Enzymatic colorimetric coding-decoding system for weighted detection of multiple biomarkers and application

Abstract

The invention discloses an enzymatic colorimetric coding-decoding system for weighted detection of multiple biomarkers and application, and belongs to the field of biomedical detection. According to the enzymatic colorimetric coding-decoding system provided by the invention, the mapping relation between the color and the multi-dimensional biomarker can be realized through direct visual interpretation or simple spectral analysis. The invention also provides an enzymatic colorimetric coding-decoding method for weighted detection of multiple biomarkers, and the method comprises the following steps: converting multi-dimensional biomarker information into quantifiable and visually analyzable optical signals through an integrase catalyzed multicolor reaction, nucleic acid driven signal amplification and optical coding-decoding mechanism; and single-tube multi-dimensional joint detection, high information density output and rapid disease state discrimination are realized. The method is successfully applied to early screening of pancreatic cancer and can be expanded to dynamic monitoring of multiple markers of other cancers and infectious diseases in the future, and an efficient and low-cost solution is provided for precise medical treatment and digital medicine.

Description

Technical Field

[0001] The present invention relates to the field of biomedical detection, and in particular to an enzymatic colorimetric encoding-decoding system for weighted detection of multiple biomarkers and its application. Background Art

[0002] In the field of biomedical testing, the combined detection of multiple biomarkers is crucial for disease diagnosis, prognosis assessment, and personalized treatment. Single biomarker detection often fails to fully capture the full picture of a disease. This is especially true for complex diseases such as cancer, cardiovascular disease, and neurodegenerative diseases, where the pathogenesis is not driven by a single biomarker but rather by abnormal changes in multiple biomarkers. The coordinated detection of multiple biomarkers can provide more accurate and comprehensive information. Multi-biomarker testing can also monitor the dynamic changes of multiple disease-related markers in real time, providing important evidence for assessing disease progression and adjusting treatment plans. Furthermore, with the advancement of personalized treatment, the detection of multiple biomarkers can help physicians develop more precise treatment plans tailored to a patient's specific pathological characteristics. In clinical practice, the use of multi-biomarker detection strategies not only improves diagnostic efficiency but also enhances the reliability of disease monitoring, making it a crucial technology in modern medical research and clinical applications, with broad prospects and application value.

[0003] Traditional multiplex nucleic acid biomarker detection methods primarily utilize fluorescent PCR technology for separate testing. However, its multi-reaction tube design results in high sample consumption, cumbersome procedures, the introduction of batch-to-batch errors, and difficulty in efficiently integrating multidimensional biomarker information. Furthermore, fluorescent multiplex PCR technology uses probes of different wavelengths to detect multiple targets in a single tube. However, this technology relies on complex optical equipment, makes signal output difficult to visualize, and requires a step-by-step procedure. This results in high testing costs, limited throughput, and restricts its application in resource-limited settings (such as primary healthcare and point-of-care testing).

[0004] In addition, the colorimetric detection method is a detection technology that directly reflects the content of biomarkers by causing the color change of the detection system through an enzymatic reaction. Although the results can be directly observed by the naked eye, it has the advantages of intuitiveness, low cost, simple operation, and no need for professional equipment. However, the single color coding information density of the existing colorimetric detection method is low, and it is impossible to efficiently compress multi-dimensional biomarker information and reflect the coordinated changes of multi-dimensional biomarkers. Although multi-target information can be reflected by color mixing, there is a lack of a strict mathematical encoding-decoding system. It is difficult to establish a linear mapping relationship between the color ratio and the biomarker concentration, resulting in low detection sensitivity and insufficient quantitative ability. In the existing technology, there is no systematic solution that can simultaneously realize multi-dimensional signal encoding, continuous weighted calculation and single-tube high-density output.

[0005] The above defects seriously restrict the popularity and accuracy of multi-marker testing in clinical diagnosis, and there is an urgent need for an innovative technological breakthrough that combines high information density, mathematical interpretability and ease of operation. Summary of the Invention

[0006] The purpose of the present invention is to provide an enzymatic colorimetric encoding-decoding system and application for weighted detection of multiple biomarkers to solve the problems existing in the above-mentioned prior art. The system is based on the integration of nucleic acid signal amplification and enzymatic colorimetric coding to perform single-tube multidimensional biomarker detection. The detection method converts multidimensional biomarker information (such as miRNA, etc.) into quantifiable and visually analyzable optical signals through integrase-catalyzed multicolor reactions, nucleic acid-driven signal amplification and optical encoding and decoding mechanisms, thereby realizing single-tube multidimensional joint detection, high information density output and rapid disease status discrimination.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] The present invention provides an enzymatic colorimetric encoding-decoding system for weighted detection of multiple biomarkers, comprising:

[0009] (1) Specific template DNA designed for the biomarker to be tested;

[0010] (2) Capture chain;

[0011] (3) Horseradish peroxidase and alkaline phosphatase are conjugated to DNA barcodes to form enzyme-DNA covalent conjugates;

[0012] (4) Chromogenic substrate;

[0013] Wherein, different specific template DNAs are designed for different biomarkers to be detected, and the specific template DNA includes a functional region that binds to the biomarker to be detected and the capture chain and a DNA barcode functional region.

