System and method for identifying early stage pancreatic cancer risk using automated analysis of pcr detection results

Abstract

This invention relates to a system and method for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in individuals. The system includes a computation module, which performs the following computational processes: S1, calculating the corresponding ΔCT based on the CT values ​​of miRNAs and internal controls, and preprocessing abnormal data; S2, removing data exceeding a preset threshold and calculating the Dx value; S3, determining the positive or negative value of the Dx value based on the preset Dx judgment threshold of the selected miRNA combination to determine the sample type. The miRNA combination is a diagnostic reagent used to determine the likelihood of a subject having pancreatic cancer. The miRNA combination is selected from at least three of miR-200c / miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-24 / miR-23a, with one of them being miR-23a.

Description

Technical Field

[0001] This invention relates to the field of PCR detection and identification technology, and in particular to a system and method for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in individuals. Background Technology

[0002] Polymerase chain reaction (PCR) detection technology, as one of the core technologies in the field of molecular diagnostics, typically consists of detection reagents, detection instruments, and analysis software. With the increasing demand for early disease screening, its application in early cancer diagnosis is becoming increasingly widespread. However, current PCR-based early cancer detection and analysis solutions still face many technical bottlenecks that fail to meet actual clinical needs, especially in the diagnosis of early pancreatic cancer, a specific type of cancer where these problems are even more pronounced.

[0003] First, existing PCR detection result analysis methods mostly focus on a general detection framework for pan-cancer types, without fully considering the unique pathological mechanisms and molecular expression characteristics of pancreatic cancer. As a highly malignant digestive system tumor with insidious early symptoms, pancreatic cancer requires much higher specificity and sensitivity of molecular markers for early diagnosis than other common cancer types. Because the general analysis model for pan-cancer types has not been optimized for the specific molecular profile of pancreatic cancer, it is often difficult to effectively distinguish pancreatic cancer from other pancreatic diseases or digestive tract diseases, which can easily lead to missed diagnoses or misdiagnoses and cannot meet the needs of early and accurate diagnosis of pancreatic cancer.

[0004] Secondly, although some methods have attempted to utilize microRNAs as detection biomarkers and conduct data analysis using logistic regression models, current technologies only provide a vague framework for miRNA quantity. They neither clearly identify the specific miRNA types suitable for early pancreatic cancer diagnosis nor provide precise calculation formulas for fitting large datasets of clinical pancreatic cancer samples, nor do they offer a basis for determining positive or negative thresholds. When applying these methods to pancreatic cancer detection, healthcare professionals must manually explore miRNA combinations and calculation parameters, which not only increases operational complexity but also makes it difficult to obtain stable and consistent diagnostic results, severely impacting the reliability and repeatability of the test.

[0005] Furthermore, existing technologies generally lack systematic clinical validation data, especially for multicenter, large-sample clinical trial data for pancreatic cancer. The diagnostic efficacy (such as sensitivity, specificity, and area under the curve AUC) of the protocols has not been validated through statistical methods such as receiver operating characteristic (ROC) curves, nor has its efficacy been compared with routinely used pancreatic cancer detection indicators (such as CA19-9). As a result, the protocols remain at the laboratory theoretical level and cannot be transformed into diagnostic tools that can be directly applied in clinical practice, making it difficult for medical staff to accurately assess their practical value in the early diagnosis of pancreatic cancer.

[0006] Furthermore, existing miRNA combinations are relatively simple and fixed, failing to adapt to the diverse needs of different medical scenarios. Primary healthcare institutions may require simple, low-cost, low-volume miRNA detection solutions, while high-end medical settings such as tertiary hospitals require high-precision, high-volume miRNA detection solutions. A single miRNA combination cannot balance detection cost and diagnostic accuracy, limiting the widespread adoption and application of the technology. Simultaneously, existing solutions lack detailed design for pancreatic cancer detection data in the data preprocessing stage. The mechanisms for identifying and handling invalid data (such as abnormal internal reference CT values ​​or abnormal miRNA CT values) are inadequate, easily leading to invalid data interfering with the final diagnostic results, further reducing the accuracy and reliability of the test results.

[0007] When faced with a large amount of raw PCR test data for early pancreatic cancer screening, medical staff not only have to undertake the heavy workload of manual data processing and parameter input, but also have to deal with the dilemma of low diagnostic efficiency and poor result reliability caused by problems such as lack of specific analysis models, insufficient clinical validation, and poor scenario adaptability. Targeted technical improvements are urgently needed to break through the above bottlenecks.

[0008] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a system and method for automatically analyzing PCR test results to assess the risk of early pancreatic cancer, thereby solving at least some of the aforementioned technical problems. This system and method only require direct import of raw data, and automatically analyzes the data and outputs results, thus reducing erroneous interpretations due to human error. By automatically analyzing and processing the data, the workload of medical personnel is reduced, and the risk of misdiagnosis due to human error is also decreased.

[0010] This invention discloses a system for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in individuals. The system includes a computation module, and the computation process performed by the computation module includes: S1. Based on the CT values ​​of miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal reference, the corresponding ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 is calculated, and the abnormal data is preprocessed. S2. Data whose CT values ​​exceed the preset internal control CT threshold, or whose CT values ​​for miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 exceed the preset miRNA CT threshold, are discarded. After notifying the user, valid data is identified and Dx values ​​are calculated. The formula for calculating Dx is: Dx = e y / (1+e y ), where Dx is the value used for determining positive or negative, e is the natural constant, and y is the value calculated by ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 according to the preset y value formula for the corresponding miRNA combination; S3. Based on the preset Dx judgment threshold of the selected miRNA combination, the Dx value is judged as positive or negative to determine whether the sample is a pancreatic cancer positive sample or a pancreatic cancer negative sample.

