Application of serum alpha-non-erythrocyte ghost protein 1 in preparation of medicine for diagnosing heart light-chain amyloidosis disease

Abstract

The invention discloses application of serum alpha-non-red blood cell ghost protein 1 in preparation of drugs for diagnosing heart light chain amyloidosis diseases, and belongs to the technical field of biological medicines. The serum SPTAN1 protein is used for preparing the medicine for diagnosing the heart light-chain amyloidosis disease, the medicine is limited to be an enzyme linked immunosorbent assay kit, and the kit comprises an antibody of the SPTAN1 protein and further comprises a pre-coated plate, a standard substance, a coating buffer solution, a sample diluent, a confining liquid, horse radish peroxidase, an eluent and a color developing agent. The concentration of the SPTAN1 protein in the serum is measured through an enzyme linked immunosorbent assay kit, and early diagnosis of the heart AL amyloidosis disease is carried out. The invention provides a novel, efficient and sensitive biomarker, namely the serum SPTAN1 protein, early diagnosis of heart AL amyloidosis is realized by detecting the concentration of the SPTAN1 protein in serum, and the serum SPTAN1 protein has an important clinical application prospect.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to the application of serum α-non-erythrocyte spectroscopy protein 1 (SPTAN1) in the preparation of drugs for diagnosing cardiac light chain amyloidosis. Background Technology

[0002] Amyloidosis is a rare protein folding disorder that can lead to organ damage and even death. Light chain (AL) amyloidosis is the most common systemic amyloidosis, characterized by the deposition of extracellular misfolded immunoglobulin light chains. AL amyloidosis is a systemic disease that can affect multiple organs throughout the body. Previous studies have found that different light chain proteins contain different variable domains, which may lead to involvement of different organs. Cardiac amyloidosis is highly prone to refractory heart failure, has a high risk of disease progression, and a poor prognosis. According to literature, the annual mortality rate per 100,000 people due to cardiac amyloidosis in the United States increased from 0.261 in 1999 to 0.608 in 2020, with an overall annual growth rate of 3.96% and an estimated median survival of 1.72 years.

[0003] Clinical diagnosis of cardiac amyloliquefaciens (AL) relies on biomarkers, labial gland and fat biopsy staining. Currently used biomarkers include M protein quantification and free light chains, but none can effectively differentiate light chain amyloidosis from other monoclonal immunoglobulin proliferation lesions, exhibiting limitations in sensitivity and specificity. Traditional cardiac biomarkers and imaging methods, including troponin, N-terminal pro-B-type glycopeptide (NT-proBNP), and echocardiography, have been established as key indicators of disease burden, reflecting myocardial damage and hemodynamic stress, but they cannot definitively diagnose the disease and fail to meet the clinical need for early and accurate disease identification. Due to the importance of the heart, routine myocardial biopsy for direct detection of amyloid deposition is not feasible clinically. Furthermore, biopsy is an invasive procedure with bleeding risks and is not suitable for patients undergoing long-term anticoagulation therapy. Specimens requiring Congo red staining and mass spectrometry for amyloid deposition detection have high quality requirements and long processing times. Fat and labial gland biopsies are also limited by their invasiveness and time-consuming specimen processing. Therefore, neither biochemical indicators nor detection methods fully meet clinical needs. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide the application of serum α-non-erythrocyte spectroscopy protein 1 (SPTAN1) in the preparation of drugs for diagnosing cardiac light chain amyloidosis. By integrating proteomics, machine learning, and PRM detection, serum SPTAN1 protein is screened as a biomarker for cardiac light chain amyloidosis. Cardiac AL amyloidosis is diagnosed by the concentration of SPTAN1 protein in the blood, providing a novel, efficient, and sensitive biomarker that is unaffected by treatment methods, enabling early diagnosis of cardiac AL amyloidosis.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides the application of serum α-non-erythrocyte spectroscopy protein 1 (SPTAN1) in the preparation of drugs for diagnosing cardiac light chain amyloidosis.

[0007] Preferably, the diagnosis is performed by quantitative detection of the concentration of SPTAN1 protein in serum using enzyme-linked immunosorbent assay (ELISA).

