Diagnostic biomarkers for differentiating igg4-related pancreatitis from pancreatic cancer and applications thereof

Abstract

The application provides a diagnostic biomarker for identifying IgG4-related pancreatitis and pancreatic cancer and an application thereof, and the diagnostic biomarker is used in the preparation of a detection reagent for diagnosing IgG4-related pancreatitis and pancreatic cancer. By detecting the diagnostic biomarker, it can be identified whether it is IgG4-related pancreatitis or pancreatic cancer, and the operation is convenient, practical, and has good application value.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology, specifically relating to diagnostic biomarkers for differentiating IgG4-associated pancreatitis from pancreatic cancer and their applications. Background Technology

[0002] In the current technology, IgG4-related disease (IgG4-RD) is a newly defined autoimmune disease in recent years, characterized by immune-mediated chronic inflammation and fibrosis. Its main pathological features are lymphoplasmacytic infiltration of affected tissues, predominantly composed of IgG4+ plasma cells, often accompanied by some degree of fibrosis, obliterative phlebitis, and eosinophilia. Approximately two-thirds of patients have elevated serum IgG4 levels. This disease can affect any part of the body; single-organ involvement is rare, and most patients have multiple organ involvement, leading to organ dysfunction through swelling and sclerosis of the affected organs. IgG4-RD can be classified into several subtypes depending on the affected organs, including IgG4-related pancreatitis (IgG4-RP), IgG4-related sclerosing cholangitis, IgG4-related sialadenitis, and IgG4-related kidney disease. Because patients with involvement of different organs present with significant differences in clinical manifestations, and because mass-like lesions are easily misdiagnosed as tumors, some patients often have to visit more than three departments before being diagnosed, resulting in unnecessary treatments, increasing the economic burden on patients and wasting medical resources.

[0003] Currently, the internationally accepted diagnostic criteria for IgG4-RD include the 2020 Revised Comprehensive Diagnostic Criteria for IgG4-RD (Japan) and the 2019 American College of Rheumatology / European League Against Rheumatism Classification. The diagnosis of IgG4-RD requires a combined approach based on the patient's clinical symptoms, imaging findings, pathology, and laboratory indicators. Serum IgG4 is the only recommended diagnostic biomarker.

[0004] Pancreatic cancer (PC) is a malignant tumor with an extremely high mortality rate, while patients with IgG4-RP have a good prognosis. CA19-9 has a sensitivity of 70%-92% and a specificity of 68%-92% for PC. However, sensitivity is closely related to tumor size. CA19-9 levels are not very sensitive for small tumors. Misdiagnosing IgG4-RP as PC can impose a huge psychological burden on patients and lead to excessive medical interventions such as surgical resection. IgG4-RP is also known as type 1 autoimmune pancreatitis, characterized by a "sausage-like" enlargement of the pancreas on cross-sectional imaging. However, IgG4-RP can also present as focal pancreatitis, accounting for approximately 28-41% of IgG4-RP cases. Because the imaging and clinical features of focal masses forming IgG4-RP and PC often overlap, it is difficult to distinguish between the two entities, making it difficult to differentiate between IgG4-RP and PC.

[0005] Elevated serum IgG4 levels are observed in approximately two-thirds of patients with IgG4-RD, and this elevation correlates with the number of organs affected. However, elevated serum IgG4 has poor diagnostic utility and is largely nonspecific, as it can also occur in a broad spectrum of clinically similar diseases, particularly tumors. Serum IgG4 levels and CA19-9 are of some value in the differential diagnosis between IgG4-RP and PC. However, serum IgG4 levels are also elevated in 7–10% of PC patients. Furthermore, CA199 may not be elevated in PC patients, especially in some PC patients with smaller tumor diameters.

[0006] Therefore, the development of new biomarkers still has important clinical value in differentiating IgG4-RP and PC, and there is an urgent need for biomarkers for the diagnosis and differential diagnosis of IgG4-RD. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides diagnostic biomarkers for differentiating IgG4-related pancreatitis and pancreatic cancer, and their applications. These biomarkers overcome the shortcomings of existing biomarkers in differentiating the clinical efficacy of IgG-RP and PC, and discover metabolites for differentiating and diagnosing IgG4-RP and PC, thereby further enhancing the diagnostic ability to differentiate between IgG4-RP and PC.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] Diagnostic biomarkers for differentiating IgG4-associated pancreatitis from pancreatic cancer include D-glutamate and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine.

