Application of biomarker in preparation of product for diagnosis and / or prognosis evaluation of esophageal cancer

Abstract

The invention belongs to the technical field of biological medicine, and particularly relates to application of a biomarker or a reagent for detecting the biomarker in preparation of products for diagnosis and / or prognosis evaluation of esophageal cancer, and the biomarker comprises one or more than two of SCC, Cyfra21-1, HGF, VEGF, IL-6, IL-8 or IGFBP-1. Experiments prove that the biomarker disclosed by the invention has remarkably high sensitivity and specificity when being used for diagnosis and prognosis evaluation of esophageal cancer, and has very good application and research values in the field of biological medicines.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to the use of biomarkers in the preparation of products for diagnosis and / or prognosis evaluation of esophageal cancer. Background Art

[0002] Esophageal cancer (EC) is one of the major malignant tumors that threaten the lives and health of residents. Esophageal cancer is divided into esophageal squamous cell carcinoma (ESCC) and esophageal adenocacinoma (EAC) according to histological subtypes. At present, the clinical diagnosis of EAC usually includes medical history and physical examination, endoscopy, histopathological examination, and imaging examination. Although esophageal endoscopy is currently the gold standard for the discovery and diagnosis of esophageal cancer, its invasive method is not conducive to the promotion of clinical application. The serum tumor markers currently used in clinical practice, such as squamous cell carcinoma antigen (SCCA), carcinoembryonic antigen (CEA) and Cyfra21-1, are insufficient in specificity and sensitivity for the diagnosis of early EC. Therefore, there is an urgent need to screen esophageal cancer markers or marker combinations to improve diagnostic performance.

[0003] DIA-MS (Data-Independent Acquisition Mass Spectrometry) technology is a mass spectrometry technology used for protein identification and quantification in proteomics research. Compared with traditional DIA-MS technology, DIA-MS technology has higher sensitivity and coverage, and can simultaneously identify and quantify a large number of proteins in a sample. The main principle of this technology is to use a fixed mass-to-charge ratio window in the mass spectrometer for ion selection so that all ions in the sample can be measured simultaneously. By collecting a large amount of mass spectrometry data at different time periods, and then using computational algorithms to process and interpret the data, efficient identification and quantification of proteins in the sample can be achieved.

[0004] Antibody microarray is a high-throughput proteomics technology used to detect and quantify the expression levels and interactions of a large number of proteins in cells or tissues. It is based on the principle of immunoassay, using specific antibodies on a solid phase to interact with proteins in a sample, and then quantitatively measuring protein expression levels through methods such as fluorescence, chemiluminescence or mass spectrometry. This technology uses multiple different antibodies fixed on a microarray chip to detect multiple target proteins at the same time. Each antibody is specific and can bind to a specific protein. By comparing with a reference sample or a control group, differences between different samples can be found.

[0005] Compared with mass spectrometry, antibody microarray technology has the following advantages in proteomics research: antibody microarray can detect and quantify a large number of protein molecules at the same time, and can obtain the expression levels of multiple proteins in a sample at one time. Antibody microarray can detect low expression levels of proteins and has good sensitivity for detecting rare or small amounts of proteins. By using specific antibodies, antibody microarray can identify and detect target proteins with high specificity. Antibody microarray can compare different samples at the same time, which is helpful for studying differential expression and interaction of proteins. Antibody arrays have superb performance and are suitable for knowledge-based bioanalysis, which can overcome the sensitivity issues associated with non-targeted proteomics technology. Antibody arrays are particularly useful for serological analysis because most TAPs are low-abundance cell effluents, such as hormones, cytokines, chemokines, intracellular signaling molecules, and post-translational modifications. Antibody arrays have been applied to find diagnostic features for bladder cancer. Its application in prostate cancer, ovarian cancer, colorectal cancer, etc. has also been confirmed. Antibody microarray technology has a wide range of applications in proteomics research, and can be used to discover and verify biomarkers, understand the mechanism of disease development, study signaling pathways and protein interaction networks, etc. It plays an important role in understanding the changes of proteome in diseases and developing new treatment strategies. Therefore, it is suitable for detecting low-abundance proteins in serum or plasma samples.

