Diagnostic biomarker miR-940 for cerebral hemorrhage and application thereof

Abstract

The invention discloses a diagnostic biomarker miR-940 for cerebral hemorrhage and application of the diagnostic biomarker miR-940, application of the miR-940 in preparation of the diagnostic biomarker for cerebral hemorrhage, application of the miR-940 as the diagnostic biomarker in preparation of a detection reagent for cerebral hemorrhage, and a diagnostic kit or a diagnostic preparation for cerebral hemorrhage. Comprising a reagent for measuring the expression quantity of the miR-940. According to the application, the miR-940 is used as a marker for early diagnosis of cerebral hemorrhage, the miR-940 axis regulates mitochondrial-immune interaction, the miR-940 axis regulates mitochondrial-immune interaction, the miR-940 axis regulates mitochondrial-immune interaction and can be used as a potential ICH diagnosis and treatment target, and a potential target is provided for developing a new therapy for relieving secondary brain injury.

Description

TECHNICAL FIELD

[0001] The present invention belongs to the biotechnology field, in particular relates to diagnostic biomarkers for cerebral hemorrhage

[0002] miR-940

[0003] and its application. BACKGROUND

[0004] Cerebral hemorrhage (ICH) is a fatal stroke, accounting for 10-15% of all cerebrovascular events, with high mortality and long-term morbidity. The disease has a heavy burden on patients, families, and global health systems. Although progress has been made in acute phase treatment, the understanding of the molecular mechanisms of secondary brain injury is still incomplete, limiting the development of effective therapies. The pathological process after ICH involves complex interactions between mitochondrial dysfunction and immune dysregulation, leading to neuronal death, neuroinflammation, and brain edema. Mitochondria play a central role in cellular energy metabolism and redox balance, and are extremely susceptible to oxidative stress damage after hemorrhage. In addition, abnormal immune cell infiltration exacerbates tissue destruction. However, the regulatory network connecting mitochondrial-related genes, immune microenvironment remodeling, and ICH progression remains largely unknown.

[0005] Recent advances in bioinformatics have provided new opportunities for systematic identification of disease-related molecular signatures. Although previous studies have reported differentially expressed genes (DEGs) in ICH, research on mitochondrial-specific DEGs (MitoDEGs) and their interactions with non-coding RNAs is still in its infancy. MicroRNAs (miRNAs), as key post-transcriptional regulators, have been identified as important regulators of mitochondrial homeostasis and neuroinflammation. For example, miR-155 exacerbates blood-brain barrier disruption in ICH by targeting complement factor H, while miR-124 inhibits neuronal apoptosis through a mitochondrial pathway. In addition, increasing evidence suggests that miRNAs play a role in regulating immune cell function and inflammatory responses. Studies have shown that miR-223 reduces neuroinflammation after ICH by regulating microglial polarization, while miR-181 inhibits inflammatory responses by targeting Toll-like receptor 4. However, the pathological role of miR-940 in ICH remains to be elucidated, although it has been confirmed to be associated with cancer and neurodegenerative diseases.

[0006] Immune cell infiltration also plays a key role in the pathophysiology of ICH. Studies have shown that the composition and function of immune cells undergo significant changes after ICH, including increased infiltration of monocytes and natural killer cells, and decreased counts of T and B lymphocytes. This pattern of immune cell infiltration may be closely related to changes in the immune microenvironment caused by mitochondrial dysfunction. A deeper understanding of the dynamic changes in immune cell infiltration and its interaction with mitochondrial dysfunction is crucial for revealing the pathogenesis of ICH and developing new treatments. Summary of the Invention

[0007] In response to the problems existing in the prior art, the present invention aims to provide a diagnostic biomarker for cerebral hemorrhage, miR-940, and its application, which is specifically achieved through the following technical solutions:

[0008] The first aspect of the present invention provides the use of miR-940 in preparing a diagnostic biomarker for cerebral hemorrhage.

[0009] A second aspect of the present invention provides the use of miR-940 as a diagnostic biomarker in the preparation of a detection reagent for cerebral hemorrhage.

[0010] Furthermore, the reagent is used to detect the content of immune cells in brain tissue.

[0011] Furthermore, the immune cells include T cells and B cells.

