An enzymatic colorimetric encoding-decoding system for multiplex biomarker weighted detection and applications

Abstract

The application discloses an enzymatic colorimetric encoding-decoding system and application for multiple biomarker weighted detection, and belongs to the field of biomedical detection. The enzymatic colorimetric encoding-decoding system can realize the mapping relationship between color and multidimensional biomarkers through direct visual interpretation or simple spectrum analysis. The application further provides an enzymatic colorimetric encoding-decoding method for multiple biomarker weighted detection. By integrating enzyme-catalyzed multicolor reaction, nucleic acid-driven signal amplification and optical encoding-decoding mechanism, the multidimensional biomarker information is converted into quantifiable and visually analyzable optical signals, realizing single-tube multidimensional joint detection, high information density output and rapid disease state discrimination. The application has been successfully applied to early screening of pancreatic cancer, and can be extended to dynamic monitoring of multiple markers of other cancers and infectious diseases in the future, providing an efficient and low-cost solution for precision medicine and digital medicine.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection, and in particular to an enzyme-catalyzed colorimetric encoding-decoding system and its application for weighted detection of multiple biomarkers. Background Technology

[0002] In the field of biomedical testing, the combined detection of multiple biomarkers is crucial for disease diagnosis, prognostic assessment, and personalized treatment. The detection of a single biomarker often fails to comprehensively reflect the full picture of a disease, especially in the diagnosis of complex diseases such as cancer, cardiovascular disease, and neurodegenerative diseases. The pathogenesis of these diseases is not caused by a single biomarker but by abnormal changes in multiple biomarkers. The synergistic detection of multiple biomarkers can provide more accurate and comprehensive information. Multi-biomarker detection can also monitor the dynamic changes of multiple disease-related biomarkers in real time, providing important evidence for assessing disease progression and adjusting treatment plans. Furthermore, with the development of personalized medicine, multi-biomarker detection can help doctors formulate more precise treatment plans based on the specific pathological characteristics of patients. In clinical practice, the use of multi-biomarker detection strategies not only improves diagnostic efficiency but also enhances the reliability of disease monitoring, becoming an important technology in modern medical research and clinical application with broad prospects and application value.

[0003] Traditional multiplex nucleic acid biomarker detection methods mainly utilize fluorescent PCR technology for multi-tube detection. However, this multi-reaction tube design leads to high sample consumption, cumbersome procedures, and is prone to batch-to-batch errors, making it difficult to efficiently integrate multidimensional biomarker information. Furthermore, while fluorescent multiplex PCR technology achieves multi-target detection in a single tube using probes of different wavelengths, it relies on complex optical equipment, its signal output is not easily visualized, and it requires step-by-step operation, resulting in high detection costs, limited throughput, and restricting its application in resource-constrained scenarios (such as primary healthcare and point-of-care testing).

[0004] Furthermore, colorimetric detection methods are techniques that directly reflect biomarker levels by causing color changes in the detection system through enzymatic reactions. While offering advantages such as intuitiveness, low cost, ease of operation, and no need for specialized equipment, the low information density of single-color encoding in existing colorimetric methods makes it difficult to efficiently compress multidimensional biomarker information and reflect the synergistic changes of multiple biomarkers. Although color mixing can reflect multi-target information, the lack of a rigorous mathematical encoding-decoding system makes it difficult to establish a linear mapping relationship between color ratios and biomarker concentrations, resulting in low detection sensitivity and insufficient quantitative capabilities. Currently, there is no systematic solution that can simultaneously achieve multidimensional signal encoding, continuous weighted calculation, and high-density single-tube output.

[0005] The above-mentioned shortcomings severely restrict the widespread use and accuracy of multi-marker detection in clinical diagnosis, and there is an urgent need for an innovative technology breakthrough that combines high information density, mathematical interpretability, and ease of operation. Summary of the Invention

[0006] The purpose of this invention is to provide an enzyme-catalyzed colorimetric coding-decoding system and its application for weighted detection of multiple biomarkers, in order to solve the problems existing in the prior art. Based on the integration of nucleic acid signal amplification and enzyme-catalyzed colorimetric coding, this invention enables single-tube multidimensional biomarker detection. By integrating enzyme-catalyzed multicolor reactions, nucleic acid-driven signal amplification, and optical coding-decoding mechanisms, this method converts multidimensional biomarker information (such as miRNA) into quantifiable and visually interpretable optical signals, thereby achieving single-tube multidimensional joint detection, high information density output, and rapid disease state identification.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] This invention provides an enzyme-catalyzed 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 coupled to DNA barcodes to form enzyme-DNA covalent conjugates;

[0012] (4) Developing substrate;

[0013] Different specific template DNAs are designed for different biomarkers to be tested. The specific template DNA includes a functional region that binds to the biomarker to be tested and the capture strand, as well as a DNA barcode functional region.

