Application of Lnc-PLBD1-AS1 and MCEMP1 as prognosis biomarkers of acute ischemic stroke

Abstract

The invention belongs to the technical field of biology, and particularly relates to application of Lnc-PLBD1-AS1 and MCEMP1 as prognosis biomarkers of acute ischemic stroke. According to the application disclosed by the invention, it is found that the expression levels of baseline Lnc-PLBD1-AS1 and MCEMP1 of people who are dead within one year after an acute cerebral arterial thrombosis patient is attacked are obviously higher than those of survival patients, and the Lnc-PLBD1-AS1 and MCEMP1 have good prediction efficiency for evaluating the risk of death of the acute cerebral arterial thrombosis patient within one year after the acute cerebral arterial thrombosis patient is attacked by the acute cerebral arterial thrombosis patient is attacked by the acute cerebral arterial thrombosis patient; the expression levels of the baselines Lnc-PLBD1-AS1 and MCEMP1 of people suffering from the adverse function outcome (death or severe disability) of acute cerebral arterial thrombosis patients after attack for 3 months are obviously higher than those of other patients, and the Lnc-PLBD1-AS1 and MCEMP1 have good prediction efficiency for evaluating the risk of the adverse function outcome of the acute cerebral arterial thrombosis patients after attack for 3 months.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biotechnology, and particularly relates to application of Lnc-PLBD1-AS1 and MCEMP1 as biomarkers for prognosis of acute ischemic stroke. BACKGROUND

[0002] Acute ischemic stroke has the characteristics of high disability rate, high mortality rate and high recurrence rate, and intravenous thrombolytic therapy is currently the highest recommended treatment for patients with acute ischemic stroke. However, even in the case of popularization and application of intravenous thrombolytic therapy, nearly half of the patients still cannot benefit from it.

[0003] A biomarker refers to a biochemical index that can mark the changes in system, organ, tissue, cell and subcellular structure or function, and can be used for diagnosing diseases, judging disease stages, and evaluating the safety and effectiveness of new drugs or new therapies in target populations. However, there are relatively few effective biomarkers for the prognosis and early warning of acute ischemic stroke on the international and domestic markets. Therefore, screening new biomarkers that can more accurately predict the clinical adverse outcomes of acute ischemic stroke has important clinical value and significance.

[0004] Mast cell expressed membrane protein 1 (MCEMP1) is a transmembrane protein mainly expressed in immune-related cells (such as mast cells, macrophages). MCEMP1 is one of the most important induced genes in inflammatory diseases, which is attributed to the presence of nuclear factor-kappa B (NF-κB) and activated T cell nuclear factor (NFAT) transcription factor binding motifs in its promoter region. Previous studies have shown that MCEMP1 has various mechanisms of action in stroke pathology, including regulating mast cell differentiation, immune response (such as neutrophils, immature B cells and CD8 + T cells), and enhancing NLRP3 inflammasome and interleukin-1 signaling pathways, but it has not been analyzed separately for acute ischemic stroke.

[0005] Long non-coding RNA (LncRNA) belongs to non-coding RNA with a length of more than 200 nucleotides. One of the functions of LncRNA is that they can act as "sponges" for microRNAs (miRNAs) to inhibit their activity through miRNA response elements (MREs), thereby regulating the expression level of downstream target genes. PLBD1-AS1 is a long non-coding RNA gene, which is highly expressed in the brain and has been shown to be involved in the regulation of neuronal differentiation and synaptic plasticity. PLBD1The antisense strand of the gene overlaps. Studies have found that five autophagy-related LncRNAs, including Lnc-PLBD1-AS1, are all expressed in the high-risk group of hepatocellular carcinoma and are associated with poor overall survival, and can also predict the effect of chemotherapy and immunotherapy on hepatocellular carcinoma patients. In non-small cell lung cancer samples, compared with matched non-tumor control tissues, autophagy-related Lnc-PLBD1-AS1 expression was significantly increased. Further ROC curve analysis showed that in the differentiation of non-small cell lung cancer and small cell lung cancer, the area under the ROC curve of Lnc-PLBD1-AS1 reached 0.81, and in the differentiation of non-small cell lung cancer and healthy control group, the area under the ROC curve of Lnc-PLBD1-AS1 reached 0.82, so Lnc-PLBD1-AS1 was found to be a potential lung cancer-related biomarker. However, no study has established the relationship between Lnc-PLBD1-AS1 and MCEMP1, and there is no record of Lnc-PLBD1-AS1 affecting the prognosis of acute ischemic stroke. SUMMARY

[0006] The purpose of the present application is to enrich the biomarkers for the prognosis of acute ischemic stroke, to evaluate or predict the poor prognosis of acute ischemic stroke, and in particular to evaluate or predict the death within 1 year and / or poor functional outcome within 3 months of acute ischemic stroke, and to distinguish between high-risk and low-risk acute ischemic stroke patients with poor prognosis.

[0007] In order to achieve the above-mentioned purpose, the present application provides the use of a substance for detecting the expression level of a biomarker in one or more of the following; (1) preparing a product for evaluating or assisting in evaluating the poor prognosis of acute ischemic stroke patients; (2) preparing a product for predicting or assisting in predicting the poor prognosis of acute ischemic stroke patients; (3) preparing a product for distinguishing between high-risk and low-risk acute ischemic stroke patients with poor prognosis; The biomarker comprises Lnc-PLBD1-AS1 and / or MCEMP1; The expression level of Lnc-PLBD1-AS1 and / or MCEMP1 is up-regulated in acute ischemic stroke patients with poor prognosis.