[0012] A third aspect of the present invention provides a diagnostic kit or a diagnostic preparation for cerebral hemorrhage, comprising a reagent for measuring the expression level of miR-940.

[0013] The application of the present invention utilizes miR-940 as a marker for early diagnosis of cerebral hemorrhage. The miR-940 axis regulates mitochondrial-immune interactions and can serve as a potential diagnostic and therapeutic target for ICH, providing a potential target for the development of new therapies to reduce secondary brain damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the process of the present invention;

[0015] Figure 2 (A) Volcano plots of differentially expressed genes in the ICH dataset (AB) GSE24265 and GSE125512 datasets; (C) Common differentially expressed genes between GSE24265 and GSE125512; (D) GSEA maps highlighting significant GSEA sets in KEGG and Reactome; (E) GSEA maps depicting significant pathway sets in KEGG);

[0016] Figure 3(A) Visualization of differentially expressed genes; B-C) Analysis of DEGs enrichment using GO and KEGG; C) PPI network of MitoDEGs; D) Central MitoDEGs associated with ICH disease based on CTD database) ;

[0017] Figure 4 (A) TF-MitoDEGs regulatory network; B) miRNA-MitoDEGs regulatory network; C) Heatmap of DEGs in GSE43618 dataset; D) Predicted and intersecting miRNAs) ;

[0018] Figure 5 (A) HE staining around hematoma in different rat groups (x200); B) HE staining of brain tissue in different groups of rats (x400); C) Nissl staining of hippocampal neurons in different groups) ;

[0019] Figure 6 Display of qRT-PCR results;

[0020] Figure 7 (A) mRNA expression qRT-PCR analysis of MitoDEGs and their predicted TFs in each group; B) Western blot analysis of MitoDEGs protein expression levels in each group of rats; C-I) Quantitative analysis of brain tissue) ;

[0021] Figure 8 (A) Boxplot showing the proportion of immune cells; B) Stacked bar chart showing the proportion of immune cells; C) Heatmap showing the proportion of various immune cell types; D) Matrix showing the correlation of immune cell proportions; E) Association between central MitoDEGs and immune cells) ;

[0022] Figure 9 Flow cytometry analysis of peripheral blood immune cell differentiation in rats 72 hours after intracerebral hemorrhage (n=8) (A) Flow cytometry analysis of T and B cell expression in rats with intracerebral hemorrhage; B) Absolute number of T cells in each group; C) Absolute number of B cells in each group) ;

[0023] Figure 10 Flow cytometry analysis of peripheral blood immune T cell changes in rats 72 hours after intracerebral hemorrhage (n=5) (A) CD3 + cells were screened from all lymphocytes, showing the proportion of CD3 + cells in total lymphocytes; B-C) Cell sorting second step: one for CD4 +T cells, and another for CD8 + T cells, showing all CD3 + CD4 + and CD8 + The proportion of T cells; DF) CD3 + 、CD4 + and CD8 + Cell density trend; G) CD3 + The absolute number of cells; H) CD4 + and CD8 + ratio of T cells). DETAILED DESCRIPTION

[0024] The present invention is further described below in conjunction with the accompanying drawings to facilitate a better understanding of the present technical solution.

[0025] This study aims to elucidate the molecular mechanism by which miR-940 regulates the mitochondrial-immune axis in ICH, filling a significant gap in the current literature. By integrating bioinformatics analysis with experimental validation, we explored the role of miR-940 in regulating mitochondrial metabolism and immune cell interactions, providing new insights and potential therapeutic targets for ICH treatment. Figure 1 Schematic diagram of the workflow of the method of the present invention.