[0006] As mentioned above, in order to try to fully explore the proteome profile in the plasma of EC patients, combined with the existing conditions of our laboratory, we tried to use the new customizable antibody chip technology and data-independent acquisition (DIA) mass spectrometry detection technology to establish a deep coverage serum proteome research platform to find more efficient EC-related diagnostic and prognostic markers. The establishment of this platform aims to find more efficient and accurate EC-related diagnostic and prognostic markers, and open up new avenues for individualized precision diagnosis and treatment research of EC. Summary of the invention

[0007] The inventors have identified 213 differentially expressed genes or proteins related to esophageal cancer through creative work. By combining statistics with bioinformatics, the inventors can comprehensively and systematically analyze the systematic changes in the expression levels of differentially expressed genes or proteins in esophageal cancer patients, and reveal the relationship between differentially expressed genes and proteins and clinical physiological and biochemical indicators, thereby providing guidance for clinical research, diagnosis and treatment. In addition, the inventors further screened and verified that 4 biomarkers (SCC, IL-6, VEGF and IL-8) have a high correlation with prognosis, and 7 biomarkers (SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8) have a high correlation with diagnosis. After combined diagnosis, the sensitivity and specificity are further improved.

[0008] A first aspect of the present invention provides a use of a biomarker or a reagent for detecting a biomarker in the preparation of a product for diagnosing and / or prognostic evaluation of esophageal cancer.

[0009] The biomarkers include at least two of SCC, IL-6, VEGF or IL-8. Preferably, the biomarkers also include one or more of Cyfra21-1, HGF or IGFBP-1.

[0010] In a specific embodiment of the present invention, the biomarker is a combination of SCC, IL-6, VEGF and IL-8.

[0011] In a specific embodiment of the present invention, the biomarker is a combination of SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8.

[0012] In a specific embodiment of the present invention, the biomarker is selected from IGFBP-1, CD86, RBP1, ENO2, VWF, LRRFIP1, CCND1, PRDX1, CAT, TAGLN2, BLVRB, CA1, PRDX6, HBB, PRDX2, ACTB, ARF3, HBA2, CA2, CNDP1, DCD, HBD, AK1, C2, MFAP4, DBH, HGF, PPIA, C9, ACTA2, HBG2, SERPINA5, IGLV5-45, APOC1, ENPP2, ITIH3, YWHAB, CHGA, ELANE, AMY1B, KRT10, EPO , LCAT, KRT2, APOC4, EZR, GPLD1, MPO, COLEC10, CLEC3B, VEGF, FGB, LAMP2, PLK1, IGHV3-11, TNFSF15, APOL1, APOC3, SERPINA4, PRSS1, MUC1, IL-4, TNC, K RT1, FCGBP, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFB P3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGKV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LC N2, IGHG2, PSAP, CTSG, IGLV1-36, SIGLEC1, SERPINA6, KPNA2, PIP, ERVW-1, MMRN1, BMP1, FABP3, SERPINF1, TXN, FETUB, LRG1, NFE2L2, LAMP1, IL-5, HCRT , AGER, SAA, PRTN3, WBSCR22, IL10RB, IGKV6-21, HMGA2, MPC1, IL-8, IGHV3-20, APOA1, FGF12, TGFB3, FGF22, GSK3B, GAPDH, PSMB8, RNASE3, SMAD4, SELL, CPB2, MS4A1, IGLV3-1, CDH2, APOB, H4-16, LDHB, MSMB, IGLV2-23, PON3, IL-12-p70, AZGP1, C4B, CPN2, F12, CD8A, HSPA8, AHSG, IL11, PEBP4, PROM1, APOD,At least two of PTGDS, LUM, HSPG2, KHSRP, ALB, KRT5, CORO2A, LCP1, APOE, FBLN1, IGHE, C8G, LY6E, FGG, PROZ, IGKV3-7, CD22, ABI3BP, CRH, IGHA1, LBP, APOC2, PTPN1, IGKV1-17, CCL8, SWAP70, IGHV4-30-2, IGKV3-11, CACNA2D1, TNXB, P2RX7, PI16, SLC30A10, PTH, RALGAPB, IL31, APOM, BPIFB1, ABHD2, TSHR, CD5L, PROC, Cyfra21-1, SCC, or IL-6.

