CN114196748A - Early prediction biomarker and prediction model for acute pancreatitis and construction method thereof - Google Patents
Early prediction biomarker and prediction model for acute pancreatitis and construction method thereof Download PDFInfo
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Abstract
The invention provides an early prediction biomarker for acute pancreatitis, a prediction model and a construction method thereof, wherein the significant correlation between three differential expression exocrine body microRNAs markers and the acute pancreatitis is obtained by analyzing the expression levels of the serum exocrine body microRNAs-Novel 1, Novel2 and Novel3 of an acute pancreatitis patient, and the prediction model C is constructedmicRNA(-9.552) +0.025 Novel1+2.033 Novel2+2.384 Novel 3; the Novel1, the Novel2 and the Novel3 are respectively relative expression quantities of secretion microRNAs in serum of a sample to be detected, are used for prognosis evaluation and early screening of patients with acute pancreatitis, and are beneficial to providing more accurate prognosis and diagnosis information for the patients.
Description
Technical Field
The invention relates to the technical field of biology and disease prediction, in particular to an acute pancreatitis early-stage prediction biomarker, a prediction model and a construction method thereof.
Background
Acute pancreatitis is a disease which seriously threatens human health and life, and the incidence rate is in an increasing trend. According to the clinical features of acute pancreatitis, it can be defined as two types of acute pancreatitis (Mild acid pancreatits, MAP) and acute pancreatitis (SAP). Although from a pathophysiological point of view, these types of mechanisms of occurrence and development share common features, including the recruitment and activation of neutrophils, the production of various cytokines and vasoactive substances, and the activation of associated signaling pathways, the course and severity of two types of Acute Pancreatitis (AP) are diametrically opposed. Over 20% of acute pancreatitis, AP, can gradually be exacerbated to SAP with severe complications such as pancreatic necrosis, Systemic Inflammatory Response Syndrome (SIRS), and even Multiple Organ Dysfunction Syndrome (MODS), with a total mortality rate of about 40%. Therefore, if SAP patients can be predicted early, more positive medical support can be provided in the early stage of the disease, which is significant for improving SAP disease outcome. Although there are some current clinical practice guidelines that suggest the use of scoring systems to predict the severity of AP onset, such as the Ranson scoring system, the acute physiological and chronic health detection (APACHE) system, and the bedside index of AP severity (BISAP). But these scoring systems are not only complex to operate, but also the prediction efficiency is yet to be further improved. In addition, some clinical parameters, such as CRP, PCT, Blood Urea Nitrogen (BUN) and creatinine (SCr), etc., have been suggested by scholars to predict the severity of acute pancreatitis. However, it seems not ideal from clinical practice, and therefore more effective early prediction molecular markers remain to be discovered.
The exosome is mostly circular or elliptical in shape and has a lipid membrane. Exosomes can carry a variety of proteins and nucleic acids, including long coding RNAs (lncRNAs), circular RNAs (circular RNAs), smiling RNAs (micro RNAs ), mRNAs, and the like. During the extracellular circulation, exosomes can be taken up by other cells, and various functions of the cells, such as autophagy and cell homeostasis, are regulated by proteins and nucleic acids carried by the exosomes. The microRNAs are endogenous non-coding RNAs and can regulate the expression level of genes after transcription so as to regulate the occurrence and development of diseases.
Disclosure of Invention
Aiming at the problems in the background technology, the invention provides an early prediction biomarker and a prediction model for acute pancreatitis and a construction method thereof, wherein the three differential expression exocrine body mic-RNAs markers and the significant correlation relationship between the acute pancreatitis are obtained by analyzing the expression levels of the serum exocrine body micRNAs-Novel1, Novel2 and Novel3 of an acute pancreatitis patient, and the prediction model is constructed for prognosis evaluation and early screening of the acute pancreatitis patient, and is favorable for providing more accurate prognosis and diagnosis information for the patient.
In order to achieve the above purpose, the invention adopts the technical scheme that:
in a first aspect, the invention provides an early acute pancreatitis prediction molecular marker, which comprises any one or a combination of at least two of exocrine micRNAs-Novel1, micRNAs-Novel2 or micRNAs-Novel 3;
the microRNAs-Novel 1 have a sequence shown by SEQ ID NO.1 in a sequence table:
the microRNAs-Novel 2 have a sequence shown by SEQ ID NO.2 in a sequence table;
the microRNAs-Novel 3 have a sequence shown by SEQ ID NO.3 in a sequence table.
In a second aspect, the present invention provides an early prediction model for acute pancreatitis, wherein the prediction model is: cmicRNA(-9.552) +0.025 Novel1+2.033 Novel2+2.384 Novel 3; the Novel1, the Novel2 and the Novel3 are respectively relative expression amounts of secreted microRNAs-Novel 1, microRNAs-Novel 2 and microRNAs-Novel 3 in serum of a sample to be detected.
