Application of PLOD3 and LRRN3 as combined markers in the early diagnosis of Parkinson's disease
Through RT-qPCR detection of the combined markers of PLOD3 and LRRN3, the problem of early diagnosis of Parkinson's disease has been solved, and minimally invasive and efficient early screening and diagnosis have been achieved, with significant diagnostic accuracy and the effect of delaying disease progression.
Patent Information
- Application Number
- CN202310170016.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-02-27
- Publication Date
- 2025-09-09
- Estimated Expiration
- 2043-02-27
AI Technical Summary
Existing technologies lack reliable biomarkers for the early diagnosis of Parkinson's disease, especially sporadic Parkinson's disease with atypical symptoms, resulting in diagnosis relying on clinical manifestations in the late stages of the disease and lacking minimally invasive and early screening methods.
PLOD3 and LRRN3 were used as joint markers. The expression levels of PLOD3 and LRRN3 in peripheral blood were detected by RT-qPCR and other methods, and a nomogram model was constructed to assist in the early diagnosis of Parkinson's disease.
It has achieved minimally invasive and easy-to-operate early screening and diagnosis of Parkinson's disease, enabled early intervention to delay disease progression, and improved the accuracy and stability of diagnosis.
Smart Images
Figure CN116042816B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of biological detection technology and relates to the application of PLOD3 and LRRN3 as a combined marker in the early diagnosis of Parkinson's disease. Background Art
[0002] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not necessarily be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art.
[0003] According to the inventors' research, the current diagnostic methods for Parkinson's disease rely heavily on typical clinical manifestations of the late stage of the disease, such as resting tremor, bradykinesia, and muscle rigidity. However, studies have shown that early diagnosis and intervention can slow the progression of Parkinson's disease. As a molecular diagnostic technology, liquid biopsy technology has the advantages of easy extraction of test samples, non-invasive or minimally invasive, low cost, and low risk. It has been widely used in the fields of tumors and microbial infections. However, for the diagnosis of Parkinson's disease, especially the diagnosis of sporadic Parkinson's disease with atypical symptoms, there are still no objective biomarkers for early screening of Parkinson's disease. Therefore, there is an urgent need to identify and verify reliable biomarkers for auxiliary diagnosis of Parkinson's disease, which is very important for the management and early intervention of Parkinson's disease. Summary of the Invention
[0004] In order to address the deficiencies of the prior art, the present invention aims to provide the use of PLOD3 and LRRN3 as a combined marker for the early diagnosis of Parkinson's disease. The use of PLOD3 and LRRN3 as a combined marker can enable early screening and diagnosis of Parkinson's disease in the population. Compared with traditional diagnostic methods that rely on typical clinical manifestations of Parkinson's disease, the present invention has the advantages of early diagnosis and clarification of the onset and progression of Parkinson's disease, which is of great significance for early follow-up, management, and intervention of the disease, thereby delaying the progression of Parkinson's disease.
[0005] In order to achieve the above object, the technical solution of the present invention is:
[0006] On the one hand, a biomarker panel for early diagnosis of Parkinson's disease consists of a PLOD3 encoding gene or an expression product of a PLOD3 encoding gene and a LRRN3 encoding gene or an expression product of a LRRN3 encoding gene.
[0007] In another aspect, a reagent for detecting the expression level of PLOD3 and LRRN3 is used in the preparation of a diagnostic reagent for early diagnosis of Parkinson's disease.
[0008] Furthermore, the sample object for detecting the expression level of PLOD3 is peripheral blood.
[0009] Detection of PLOD3 expression levels can be performed by combining PLOD3 expression products with antibodies against PLOD3 expression products, by RT-qPCR, or by other methods. Furthermore, RT-qPCR is used to detect PLOD3 levels.
[0010] Furthermore, the reagent for detecting PLOD3 expression level by RT-qPCR includes a total RNA extraction reagent, such as TRIzol reagent.
[0011] Furthermore, the reagents for detecting the expression level of PLOD3 by RT-qPCR include a TaqMan reverse transcription kit for reverse transcription of the cDNA encoding the PLOD3 gene.
