Application of serum secretory protein-based composition in predicting curative effect of liver cancer hepatic artery perfusion chemotherapy and kit
By detecting the specific protein molecular composition in serum and combining it with a weighted scoring model, the problem of predicting the accuracy of the efficacy of hepatic artery infusion chemotherapy for liver cancer was solved, efficient multi-dimensional prediction was achieved, and the ability to predict treatment effects was improved.
Patent Information
- Application Number
- CN202510854495.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-25
- Publication Date
- 2025-09-05
AI Technical Summary
Existing technologies are insufficiently accurate in predicting the efficacy of hepatic arterial infusion chemotherapy for liver cancer, imaging assessments are delayed, and there is a lack of molecular-level predictive tools, making it difficult to predict treatment outcomes.
Biomarkers based on serum secretory protein compositions, including treatment resistance-related markers such as TFPI, SAA1, SLPI, ST6GAL1, GDF15, and treatment sensitivity-related markers such as MFAP4, OGN, CRHBP, and FBLN5, are used. Multiple immunoassays are performed using specific capture antibodies and fluorescent-encoded magnetic beads, and the efficacy is predicted in combination with a weighted scoring model.
It has achieved efficient prediction of the efficacy of hepatic arterial infusion chemotherapy for liver cancer, improved the accuracy and sensitivity of the prediction, avoided the limitations of a single marker, and provided a multi-dimensional prediction system for molecular mechanisms.
Smart Images
Figure CN120594823A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of biomarker detection, and in particular to an application of a serum secretory protein composition in predicting the efficacy of hepatic artery perfusion chemotherapy for liver cancer and a kit. Background Art
[0002] Hepatic arterial infusion chemotherapy (HAIC), a newly emerging local interventional therapy for the treatment of hepatocellular carcinoma (HCC), has demonstrated breakthrough efficacy in advanced HCC and significant clinical potential. However, the efficacy of HAIC varies significantly among patients: some patients demonstrate treatment sensitivity and achieve good results, while others may develop treatment resistance, leading to disease progression. Therefore, predicting the efficacy of HAIC and identifying patients who may benefit are crucial for optimizing treatment strategies, improving treatment efficiency, and stabilizing patient outcomes.
[0003] Currently, clinical evaluation of the efficacy of hepatic arterial infusion chemotherapy relies primarily on imaging studies and tumor marker testing, such as changes in alpha-fetoprotein (AFP) levels. However, these methods have limitations in predictive accuracy. The traditional mRECIST criteria require regular follow-up assessments after treatment, making it impossible to predict efficacy before treatment, potentially leading to delays in ineffective treatment. Furthermore, the AUC for AFP in predicting the efficacy of hepatic arterial infusion chemotherapy is only 0.62, with a sensitivity and specificity of 54.1% and 69.2% respectively, which is difficult to meet clinical needs. The differences in sensitivity and resistance to hepatic arterial infusion chemotherapy have not yet been revealed at the molecular level, and a multidimensional prediction system based on serum proteomics is lacking.
[0004] In recent years, biomarker research based on serum secretory proteins has provided a new perspective for predicting tumor efficacy. Specific protein molecules in serum directly reflect the interaction between the tumor microenvironment and the host, and changes in their expression levels are closely associated with tumor development, progression, and treatment response. Therefore, measuring the expression levels of specific serum proteins may offer the potential for effectively predicting the efficacy of hepatic arterial infusion chemotherapy.
[0005] Therefore, we propose an application and kit based on a serum secretory protein composition in predicting the efficacy of hepatic artery infusion chemotherapy for liver cancer, in order to solve the above-mentioned problems. Summary of the Invention
[0006] The purpose of the present invention is to provide an application and kit based on a serum secretory protein composition for predicting the efficacy of hepatic arterial infusion chemotherapy for liver cancer, so as to solve one of the problems raised in the above background technology, such as the insufficient efficacy of single markers on the current market, the lag in imaging assessment, and the lack of molecular-level prediction tools.
[0007] To achieve the above object, the present invention provides the following technical solutions:
[0008] A biomarker composition for predicting the efficacy of hepatic arterial infusion chemotherapy in patients with liver cancer, comprising the following protein molecules:
[0009] Treatment resistance-related markers: TFPI, SAA1, SLPI, ST6GAL1, GDF15;
[0010] Treatment sensitivity-related markers: MFAP4, OGN, CRHBP, FBLN5;
[0011] The composition predicts the sensitivity and prognosis of hepatic artery infusion chemotherapy by detecting the expression levels of the above proteins in the patient's serum.