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

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

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

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

[0018] (1) Specific template DNA is designed for various biomarkers to be tested. After the specific template DNA specifically binds to the biomarker to be tested, it forms circular DNA under the action of DNA ligase. The specific template DNA includes a functional region that binds to the biomarker to be tested and the capture strand, as well as a DNA barcode region.

[0019] (2) Using circularized DNA as a template for the RCA reaction and the capture strand as an amplification primer, an enzyme-DNA covalent conjugate is added to carry out the reaction, converting the signal of the target marker into an enzyme signal; wherein, the enzyme-DNA covalent conjugate is formed by coupling a colorimetric enzyme with a DNA barcode.

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

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

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

[0023] Preferably, the horseradish peroxidase and alkaline phosphatase are modified with azide groups, 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 regulation mechanism for weighting the encoded signal of the biomarker to be tested includes any one of the following regulation methods:

[0027] (1) Template ratio regulation: By regulating the ratio of specific template DNA strands with DNA barcoding functional regions to non-specific template DNA strands without DNA barcoding functional regions, continuous weighting is achieved;

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

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

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

[0031]

[0032] The formula for the linear superposition model of RGB values ​​is:

[0033]

[0034] In the above formula, C i C represents the enzyme concentration of each component in the mixed colorimetric enzyme system. reference Absorption is the reference concentration of each component enzyme in the mixed colorimetric enzyme. reference Absorption spectrum at the reference concentration of the enzyme in a single-enzyme system. Blank Absorption spectrum of the blank control; RGB_Value reference The RGB values ​​represent the enzyme reference concentration in the single-enzyme system, and the absorption... mixed Absorption spectrum of the mixed colorimetric enzyme system, RGB_Value mixed RGB_Value represents the RGB values ​​of the mixed colorimetric enzyme system. Blank The RGB values ​​are for the blank control.

[0035] The present invention also provides the application of the described enzyme-catalyzed colorimetric encoding-decoding system or the method in the preparation of multiplex miRNA biomarker detection products.

[0036] This invention also provides the application of the described enzyme-catalyzed colorimetric coding-decoding system or the method in any of the following:

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

[0038] (2) Application in the preparation of products for diagnosing tumor disease states or tumor prognoses;

[0039] Preferably, the tumor includes pancreatic cancer, and the multiple biomarkers include multiple miRNA biomarkers.

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

[0041] This invention, by integrating enzymatic colorimetric coding, nucleic acid signal amplification, and continuous weighted calculation, exhibits the following significant advantages in the detection of multidimensional biomarkers:

[0042] 1. High information density and intuitiveness: Through red, green, yellow, and more color coding and a rigorous mathematical mapping model (linear superposition formula), multidimensional biomarker information is compressed into optical signals, resulting in higher information density than traditional fluorescence detection. It also supports direct visual interpretation (e.g., color differences are discernible to the naked eye) or absorption spectral analysis, eliminating the need for expensive spectrometers. Subsequent analysis can be more convenient and accurate by collecting reflectance spectra via smartphones, making it suitable for primary healthcare and point-of-care testing (POCT).

[0043] 2. Single-tube high-efficiency detection: Based on orthogonal template design and RCA isothermal amplification technology, it enables simultaneous detection of multiple miRNA targets in a single reaction tube, reducing sample consumption to below 2 mL and simplifying the operation. It also avoids batch-to-batch errors caused by multi-tube reactions, increasing throughput to 96 samples / batch, suitable for large-scale screening. When the number of targets matches the number of colorimetric enzymes, decoding of each target concentration is possible; when the number of targets exceeds the number of colorimetric enzymes, targets with similar functions in the disease can be mapped to the same colorimetric enzyme, thus enabling the final detection result color to reflect the overall expression trend of the miRNA spectrum and accurately reflect the overall disease status.

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

[0045] 4. High-dimensional expansion and intelligent analysis compatibility: Encoding more color systems can support the integration and decoding of higher-dimensional information, enabling more refined disease classification, such as disease subtyping (e.g., stage I / II pancreatic cancer) or multi-disease joint detection (e.g., cancer and cardiovascular biomarkers). Further integration with AI-driven reflectance spectral analysis (e.g., convolutional neural networks) or portable devices (e.g., smartphones) is possible.