[0008] Preferably, the drug includes a kit.

[0009] More preferably, the kit is an enzyme-linked immunosorbent assay (ELISA) kit.

[0010] More preferably, the enzyme-linked immunosorbent assay kit contains an antibody against the SPTAN1 protein.

[0011] More preferably, the enzyme-linked immunosorbent assay kit further includes a pre-coated plate, standards, coating buffer, sample diluent, blocking buffer, horseradish peroxidase, elution buffer, and chromogenic agent.

[0012] More preferably, the concentration of SPTAN1 protein in peripheral blood serum can be measured using the enzyme-linked immunosorbent assay kit for early diagnosis of cardiac AL amyloidosis.

[0013] More preferably, by collecting peripheral blood samples from the subject, separating the serum, and then detecting the concentration of SPTAN1 protein in the serum using the enzyme-linked immunosorbent assay kit, if the concentration of SPTAN1 protein in the serum is less than 81.21 pg / mL, it is judged as negative; when the concentration of SPTAN1 protein in the serum exceeds 81.21 pg / mL, it indicates cardiac AL amyloidosis in the subject.

[0014] Compared with existing technologies, this invention provides a novel, efficient, and sensitive biomarker—serum SPTAN1 protein—as a standalone diagnostic marker for cardiac AL amyloidosis, enabling early prediction and diagnosis of cardiac AL amyloidosis. It features rapid detection, ease of operation, high accessibility, cost-effectiveness, and high sensitivity and specificity, and is unaffected by treatment methods. Its beneficial effects include at least the following:

[0015] 1. Early diagnosis, high sensitivity and specificity:

[0016] Experimental results showed that serum SPTAN1 protein, as a standalone diagnostic marker for cardiac AL amyloidosis, demonstrated outstanding ability to distinguish it from other M proteinemias, with an AUC of 0.816 (95% CI: 0.739-0.894, p<0.001), a sensitivity of 65.3%, and a specificity of 89.7%. This finding fills the gaps in conventional blood indicators such as free light chains, and serum SPTAN1 protein levels, while enabling accurate diagnosis, also possess the ability for early detection, making it an ideal biomarker for cardiac AL amyloidosis.

[0017] 2. Unaffected by clinical treatment:

[0018] Serum SPTAN1 protein not only aids in the accurate diagnosis of cardiac AL amyloidosis, but its serum concentration also shows no significant change in patients who have achieved clinical remission. While current treatments aim to inhibit amyloid deposition, reduce inflammation, and protect kidney function, they do not clear existing amyloid deposits. Serum SPTAN1 protein concentration directly reflects the presence or absence of amyloid, is unaffected by factors such as inflammation, and is therefore more stable.

[0019] 3. Clinical translation potential:

[0020] The serum SPTAN1 protein of this invention, as a biomarker, has the advantages of simple operation, high cost and high accessibility, and is easy to promote and apply in clinical practice. By detecting the concentration level of serum SPTAN1 protein, it can be used to diagnose cardiac AL amyloidosis, providing a new strategy to improve patient prognosis, and has important clinical application prospects. Attached Figure Description

[0021] Figure 1 This is an experimental flowchart for screening key biomarkers in the development of cardiac light chain amyloidosis, as shown in the examples.

[0022] Figure 2 This is a volcano diagram showing the differential protein expression after proteomics screening in the examples.

[0023] Figure 3 The results of differential protein enrichment analysis in the examples are as follows: (A) GO enrichment analysis of differential proteins; (B) KEGG enrichment analysis of differential proteins.

[0024] Figure 4In this example, machine learning is used to screen candidate biomarkers for cardiac AL amyloidosis: Figures (A) and (B) on the left show the performance of Random Forest (RF) and Support Vector Machine (SVM) models evaluated using nested 10-fold cross-validation (k=10, repeated n=5 times), with the vertical axis representing accuracy and the horizontal axis representing the number of variables (features); orange dots indicate the size of the optimal feature subset, and the corresponding accuracy is indicated by the horizontal line; the right side of the figure shows a bar chart representing the importance of each gene selected for the optimal feature subset; (C) Venn plot shows the overlapping differentially expressed proteins (DEPs) jointly identified by the RF and SVM algorithms; (D) Flowchart shows the workflow of Parallel Response Monitoring (PRM) technology, and dot plot (n=8) shows the results of quantitative detection of SPTAN1 protein in the Non-AL and CA-AL groups using PRM technology.