In a third aspect, the invention provides a method for constructing an acute pancreatitis early prediction model according to the second aspect, wherein the method comprises the following steps:
(1) separating and screening a serum sample of an acute pancreatitis patient to obtain a serum exosome;
(2) further extracting and identifying three kinds of microRNAs (Novel 1, Novel2 and Novel 3) in qualified exosomes, and detecting the relative expression quantities of the three kinds of microRNAs by an RT-qPCR method;
(3) and modeling the three differentially expressed microRNAs by adopting binomial logistic regression, and constructing a prediction model equation according to a multi-factor logistic regression result.
In a fourth aspect, the invention provides a use of the acute pancreatitis early prediction molecular marker in the first aspect in preparing an acute pancreatitis diagnosis reagent.
Compared with the prior art, the invention has the following advantages:
(1) the marker exocrine micRNAs-Novel1, the micRNAs-Novel2 and the micRNAs-Novel3 have obvious correlation with acute pancreatitis, the three differentially expressed micRNAs are used as biomarkers to be applied to the early prediction of SAP, and the early prediction of SAP has very good efficiency in the early prediction of SAP conditions no matter single detection or combined detection, and is more obvious in combined detection.
(2) Constructing a prediction model C by taking the difference relative expression quantity of exocrine micRNAs-Novel1, micRNAs-Novel2 or micRNAs-Novel3 as variablesmicRNAAs a prediction model of acute pancreatitis, the probability of AP patients developing into SAP is determined according to a well-established fitting curve, so that high-risk patients are captured early, more active intervention is given early, and specificity and accuracy of early diagnosis of severe acute pancreatitis can be greatly improved.
Drawings
FIG. 1A basic information of three kinds of microRNAs; FIG. 1B shows the sequence structures of three kinds of microRNAs.
FIG. 2A exosome morphology under transmission microscope (arrows indicate exosomes); figure 2B exosome volume size distribution.
FIG. 3A is a boxplot of the relative expression of Novel1 in the Non-SAP and SAP groups; FIG. 3B is a boxplot of the relative expression of Novel2 in the Non-SAP and SAP groups; FIG. 3C is a boxplot of the relative expression of Novel3 in the Non-SAP and SAP groups.
FIG. 4CmicRNAModel and independent prediction and Ran of three microRNAsComparison of son score predictive power.
Detailed Description
The following describes in detail specific embodiments of the present invention. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The specific embodiment is as follows:
plasma samples of 4 SAPs and 4 healthy subjects were collected in the previous study by the team and large amounts of micro rnas (mirnas) were found in plasma exosomes. Compared with a healthy control group, three kinds of differentially expressed microRNAs are identified in exosomes of SAP patients, namely, down-regulated Novel1 and up-regulated Novel2 and 3, and the three kinds of differentially expressed microRNAs are supposed to be expected to be used as biomarkers for early prediction of SAP. The basic information of these three kinds of microRNAs is shown in FIG. 1.
The microRNAs-Novel 1 have a sequence shown by SEQ ID NO.1 in a sequence table:
CAGGGCUGGGCUGGAAUUUUAAGAAAGGUGGCCAAGGUGGGGUUUGCU；
the microRNAs-Novel 2 have a sequence shown by SEQ ID NO.2 in a sequence table:
UUUCAUGUUCUCACUCAUGUAUGAUAGCUAAAAACGUUGAUCUCAUAGAGGGAGAGAAUGGAAUA；
the microRNAs-Novel 3 have a sequence shown by SEQ ID NO.3 in a sequence table.
AGCGGGGCUGGUGGCAUUUUGUUUCCAGUUCUGCACU。
Therefore, after the plasma of the SAP patient is further collected, and the microRNAs in the exosomes are extracted and detected, the three microRNAs have very good efficacy in early predicting the SAP condition no matter the three microRNAs are detected separately or in combination, and the three microRNAs are more remarkable in combined detection.
First, AP patient data is collected.
Inclusion criteria were: (ii) the AP diagnosis conforms to the International consensus of Atlanta revised 2012; ② the method comprises the following steps: sex, height, weight, blood fat and 5 indexes (age, blood sugar, aspartate transferase, lactate dehydrogenase and white blood cell count) required by a Ranson scoring system for hospitalization, 6 indexes (blood calcium, oxygen partial pressure, alkali deficiency, blood urea nitrogen, hematocrit and body fluid loss) required by a Ranson scoring system for hospitalization for 48h and the like; thirdly, three kinds of differentially expressed microRNAs in plasma exosomes are detected within 24 hours after admission; and fourthly, agreeing to participate in the study and signing an informed consent.
Exclusion criteria: AP already treated prior to admission; the disease distance of AP is more than 48 h; acute exacerbation of chronic pancreatitis; serious heart, lung and kidney diseases, rheumatic immune system diseases, metabolic diseases and other basic diseases; a history of trauma within a month; incomplete clinical data; patients who need surgical treatment one month before and after admission are excluded; patients with malignant tumor and the like are merged; patients with special medical history such as immunosuppressant and hormone and the like are taken for a long time.