[0026] 1. Methods

[0027] 1.1 Bioinformatics analysis

[0028] To systematically investigate the molecular mechanisms associated with mitochondria in intracerebral hemorrhage (ICH), we obtained ICH data from the NCBI GeneExpression Omnibus (GEO) database (http: / / www.ncbi.nlm.nih.gov / geo). Differential expression analysis was performed using the R-based online tool GEO2R, ​​with the filtering criteria set to |log2FC| > 1 and P < 0.05. Two mRNA datasets (GSE24265 and GSE125512) and one miRNA dataset (GSE43618) were analyzed. Volcano plots, heat maps, and Venn diagrams of differentially expressed genes were created using R 4.2.1. The TargetScan database (http: / / www.targetscan.org / Vert_72 / ) and the miRDB database (http: / / mirdb.org / miRDB / ) were used to predict miRNAs that regulate mRNAs. The STRING database (https: / / cn.string-db.org / ) was used to predict and provide protein-protein interaction (PPI) networks. The DAVID database 6.8 (https: / / david.ncifcrf.gov / ) was used for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The Gseaplot 2 R package was used to perform gene set enrichment analysis (GSEA). To identify mitochondrial-specific signatures, the MitoCarta 3.0 database (http: / / www.broadinstitute.org.mitocarta) was queried, and differentially expressed genes were aligned with 27,103 mitochondrial-related genes to generate mitochondrial-related differentially expressed genes (MitoDEGs). Spatial expression patterns were visualized using the "ggplot2" R package. Key network components were identified through PPI analysis using STRING and Cytoscape 3.8.2, and hub genes were identified using the CytoHubba and MCODE plugins. Disease associations were confirmed by comparing toxicogenomics databases.

[0029] 1.2 Transcription factor (TF) and miRNAs network

[0030] In order to reveal the upstream regulatory mechanism, the iRegulon Cytoscape plug-in was used to predict the transcription factors that regulate MitoDEGs.

[0031] (http: / / mirwalk.umm.uni-heidelberg.de) was used to identify miRNAs targeting central MitoDEGs. Integrated network modeling was performed in Cytoscape 3.8.2, and a MitoDEG-TF-miRNA interaction network was constructed.

[0032] 1.3 Establishment of a rat model of intracerebral hemorrhage

[0033] To mimic the pathophysiology of human ICH, an ICH model was established by collagenase induction. Rats were anesthetized with Zoletil 50 and atropine, and collagenase / normal saline was injected after drilling. The injection needle was withdrawn after 10 minutes of retention. Animals that died or had no limb hemiplegia were excluded. According to previous literature, miR-940 inhibitors and controls were administered locally before surgery. This study was approved by the IACUC of Zhejiang Chinese Medical University (No. IACUC-20230904-14) and followed the ARRIVE guidelines.

[0034] 1.4 Histopathological examination

[0035] To perform histopathological evaluation, rats were subjected to transcardial perfusion with PBS and 4% paraformaldehyde 72 hours after ICH. Coronal brain sections (5 pm) containing the hematoma were paraffin-embedded, stained with hematoxylin-eosin (HE) and Nissl, and imaged using a Nikon Eclipse E100 microscope.

[0036] 1.5 RNA extraction and qRT-PCR

[0037] Total RNA was extracted from the perihematoma tissue using TRIzol reagent (Invitrogen), followed by cDNA synthesis using a Roche reverse transcription kit. Quantitative PCR amplification was performed on a LightCycler 480 II system using SYBR Green Master Mix (Roche). Expression was normalized to GAPDH, and fold changes were calculated using the 2 -ΔΔCt method.

[0038] 1.6 Western Blotting

[0039] Protein lysates were extracted using RIPA buffer supplemented with protease inhibitors, quantified by BCA assay (ThermoFisher), and separated on 10% SDS-PAGE gels. Proteins were transferred to PVDF membranes, blocked with 3% BSA, and then incubated with primary antibodies (1:1000; Abcam) overnight at 4°C. Signals were detected using HRP-conjugated secondary antibodies (1:5000; CellSignaling) and ECL developer (Bio-Rad).

[0040] 1.7 Immune cell infiltration analysis

[0041] The present invention uses the CIBERSORT algorithm to analyze the infiltration of 36 immune cell types in the data set samples to explore the potential association between the target central gene and the changes in the immune microenvironment of patients with cerebral hemorrhage. First, the expression matrix was standardized to eliminate the batch effect, and the LM22 feature matrix was used as the reference data set. The proportion of immune cells in each sample was obtained by permutation test (Perm=1000). The analysis included: comparing the differences in immune cell composition between the ICH group and the control group (T test), generating a heat map showing the overall distribution pattern of immune cell infiltration, and calculating the Spearman correlation coefficient (P<0.05) between the core gene expression level and the immune cell infiltration level. Statistical analysis and visualization were performed using R 4.4.2 and its compatible statistical software packages.