[0008] The present application provides the use of a substance for detecting the expression level of a biomarker in combination with risk factors in one or more of the following; (1) preparing a product for evaluating or assisting in evaluating the poor prognosis of acute ischemic stroke patients; (2) preparing a product for predicting or assisting in predicting the poor prognosis of acute ischemic stroke patients; (3) preparing a product for distinguishing high-risk and low-risk acute ischemic stroke patients with poor prognosis; The biomarker comprises Lnc-PLBD1-AS1 and / or MCEMP1. The expression level of the Lnc-PLBD1-AS1 and / or MCEMP1 is up-regulated in the acute ischemic stroke patient with poor prognosis. The risk factor comprises one or more of age, onset-to-admission time, NIHSS score at admission, history of atrial fibrillation, history of hypertension, history of dyslipidemia, and history of smoking.

[0009] Preferably, the poor prognosis comprises death within 1 year and / or poor functional outcome within 3 months.

[0010] Preferably, detecting the expression level of the MCEMP1 comprises detecting the mRNA expression level of the MCEMP1.

[0011] Preferably, the product comprises a kit.

[0012] The present application provides a kit for evaluating or predicting the prognosis of an acute ischemic stroke patient, the kit comprising a substance for detecting the expression level of a biomarker in whole blood; The biomarker comprises Lnc-PLBD1-AS1 and / or MCEMP1. The expression level of the Lnc-PLBD1-AS1 and / or MCEMP1 is up-regulated in the acute ischemic stroke patient with poor prognosis.

[0013] The present application provides a system for evaluating or predicting the prognosis of an acute ischemic stroke patient, the system comprising a data analysis module; the data analysis module is used for analyzing the detection value of a biomarker, the biomarker comprising Lnc-PLBD1-AS1 and / or MCEMP1. The expression level of the Lnc-PLBD1-AS1 and / or MCEMP1 is up-regulated in the acute ischemic stroke patient with poor prognosis.

[0014] The present application provides a use of a substance for inhibiting the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 in the preparation of a product for reducing the risk of poor prognosis of an acute ischemic stroke patient.

[0015] Preferably, the substance for inhibiting the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 comprises hsa-miR-4685-5p, and the nucleotide sequence is shown as SEQ ID NO: 1.

[0016] Preferably, the poor prognosis comprises death within 1 year and / or poor functional outcome within 3 months.

[0017] Beneficial effects: The present application first discovers that the expression levels of Lnc-PLBD1-AS1 and MCEMP1 of the population of acute ischemic stroke patients within 1 year of onset of death are significantly higher than those of the survival patients, and Lnc-PLBD1-AS1 and MCEMP1 have good prediction efficiency for evaluating the risk of death within 1 year of onset of ischemic stroke patients. The present application also discovers that the expression levels of Lnc-PLBD1-AS1 and MCEMP1 of the population of acute ischemic stroke patients with poor functional outcome (death or severe disability) within 3 months of onset are significantly higher than those of other patients, and Lnc-PLBD1-AS1 and MCEMP1 have good prediction efficiency for evaluating the risk of poor functional outcome within 3 months of onset of ischemic stroke patients.

[0018] Further, the present application also constructs a competitive endogenous RNA (ceRNA) regulation network, and the results show that hsa-miR-4685-5p significantly reduces the luciferase activity of Lnc-PLBD1-AS1 and MCEMP1. After mutating the predicted target sites of Lnc-PLBD1-AS1 and MCEMP1, compared with the miRNA negative control group, hsa-miR-4685-5p does not significantly change the luciferase activity of Lnc-PLBD1-AS1 and MCEMP1, confirming the ceRNA regulation network of Lnc-PLBD1-AS1-hsa-miR-4685-5p-MCMEP1. The present application proves that there is a direct interaction relationship between Lnc-PLBD1-AS1, MCEMP1 and hsa-miR-4685-5p, which provides important molecular biological evidence for the regulation relationship between Lnc-PLBD1-AS1 and MCEMP1, and provides an important basis for reducing the risk of poor prognosis of acute ischemic stroke. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below.