[0012] Preferably, the detection targets of serum protein molecules include:
[0013] The product of ST6GAL1 enzymatic activity is a serum glycoprotein modified by α-2,6-sialic acid glycosylation;
[0014] Functional monomer of the non-polymeric form of SAA1.
[0015] Application of a biomarker composition in the preparation of a kit for predicting the efficacy of hepatic arterial infusion chemotherapy.
[0016] A kit for predicting the efficacy of hepatic arterial infusion chemotherapy, comprising:
[0017] Specific capture antibodies against the above 9 serum protein molecules;
[0018] a specific detection antibody paired with the capture antibody;
[0019] Contains recombinant protein calibrators in a gradient of concentrations.
[0020] Preferably, the capture antibody is immobilized on an encoding microsphere carrier, wherein:
[0021] TFPI, GDF15, and MFAP4 antibodies were coupled to magnetic beads with different fluorescence codes;
[0022] The surface of the SAA1 antibody-coupled microspheres is coated with a polymer dissociation agent.
[0023] Preferably, the detection antibody is a biotin-labeled antibody, and a streptavidin-phycoerythrin (SA-PE) complex is provided in combination.
[0024] Preferably, the capture antibody is directed against the following epitope:
[0025] GDF15: the antigen epitope sequence is SEQ ID NO: 1 (GVGPARARPAEGPGG);
[0026] FBLN5: The antigen epitope sequence is SEQ ID NO: 2 (KPDVVLNQPQVTI).
[0027] The method for predicting the efficacy of hepatic arterial infusion chemotherapy according to the kit comprises the following steps:
[0028] Step 1: Obtain serum samples from patients with hepatocellular carcinoma before treatment;
[0029] Step 2: using the kit to detect the concentrations of 9 protein molecules in serum;
[0030] Step 3: Calculate the efficacy risk value through the weighted scoring model;
[0031] Step 4: Divide the efficacy risk level according to the risk value threshold.
[0032] Preferably, in step three, the formula used is:
[0033]
[0034] Among them, α i , β j is the pre-training weight coefficient, and C is the protein concentration.
[0035] Preferably, the coefficient α5 of GDF15 is 0.18 to 0.25; the coefficient β4 of FBLN5 is -0.15 to -0.20;
[0036] The low risk value threshold is: Score < -1.0; the high risk value threshold is: Score > 0.4.
[0037] Compared with the prior art, the present invention has the following beneficial effects:
[0038] The five treatment resistance-related markers (TFPI, SAA1, SLPI, ST6GAL1, GDF15) and four treatment sensitivity-related markers (MFAP4, OGN, CRHBP, FBLN5) in the present invention reveal the differences in therapeutic efficacy from the positive and negative bidirectional molecular mechanisms, avoiding the limitations of a single marker.
[0039] The present invention adopts a multiple immunoassay system of coded microsphere carriers coupled with specific antibodies. TFPI, GDF15, and MFAP4 antibodies are coupled with different fluorescent-coded magnetic beads to achieve simultaneous detection of multiple indicators. The surface of the SAA1 antibody microspheres is coated with a polymer dissociation agent (such as 0.2% CHAPS) to ensure the detection of functional monomers in non-polymer form and avoid interference.
[0040] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 This is a simplified detection flow chart in Example 2 of the present invention. DETAILED DESCRIPTION
[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0043] Example 1: Biomarker Screening and Validation
[0044] The samples were collected from 87 patients with hepatocellular carcinoma (HCC) who received FOLFOX chemotherapy via hepatic artery infusion.
[0045] Sample grouping:
[0046] 43 cases in the sensitive group: objective response after treatment (ORR, CR+PR according to mRECIST standards).
[0047] 44 cases in the resistance group: progressive disease (SD+PD).
[0048] Tissue sequencing and serum validation
[0049] Tissues from 87 patients with liver cancer were punctured before hepatic arterial infusion chemotherapy and stored in liquid nitrogen.
[0050] RNA was extracted using the Qiagen RNeasy FFPE Kit. First, 20 μm thick tissue sections were dewaxed with xylene and digested with proteinase K at 56°C for 30 minutes. The sections were then attached to RNA adsorption columns, genomic DNA was digested with DNase I, and total RNA was eluted.
[0051] RNA integrity number (RIN) ≥ 7.0 (Agilent 2100 Bioanalyzer), concentration ≥ 50 ng / μL (Nanodrop assay).
