A platelet marker and detection kit for detecting pancreatic cancer
Through the detection of platelet markers DERA, TPP1 and CREG1, a high sensitivity and specific pancreatic cancer diagnosis model was constructed, which solved the problem of insufficient pancreatic cancer diagnosis sensitivity and specificity in the prior art, achieved accurate diagnosis and reduced detection cost.
Patent Information
- Application Number
- CN202411351070.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-26
- Publication Date
- 2024-12-06
- Estimated Expiration
- 2044-09-26
AI Technical Summary
The existing pancreatic cancer diagnosis methods have insufficient sensitivity and specificity, which leads to difficulty in early diagnosis, which in turn affects the rate of curative surgical resection and reduces mortality.
The platelet markers DERA, TPP1 and CREG1 were used as new biomarkers to construct diagnostic models and use RT-qPCR technology to detect their expression levels, and develop detection kits for pancreatic cancer.
A high sensitivity and specificity of more than 95% pancreatic cancer diagnosis has been achieved, reducing patient trauma and detection costs, and has the potential to promote in clinical practice, especially in areas with limited resources.
Smart Images
Figure CN118853891B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of pancreatic cancer diagnosis, and in particular relates to a platelet marker and a detection kit for detecting pancreatic cancer. Background Art
[0002] Pancreatic adenocarcinoma (PAAD), as a highly malignant tumor, is one of the most lethal malignant tumors in the world, with a 5-year overall survival rate of only 9%. In recent years, the incidence of this tumor has shown a sharp upward trend. Although surgical resection can provide patients with the opportunity for curative treatment in the early stages of the disease, due to the deep location of the disease in the abdominal cavity, the early symptoms are not obvious, and the disease progresses rapidly, about 85% of cases are already in the advanced or metastatic stage at the time of diagnosis. Although some progress has been made in new diagnostic methods and effective treatments in the past decade, research progress has been relatively slow. To date, serum carbohydrate antigen 19-9 (CA19-9) is the only biomarker approved by the US Food and Drug Administration for the diagnosis and preoperative prognosis of pancreatic cancer. However, CA19-9 has obvious limitations, with a median sensitivity and specificity of 79% and 82%, respectively, and a high false positive rate. This has led to the continued increase in the incidence and related mortality of PAAD.
[0003] Therefore, finding new biomarkers, especially non-invasive early diagnosis methods with high sensitivity and specificity, is crucial to improve the curative surgical resection rate and reduce the mortality rate of PAAD patients. Platelets, as non-nuclear cell fragments shed by mature megakaryocytes, are second only to red blood cells in peripheral blood. They not only play a key role in hemostasis, thrombosis and inflammation, but also participate in cancer-related processes such as epithelial-mesenchymal transition, metastasis, angiogenesis and immune escape. In liquid biopsy technology, platelets show unique advantages due to their non-invasive, easy to obtain and handle characteristics. Their high abundance and stability in the blood facilitate analysis, and they can capture and carry rich tumor-related molecular information, such as RNA and proteins, which are of great value for early diagnosis, real-time monitoring of disease progression, and evaluation of treatment response. These characteristics of platelets make them a promising tool in the field of liquid biopsy, which will help promote the development of personalized medicine and provide patients with more accurate and efficient cancer management strategies.
[0004] It is also based on this that the technical solution of the present invention is proposed. Summary of the invention
[0005] In order to solve the problems existing in the prior art, the present invention provides a platelet marker for detecting pancreatic cancer, wherein the platelet marker comprises DERA, TPP1 and CREG1.
[0006] Based on the same technical concept, another embodiment of the present invention is to provide a use of a platelet marker in preparing a kit for detecting pancreatic cancer, wherein the platelet marker includes DERA, TPP1 and CREG1.
[0007] Based on the same technical concept, another embodiment of the present invention is to provide a detection kit for pancreatic cancer, wherein the detection kit comprises a detection reagent for detecting platelet markers, wherein the platelet markers include DERA, TPP1 and CREG1.
