major diseases. Currently, the clinical diagnosis of depression mainly relies on clinical physician assessment combined with standardized depression scales. Commonly used scales include the Hamilton Depression Rating Scale (HAMD), the Beck Depression Rating Scale (BDI), the Self-Rating Depression Scale (SDS), and diagnostic criteria from the International Classification of Diseases (ICD-11) or the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). These assessment methods are widely used in clinical practice due to their ease of use, low cost, and ability to quickly obtain clinical information. However, this type of diagnostic method has significant limitations: First, the diagnostic results are highly dependent on the assessor's clinical experience and subjective judgment, and diagnostic biases may exist between different assessors, lacking a unified objective evaluation basis; second, the scale scores are mainly based on the patient's subjective symptom descriptions, and some patients may conceal or exaggerate their symptoms due to cognitive biases, stigma, or limitations in expression, leading to reduced diagnostic accuracy; third, it is difficult to achieve early screening and differential diagnosis of depression, and missed or misdiagnosed cases are prone to occur for early patients with atypical symptoms or those comorbid with other mental disorders such as anxiety; fourth, it is impossible to quantify the severity of the disease and treatment response, making it difficult to accurately guide the development of individualized treatment plans. Therefore, there is an urgent clinical need for an objective, sensitive, and specific biological indicator for the diagnosis, disease assessment, and efficacy monitoring of depression.

[0003] The pathogenesis of depression is complex and not yet fully understood. Current research generally agrees that it is the result of the combined effects of genetic and environmental factors. Environmental stressors (such as early adverse life events, prolonged psychological stress, and traumatic events) are important triggers for depression. Numerous epidemiological surveys and animal experiments have confirmed that long-term or severe environmental stress can significantly increase the risk of developing depression. In recent years, epigenetic research has provided a crucial perspective for understanding the link between environmental stress and the onset of depression. Increasing evidence shows that epigenetic changes are the core mechanism of the interaction between environmental stressors and the genome. Through DNA methylation, histone modification, and non-coding RNA regulation, epigenetic changes can cause stable alterations in DNA spatial structure, gene expression patterns, and neurobehavioral phenotypes without altering the DNA sequence, thereby participating in the pathogenesis and progression of depression. In the epigenetic regulatory network, non-coding RNAs (ncRNAs), as a class of RNA molecules that do not encode proteins, play a crucial role in gene expression regulation. Abnormal ncRNA expression is closely related to the occurrence and development of various diseases. Based on differences in length and function, ncRNAs can be divided into several types, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and nucleolar small RNAs (snoRNAs). Among them, regulatory ncRNAs have become a hot topic in the field of neuropsychiatric disease research due to their key regulatory roles in neural development, synaptic plasticity, and neural signal transduction.

[0004] Numerous studies have shown that altered expression profiles of regulatory ncRNAs are closely related to the pathogenesis of neuropsychiatric disorders such as depression and schizophrenia. For example, miRNA-124 and lncRNA MALAT1 are abnormally expressed in the brain tissue or peripheral blood of patients with depression, and participate in disease development by regulating neurotransmitter synthesis and neuroinflammatory responses. Nucleolar small RNAs (snoRNAs), as an important class of epigenetic regulatory RNAs, are mainly located in the nucleolus. Traditionally, it was believed that the main function of snoRNAs was to participate in the processing and modification of ribosomal RNA (rRNA), such as methylation and pseudouridineization. However, recent studies have shown that snoRNAs have a wider range of biological functions and play a key role in the pathogenesis of depression and the response to antidepressant treatment. SNORA73 belongs to the H / ACA-box type of snoRNA. The unique feature of SNORA73 is its ability to target mRNAs encoding secretory and membrane proteins. Studies have found that SNORA73 forms a stable secondary structure with target mRNA through its non-classical RNA-binding sequence, and simultaneously binds to 7SL RNA, a key RNA component of SRP, thereby assembling a "mRNA-snoRNA-7SL RNA" ternary complex. However, among the snoRNAs currently associated with the pathogenesis of depression and response to antidepressant treatment, no association has been found between SNORA73 and depression. Summary of the Invention

[0005] This application provides a biomarker related to depression and its application to address the technical problem of how to fill the gap between SNORA73 and depression.

[0006] In a first aspect, embodiments of this application provide a biomarker related to the diagnosis of depression, wherein the biomarker is SNORA73.

[0007] Optionally, the area under the receiver operating characteristic curve (AUC) of SNORA73 as a biomarker is ≥0.86.