[0020] Fig. 1 The scatter plot of LncRNA differential expression in high-throughput sequencing of baseline whole blood samples of 50 acute ischemic stroke patients; wherein, the red dot represents the up-regulated LncRNA, the blue dot represents the down-regulated LncRNA, and the gray dot represents the LncRNA with no significant difference; Fig. 2 The OPLS-DA analysis chart of LncRNA in high-throughput sequencing of baseline whole blood samples of 50 acute ischemic stroke patients; Fig. 3Scatter plot of mRNA differential expression in high-throughput sequencing of baseline whole blood samples of 50 patients with acute ischemic stroke; red dots represent up-regulated mRNA, blue dots represent down-regulated mRNA, and gray dots represent no significant difference in mRNA; Fig. 4 OPLS-DA analysis chart of mRNA in high-throughput sequencing of baseline whole blood samples of 50 patients with acute ischemic stroke; Fig. 5 Violin plot of Lnc-PLBD1-AS1 differential expression in high-throughput sequencing of baseline whole blood samples of death group and survival group among 50 patients with acute ischemic stroke; Fig. 6 Violin plot of MCEMP1 differential expression in high-throughput sequencing of baseline whole blood samples of death group and survival group among 50 patients with acute ischemic stroke; Fig. 7 Spearman correlation analysis results of Lnc-PLBD1-AS1 and MCEMP1 mRNA in high-throughput sequencing of baseline whole blood samples of 50 patients with acute ischemic stroke; Fig. 8 ROC curve of Lnc-PLBD1-AS1 and MCEMP1 mRNA in high-throughput sequencing of baseline whole blood samples of 50 patients with acute ischemic stroke for distinguishing whether death within one year; Fig. 9 Relative luciferase activity detection results of Lnc-PLBD1-AS1 and miR-4685 co-transfection; WT represents wild type; MUT represents mutant type; Fig. 10 Relative luciferase activity detection results of MCEMP1 and miR-4685 co-transfection; WT represents wild type; MUT represents mutant type; Fig. 11 Kaplan-Meier curve of 1-year death of Lnc-PLBD1-AS1 tertile group of 313 patients; Fig. 12 Kaplan-Meier curve of 1-year death of MCEMP1 mRNA tertile group of 313 patients; Fig. 13 Distribution of 3-month mRS score of Lnc-PLBD1-AS1 tertile group of 313 patients; Fig. 14 Distribution of 3-month mRS score of MCEMP1 mRNA tertile group of 313 patients; Fig. 15The ROC curve of Lnc-PLBD1-AS1, MCEMP1 mRNA, a traditional model and the combination of the three for 313 patients to distinguish whether death will occur within 1 year; Fig. 16 The ROC curve of Lnc-PLBD1-AS1, MCEMP1 mRNA, a traditional model and the combination of the three for 313 patients to distinguish whether an adverse functional outcome exists within 3 months. DETAILED DESCRIPTION

[0021] The application provides application of a substance for detecting expression levels of biomarkers in one or more of the following: (1) preparing a product for evaluating or assisting in evaluating an adverse prognosis of an acute ischemic stroke patient; (2) preparing a product for predicting or assisting in predicting an adverse prognosis of an acute ischemic stroke patient; (3) preparing a product for distinguishing an acute ischemic stroke patient with a high risk of an adverse prognosis from an acute ischemic stroke patient with a low risk of an adverse prognosis; the biomarkers include Lnc-PLBD1-AS1 and / or MCEMP1; the expression levels of the Lnc-PLBD1-AS1 and / or MCEMP1 are up-regulated in an acute ischemic stroke patient with an adverse prognosis.

[0022] The application provides application of a substance for detecting expression levels of biomarkers in one or more of the following: (1) preparing a product for evaluating or assisting in evaluating an adverse prognosis of an acute ischemic stroke patient; (2) preparing a product for predicting or assisting in predicting an adverse prognosis of an acute ischemic stroke patient; (3) preparing a product for distinguishing an acute ischemic stroke patient with a high risk of an adverse prognosis from an acute ischemic stroke patient with a low risk of an adverse prognosis; the biomarkers include Lnc-PLBD1-AS1 and / or MCEMP1; the expression levels of the Lnc-PLBD1-AS1 and / or MCEMP1 are up-regulated in an acute ischemic stroke patient with an adverse prognosis; the risk factors include one or more of age, time from onset to admission, NIHSS score at admission, history of atrial fibrillation, history of hypertension, history of dyslipidemia and history of smoking; as an implementation manner, the risk factors are age, time from onset to admission, NIHSS score at admission, history of atrial fibrillation, history of hypertension, history of dyslipidemia and history of smoking The application constructs and verifies an Lnc-PLBD1-AS1-hsa-miR-4685-5p-MCMEP1 ceRNA network in an acute ischemic stroke patient by high-throughput transcriptome sequencing, bioinformatics analysis, luciferase reporter gene detection and a prospective cohort, and further evaluates the influence of Lnc-PLBD1-AS1 and MCEMP1 on long-term prognosis of an acute ischemic stroke patient, and the results show that both Lnc-PLBD1-AS1 and MCEMP1 can evaluate an adverse prognosis of an acute ischemic stroke patient.

[0023] As an implementation form, the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 in the application is the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 in a whole blood sample. As an implementation form, the application detects the expression level of Lnc-PLBD1-AS1. As an implementation form, the application detects the expression level of MCEMP1. The nucleotide sequences of Lnc-PLBD1-AS1 and MCEMP1 in the application are shown in SEQ ID NO: 3 and SEQ ID NO: 5, respectively.

[0024] As an implementation form, the poor prognosis in the application includes death within 1 year and / or poor functional outcome within 3 months.

[0025] As an implementation form, the product in the application includes a kit.

[0026] The application provides a kit for evaluating or predicting the prognosis of an acute ischemic stroke patient, the kit comprising a substance for detecting the expression level of a biomarker in a whole blood sample; the biomarker comprises Lnc-PLBD1-AS1 and / or MCEMP1; the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 is up-regulated in an acute ischemic stroke patient with a poor prognosis.

[0027] The application provides a system for evaluating or predicting the prognosis of an acute ischemic stroke patient, the system comprising a data analysis module; the data analysis module is used for analyzing the detection value of a biomarker, the biomarker comprising Lnc-PLBD1-AS1 and / or MCEMP1; the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 is up-regulated in an acute ischemic stroke patient with a poor prognosis.

[0028] The application provides a substance for inhibiting the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 in the preparation of a product for reducing the risk of poor prognosis in an acute ischemic stroke patient.