[0014] Preferably, the biomarker to be detected includes miRNA;

[0015] and / or the chromogenic substrate comprises 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) and p-nitrophenyl phosphate;

[0016] And / or the horseradish peroxidase and alkaline phosphatase are respectively modified with an azide group, and the DNA barcode is modified with DBCO.

[0017] The present invention also provides an enzymatic colorimetric encoding-decoding method for weighted detection of multiple biomarkers, comprising the following steps:

[0018] (1) Designing specific template DNA for each of the multiple biomarkers to be detected, wherein the specific template DNA specifically binds to the biomarker to be detected and then forms a circularized DNA under the action of DNA ligase; wherein the specific template DNA includes a functional region that binds to the biomarker to be detected and the capture strand and a DNA barcode region;

[0019] (2) using the circularized DNA as a template for the RCA reaction, capturing the strand as an amplification primer, and simultaneously adding an enzyme-DNA covalent conjugate to react, thereby converting the marker signal to be detected into an enzyme signal; wherein the enzyme-DNA covalent conjugate is formed by coupling a colorimetric enzyme with a DNA barcode;

[0020] (3) adding a substrate corresponding to the colorimetric enzyme to the reaction system of step (2) to generate a visible color coding signal through an enzymatic colorimetric reaction;

[0021] (4) By detecting the RGB values ​​or absorbance spectra of the colors generated by the enzymatic reaction, the concentrations of multiple biomarkers are decoded by correspondingly combining the linear superposition model of the absorption spectrum or the linear superposition model of the RGB values.

[0022] Preferably, the colorimetric enzyme comprises horseradish peroxidase and alkaline phosphatase, and the substrates corresponding to the colorimetric enzyme comprise 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) and p-nitrophenyl phosphate.

[0023] Preferably, the horseradish peroxidase and alkaline phosphatase are modified with an azide group, and the DNA barcode is modified with DBCO;

[0024] and / or the molar ratio of the horseradish peroxidase to the DNA barcode is 1:10;

[0025] And / or the molar ratio of the alkaline phosphatase to the DNA barcode is 1:10.

[0026] Preferably, the dynamic control mechanism for weighted calculation of the coding signal of the biomarker to be detected includes any one of the following control methods:

[0027] (1) Template ratio control: Continuous weighting is achieved by regulating the ratio of specific template DNA chains with DNA barcode functional regions to nonspecific template DNA chains without DNA barcode functional regions;

[0028] (2) Regulation of the number of template functional domains: By regulating the number of DNA barcode functional regions on the specific template DNA, discontinuous weighting of specific markers can be achieved;

[0029] (3) Enzyme-DNA barcode ratio regulation: Continuous weighting is achieved by regulating the ratio of enzyme-DNA covalent conjugates to pure DNA barcodes.

[0030] Preferably, the formula of the absorption spectrum linear superposition model is:

[0031]

[0032] The formula of the RGB value linear superposition model is:

[0033]

[0034] In the above formula, C i is the concentration of each component enzyme in the mixed colorimetric enzyme system, C reference is the reference concentration of each component enzyme in the mixed colorimetric enzyme, Absorption reference is the absorption spectrum of the enzyme reference concentration in the single enzyme system, Blank is the absorbance spectrum of the blank control; RGB_Value reference is the RGB value at the reference enzyme concentration in the single enzyme system, Absorption mixed Absorption spectrum of mixed colorimetric enzyme system, RGB_Value mixed RGB_Value is the RGB value of the mixed colorimetric enzyme system, Blank is the RGB value of the blank control.

[0035] The present invention also provides the use of the enzymatic colorimetric encoding-decoding system or the method in preparing a multiple miRNA biomarker detection product.

[0036] The present invention also provides the use of the enzymatic colorimetric encoding-decoding system or the method in any of the following:

[0037] (1) Application in the preparation of products for detecting multiple tumor biomarkers;

[0038] (2) Use in the preparation of products for diagnosing tumor disease status or tumor prognosis;

[0039] Preferably, the tumor comprises pancreatic cancer, and the multiple biomarkers comprise a plurality of miRNA biomarkers.

[0040] The present invention discloses the following technical effects:

[0041] By integrating enzymatic color coding, nucleic acid signal amplification, and continuous weighted calculation, the present invention demonstrates the following significant advantages in multidimensional biomarker detection:

[0042] 1. High Information Density and Intuitiveness: Through the encoding of red, green, yellow, and other colors and a rigorous mathematical mapping model (linear superposition formula), multidimensional biomarker information is compressed into an optical signal, improving information density compared to traditional fluorescence detection. It also supports direct visual interpretation (e.g., color differences discernible to the naked eye) or absorption spectrum analysis, eliminating the need for expensive spectrometers. Subsequent reflection spectra can be collected via smartphone for more convenient and accurate analysis, making it suitable for primary care and point-of-care testing (POCT).