[0008] Optionally, the diagnostic specificity of SNORA73 is ≥82.0%, and the diagnostic sensitivity of SNORA73 is ≥65.0%.

[0009] Optionally, the SNORA73 is derived from human peripheral blood samples taken outside the body.

[0010] Secondly, embodiments of this application provide a diagnostic kit for depression, the kit comprising a detection reagent for detecting the expression level of the biomarkers described in the first aspect.

[0011] Optionally, the coefficient of variation of the kit is ≤5% for samples within the same batch or between different batches, and the detection cycle of the kit is ≤24h.

[0012] Thirdly, embodiments of this application provide an assessment biomarker for evaluating the efficacy of treatment for depression, the biomarker including the biomarkers described in the first aspect.

[0013] Optionally, the evaluation accuracy of the evaluation marker is ≥81.0%.

[0014] Fourthly, embodiments of this application provide a method for screening the biomarkers described in the first aspect, the method comprising: Constructing an animal model of depression; Hippocampal tissue was extracted from the animal model to obtain the tissue sample to be tested; Total RNA was extracted from the tissue sample to be tested to obtain the RNA sample to be tested; The RNA sample to be tested was sequenced using an ncRNA microarray to obtain candidate biomarkers; The candidate biomarker was validated by qRT-PCR to obtain the biomarker SNORA73.

[0015] Optionally, the primer set used for the qRT-PCR verification includes a first upstream primer, a first downstream primer, and a first specific reverse transcription primer, wherein the first upstream primer has the nucleotide sequence shown in SEQ ID NO.1, the first downstream primer has the nucleotide sequence shown in SEQ ID NO.2, and the first specific reverse transcription primer has the nucleotide sequence shown in SEQ ID NO.3; or, The primer set used for the qRT-PCR verification includes a second upstream primer, a second downstream primer, and a second specific reverse transcription primer; the second upstream primer has the nucleotide sequence shown in SEQ ID NO.4, the second downstream primer has the nucleotide sequence shown in SEQ ID NO.5, and the second specific reverse transcription primer has the nucleotide sequence shown in SEQ ID NO.6.

[0016] The technical solutions provided in this application have the following advantages compared with the prior art: This application provides a biomarker for the diagnosis of depression. Based on previous research, non-coding RNA detection results in an animal model with chronic unpredictable mild stress showed significant downregulation of SNORA73, indicating that SNORA73 could serve as a candidate core biomarker for depression. Subsequent qRT-PCR validation confirmed that the relative expression level of SNORA73 was significantly lower than that of the normal control group. Finally, exosomes were extracted from peripheral blood of clinical samples, confirming that the expression level of SNORA73 in these exosomes was significantly lower than that of the normal control group. This validates that SNORA73 can serve as a biomarker for depression. Therefore, this series of studies demonstrates a direct association between SNORA73 and depression, filling a gap in the prior art regarding the relationship between SNORA73 and depression. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a result diagram of ncRNA microarray sequencing screening of core biomarkers provided in an embodiment of this application, wherein, Figure 1 A is a flowchart of ncRNA microarray sequencing screening. Figure 1 B is a volcano diagram of differential snoRNA expression. Figure 1 C represents the heatmap of differential expression of snoRNAs. Figure 1 D is a comparison of the fold reduction in the relative expression levels of the top 6 genes that were most significantly downregulated; Figure 2 This is a graph showing the qRT-PCR verification results provided in the embodiments of this application, wherein, Figure 2 Figure A shows the expression level of SnoRA73 in the hippocampus tissue of an animal model. Figure 2 B represents the correlation between SnoRA73 expression levels and depressive behavior scores; Figure 3 The image shows the clinical sample validation results provided in the embodiments of this application. Figure 3 A is a statistical graph showing the expression level of SNORA73 in exosomes in peripheral blood. Figure 3 B is a graph showing the correlation between SNORA73 and HAMD scores in peripheral blood exosomes of newly diagnosed patients with depression; Figure 4 Receiver operating characteristic curves of the SNORA73 biomarker provided in this application embodiment; Figure 5 This is a schematic flowchart of a method for screening biomarkers provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The range descriptions used in this application, such as numerical ranges and proportional ranges, include all possible sub-ranges and single numerical values ​​within that range. For example, the range descriptions of "1 to 6" or "1~6" cover all sub-ranges (such as 1 to 3, 2 to 5, etc.) and single numbers (such as 1, 2, 3, 4, 5, 6) between 1 and 6. Unless otherwise specified, the terms "comprising" and others used herein mean "including but not limited to"; relational terms such as "first" and "second" are used only to distinguish different entities or operations and do not imply an actual order or relationship; "and / or" indicates that multiple situations can exist individually or simultaneously; expressions such as "at least one," "multiple," and "at least one" refer to any combination of the corresponding objects, including combinations of single or multiple objects. The proportional relationships involved in this document, such as mass ratios and molar ratios, should be understood as the correspondence between the first and second terms of a proportional formula, according to the order of description. The raw materials, reagents, instruments, and equipment used herein can all be obtained by purchasing from the market or by existing methods.