[0029] As an implementation form, the substance for inhibiting the expression level of Lnc-PLBD1-AS1 and / or MCEMP1 in the application comprises hsa-miR-4685-5p, and the nucleotide sequence is shown in SEQ ID NO: 1. As an implementation form, the poor prognosis in the application includes death within 1 year and / or poor functional outcome within 3 months.

[0030] In order to further illustrate the present application, the application of Lnc-PLBD1-AS1 and MCEMP1 as prognostic biomarkers for acute ischemic stroke provided by the present application is described in detail below in conjunction with the accompanying drawings and examples, but they cannot be understood as limiting the scope of protection of the present application.

[0031] Example 1 Construction of ceRNA regulatory network related to acute ischemic stroke 1. Establish a prospective cohort study (1) A prospective prognostic cohort including 313 patients with acute ischemic stroke was established, all patients were admitted to the Department of Neurology of Nantong University Affiliated Hospital during December 2019 to July 2021. Inclusion criteria: age ≥ 18 years old; onset time ≤ 72 hours; the clinical symptoms and signs of the case should meet the definition of stroke by WHO, and be diagnosed as ischemic stroke by CT or MRI scan; willing and can participate in this study, can sign the informed consent form. Exclusion criteria: suffering from malignant tumor, autoimmune disease or other serious diseases.

[0032] (2) Collect baseline data and blood samples of patients admitted. Among them, the baseline data include demographic characteristics, neurological signs, past medical history, medication, NIHSS score and modified Rankin scale (mRS) at admission, time from onset to admission, etc.; baseline test indicators include blood routine, blood glucose, blood biochemistry, blood lipid indicators, etc. Use PAXgene tube to collect all fasting blood samples of patients, and store them in an environment of-80℃. After all samples are collected, RNA extraction and quality control are carried out.

[0033] (3) Collect the physical condition and follow-up information of patients 1 year after onset. The main outcome is death within 1 year after onset, and the secondary outcome is death or severe disability (mRS score 3-6) within 3 months after onset.

[0034] 2. High-throughput transcriptome sequencing and differential expression analysis based on nested case-control (1) Based on the above prospective prognostic cohort, age-gender matched baseline blood samples of patients who died within 1 year (25 cases) and survived (25 cases) were retrieved, and Illumina high-throughput sequencing technology was used for detection. Total RNA was extracted using TRIzol reagent, and ribosomal RNA was removed by RiboErase in KAPA RNA HyperPrep kit. According to the manufacturer's protocol, a chain-specific long non-coding RNA library was constructed. Then the quality and concentration of cDNA library were verified by Agilent 2100 Bioanalyzer. Finally, the library was subjected to Illumina double-end sequencing with a sequencing mode of 2x150.

[0035] (2) RNA identification and differential expression analysis: The screened high-quality reads were aligned with the human reference genome by HISAT2. The StringTie software was used to estimate the gene expression level based on the transcription per million mapped reads (FPKM) per kilobase. The edgeR package was used for differential expression analysis of LncRNA and mRNA between the dead and surviving patients (|Fold Change|>1.5, q<0.05). The ggplot2, ggpubr and ropls packages were used to generate scatter plots and orthogonal partial least squares discriminant analysis (OPLS-DA) charts for display.

[0036] Results as shown in Figs. 1-4 The results showed that there were 1071 differential expression (DE) LncRNAs (529 LncRNAs were significantly up-regulated and 542 LncRNAs were significantly down-regulated) and 1503 DE mRNAs (including MCEMP1, 669 mRNAs were significantly up-regulated and 834 mRNAs were significantly down-regulated) in the two groups of patients who died or survived within one year. The OPLS-DA chart showed that LncRNAs and mRNAs could better distinguish between dead and surviving patients.

[0037] 3. Construction of LncRNA-miRNA-MCEMP1 ceRNA network (1) The miRanda software was used to predict the target miRNAs that could bind to DELncRNA and MCEMP1, respectively, and the intersection of the two was taken. The results showed that Lnc-PLBD1-AS1 and MCEMP1 could both bind to miRNA-4685.

[0038] (2) The ROC curve was used to calculate the prediction ability of Lnc-PLBD1-AS1 and MCEMP1 for death, and the Spearman rank correlation coefficient was used to evaluate the expression correlation between Lnc-PLBD1-AS1 and MCEMP1.

[0039] Results as shown in Figs. 5-8As shown, the baseline Lnc-PLBD1-AS1 expression level of the dead patient was 1.83 times that of the surviving patient (q<0.001), the area under the curve of Lnc-PLBD1-AS1 was 0.899 (95% CI: 0.817-0.982), the sensitivity was 76%, and the specificity was 92%; the baseline MCEMP1 expression level of the dead patient was 5.67 times that of the surviving patient (q<0.001), the area under the curve of MCEMP1 was 0.950 (95% CI: 0.892-1.000), the sensitivity was 92%, and the specificity was 92%; the expression levels of Lnc-PLBD1-AS1 and MCEMP1 were significantly positively correlated, and the Spearman rank correlation coefficient reached 0.808. Thus, the ceRNA regulatory network of Lnc-PLBD1-AS1-hsa-miR-4685-5p-MCEMP1 was constructed.