[0011] The miRNA combination is a diagnostic reagent used to determine the likelihood of a subject having pancreatic cancer. The miRNA combination is selected from at least three of miR-200c / miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-24 / miR-23a, and one of them is miR-23a.

[0012] The gene sequences of the above optional miRNAs are shown in the table below: According to a preferred embodiment, the miRNA combination includes one or more of the following combinations: Combination 1: miR-132 / miR-30c / miR-24 / miR-23a; Combination 2: miR-132 / miR-24 / miR-23a; Combination 3: miR-200c / miR-154 / miR-130b / miR-30c / miR-23a; Combination 4: miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-23a.

[0013] According to a preferred embodiment, the formula for the value of y is: .

[0014] Among them, α1, α2, α3, α4, α5, and α6 are calculation constants, and Constant is another constant.

[0015] According to a preferred embodiment, the formula for the y-value of combination 1 is: .

[0016] Where x1 is the ΔCT value of miR-132, x2 is the ΔCT value of miR-30c, x3 is the ΔCT value of miR-24, and x4 is the ΔCT value of miR-23a.

[0017] According to a preferred embodiment, the formula for the y-value of combination 2 is: .

[0018] Where x1 is the ΔCT value of miR-132, x2 is the ΔCT value of miR-24, and x3 is the ΔCT value of miR-23a.

[0019] According to a preferred embodiment, the formula for the y-value of combination 3 is: .

[0020] Where x1 is the ΔCT value of miR-200c, x2 is the ΔCT value of miR-154, x3 is the ΔCT value of miR-130b, x4 is the ΔCT value of miR-30c, and x5 is the ΔCT value of miR-23a.

[0021] According to a preferred embodiment, the formula for the y-value of combination 4 is: .

[0022] Where x1 is the ΔCT value of miR-154, x2 is the ΔCT value of miR-132, x3 is the ΔCT value of miR-130b, x4 is the ΔCT value of miR-577, x5 is the ΔCT value of miR-30c, and x6 is the ΔCT value of miR-23a.

[0023] According to a preferred embodiment, after calculating the Dx value, a positive or negative result is determined based on a preset threshold for the selected miRNA combination. The specific determination criteria are as follows: Combination 1: When the Dx value of a sample is <0.62, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.62, it is judged as a positive sample for pancreatic cancer. Combination 2: When the Dx value of a sample is <0.562, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.562, it is judged as a positive sample for pancreatic cancer. Combination 3: When the Dx value of a sample is <0.58, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.58, it is judged as a positive sample for pancreatic cancer. Combination 4: When the Dx value of a sample is <0.531, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.531, it is judged as a positive sample for pancreatic cancer.

[0024] According to a preferred embodiment, the system further includes: The user interface module is used for visualization operations and result display. The user interface module is also used to input the CT values ​​of 3 to 6 miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 generated by the real-time fluorescence quantitative PCR instrument and their corresponding internal control CT values. It automatically identifies and combines the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal control, and calculates ΔCT from the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal control and presents it to the calculation module. The data storage module is used to store the original data entered by the user interface module and the calculation results output by the calculation module.

[0025] The present invention also discloses a method for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in others, which includes the following steps: using a computing module to perform calculations on the received data.

[0026] The calculation process performed by the calculation module includes: S1. Based on the CT values ​​of miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal reference, the corresponding ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 is calculated, and the abnormal data is preprocessed. S2. Data whose CT values ​​exceed the preset internal control CT threshold, or whose CT values ​​for miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 exceed the preset miRNA CT threshold, are discarded. After notifying the user, valid data is identified and Dx values ​​are calculated. The formula for calculating Dx is: Dx = e y / (1+e y ), where Dx is the value used for determining positive or negative, e is the natural constant, and y is the value calculated by ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 according to the preset y value formula for the corresponding miRNA combination; S3. Based on the preset Dx judgment threshold of the selected miRNA combination, the Dx value is judged as positive or negative to determine whether the sample is a pancreatic cancer positive sample or a pancreatic cancer negative sample.

[0027] The miRNA combination is a diagnostic reagent used to determine the likelihood of a subject having pancreatic cancer. The miRNA combination is selected from at least three of miR-200c / miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-24 / miR-23a, and one of them is miR-23a.

[0028] According to a preferred embodiment, the miRNA combination includes one or more of the following combinations: Combination 1: miR-132 / miR-30c / miR-24 / miR-23a; Combination 2: miR-132 / miR-24 / miR-23a; Combination 3: miR-200c / miR-154 / miR-130b / miR-30c / miR-23a; Combination 4: miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-23a.

[0029] According to a preferred embodiment, the method further includes one or more of the following steps: Users can enter the CT values ​​of 3-6 miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 generated by the real-time fluorescence quantitative PCR instrument and their corresponding internal control CT values ​​in txt document format through the interface buttons of the user interface module. The system automatically identifies and combines the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 with the internal control based on the user's selection. The CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal reference are calculated and presented to the calculation module; After the calculation is completed, the results are presented to the user through the user interface module; historical data stored in the data storage module can be queried through the interface buttons and displayed in the user interface module.

[0030] The beneficial technical effects of this invention include: This invention relates to an automated PCR test result analysis system and method for identifying the risk of early pancreatic cancer. It utilizes a proprietary calculus calculation model to automatically preprocess the raw data generated by the instrument. Then, it performs calculations and analysis using the established model, and the comprehensive analysis results assist medical personnel in disease diagnosis. Medical personnel simply import the data directly through the software interface; the software automatically analyzes and processes the data, and automatically outputs the calculation results and diagnostic results. Simultaneously, experimental data can be organized and saved, and historical test data can be exported later.