[0012] Furthermore, the reagents for detecting the expression level of PLOD3 by RT-qPCR include M-MLV reverse transcriptase, which is used to reverse transcribe the cDNA of the PLOD3 encoding gene to detect mRNA.
[0013] Furthermore, the reagents for detecting the expression level of PLOD3 by RT-qPCR include a forward primer for PLOD3 and a reverse primer for PLOD3. The forward primer for PLOD3 is shown in SEQ ID NO. 1. The reverse primer for PLOD3 is shown in SEQ ID NO. 2.
[0014] Furthermore, the sample object for detecting the expression level of LRRN3 is peripheral blood.
[0015] The LRRN3 expression level can be detected by combining the LRRN3 expression product with an antibody against the LRRN3 expression product, by RT-qPCR, or by other methods. Furthermore, the LRRN3 expression level can be detected by RT-qPCR.
[0016] Furthermore, the reagent for detecting the expression level of LRRN3 by RT-qPCR includes a total RNA extraction reagent, such as TRIzol reagent.
[0017] Furthermore, the reagents for detecting the expression level of LRRN3 by RT-qPCR include a TaqMan reverse transcription kit, which is used to reverse transcribe the cDNA of the LRRN3 encoding gene.
[0018] Furthermore, the reagents for detecting the expression level of LRRN3 by RT-qPCR include M-MLV reverse transcriptase, which is used to reverse transcribe the cDNA of the LRRN3 encoding gene to detect mRNA.
[0019] Furthermore, the reagents for detecting the expression level of LRRN3 by RT-qPCR include a forward primer for LRRN3 and a reverse primer for LRRN3. The forward primer for LRRN3 is shown in SEQ ID NO. 1. The reverse primer for LRRN3 is shown in SEQ ID NO. 2.
[0020] Thirdly, a product for detecting and diagnosing Parkinson's disease in the early stages, comprising reagents for detecting PLOD3 expression levels and LRRN3 expression levels.
[0021] The product of the present invention may be a kit.
[0022] Furthermore, the reagent for detecting the expression level of PLOD3 is a reagent for detecting the expression level of PLOD3 by RT-qPCR.
[0023] Furthermore, the reagent for detecting PLOD3 expression level by RT-qPCR includes a total RNA extraction reagent, such as TRIzol reagent.
[0024] Furthermore, the reagents for detecting the expression level of PLOD3 by RT-qPCR include a TaqMan reverse transcription kit for reverse transcription of the cDNA encoding the PLOD3 gene.
[0025] Furthermore, the reagents for detecting PLOD3 expression levels by RT-qPCR include M-MLV reverse transcriptase, which is used to reverse transcribe the cDNA of the PLOD3 expression coding gene to detect mRNA.
[0026] Furthermore, the reagents for detecting the expression level of PLOD3 by RT-qPCR include a forward primer for PLOD3 and a reverse primer for PLOD3. The forward primer for PLOD3 is shown in SEQ ID NO. 1. The reverse primer for PLOD3 is shown in SEQ ID NO. 2.
[0027] Furthermore, the reagent for detecting the expression level of LRRN3 is a reagent for detecting the expression level of LRRN3 by RT-qPCR.
[0028] Furthermore, the reagent for detecting the expression level of LRRN3 by RT-qPCR includes a total RNA extraction reagent, such as TRIzol reagent.
[0029] Furthermore, the reagents for detecting the expression level of LRRN3 by RT-qPCR include a TaqMan reverse transcription kit, which is used to reverse transcribe the cDNA of the LRRN3 encoding gene.
[0030] Furthermore, the reagents for detecting the expression level of LRRN3 by RT-qPCR include M-MLV reverse transcriptase, which is used to reverse transcribe the cDNA of the LRRN3 encoding gene to detect mRNA.
[0031] Furthermore, the reagents for detecting the expression level of LRRN3 by RT-qPCR include a forward primer for LRRN3 and a reverse primer for LRRN3. The forward primer for LRRN3 is shown in SEQ ID NO. 1. The reverse primer for LRRN3 is shown in SEQ ID NO. 2.