[0025] Figure 5 In the example, (A) compared with the Non-AL group, the serum SPTAN1 protein concentration in the CA-AL group was significantly increased. (B) The receiver operating characteristic (ROC) curve shows the efficacy of SPTAN1 in identifying cardiac AL amyloidosis. The specific values ​​are shown in (C). The data are expressed as mean ± standard error. ****, p < 0.0001, **, p < 0.01.

[0026] Figure 6 The example shows the concentration changes of serum SPTAN1 protein in VGPR patients. Detailed Implementation

[0027] To more fully understand and demonstrate the technical solutions, objectives, and advantages of the present invention, the technical effects produced by the present invention will be further described in detail and completely below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be noted that other embodiments obtained by those skilled in the art without departing from the concept of the present invention are all within the protection scope of the present invention.

[0028] Unless otherwise specified, all reagents and materials used in the following examples are commercially available.

[0029] Example 1

[0030] This embodiment integrates proteomics, machine learning, and PRM detection to screen and identify key biomarkers in the development of cardiac light chain amyloidosis. The experimental procedure is as follows: Figure 1 As shown, the experimental method is as follows:

[0031] 1. Biomarker screening

[0032] By constructing a bioinformatics analysis workflow using proteomics, mining high-order statistical information and combining it with molecular biological validation, we can accurately capture early diagnostic signals of diseases.

[0033] 2. Patient sample validation

[0034] All patient specimens were collected from routine clinical cases handled by various departments of Zhongshan Hospital affiliated with Fudan University between 2022 and 2025. Inclusion criteria were based on the guidelines for the diagnosis and treatment of systemic light chain amyloidosis as follows:

[0035] 1. Control group

[0036] a) Presence of M proteinemia

[0037] b) Negative Congo red staining of labial gland and fat biopsies

[0038] c) Mean ventricular wall thickness >12 mm on echocardiography, excluding other cardiac diseases; or NT-proBNP >332 ng / L in the absence of renal insufficiency and atrial fibrillation.

[0039] 2. Experimental Group

[0040] a) Positive Congo red staining in labial gland or fat biopsy

[0041] b) Mean ventricular wall thickness >12 mm on echocardiography, excluding other cardiac diseases; or NT-proBNP >332 ng / L in the absence of renal insufficiency and atrial fibrillation.

[0042] Detailed patient information (age, gender), clinical test results (complete blood count, free light chains, cardiac function, etc.), and biopsy information were recorded. A total of 141 valid blood samples were collected from Zhongshan Hospital affiliated with Fudan University, including the disease group (n=98), the control group (n=29), and blood samples from patients diagnosed with cardiac AL amyloidosis who achieved VGPR (n=14). Peripheral blood samples were collected upon admission, and the concentration levels of candidate serum biomarkers were detected by ELISA. The study protocol was approved by the Ethics Committee of Zhongshan Hospital affiliated with Fudan University (No. B2023-185R) and followed the Declaration of Helsinki.

[0043] 3. Methods

[0044] 1) Serum separation

[0045] Whole blood samples were collected from patients and allowed to coagulate naturally at room temperature (RT) for 30 minutes. They were then centrifuged at 12,000 rpm for 10 minutes at 4°C. The resulting serum supernatant was transferred to sterile cryovials and stored at -80°C until analysis. All collected samples were anonymized. Specimens exhibiting visible hemolysis, hemoglobin contamination, or lipemia were excluded from subsequent testing.