[0013] Preferably, the biomarker is IGFBP-1, CD86, RBP1, ENO2, VWF, LRRFIP1, CCND1, PRDX1, CAT, TAGLN2, BLVRB, CA1, PRDX6, HBB, PRDX2, ACTB, ARF3, HBA2, CA2, CNDP1, DCD, HBD, AK1, C2, MFAP4, DBH, HGF, PPIA, C9, ACTA2, HBG2, SERPINA5, IGLV5-45, APOC1, ENPP2, ITIH3, YWHAB, CHGA, ELANE, AMY1B, KRT10, EPO, LCAT, KRT2, APOC4, EZR, GPLD1, MPO, COLEC10, CLEC3B, VEGF, FGB, LAMP2, PLK1, IGHV3-11, TNFSF15, APOL1, APOC3, SERPINA4, PRSS1, MUC1, IL-4, TNC, KRT1, FCGBP, I FNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KR T9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGKV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, IGHG2, PS AP, CTSG, IGLV1-36, SIGLEC1, SERPINA6, KPNA2, PIP, ERVW-1, MMRN1, BMP1, FABP3, SERPINF1, TXN, FETUB, LRG1, NFE2L2, LAMP1, IL-5, HCRT, AGER, SAA, P RTN3, WBSCR22, IL10RB, IGKV6-21, HMGA2, MPC1, IL-8, IGHV3-20, APOA1, FGF12, TGFB3, FGF22, GSK3B, GAPDH, PSMB8, RNASE3, SMAD4, SELL, CPB2, MS4A1, IGLV3-1, CDH2, APOB, H4-16, LDHB, MSMB, IGLV2-23, PON3, IL-12-p70, AZGP1, C4B, CPN2, F12, CD8A, HSPA8, AHSG, IL11, PEBP4, PROM1, APOD, PTGDS, LUM,A combination of HSPG2, KHSRP, ALB, KRT5, CORO2A, LCP1, APOE, FBLN1, IGHE, C8G, LY6E, FGG, PROZ, IGKV3-7, CD22, ABI3BP, CRH, IGHA1, LBP, APOC2, PTPN1, IGKV1-17, CCL8, SWAP70, IGHV4-30-2, IGKV3-11, CACNA2D1, TNXB, P2RX7, PI16, SLC30A10, PTH, RALGAPB, IL31, APOM, BPIFB1, ABHD2, TSHR, CD5L, PROC, Cyfra21-1, SCC, and IL-6.

[0014] In a specific embodiment of the present invention, the application is: the use of SCC, IL-6, VEGF and IL-8 in combination as biomarkers in the preparation of products for diagnosis and / or prognosis evaluation of esophageal cancer.

[0015] In a specific embodiment of the present invention, the application is: the use of SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8 in combination as biomarkers in the preparation of products for diagnosis and / or prognosis evaluation of esophageal cancer.

[0016] The biomarkers include genes or proteins.

[0017] The reagent for detecting the biomarker may be an antibody, for example, a specific antibody that can bind to the protein to be detected.

[0018] The biomarker is a biomarker in plasma or serum; preferably, the biomarker is a biomarker in serum.

[0019] The product includes a chip, a test kit, a test paper, a membrane strip or a device. The device can be selected from a liquid phase or a mass spectrometer. The chip can include an antibody chip, such as a liquid phase chip (Multiplexbead-based immunoassay by flow cytometry).