[0042] 1.8 Flow cytometry

[0043] Seventy-two hours after ICH, the rats' blood was analyzed for immune cell counts and percentages. Mononuclear cells were isolated by density gradient centrifugation. The cells were purified, counted, and incubated with fluorescent dye-labeled antibodies. 100,000 cells per sample were analyzed by flow cytometry. Data were analyzed using FACSDiva 8.0 software.

[0044] 1.9 Statistical analysis

[0045] Data are presented as mean ± standard deviation (SD) and are derived from four experiments. Data were analyzed using GraphPad Prism 8.0 (GraphPad, San Diego, USA). The Shapiro-Wilk test was used to check for normal distribution. Differences between groups were assessed using the Student's t-test, with P < 0.05 considered statistically significant.

[0046] 2. Results

[0047] 2.1 Computer simulation of differentially expressed genes in the ICH dataset

[0048] To explore the mRNA expression differences between intracerebral hemorrhage (ICH) and normal brain tissue, two mRNA datasets (GSE24265 and GSE125512) were selected from GEO database for analysis. By setting |log2FC| > 1 and P < 0.05 as the screening conditions, differentially expressed mRNAs were identified in these datasets. Volcano plots generated by R programming language showed the differentially expressed mRNAs in each dataset (A, B). In the GSE24265 dataset, 1,596 genes were found to be differentially expressed by differential gene analysis, of which 550 genes were up-regulated and 1,046 genes were down-regulated in ICH samples. The GSE125512 dataset revealed 544 differentially expressed genes, including 69 up-regulated genes and 62 down-regulated genes. The Venn diagram showed that there were 7 common differentially expressed genes between the two datasets, which were EFHC2, TRIM9, BCL2A1, PTGES, SPP1, CAV2 and RHOBTB1 (C). Gene set enrichment analysis (GSEA) showed that these common differentially expressed genes were mainly involved in hematopoietic cell function and immune-related pathways. The expression patterns of these common genes were also visualized (D, E, F). The most enriched pathways were related to mitochondrial metabolism and function, hypoxia, redox reactions, material production and immune response. Figure 2 A, B). In the GSE24265 dataset, 1,596 genes were found to be differentially expressed by differential gene analysis, of which 550 genes were up-regulated and 1,046 genes were down-regulated in ICH samples. The GSE125512 dataset revealed 544 differentially expressed genes, including 69 up-regulated genes and 62 down-regulated genes. The Venn diagram showed that there were 7 common differentially expressed genes between the two datasets, which were EFHC2, TRIM9, BCL2A1, PTGES, SPP1, CAV2 and RHOBTB1 Figure 2 C). Gene set enrichment analysis (GSEA) showed that these common differentially expressed genes were mainly involved in hematopoietic cell function and immune-related pathways. The expression patterns of these common genes were also visualized Figure 2 D, E, F). The most enriched pathways were related to mitochondrial metabolism and function, hypoxia, redox reactions, material production and immune response.

[0049] 2.2 Computer prediction of downstream pathways of common differentially expressed genes and protein-protein interaction (PPI) network

[0050] To further explore the biological processes and molecular functions related to differentially expressed genes, these genes were analyzed in detail. In the hemorrhagic model group, four genes were up-regulated (EFHC2, TRIM9, BCL2A1, PTGES), and three genes were down-regulated (SPP1, CAV2, RHOBTB1). The relevant visualization results are shown in Figure 3 A. Gene ontology (GO) enrichment analysis showed that the target genes were mainly related to biological processes (BP) such as vesicle fusion and organelle membrane fusion. In terms of cellular components (CC), these genes were enriched in nuclear membrane lumen, presynaptic cytoplasm and transport vesicles. Molecular function (MF) analysis highlighted items such as glutathione binding, oligopeptide binding and various oxidoreductase activities Figure 3 B). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis Figure 3C) showed that stroke-related pathways (including metabolic and immune pathways) were significantly involved, which is consistent with the known pathogenesis of ICH. By querying the MitoCarta3.0 database, which specifically catalogs mitochondrial proteins, it was found that these seven common genes were associated with ICH-related mitochondrial diseases, so they were called mitochondrial-related differentially expressed genes (MitoDEGs). Protein-protein interaction (PPI) analysis of these seven MitoDEGs was performed using the STRING database and visualized using Cytoscape ( Figure 3 D). Comparative Toxicogenomics Database (CTD) analysis predicted the association between MitoDEGs and ICH, among which SPP1, PTGES, CAV2, and TRIM9 had the highest correlation with ICH ( Figure 3 E).