[0046] 5. This invention has been successfully applied to early screening of pancreatic cancer (32 clinical cases). In the future, it can be extended to dynamic monitoring of multiple biomarkers for other cancers (such as lung cancer and breast cancer) and infectious diseases, providing an efficient and low-cost solution for precision medicine and digital medicine. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This invention presents a schematic diagram and results of a two-dimensional enzymatic colorimetric encoding and decoding process; 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 diagram b; d: Spectral decoding formula; e: RGB values ​​of four representative mixed coloring solutions in diagram b; f: Color decoding formula; g: Schematic diagram of applying the spectral decoding formula and color decoding formula to decode a set of colorimetric encoded solutions; hi: Analysis results of the colorimetric encoded solutions using spectral decoding and color decoding.

[0049] Figure 2 This invention provides a schematic diagram and results of the enzymatic colorimetric encoding and decoding method for dual miRNA target detection; a: Schematic diagram of enzymatic colorimetric encoding technology used 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 values ​​in figure c; e: Decoding of the encoding results using spectral or colorimetric methods in figure c.

[0050] Figure 3 The diagrams (a, b) and (c) show the weighted detection implementation of the enzyme-catalyzed colorimetric encoding and decoding method of the present invention.

[0051] Figure 4 The diagram (a) and the detection result (b) show the implementation of the enzyme-catalyzed colorimetric encoding and decoding method of the present invention in the detection of a simulated disease model.

[0052] Figure 5 The diagram shows the detection results and performance of the pancreatic cancer diagnostic model of this invention; a: Schematic diagram of the diagnostic model and the detection results of actual samples using this invention; b: Area under the ROC curve of this invention; c: Detection performance of this invention; d: qPCR detection results; e: Area under the ROC curve of qPCR. Detailed Implementation

[0053] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of 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 terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0055] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0056] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

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

[0058] HRP: Horseradish peroxidase;

[0059] AP: Alkaline phosphatase;

[0060] pNPP: p-Nitrophenyl phosphate;

[0061] ABTS: 2,2'-Azabis(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 coding and decoding

[0065] 1. Experimental Methods

[0066] In the mixed-encoding reaction system, HRP (horseradish peroxidase) and AP (alkaline phosphatase) were selected as the core catalytic enzymes to catalyze the ABTS (green) and pNPP (yellow) color development reactions, respectively. Adjustable color levels were generated by controlling different enzyme concentrations, thus achieving two-dimensional color encoding. The encoding dimension can be expanded by introducing other color-developing enzymes. The specific operation is as follows:

[0067] (1) Enzyme-catalyzed multidimensional colorimetric system coding and construction

[0068] Prepare a chromogenic substrate mixture by mixing 2 mM pNPP, 2 mM MABTS, and 0.5 mM H2O2, and precisely adjust the concentrations of AP and HRP. Figure 1 (b) The enzyme-catalyzed colorimetric reaction was achieved. 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 multi-functional microplate reader (Infinite 200PRO, Tecan, Switzerland). Simultaneously, photographs of the solution were taken, and the average RGB values ​​of the solution were analyzed using Photoshop software.

[0069] (2) Construction of mathematical mapping model

[0070] For the above colorimetric coding system, a linear superposition model for color mixing is established, and the coding formula is defined using the linear relationship between absorption spectrum and RGB values:

[0071]

[0072] The formula for the linear superposition model of RGB values ​​is:

[0073]

[0074] In the above formula, C i C represents the enzyme concentration of each component in a mixed colorimetric enzyme. reference Absorption is the reference concentration of each component enzyme in the mixed colorimetric enzyme. reference Absorption spectrum at the reference concentration of the enzyme in a single-enzyme system. Blank Absorption spectrum of the blank control; RGB_Value reference The RGB values ​​represent the enzyme reference concentration in the single-enzyme system, and the absorption... mixed Absorption spectrum of mixed colorimetric enzymes, RGB_Value mixed The above refers to the RGB values ​​of the mixed colorimetric enzyme. The mixed colorimetric enzyme mentioned above refers to a mixture of HRP and AP colorimetric enzymes. RGB_Value BlankThe RGB values ​​are for the blank control.

[0075] (3) Visual / Spectral Dual-Mode Decoding

[0076] Based on the aforementioned mathematical mapping relationship, the concentration 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 the reference concentrations of each component in the single-enzyme system and in the mixed-encoding system, thus achieving a precise mapping between color signals and colorimetric enzyme concentrations. By precisely correlating the concentrations of multiple biomarkers with the colorimetric enzyme concentrations, a precise mapping between color signals and biomarker concentrations can be achieved.