[0040] Example 2 Dual-luciferase reporter assay for detecting the interaction of Lnc-PLBD1-AS1 and MCEMP1 with hsa-miR-4685-5p 1. According to the prediction results of miRanda software, the sequences of hsa-miR-4685-5p mimic, wild-type Lnc-PLBD1-AS1, and wild-type MCEMP1 were obtained from the database, and Lnc-PLBD1-AS1 wild-type and mutant recombinant vectors and MCEMP1 wild-type and mutant recombinant vectors were constructed, which were sequentially recorded as Lnc-PLBD1-AS1-WT, Lnc-PLBD1-AS1-MUT, MCEMP1-WT, and MCEMP1-MUT, and miR-4685 plasmid and its negative control were constructed, which were recorded as miR-4685 and miR-NC. The basic vector was pmirGLO vector, and the nucleotide sequences of the related genes were as follows: hsa-miR-4685-5p mimics: cccagggcuuggaguggggcaagguu (SEQ ID NO: 1); hsa-miR-4685-5p NC: ccuugccccacuccaagcccuggguu (SEQ ID NO: 2); Lnc-PLBDl-ASl-WT: CTTCTCAAGTGCCCTCGCTGCGCAGCCGCAGGGCTGGAGCGAGACCGCCGGCCGGAGGCGCGTTTCTCTGAGTTCACCCAGGAACCAGACGCCCGCTGGACGTCAACTCGGCCGGACGCGGGGCACGGGCGCTAACAGGCTCCTAGGAGCCTCCCCGGGCGCCCCCCC CC CAGGGCGCGCTCTGCTCACCTGCAGGTTTCGGCGGCTCCGCGGTGACTAACAACAGCGGCAGCAGCAGCAGCAGCAGAAGCGGTGGCGGCTGTGGCAGCCCCGGGCGCCCGCCCGGACCGCCGCGGGTCATCGCTCCACGGCCGCGACCTTCCTCTGCGGGATCAGGCGGCCGCGGTGGCCCGCGGTGGCAGAAGTTGCAAGAGAGGCTCCTGGCCTACTCAGGGGCGCCGCCTCTCCGAGGTGGGGCGTCCTCAACTTTCCTCTTTCTTGAGCCCGGCCTGCTCCGGGCTCTGAGGGGCGAGGACGCTTCACGTGGTGGCCTGGGGCCCACCCCGCCTAGGAAAGGCGGATCCTTTACGTCTGACCCTCTAGCAACCCCTGGGCCGGTCTGGGAAAGTTCCCCAGCTGCACTTCTGGGAAATGCAG (SEQ ID NO: 3); Lnc-PLBD1-AS1-MUT: CTTCTCAAGTGCCCTCGCTGCGCAGCCGCAGGGCTGGAGCGAGACCGCCGGCCGGAGGCGCGTTTCTCTGAGTTCACCCAGGAACCAGACGCCCGCTGGACGTCAACTCGGCCGGACGCGGGGCACGGGCGCTAACAGGCTCCTAGGAGCCTCCCCGGGCGCCCCCCCCTAGGGCGCGCTCTGCTCACCTGCAGGTTTCGGCGGCTCCGCGGTGACTAACAACAGCGGCAGCAGCAGCAGCAGCAGAAGCGGTGGCGGCTGTGGCAGCCCCGGGCGCCCGCCCGGACCGCCGCGGGTCATCGCTCCACGGCCGCGACCTTCCTCTGCGGGATCAGGCGGCCGCGGTGGCCCGCGGTGGCAGAAGTTGCAAGAGAGGCTCCTGGCCTACTCAGGGGCGCCGCCTCTCCGAGGTGGGGCGTCCTCAACTTTCCTCTTTCTTCTCGGGCCGGACGAGGCCCGAGACTCGGGCGAGGACGCTTCACGTGGTGGCCTGGGGCCCACCCCGCCTAGGAAAGGCGGATCCTTTTGCAGACTGGGAGATCGTTGGGGACCCCCGGTCTGGGAAAGTTCCCCAGCTGCACTTCTGGGAAATGCAG (SEQ ID NO:4); MCEMP1-WT: GTGGAGGAAATCTACAAGCACCAGGAAGTCAAGATGCAAGCACCAGCCTTCAGGGACAAGAAACAGGGGGTCTCAGCCAAGAATCAAGTCCCAGCCCAGTGCAGGCCGCCCTCAGACTCCACCCAGGTCCCCTGCTGGTTGTACAGAGCCATCCTGAGCCTGTACATCCTCCTGGCCCTGGCCTTTGTCCTCTGCATCATCCTGTCAGCCTTCATCATGGTGAAGAATGCTGAGATGTCCAAGGAGCTGCTGGGCTTTAAAAGGGAGCTTTGGAATGTCTCAAACTCCGTACAAGCATGCGAAGAGAGACAGAAGAGAGGCTGGGATTCCGTTCAGCAGAGCATCACCATGGTCAGGAGCAAGATTGATAGATTAGAGACGACATTAGCAGGCATAAAAAACATTGACACAAAGGTACAGAAAATCTTGGAGGTGCTGCAGAAAATGCCACAGTCCTCACCTCAATAAATGAGAGGACATTGTGGCAGCCAAAGCCACAACTTGGAAGATGGGGCTGCACCTGCCAACGAAGACGGGAAATGACCCCCCCCCCCCAGCCTAGTGTGAACCTGCCCCTCGTCCCACGTATAGAAAAACCTCGAGTCATGGTGAATGAGTGTCTCGGAGTTGCTCGTGTGTGTGTACACCTGCGTGCGTGTGTGTGCGTGTGTGCGCGTGTGTTCGTGTATGTGCGTGTGTGCGTGCGCGTGTGTGTGCATTTTGCAAAGGGTGGACATTTCAGTGTATCTCCCAGAAAGGTGAT (SEQ ID NO: 5); MCEMP1-MUT: GTGGAGGAAATCTACAAGCACCAGGAAGTCAAGATGCAAGCACCAGCCTTCAGGGACAAGAAACAGGGGGTCTCAGCCAAGAATCAAGTCCCAGCCCAGTGCAGGCCGCCCTCAGACTCCACCCAGGTCCCCTGCTGGTTGTACAGAGCCATCCTGTCGGACATGTAGGAGGACCGGGACCGCTTTGTCCTCTGCATCATCCTGTCAGCCTTCATCATGGTGAAGAATGCTGAGATGTCCAAGGAGCTGCTGGGCTTTAAAAGGGAGCTTTGGAATGTCTCAAACTCCGTACAAGCATGCGAAGAGAGACAGAAGAGAGGCTGGGATTCCGTTCAGCAGAGCATCACCATGGTCAGGAGCAAGATTGATAGATTAGAGACGACATTAGCAGGCATAAAAAACATTGACACAAAGGTACAGAAAATCTTGGAGGTGCTGCAGAAAATGCCACAGTCCTCACCTCAATAAATGAGAGGACATTGTGGCAGCCAAAGCCACAACTTGGAAGATGGGGCTGCACCTGCCAACGAAGACGGGAAATGACCCCCCCCCCCCAGCCTAGTGTGAACCTGCCCCTCGTCCCACGTATAGAAAAACCTCGAGTCATGGTGAATGAGTGTCTCGGAGTTGCTCGTGTGTGTGTACACCTGCGTGCGTGTGTGTGCGTGTGTGCGCGTGTGTTCGTGTATGTGCGTGTGTGCGTGCGCGTGTGTGTGCATTTTGCAAAGGGTGGACATTTCAGTGTATCTCCCAGAAAGGTGAT (SEQ ID NO: 6).