[0008] Preferably, the detection reagent is a primer, and the primer is used to detect the expression level of DERA, TPP1 and CREG1.
[0009] Preferably, the detection kit also includes the sample cDNA to be tested, DNA polymerase, four deoxynucleotide triphosphates, SYBR Green I, Mg 2+ , buffer, enzyme-free water, EDTA reagent, Trizol reagent and primers of internal reference genes GAPDH and β-actin.
[0010] Preferably, the sample to be tested is a human blood sample.
[0011] The beneficial effects of the present invention are:
[0012] In the present invention, by analyzing the platelet transcriptome of non-cancer individuals and pancreatic cancer patients, it was found that DERA, TPP1 and CREG1 genes were significantly highly expressed in cancer patients, and the diagnostic model constructed by the above three genes showed excellent performance. This model not only showed high sensitivity and specificity in distinguishing pancreatic cancer patients from non-cancer individuals, but also achieved an accuracy of more than 95% in the independent validation set, showing its great potential in clinical applications. At present, there is still a blank in the research on platelet DERA, TPP1 and CREG1 in pancreatic cancer, and the molecular mechanism of these genes in pancreatic cancer has not been fully revealed. In addition, platelet DERA, TPP1 and CREG1 have not yet been used in the development of pancreatic cancer diagnostic reagents. However, with the deepening of research, the combination of these genes is expected to provide new strategies and targets for the accurate diagnosis of pancreatic cancer.
[0013] In summary, the present invention provides a new biological marker for differential diagnosis of pancreatic cancer. Using the mRNA expression levels of platelet genes DERA, TPP1 and CREG1 as highly specific markers for diagnosing pancreatic cancer not only reduces the trauma of patients and reduces the cost of detection, but also makes the method easier to promote in clinical practice, especially in underdeveloped areas with limited resources, because RT-qPCR technology has strong anti-interference ability and easy to standardize operating procedures. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0015] Figure 1 This is a result graph of genes that are significantly differentially expressed in platelets of non-cancer individuals and pancreatic cancer patients, screened out through differential expression analysis.
[0016] Figure 2 This is the result of univariate logistic regression analysis of the five genes with the largest odds ratio among the differentially expressed genes.
[0017] Figure 3 This is the expression difference result of DERA in platelets of non-cancer individuals (n=55) and pancreatic cancer patients (n=35) obtained by transcriptome sequencing (Wilcoxon rank sum test was used for statistics, ****P<0.0001).
[0018] Figure 4 This is the expression difference result of TPP1 in platelets of non-cancer individuals (n=55) and pancreatic cancer patients (n=35) obtained by transcriptome sequencing (Wilcoxon rank sum test was used for statistics, ****P<0.0001).
[0019] Figure 5 This is the expression difference result of CREG1 in platelets of non-cancer individuals (n=55) and pancreatic cancer patients (n=35) obtained by transcriptome sequencing (Wilcoxon rank sum test was used for statistics, ****P<0.0001).
[0020] Figure 6 The expression level of DERA in platelets of non-cancer individuals (n=10) and pancreatic cancer patients (n=15) obtained by RT-qPCR (Wilcoxon rank sum test was used for statistics; ****P<0.0001).
[0021] Figure 7 The expression level of TPP1 in platelets of non-cancer individuals (n=10) and pancreatic cancer patients (n=15) obtained by RT-qPCR (Wilcoxon rank sum test was used for statistics; ****P<0.0001).
[0022] Figure 8 The expression level of CREG1 in platelets of non-cancer individuals (n=10) and pancreatic cancer patients (n=15) obtained by RT-qPCR (Wilcoxon rank sum test was used for statistics; ****P<0.0001).
[0023] Fig. 9 This is the ROC curve of the univariate logistic regression model constructed based on DERA expression level in the training set and validation set (the X-axis represents the true negative rate, i.e., specificity %; the Y-axis represents the true positive rate, i.e., sensitivity %).