[0043] 2. Single-tube efficient detection: Based on orthogonal template design and RCA isothermal amplification technology, it realizes the simultaneous detection of multiple miRNA targets in a single reaction tube, reduces sample consumption to less than 2 mL, and simplifies the operation steps. At the same time, it avoids batch-to-batch errors caused by multiple-tube reactions, and the detection throughput is increased to 96 samples / batch, which is suitable for large-scale screening. When the number of targets is consistent with the number of colorimetric enzymes, the concentration of each target can be decoded; when the number of targets is greater than the number of colorimetric enzymes, targets with similar effects in the disease can be mapped to the same colorimetric enzyme, so that the color of the final test result responds to the overall expression trend of the miRNA spectrum and accurately responds to the overall status of the disease.

[0044] 3. Continuous Weighted Calculation and Model Adaptability: Through dynamic template ratio regulation and competitive binding mechanisms, continuous multiplication and addition of biomarker concentrations is achieved, adapting to complex disease prediction models. In clinical validation of pancreatic cancer, the present invention was highly consistent with qPCR results, with high sensitivity and specificity (qPCR: AUC value was 0.96 (95% CI: 89.41%, 100%), sensitivity was 100% (95% CI: 75.75%, 100%), specificity was 90% (95% CI: 69.90%, 98.22%), and overall accuracy was 93% (95% CI: 79.2%, 99.2%); the present method: AUC value was 0.97 (95% CI: 92.31%, 100%), sensitivity was 100% (95% CI: 75.75%, 100%), and specificity was 85% (95% CI: 63.96%, 94.76%)).

[0045] 4. High-dimensional expansion and intelligent analysis compatibility: By encoding more color systems, it can support the integrated decoding of higher-dimensional information, enabling more detailed disease classification, such as disease subtyping (e.g., pancreatic cancer stage I / II) or multi-disease testing (e.g., cancer and cardiovascular markers). This can be subsequently combined with AI-driven reflectance spectroscopy analysis (e.g., convolutional neural networks) or integrated with portable devices (e.g., smartphones).

[0046] 5. This invention has been successfully applied to the early screening of pancreatic cancer (32 cases clinically verified) and can be expanded to the dynamic monitoring of multiple markers for other cancers (such as lung cancer, breast cancer) and infectious diseases in the future, providing efficient and low-cost solutions for precision medicine and digital medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 Schematic diagram and result diagram of two-dimensional enzymatic colorimetric encoding and decoding of the present invention; a: Schematic diagram of two-dimensional enzymatic colorimetric encoding and decoding; b: Matrix diagram of different concentrations of AP and HRP mixed for enzymatic colorimetric encoding; c: Absorption spectra of four representative mixed coloring solutions in Figure b; d: Spectral decoding formula; e: RGB values ​​of four representative mixed coloring solutions in Figure b; f: Color decoding formula; g: Schematic diagram of the application of the spectral decoding formula and the color decoding formula to decode a set of colorimetrically coded solutions; hi: Analysis results of colorimetrically coded solutions using spectral decoding and color decoding;

[0049] Figure 2 Schematic diagram and results of the enzymatic colorimetric encoding and decoding method of the present invention for dual miRNA target detection; a: Schematic diagram of enzymatic colorimetric encoding technology for dual miRNA target detection; b: Schematic diagram of the relationship between absorbance and RGB values ​​and target concentration; c: A set of enzymatic colorimetric reaction results; d: Linear analysis of target concentration, absorbance, and RGB in Figure c; e: Decoding of the encoding results in Figure c using spectral or colorimetric methods;

[0050] Figure 3 Schematic diagrams (a, b) and a detection performance graph (c) of the enzymatic colorimetric encoding and decoding method of the present invention for achieving weighted detection;

[0051] Figure 4 Schematic diagram (a) and detection result diagram (b) of the enzymatic colorimetric encoding and decoding method of the present invention for realizing simulated disease model detection;

[0052] Figure 5 Figures 2 and 3 show the detection results and detection performance of the pancreatic cancer diagnostic model implemented by the present invention; a: Diagnostic model schematic and actual sample detection results achieved by applying the present invention; b: Area under the ROC curve of the present invention; c: Detection performance of the present invention; d: qPCR detection results; e: Area under the qPCR RCA curve. DETAILED DESCRIPTION

[0053] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0054] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. The intermediate value within any stated value or stated range, and each smaller range between any other stated value or intermediate value within the stated range, is also encompassed within the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0055] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein may also be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of any conflict with any incorporated document, the contents of this specification shall prevail.

[0056] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from the description of the invention. The description and examples are intended to be illustrative only.

[0057] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0058] HRP: horseradish peroxidase;

[0059] AP: alkaline phosphatase;

[0060] pNPP: p-nitrophenyl phosphate;

[0061] ABTS: 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid);

[0062] NHS-PEG4-N3: N-hydroxysuccinimide-polyethylene glycol-azide;

[0063] RCA: rolling circle amplification.