[0041] 2. Preparation of cells: Take Hela cells in logarithmic growth phase, inoculate 2.0 x 10 4 cells per well into a 48-well plate, and place 250 μl of complete medium per well in a cell incubator for overnight culture. 18 h after cell plating, when the cell confluence is about 70% under a microscope, use LipofectMax transfection reagent for transfection, and set 3 replicate wells per group.

[0042] 3. For each transfection sample, the preparation method is as follows: (1) Take each group of plasmid according to the pre-set amount, add 25 μl Opti-MEM low serum medium, gently blow several times to mix, room temperature for 5 minutes; At the same time, prepare each group of transfection reagent diluent, 1.0 μl LipofectMax is added to 25 μl Opti-MEM low serum medium, gently blow several times to mix, room temperature for 5 minutes.

[0043] (2) Mix each group of plasmid diluent with the corresponding transfection reagent diluent, gently blow several times to mix, incubate at room temperature for 20 minutes to form DNA-liposome complex.

[0044] (3) Transfection: discard the medium in the 48-well culture plate, add 200 μl of medium without serum and antibiotic, add 50 μl of DNA / liposome mixture to each well of cells, shake to mix, then place in a 37℃ cell incubator with 5% CO2, culture for 6h. Take out the culture plate, aspirate the DNA / liposome mixture, add 250 μl of fresh serum-containing complete medium without antibiotic to each well, continue to culture at 37℃, 5% CO2 for 24-72h. Observe and perform subsequent experiments under a fluorescence microscope.

[0045] 4. Screening of luciferase reporter vector system: the strain identified by sequencing is expanded and cultured, and the E.Z.N.A. endotoxin-free plasmid extraction kit produced by Omega is used to extract the endotoxin-free plasmid for transfection, and the A260 / 280 ratio of the obtained plasmid is required to be between 1.8-2.0, and A160 / 230>2.

[0046] 5. Detect luciferase activity 48h after transfection, using Dual-Luciferase Reporter Assay System kit, the operation steps are as follows: (1) Take the 48-well plate with Hela cells from the constant temperature carbon dioxide incubator, and preferably place the cells at room temperature for a few minutes before proceeding to the next step.

[0047] (2) Aspirate the medium in each well, add 200 μl of cell lysis solution to each well, place the culture plate on a shaker, and shake at low speed to lyse the cells for 30 minutes at room temperature.

[0048] (3) After lysis, take 25 μl of lysis solution from each well and add it to a white plate, add 40 μl of Luc reagent in the Dual-Luciferase Reporter Assay System kit to each well, gently blow to mix, incubate at room temperature for an appropriate time, and then use the enzyme marker Lum program to detect the firefly luciferase (Luc) activity data.

[0049] (4) After the first detection, 40 μl of stop reagent was immediately added to each well, and the mixture was mixed gently by blowing and mixed, and then incubated at room temperature for an appropriate time. The Renilla luciferase activity data was detected by enzyme marker Lum program. It should be noted that the measurement order of each well should be the same as the previous detection.

