A biomarker for cerebral infarction diagnosis and detection and related applications thereof

Abstract

The application belongs to the field of biological medicine, and relates to a biomarker for cerebral infarction diagnosis and detection and related application thereof, wherein the biomarker comprises any one or several of ZYX gene, CST3 gene, RAB11B gene, SPHK1 gene, TGFB1 gene and NME4 gene. Compared with the prior art, the five biomarkers provided by the application have better specificity and sensitivity when used alone, and are especially suitable for combined use and have higher detection stability.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine and relates to a biomarker for the diagnosis and detection of cerebral infarction and its related applications. Background Technology

[0002] Cerebral infarction, also known as ischemic stroke, is called stroke or apoplexy in traditional Chinese medicine. This disease is caused by various factors leading to impaired blood supply to a localized area of ​​brain tissue, resulting in ischemic and hypoxic lesions and necrosis of the brain tissue, thus producing corresponding neurological deficits. Clinical symptoms of cerebral infarction manifest in areas of consciousness, cognition, motor function, language, and sensation. Patients with milder cases may experience numbness or weakness in the limbs or even dizziness within a short period; while those with severe cases are prone to hemiplegia, brain herniation, and even death. Cerebral infarction is characterized by high incidence and mortality rates, and a low prevention rate. It is one of the major diseases seriously endangering human health and the leading cause of disability, with a mortality rate second only to myocardial infarction and cancer, ranking third. Cerebral infarction is classified into several main types based on its pathogenesis, including cerebral thrombosis, cerebral embolism, and lacunar infarction. Cerebral thrombosis is the most common type of cerebral infarction, accounting for approximately [percentage missing] of all cerebral infarctions.

[0003] Currently, the diagnosis of cerebral infarction is based on clinical history, physical examination, neuroimaging, and laboratory tests. There are many imaging methods, among which CT (computed tomography), MRI (magnetic resonance imaging), and DSA (digital subtraction angiography) are the most commonly used. However, each method has its limitations. For example, CT has low sensitivity for ischemic stroke, MRI is not suitable for pregnant women or those with pacemakers, and DSA cannot be performed on patients allergic to contrast agents or anesthetics. Therefore, there is an urgent need for new methods for early and accurate diagnosis of cerebral infarction.

[0004] In response, some biomarkers for cerebral infarction have been studied, and the existing technologies are as follows: CN113063718A discloses the use of plasma endothelial cell membrane microparticles (EMVs) as biomarkers for cerebral infarction.

[0005] CN113447601B discloses the use of galactosylceramide as a biomarker for diagnosing cerebral infarction and white matter lesions.

[0006] CN114137226A discloses diagnostic biomarkers for cerebral infarction, composed of the following 10 substances: 4-dimethylalyltryptophan, taurochenodeoxycholate-3-sulfate, trihexosylceramide (d18:1 / 18:0), lysophosphatidylcholine (18:0), and L... ysoPC(18:0)】, Arginyl-Alanine, Asparaginyl-Tryptophan, Methionyl-Arginine, Sphingomyelin d37:5 (SMd37:5), Phosphatidylglycerol (12:0 / 21:0) [PG(12:0 / 21:0)] and Glucose Ceramide (d18:0 / 18:0) [GlcCer(d18:0 / 18:0)].

[0007] While these biomarkers can detect cerebral infarction to some extent, they are not comprehensive, and further research is needed to discover more suitable biomarkers with more stable detection results. Summary of the Invention

[0008] The purpose of this invention is to provide cerebral infarction biomarkers and related applications, which provide more stable detection of cerebral infarction results and enrich the cerebral infarction biomarker library.

[0009] In a first aspect, the present invention provides cerebral infarction biomarkers, including any one or more of the ZYX gene, CST3 gene, RAB11B gene, SPHK1 gene, TGFB1 gene, and NME4 gene.

[0010] The second aspect of the present invention provides the application of the biomarkers described in the first aspect of the present invention in the preparation of diagnostic or detection products for diagnosing cerebral infarction.

[0011] Preferably, the biomarkers ZYX gene, CST3 gene, RAB11B gene, SPHK1 gene, TGFB1 gene, and NME4 gene are used in combination, which has the advantages of high sensitivity, high specificity, good stability, and rapid detection.

[0012] Thirdly, the present invention provides a cerebral infarction diagnosis or detection kit, including reagents for detecting the expression level of cerebral infarction biomarkers as described in the first aspect of the present invention.

[0013] Preferably, the reagents for detecting the expression level of the cerebral infarction biomarker include RNA extraction reagents, reverse transcription reagents, and PCR amplification reagents.