[0032] The beneficial effects of the present invention are:
[0033] This study used qPCR to verify that PLOD3 is highly expressed in the peripheral blood of Parkinson's patients, while LRRN3 is lowly expressed. Receiver operating characteristic (ROC) and calibration curves were used to assess the accuracy and stability of the two genes in predicting Parkinson's disease. A nomogram model was constructed combining PLOD3 and LRRN3 with age, confirming the feasibility of PLOD3 and LRRN3 as Parkinson's disease biomarkers.
[0034] Compared to traditional Parkinson's disease diagnosis based on typical clinical manifestations, this method involves minimally invasively collecting blood samples, centrifuging them, and then extracting circulating RNA for analysis of PLOD3 and LRRN3 expression. This method is more suitable for screening and auxiliary diagnosis of Parkinson's disease in the general population. Its minimally invasive nature and ease of use facilitate early auxiliary diagnosis, early follow-up, and intervention to slow Parkinson's disease progression. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.
[0036] Figure 1Figure 1 shows the results of the process of screening PLOD3 and LRRN3 in the Parkinson's disease risk score model constructed using 9 key genes in an embodiment of the present invention: A. Lasso regression analysis of the biomarker gene set (58 genes) that are upregulated or downregulated in the substantia nigra tissue and peripheral serum of Parkinson's patients compared with the control group, and the inclusion of variables at different lamada values; B. Lasso regression cross-validation is used to determine the optimal lamada value, thereby determining the inclusion of 9 key genes as variables; C. Comparison of risk scores in the PD group and the control group in the training set; D. Comparison of risk scores in the PD group and the control group in the validation set; E. Calculation of calibration curve and AUC value in the training set. ; F. Calibration curve and AUC value calculated in the validation set; G. Expression of PLOD3 in the control group and Parkinson's disease group in the merged human brain substantia nigra tissue dataset; H. Expression of LRRN3 in the control group and Parkinson's disease group in the merged human brain substantia nigra tissue dataset; I. Expression of PLOD3 in the control group and Parkinson's disease group in the GSE99039 dataset; J. Expression of LRRN3 in the control group and Parkinson's disease group in the GSE99039 dataset; K. Expression of PLOD3 along with Parkinson's disease Braak stage in the GSE49036 dataset; L. Expression of LRRN3 along with Parkinson's disease Braak stage in the GSE49036 dataset.
[0037] Figure 2 Figure 1 is a characterization result diagram for validating the combination of PLOD3 and LRRN3 as a biomarker for Parkinson's disease in an embodiment of the present invention: A is the expression of PLOD3 in the serum of patients in the PD group and the control group; B is the proportion of patients in the high and low expression groups of PLOD3 in the PD group and the control group, respectively; C is the expression of LRRN3 in the serum of patients in the PD group and the control group; D is the proportion of patients in the high and low expression groups of LRRN3 in the PD group and the control group, respectively; E is the ROC curve for distinguishing PD and control groups by PLOD3 and LRRN3; F is the calibration curve for distinguishing PD and control groups by PLOD3 and LRRN3; G is a nomogram for distinguishing PD and control groups by integrating PLOD3, LRRN3 expression and age factors; H is the difference in risk score constructed based on the nomogram between the PD group and the control group. DETAILED DESCRIPTION
[0038] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to specific embodiments.
[0039] Example
[0040] 1. Download sequencing data from the substantia nigra of Parkinson's disease patients from the GEO database (GSE20141, GSE20163, GSE20164, GSE20292, GSE24378, and GSE7621). Merge the data using the R language "combat" to remove batch effects. Perform differential analysis using the "limma" package, setting a p-value of less than 0.01. Select RNAs that are significantly differentially expressed in the substantia nigra of Parkinson's disease patients compared to controls.
[0041] 2. Obtain GSE49036 data from the GEO database, which contains clinical data on the Braak stage of Parkinson's disease, which can be used to assess the progression of Parkinson's disease. Based on the GSE49036 data, perform weighted gene co-expression network analysis (WGCNA) on the differentially expressed genes obtained in step 1 to identify biomarkers associated with Braak stage.