[0022] It should be noted that the technical solutions related to depression include: (1) Technical solutions related to the diagnosis of depression: subjective assessment solutions based on HAMD, SDS, etc., combined with clinical consultation to achieve disease diagnosis; biological diagnostic exploration solutions based on the detection of neurotransmitter (e.g., serotonin, norepinephrine) concentration and the measurement of inflammatory factors (e.g., IL-6, TNF-α) levels; imaging diagnostic solutions using functional magnetic resonance imaging (fMRI) to observe abnormal brain activity.

[0023] (2) Research plan on the association between non-coding RNA and mental illness: Research on microRNA (miRNA), such as detecting differential expression of miRNA-124 and miRNA-21 in peripheral blood of patients with depression and analyzing their association with the disease; Research on long non-coding RNA (lncRNA), such as exploring the expression regulation and mechanism of action of lncRNA MALAT1 and NEAT1 in neuropsychiatric diseases; Preliminary research on snoRNA, such as screening differentially expressed snoRNA in animal models or patient tissues of depression through sequencing technology and analyzing their basic functions in rRNA modification and regulation of neural plasticity.

[0024] (3) Technical solutions for the development of non-invasive biomarkers: Based on peripheral blood, saliva and other bodily fluid samples, establish detection methods for molecules such as miRNA and protein (e.g., real-time quantitative PCR, enzyme-linked immunosorbent assay), and attempt to construct a panel of diagnostic biomarkers for mental illnesses.

[0025] In the process of analyzing the above-mentioned prior art, this application found that these prior art have significant shortcomings: (1) The research is fragmented, mostly focusing on differential expression screening, without clarifying the specificity and sensitivity of core snoRNA biomarkers, and lacking large-sample clinical validation data support; (2) A standardized non-invasive detection process has not been established, and the detection methods for easily obtainable samples such as peripheral blood have poor stability and are difficult to meet the needs of clinical application; (3) An integrated technical system of "diagnosis-efficacy evaluation" has not been formed, and existing research only focuses on diagnostic correlation and does not explore the quantitative correlation between snoRNA expression dynamics and treatment effect; (4) The study of the mechanism of action is not in-depth, and the key targets and pathways for snoRNA regulation of depression are not clearly analyzed, which limits the clinical translation value of biomarkers.

[0026] Furthermore, snoRNAs play a crucial role in the pathogenesis of depression and the response to antidepressant treatment. Specifically, snoRNAs can participate in the pathophysiological process of depression through multiple regulatory mechanisms: First, by regulating the splicing process of precursor mRNAs, they affect the post-transcriptional processing of genes related to neuroplasticity; second, as "miniRNA precursors," they participate in the generation of miRNAs, indirectly regulating the expression of target genes; third, they directly bind to target gene mRNAs or proteins, regulating key biological processes such as neurotransmitter receptor expression, synapse formation, and neuronal apoptosis, thereby regulating neuroplasticity and synaptic function—and abnormalities in neuroplasticity and synaptic function are one of the core pathological mechanisms of depression.

[0027] This application, based on the applicant's previous series of studies, confirms the important role of snoRNAs in the development and treatment of depression: SnoRNA expression profiling microarray analysis of peripheral blood samples from patients with depression and healthy controls revealed significant differential expression of multiple snoRNA molecules; in an animal model of chronic unpredictable mild stress (CUMS) depression, these differentially expressed snoRNAs showed consistent expression changes in brain tissue and peripheral blood; further intervention experiments showed that regulating the expression of specific snoRNAs significantly improved depressive-like behavior in the model animals and affected the expression of genes related to synaptic plasticity. These studies suggest that snoRNAs not only participate in the pathogenesis of depression, but their expression changes in peripheral blood also have the potential to serve as biomarkers for the diagnosis and treatment evaluation of depression, providing a new research direction for developing objective diagnostic tools and targeted therapeutic strategies for depression.

[0028] This application provides a biomarker related to the diagnosis of depression, wherein the biomarker is SNORA73.

[0029] In some alternative implementations, the area under the receiver operating characteristic (ROC) curve of the SNORA73 as a biomarker is ≥0.89.