[0024] Fig.10 This is the ROC curve of the univariate logistic regression model constructed based on TPP1 expression level in the training set and validation set (the X-axis represents the true negative rate, i.e., specificity %; the Y-axis represents the true positive rate, i.e., sensitivity %).
[0025] Fig.11 This is the ROC curve of the univariate logistic regression model constructed based on CREG1 expression level in the training set and validation set (the X-axis represents the true negative rate, i.e., specificity %; the Y-axis represents the true positive rate, i.e., sensitivity %).
[0026] Fig.12 It is the ROC curve diagram of the multivariate logistic regression model constructed by integrating the expression levels of the three genes DERA, TPP1 and CREG1 in the training set and the validation set (the X-axis represents the true negative rate, that is, specificity %; the Y-axis represents the true positive rate, that is, sensitivity %). DETAILED DESCRIPTION
[0027] In order to fully illustrate the purpose, technical solutions and advantages of the present invention, the technical solutions will be described in detail below, and supplemented by specific embodiments and drawings. It should be noted that the embodiments shown are only part of the many embodiments of the present invention, and their purpose is to illustrate rather than define the scope of the present invention. All other implementation methods derived from these embodiments by those skilled in the art without creative thinking are within the scope of protection of the present invention. In addition, the professional vocabulary and terms used in this article, unless otherwise defined, should be understood in accordance with the common understanding of technical experts in the relevant fields of the invention.
[0028] Example 1: Identification of key platelet genes for diagnosis of pancreatic cancer
[0029] 1.1 Platelet transcriptome sequencing of clinical samples
[0030] 1. Collection of Clinical Samples
[0031] In order to screen new highly specific biomarkers that can diagnose pancreatic cancer, the present study collected peripheral whole blood from 90 patients recruited from West China Hospital of Sichuan University by venipuncture. Among these 90 patients, 35 were diagnosed with pancreatic cancer, while the remaining 55 had other non-cancer diseases or were asymptomatic individuals. The collected whole blood was stored in 10 ml EDTA-coated BD vacuum blood collection tubes containing the anticoagulant EDTA. EDTA inhibits the immediate activation and aggregation of platelets through calcium chelation, thereby blocking the coagulation cascade reaction. In order to minimize the degradation of platelet RNA, whole blood stored in vacuum tubes needs to be separated within 48 hours at room temperature or 4°C.
[0032] 2. Platelet Separation
[0033] In order to ensure the quality and integrity of platelet RNA, the present invention adopts a set of optimized platelet separation steps. First, the whole blood stored in the EDTA-coated vacuum blood collection tube is gently inverted and mixed at room temperature or 4°C to ensure that the anticoagulant is evenly distributed. Subsequently, the platelets are separated according to the following steps: First, the blood sample is centrifuged at room temperature for 15 minutes at a centrifugal force of 200g to separate the platelet-rich plasma from the red blood cells and white blood cells. Then, the plasma is transferred to a 15 ml BD tube. During the operation, be careful to avoid inhaling the lower cell sediment, and some more plasma can be appropriately retained. Subsequently, the plasma is centrifuged again at room temperature for 5 minutes at a centrifugal force of 200g, and the supernatant is carefully transferred to a new BD tube, and the sediment is discarded. Immediately thereafter, it is centrifuged again at room temperature for 3 minutes at a centrifugal force of 300g, the supernatant is transferred to a new tube, and the sediment is discarded. Finally, centrifuge at 1500g for 10 minutes at room temperature, pour the supernatant directly into the waste bottle, and use a pipette to suck up and discard the remaining liquid, then invert the tube for a moment to ensure that as little plasma as possible is left. Use 1mL of Trizol reagent to resuspend the platelets in the pellet and transfer the resuspended liquid to the seed tube.
[0034] Under the protection of RNA stabilizing solution, platelet samples can be stored for a long time at a low temperature of -80°C while maintaining the integrity and quality of their RNA. Through this sophisticated platelet separation and storage process, the present invention ensures the purity of the sample and the long-term stability of RNA, providing a solid foundation for subsequent biomarker screening and diagnostic analysis.