[0077] 2. Results and Analysis

[0078] Constructing a rigorous mathematical encoding-decoding system based on colorimetric coding is fundamental to realizing multi-target single-tube colorimetric detection, such as... Figure 1 As shown in Figure a, this is a schematic diagram of two-dimensional color encoding and decoding using two colorimetric enzymes, HRP and AP.

[0079] Different concentrations of AP and HRP were directly mixed for enzymatic colorimetric coding. Figure 1 (b) Intuitively, the color of the mixed solution exhibits a visual color code as the type and concentration of enzymes change. Further analysis of the absorption spectrum shows 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. Meanwhile, the RGB values ​​of the mixed solutions also exhibit a similar relationship ( Figure 1 (e). These rigorous mathematical relationships laid the foundation for the encoding and decoding of enzyme-catalyzed colorimetric signals. Figure 1 (d, f). Applying this formula to a set of decoded colorimetric encoded solutions showed good results. Furthermore, the RGB values ​​of the colorimetric encoded 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 testing principles:

[0082] (1) Design of 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, a capture binding region, and a DNA barcode region. Approximately 11 nt of the 5' end of template A is complementary to the 5' end of target A, and approximately 11 nt of the 3' end of template A is complementary to the 3' end of target A. The specific number of complementary bases is related to the number of bases in target A, ensuring complete complementarity between target A and template A. After complementarity, template A forms a circular structure using target A as the linking primer. Simultaneously, template A has a 13 nt DNA barcode region, allowing the rolling circle amplification product using template A as the template to bind complementary to different enzyme-DNA barcode covalent conjugates. Finally, template A has a universal 20 nt capture binding region, allowing template A to be pulled down and fixed onto an enzyme-linked immunosorbent assay (ELISA) plate. Different template sequences can be designed according to different targets.

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

[0084] (3) Immobilizing the miRNA-circularized template DNA complex at the solid-phase interface: A biotin-modified capture DNA strand, perfectly complementary to the template strand capture-binding region, was added to a streptavidin-coated ELISA plate and incubated, resulting in the capture DNA strand being coated on the ELISA plate. The miRNA-circularized template DNA complex was then incubated on the ELISA plate coated with the capture DNA strand, thereby immobilizing the miRNA-circularized template DNA complex at the solid-phase interface.

[0085] (4) Rolling Circulation Amplification and Introduction of Colorimetric Enzyme Signal: Captured DNA strands were used as amplification primers, and circularized template DNA was used as the template for RCA amplification. Simultaneously, enzyme-DNA covalent conjugates formed by the coupling of all enzymes and DNA barcodes were added to the reaction system. The target concentration and the RCA amplification product content showed a linear quantitative relationship in absorbance. The enzyme-DNA covalent conjugate bound 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, a visible encoded color is generated through an enzymatic colorimetric reaction, reflecting the collective expression level of the target miRNA panel. The color can be decoded by visual color (RGB values ​​or chromaticity values) or by detecting the absorption spectrum, and complex target miRNA content can be extracted from the color result. Subsequently, reflectance spectra can be collected by a smartphone and decoded using a pre-trained convolutional neural network (CNN), which is more accurate and convenient.

[0087] Based on the reagent combination and performance optimization of this invention, an enzyme-catalyzed colorimetric coding method combined with nucleic acid signal amplification is used for multiplex 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 cross-linking agent NHS-PEG4-N3 in 1×PBS buffer. The mixture was gently stirred at 300 rpm for 1 hour at 25°C to convert the amino group of the enzyme to an azide group. After the reaction was complete, the enzyme was purified using a 30 kDa ultrafiltration centrifuge tube and washed 6 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., an enzyme-DNA covalent conjugate), in which AP binds DNA barcode A and HRP binds 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. 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, template DNA circularization was performed by preparing a 100 μL reaction mixture. This mixture included 1×SplintR Ligase buffer, miRNA sample, 1 μM of the two corresponding template DNAs (template A, template B), and 40 U / μL of SplintR Ligase. The mixture was incubated at 16°C for 1 hour to form a circularized template DNA-target heterodimer. Simultaneously, the capture strand was coated onto the enzyme plate by adding 10 μM of 5'-biotinylated capture strand to each well of a 96-well plate coated with streptavidin, and then incubated at room temperature for 2 hours. Next, the wells were washed three times with PBS to remove excess capture strand. The circularized template DNA-target was then incubated at room temperature with the capture strand-coated enzyme plate for 0.5 hours, followed by three washes. Then, 0.4 mM dNTP, 0.2 U / μL phi29 DNA polymerase, 100 nM HRP-DNA barcode H, and 100 nM AP-DNA barcode A were mixed in a 200 μL 1×phi29 DNA polymerase buffer system for rolling circle amplification, and incubated at 30 °C for 0.5 h. Finally, an enzyme-catalyzed colorimetric reaction was performed by adding 200 μL of colorimetric solution (composed of 5 mM pNPP, 5 mM ABTS, and 0.5 mM H2O2 mixed in 1×PBS) to the enzyme plate. The reaction was carried out at room temperature for 30 min. The absorbance was measured using a multi-well microplate reader, and the colorimetric results were analyzed by imaging.