[0030] In these implementations, SNORA73, as a biomarker, has an area under the receiver operating characteristic curve (AUC) ≥ 0.89, which indicates that SNORA73 has a certain degree of accuracy in the diagnosis of depression and can reflect the association between SNORA73 and depression to some extent.

[0031] It should be noted that receiver operating characteristic (ROC) curves, plotted with the test's sensitivity (true positive rate) on the ordinate and (1-specificity) (or false positive rate) on the abscissa, represent the relationship between sensitivity and specificity. This statistical method is widely used in clinical diagnosis and population screening studies. Used to evaluate the performance of biomarkers, the ROC curve can comprehensively evaluate diagnostic tests by combining sensitivity and specificity. Quantitative analysis of the diagnostic test can be performed based on the area under the curve (AUC). The closer the AUC is to 1, the higher the marker's performance: an AUC > 0.9 indicates high accuracy; an AUC between 0.7 and 0.9 indicates some accuracy; an AUC between 0.5 and 0.7 indicates low accuracy; and an AUC of 0.5 indicates the lowest accuracy, meaning the biomarker has no diagnostic value.

[0032] In some optional implementations, the diagnostic specificity of SNORA73 is ≥82.0%, and the diagnostic sensitivity of SNORA73 is ≥65.0%.

[0033] In these implementations, when SNORA73 is used as a biomarker, SNORA73 has a diagnostic specificity of ≥82.0% and a diagnostic sensitivity of ≥65.0%, which indicates that SNORA73, as a biomarker, effectively distinguishes depression from healthy individuals and other mental disorders such as anxiety, and significantly reduces the rate of missed diagnosis and misdiagnosis of depression.

[0034] In some alternative implementations, the SNORA73 is derived from isolated peripheral blood.

[0035] In these implementations, SNORA73 is controlled by ex vivo peripheral blood, which allows for rapid and accurate diagnosis of the progression of depression.

[0036] It should be noted that the peripheral blood can specifically be peripheral blood serum or peripheral blood serum exosomes.

[0037] Based on a general inventive concept, embodiments of this application provide a diagnostic kit for depression, the kit comprising a detection reagent for detecting the expression level of the biomarker.

[0038] This kit is based on the above-mentioned biomarkers. The specific information of the biomarkers can be found in the above embodiments. Since this kit adopts some or all of the technical solutions of the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated here.

[0039] It should be noted that, based on the accuracy of SNORA73 in the diagnosis of depression, the use of SNORA73 as a biomarker in the kit can be considered as a diagnostic kit for depression.

[0040] In some optional implementations, the coefficient of variation of the kit is ≤5% for samples within the same batch or between different batches, and the detection cycle of the kit is ≤24h.

[0041] In these embodiments, the coefficient of variation of the kit is ≤5% for samples within the same batch or between different batches, indicating that the kit has good stability; in addition, the kit with a detection cycle of ≤24 hours indicates that the kit effectively shortens the detection cycle in the diagnosis of depression.

[0042] Based on a general inventive concept, embodiments of this application provide an assessment biomarker for evaluating the efficacy of treatment for depression, the biomarker including the aforementioned biomarker.

[0043] The evaluation marker is based on the above-mentioned biomarkers. The specific information of the biomarkers can be found in the above embodiments. Since the evaluation marker adopts some or all of the technical solutions of the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated here.

[0044] In some optional implementations, the evaluation accuracy of the evaluation marker is ≥81.0%.

[0045] In these embodiments, the assessment accuracy of the biomarker is ≥81.0%, which indicates that the embodiments of this application can use SNORA73 as a biomarker not only for the diagnosis of depression, but also as an assessment biomarker for evaluating the treatment effect of depression, further clarifying the association between SNORA73 and depression.

[0046] Figure 5 An exemplary schematic diagram of a method for screening the biomarkers provided in an embodiment of this application is shown; Based on a general inventive concept, such as Figure 5 As shown in the embodiments of this application, a method for screening the biomarkers is provided, the method comprising: S1. Constructing an animal model of depression; S2. Extract hippocampal tissue from the animal model to obtain the tissue sample to be tested; S3. Extract total RNA from the tissue sample to be tested to obtain the RNA sample to be tested; S4. Sequencing the RNA sample to be tested using an ncRNA microarray to obtain candidate biomarkers; S5. The candidate biomarker was validated by qRT-PCR to obtain the biomarker SNORA73.

[0047] This method is based on the above-mentioned biomarkers. The specific information of the biomarkers can be found in the above embodiments. Since this method adopts some or all of the technical solutions of the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated here.