[0042] 3. Obtain GSE99039 data from the GEO database. This data contains RNA sequencing data from peripheral serum of Parkinson's disease patients and normal controls. Use the "limma" package to perform differential analysis, setting the p-value to less than 0.01, and screen for differentially expressed genes.
[0043] 4. The intersection of the "Braak stage-related biomarkers" obtained in #2 and the differentially expressed genes in #3 was taken, and the intersection genes were analyzed to screen out the biomarker gene set (58 genes) that was upregulated or downregulated in the substantia nigra tissue and peripheral serum of Parkinson's patients compared with the control group.
[0044] 5. GSE99039 was randomly divided into a training set and a validation set in a ratio of 7:3. Least absolute shrinkage and selection operator regression (LASSO) analysis was performed on the training set using the "glmnet" package. The optimal lambda value was selected to construct a Parkinson's disease-associated gene set. Nine key genes (ABHD2, BASP1, CTBP2, GCM1, GMPR2, GPX3, LRRN3, PLOD3, and RBM38) from the 58 biomarker gene set were further subjected to logistic regression to construct a Parkinson's disease risk score model.
[0045] 6. The Parkinson's disease-related gene set and risk score model were applied to the training set and validation set, and the receiver operating characteristic curve (ROC) and calibration curve were used to evaluate the accuracy and stability of the prediction model.
[0046] 7. Further analysis of the nine key genes in the gene set revealed that PLOD3 (procollagen-lysine-2-oxoglutarate 5-dioxygenase 3) and LRRN3 (leucine-rich repeat neuron 3) were significantly correlated with Parkinson's disease Braak stage. Single-sample gene functional enrichment analysis (ssGSEA) was used to analyze the relationship between these two genes and tissue immune cell infiltration.
[0047] 8. GSE184950 data, single-cell sequencing data from substantia nigra tissue of the human brain, was obtained from the GEO database. The data were analyzed using the "Seraut" package. Single cells were clustered using the "UMAP" function. The clustering results were annotated to cell subpopulations based on the expression of classic cell subpopulation markers. The expression of PLOD3 and LRRN3 in each cell subpopulation was analyzed.
[0048] 9. Peripheral blood samples from Parkinson's disease patients and normal controls were obtained from the Department of Neurosurgery, Qilu Hospital, Shandong University. After resting at 4°C for 2 hours, the blood samples were centrifuged (1000 rpm, 10 minutes), and serum components were collected. Total RNA was isolated from cells using TRIzol reagent (Invitrogen) according to the manufacturer's protocol. cDNA for detection was reverse transcribed using the TaqMan Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). mRNA was detected by reverse transcription of cDNA using M-MLV Reverse Transcriptase (BioTeke, Beijing, China). RT-qPCR was performed using PowerSYBR Green PCR Master Mix (Applied Biosystems) using an ABI 7500 real-time PCR system (Applied Biosystems). GAPDH was used as an internal control to normalize the expression of PLOD3 and LRRN3. The primer sequences are as follows: PLOD3, F: 5′-ACCTGAAGGCGGTCTCTGT-3′ (SEQ ID NO. 1) and R: 5′-CCTTGCTGCCATCTCGAATC-3′ (SEQ ID NO. 2); LRRN3, F: 5′-TGGTACCATTGAGTCTCTGCCA-3′ (SEQ ID NO. 3) and R:
[0049] 5′-TGCCGAACATTCTGACCTTGG-3′ (SEQ ID NO. 4); GADPH F:
[0050] 5′-GCACCGTCAAGGCTGAGAAC-3′ (SEQ ID NO. 5) and R: 5′-TGGTGAAGACGCCAGTGGA-3′ (SEQ ID NO. 6). qPCR confirmed that PLOD3 was highly expressed in the peripheral blood of Parkinson's patients, while LRRN3 was lowly expressed in the peripheral blood of Parkinson's patients. Receiver operating characteristic (ROC) and calibration curves were used to evaluate the accuracy and stability of the two genes in predicting Parkinson's disease. A nomogram model was constructed combining PLOD3, LRRN3, and age, confirming the feasibility of PLOD3 and LRRN3 as Parkinson's disease biomarkers.