[0051] 2.3 Regulatory network analysis of MitoDEGs

[0052] After identifying the key genes and pathways, the regulatory mechanisms of their expression were further explored. In order to further explore the regulatory mechanism of MitoDEGs, the iRegulon plug-in in Cytoscape software was used to predict the transcription factors (TFs) associated with the central MitoDEGs. The analysis results constructed a MitoDEGs-TFs regulatory network containing 11 transcription factors: CUX1, TCF15, HMGA1, FOXB1, TAL1, CEBPE, TEF, NFKB1, IRF1, DBP and SOX15 ( Figure 4 A).

[0053] Then, miRDB and TargetScan were used to predict miRNAs related to MitoDEGs, and a MitoDEGs-miRNA regulatory network was constructed ( Figure 4 B). In addition, by analyzing the ICH-related miRNA dataset GSE43618, 11 differentially expressed miRNAs were identified ( Figure 4 C). By performing intersection analysis of these prediction results with differentially expressed miRNAs, hsa-miR-940 was found to be the only shared miRNA. Given its potential regulatory effects on the seven key genes mentioned above, hsa-miR-940 is speculated to be a key miRNA affecting the progression and outcome of ICH ( Figure 4 D).

[0054] 2.4 Pathological observation

[0055] Hematoxylin-eosin (HE) staining showed differences between groups. The model group showed a hematoma containing red blood cells and severe edema. The brain tissue of the has-miR-940 antagomir group was better than that of the model group Figure 5 A-B). The neurons of the sham group were complete, arranged in order, with deep cytoplasm and abundant Nissl bodies, and the dendrite morphology was normal. The number of neurons of the model group was less, arranged in disorder, with lighter staining, less Nissl bodies, and looser structure. The Nissl body damage of the has-miR-940 antagomir group was lighter, with darker staining, and the number of neurons was more than that of the model group Figure 5 C). Inhibition of miR-940 may be an important way to protect brain hemorrhage injury.

[0056] 2.5 Experimental verification of hub expression of MitoDEGs in ICH rats

[0057] To further verify the experimental results at the molecular level, reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blotting analysis were performed. RT-qPCR analysis confirmed that the expression of 7 mitochondrial-related differentially expressed genes (MitoDEGs: SPP1, PTGES, CAV2, TRIM9, RHOBTB1, EFHC2, BCL2A1) and 9 transcription factors (TFs: CUX1, TCF15, HMGA1, FOXD1, TAL1, TEF, NFKB1, DBP, SOX15) in the ventricle tissue of intracerebral hemorrhage (ICH) rats showed a tendency of disorder compared with the control group. In the miR-940 inhibition group (antagomir group), the expression of specific target genes changed: SPP1 mRNA expression increased by 1.8 times (P<0.001), while RHOBTB1 and EFHC2 decreased by 60% (P<0.05) and 45% (P<0.01), respectively Figure 6 ). Among the transcription factors, the expression of HMGA1 and CAV2 decreased by 55% (P<0.0001) and 42% (P<0.001), respectively, while SOX15 and TCF15 increased by 2.1 times (P<0.01) and 1.7 times (P<0.05), respectively.

[0058] Western blotting analysis showed that the changes in protein levels in the intracerebral hemorrhage rat model were consistent with the gene expression trend Figure 7BI). In the ICH model, knockdown of miR-940 significantly upregulated the expression of BCL2A1 and TRIM9, while inhibiting the pathological overexpression of EFHC2 and RHOBTB1. Notably, the protein levels of SPP1 and PTGES were significantly increased in the ICH group compared with the sham control group (P<0.001), but no significant changes in these two proteins were detected in the miR-940 knockdown group ( Figure 7 A).