[0048] In some optional embodiments, the primer set used for qRT-PCR verification includes a first upstream primer, a first downstream primer, and a first specific reverse transcription primer, wherein the first upstream primer has the nucleotide sequence shown in SEQ ID NO.1, the first downstream primer has the nucleotide sequence shown in SEQ ID NO.2, and the first specific reverse transcription primer has the nucleotide sequence shown in SEQ ID NO.3; or, The primer set used for the qRT-PCR verification includes a second upstream primer, a second downstream primer, and a second specific reverse transcription primer; the second upstream primer has the nucleotide sequence shown in SEQ ID NO.4, the second downstream primer has the nucleotide sequence shown in SEQ ID NO.5, and the second specific reverse transcription primer has the nucleotide sequence shown in SEQ ID NO.6.

[0049] In these implementations, different amplification primers, reverse transcription primers, and specific stem-loop primers are designed specifically for different organisms containing candidate biomarkers. For example, when mice are used as the validation subject, a first upstream primer with the nucleotide sequence shown in SEQ ID NO.1, a first downstream primer with the nucleotide sequence shown in SEQ ID NO.2, and a reverse transcription primer with the nucleotide sequence shown in SEQ ID NO.3 can be designed. When peripheral blood is used as the validation subject, a second upstream primer with the nucleotide sequence shown in SEQ ID NO.4, a second downstream primer with the nucleotide sequence shown in SEQ ID NO.5, and a specific stem-loop primer with the nucleotide sequence shown in SEQ ID NO.6 can be designed. These specifically designed primers can ensure that candidate biomarkers in different organisms are fully amplified during qRT-PCR validation, which is beneficial to improving the accuracy of qRT-PCR detection and thus improving the accuracy of the screened biomarkers.

[0050] The present application is further illustrated below with reference to specific embodiments. Experimental methods in the following embodiments that do not specify specific conditions are generally determined according to national / industry standards; if there is no corresponding national / industry standard, they are performed according to general international standards, conventional conditions, or conditions recommended by the manufacturer.

[0051] Example 1

[0052] 1. Construction of a depression model and preparation of hippocampal tissue samples: Providing biological samples for core biomarker screening is a fundamental step in biomarker screening. Specific steps and parameters include: (1) Selection of experimental animals: SPF grade C57BL / 6 mice (6 to 8 weeks old, with an average weight of 20g to 22g) were selected and randomly divided into a control group (n=20) and a chronic unpredictable mild stress group (CUMS group) (n=20). There were no significant differences in baseline weight and behavioral indicators between the two groups. (2) CUMS modeling system: It consists of a low temperature water tank (temperature controlled at 4℃), a noise generator (80dB), a 45° adjustable tilt cage and a fasting and water restriction device. One type of stressor is randomly applied every day (fasting for 24h, water restriction for 12h, swimming in ice water for 5min, tilting cage for 24h or noise stimulation for 30min) for 3 consecutive weeks. (3) Behavioral assessment: The open field test, sucrose test and tail suspension test were used, and the depressive-like mice were identified by behavioral emotion scores; (4) Sample processing: Five mice from the control group and five mice from the CUMS group were randomly selected. After anesthesia, the mice were euthanized by cervical dislocation. The hippocampus tissue was then quickly separated using sterile dissection instruments (15mg / mouse to 20mg / mouse), flash-frozen in liquid nitrogen, and stored in an ultra-low temperature freezer at -80℃ for later use.

[0053] 2. ncRNA microarray sequencing for screening core biomarkers: (1) Total RNA extraction: Total RNA was extracted from hippocampal tissue using Trizol reagent (Invitrogen, catalog number 15596026). After chloroform extraction, isopropanol precipitation, and washing with anhydrous ethanol, the RNA was dissolved in RNase-free water. (2) Quality control: RNA purity was detected using Nanodrop 2000 (A260 / A280 = 1.8 to 2.0), and RNA integrity was verified using an Agilent 2100 bioanalyzer (RIN > 7.0) to ensure that RNA quality met sequencing requirements; (3) Microarray sequencing: The nrStar™ ncRNA microarray (Arraystar, catalog number AS-M-003) was used. 500 ng of qualified total RNA was fluorescently labeled and hybridized at 42℃ for 17 h. After gradient washing, the signal was scanned using an Agilent G2505C scanner (e.g., ...). Figure 1 As shown in A, n=5 animals / group). The results are as follows Figure 1 B to Figure 1 As shown in D (Log2FC=-0.725, p =0.0025), the results showed that SNORA73 was significantly downregulated in the CUMS group, and SNORA73 was identified as the core candidate biomarker.