[0092] The corresponding nucleic acid sequences are shown in Table 1.

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

[0094]

[0095] The results showed that the platform's detection limit for miRNA concentration was as low as 3.19 pM. The absorbance and RGB values ​​showed a linear relationship with the target concentration, consistent with the enzymatic colorimetric reaction, demonstrating a good mapping relationship between miRNA content and colorimetric enzyme content. Figure 2 (bd). The encoding results can also be decoded using spectral or colorimetric methods to extract information on the target miRNA content. Figure 2 (e).

[0096] Example 3: Application of the Continuous Weighted Calculation of the Invention

[0097] Since different targets have different weights in different diseases, this invention provides a scheme for weighted calculation of biomarker concentrations to directly and accurately map complex disease models (the weighted calculation principle is as follows). Figure 3As shown in ab), the final color signal can directly reflect the disease model score. Specific methods are as follows: (i)-(iii) (Any one):

[0098] (i) Template DNA Ratio Regulation: In the procedure described in Example 2 above, two template DNA strands are designed for the same target miRNA (miR-150). One is a signal template strand 1, which contains a DNA barcoding functional region, and the RCA product can pair with the DNA barcoding bases linked by the enzyme. The other is a non-signal template strand 0, which does not contain a DNA barcoding functional region. By controlling the ratio of these two strands in the system, weighting can be achieved, with the ratio of the signal strand serving as the weighting coefficient. This method enables continuous weighting of the target. 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 control: In the above-described Example 2 operation process, the weighting of specific targets 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 the 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 strand C has 2 DNA barcode functional regions, and the weighting coefficient is specified as 2; template strand D has 3 DNA barcode functional regions, and the weighting coefficient is specified as 3; template strand E has 4 DNA barcode functional regions, and the weighting coefficient is specified as 4.

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

[0101] The corresponding nucleic acid sequences are shown in Table 2.

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

[0103]

[0104]

[0105] like Figure 3 As shown in Figure c, the performance of the three schemes was verified, and 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 was 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 were assigned to HRP enzymes, and the negative coefficients of miR-143 and miR-223 were assigned to AP enzymes, thus allowing the detection results to clearly and intuitively display the disease prediction model score. Weighting was performed using a template DNA ratio adjustment method. Specific operating steps are detailed in Example 3.

[0108] The corresponding nucleic acid sequences are shown in Table 3.

[0109] Table 3 Nucleic acid sequences used for disease model testing and validation.

[0110]

[0111]

[0112] The results show that both spectral features and visual colorimetry can effectively reflect the model score and classify the samples well. Figure 4 The enzyme-catalyzed colorimetric coding detection system, which combines a weighted calculation model, provides a more comprehensive digital medical computing platform, enabling more precise 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 Database (GEO), and differentially expressed miRNAs were identified. Subsequently, binary logistic regression was used to screen for the final pancreatic cancer diagnostic multi-miRNA markers miR-154-5p, miR-629-5p, miR-99a-5p, miR-5006-5p, and miR-575. Finally, a pancreatic cancer diagnostic model was constructed, with the 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 a diagnostic model for pancreatic cancer:

[0116] The construction and validation of the pancreatic cancer prediction model were based on the microarray datasets GSE21169241, GSE163031, and GSE10681742 downloaded from the Gene Expression Comprehensive Database (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-cancer pancreatic samples. The GSE106817 dataset contains serum samples from 115 pancreatic cancer patients and 2759 healthy individuals.

