The Research On Circulating MicroRNAs For Early Diagnosis Of Acute Cerebral Infarction

Stroke remains one of the top causes of mortality and disability in the modern society and many of the surviving stroke patients suffer from severe disability for all their lives. Ischemic stroke accounts for 80%-85% of total stroke.Currently the diagnosis and prediction of acute cerebral infarction is mostly depending on physical examination and image data.Imageological examination is associated with significant salary cost、time cost and limited availability with atypical Image. Given the limited recommended therapeutic window for thrombolysis(4.5 hours), biomarkers for stroke have the potential to expedite diagnosis and institution of treatment. The accurate prediction of stroke outcomes is important for personalized treatment in clinical practice. Blood sample is the material that can be obtained most easily and used to monitor the brain changes during the acute and recovery period of ischemic stroke in humans.Micro RNAs(mi RNAs) are a novel class of endogenous, noncoding small RNAs that negatively regulate gene expression at the post-transcriptional level by binding to the 3’-untranslated regions of their target m RNA.Over the past decade, their roles in several human diseases, from cancer to cardiovascular disease, have been established by a wealth of evidence. And some scholars found that the nuclease in the blood can’t destroy circulating mi RNAs. The in-depth study of some scholars found that multiple examinational results of mi RNAs in the blood are stable,and their content is related to the severity of the disease. However, mi RNAs research in stroke is still in its infancy.Mi RNAs,a unique class of endogenous riboregulators of gene function,offer tremendous potential in unraveling the mechanisms underlying stroke pathogenesis, and have an important role in diagnosising and prognosising of cerebral infarction. Therefore, we postulate that there is the specific serum/plasma mi RNAs expression profile constituting the fingerprint of a pathophysiological or diseased condition, and some of these circulating mi RNAs may be potential biomarkers for early noninvasive diagnosis and prediction of acute cerebral infarction.The major objectives of this study are:1、establish efficient、sensitive and accurate detection platform of plasma mi RNAs in lab;2、explore potential value of the plasma mi RNAs as the early biomarkers of acute cerebral infarction.The main techniques and methods involved in this study: preliminary processing and storage of blood specimen, extraction of total plasma RNA, q RT-PCR, plasma mi RNAs microarray, medical statistics.To identify the hypothesis we start our research from three three aspects.Part I The change of circulating mi RNAs microarray profilings in acute cerebral infarction patientsObjectives: explore plasma micro RNAs content and distribution of healthy volunteers,analysis the plasma mi RNAs different profilings between healthy people and acute cerebral infarction patients.Methods: to collect plasma of 4 cases of healthy volunteers and 7 cases of acute cerebral infarction patients, to extract total RNA with mir Vana PARIS Kit(Applied Biosystem p/n AM1556), using Agilent micrornas microarray technology(version 19.0) for quality inspection,fluorescent tags,hybridization and scanning. To analyze plasma mi RNAs expression profile of healthy volunteers,then to analyze the difference of plasma mi RNAs expression profiles between patients with acute cerebral infarction and healthy volunteers with an average signal value > 2 as effective signals。Results:1、mi RNAs microarray technology can detect 142 mi RNAs in plasma, the average signal value of 106 mi RNAs is less than 100, which accounts for 74.6% of the total. The signal of most plasma mi RNAs is very low.2、Most brain- related mi RNAs is undetectable in the plasma of healthy volunteers except mi R-21-5p(2.87±5.41).3、Most tissue-specific mi RNAs is undetectable in the plasma of healthy volunteers except mi R-122(72.75 + 145.15).4、The plasma mi RNAs expression is obviously different between acute cerebral infarction patients and healthy volunteers.The expression of 11 mi RNAs ascends while the expression of 1 mi RNA descends. Most tissue-specific and brain-related mi RNAs did not show obvious change except mi R-223-3p and mi R-16-5p.Conclusion: Micrornas microarray technology can detect mi RNAs in plasma; The signal of most plasma mi RNAs is very low; most brain-specific and tissue-specific mi RNAs is undetectable in the plasma of healthy volunteers; the plasma mi RNAs expression is obviously different between acute cerebral infarction patients and healthy volunteers, most tissue-specific and brain-related mi RNAs did not show obvious change. quantitative dectection circulating mi RNAsObjective: Establishing and optimizing the system of dectection plasma mi RNAs in our lab, discuss the technical details to form the standardized process.Methods: 1, We separate plasma by two-step method.We extract plasma total RNA with “TRI REAGENT kit for BD” and “S/P RNAiso kit”.We compared the yield of this two