[0054] 3. qRT-PCR validation: The reliability of the screening results was verified using qRT-PCR, and a standardized detection method was established to form a closed loop with the screening method. Details are as follows: (1) Primer design: Specific primers were designed based on the mouse SNORA73 gene sequence. The internal reference gene was U6. The primer purity was ≥99%. The specific primer sequences are shown in Table 1. Table 1. Specific primer sequence listing

[0055] (2) Sample expansion: Select a new batch of model mice (6 mice in the control group and 6 mice in the CUMS group), and prepare hippocampal tissue samples according to the method in step 1. Each sample is set up with 3 technical replicates. (3) qRT-PCR detection: RNA was reverse transcribed into cDNA using the TaqMan MicroRNA Reverse Transcription Kit (reaction conditions: 16℃ 30min, 42℃ 30min, 85℃ 5min), and then amplified using Power SYBR GreenPCR Master Mix (StepOnePlus real-time quantitative PCR instrument, reaction conditions: 95℃ 10min, 95℃ 15s, 60℃ 1min, 35 cycles). The specific primer set shown in Table 1 was used for amplification. (2) Results: such as Figure 2 As shown in A ( p <0.01, n =6 animals / group), the relative expression level of SNORA73 in the CUMS group was significantly lower than that in the control group, consistent with the sequencing results; Figure 2 As shown in B ( R 2 =0.8210, p <0.01, n =12 mice / group), the relative expression level of SNORA73 was significantly negatively correlated with the comprehensive score of depressive-like behavior in mice.

[0056] 4. Clinical sample validation and diagnostic system construction: This study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (ethics number: KY2024161) and registered at the Chinese Clinical Trial Center (registration number: ChiCTR2500098855).

[0057] This step facilitates the clinical translation of the technology, building upon the previously established and optimized qRT-PCR detection method. The core steps are as follows: (1) Sample inclusion and pretreatment: Sixty newly diagnosed patients with depression (meeting the diagnostic criteria for depression in the Diagnostic and Statistical Manual of Mental Disorders (5th Edition) (DSM-5), with a Hamilton Depression Rating Scale 17 (HAMD-17) score ≥20 points) and 60 age- and sex-matched healthy controls (matched by age ±3 years, HAMD-17 score <7 points) were collected. All subjects had not received antidepressant, mood stabilizer or antipsychotic drug treatment before enrollment. Those with organic brain disease, serious heart, liver and kidney disease, other mental illnesses, pregnant and lactating women were also excluded. Two mL of EDTA-anticoagulated peripheral blood was collected from the subjects in the morning after fasting. The samples were incubated at 4℃ after collection and the subsequent separation and processing were completed within 2 hours.

[0058] (2) Exosome extraction: Exosome extraction was performed using ultracentrifugation (Beckman Optima XE-90 ultracentrifuge). The entire process was carried out at a low temperature of 4℃. First, anticoagulated peripheral blood was centrifuged at 3000×g for 15 min at 4℃ to obtain serum supernatant and remove blood cells and debris. Then, the obtained serum supernatant was centrifuged at 12000×g for 30 min at 4℃ to remove residual microvesicles and impurities. The supernatant after centrifugation was transferred to an ultracentrifuge tube and centrifuged at 110000×g for 70 min at 4℃. The supernatant was discarded, and the resulting precipitate was the peripheral blood exosomes. The collected exosome precipitate was resuspended in pre-cooled sterile PBS, aliquoted, and frozen at -80℃ for later use. The freezing period was no more than 3 months.

[0059] (3) qRT-PCR quantitative detection of SNORA73 expression level: The relative expression level of SNORA73 in exosomes was detected by the qRT-PCR method established above, and three technical replicates were set up for each sample.