[0035] 3. Isolation, library construction and sequencing of platelet RNA
[0036] For the extraction of platelet RNA, first, the frozen platelet samples were placed on ice for slow thawing to minimize RNA degradation, and then total RNA was isolated from the platelets. The isolated RNA samples were subjected to strict quality assessment on the Bioanalyzer 2100 system using RNA 6000Picochip. The present invention only selected platelet RNA samples with an RNA integrity number (RIN) greater than 7 and / or showing an obvious rRNA characteristic curve for inclusion in subsequent experiments, including reverse transcription to generate cDNA, synthesis of double-stranded DNA, library construction, and final sequencing.
[0037] 2. Identification of platelet genes as novel markers for pancreatic cancer
[0038] 1. Processing of Raw Platelet Transcriptome Data
[0039] First, the raw data images obtained by sequencing were converted into standard Fastq format sequence information with double-end 150 base pairs using Illumina's bcl2fastq software. Then, the raw RNA sequencing reads data were strictly quality controlled using TrimGalore (v0.6.7) software, including the removal of low-quality data and trimming and shearing of adapter sequences to improve data quality. Subsequently, the clean reads were accurately aligned to the human reference genome (GRCh38) using HISAT2 (v2.2.1). Finally, featureCounts (v2.0.0) software was used in combination with the NCBI gene annotation gtf file GRCh38.84 version to calculate the number of reads mapped on genomic features (such as genes, exons, promoters, etc.).
[0040] 2. Screening of differentially expressed genes
[0041] In order to find differentially expressed genes in pancreatic cancer platelets, the present invention used edgeR (v3.40.2) to perform statistical tests between non-cancerous platelets and pancreatic cancer platelets. The present invention set the threshold of logFC (log-fold change) to 1 to ensure the significance of gene expression changes; at the same time, the threshold of logCPM (log-counts per million) was set to 3 to exclude the interference of low-expressed genes. In addition, the present invention adopted FDR (false discovery rate) correction and set the threshold to 0.05 to control the false positive rate caused by multiple comparisons. The present invention also excluded genes located on the Y chromosome and limited the analysis objects to protein-coding genes to ensure the biological significance and clinical relevance of the research results. After this series of screening conditions, we finally identified a series of genes that were significantly upregulated or downregulated in pancreatic cancer platelets (such as Figure 1 These genes may serve as potential biomarkers and provide new perspectives for the diagnosis of pancreatic cancer.
[0042] 3. Screening for genes with specificity for pancreatic cancer diagnosis
[0043] In order to evaluate the relationship between the mRNA expression level of a single gene and the binary dependent variable (pancreatic cancer vs. non-cancer), the present invention performed a univariate logistic regression analysis on the genes significantly upregulated in pancreatic cancer. Among these genes, DERA, TPP1, and CREG1 genes showed significantly better odds ratios (OR, OR>100; such as Figure 2 As shown in Figure 3 ), this indicates that the expression levels of DERA, TPP1 and CREG1 genes in pancreatic cancer are significantly increased compared with those in the non-cancerous state, and their expression changes have a strong predictive ability for the occurrence of pancreatic cancer.
[0044] Example 2: Verification of DERA, TPP1 and CREG1 expression levels in platelets from clinical samples
[0045] First, the present invention uses the platelet transcriptome data in Example 1, takes the platelets of non-cancer patients as the control, normalizes the gene expression values, and calculates the expression levels of DERA, TPP1, and CREG1 in non-cancer individuals and pancreatic ductal patients. Figure 3~Figure 5 As shown, DERA, TPP1, and CREG1 were significantly upregulated in platelets from patients with pancreatic ductal carcinoma.
[0046] At the same time, in the present invention, according to the platelet separation method described in Example 1, additional platelet samples from 10 non-cancer individuals and 15 pancreatic cancer patients were collected. Further, in this example, the expression levels of DERA, TPP1 and CREG1 in these samples were detected using RT-qPCR technology. Table 1 below shows the primers used.