[0031] Furthermore, compared to existing technologies (such as CN115101127A), the technical solution of this application demonstrates multi-dimensional and groundbreaking beneficial technical effects in the clinical pain point of early pancreatic cancer diagnosis. Its inventiveness and non-obviousness are fully highlighted by solving a long-standing diagnostic problem in the industry, specifically manifested in: First, addressing the shortcomings of existing technologies' general framework for "early-stage cancer" that cannot adapt to the characteristics of pancreatic cancer, this invention precisely limits the technical scenario to early-stage pancreatic cancer. As a highly malignant digestive system tumor with insidious early symptoms, the early diagnosis of pancreatic cancer has long been limited by the pain points of "low specificity of molecular markers and difficulty in differentiating it from other pancreatic diseases." However, this invention, based on the unique pathological mechanism and molecular expression profile optimization technology of pancreatic cancer, completely avoids the sensitivity deviation caused by "insufficient specificity" in general cancer detection schemes, effectively reducing the risk of missed diagnosis and misdiagnosis of early-stage pancreatic cancer. It provides key technical support for the early intervention and treatment of pancreatic cancer, a "refractory cancer," and fills the technical gap in the precise molecular diagnosis of early-stage pancreatic cancer.

[0032] Secondly, existing technologies only provide a vague general framework of "4 miRNAs" and formulas without specific parameters. In contrast, the four specific pancreatic cancer-targeting miRNA combinations and precise calculation formulas disclosed in this invention are core technological achievements obtained through screening a large number of clinical samples (covering pancreatic cancer stages I-IV and non-case group samples) and fitting with big data. These combinations (such as miR-132 / miR-30c / miR-24 / miR-23a) are designed for the expression characteristics of pancreatic cancer-related miRNAs. The precise coefficients in their y-value formulas (such as -0.474 and -0.550) can accurately quantify the association between each miRNA and the occurrence of pancreatic cancer, ultimately achieving a very high level of diagnostic efficacy (combination 1 AUC=0.9855, sensitivity 93.85%, specificity 94.95%), which is significantly better than the CA19-9 detection commonly used in clinical practice (AUC=0.82). This fundamentally solves the core pain point of "insufficient accuracy in early molecular diagnosis of pancreatic cancer" in the industry, providing medical staff with a more reliable diagnostic basis.

[0033] Furthermore, existing technologies lack any clinical validation data, leaving their solutions at the theoretical level. In contrast, this invention, through ROC curve validation and multi-center clinical trials (covering four tertiary hospitals including Shanghai Changhai Hospital), not only scientifically determined the optimal Dx threshold for each miRNA combination using statistical methods (Youden index), ensuring the uniformity and objectivity of diagnostic standards (avoiding subjective judgment errors by medical staff), but also eliminated the random bias of single-center experiments through repeated validation of multi-center samples, proving the stability and reliability of the technical solution in different medical scenarios. This ability to transform "from laboratory theory to clinical application" is something that comparative documents cannot achieve. It not only gives the technical solution practical clinical application value, but also enhances the trust of medical staff in the test results, promoting the clinical implementation of molecular diagnostic technology for early pancreatic cancer.

[0034] Finally, the existing fixed "4 miRNA" framework cannot adapt to the differentiated needs of different medical scenarios. However, the 3, 5, and 6 miRNA combinations extended in this invention achieve a flexible balance between "detection cost and diagnostic accuracy": For primary healthcare institutions with limited sample size and sensitive detection costs, the combination of 3 miRNAs (such as combination 2) can simplify the operation process, reduce reagent and time costs, and ensure basic diagnostic accuracy. For tertiary hospitals or the diagnosis of difficult cases that pursue high accuracy, the combination of 6 miRNAs (such as combination 4) can improve diagnostic specificity through the synergistic effect of multiple biomarkers. This scenario-based adaptability breaks the limitation of a single detection scheme's "one-size-fits-all" approach, greatly improves the universality of the technical solution, and enables it to cover all levels of medical needs from primary to high-end healthcare. This further expands the application scope of early pancreatic cancer screening and has important public health significance for improving the overall early diagnosis rate of pancreatic cancer. Attached Figure Description

[0035] Figure 1 This is a system hardware connection diagram of a preferred embodiment of the present invention; Figure 2 This is a summary diagram of example sample data for miRNA combination 1 according to a preferred embodiment of the present invention; Figure 3 This is a schematic diagram of the ROC curve and optimal threshold of miRNA combination 1 according to a preferred embodiment of the present invention; Figure 4 This is a schematic diagram of the ROC curve and optimal threshold of miRNA combination 2 according to a preferred embodiment of the present invention; Figure 5 This is a schematic diagram of the ROC curve and optimal threshold of miRNA combination 3 according to a preferred embodiment of the present invention; Figure 6 This is a schematic diagram of the ROC curve and optimal threshold of miRNA combination 4 according to a preferred embodiment of the present invention. Figure 7 This is a comparison of the ROC curves of miRNA combination 1 and CA19-9 according to a preferred embodiment of the present invention. Figure 8 This is a comparison of the ROC curves of miRNA combination 2 and CA19-9 according to a preferred embodiment of the present invention. Figure 9 This is a comparison of the ROC curves of miRNA combination 3 and CA19-9 according to a preferred embodiment of the present invention. Figure 10 This is a comparison of the ROC curves of miRNA combination 4 and CA19-9 according to a preferred embodiment of the present invention. Detailed Implementation

[0036] The following is a detailed explanation with reference to the accompanying drawings.

[0037] Example 1 like Figure 1 As shown, this invention discloses a system for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in individuals, comprising: The user interface module is used for visual operations and displaying results; The calculation module is used to receive data from the user interface module, perform calculations and analysis, and submit the results back to the user interface module. It also organizes the raw data according to a set pattern and transmits it to the data storage module. Data storage module: Used to store the original data and calculation results of the user interface module and the calculation module. The data storage module can automatically save the data in local and remote servers.

[0038] Preferably, in the user interface module, the CT values ​​of 3-6 miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 generated by the real-time quantitative PCR instrument and their corresponding internal control CT values ​​are entered in txt document format. The CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal control are arranged automatically and combined. Then, ΔCT is calculated from the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal control and presented to the calculation module.