[0014] Preferably, the PCR amplification primers for detecting the expression level of the cerebral infarction biomarker are:

[0015] The reagent kit of this invention has greater application value for diagnosing cerebral infarction compared to traditional imaging-based diagnostic methods. It helps to detect and prevent disease early, reduce the medical costs of disease progression, lower the probability of acute and severe cerebral infarction, and improve the quality of life of patients.

[0016] Compared with existing technologies, the seven biomarkers provided by this invention have better specificity and sensitivity when used alone, and are especially suitable for combined use, with higher detection stability. Attached Figure Description

[0017] Appendix Figure 1 ROC curves for the relevant genes; Appendix Figure 2 The results of q-PCR gene detection of biomarkers ZYX, CST3, RAB11B, and SPHK1 in different samples; Appendix Figure 3 The results of q-pcr gene detection for biomarkers SHARPIN, NME4, and TGFB1 in different samples. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. Example

[0019] Screening of cerebral infarction biomarkers Information was collected from 25 stroke patients and 25 healthy individuals, as shown in Table 1 below: Table 1 Sample Information

[0020] 1. Platelet separation and RNA extraction from samples Blood samples were collected, and platelets were isolated from the blood samples within 24 hours of collection to minimize the loss of platelet RNA quality and quantity. For platelet isolation, platelet-rich plasma (PRP) was centrifuged at 1000 rpm. The extracted PRP was transferred to 1.5 ml Eppendorf tubes, and platelets were precipitated by centrifugation at 3000 rpm for 20 minutes. The supernatant was then removed, and the platelets were observed as a white precipitate. The platelets were washed with phosphate-buffered saline (PBS) solution and removed after centrifugation at 3000 rpm for 15 minutes, followed by a brief centrifugation to remove residual PBS solution. The precipitated platelets were treated with TRIzol reagent (Invitrogen) to purify total RNA.

[0021] 2. Library construction and mRNA sequencing Samples that passed quality checks (OD260 / OD280 values ​​between 1.8 and 2.0) were enriched with RNA using magnetic beads with Oligo (dT) inclusions. Subsequently, fragmentation buffer was added to randomly fragment the mRNA. Using the mRNA as a template, the first strand of cDNA was synthesized using six-base random primers. Then, buffer, dNTPs, and DNA polymerase I were added to synthesize the second strand of cDNA. The double-stranded cDNA was then purified using AMPure XP beads. The purified double-stranded cDNA underwent end repair, A-tailing, and ligation with sequencing adapters. Fragment size selection was then performed using AMPure XP beads, followed by PCR enrichment to obtain the final cDNA library. After library testing, different libraries were pooled according to effective concentration and target data volume and then sequenced using an Illumina Novaseq 6000.

[0022] 3. Differential Gene Analysis DEseq2 was used to compare the expression differences of mRNA between healthy individuals and stroke patients, identifying 4224 differentially expressed genes. Compared to the healthy group, 3692 genes were upregulated and 532 genes were downregulated in the stroke patient group (|Log2FC|>2 and q-value ≤ 0.05). To identify valuable genes, we further selected the top 34 genes by BaseMean value (a low BaseMean value indicates low gene abundance; for example, a gene with a mean expression level of 16 in group A and a mean expression level of 2 in group B, although an 8-fold difference, has low confidence due to its low abundance and may be considered as having no difference) as potential biomarkers, including: MT-CO1, MT-ND4L, NRGN, MMD, ZYX, RNF11, CST3, TRIM58, FKBP8, C12orf75, 5-sep, CD151, CDKN2D, GAS2L1, MTND2P28, SCN1B, UNC13D, RAB32, FAM110A, RASGRP2, ARHGAP45, CRAT, TAGLN2P1, LRP10, NENF, RAB11B, MTCO1P12, SPHK1, PPDPF, ACTBP2, TGFB1, NME4, KIFC3, SHARPIN. See Table 2 for detailed information.

[0023] Table 2 Information on 34 potential biomarkers

[0024] 4. KEGG pathway enrichment analysis KEGG is a database that integrates genomic, chemical, and systemic functional information. In the KEGG PATHWAY database, biological metabolic pathways are classified into six categories: Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases, Metabolism, and Organismal Systems. Pre-treated differentially expressed genes were subjected to KEGG pathway enrichment analysis; the results are attached. Figure 1 To understand the related functions and pathways of differentially expressed genes, and to screen out the corresponding core genes, see Table 3 for details.