[0051] A Parkinson's disease risk score model was constructed using 9 key genes (hub genes), and the accuracy and stability of the prediction model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve. The 9 key gene biomarkers were further analyzed, and PLOD3 and LRRN3 were screened out to have a significant correlation with Parkinson's Braak stage, such as Figure 1 As shown: First, LASSO regression was used to construct a Parkinson's disease risk score model with 9 key genes ( Figure 1 A, B), comparing the risk scores of the PD group and the control group, it was found that the risk scores of the PD group were significantly higher than those of the control group ( Figure 1 C, D); further calculation of the calibration curve and AUC value revealed that the risk score had a good discriminative effect on the PD group and the control group ( Figure 1 E, F); by comparing 9 key gene biomarkers in the combined human substantia nigra tissue dataset ( Figure 1 G, H), GSE99039 dataset ( Figure 1 I, J), and GSE49036 datasets ( Figure 1 K, L), and found that PLOD3 and LRRN3 were highly expressed and lowly expressed in the substantia nigra tissue and blood of Parkinson's disease patients, respectively, and were significantly correlated with the Braak stage of Parkinson's disease.
[0052] Peripheral blood samples from Parkinson's disease patients and normal controls were obtained from the Department of Neurosurgery, Qilu Hospital, Shandong University. PCR was performed to detect the expression of PLOD3 and LRRN3, and the results were analyzed to confirm the feasibility of combining PLOD3 and LRRN3 as PD biomarkers. Figure 2As shown: First, qPCR verified the expression of PLOD3 and LRRN3 in peripheral blood samples of Parkinson's patients and normal controls, and found that the expression of PLOD3 in the peripheral blood of PD patients was significantly higher than that in the normal control group ( Figure 2 A, B); the expression of LRRN3 in the peripheral blood of PD patients was significantly lower than that in the normal control group ( Figure 2 C, D). Receiver operating curve (ROC) and calibration curve (Calibration curve) were used to demonstrate that PLOD3 and LRRN3 have high accuracy and stability in distinguishing PD from normal controls ( Figure 2 E, F). The Nomogram model was further constructed based on the expression of PLOD3 and LRRN3 and age ( Figure 2 G) and obtained the Nomogram score; by comparing the Nomogram scores of the normal control group and the PD group, it was found that the Nomogram score of the PD group was significantly higher than that of the normal control group ( Figure 2 H), ultimately verifying the effectiveness and accuracy of combined detection of PLOD3 and LRRN3 for diagnosing PD and confirming the feasibility of combining PLOD3 and LRRN3 as PD biomarkers.
[0053] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.
Claims
1. Use of a reagent for detecting PLOD3 expression level and LRRN3 expression level in the preparation of a diagnostic reagent for early diagnosis of Parkinson's disease, characterized in that: The early diagnosis of Parkinson's disease comprises the following steps: constructing a Nomogram model based on the expression of PLOD3 and LRRN3 and age to obtain a Nomogram score; The samples for detecting the expression levels of PLOD3 and LRRN3 were peripheral blood.
2. Use of the reagent for detecting PLOD3 expression level and LRRN3 expression level according to claim 1 in the preparation of a diagnostic reagent for early diagnosis of Parkinson's disease, characterized in that: The PLOD3 level was detected by real-time qPCR.
3. Use of the reagent for detecting PLOD3 expression level and LRRN3 expression level according to claim 2 in the preparation of a diagnostic reagent for early diagnosis of Parkinson's disease, characterized in that: The reagents for real-time qPCR detection of PLOD3 expression levels include PLOD3 forward primer and PLOD3 reverse primer.
4. Use of the reagent for detecting PLOD3 expression level and LRRN3 expression level according to claim 1 in the preparation of a diagnostic reagent for early diagnosis of Parkinson's disease, characterized in that: The expression level of LRRN3 was detected by real-time qPCR.
5. Use of the reagent for detecting PLOD3 expression level and LRRN3 expression level according to claim 4 in the preparation of a diagnostic reagent for early diagnosis of Parkinson's disease, characterized in that: The reagents for real-time qPCR detection of LRRN3 expression levels include LRRN3 forward primer and LRRN3 reverse primer.