[0050] According to Fig. 9 and Fig. 10 It can be seen that, in the wild type, compared with miR-NC, hsa-miR-4685-5p mimic significantly reduces the luciferase activity of Lnc-PLBD1-AS1 and MCEMP1. After mutating the predicted target site of Lnc-PLBD1-AS1 and MCEMP1, compared with miR-NC, hsa-miR-4685-5p mimic does not significantly change the luciferase activity of Lnc-PLBD1-AS1 and MCEMP1. The above results prove that there is a direct interaction relationship between Lnc-PLBD1-AS1, MCEMP1 and hsa-miR-4685-5p.

[0051] Example 3 Baseline Lnc-PLBD1-AS1 and MCEMP1 as biomarkers for prognosis of acute ischemic stroke 1. Baseline characteristics The expression levels of baseline Lnc-PLBD1-AS1 and MCEMP1 of 313 patients were detected by RT-qPCR, and were grouped according to their three-part level. The baseline variables were compared by one-way analysis of variance, rank sum test and chi-square test. The results are shown in Table 1.

[0052] Table 1 Comparison of baseline characteristics of MCEMP1 and Lnc-PLBD1-AS1 three-part groups of patients with acute ischemic stroke

[0053] According to Table 1, among the 313 acute ischemic stroke patients included in the application, there were 198 males and 115 females, with an average age of 67.3±12.6 years. According to the three-part level of Lnc-PLBD1-AS1 and MCEMP1 at admission, compared with the lowest three-part group, patients with higher Lnc-PLBD1-AS1 levels were more likely to smoke, had higher admission NIHSS scores and were more likely to have a history of atrial fibrillation; as the MCEMP1 level increased, the patients tended to be older, had higher admission NIHSS scores, higher high-density lipoprotein cholesterol levels, higher prevalence of atrial fibrillation and hypertension, and shorter time from onset to admission (P<0.05). P <0.05).

[0054] 2. Kaplan-Meier curves and Log-rank test were used to calculate and compare the difference of 1-year mortality rate among Lnc-PLBD1-AS1 and MCEMP1 tertile groups; ordinal logistic regression model was used to analyze the association of Lnc-PLBD1-AS1 and MCEMP1 tertile groups with 3-month mRS score. The results are shown in Figs. 11-14

[0055] According to Figs. 11-14 It can be seen that, in the one-year follow-up period, 48 subjects died and 103 subjects had adverse functional outcomes within three months of onset. The mortality rates of Lnc-PLBD1-AS1 high, medium and low tertile groups were 21.9%, 17.3% and 6.7% (log-rank p = 0.008), respectively; the mortality rates of MCEMP1 high, medium and low tertile groups were 25.7%, 11.7% and 8.6% (log-rank p = 0.001), respectively. In ordinal logistic regression analysis, with each increase of one tertile group of Lnc-PLBD1-AS1 (OR: 1.54, 95% CI: 1.21-1.97, P trend = 0.001) and MCEMP1 (OR: 1.49, 95% CI: 1.17-1.89, P trend = 0.001), the risk of 3-month mRS score deterioration of ischemic stroke patients increased significantly by 54% and 49%, respectively.

[0056] 3. Multivariate regression analysis Baseline variables with P P ≤0.05 were included in the multivariate Cox model and logistic model, with the lowest tertile group of Lnc-PLBD1-AS1 and MCEMP1 as the reference group, to calculate the hazard ratios (HRs), odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) of other tertile groups, and the results are shown in Table 2.

[0057] Table 2 Association of Lnc-PLBD1-AS1 and MCEMP1 tertile groups with adverse clinical outcomes of acute ischemic stroke patients

[0058] Note: a represents a model containing NIHSS score at admission, history of atrial fibrillation, history of smoking and Lnc-PLBD1-AS1; b represents a model containing age, time from onset to admission, NIHSS score at admission, history of atrial fibrillation, history of hypertension, history of dyslipidemia and MCEMP1 mRNA ​As shown in Table 2, after multivariate adjustment, patients in the highest tertile of Lnc-PLBD1-AS1 and MCEMP1 had a significantly higher risk of 1-year mortality compared with those in the lowest tertile, with HRs of 2.98 (95% CI: 1.27-7.03; P trend = 0.013) and 2.82 (95% CI: 1.27-6.25; P trend = 0.006), respectively. Similarly, there was a significant positive dose-response relationship between baseline levels of Lnc-PLBD1-AS1 and MCEMP1 and 3-month adverse functional outcome, with ORs of 4.23 (95% CI: 2.17-8.27; P trend <0.0001) and 1.91 (95% CI: 1.01-3.62; P trend = 0.040) for patients in the highest tertile of Lnc-PLBD1-AS1 and MCEMP1 compared with those in the lowest tertile, respectively.

[0059] 4. Evaluation of the predictive performance of Lnc-PLBD1-AS1 and MCEMP1 in ischemic stroke ROC curves were used to evaluate the performance of Lnc-PLBD1-AS1 and MCEMP1 in identifying patients with ischemic stroke who died within 1 year and had adverse functional outcomes at 3 months, and whether Lnc-PLBD1-AS1 and MCEMP1 improved the ability of the traditional model (using age, time from onset to admission, NIHSS score at admission, history of atrial fibrillation, history of hypertension, history of dyslipidemia, and history of smoking as discriminators) to identify adverse outcomes. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to evaluate whether Lnc-PLBD1-AS1 and MCEMP1 improved the reclassification ability of the traditional model for adverse outcomes. Likelihood ratio tests were used to evaluate whether Lnc-PLBD1-AS1 and MCEMP1 improved the fitting ability of the model. Hosmer-Lemeshow goodness-of-fit tests and calibration plots were used to evaluate whether the inclusion of Lnc-PLBD1-AS1 and MCEMP1 in the traditional model improved the calibration ability of the model, and the results are shown in Table 3 and Figs. 15-16 .