[0064] Example 1: Two-dimensional enzymatic encoding and decoding

[0065] 1. Experimental methods

[0066] In the hybrid coding reaction system, HRP (horseradish peroxidase) and AP (alkaline phosphatase) are selected as the core catalytic enzymes to catalyze the ABTS (green) and pNPP (yellow) color development reactions, respectively. By regulating different enzyme concentrations to generate adjustable color scales, two-dimensional color coding is achieved. The coding dimension can be expanded by introducing other color development enzymes. The specific operation is as follows:

[0067] (1) Construction of the coding system for enzymatic multidimensional color development

[0068] Prepare a colorimetric substrate mixture by mixing 2 mM pNPP, 2 mM ABTTS, and 0.5 mM H2O2, and adjust the concentrations of AP and HRP precisely ( Figure 1 b) to achieve an enzyme-catalyzed colorimetric reaction. After the reaction was carried out at room temperature for 30 minutes, the absorbance (characteristic absorption peaks at 400 nm, 415 nm, and 730 nm) spectrum of the resulting mixed solution in a 96-well plate was measured using a multifunctional microplate reader (Infinite 200PRO, Tecan, Switzerland). Simultaneously, photos of the solutions were taken, and the average RGB values ​​of the solutions were analyzed using Photoshop software.

[0069] (2) Construction of mathematical mapping model

[0070] For the above colorimetric coding system, a linear superposition model of color mixing is established, and the linear relationship between the absorption spectrum and the RGB value is used to define the coding formula:

[0071]

[0072] The formula of the RGB value linear superposition model is:

[0073]

[0074] In the above formula, C i is the concentration of each component enzyme in the mixed colorimetric enzyme, C reference is the reference concentration of each component enzyme in the mixed colorimetric enzyme, Absorption reference is the absorption spectrum of the enzyme reference concentration in the single enzyme system, Blank is the absorbance spectrum of the blank control; RGB_Value reference is the RGB value at the reference enzyme concentration in the single enzyme system, Absorption mixed Absorption spectrum of mixed colorimetric enzyme, RGB_Value mixed The above mixed colorimetric enzyme refers to the mixed system of HRP and AP colorimetric enzymes. Blankis the RGB value of the blank control.

[0075] (3) Visual / spectral dual-mode decoding

[0076] Based on this mathematical mapping relationship, the concentrations of each component can be calculated using only the absorption spectra (characteristic absorption peaks at 400nm, 415nm, and 730nm) or RGB channels (R, G, B values) at reference concentrations for each component in a single enzyme system and in a mixed encoding system, achieving a precise mapping of color signals to colorimetric enzyme concentrations. By precisely correlating multiple biomarker concentrations with colorimetric enzyme concentrations, a precise mapping of color signals to biomarker concentrations can be achieved.

[0077] 2. Results and Analysis

[0078] Constructing a strict mathematical encoding-decoding system based on colorimetric coding is the basis for realizing multi-target single-tube colorimetric detection, such as Figure 1 As shown in (a), this is a schematic diagram of two-dimensional encoding or decoding of colors using two colorimetric enzymes, HRP and AP.

[0079] Directly mix different concentrations of AP and HRP for enzymatic colorimetric coding ( Figure 1 (b) Visually, the color of the mixed solution exhibits a visual color coding as the enzyme type and concentration change. Further analysis of the absorption spectra reveals that the absorption spectrum of the mixed solution is equal to the sum of the absorption spectra of enzyme solutions of the same concentration minus the absorption spectrum of the blank sample (enzyme concentration of 0). Figure 1 Figure c shows the absorption spectra of four representative mixed coloring solutions. At the same time, the RGB values ​​of the mixed solutions also show a similar relationship ( Figure 1 These rigorous mathematical relationships lay the foundation for the encoding and decoding of enzymatic colorimetric signals ( Figure 1 The formula was applied to a set of decoded colorimetric coded solutions and the results showed good results. In addition, the RGB values ​​of the colorimetric coded solutions can also be used for decoding with the same resolution capability ( Figure 1 in gi).

[0080] Example 2: Application of the present invention in dual miRNA target detection

[0081] Template design principles and detection principles:

[0082] (1) Design target miRNA-specific template DNA: Template A is designed for target A. Template A has functional binding regions, including a specific binding region for target A and a capture binding region, as well as a DNA barcode region. The 5' end of template A has an 11nt region that is complementary to the 5' end region of target A, and the 3' end of template A has an 11nt region that is complementary to the 3' end region of target A. The specific number of complementary bases is related to the number of bases in target A, so that target A and template A are completely complementary, and after complementation, template A forms a ring with target A as the connecting primer. At the same time, there is a 13nt DNA barcode region on template A, so that the rolling circle amplification product using template A as the rolling circle amplification template can be complementary to the bases of different enzyme-DNA barcode covalent conjugates. Finally, there is a universal 20nt capture binding region on template A, so that template A can be pulled down and fixed on the enzyme-linked immunosorbent assay (ELISA) plate. Different template sequences can be designed according to different targets.