[0020] Preferably, the product includes reagents for detecting the presence or expression level of biomarkers in a sample.

[0021] Preferably, the sample is a plasma sample or a serum sample.

[0022] Preferably, the method for detecting biomarkers in a sample comprises one or more of mass spectrometry, liquid chromatography, antibody chip (preferably liquid phase chip) or ELISA.

[0023] Preferably, the diagnosis and / or prognosis assessment of esophageal cancer includes detecting the presence or expression level of biomarkers in the sample, and further preferably includes the step of comparing the detection result with a threshold value.

[0024] Preferably, the threshold is obtained from preliminary experiments, that is, the threshold is determined by experiments and data analysis based on the difference in biomarkers between esophageal cancer patients and healthy people.

[0025] When the expression level of the biomarkers described in the present application is different or significantly different from the threshold, the disease is diagnosed, for example:

[0026] A) The expression level of SCC is higher than the threshold or significantly higher than the threshold, indicating the occurrence of esophageal cancer; and / or,

[0027] B) the expression level of Cyfra21-1 is higher than the threshold or significantly higher than the threshold, indicating the occurrence of esophageal cancer; and / or,

[0028] C) the expression level of HGF being higher than the threshold or significantly higher than the threshold indicates the occurrence of esophageal cancer; and / or,

[0029] D) the expression level of IGFBP-1 being higher than the threshold or significantly higher than the threshold indicates the occurrence of esophageal cancer; and / or,

[0030] E) the expression level of VEGF being higher than the threshold or significantly higher than the threshold indicates the occurrence of esophageal cancer; and / or,

[0031] F) the expression level of IL-6 is higher than the threshold or significantly higher than the threshold, indicating the occurrence of esophageal cancer; and / or,

[0032] G) The expression level of IL-8 lower than the threshold or significantly lower than the threshold indicates the occurrence of esophageal cancer.

[0033] When the expression of the biomarkers described in the present application is different or significantly different from the threshold, a good prognosis or risk of recurrence is determined, for example:

[0034] A) The expression level of SCC is higher than the threshold or significantly higher than the threshold, indicating a high risk of recurrence; and / or,

[0035] B) the expression level of IL-6 is higher than the threshold or significantly higher than the threshold, indicating a high risk of recurrence; and / or,

[0036] C) the expression level of VEGF is higher than the threshold or significantly higher than the threshold, indicating a high risk of recurrence of the disease; and / or,

[0037] D) The expression level of IL-8 is lower than the threshold or significantly lower than the threshold, indicating a high risk of recurrence.

[0038] or,

[0039] A) The expression level of SCC is lower than the threshold or significantly lower than the threshold, indicating a good prognosis; and / or,

[0040] B) the expression level of IL-6 is lower than the threshold or significantly lower than the threshold, indicating a good prognosis; and / or,

[0041] C) the expression level of VEGF being lower than the threshold or significantly lower than the threshold indicates a good prognosis; and / or,

[0042] D) The expression level of IL-8 higher than the threshold or significantly higher than the threshold indicates a good prognosis.

[0043] Preferably, the esophageal cancer includes esophageal squamous cell carcinoma and / or esophageal adenocarcinoma.

[0044] A second aspect of the present invention provides a method for diagnosing and / or prognostic evaluation of esophageal cancer, wherein the method comprises detecting a biomarker in a sample of a subject.

[0045] The biomarkers include at least two of SCC, IL-6, VEGF or IL-8. Preferably, the biomarkers also include one or more of Cyfra21-1, HGF or IGFBP-1.

[0046] In a specific embodiment of the present invention, the biomarkers are SCC, IL-6, VEGF and IL-8.

[0047] In a specific embodiment of the present invention, the biomarkers are SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8.

[0048] Preferably, the method comprises detecting the presence or content of a biomarker, such as the expression level of a protein.