[0059] Compared with the sham group, the expression of BCL2A1 and TRIM9 in the ICH group was significantly downregulated (P<0.001). Inhibition of miR-940 restored the expression of both (BCL2A1: P<0.01; TRIM9: P<0.001) ( Figure 7 F, G). Similarly, compared with the sham control group, the protein expressions of EFHC2, RHOBTB1, SPP1, and PTGES were significantly upregulated in the ICH group (EFHC2 and RHOBTB1: P < 0.01; SPP1 and PTGES: P < 0.001). Knockdown of miR-940 inhibited the expression of EFHC2 and RHOBTB1 (P < 0.01) ( Figure 7 B, C), but there was no significant difference in SPP1 and PTGES levels between the ICH group and the miR-940 knockdown group ( Figure 7 D, E), suggesting that the regulation of these genes may not depend on miR-940.

[0060] In summary, knockdown of miR-940 significantly altered the expression of key downstream genes involved in the pathogenesis of ICH. These results suggest that miR-940 regulates immune regulatory and metabolic pathways by selectively targeting BCL2A1, TRIM9, EFHC2, and RHOBTB1, providing mechanistic insights for subsequent studies.

[0061] Immune Cell Infiltration in ICH

[0062] After clarifying the molecular and pathological findings, this application turned its attention to the immune cell infiltration in ICH. CIBERSORT analysis showed that the immune cell profile of ICH tissue was significantly different compared with normal tissue ( Figure 8 A, B). Especially in brain tissue, adaptive immune cells (T cells and B cells) are significantly reduced. Specifically for T cells, activated CD4 + Memory T cells decreased, while resting CD8 + Memory T cells increased. In terms of innate immune cells, monocytes and natural killer cells showed higher infiltration and phagocytic activity, with a decrease in resting cells and an increase in activated cells ( Figure 8C, D). Correlation analysis showed that the central gene was associated with B cells, activated CD4 + There is a strong association between the infiltration of memory T cells and monocytes. In addition, central genes were found to regulate the polarization of natural killer cells ( Figure 8 E). Taken together, these findings suggest that central genes may reshape the immune microenvironment after hemorrhage by regulating the dynamic changes of immune cells.

[0063] 2.7 Identification of T and B cells in peripheral blood of ICH rats

[0064] To investigate the role of T cells and B cells in the pathophysiology of ICH, peripheral blood samples were obtained from rats 72 hours after ICH. + 、CD8 + 、CD3 + and B cells) to stain blood samples ( Figure 9 A). The results showed that 72 hours after cerebral hemorrhage, the peripheral T cell counts in the ICH group were significantly lower than those in the control group ( Figure 9 B), the proportion of T cells in the ICH group was significantly lower than that in the normal group (P<0.001). This finding suggests that there is a correlation between the exhaustion of peripheral T cells and the recovery of acute neuroinflammation and injury after intracerebral hemorrhage. In addition, the average B lymphocyte count in the ICH group was higher than that in the control group, and silencing miR-940 significantly mobilized B cells ( Figure 9 C), the proportion of B cells in the anti-miR-940 group was significantly higher than that in the ICH group (P=0.0065).

[0065] 2.8 Analysis of T Cell Function in ICH Pathogenesis

[0066] Peripheral blood was collected from rats 72 hours after ICH and CD4 + 、CD8 + and CD3 + and B cell markers ( Figure 10 A). The results showed that the peripheral T cell counts in the ICH group were significantly lower than those in the control group 72 hours after bleeding ( Figure 10 B), and the proportion of T cells in the ICH group was significantly lower than that in the normal group (P<0.001), indicating that peripheral T cell exhaustion may be related to the recovery of acute neuroinflammation and injury after cerebral hemorrhage. In addition, the average B lymphocyte count in the ICH group was higher than that in the control group, and silencing miR-940 could significantly mobilize B cells ( Figure 10 C), the proportion of B cells in the anti-miR-940 group was significantly higher than that in the ICH group (P=0.0065).

Claims

1. Use of miR-940 in the preparation of diagnostic biomarkers for cerebral hemorrhage.

2. Application of miR-940 as a diagnostic biomarker in the preparation of detection reagents for cerebral hemorrhage.

3. The use according to claim 2, characterized in that The reagent is used to detect the content of immune cells in brain tissue.

4. The use according to claim 3, characterized in that The immune cells include T cells and B cells.

5. A diagnostic kit or preparation for cerebral hemorrhage, characterized in that: The invention also includes reagents for measuring the expression level of miR-940.

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