[0039] Preferably, the calculation module may include the following calculation steps: S1. Based on the CT values ​​of miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal reference, the corresponding ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 is calculated, and the abnormal data is preprocessed.

[0040] Preferably, in step S1, the calculation module first receives the raw data transmitted by the user interface module, including the sample number, sample type, CT value of each target miRNA, and CT value of the internal reference U6. Then, for the selected miRNA combination, it calculates the ΔCT value of each target miRNA. The formula for calculating the ΔCT value is: ΔCT = CT value of target miRNA - CT value of internal reference U6. miRNA1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 can correspond to miR-132, miR-30c, miR-24, miR-23a, miR-200c, miR-154, miR-130b, and miR-577, respectively.

[0041] The gene sequences of the above optional miRNAs are shown in the table below: Furthermore, after calculating the ΔCT value, the computation module can preprocess abnormal data, which includes the following two categories: The first category is data whose ΔCT value exceeds the reasonable range. Based on extensive clinical trials, a normal range of ΔCT values ​​for target miRNAs in early pancreatic cancer samples can be obtained. If the ΔCT value of a sample exceeds this range, the system can mark it as "abnormal ΔCT value". The second category is data with inconsistent repeated testing. If the same sample has undergone multiple PCR tests (e.g., 3 repeated tests), and the difference in CT values ​​for the same miRNA exceeds a threshold (e.g., 1.0), it is marked as "inconsistent repeated data". For the marked abnormal data, the computation module can temporarily retain it and further judge it in subsequent steps in conjunction with data removal conditions, while feeding back the abnormal marking results to the user interface module.

[0042] S2. Data with CT values ​​>34 for the internal control and CT values ​​>40 for miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 are removed. After notifying the user, valid data are identified and processed. X The value is calculated using the following formula: Dx=e y / (1+e y ).

[0043] Where Dx is the value used for positive / negative determination after calculation, e is the natural constant, and y is the value obtained by calculating ΔCT after miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 with the CT value of the internal reference, according to the following formula: .

[0044] In this formula, α1, α2, α3, α4, α5, and α6 are calculation constants, and Constant is another constant. Furthermore, depending on the number of miRNAs selected, some of these calculation constants can be set to 0. For example, when the miRNA combination contains 3 miRNAs, α4, α5, and α6 can be set to 0; when the miRNA combination contains 4 miRNAs, α5 and α6 can be set to 0; when the miRNA combination contains 5 miRNAs, α6 can be set to 0; and when the miRNA combination contains 6 miRNAs, the above calculation constants may not be set to 0. Furthermore, when the miRNA combination contains more than 6 miRNA types, the above y-value calculation formula can continue to add a subtraction term according to a set pattern. For example, when the miRNA combination contains 7 miRNAs, ΔCT7×α7 can be subtracted from the above y-value calculation formula.

[0045] Preferably, if a sample simultaneously satisfies the condition that the CT value of the internal control is ≤34 and the CT value of all target miRNAs is ≤40, it is considered valid data and can proceed with the subsequent Dx value calculation. For valid data, the calculation module calls the corresponding specific calculation model to calculate the Dx value based on the selected miRNA combination. The Dx value calculation requires first calculating the y-value based on the formula for that combination, and then substituting it into the Dx calculation formula. Specifically, the y-value formula and Dx value calculation process for the four miRNA combinations are as follows: (1) Combination 1: miR-132 / miR-30c / miR-24 / miR-23a, with a corresponding Dx=0.62.

[0046] The formula for the y-value of combination 1 is: .

[0047] Where x1 is the ΔCT value of miR-132, x2 is the ΔCT value of miR-30c, x3 is the ΔCT value of miR-24, and x4 is the ΔCT value of miR-23a.

[0048] (2) Combination 2: miR-132 / miR-24 / miR-23a, with a corresponding Dx=0.562.

[0049] The formula for the y-value of combination 2 is: .

[0050] Where x1 is the ΔCT value of miR-132, x2 is the ΔCT value of miR-24, and x3 is the ΔCT value of miR-23a.

[0051] (3) Combination 3: miR-200c / miR-154 / miR-130b / miR-30c / miR-23a, with a corresponding Dx=0.58.

[0052] The formula for the y-value of combination 3 is: .

[0053] Where x1 is the ΔCT value of miR-200c, x2 is the ΔCT value of miR-154, x3 is the ΔCT value of miR-130b, x4 is the ΔCT value of miR-30c, and x5 is the ΔCT value of miR-23a.

[0054] (4) Combination 4: miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-23a, with a corresponding Dx=0.531.

[0055] The formula for the y-value of combination 4 is: .

[0056] Where x1 is the ΔCT value of miR-154, x2 is the ΔCT value of miR-132, x3 is the ΔCT value of miR-130b, x4 is the ΔCT value of miR-577, x5 is the ΔCT value of miR-30c, and x6 is the ΔCT value of miR-23a.

[0057] S3. After calculating the Dx value, determine the positive or negative result based on the preset threshold of the selected miRNA combination. The specific criteria are as follows: Combination 1: When the Dx value of a sample is <0.62, it is judged as a pancreatic cancer negative sample (NS); when the Dx value is ≥0.62, it is judged as a pancreatic cancer positive sample (PDAC). Combination 2: When the Dx value of a sample is <0.562, it is judged as a pancreatic cancer negative sample (NS); when the Dx value is ≥0.562, it is judged as a pancreatic cancer positive sample (PDAC). Combination 3: When the Dx value of a sample is <0.58, it is judged as a pancreatic cancer negative sample (NS); when the Dx value is ≥0.58, it is judged as a pancreatic cancer positive sample (PDAC). Combination 4: When the Dx value of a sample is <0.531, it is judged as a pancreatic cancer negative sample (NS); when the Dx value is ≥0.531, it is judged as a pancreatic cancer positive sample (PDAC).