[0010] The application of diagnostic biomarkers for differentiating IgG4-associated pancreatitis from pancreatic cancer in the preparation of test reagents. The diagnostic biomarkers include D-glutamate and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine.

[0011] As described above, the use of this method involves detecting the levels of D-glutamate and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine in the blood to determine whether it is IgG4-related pancreatitis or pancreatic cancer.

[0012] This invention, through extensive research, has discovered that the levels of D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine in the blood are higher in patients with IgG4-associated pancreatitis and lower in patients with pancreatic cancer. When pancreatic cancer or IgG4-associated pancreatitis is suspected, testing the levels of these two metabolites can help. Elevated levels suggest that IgG4-associated pancreatitis is more likely than pancreatic cancer. Conversely, low levels of these metabolites in the blood suggest that the patient may have pancreatic cancer rather than IgG4-associated pancreatitis.

[0013] In the application described above, preferably, the contents of D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine are substituted into the calculation equation (1) to obtain the logit(P) value, where logit(P) = -78.997386 + 0.000726 * D-glutamic acid + 0.000079 * 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine (1); then the logit(P) value is substituted into the equation (2) to obtain the P value. (2) If P≥0.5, the patient is identified as an IgG4-RP patient; if P<0.5, the patient is identified as a PC patient.

[0014] As described above, liquid chromatography-mass spectrometry was used to detect the levels of two metabolites, D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine.

[0015] The beneficial effects of this invention are as follows:

[0016] This invention provides diagnostic biomarkers for differentiating IgG4-related pancreatitis from pancreatic cancer, comprising D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine. Based on the detection of their levels in blood samples from suspected IgG4-RP or PC patients, a p-value is calculated using a logistic regression equation to determine IgG4-RP and PC, further improving the diagnostic ability to differentiate between IgG4-RP and PC. The method is convenient, practical, and has good application value. Using this method can effectively reduce the medical burden on patients, decrease unnecessary pathological biopsies, and shorten the diagnosis time for both patients and clinicians. Attached Figure Description

[0017] Figure 1 OPLS-DA plot of IgG4-RP and PC in ESI-mode;

[0018] Figure 2 OPLS-DA plot of IgG4-RP and PC in ESI+ mode;

[0019] Figure 3 The cumulative AUC of different expressed metabolites between IgG4-RD and PC;

[0020] Figure 4 Score the importance of each different expressed metabolite and selected metabolites between IgG4-RD and PC;

[0021] Figure 5 ROC curves for selected metabolites between IgG4-RD and PC. Detailed Implementation

[0022] The following embodiments are used to further illustrate the present invention, but should not be construed as limiting the present invention. Any modifications or substitutions made to the present invention without departing from its spirit and essence are within the scope of the present invention.

[0023] Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art. Unless otherwise specified, all reagents used in this invention are of analytical grade or higher.

[0024] Example 1

[0025] Untargeted liquid chromatography-tandem mass spectrometry (LC-MS / MS) metabolomics analysis was performed on plasma samples from sex- and age-matched patients with untreated IgG4-RP (n=33) and PC (n=33). A random forest machine learning model was used to validate the efficacy of identified metabolites in the differential diagnosis of the two diseases. IgG4-RP was diagnosed according to the 2019 American College of Rheumatology / European League Against Rheumatism classification criteria. Patients with other autoimmune diseases, infectious diseases, or malignancies were excluded from this study. PC was diagnosed by pathologists based on histological examination (resected or fine-needle aspiration specimens of the pancreas).

[0026] Whole blood samples were collected from patients in tubes containing EDTA anticoagulant. Each participant was required to fast for at least 8 hours prior to sample collection. Samples were then centrifuged for 15 minutes (1500 g, 4 °C). Plasma samples were thawed at 4 °C, and 100 μL aliquots were mixed with 400 μL of cold methanol / acetonitrile (1:1, v / v) to remove proteins. The mixture was then centrifuged for 15 minutes (14000 g, 4 °C). The supernatant was dried in a vacuum centrifuge. Samples were redissolved in 100 μL of acetonitrile / water (1:1, v / v) for analysis by liquid chromatography-mass spectrometry (LC-MS).