[0083] (2) Circularization of the template DNA chain: The target miRNA is used as a ligation primer and mixed with the corresponding template DNA. The template DNA is circularized using DNA ligase. Different target miRNAs can be used as ligation primers for different template DNAs for circularization.

[0084] (3) Immobilizing the miRNA-circularized template DNA complex on the solid phase interface: Adding a biotin-modified capture DNA strand that is completely complementary to the capture binding region of the template strand to a streptavidin-coated ELISA plate and incubating the strand, thereby coating the ELISA plate with the capture DNA strand. Incubating the miRNA-circularized template DNA complex on the ELISA plate coated with the capture DNA strand, thereby immobilizing the miRNA-circularized template DNA complex on the solid phase interface.

[0085] (4) Rolling circle amplification and colorimetric enzyme signal introduction: The capture DNA chain is used as the amplification primer, and the circularized template DNA is used as the template for RCA to amplify the signal. At the same time, the enzyme-DNA covalent conjugate formed by coupling all enzymes and DNA barcodes is added to the reaction system. The target concentration and the content of the RCA amplification product have a linear quantitative relationship in terms of absorbance. The enzyme-DNA covalent conjugate binds to the sequence of the RCA amplification product, thereby converting the target miRNA signal into a colorimetric enzyme signal.

[0086] (5) Enzymatic colorimetric reaction and signal detection and decoding: By adding the corresponding substrate of the colorimetric enzyme, an enzymatic colorimetric reaction generates a visible coded color, reflecting the collective expression level of the target miRNA panel. The color can be decoded by visual color (RGB value or chromaticity value) or by detecting the absorption spectrum, and the complex target miRNA content can be extracted from the color result. Subsequently, the reflectance spectrum can be collected by a smartphone and decoded by a pre-trained convolutional neural network (CNN), which is more accurate and convenient.

[0087] Based on the reagent combination and performance optimization of the present invention, the enzymatic colorimetric coding method is combined with nucleic acid signal amplification to perform multiple quantitative detection of simulated dual miRNA targets (target A, target B) ( Figure 2 a). The specific steps are as follows:

[0088] 1. Preparation of enzyme-DNA covalent conjugates

[0089] First, a 30 μM enzyme solution (HRP or AP) was mixed with 1.5 mM of the bifunctional crosslinker NHS-PEG4-N3 in 1× PBS buffer. The mixture was gently stirred at 300 rpm at 25°C for 1 hour to convert the enzyme's amino groups to azido groups. After the reaction was complete, the enzyme was purified using a 30 kDa ultrafiltration centrifuge tube and washed six times with 1× PBS buffer (pH 7.4) to remove excess NHS-PEG4-N3. Next, the azide-modified enzyme was mixed with a DBCO-modified DNA barcode at a concentration ratio of 10 μM:100 μM and incubated at 4°C for 24 hours to generate an enzyme-DNA barcode conjugate (i.e., enzyme-DNA covalent conjugate), in which AP bound to DNA barcode A and HRP bound to DNA barcode H. To remove excess DBCO-DNA, the product was filtered again through a 30 kDa ultrafiltration centrifuge tube and washed with 1× PBS buffer (pH 7.2). Finally, the enzyme-DNA barcode conjugates were collected and stored at 4°C for later use. A total of two enzyme-DNA covalent conjugates were obtained: HRP-DNA barcode H and AP-DNA barcode A.

[0090] 2. Dual miRNA target detection based on nucleic acid amplification technology

[0091] First, circularization of the template DNA was achieved by preparing a 100 μL reaction mixture consisting of 1× SplintR Ligase buffer, the miRNA sample, 1 μM of each of the two corresponding template DNAs (template A and template B), and 40 U / μL of SplintR Ligase. This mixture was incubated at 16°C for 1 hour to form circularized template DNA-target heterodimers. Simultaneously, the capture strands were coated onto the plate by adding 10 μM of 5'-biotinylated capture strands to each well of a streptavidin-coated 96-well plate, followed by incubation at room temperature for 2 hours. The wells were then washed three times with PBS to remove excess capture strands. The circular template DNA-targets were then incubated with the capture strand-coated plate for 0.5 hours at room temperature, followed by three washes. Rolling circle amplification (RCA) was then performed by mixing 0.4 mM dNTPs, 0.2 U / μL phi29 DNA polymerase, 100 nM HRP-DNA barcode H, and 100 nM AP-DNA barcode A in 200 μL of 1× phi29 DNA polymerase buffer system and incubating at 30°C for 0.5 h. Finally, an enzyme-catalyzed colorimetric reaction was performed by adding 200 μL of a colorimetric solution (5 mM pNPP, 5 mM ABTS, and 0.5 mM H2O2 in 1× PBS) to the enzyme plate. The reaction was allowed to proceed at room temperature for 30 minutes. The absorbance was measured using a multi-function microplate reader, and the color development was photographed for analysis.

[0092] The corresponding nucleic acid sequences can be found in Table 1.