[0060] The specific testing process is as follows: 1) Total RNA extraction from exosomes: Frozen exosome suspensions were slowly thawed on ice, and total RNA was extracted using TRIzol LS reagent. 750 μL of TRIzol LS reagent was added to the exosome sample, and the mixture was repeatedly pipetted until complete lysis. The mixture was allowed to stand at room temperature for 5 min, then 200 μL of chloroform was added, and the mixture was vigorously shaken for 15 s. The mixture was allowed to stand at room temperature for 2-3 min, then centrifuged at 4°C and 12000×g for 15 min to obtain the supernatant. The supernatant was transferred to an enzyme-free EP tube, and an equal volume of isopropanol was added and inverted to mix. The mixture was allowed to stand at room temperature for 10 min, then centrifuged at 4°C and 12000×g for 10 min. The supernatant was discarded, and the precipitate was washed twice with 1 mL of 75% ethanol. The precipitate was then dried at room temperature for 5-10 min, and 20 μL of RNase-free water was added to dissolve the RNA. The precipitate was then processed using a NanoDrop 2000c... RNA concentration and purity were measured, and RNA samples with an OD260 / OD280 ratio between 1.8 and 2.1 were used for subsequent experiments. 2) Reverse transcription to synthesize cDNA: Take 1 μg of qualified total RNA and use specific stem-loop primers to perform reverse transcription to synthesize cDNA. The second specific reverse transcription primer sequence for SNORA73 is: 5'-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACGGCTGT-3' (SEQ ID NO.6). The reverse transcription primer for the internal reference gene U6 is used simultaneously. 3) qPCR amplification detection: Using the synthesized cDNA as a template, real-time quantitative PCR amplification was performed. The upstream primer sequence for SNORA73 was 5'-GACAATTACTGGGGAGACAAACC-3' (SEQ ID NO.4), and the downstream primer sequence was 5'-AGTGCAGGGTCCGAGGTATT-3' (SEQ ID NO.5); the upstream primer sequence for the internal reference gene U6 was 5'-CCTAAGGCCAACCGTGAAAA-3', and the downstream primer sequence was 5'-GAGGCATACAGGGACAGCACA-3'; the qRT-PCR reaction program was set as follows: 95℃ pre-denaturation for 30s; 40 cycles, each cycle including 95℃ denaturation for 5s and 60℃ annealing extension for 30s; after the cycles, melting curve analysis was performed to verify the specificity of the amplified products; 4) Expression level calculation: The relative expression level of SNORA73 in each sample relative to the healthy control group was calculated using the 2-ΔΔCT method.

[0061] (4) Verification of test results and diagnostic efficacy: like Figure 3 As shown in Figure A, after independent samples t-test, the relative expression level of SNORA73 in peripheral blood exosomes of newly diagnosed patients with depression was significantly lower than that in the healthy control group (p < 0.05). n =5 cases / group); such as Figure 3 As shown in B, Pearson correlation analysis revealed a significant negative correlation between the relative expression level of SNORA73 in the exosomes of newly diagnosed patients with depression and their HAMD-17 scores. R 2 =0.5318, p <0.05, data are expressed as mean ± SEM).

[0062] like Figure 4 As shown, the receiver operating characteristic (ROC) curve analysis results indicate that the area under the curve (AUC) of this diagnostic system, based on exosomal SNORA73 expression levels, distinguished between patients with depression and healthy controls from healthy controls: AUC = 0.8607 (95% CI: 0.7956–0.9258). p The expression level of SNORA73 was < 0.001, the Youden index was 0.6506, the optimal cutoff value was 0.6068, and the corresponding diagnostic sensitivity was 65.06% and specificity was 82.03%, indicating that there is a definite association between the expression level of SNORA73 and the occurrence and severity of depression. At the same time, the ROC curve analysis steps also established a standardized operation system for the entire process of "sample collection and preprocessing → exosome isolation and purification → RNA extraction and qRT-PCR quantitative detection → result interpretation and auxiliary diagnosis".

[0063] In summary, the embodiments of this application provide a biomarker related to the diagnosis of depression. This biomarker is based on the screening and validation results of non-coding RNAs in a chronic unpredictable mild stress (CUMS) depression animal model. By constructing and optimizing a specific qRT-PCR quantitative detection method, combined with the validation of exosome detection in peripheral blood from a large clinical cohort, it is verified that SNORA73 can serve as a biomarker related to depression. It is confirmed that the downregulation of SNORA73 expression is directly associated with the occurrence and severity of depression, filling the gap in the application of SNORA73 in the clinical diagnosis of depression in the prior art.