[0058] Furthermore, if the CT value of the internal reference U6 of the tested sample is >34, regardless of the Dx value, the test result is directly determined to be invalid, and medical staff can be notified to repeat the test.

[0059] Preferably, the data storage module is responsible for receiving the raw data (including imported txt document data and abnormal data marking results) and calculation results (including ΔCT value, y value, Dx value, and diagnostic results) transmitted by the calculation module, and storing them according to preset rules. At the same time, it supports the query and retrieval of historical data by the user interface module.

[0060] Figure 2 This is an overview of example sample data for miRNA combination 1. In determining the positive cutoff value, this invention selected clinical samples for testing, including confirmed pancreatic cancer samples, diabetes samples, obesity samples, and non-pancreatic cancer confirmed samples from other pancreatic and digestive tract diseases. Combining the test results of the kit with clinical diagnostic results, this invention used receiver operating characteristic (ROC) curves to determine the positive cutoff value of the kit.

[0061] The ROC curve method offers advantages such as simplicity and intuitiveness. Users can visually observe the clinical accuracy of analytical methods and make judgments. The ROC curve combines sensitivity and specificity graphically, accurately reflecting the relationship between the specificity and sensitivity of an analytical method, and providing a comprehensive representation of test accuracy. The ROC curve does not have a fixed classification threshold, allowing for intermediate states, which facilitates users in combining their professional knowledge to weigh the impact of missed diagnoses and misdiagnoses, thereby selecting a better cutoff point as a diagnostic reference value. This method provides intuitive comparisons of different tests on a common scale. The more convex the ROC curve and the closer it is to the upper left corner, the higher its diagnostic value, which is beneficial for cross-sectional comparisons between different indicators. The area under the curve (AUC) can be used to evaluate diagnostic accuracy; the larger the AUC, the stronger the diagnostic power of the test. The area under the ROC curve (AUC) is between 0.5 and 1.0. When AUC > 0.5, the closer the AUC is to 1, the better the diagnostic accuracy. AUC between 0.5 and 0.7 indicates low diagnostic accuracy; AUC between 0.7 and 0.9 indicates some diagnostic accuracy; and AUC above 0.9 indicates high diagnostic accuracy. If AUC = 0.5, the diagnostic method has no diagnostic value whatsoever; AUC < 0.5 does not conform to practical testing logic and is extremely rare in clinical practice.

[0062] The optimal cutoff value is typically determined using the "Youden index," which is calculated as: Youden index = sensitivity + specificity - 1. The diagnostic indicator value corresponding to the maximum value of this index is considered the optimal cutoff value. (Combined with...) Figure 2 The curve coordinates formed by the data can be used to calculate the value of the sum of sensitivity and specificity minus 1 for each coordinate point. The diagnostic index value corresponding to the maximum value of this value is the optimal threshold.

[0063] This invention uses receiver operating characteristic (ROC) curves to confirm the critical value for result judgment and establishes the positive judgment value of the product accordingly.

[0064] For combination 1, the results obtained from ROC curve analysis are shown in the table below. Figure 3 As shown, when Dx=0.62, the Youden index corresponding to sensitivity (93.85%) and specificity (94.95%) is the largest, so Dx=0.62 is the positive judgment value of combination 1. For combination 2, the results obtained from ROC curve analysis are shown in the table below. Figure 4 As shown, when Dx=0.562, the Youden index corresponding to sensitivity (92.66%) and specificity (92.93%) is the largest, so Dx=0.562 is the positive judgment value of combination 2. For combination 3, the results obtained from ROC curve analysis are shown in the table below. Figure 5 As shown, when Dx=0.58, the Youden index corresponding to sensitivity (91.96%) and specificity (92.42%) is the largest, so Dx=0.58 is the positive judgment value for combination 3. For combination 4, the results obtained from ROC curve analysis are shown in the table below. Figure 6 As shown, when Dx=0.531, the Youden index corresponding to sensitivity (93.01%) and specificity (90.91%) is the largest, so Dx=0.531 is the positive judgment value of combination 4. By comparing the AUC values ​​of the four miRNA combinations, it can be found that miRNA combination 1 has the best detection effect among the four miRNA combinations.

[0065] Example 2 This embodiment is a further improvement on embodiment 1, and repeated content will not be described again.

[0066] This invention discloses a method for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in individuals, which may include the following steps: T1. Users can enter the CT values ​​of 3-6 miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 generated by the real-time fluorescence quantitative PCR instrument and their corresponding internal control CT values ​​in txt document format through the interface buttons of the user interface module. T2. Automatically identify and combine the CT values ​​of miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 with the internal control according to the user's selection; T3, the CT values ​​of miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal reference are calculated and presented to the calculation module; T4. Use the processing module to process the received data; T5. After the calculation is completed, the calculation result is presented to the user through the user interface module; historical data stored in the data storage module can be queried through the interface buttons and displayed in the user interface module.

[0067] Preferably, in step T1, medical personnel can obtain raw CT data generated by a real-time quantitative PCR instrument, wherein the PCR instrument meets the following conditions: supports fluorescent probe detection, has ≥4 detection channels (adapted to simultaneous detection of multiple miRNAs), and has a CT value detection range of 10-40 (ensuring matching with the data rejection conditions of this invention), for example, using an ABI 7500 real-time quantitative PCR instrument. Medical personnel can organize the CT data exported from the PCR instrument according to the txt format required by the user interface module, then open the system software of this invention, enter the user interface module, click the "Import Data" button, locate the organized txt file in the file selection window, select it, and click "OK". The system automatically completes the data import and displays the data in the preview window. After confirming that the data is complete and has no format errors, the medical personnel click "Confirm Import" to complete step T1. If the data exported from the PCR instrument contains duplicate samples (e.g., the same sample was tested three times), the duplicate data can be merged before import. The merging method is to calculate the average CT value of the duplicate tests.