[0046] 2) Peptide preparation

[0047] After thawing the serum samples at 4°C, an appropriate amount was transferred to a new 1.5 mL microcentrifuge tube. 50 mM ammonium bicarbonate solution was added to adjust the total volume to 100 μL, and the samples were heated at 95°C for 3 minutes to denature the proteins. After cooling to room temperature, the samples were digested with trypsin at 37°C for 16 hours. The samples were then extracted, lyophilized, and desalted for purification. The final peptides were reconstituted in 100 μL of 0.1% formic acid solution, and 2% of the solution was injected for analysis.

[0048] 3) Nanoscale liquid chromatography-tandem mass spectrometry

[0049] Serum proteomics analysis was performed on 16 samples (8 from the control group and 8 from the disease group) from Zhongshan Hospital affiliated with Fudan University that met the inclusion and exclusion criteria. Peptide samples were analyzed using an EASY-nLC 1200 ultra-high performance liquid chromatography system (Thermo Fisher Scientific) coupled with a QE480 quadrupole-orbit trap hybridization mass spectrometer (Thermo Fisher Scientific). Lyophilized peptides were reconstituted in mobile phase A (0.1% formic acid aqueous solution) and loaded onto a self-packed capture column (100 μm inner diameter × 2 cm, packed with 3 μm ReproSil-Pur C18-AQ packing material, Dr. Maisch GmbH). Separation was achieved by binary gradient elution on an analytical column (75 μm inner diameter × 10 cm, 1.9 μm ReproSil-Pur C18-AQ packing material). The eluted peptides were ionized using a 2.1 kV electrospray ionization source. Mass spectrometry acquisition employed a data-independent (DIA) mode, with MS1 scanning ranging from 300 to 1400 m / z at a resolution of 60,000 (AGC target = custom; maximum injection time mode = custom). Subsequently, 30 DIA windows were acquired at 15,000 resolution, with the AGC target set to automatic to maximize injection time. HCD fragmentation normalized collision energy was set to 30%, and spectra were recorded in Profile mode. MS2 default charge state was set to 3+, and peptide recognition mode was enabled on the instrument. Raw mass spectrometry data were RAW files, and qualitative and quantitative analysis was performed using the iProteome one-stop data analysis cloud platform. Peptide mass tolerance was set to 20 ppm, and fragment ion tolerance was set to 0.05 Da. Cysteine ​​carbamylation at trypsin cleavage sites was set as a fixed modification, while methionine oxidation was set as a variable modification. The false positive rate for peptide annotation was controlled to 1%, and protein identification was based on razorpeptides and unique peptides. The mean protein intensity value was calculated for each sample, and missing values ​​of low-abundance proteins were imputed using the minimum value from whole-protein detection. The processed intensity values ​​were then compared between groups. Statistical differences between the disease group and the control group were calculated using an unpaired t-test in Microsoft Excel 2016 (Microsoft Corporation, Redmond).

[0050] 4) Machine Learning

[0051] This embodiment employs a dual-algorithm approach—Random Forest (RF) and Support Vector Machine (SVM)—to screen biomarkers. RF classifies biomarkers by constructing multiple decision trees, using bootstrapping and random sample splitting techniques to generate each decision tree from the original dataset, and reserving approximately one-third of the samples to construct a cross-validation set. Its variable importance measurement can accurately identify key genes. SVM, as an efficient supervised machine learning technique, maps data points to an n-dimensional feature space through a nonlinear kernel function to achieve classification and segmentation; the specific algorithm follows the scheme of Lihore et al. The analysis is implemented using the R language packages "randomForest" and "caret". Both algorithms have automatic feature selection capabilities and utilize cross-validation to ensure model accuracy and predictive reliability. Finally, the model performance is evaluated based on the cross-validation accuracy.

[0052] 5) Bioinformatics data analysis

[0053] Differential expression analysis: The limma package (version 3.58.1) in R was used to perform differential expression analysis on proteins in the control and disease groups to identify differentially expressed proteins (DEPs). The identification of differentially expressed proteins was based on the criteria of p-value < 0.05 and |logFC| > 1.

[0054] Functional enrichment analysis: Biological processes and pathways enriched in differentially expressed proteins were identified through the GO and KEGG pathways (p<0.05).

[0055] Analysis environment: All analysis and visualization were performed in R (version 4.2.3).