[0027] Analysis was performed using UHPLC (1290 Infinity LC, Agilent Technologies, Palo Alto, CA, USA) and Q-TOF-MS (TripleTOF 6600, ABSciex, Framingham, MA, USA). For hydrophilic interaction HPLC separation, samples were analyzed using a 2.1-mm × 100-mm ACQUIYUPLCBEH 1.7-μm column (Waters, Ireland). In both positive and negative electrospray ionization (ESI) modes, the mobile phase contained A = 25 mM ammonium acetate and 25 mM ammonium hydroxide aqueous solution, and B = acetonitrile. The gradient was 85% B for 1 min, linearly decreasing to 65% within 11 min, then decreasing to 40% within 0.1 min and holding for 4 min. It was then increased to 85% within 0.1 min, with a reequilibration time of 5 min.

[0028] For reversed-phase liquid chromatography (RP-LC) separation, a 2.1 mm × 100 mm ACQUIYUPLCHSST 31.8 μm column (Waters) was used. In ESI positive mode, the mobile phase contained A = water with 0.1% formic acid and B = acetonitrile with 0.1% formic acid. In ESI negative mode, the mobile phase contained A = 0.5 mM ammonium fluoride aqueous solution and B = acetonitrile. The gradient was 1% B for 1.5 min, linearly increasing to 99% within 11.5 min and holding for 3.5 min. It was then reduced to 1% within 0.1 min, with a reequilibration time of 3.4 min. The gradient flow rate was 0.3 mL / min, and the column temperature was maintained at 25 °C. 2 μL aliquots of each sample were injected. The ESI source conditions were set as follows: ion source gas 1 (Gas1) 60, ion source gas 2 (Gas2) 60, curtain gas (CUR) 30, source temperature 600 °C, and ion spray voltage fluctuation ±5500 V. In MS-only acquisition, the instrument was set to acquire Da in the m / z range of 60–1000, and the cumulative time for TOFMS scans was set to 0.20 s / spectrum. In automated MS / MS acquisition, the instrument was set to acquire in the m / z range of 25–1000 Da, and the cumulative time for daughter ion scans was set to 0.05 s / spectrum. Product ion scans were obtained using information-correlated acquisition in high-sensitivity mode. The parameter settings were as follows: collision energy fixed at 35 V, ±15 eV; declustering potential (DP), 60 V (+) and -60 V (-); exclusion of isotopes within 4 Da; and 10 candidate ions per period for monitoring.

[0029] Before importing into the free XCMS software, the raw MS data (wiff.scan file) was converted to an MzXML file using ProteoWizard MSConvert. The following parameters were used for peak selection: centWave m / z = 25 ppm, peak width = c(10, 60), pre-filter = c(10, 100). For peak grouping, bw = 5, mzwid = 0.025, and minfrac = 0.5 were used. A set of algorithms for Metabolite pRofile Annotation was used for isotope and adduct annotation. Among the extracted ion features, only variables with >50% non-zero measurements in at least one group were retained. Metabolites were identified as compounds by comparing the accuracy of m / z values ​​(<25 ppm) and MS / MS spectra with an internal database established using available real standards.

[0030] After summation normalization and pareto-scaling, mass spectrometry metabolite data underwent multidimensional statistical analysis: orthogonal partial least squares discriminant analysis (OPLS-DA). The variable importance in the projected (VIP) values ​​of each variable in the OPLS-DA model was calculated to assess its contribution to classification. A Student's t-test was performed to determine the significance of differences between two independent groups. VIP > 1 and p-value < 0.05 were used to screen for metabolites with significant variations. In the random forest analysis, one thousand trees were constructed using the R package randomForest, and 10-fold cross-validation was performed. This was repeated 100 times.

[0031] The original data was initially divided into training and test sets in a 7:3 ratio. Then, in the training set, to reduce the impact of data randomness and model overfitting, 10-fold cross-validation was performed.

[0032] After the model was built, it was validated using 30% of the original data as the test set. For each tree, the classification accuracy of out-of-bag samples was determined using random permutations with and without variable values. The prediction accuracy after permutation was subtracted from the prediction accuracy before permutation, and then averaged across all trees in the forest to obtain an importance score. This importance score was used to identify biomarkers and remove non-informative variables. The results showed that an OPLS-DA plot was generated to characterize the differences. Figure 1 and Figure 2 OPLS-DA plots of ESI+ and ESI- modes between IgG4-RP (n=33, blue) and PC (PC, n=33, red). The OPLS-DA plots show that IgG4-RP and PC patient samples were separated in both positive and negative ion modes.

[0033] In the metabolomics profile, many metabolites changed as shown in Table 1. Significant differences were found in 171 metabolites between the IgG4-RP group and the PC group.