[0117] In the Bioconductor project of R software, the LIMMA package was used to identify differentially expressed miRNAs. Given that the goal of this invention is to analyze serum miRNAs and perform subsequent detection, the aim is to identify miRNAs with significant differential expression in serum samples. In serum samples from 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, and a false detection rate (FDR) < 0.05; while in tissue samples from the GSE163031 dataset, the screening criteria were |log2 fold change (FC)| ≥ 1, and an FDR < 0.05. The miRNA expression overlap characteristics between the GSE211692 and GSE163031 datasets were preserved for further investigation.

[0118] Subsequently, binary logistic regression analysis was performed using SPSS (Social Science Statistical Software), and forward selection was used to select the most important miRNA biomarkers to construct a pancreatic cancer diagnostic prediction model. Finally, ROC (Receptor Operating Characteristic) analysis was used to evaluate the model's performance.

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

[0120] (1) Extraction of whole miRNA from serum: Extraction was performed using a column-based miRNA extraction kit provided by Sangon Biotech (Shanghai, China). All samples were purified according to the manufacturer's instructions, and the eluted RNA was stored in nucleic acid-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 Biotech (Shanghai, China). Subsequently, quantitative PCR (qPCR) was performed using a real-time PCR kit provided by TaKaRaBio (Japan). All samples were processed according to the manufacturer's instructions. After miRNA was reverse transcribed into cDNA, LATE-PCR amplification was performed using 2×TaqMan rapid qPCR mix provided by Sangon Biotech (Shanghai, China). The concentrations of the upstream and downstream primers were adjusted to excess and restriction concentrations, respectively. The excess primer concentration was set to 2 μM, and the restriction primer concentration was set to 50 nM. The restriction primers were universal primers from the one-step miRNA reverse transcription kit provided by China National Biotec Group (CNBG). All other steps were performed according to the manufacturer's instructions.

[0122] (3) The weighted template DNA ratio regulation method was used to apply the above method to the detection of disease prediction models. The corresponding nucleic acid sequences are shown in Table 4.

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

[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 revealed that the diagnostic performance for pancreatic cancer was as follows: area under the ROC curve of 0.97 (95% CI: 92.31%, 100%), sensitivity of 100% (95% CI: 75.75%, 100%), and 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%). For qPCR, the 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 the overall accuracy was 93% (95% CI: 79.2%, 99.2%). Therefore, the method provided by this invention has a high degree of consistency with qPCR.

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

Claims

1. An enzyme-catalyzed 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 coupled to DNA barcodes to form enzyme-DNA covalent conjugates; (4) Developing substrate; Different specific template DNAs are designed for different biomarkers to be tested. The specific template DNA includes a functional region that binds to the biomarker to be tested and the capture strand, as well as a DNA barcode functional region. The biomarkers to be tested include miRNAs; The chromogenic substrate includes 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) and p-nitrophenyl phosphate; The horseradish peroxidase and alkaline phosphatase are modified with azide groups, and the DNA barcode is modified with DBCO. The enzyme-catalyzed colorimetric encoding-decoding method for weighted detection of multiple biomarkers includes: (1) Specific template DNA is designed for each of the various biomarkers to be tested. After the specific template DNA specifically binds to the biomarker to be tested, it forms circular DNA under the action of DNA ligase. The specific template DNA includes a functional region that binds to the biomarker to be tested and the capture strand, as well as a DNA barcode functional region. (2) Using circularized DNA as a template for the RCA reaction and the capture strand as an amplification primer, an enzyme-DNA covalent conjugate is added to carry out the reaction, converting the signal of the target marker into an enzyme signal; wherein the enzyme-DNA covalent conjugate is formed by coupling a colorimetric enzyme with a DNA barcode. (3) Add the substrate corresponding to the colorimetric enzyme to the reaction system of step (2) and generate a visible color coding signal through enzymatic colorimetric reaction; (4) By detecting the RGB values ​​or absorption spectra of the colors generated by the enzyme-catalyzed reaction, the concentrations of multiple biomarkers are decoded by combining the linear superposition model of absorption spectra or the linear superposition model of RGB values.

2. The application of the enzyme-catalyzed colorimetric encoding-decoding system as described in claim 1 in the preparation of multiplex miRNA biomarker detection products.

3. The application of the enzyme-catalyzed colorimetric encoding-decoding system as described in claim 1 in any of the following: (1) Application in the preparation of products for detecting multiple biomarkers of tumors; (2) Application in the preparation of products for diagnosing tumor disease status or tumor prognosis.

4. The application as described in claim 3, characterized in that, The tumor includes pancreatic cancer, and the multiple biomarkers include various miRNA biomarkers.

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