[0056] 6) ELISA testing

[0057] Approximately 1 mL of whole blood was collected from untreated subjects and allowed to coagulate naturally at room temperature (RT) for 30 minutes. Subsequently, the blood was centrifuged at 12,000 rpm for 10 minutes at 4°C. The resulting serum supernatant was transferred to sterile cryovials and stored at -80°C until analysis. Samples exhibiting visible hemolysis, hemoglobin contamination, or lipemia were excluded from subsequent testing. Before testing, samples were thawed at room temperature. The procedure employed a double-antibody sandwich method, where the target analyte (SPTAN1) was captured by a monoclonal antibody conjugated to a dendritic polymer on a microplate. Horseradish peroxidase (HRP)-labeled secondary antibody was added to form an antibody-antigen-labeled antibody complex, and unbound labeled antibody was eluted. The rabbit anti-human SPTAN1 antibody (catalog number: YB71529Hu) used in the ELISA was purchased from Shanghai Yubo Biotechnology Co., Ltd. (Shanghai, China). As verified by the manufacturer, due to the high specificity of monoclonal antibodies, the method exhibits negligible cross-reactivity with non-target analytes, and interference from biological matrices or drug compounds is extremely low. Standards are prepared using full-length human protein strictly according to the instructions of the commercial kit.

[0058] 7) Data Analysis

[0059] Statistical analysis was performed using GraphPad Prism 9.0 software (La Jolla, California, USA). Continuous variables (such as PRSS57 concentration) were expressed as mean ± standard error (SEM). Between-group comparisons were performed using either the Student's t-test (parametric test) or the Mann-Whitney U test (non-parametric test) based on the data distribution characteristics. The diagnostic efficacy of PRSS57 was assessed using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) automatically generated from the test data. The statistical significance (p-value) of the ROC curve was determined using a two-tailed z-test (null hypothesis: AUC = 0.5) with a two-tailed p-value < 0.05 as the statistical significance threshold.

[0060] Example 2: Biomarkers for identifying cardiac AL amyloidosis based on proteomics methods

[0061] In this embodiment, proteomics analysis was performed on the serum of the above-mentioned 8 control groups and 8 subjects, and a total of 81 differentially expressed proteins were screened out. Figure 2 GO enrichment analysis was performed on these differentially expressed proteins. Figure 3 A), found that its protein function is significantly enriched in pathways such as cytoskeleton structure. KEGG pathway analysis showed that it is involved in bacterial infection (log p = -3.6), fructose and mannose metabolism (log p = -3.0), and lipid and atherosclerosis (log p = -2.0). Figure 3Significant enrichment was observed in proteins such as B. Subsequently, two machine learning algorithms, RF and SVM, were used to derive the differentially expressed protein subsets with the best diagnostic efficacy. Figure 4 AB). Overlapping proteins of two subsets were selected using Venn diagrams. Figure 4 C), using PRM technology, it was further verified that the serum concentration of SPTAN1 was significantly upregulated in subjects compared to the control group, consistent with the proteomics results. Figure 4 D).

[0062] Example 3: Serum Sptanan1 is a major diagnostic marker in cardiac AL amyloidosis.

[0063] This study investigated the changes in SPTAN1 concentration in the serum of subjects. Blood samples were collected again from 98 patients in the disease group and 29 patients in the control group admitted to Zhongshan Hospital affiliated with Fudan University, according to inclusion and exclusion criteria. Basic patient information is shown in Table 1. ELISA and quantitative analysis showed that SPTAN1 (NCBI Gene ID: 6709) was significantly elevated in the subjects compared to the control group (P < 0.001). Figure 5 A) indicates that candidate protein biomarkers change significantly in cardiac AL amyloidosis. The identification effect of SPTAN1 on cardiac AL amyloidosis was also investigated, and the area under the curve (AUC), identification sensitivity, and specificity were analyzed using ROC curve analysis.