[0093] Table 1 Nucleic acid sequences for dual miRNA target detection

[0094]

[0095] The results showed that the platform can detect miRNA concentrations as low as 3.19 pM. The absorbance and RGB values ​​are linearly related to the target concentration, consistent with the enzymatic colorimetric reaction, demonstrating a good mapping relationship between miRNA content and colorimetric enzyme content ( Figure 2 The encoded results can also be decoded by spectral or colorimetric methods to extract information about the target miRNA content ( Figure 2 (e)

[0096] Example 3: Continuous weighted calculation application of the present invention

[0097] Since different targets have different weights in different diseases, in order to directly and accurately map complex disease models, the present invention provides a solution for weighted calculation of biomarker concentrations (the weighted calculation principle is as follows Figure 3The final color signal can directly reflect the disease model score. The specific method is any of the following (i)-(iii):

[0098] (i) Template DNA Ratio Control: In the process described in Example 2 above, two template DNA strands were designed for the same target miRNA (miR-150). One strand, signaling template strand 1, contains a DNA barcode region, allowing the RCA product to base-pair with the enzyme-linked DNA barcode. The other strand, non-signaling template strand 0, lacks the DNA barcode region. By controlling the ratio of these two strands in the system, weighting can be achieved, with the signaling strand ratio serving as the weighting factor. This method allows for continuous target weighting. For example: when signal template chain 1 / (signal template chain 1 + non-signal template chain 0) = 0, the weighting coefficient is specified as 0; when signal template chain 1 / (signal template chain 1 + non-signal template chain 0) = 0.25, the weighting coefficient is specified as 1; when signal template chain 1 / (signal template chain 1 + non-signal template chain 0) = 0.5, the weighting coefficient is specified as 2; when signal template chain 1 / (signal template chain 1 + non-signal template chain 0) = 0.75, the weighting coefficient is specified as 3; when signal template chain 1 / (signal template chain 1 + non-signal template chain 0) = 1, the weighting coefficient is specified as 4.

[0099] (ii) Template type regulation: In the above-mentioned Example 2 operation process, the weighting of a specific target can be controlled by designing the number of DNA barcode functional regions on the template DNA. The number of DNA barcode functional regions is used as a weighting coefficient. This method cannot achieve continuous weighting of targets. For example: Template A has 0 DNA barcode functional regions, and the weighting coefficient is specified as 0; Template B has 1 DNA barcode functional region, and the weighting coefficient is specified as 1; Template chain C has 2 DNA barcode functional regions, and the weighting coefficient is specified as 2; Template chain D has 3 DNA barcode functional regions, and the weighting coefficient is specified as 3; Template chain E has 4 DNA barcode functional regions, and the weighting coefficient is specified as 4;

[0100] (iii) Enzyme-DNA barcode ratio control: By adding enzyme-DNA barcode conjugates and pure DNA barcodes in different ratios, weighting can be achieved by controlling the ratio of the two in the system, where the enzyme-barcode ratio serves as a weighting coefficient. For example, when enzyme-DNA barcode conjugate / (enzyme-DNA barcode conjugate + DNA barcode) = 0, the weighting coefficient is set to 0; when enzyme-DNA barcode conjugate / (enzyme-DNA barcode conjugate + DNA barcode) = 0.25, the weighting coefficient is set to 1; when enzyme-DNA barcode conjugate / (enzyme-DNA barcode conjugate + DNA barcode) = 0.5, the weighting coefficient is set to 2; when enzyme-DNA barcode conjugate / (enzyme-DNA barcode conjugate + DNA barcode) = 0.75, the weighting coefficient is set to 3; and when enzyme-DNA barcode conjugate / (enzyme-DNA barcode conjugate + DNA barcode) = 1, the weighting coefficient is set to 4.

[0101] The corresponding nucleic acid sequences can be found in Table 2.

[0102] Table 2 Nucleic acid sequences verified by continuous weighted calculation

[0103]

[0104]

[0105] like Figure 3 As shown in Figure c, the performance of the above three schemes is verified. The results show that all three schemes can achieve good weighted calculation.

[0106] Example 4: Application of the present invention in disease model detection

[0107] like Figure 4 As shown, a simulated disease prediction model is set up: Score = 0.5 × miR-150 + 0.5 × miR-636 - (0.75 × miR-143 + 0.25 × miR-223). The positive coefficients of miR-150 and miR-636 in the model correspond to the HRP enzyme, and the negative coefficients of miR-143 and miR-223 in the model correspond to the AP enzyme. This allows the test results to clearly and intuitively display the disease prediction model score. The weighting method uses the template DNA ratio control method. The specific operation steps are shown in Example 3.

[0108] The corresponding nucleic acid sequences can be found in Table 3.

[0109] Table 3 Nucleic acid sequences for disease model detection and verification

[0110]

[0111]

[0112] The results show that both spectral features and visual colorimetry can effectively reflect the model scores and can classify samples well ( Figure 4 The enzymatic colorimetric coding detection system combined with the weighted computational model provides a more comprehensive digital medical computing platform that can achieve more sophisticated calculations to better meet the needs of clinical testing.