[0025] Table 3. Core genes selected Example

[0026] Marker identification Receiver operating characteristic (ROC) reflects the balance between sensitivity and specificity. The area under the ROC curve (AUC) is an important indicator of test accuracy; the larger the AUC, the greater the diagnostic value of the test. When AUC > 0.5, the closer the AUC is to 1, the better the diagnostic efficacy of the diagnostic biomarker. The Youden index, also known as the accuracy index, is a method for evaluating the validity of screening tests. It can be applied when the harm of false negatives (missed diagnoses) and false positives (false diagnoses) is considered equally significant. The Youden index is the sum of sensitivity and specificity minus 1. It represents the overall ability of the screening method to identify true patients and non-patients. A larger index indicates a better screening test effect and greater validity. ROC curves for relevant genes are attached. Figure 1 As shown.

[0027] The core genes of each pathway were intersected with the 34 previously screened differentially expressed genes, yielding the following results: ZYX, CST3, CDKN2D, CRAT, RAB11B, SPHK1, TGFB1, NME4, and SHARPIN. Based on the criteria of Youden index ≥ 0.8, AUC ≥ 0.5, and being core genes of the pathway, these seven genes—ZYX, CST3, RAB11B, SPHK1, TGFB1, NME4, and SHARPIN—were selected as biomarkers for the diagnosis of cerebral infarction.

[0028] Table 4. AUC values, sensitivity, specificity, and Youden index for the seven biomarkers. Example

[0029] Genetic verification The target gene sequences were obtained by sequencing, and qPCR primers for the seven selected biomarker genes were designed using Primer Premier 6 software. The primer sequences are shown in Table 5.

[0030] Table 5. qPCR primers for 7 biomarker genes

[0031] Samples were collected from 16 stroke patients and 25 healthy individuals. q-PCR gene copy number detection was performed using designed primers. The experimental procedure is as follows, and the results are attached. Figure 3 .

[0032] After platelet separation and RNA extraction and quality control (as described above), the samples underwent reverse transcription. The transcription system is as follows (all reverse transcription reagents were purchased from Thermo Fisher Scientific): 50uM OligodT(20) primer: 1ul; 10mM dNTP mix: 1ul; RNA: 2ul; Rnasy-free H2O: 9ul.

[0033] The reaction procedure is: 65℃ for 5 min, then icing for 1 min; 5X SSIV buffer: 4ul; 100 mM DTT: 1 μL; RRI: 1ul; SuperScript IV: 1ul; The reaction procedure was: 50℃ for 10 min, 80℃ for 10 min.

[0034] The transcribed product (cDNA) can then be used for q-PCR. The reaction system is as follows: 2x SYBR GREEN Mix: 5ul; Primer-F (10µM): 0.2µl; Primer-R (10uM): 0.2ul; RNeasy-free H2O: 3.6ul; cDNA: 1ul; The reaction procedure is as follows: 50℃ for 2 minutes

[0035] 60℃ l min 95℃ 15s 60℃ 15s 95℃ 15s Final experimental results: The relative expression levels of ZYX, CST3, RAB11B, SPHK1, TGFB1, and NME4 showed statistically significant differences between healthy individuals and stroke patients (p<0.05). The relative expression levels of the SHARPIN biomarker showed no significant difference between healthy individuals and stroke patients (P=0.798).

[0036] Table 6. Relative expression levels and P-values ​​of seven biomarker genes in different samples.

[0037] Cutoff value The cutoff value serves as the judgment criterion. For binary data, the cutoff value for continuous independent variables is determined through ROC analysis. The point with the highest Youden index (sensitivity + specificity - 1) is selected as the optimal cutoff value, which serves as the boundary between healthy individuals and stroke patients, defining the range of normal and pathological values ​​in gene expression levels to distinguish between normal and abnormal. The calculated maximum Youden indices for ZYX, CST3, RAB11B, SPHK1, NME4, and TGFB1 are 0.8, 0.8, 0.7, 0.8, 0.8, and 0.7, respectively. The corresponding cutoff values ​​for these maximum Youden indices are 0.527, 0.470, 1.002, 0.856, 0.845, and 1.590, respectively. When the relative gene expression level is lower than the corresponding cutoff value, the incidence of stroke in patients is assessed based on the sensitivity and specificity corresponding to the maximum Youden index.

[0038] Table 7. Specificity, sensitivity, Youden index, and cutoff value of six biomarker genes.

[0039] Validation has shown that the six genes ZYX, CST3, RAB11B, SPHK1, TGFB1, and NME4 can serve as biomarkers for the diagnosis of cerebral infarction.

Claims

1. Application of products that specifically detect CST3 gene expression levels in the preparation of diagnostic or detection products for cerebral infarction.

2. The application according to claim 1, characterized in that, The product for specifically detecting the expression level of the CST3 gene is primers for PCR amplification of the CST3 gene, and the primer sequences for PCR amplification are shown in SEQ No. 3-4.

Images