[0068] Preferably, in step T2, medical staff can select the corresponding miRNA combination in the user interface module according to the detection scenario and accuracy requirements.

[0069] Preferably, in step T3, after the system completes the selection of miRNA combinations and data arrangement, it can automatically calculate the ΔCT value for the target miRNA of each valid sample. The formula for calculating the ΔCT value is: ΔCT = CT value of target miRNA - CT value of internal reference U6. After the calculation is completed, the system displays the ΔCT value of each sample in the user interface module for medical staff to confirm. After the medical staff confirms that the ΔCT value calculation is correct, they click the "Send Data to Calculation Module" button in the user interface module. The system then transmits the ΔCT value data and the instructions for the selected combination to the calculation module, completing step T3. If the absolute value of the ΔCT value of a sample is too large, the system can pop up a warning message: "The ΔCT value of miRNA XXX of sample XXX is XXX, the absolute value is too large, and there may be a detection error. Please confirm whether the original CT value is correct." The medical staff needs to check the original CT value exported by the PCR instrument. If the original CT value is correct, data transmission can continue; if the original CT value is incorrect, PCR testing needs to be performed again.

[0070] Preferably, in step T4, after receiving the ΔCT value data and combination instructions, the calculation module can perform the calculation through steps S1 to S3 as described in Embodiment 1. During the calculation process, medical staff can view the calculation progress and intermediate results through the user interface module. If an anomaly is found in the data of a certain sample (such as the CT value of the internal reference > 34), they can click the "Pause Analysis" button, check the original data, and then click "Continue Analysis" or "Remove Sample". After the calculation is completed, the calculation module transmits the Dx values ​​and diagnostic results of all samples to the user interface module, and transmits the original data, ΔCT value, y value, Dx value, and diagnostic results to the data storage module for storage, thus completing step T4.

[0071] Preferably, in step T5, the user interface module can display the diagnostic results of all valid samples in tabular form. The table columns include sample number, sample type, selected combination, ΔCT value, y value, Dx value, and diagnostic result for each miRNA. Pancreatic cancer positive samples are marked in red, and pancreatic cancer negative samples are marked in green. Medical staff can click the "Export Results" button to export the diagnostic results to Excel format. In addition to the tabular data, the Excel file also includes a histogram of Dx value distribution (the horizontal axis represents the Dx value range, and the vertical axis represents the number of samples) and a pie chart showing the proportion of PDAC samples, NS samples, and samples from other diseases. To query historical data, medical staff can enter the date range (e.g., "2024-01-01 to 2024-01-31"), sample number (e.g., "CO1-CO50"), and diagnosis result type (e.g., "positive" or "PDAC") in the historical data query area, and click the "Query Historical Data" button. The system will retrieve the corresponding data from the data storage module and display it in the sub-window below the results display area. It also supports recalculating historical data (select a new miRNA combination and click the "Reanalyze" button). For example, for sample CO1-CO50 tested in January 2024, if combination 1 was originally selected, combination 4 can be selected for recalculation. The diagnostic results of the two combinations can be compared to evaluate the accuracy of combination 4 and complete step T5.

[0072] Furthermore, after medical staff press the analysis button in the user interface module, the system can start calculations and display the results on the user interface module in the form of negative / positive labels and risk coefficients.

[0073] Furthermore, the data storage module can simultaneously store data on a local hard drive and a cloud server. The user interface module can retrieve historical data stored on the local hard drive or the cloud server and present the data results on the user interface module.

[0074] Example 3 This embodiment is a further improvement on Embodiment 1 and / or 2, and the repeated content will not be described again.

[0075] The main design concept of this embodiment is to address the problem that existing users need to manually input raw data when analyzing raw data, and to propose software that automates the analysis of experimental data to determine the risk of early-stage pancreatic cancer in humans. This embodiment uses a visual user interface to import raw test data. The software automatically analyzes the data results and organizes the data. Test results are displayed on the software interface as negative / positive, along with the calculated risk data, and also the risk data calculated by conventional molecular testing techniques used in clinical practice. This achieves automatic data entry and result generation, significantly reducing manual processing.

[0076] This invention discloses software for automatically analyzing the risk of early-stage pancreatic cancer in humans, comprising: The user interface module is used to display user-visualized operations and results. The computation module is used to perform calculations, analysis, and processing on the raw data. The data storage module is used to store historical data.

[0077] The user interface module, also known as the UI module, provides users with an interface for processing data. Users can input the CT values ​​of 3-6 miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 generated by the real-time quantitative PCR instrument and their corresponding internal control CT values ​​in TXT document format via interface buttons. The system automatically identifies and combines the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 with the internal control, and then calculates ΔCT based on the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 with the internal control, which is then presented to the calculation module. After the calculation is completed, the result is presented to the user through the user interface module. Users can also query historical data stored in the data storage module via interface buttons, which is then displayed in the user interface module.

[0078] The calculation module can receive data from the user interface module, perform calculation and analysis according to its own calculation model (analysis steps are shown in Examples 1 and / or 2), and present the results back to the user interface. It also organizes the raw data according to the rules and transmits it to the data storage module.

[0079] The data storage module can save the original data and calculation results to the database according to certain rules through the user interface module and the calculation module, and can also read them directly through the user interface module; the data storage module automatically saves the data in local and remote servers.

[0080] The software can organize and save data to local hard drives and cloud servers. Historical data stored on local hard drives can be read directly through the software.

[0081] This software has the following features: 1. It has strong compatibility with the operating environment and can run directly on ordinary Windows home computers.

[0082] 2. Easy to operate. Users can complete the operation through the function buttons of the user interface module and enter data into the system by selecting the data path.