[0113] Example 5: Application of the present invention in actual pancreatic cancer sample detection

[0114] A pancreatic cancer dataset meeting the requirements was selected from the Gene Expression Omnibus (GEO) database, and differentially expressed miRNAs were screened. Subsequently, binary logistic regression was used to identify the final pancreatic cancer diagnostic multi-miRNA markers: miR-154-5p, miR-629-5p, miR-99a-5p, miR-5006-5p, and miR-575. A pancreatic cancer diagnostic model was constructed with the following scoring formula: Score = 0.43 × miR-154-5p + 0.57 × miR-629-5p + 0.41 × miR-99a-5p - (0.09 × miR-5006-5p + 0.51 × miR-575).

[0115] Construction of pancreatic cancer diagnostic model:

[0116] The pancreatic cancer prediction model was constructed and validated based on microarray datasets GSE21169241, GSE163031, and GSE10681742 downloaded from the Gene Expression Omnibus (GEO). The GSE211692 dataset contains serum samples from 851 pancreatic cancer patients and 1972 healthy individuals. The GSE163031 dataset includes tissue samples from 25 pancreatic cancer patients and 13 non-cancerous pancreatic tissue samples. The GSE106817 dataset contains serum samples from 115 pancreatic cancer patients and 2759 healthy individuals.

[0117] The LIMMA package was used in the Bioconductor project in R software to identify differentially expressed miRNAs. Given the goal of this study to analyze serum miRNAs and conduct subsequent testing, the aim was to identify miRNAs with significant differential expression in serum samples. For serum samples in the GSE211692 dataset, the screening criteria for differentially expressed miRNAs were a log2 fold change (FC) ≥ 3.5 or a log2 fold change (FC) ≤ ​​-2.5, with a false discovery rate (FDR) < 0.05; for tissue samples in the GSE163031 dataset, the screening criteria were |log2 fold change (FC)| ≥ 1, with an FDR < 0.05. Overlapping miRNA expression features between the GSE211692 and GSE163031 datasets were retained for further investigation.

[0118] Subsequently, binary logistic regression analysis was performed using Statistical Software for Social Sciences (SPSS), and a forward selection method was used to select the most important miRNA biomarkers and construct a pancreatic cancer diagnosis and prediction model. Finally, ROC (Receiver Operating Characteristic Curve) analysis was used to evaluate the performance of the model.

[0119] Based on the established pancreatic cancer diagnostic model, 32 serum samples were collected, including 12 pancreatic cancer samples and 20 normal human samples. The specific steps are as follows:

[0120] (1) Total miRNA extraction from serum: Extraction was performed using a column-based miRNA extraction kit provided by Sangon Biotechnology (Shanghai, China). All samples were purified according to the manufacturer's instructions, and the eluted RNA was stored in nuclease-free water at −80°C until needed.

[0121] (2) LATE-PCR amplification: The extracted miRNA was first transcribed into cDNA using a one-step miRNA reverse transcription kit provided by Sangon Biotechnology (Shanghai, China). Subsequently, quantitative PCR (qPCR) detection was performed using a real-time PCR kit provided by TaKaRaBio (Japan). All samples were processed according to the manufacturer's instructions. After the miRNA was reverse transcribed into cDNA, LATE-PCR amplification was performed using a 2× TaqMan fast qPCR mixture provided by Sangon Biotechnology (Shanghai, China). The concentrations of the upstream primer and the downstream primer were adjusted to excess and limiting concentrations, respectively. The excess primer concentration was set to 2 μM, and the limiting primer concentration was set to 50 nM. The limiting primer was derived from the universal primer in the one-step miRNA reverse transcription kit provided by China Biological Products Company. All other steps were processed according to the manufacturer's instructions.

[0122] (3) The weighted template DNA ratio control method was used to apply the above method to the disease prediction model detection. The corresponding nucleic acid sequences can be seen in Table 4.

[0123] Table 4 Nucleic acid sequences detected by clinical pancreatic cancer diagnostic model

[0124]

[0125] 5006-5p)AGGTTATCAGGCAAGCACGAATTCCACCTCCT

[0126] Signal template chain PO4-

[0127] 1(miR-575)ACTGGCTCAGAATCAAGTAATCCAGAATCAAGTAATCCAGAA SEQ ID NO.38

[0128] AGGTTATCAGGCAAGCACGAAGCTCCTGTCCA

[0129] Signal template chain PO4-

[0130] 0(miR-575)ACTGGCTCAGAAGTCAGTCTGTCAAAAGTCAGTCTGTCAAAA SEQ ID NO.39

[0131] AGGTTATCAGGCAAGCACGAAGCTCCTGTCCA

[0132] like Figure 5 As shown, the results analysis showed that the diagnostic performance of pancreatic cancer was an area under the ROC curve of 0.97 (95% CI: 92.31%, 100%), a sensitivity of 100% (95% CI: 75.75%, 100%), and a specificity of 85% (95% CI: 63.96%, 94.76%). The overall accuracy of this method in diagnosing pancreatic cancer in clinical samples was 91% (95% CI: 75% to 98%). qPCR: The AUC value was 0.96 (95% CI: 89.41%, 100%), the sensitivity was 100% (95% CI: 75.75%, 100%), the specificity was 90% (95% CI: 69.90%, 98.22%), and the overall accuracy was 93% (95% CI: 79.2%, 99.2%). It can be seen that the method provided by the present invention is highly consistent with qPCR.