methods.2、We use q RT- PCR to detect plasma mi RNAs with SYBR Green [Rever Tra Ace Qpcr RT Kit and Real- time PCR Master Mix(TOYOBO) ]and S- Poly(T).We perform a preliminary comparison between sensitivity and specificity of the two methods.3、We explore the linear range of amplification curve of these two system by fluorescence quantitative PCR.Results: 1、The yield of “TRI REAGENT kit for BD” is lower than “TRI REAGENT kit for BD” in plasma total RNA extraction.2、 The comparison to two q RT PCR method showed that Poly(A) has better sensitivity and SYBR Green has better specificity.3、When its CT value was above 27, the relationship between the CT value and concentration did not still conform to the ideal linear state for exogenous referential gene(cel-mi R–54) in SPoly(T) q RT-PCR system。When its CT value was above 28, the relationship between the CT value and concentration did not still conform to the ideal linear state for endogenous referential gene(mi R–16).Conclusion: We separate plasma by two-step method,and we extract plasma total RNA with S/P RNAiso kit;we selected cel-mi R–54 as exogenous referential gene; we use q RT- PCR to detection plasma mi RNAs with S-Poly(T); when its CT value was above 28, the relationship between the CT value and concentration did not still conform to good linear state for endogenous referential gene.Part Ⅲ screening and identify the candidate circulating mi RNAs biomarkers for early diagnosis and prediction of acute cerebral infarction in patientsPurpose: 1、We evaluated the value of candidate biomarkers in the diagnosis of acute cerebral infarction in a small sample of patients with acute cerebral infarction and healthy volunteers. Then chronic cerebral infarction was brought into the evaluation system;2、We evaluated the value of candidate biomarkers in the prognosis of outcome in the small sample of patients with acute cerebral infarction. Part ⅡEstablishing and optimizing the system ofMethod: 1、We collected the plasma of 27 acute cerebral infarction patients、28 chronic cerebral infarction patients and 26 healthy volunteers.We used q RT-PCR to perform quantitative analysis of mi RNAs;2 、 We selected mi RNAs which had high fluorescent signal from microarray different expression profile and literature to form the candidate markers.We analyzed their differences between acute patients and healthy subjects and their differences between acute patients 、 chronic patients and healthy subjects;3、We divided patients with acute cerebral infarction into poor outcome group(MRS 3-6) and good outcome group(MRS 0-2).We choose mi RNAs which are different between acute patients and healthy volunteers and analyzed their differences between poor outcome group and good outcome group.Results: 1、When evaluated between acute patients and healthy volunteers,The average level of plasma mi R-16 and let-7i in patients with acute cerebral infarction was obviously higher than healthy volunteers,it has significant difference(p<0.05).There was no statistical difference between two groups of mi R- 106 b,mi R-130 a,mi R-223,mi R-25,mi R-140 and mi R-4454. When evaluated in three groups,mi R-16 and let-7i can also distinguish acute patients and volunteers.And let-7i could distinguish acute and chronic cerebral infarction while mi R-16 could’t.Let-7i had stronger specificity of acute cerebral infarction.2、The average plasma mi R-16 and let-7i level of poor outcome group was markedly higher than good outcome group, there was statistical differences between two groups(p<0.05).Conclusion: Mi R-16 from the microarray different expression profile and let-7i from literature review had significant difference in plasma of acute cerebral infarction patients and healthy volunteers。Let-7i could distinguish acute and chronic cerebral infarction.It had higher value in diagnosis of acute cerebral infarction.It could be used to exclude recurrence of cerebral infarction.Mi R-16 and let-7i had significant difference in plasma of acute cerebral infarction patients with poor outcome and good outcome.Summary: This study discussed the early diagnosis of acute cerebral infarction. We comprehensively analysis the plasma mi RNAs expressive profile of healthy volunteers and plasma mi RNAs different expression profile between patients with acute cerebral infarction and healthy volunteers with the latest technology developments of mi RNAs microarray and fluorescent quantitative PCR.We set up a optimal blood mi RNAs detection platform.We explored the change of microarray different expressive mi RNAs in plasma of patients after acute cerebral infarction. We discussed the value of candidate mi RNAs biomarkers for diagnosis and prediction of patients with acute cerebral infarction.We clarified the conception that plasma mi RNAs can be used as potential biomarkers for early diagnosis of acute cerebral infarction. We selected and identified 2 mi RNAs represented by let-7i which had potential clinical value.At the same time we puts forward a research approach that researchers can screen plasma biomarkers of acute cerebral infarction from plasma mi RNAs microarray different expression profile.