[0052] In the initial screening of candidate molecules, the number of differentially expressed genes in the sensitive group was 217, and the number of secreted protein candidates was 38; the number of differentially expressed genes in the resistant group was 195, and the number of secreted protein candidates was 41.
[0053] For key genes, TFPI (log2FC=2.1, FDR=3.2e-6) and SAA1 (log2FC=3.4, FDR=1.8e-8) were highly expressed in the resistance group, while MFAP4 (log2FC=1.9, FDR=4.7e-5) and FBLN5 (log2FC=2.3, FDR=2.1e-7) were highly expressed in the sensitive group.
[0054] Subsequently, the same batch of patients' pre-treatment serum was centrifuged at 3000g for 15 min to remove the precipitate and aliquoted at −80°C.
[0055] R&D Systems Quantikine ELISA was used for detection. First, 100 μL of serum / calibrator was added to the pre-coated plate and incubated at 37°C for 2 hours. The biotinylated antibody was reacted at room temperature for 1 hour, and Streptavidin-HRP was reacted for 30 minutes. TMB was used for color development and the reading was performed at 450 nm.
[0056] Finally, concentration calibration was performed, using healthy human serum (n=30) as a reference.
[0057]
[0058]
[0059] Table 1 Validation results of HAIC efficacy prediction markers Example 2: Construction of kit
[0060] Antibody conjugation to microspheres:
[0061]
[0062] Table 2 Capture Antibodies, Dissociation Agents Added SAA1 Antibody Microspheres Coated with 0.2% CHAPS Buffer (Disaggregated SAA1 Polymers)
[0063] index result Limit of detection (LOD) 0.1-0.5 pg / mL Linear range <![CDATA[0.5-5000pg / mL(R 2 >0.99)]]> Intra-batch CV 4.2％-7.8％ Inter-batch CV 8.5％-12.3％
[0064] Table 3 Performance parameters
[0065] The antibody was labeled with EZ-Link Sulfo-NHS-LC-Biotin at a ratio of 3-5 biotin molecules per antibody, and the antibody was determined by the HABA method.
[0066] The marker SLPI was mouse mAb 3D8, with a labeled concentration of 1.5 μg / mL; the marker FBLN5 was rabbit pAb H-140, with a labeled concentration of 2.0 μg / mL.
[0067] The samples were pretreated. First, 50 μL of serum was taken and added to 200 μL of dilution buffer (PBS+1% BSA+0.05% Tween-20). The mixture was vortexed and centrifuged at 10,000 g for 5 minutes at 4°C to remove the precipitate.
[0068] Add CHAPS to a final concentration of 0.2% and incubate at 37°C for 15 minutes.
[0069] For microsphere-antibody complex incubation, nine antibody-coupled microspheres were mixed in proportion, with 2,500 microspheres of each type per well. 50 μL of pretreated serum was added to a 96-well plate and incubated at 37°C with shaking for 2 hours.
[0070] The microspheres were adsorbed on a magnetic rack, the supernatant was discarded, and 200 μL of 0.1% Tween-20 / PBS washing buffer was added, and the washing was repeated three times.
[0071] To detect antibody reaction, add 50 μL of biotinylated detection antibody mixture to each well and shake in the dark at room temperature for 1 hour at a shaking speed of 400 rpm.
[0072] After signal amplification, add 50 μL Streptavidin-Phycoerythrin at a dilution of 1:200 after washing, shake at 300 rpm for 30 minutes at room temperature, and add 100 μL 1% BSA / PBS stop solution. Figure 1 shown.
[0073] Example 3: Prediction model construction and verification
[0074] Weighted scoring formula:
[0075] Score=0.21·ln(GDF15)+0.17·ln(SAA1)-0.16·ln(FBLN5)+...+0.32·I(AFP>400)
[0076] LASSO regression analysis was based on a discovery cohort of 87 cases, where AFP was set to 1 when it was >400 ng / mL and 0 otherwise.