[0034] Table 1. Differential metabolites between IgG4-RP and PC

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[0043] After revealing the unique metabolomics characteristics of IgG4-RP and PC, the existence of metabolic biomarkers to differentiate IgG4-RP from PC was further explored. A random forest machine learning model based on metabolomics data and recipient operating characteristic (ROC) analysis was used to validate the relevance of identified metabolites in the differential diagnosis of IgG4-RP and PC. Importance scores for each metabolite were calculated using random forest, and ROC curves were generated by accumulating the ranked metabolites (based on their importance scores). Results are as follows: Figure 3 , 4 As shown in Figures 5 and 6. Among them, Figure 3 The cumulative AUC of different expressed metabolites between IgG4-RD and PC. Figure 4 The importance of each different expressed metabolite and selected metabolite between IgG4-RD and PC was scored. Figure 5 ROC curves were selected for metabolites between IgG4-RD and PC. Specifically, Figure 3 When the top two metabolites in the combined detection ranking were displayed, the cumulative AUC result was already equal to 1, indicating that these two metabolites can be effectively used to distinguish between IgG4-RP and PC. Figure 4 The ranking of metabolite importance scores is displayed, with the top two metabolites being D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine. Figure 5 The D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine obtained from the above results were validated in samples divided into training and test sets. This part of the results indicates that these two metabolites have good diagnostic discrimination ability in both datasets (AUC = 1). The results show that the combination of D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine can effectively distinguish between IgG4-RP and PC (AUC = 1).

[0044] To further demonstrate the specific combined role of these two metabolites in the differential diagnosis of IgG4-RP and PC, this invention performed logistic regression analysis on the relative absorptivity of D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine in the IgG4-RP and PC groups, establishing the calculation equation:

[0045] logit(P)=-78.997386+0.000726*D-Glutamic acid+0.000079*1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine(1);

[0046] Then, based on the logit(P) value of each patient sample, the following equation (2) is performed to obtain the P value of each patient.

[0047]

[0048] After analyzing the diagnostic efficacy of p-values ​​in untreated IgG4-RP (n=33) and PC (n=33) patients, the results showed that samples with p-values ​​greater than or equal to 0.5 were considered IgG4-RP patients, while those with p-values ​​less than 0.5 were considered PC patients. The AUC for differentiating between the two metabolites for IgG4-RP and PC was 1, with both sensitivity and specificity of 100%.

[0049] Example 2

[0050] The levels of D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine in blood samples from IgG4-RP and PC patients were determined using the liquid chromatography-mass spectrometry (LC-MS) method described in Example 1. The measured levels were then analyzed according to the model established in Example 1, using equation (1):

[0051] logit(P) = -78.997386 + 0.000726 * D-Glutamicacid + 0.000079 * 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine (1), obtain the logit(P) value of the patient sample, and then perform the calculation of equation (2) based on the logit(P) value of each patient sample.

[0052] Obtain the P-value for each patient. Determine the patient type based on the P-value: if P ≥ 0.5, the patient is classified as an IgG4-RP patient; if P < 0.5, the patient is classified as a PC patient.

[0053] The results showed that detecting the levels of D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine demonstrated good diagnostic performance in differentiating IgG4-RP from PC. Receiver operating characteristic (ROC) results showed that the area under the curve (AUC) for these two metabolites was equal to 1, confirming their excellent diagnostic value. This indicates that D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine exhibit very high diagnostic ability and are suitable for clinical kit applications.

Claims

1. A diagnostic biomarker for differentiating IgG4-associated pancreatitis from pancreatic cancer, characterized in that, It includes D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine.

2. The use of the reagent for detecting the diagnostic biomarker as described in claim 1 in the preparation of reagents for detecting IgG4-related pancreatitis and pancreatic cancer, characterized in that, The presence of IgG4-associated pancreatitis or pancreatic cancer can be determined by detecting the levels of D-glutamate and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine in the blood.

3. The application as described above according to claim 2, characterized in that, Substituting the contents of D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine into the calculation equation (1) yields the logit(P) value, where logit(P) = -78.997386 + 0.000726 * D-glutamic acid + 0.000079 * 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine (1); then substituting the logit(P) value into equation (2) yields the P value. (2), If P ≥ 0.5, the patient is diagnosed as an IgG4-RP patient; if P < 0.5, the patient is diagnosed as a PC patient.

4. The application as described above according to claim 2, characterized in that, The contents of two metabolites, D-glutamic acid and 1-stearoyl-2-myristoyl-sn-glycerol-3-phosphocholine, were determined by liquid chromatography-mass spectrometry.

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