[0083] 3. Supports data editing functions, allowing users to manually or automatically edit entered data in the user interface module.

[0084] 4. It can automatically complete data recognition and calculation. The entered data will be automatically transmitted to the calculation module, which will automatically recognize the data and perform calculations according to the preset calculation model.

[0085] 5. The calculation results can be synchronized in both directions. After the calculation is completed, the results will be pushed to the user interface for viewing and simultaneously transmitted to the data storage module for storage.

[0086] 6. Supports dual-end data storage and local retrieval. The data storage module can synchronously store data to the local hard drive and the cloud server. Users can directly retrieve historical data from the local hard drive through the user interface module and view the corresponding data content on the user interface module.

[0087] The technical effects achieved by this embodiment are: 1. Users can freely choose the desired detection conclusions / results to be output, based on their requirements; 2. This technology software is the foundational software for developing AI-based diagnosis and medication guidance in the medical field (pancreatic cancer).

[0088] Example 4 This embodiment is an application of embodiments 1, 2 and / or 3, and repeated content will not be described again.

[0089] To verify the effectiveness of the system and method described in this invention, this embodiment conducted a multi-center clinical validation experiment in four hospitals: Shanghai Changhai Hospital, the First Affiliated Hospital of Dalian Medical University, Peking Union Medical College Hospital, and the Second Xiangya Hospital of Central South University. The specific experimental plan and effect analysis are as follows: 1. Sample collection and processing The case group consisted of patients with pancreatic cancer who met the diagnostic criteria of the "Guidelines for the Diagnosis and Treatment of Pancreatic Cancer" (2018 edition) and were diagnosed at their first visit, before undergoing surgical or radiotherapy / chemotherapy treatments. Serum samples were collected from these patients, including 69 cases of stage I pancreatic cancer, 108 cases of stage II, 78 cases of stage III, and 30 cases of stage IV. The non-case group consisted of subjects with other pancreatic diseases such as pancreatitis, as well as subjects with other malignant tumors such as gastric cancer, liver cancer, colorectal cancer, urinary system tumors (prostate cancer), breast cancer, lung cancer, and hematological malignancies (multiple myeloma), totaling 197 cases.

[0090] 2. Preparations before the experiment Environment: The entire experiment must be conducted in a clean room at room temperature (20-25℃). Instruments: High-speed centrifuge, Nanodrop, PCR amplification instrument, quantitative PCR instrument; Consumables: 1.5ml EP tubes of RNAase-free PCR, 0.1ml 8-strip PCR tubes, 1ml / 200ul / 10ul tips, 96-well plates; Reagents: TRIzol™ LS Reagent (Invitrogen 10296028), RNase-free ddH2O, TaqMan™ MicroRNA Reverse Transcription Kit (ABI 4366596), Premix Ex Taq™ (Probe qPCR) TAKARARR390; isopropanol, chloroform, anhydrous ethanol, RT primer (U6, miR-23a, miR-24, miR-30c, miR-577, miR-130b, miR-132, miR-154, miR-200c), qPCR primer, probe (U6 Fam, VIC: miR-23a, miR-24, miR-30c, miR-577, miR-130b, miR-132, miR-154, miR-200c).

[0091] 3. RNA Extraction - TRIzol™ LS Reagent Add 750 μL of TRIzol™ LS Reagent to every 250 μL of serum sample, vortex or pipette up and down to mix, forming a homogenate lysis buffer, and let stand at room temperature for 5 minutes.

[0092] Add 200 μL of chloroform to the homogenized lysis solution, tighten the centrifuge tube cap, mix until the solution emulsifies and turns milky white, and let stand at room temperature for 5 minutes.

[0093] After standing, centrifuge at 12,000×g and 4℃ for 15 minutes. Carefully remove the centrifuge tube from the centrifuge. At this point, the homogenate is divided into three layers: a colorless supernatant (containing RNA), a middle white protein layer (mostly DNA), and a colored lower organic layer.

[0094] Transfer the supernatant to a new centrifuge tube (do not remove the white middle layer).

[0095] Add an equal volume of isopropanol to the supernatant, invert the centrifuge tube to mix thoroughly, and let stand at room temperature for 10 minutes.

[0096] After standing, centrifuge at 12,000×g and 4℃ for 10 minutes.

[0097] Discard the supernatant, add 1 mL of 75% ethanol and mix well. Centrifuge at 7,500 × g and 4 °C for 5 minutes, then discard the supernatant.

[0098] Open the tube cap and allow it to air dry at room temperature. After drying, add 20 μL of RNase-free H2O to dissolve the RNA.

[0099] The concentration and quality of the extracted RNA were determined using Nanodrop.

[0100] 4. Reverse Transcription - TaqMan™ MicroRNA Reverse Transcription Kit (ABI 4366596) Combine any one of the miRNAs (miR-23a, miR-24, miR-30c, miR-577, miR-130b, miR-132, miR-154, miR-200c) with U6, prepare primer working solution at a concentration of 5 μM per primer, and then add the corresponding reaction reagents according to the proportions in the table below, ensuring a total volume of 15 μL. Perform the following program in the PCR amplification instrument: 16℃ 30min→42℃ 30min→85℃ 5min→4℃, and then centrifuge briefly to the bottom of the tube after completion.

[0101] 5. qPCR- Premix Ex TaqTM (Probe qPCR) TAKARARR390 Prepare the reaction system according to the table below, and dispense it into 0.1 mL eight-tube strips or 96-well plates. The reaction product from the previous step is added separately to the pre-amplified reaction mixture in eight-tube or 96-well plates. The tubes are capped or sealed with a sealing film, and then centrifuged briefly before PCR amplification on a quantitative PCR instrument. The reaction conditions are as follows: pre-denaturation, 1 cycle, 95℃ for 30 seconds; PCR reaction, 40 cycles, 95℃ for 5 seconds and 60℃ for 30 seconds; annealing, 50℃ for 30 seconds, 1 cycle.