[0047] Table 1
[0048]
[0049] The results are as follows Figure 6~Figure 8 As shown, the mRNA expression levels of DERA, TPP1, and CREG1 in platelets of pancreatic cancer patients were significantly increased compared with those of non-cancer individuals.
[0050] Example 3: Diagnostic value of platelet DERA, TPP1 and CREG1 for pancreatic cancer
[0051] By using the receiver operating curve (ROC) and its area under the curve (AUC) for verification, the present invention evaluated the differences in the mRNA expression levels of DERA, TPP1 and CREG1 in platelets between non-cancer individuals and pancreatic cancer patients, thereby determining their potential as diagnostic markers for pancreatic cancer, and further evaluating their test efficacy.
[0052] First, the platelet transcriptome data in Example 1 was used as a training set, and a univariate logistic regression model was constructed based on the expression levels of DERA, TPP1, and CREG1 to predict the occurrence of pancreatic cancer. On the training set, these univariate regression models all performed well, with AUC values reaching above 0.95 ( Figure 9~Figure 11 ). Subsequently, in this example, the qPCR values of DERA, TPP1 and CREG1 in Example 2 were used as the validation set to verify the robustness of each regression model. The validation results were also encouraging, and the AUC of each model on the validation set reached 1 ( Figure 9~Figure 11 ). In view of the excellent performance of DERA, TPP1 and CREG1 genes in the diagnosis of pancreatic cancer, the expression levels of these three genes were further integrated to construct a multifactorial logistic regression model for the accurate diagnosis of pancreatic cancer. The AUC value of this multifactorial model in the training set was increased to 0.989, and the sensitivity and specificity were as high as 100% and 94.5%, respectively ( Fig.12 ), the AUC value in the validation set remained at 1. These results fully confirm the strong ability and stability of platelet genes DERA, TPP1, and CREG1 in the prediction of pancreatic cancer.
[0053] Conclusion:
[0054] The present invention collects platelets from non-cancer individuals and pancreatic cancer patients for transcriptome sequencing, and uses bioinformatics technology to analyze the differentially expressed genes of platelets at the population sequencing level. The results show that the mRNA levels of genes represented by DERA, TPP1 and CREG1 are significantly upregulated in pancreatic cancer patients. Based on this, a logistic regression model was constructed according to the expression levels of DERA, TPP1 and CREG1 genes, and the AUC value of the model in the test set reached 0.989. Further, the mRNA expression levels of DERA, TPP1 and CREG1 were detected in additional non-cancer individuals and pancreatic cancer patients using real-time quantitative polymerase chain (RT-qPCR) technology. Not surprisingly, the relative expression of these genes in pancreatic cancer patients was more than 2 times that of non-cancer individuals, and the difference was statistically significant (P<0.05). Then, ROC analysis was performed in the validation set, and the results showed that the AUC value was as high as 1.
[0055] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.
Claims
1. A platelet marker for detecting pancreatic cancer, characterized in that: The platelet markers consist of DERA, TPP1 and CREG1.
2. Use of a platelet marker in the preparation of a kit for detecting pancreatic cancer, characterized in that: The platelet markers consist of DERA, TPP1 and CREG1.
3. A detection kit for pancreatic cancer, characterized in that: The detection kit comprises a detection reagent for detecting platelet markers, and the platelet markers consist of DERA, TPP1 and CREG1.
4. The detection kit according to claim 3, characterized in that The detection reagent is a primer, and the primer is used to detect the expression levels of DERA, TPP1 and CREG1.
5. The detection kit according to claim 3, characterized in that The detection kit also includes a sample cDNA to be tested, DNA polymerase, four deoxynucleotide triphosphates, SYBR Green I, Mg 2+ , buffer, enzyme-free water, EDTA reagent, Trizol reagent and primers of internal reference genes GAPDH and β-actin.
6. The detection kit according to claim 5, characterized in that The sample to be tested is a human blood sample.