[0077] Risk Grouping Threshold Number of cases ORR Median PFS (months) HR (95% CI) Low risk Score<-1.0 52 71.2％ 13.8 Reference High risk Score>0.4 68 22.1％ 5.2 3.41(2.15-5.38)
[0078] Table 4 Independent cohort validation
[0079] Prediction Methods AUC Sensitivity Specificity This kit 0.89 85.3％ 82.7％ Single AFP (>400) 0.62 54.1％ 69.2％ Imaging Model 0.75 73.6％ 71.8％
[0080] Table 5 Comparison of prediction effectiveness
[0081] Example 4: Stability and interference test
[0082] Storage conditions Change in GDF15 concentration (%) Change in FBLN5 concentration (%) Room temperature for 24 hours +12.3％ -8.7％ 4℃72h +5.1％ -3.2％ -80℃ for 6 months +2.8％ -1.9％
[0083] Table 6 Sample Stability As can be seen from Table 6, the kit requires a dedicated preservation solution (containing 0.1% Proclin 300 + 1% BSA).
[0084] Interference GDF15 recovery rate (%) Hemoglobin (500 mg / dL) 92.4％ Triglycerides (1000 mg / dL) 88.7％ Rheumatoid factor (1500 IU / mL) 95.1％
[0085] Table 7 Interference test
[0086] Example 5, alternative detection scheme for ST6GAL1 active product Detection target: α-2,6-sialic acid-modified α-1-acid glycoprotein (Sialylated AGP) Detection method: Use plant lectin SNA (specifically binds to α-2,6-sialic acid) to capture AGP; anti-AGP antibody quantitative detection (ELISA).
[0087]
[0088]
[0089] Table 8
[0090] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.
Claims
1. A biomarker composition for predicting the efficacy of hepatic artery infusion chemotherapy in patients with liver cancer, characterized in that: Including the following protein molecules: Treatment resistance-related markers: TFPI, SAA1, SLPI, ST6GAL1, GDF15; Treatment sensitivity-related markers: MFAP4, OGN, CRHBP, FBLN5; The composition predicts the sensitivity and prognosis of hepatic artery infusion chemotherapy by detecting the expression levels of the above proteins in the patient's serum.
2. The biomarker composition for predicting the efficacy of hepatic arterial infusion chemotherapy in patients with liver cancer according to claim 1, characterized in that: Serum protein molecules tested include: The product of ST6GAL1 enzymatic activity is a serum glycoprotein modified by α-2,6-sialic acid glycosylation; Functional monomer of non-polymeric form of SAA1.
3. Use of the biomarker composition according to claim 1 or 2 in the preparation of a kit for predicting the efficacy of hepatic arterial infusion chemotherapy.
4. A kit for predicting the efficacy of hepatic artery infusion chemotherapy, characterized in that: include: Specific capture antibodies against the nine serum protein molecules in claim 1; a specific detection antibody paired with the capture antibody; Contains recombinant protein calibrators in a gradient of concentrations.
5. The kit for predicting the efficacy of hepatic arterial infusion chemotherapy according to claim 4, characterized in that: The capture antibody is immobilized on a microsphere-encoded carrier, wherein: TFPI, GDF15, and MFAP4 antibodies were coupled to magnetic beads with different fluorescence codes; The surface of the SAA1 antibody-coupled microspheres is coated with a polymer dissociation agent.
6. The kit for predicting the efficacy of hepatic arterial infusion chemotherapy according to claim 4, characterized in that: The detection antibody is a biotin-labeled antibody and is provided with a streptavidin-phycoerythrin (SA-PE) complex.
7. The kit for predicting the efficacy of hepatic arterial infusion chemotherapy according to claim 4, characterized in that: The capture antibodies are directed against the following epitopes: GDF15: the antigen epitope sequence is SEQ ID NO: 1 (GVGPARARPAEGPGG); FBLN5: The antigen epitope sequence is SEQ ID NO: 2 (KPDVVLNQPQVTI).
8. A method for predicting the efficacy of hepatic artery infusion chemotherapy using the kit according to any one of claims 4 to 7, characterized in that: The following steps are involved: Step 1: Obtain serum samples from patients with hepatocellular carcinoma before treatment; Step 2: using the kit to detect the concentrations of 9 protein molecules in serum; Step 3: Calculate the efficacy risk value through the weighted scoring model; Step 4: Divide the efficacy risk level according to the risk value threshold.
9. The method for predicting the efficacy of hepatic arterial infusion chemotherapy according to claim 8, characterized in that: In step three, the formula used is: Among them, α i , β j is the pre-training weight coefficient, and C is the protein concentration.
10. The method for predicting the efficacy of hepatic arterial infusion chemotherapy according to claim 8, characterized in that: The coefficient α5 of GDF15 is 0.18 to 0.25; the coefficient β4 of FBLN5 is -0.15 to -0.20; The low risk value threshold is: Score < -1.0; the high risk value threshold is: Score > 0.4.