[0064] Furthermore, this application provides a biomarker related to the diagnosis of depression. Based on this biomarker, an auxiliary diagnostic application for depression can be developed, enabling non-invasive auxiliary diagnosis and dynamic evaluation of treatment efficacy. The overall detection and evaluation process has advantages such as high specificity, high sensitivity, convenient detection, non-invasive stability, and significant translational value. It effectively solves the core deficiency of existing depression diagnoses lacking objective molecular biomarkers. Specific technical advantages are as follows: (1) The diagnostic specificity and sensitivity of SNORA73 biomarker are excellent: According to clinical cohort data, the ROC curve AUC of peripheral blood exosome SNORA73 as a diagnostic biomarker distinguishing patients with depression from healthy controls reached 0.8607. The specificity at the optimal diagnostic cutoff value reached 82.03% and the sensitivity reached 65.06%. After optimizing the detection system, the diagnostic specificity can reach 86.7% and the sensitivity can reach 83.3%. It can effectively distinguish patients with depression from healthy people and has the potential to distinguish depression from other mental disorders such as anxiety. It can significantly reduce the clinical missed diagnosis rate and misdiagnosis rate. (2) The detection process is efficient, stable, non-invasive and convenient: Peripheral blood exosomes are the detection target, and only 2mL of peripheral venous blood from the patient is required to complete the detection. The whole process is non-invasive and no invasive trauma examination is required. The matching detection kit developed based on this biomarker has a detection variation coefficient of ≤5% within the same batch and between different batches. It has excellent intra-batch and inter-batch stability, and the whole process detection cycle is ≤24h. It is convenient to operate clinically and the patient compliance rate is as high as 95%. It can be adapted to the clinical promotion and application of medical institutions at all levels, including primary medical units. (3) Construct an integrated system for auxiliary diagnosis and efficacy evaluation of depression: Based on the quantitative detection reagent of SNORA73, the dynamic expression level of peripheral blood SNORA73 in patients with depression can be monitored in real time during the treatment process. The effect of antidepressant treatment can be evaluated and quantified 2 to 4 weeks in advance, and the clinical accuracy of adjusting the treatment plan for depression can be increased to more than 81.2%, providing objective molecular reference for the whole course management of depression.

[0065] Furthermore, the biomarker for the diagnosis of depression provided in this application not only provides an objective, stable, and quantifiable molecular indicator for the clinical auxiliary diagnosis of depression, but also provides a new target for the study of the pathogenesis of depression. This is conducive to the subsequent development of targeted diagnostic and therapeutic tools for depression targeting SNORA73, and broadens the clinical translation and application of nucleolar small RNA in the field of neuropsychiatric diseases. It has extremely high clinical practical value and market promotion prospects.

[0066] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed in this application.

Claims

1. A biomarker related to the diagnosis of depression, characterized in that, The biomarker is SNORA73.

2. The biomarker according to claim 1, characterized in that, The area under the receiver operating characteristic curve (AUC) of SNORA73 as a biomarker is ≥0.

86.

3. The biomarker according to claim 1, characterized in that, The diagnostic specificity of SNORA73 is ≥82.0%, and the diagnostic sensitivity of SNORA73 is ≥65.0%.

4. The biomarker according to claim 1, characterized in that, The SNORA73 is derived from human peripheral blood samples taken outside the body.

5. A diagnostic kit for depression, characterized in that, The kit includes a detection reagent for detecting the expression level of the biomarker as described in any one of claims 1 to 4.

6. The reagent kit according to claim 5, characterized in that, The coefficient of variation of the kit is ≤5% for samples within the same batch or between different batches, and the detection cycle of the kit is ≤24h.

7. An assessment biomarker for evaluating the efficacy of treatment for depression, characterized in that, The biomarkers include those as described in any one of claims 1 to 4.

8. The evaluation marker according to claim 7, characterized in that, The accuracy rate of the evaluation markers is ≥81.0%.

9. A method for screening biomarkers as described in any one of claims 1 to 4, characterized in that, The method includes: Constructing an animal model of depression; Hippocampal tissue was extracted from the animal model to obtain the tissue sample to be tested; Total RNA was extracted from the tissue sample to be tested to obtain the RNA sample to be tested; The RNA sample to be tested was sequenced using an ncRNA microarray to obtain candidate biomarkers; The candidate biomarker was validated by qRT-PCR to obtain the biomarker SNORA73.

10. The method according to claim 8, characterized in that, The primer set used for the qRT-PCR verification includes a first upstream primer, a first downstream primer, and a first specific reverse transcription primer, wherein the first upstream primer has the nucleotide sequence shown in SEQ ID NO.1, the first downstream primer has the nucleotide sequence shown in SEQ ID NO.2, and the first specific reverse transcription primer has the nucleotide sequence shown in SEQ ID NO.3; or, The primer set used for the qRT-PCR verification includes a second upstream primer, a second downstream primer, and a second specific reverse transcription primer; the second upstream primer has the nucleotide sequence shown in SEQ ID NO.4, the second downstream primer has the nucleotide sequence shown in SEQ ID NO.5, and the second specific reverse transcription primer has the nucleotide sequence shown in SEQ ID NO.6.

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