[0102] 6. Results Analysis The CT values ​​of each detected miRNA and the internal control were summarized and organized into four different combinations. The Dx value for each combination was calculated using the formula of this invention. The obtained Dx values ​​were then compared and analyzed with the CA19-9 test results to obtain the following results: Figures 7-10 The results show that the Dx results obtained by each combination of the present invention far exceed those of CA19-9 in terms of sensitivity and specificity.

[0103] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. A system for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in individuals, characterized in that, It includes a calculation module, and the calculation process performed by the calculation module includes: S1. Based on the CT values ​​of miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal reference, the corresponding ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 is calculated, and the abnormal data is preprocessed. S2. Data whose CT values ​​exceed the preset internal reference CT threshold, or whose CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 exceed the preset miRNA CT threshold, are discarded. After notifying the user, valid data is identified and Dx values ​​are calculated. The formula for calculating Dx is: Dx = e y / (1+e y ), where Dx is the numerical value used for determining positive or negative, e is a natural constant, and y is the value calculated by the preset y-value formula of the corresponding miRNA combination according to the ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8; S3. Based on the preset Dx threshold of the selected miRNA combination, determine the positive or negative value of the Dx value to identify whether the sample is a pancreatic cancer positive or negative sample. The miRNA combination is a diagnostic reagent used to determine the likelihood of a subject having pancreatic cancer. The miRNA combination is selected from at least three of miR-200c / miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-24 / miR-23a, and one of them is miR-23a.

2. The system according to claim 1, characterized in that, The miRNA combination includes one or more of the following combinations: Combination 1: miR-132 / miR-30c / miR-24 / miR-23a; Combination 2: miR-132 / miR-24 / miR-23a; Combination 3: miR-200c / miR-154 / miR-130b / miR-30c / miR-23a; Combination 4: miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-23a.

3. The system according to claim 1 or 2, characterized in that, The formula for the y-value is: ， Among them, α1, α2, α3, α4, α5, and α6 are calculation constants, and Constant is another constant.

4. The system according to any one of claims 1 to 3, characterized in that, The formula for the y-value of combination 1 is: ， Where x1 is the ΔCT value of miR-132, x2 is the ΔCT value of miR-30c, x3 is the ΔCT value of miR-24, and x4 is the ΔCT value of miR-23a.

5. The system according to any one of claims 1 to 4, characterized in that, The formula for the y-value of combination 2 is: ， Where x1 is the ΔCT value of miR-132, x2 is the ΔCT value of miR-24, and x3 is the ΔCT value of miR-23a.

6. The system according to any one of claims 1 to 5, characterized in that, The formula for the y-value of combination 3 is: ， Where x1 is the ΔCT value of miR-200c, x2 is the ΔCT value of miR-154, x3 is the ΔCT value of miR-130b, x4 is the ΔCT value of miR-30c, and x5 is the ΔCT value of miR-23a.

7. The system according to any one of claims 1 to 6, characterized in that, The formula for the y-value of combination 4 is: ， Where x1 is the ΔCT value of miR-154, x2 is the ΔCT value of miR-132, x3 is the ΔCT value of miR-130b, x4 is the ΔCT value of miR-577, x5 is the ΔCT value of miR-30c, and x6 is the ΔCT value of miR-23a.

8. The system according to any one of claims 1 to 7, characterized in that, After calculating the Dx value, a positive or negative result is determined based on a preset threshold for the selected miRNA combination. The specific criteria are as follows: Combination 1: When the Dx value of a sample is <0.62, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.62, it is judged as a positive sample for pancreatic cancer. Combination 2: When the Dx value of a sample is <0.562, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.562, it is judged as a positive sample for pancreatic cancer. Combination 3: When the Dx value of a sample is <0.58, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.58, it is judged as a positive sample for pancreatic cancer. Combination 4: When the Dx value of a sample is <0.531, it is judged as a negative sample for pancreatic cancer; when the Dx value is ≥0.531, it is judged as a positive sample for pancreatic cancer.

9. The system according to any one of claims 1 to 8, characterized in that, It also includes: The user interface module is used for visualization operations and result display. The user interface module is also used to input the CT values ​​of 3 to 6 miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 generated by the real-time fluorescence quantitative PCR instrument and their corresponding internal control CT values. The module automatically identifies and combines the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal control, and calculates ΔCT based on the CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal control, and then presents it to the calculation module. The data storage module is used to store the original data entered by the user interface module and the calculation result data output by the calculation module.

10. A method for automatically analyzing PCR test results to identify the risk of early pancreatic cancer in individuals, characterized in that, It includes the following steps: The receiving data is processed using the computing module. The calculation process performed by the calculation module includes: S1. Based on the CT values ​​of miRNA 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 and the internal reference, the corresponding ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 is calculated, and the abnormal data is preprocessed. S2. Data whose CT values ​​exceed the preset internal reference CT threshold, or whose CT values ​​of miRNAs 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 exceed the preset miRNA CT threshold, are discarded. After notifying the user, valid data is identified and Dx values ​​are calculated. The formula for calculating Dx is: Dx = e y / (1+e y ), where Dx is the numerical value used for determining positive or negative, e is a natural constant, and y is the value calculated by the preset y-value formula of the corresponding miRNA combination according to the ΔCT 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8; S3. Based on the preset Dx threshold of the selected miRNA combination, determine the positive or negative value of the Dx value to identify whether the sample is a pancreatic cancer positive or negative sample. The miRNA combination is a diagnostic reagent used to determine the likelihood of a subject having pancreatic cancer. The miRNA combination is selected from at least three of miR-200c / miR-154 / miR-132 / miR-130b / miR-577 / miR-30c / miR-24 / miR-23a, and one of them is miR-23a.

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