[0060] Table 3. Predictive value of Lnc-PLBD1-AS1 and MCEMP1 for the prognosis of patients with ischemic stroke

[0061] According to Table 3 and Figs. 15-16It can be seen that Lnc-PLBD1-AS1 and MCEMP1 showed moderate discriminative ability in predicting whether the ischemic stroke patients would die within 1 year after the onset of the disease, the area under the ROC curve of Lnc-PLBD1-AS1 was 0.656 (95% CI: 0.572-0.741), the sensitivity was 79.2%, and the specificity was 45.7%; the area under the ROC curve of MCEMP1 was 0.665 (95% CI: 0.574-0.756), the sensitivity was 37.5%, and the specificity was 90.9%. When evaluating the adverse functional outcome in 3 months, the area under the ROC curve of Lnc-PLBD1-AS1 was 0.672 (95% CI: 0.608-0.736), the sensitivity was 78.6%, and the specificity was 49.5%; the area under the ROC curve of MCEMP1 was 0.623 (95% CI: 0.553-0.692), the sensitivity was 39.8%, and the specificity was 84.8%, showing moderate discriminative ability. As can be seen from Table 3, when Lnc-PLBD1-AS1 and MCEMP1 were included in the traditional model, the area under the ROC curve, NRI and IDI were significantly improved, whether for the primary outcome or the secondary outcome. Likelihood ratio test showed that Lnc-PLBD1-AS1 and MCEMP1 improved the fitting ability of the traditional model. When taking death within 1 year as the outcome, the Hosmer-Lemeshow test showed that after Lnc-PLBD1-AS1 and MCEMP1 were included in the traditional model, the model calibration ability was further improved, P the value increased from 0.788 to 0.919; when taking death or severe disability in 3 months as the outcome, although P the value decreased slightly, but still maintained good consistency with the actual observation P =0.630).

[0062] Although the above embodiment describes the present application in detail, it is only a part of the embodiments of the present application, not all the embodiments, and other embodiments can be obtained according to the present embodiment without creativity, which are all within the protection scope of the present application.

Claims

1. The application of substances that detect the expression levels of biomarkers in one or more of the following: (1) Prepare products for assessing or assisting in the assessment of poor prognosis in patients with acute ischemic stroke; (2) To prepare products for predicting or assisting in the prediction of poor prognosis in patients with acute ischemic stroke; (3) To develop products that differentiate between patients with high and low risk of poor prognosis in acute ischemic stroke. The biomarkers include Lnc-PLBD1-AS1 and / or MCEMP1; The expression levels of Lnc-PLBD1-AS1 and / or MCEMP1 were upregulated in patients with poor prognosis of acute ischemic stroke.

2. The application of substances that detect biomarker expression levels in combination with risk factors in one or more of the following: (1) Prepare products for assessing or assisting in the assessment of poor prognosis in patients with acute ischemic stroke; (2) To prepare products for predicting or assisting in the prediction of poor prognosis in patients with acute ischemic stroke; (3) To develop products that differentiate between patients with high and low risk of poor prognosis in acute ischemic stroke. The biomarkers include Lnc-PLBD1-AS1 and / or MCEMP1; The expression levels of Lnc-PLBD1-AS1 and / or MCEMP1 were upregulated in patients with poor prognosis of acute ischemic stroke. The risk factors include one or more of the following: age, time from onset to hospitalization, NIHSS score at admission, history of atrial fibrillation, history of hypertension, history of dyslipidemia, and history of smoking.

3. The application according to claim 1 or 2, characterized in that, The adverse outcomes include death within 1 year and / or poor functional outcomes within 3 months.

4. The application according to claim 1 or 2, characterized in that, Detecting the expression level of MCEMP1 includes detecting the mRNA expression level of MCEMP1.

5. The application according to claim 1 or 2, characterized in that, The product includes a reagent kit.

6. A kit for assessing or predicting the prognosis of patients with acute ischemic stroke, characterized in that, The kit contains substances for detecting the expression levels of biomarkers in whole blood; The biomarkers include Lnc-PLBD1-AS1 and / or MCEMP1; The expression levels of Lnc-PLBD1-AS1 and / or MCEMP1 were upregulated in patients with poor prognosis of acute ischemic stroke.

7. A system for assessing or predicting the prognosis of patients with acute ischemic stroke, characterized in that, The system includes a data analysis module; the data analysis module is used to analyze the detection values ​​of biomarkers, including Lnc-PLBD1-AS1 and / or MCEMP1; The expression levels of Lnc-PLBD1-AS1 and / or MCEMP1 were upregulated in patients with acute ischemic stroke and poor prognosis.

8. Application of substances that inhibit the expression levels of Lnc-PLBD1-AS1 and / or MCEMP1 in the preparation of products that reduce the risk of poor prognosis in patients with acute ischemic stroke.

9. The application according to claim 8, characterized in that, The substance that inhibits the expression levels of Lnc-PLBD1-AS1 and / or MCEMP1 includes hsa-miR-4685-5p, the nucleotide sequence of which is shown in SEQ ID NO:

1.

10. The application according to claim 8 or 9, characterized in that, The adverse outcomes include death within 1 year and / or poor functional outcomes within 3 months.

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