[0064] Table 1: Basic information of patients used for SPTN1 concentration detection

[0065]

[0066] The results showed that the area under the curve (AUC) of SPTAN1 was 0.816 ( Figure 5 B, 95% CI: 0.739-0.894, p<0.001). Since the diagnostic efficacy of combined use was not significantly improved compared to SPTAN1 alone, and the efficiency of combined diagnosis decreased in real-world work scenarios, SPTAN1 has a better ability to differentiate cardiac AL amyloidosis when used alone, with a sensitivity of 0.653 and a specificity of 0.897. Figure 5 C).

[0067] Example 4: Verifying the correlation between SPTAN1 protein and disease progression

[0068] This embodiment established a follow-up cohort of 10 patients with cardiac AL amyloidosis who achieved very good partial remission (VGPR). Based on the Chinese Guidelines for the Diagnosis and Treatment of Systemic Light Chain Amyloidosis, the primary criterion was the difference in serum affected and unaffected light chains (dFLC). Patient prognostic indicators are shown in Table 2. ELISA quantitative analysis showed that serum SPTAN1 protein concentration did not significantly decrease from baseline after treatment. Figure 6 ).

[0069] Table 2: Key prognostic indicators for patients in the follow-up cohort

[0070]

[0071] Example 5: ELISA detection of serum SPTN1 protein concentration and preliminary diagnosis of cardiac AL amyloidosis

[0072] This embodiment uses ELISA to detect the concentration of SPTAN1 protein in serum and to preliminarily diagnose cardiac AL amyloidosis. The specific procedure is as follows: 50 μL of serum sample or concentration gradient standards are added to a 96-well microplate and incubated with 100 μL of rabbit anti-mouse HRP-labeled secondary antibody at 37°C for 1 hour. After washing three times to remove unbound antibodies, 100 μL of enzyme substrate solution is added to each well, and the plate is incubated at 37°C in the dark for the immunoreaction. 3,3',5,5'-Tetramethylbenzidine (TMB) substrate solution is added for 10 minutes. Wells containing the target protein show a blue color reaction, which turns yellow after adding stop solution. The absorbance intensity at 450 nm is measured using a ELISA reader (Biotek, Vermont, USA), and the analyte concentration is quantified according to the calibration curve generated by the standard concentration gradient.

[0073] The results showed that a serum SPTAN1 protein concentration of less than 81.21 pg / mL was considered a negative result; a serum SPTAN1 protein concentration of more than 81.21 pg / mL indicated cardiac AL amyloidosis.

[0074] In summary, this invention proposes a serum SPTAN1 biomarker for the diagnosis of cardiac AL amyloidosis, which has the advantages of simple operation, high cost and accessibility, and is easy to promote and apply in clinical practice. By detecting the serum SPTAN1 protein concentration level, it provides a new strategy for accurate diagnosis of cardiac AL amyloidosis and improves patient prognosis, and has important clinical application prospects.

[0075] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. Application of serum α-nonerythrocyte spectroscopy 1 (SPTAN1) in the preparation of drugs for diagnosing cardiac light chain amyloidosis.

2. The application according to claim 1, characterized in that, The drug includes a kit.

3. The application according to claim 2, characterized in that, The kit is an enzyme-linked immunosorbent assay (ELISA) kit.

4. The application according to claim 3, characterized in that, The enzyme-linked immunosorbent assay kit contains an antibody against the SPTAN1 protein.

5. The application according to claim 4, characterized in that, The enzyme-linked immunosorbent assay kit also includes a pre-coated plate, standards, coating buffer, sample diluent, blocking buffer, horseradish peroxidase, elution buffer, and chromogenic agent.

6. The application according to claim 1, characterized in that, Early diagnosis of cardiac AL amyloidosis can be achieved by measuring the concentration of SPTAN1 protein in peripheral blood serum using the enzyme-linked immunosorbent assay kit.

7. The application according to claim 6, characterized in that, Peripheral blood samples were collected from the subjects, and the serum was separated and the concentration of SPTAN1 protein in the serum was detected by the enzyme-linked immunosorbent assay kit. If the concentration of SPTAN1 protein in serum is less than 81.21 pg / mL, it is considered negative; When the serum SPTAN1 protein concentration exceeds 81.21 pg / mL, it indicates cardiac AL amyloidosis in the subject.

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