Dividing the selected patients into a Non-SAP group and an SAP group, wherein the SAP satisfies one of the following conditions: local complications such as pancreatic necrosis, pancreatic abscess, pseudocyst and the like exist; organ failure; CT grade II or above. The remaining inclusion subjects were included in the Non-SAP group.
Secondly, constructing a prediction model
The specific method comprises the following steps: fasting venous blood from 108 AP patients on the day of admission was collected, 4mL of the collected blood was collected, centrifuged at a radius of 6cm at 3000r/min for 12min, and the supernatant was collected. The total miRNA in the supernatant is extracted by using a miRcute miRNA extraction and separation kit (cargo number: DP501, TIANGEN), the process is particularly clean, and the miRNA is prevented from being polluted by RNA enzyme in the air, so that the miRNA is decomposed and the detection accuracy is influenced. Then, the extracted total miRNAs were reverse-transcribed into cDNAs using a miRNA first strand cDNA synthesis kit (Stem-Loop method) (cat # MR101-01, vazyme), and then the relative expression amount of each miRNA was detected by qPCR using a ChamQ Universal SYBR qPCR Master Mix (cat # Q711-02, vazyme) (specific primers used in the qPCR process are shown in Table 1). Meanwhile, U6 is adopted as an internal reference gene. By using 2-ΔΔCtCalculating the expression level of miRNA.
TABLE 1 primer sequences for row qPCR detection of three miRNAs
Serum exosomes are obtained by separating and screening serum samples of patients with acute pancreatitis, the transmission microscope result is shown in fig. 2(A), the exosomes are round particles with double-layer membrane structures, the diameters of most examples are in the range of 30-150nm, and the exosome size identification result in the research is shown in fig. 2 (B).
Then, three kinds of microRNAs (Novel 1, Novel2 and Novel 3) in the qualified exosomes are further extracted and identified, the relative expression quantity of the three kinds of microRNAs is detected by an RT-qPCR method, and the result is expressed as (2)-ΔΔCt) The box line diagram is presented as shown in fig. 3.
The three differentially expressed microRNAs are modeled by binomial logistic regression, and a prediction equation C is constructed according to a multi-factor logistic regression resultmicRNA＝(-9.552)+0.025*Novel1+2.033*Novel2+2.384*Novel3。
The severity of AP is predicted early through the relative expression levels of three kinds of microRNAs, namely Novel1, Novel2 and Novel3, and the ROC curve is shown in FIG. 4.
Third, conclusion
The marker exocrine micRNAs-Novel1, the micRNAs-Novel2 and the micRNAs-Novel3 have obvious correlation with acute pancreatitis, the three differentially expressed micRNAs are used as biomarkers to be applied to the early prediction of SAP, and the early prediction of SAP has very good efficiency in the early prediction of SAP conditions no matter single detection or combined detection, and is more obvious in combined detection.
By taking the difference relative expression quantity of exocrine micRNAs-Novel1, micRNAs-Novel2 or micRNAs-Novel3 as variables, a prediction model CmicRNA is constructed as a prediction model of acute pancreatitis, and the probability of AP patients developing into SAP is determined according to a well-established fitting curve, so that high-risk patients are captured early, more active intervention is given early, and the specificity and accuracy of early diagnosis of severe acute pancreatitis can be greatly improved.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
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Claims (5)
1. An early prediction molecular marker for acute pancreatitis, which is characterized in that the marker comprises any one or the combination of at least two of the micro RNAs named as exocrine bodies-Novel 1, micro RNAs-Novel2 or micro RNAs-Novel 3;
the microRNAs-Novel 1 have a sequence shown by SEQ ID NO.1 in a sequence table;
the microRNAs-Novel 2 have a sequence shown by SEQ ID NO.2 in a sequence table;
the microRNAs-Novel 3 have a sequence shown by SEQ ID NO.3 in a sequence table.
2. An early prediction model for acute pancreatitis is characterized in that the prediction model is as follows: cmicRNA(-9.552) +0.025 Novel1+2.033 Novel2+2.384 Novel 3; the Novel1, the Novel2 and the Novel3 are respectively relative expression amounts of secreted microRNAs-Novel 1, microRNAs-Novel 2 and microRNAs-Novel 3 in serum of a sample to be detected.
3. The method for constructing the acute pancreatitis early prediction model of claim 2, wherein the method comprises the following steps:
(1) separating and screening a serum sample of an acute pancreatitis patient to obtain a serum exosome;
(2) further extracting and identifying three kinds of microRNAs (Novel 1, Novel2 and Novel 3) in qualified exosomes, and detecting the relative expression quantities of the three kinds of microRNAs by an RT-qPCR method;
(3) and modeling the three differentially expressed microRNAs by adopting binomial logistic regression, and constructing a prediction model equation according to a multi-factor logistic regression result.
4. The method for constructing an acute pancreatitis early prediction model of claim 3, wherein the specific primers used in the qPCR process for the three kinds of microRNAs (Novel 1, Novel2 and Novel 3) are shown in the following Table 1:
5. use of the acute pancreatitis early-stage prediction molecular marker of claim 1 in the preparation of an acute pancreatitis diagnostic reagent.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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