[0133] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. An enzymatic colorimetric encoding-decoding system for weighted detection of multiple biomarkers, characterized in that: include: (1) Specific template DNA designed for the biomarker to be tested; (2) Capture chain; (3) Horseradish peroxidase and alkaline phosphatase are conjugated to DNA barcodes to form enzyme-DNA covalent conjugates; (4) Chromogenic substrate; Different specific template DNAs are designed for different biomarkers to be detected, and the specific template DNA includes a functional region that binds to the biomarker to be detected and the capture chain, as well as a DNA barcode functional region.

2. The enzymatic colorimetric encoding-decoding system according to claim 1, wherein The biomarkers to be tested include miRNA; and / or the chromogenic substrate comprises 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) and p-nitrophenyl phosphate; And / or the horseradish peroxidase and alkaline phosphatase are respectively modified with an azide group, and the DNA barcode is modified with DBCO.

3. An enzymatic colorimetric encoding-decoding method for weighted detection of multiple biomarkers, characterized in that: The following steps are involved: (1) Designing specific template DNA for each of the multiple biomarkers to be detected, wherein the specific template DNA specifically binds to the biomarker to be detected and then forms a circularized DNA under the action of DNA ligase; wherein the specific template DNA includes a functional region that binds to the biomarker to be detected and the capture strand and a DNA barcode functional region; (2) using the circularized DNA as a template for the RCA reaction, capturing the strand as an amplification primer, and simultaneously adding an enzyme-DNA covalent conjugate to react, thereby converting the marker signal to be detected into an enzyme signal; wherein the enzyme-DNA covalent conjugate is formed by coupling a colorimetric enzyme with a DNA barcode; (3) adding a substrate corresponding to the colorimetric enzyme to the reaction system of step (2) to generate a visible color coding signal through an enzymatic colorimetric reaction; (4) By detecting the RGB values ​​or absorbance spectra of the colors generated by the enzymatic reaction, the concentrations of multiple biomarkers are decoded by combining the linear superposition model of the absorption spectrum or the linear superposition model of the RGB values.

4. The method according to claim 3, wherein The colorimetric enzyme includes horseradish peroxidase and alkaline phosphatase, and the substrates corresponding to the colorimetric enzyme include 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) and p-nitrophenyl phosphate.

5. The method according to claim 4, wherein The horseradish peroxidase and alkaline phosphatase are modified with an azide group, respectively, and the DNA barcode is modified with DBCO; and / or the molar ratio of the horseradish peroxidase to the DNA barcode is 1:10; And / or the molar ratio of the alkaline phosphatase to the DNA barcode is 1:

10.

6. The method according to claim 3, wherein The dynamic control mechanism for weighted calculation of the coding signal of the biomarker to be tested includes any of the following control methods: (1) Template ratio control: Continuous weighting is achieved by controlling the ratio of specific template DNA chains with DNA barcode functional regions to nonspecific template DNA chains without DNA barcode functional regions; (2) Regulation of the number of template functional domains: By regulating the number of DNA barcode functional regions on the specific template DNA, discontinuous weighting of specific markers can be achieved; (3) Enzyme-DNA barcode ratio regulation: Continuous weighting is achieved by regulating the ratio of enzyme-DNA covalent conjugates to pure DNA barcodes.

7. The method according to claim 3, wherein The formula of the absorption spectrum linear superposition model is: The formula of the RGB value linear superposition model is: In the above formula, C i is the concentration of each component enzyme in the mixed colorimetric enzyme system, C reference is the reference concentration of each component enzyme in the mixed colorimetric enzyme, Absorption reference is the absorption spectrum of the enzyme reference concentration in the single enzyme system, Blank is the absorbance spectrum of the blank control; RGB_Value reference is the RGB value at the reference enzyme concentration in the single enzyme system, Absorption mixed Absorption spectrum of mixed colorimetric enzyme system, RGB_Value mixed RGB_Value is the RGB value of the mixed colorimetric enzyme system, Blank is the RGB value of the blank control.

8. Use of the enzymatic colorimetric encoding-decoding system according to any one of claims 1 to 2 or the method according to any one of claims 3 to 7 in the preparation of a multiple miRNA biomarker detection product.

9. Use of the enzymatic colorimetric encoding-decoding system according to any one of claims 1 to 2 or the method according to any one of claims 3 to 7 in any of the following: (1) Application in the preparation of products for detecting multiple tumor biomarkers; (2) Application in the preparation of products for diagnosing tumor disease status or tumor prognosis.

10. The use according to claim 9, characterized in that The tumor includes pancreatic cancer, and the multiple biomarkers include multiple miRNA biomarkers.

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