[0049] Preferably, the sample is a plasma sample or a serum sample.

[0050] Preferably, the method further comprises comparing the expression level of the detected biomarker with a threshold value.

[0051] When the expression level of the biomarkers described in this application is different or significantly different from the threshold, the disease is diagnosed. For example:

[0052] A) The expression level of SCC is higher than the threshold or significantly higher than the threshold, indicating the occurrence of esophageal cancer; and / or,

[0053] B) the expression level of Cyfra21-1 is higher than the threshold or significantly higher than the threshold, indicating the occurrence of esophageal cancer; and / or,

[0054] C) the expression level of HGF being higher than the threshold or significantly higher than the threshold indicates the occurrence of esophageal cancer; and / or,

[0055] D) the expression level of IGFBP-1 being higher than the threshold or significantly higher than the threshold indicates the occurrence of esophageal cancer; and / or,

[0056] E) the expression level of VEGF being higher than the threshold or significantly higher than the threshold indicates the occurrence of esophageal cancer; and / or,

[0057] F) the expression level of IL-6 is higher than the threshold or significantly higher than the threshold, indicating the occurrence of esophageal cancer; and / or,

[0058] G) The expression level of IL-8 lower than the threshold or significantly lower than the threshold indicates the occurrence of esophageal cancer.

[0059] When the expression of the biomarkers described in the present application is different or significantly different from the threshold, a good prognosis or risk of recurrence is determined, for example:

[0060] A) The expression level of SCC is higher than the threshold or significantly higher than the threshold, indicating a high risk of recurrence; and / or,

[0061] B) the expression level of IL-6 is higher than the threshold or significantly higher than the threshold, indicating a high risk of recurrence; and / or,

[0062] C) the expression level of VEGF is higher than the threshold or significantly higher than the threshold, indicating a high risk of recurrence of the disease; and / or,

[0063] D) The expression level of IL-8 is lower than the threshold or significantly lower than the threshold, indicating a high risk of recurrence.

[0064] or,

[0065] A) The expression level of SCC is lower than the threshold or significantly lower than the threshold, indicating a good prognosis; and / or,

[0066] B) the expression level of IL-6 is lower than the threshold or significantly lower than the threshold, indicating a good prognosis; and / or,

[0067] C) the expression level of VEGF being lower than the threshold or significantly lower than the threshold indicates a good prognosis; and / or,

[0068] D) The expression level of IL-8 higher than the threshold or significantly higher than the threshold indicates a good prognosis.

[0069] Preferably, the detection method can be selected from mass spectrometry, liquid chromatography, antibody chip (preferably liquid phase chip), and ELISA.

[0070] In a third aspect of the present invention, a chip, a kit, a test paper, a membrane strip or a device for diagnosing and / or evaluating the prognosis of esophageal cancer is provided, wherein the chip, the kit, the test paper, the membrane strip or the device comprises a reagent for detecting a biomarker, wherein the biomarker comprises at least two of SCC, IL-6, VEGF or IL-8. Preferably, the biomarker further comprises one or more of Cyfra21-1, HGF or IGFBP-1.

[0071] In a specific embodiment of the present invention, the biomarkers are SCC, IL-6, VEGF and IL-8.

[0072] In a specific embodiment of the present invention, the biomarkers are SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8.

[0073] In a specific embodiment of the present invention, the biomarker is selected from IGFBP-1, CD86, RBP1, ENO2, VWF, LRRFIP1, CCND1, PRDX1, CAT, TAGLN2, BLVRB, CA1, PRDX6, HBB, PRDX2, ACTB, ARF3, HBA2, CA2, CNDP1, DCD, HBD, AK1, C2, MFAP4, DBH, HGF, PPIA, C9, ACTA2, HBG2, SERPINA5, IGLV5-45, APOC1, ENPP2, ITIH3, YWHAB, CHGA, ELANE, AMY1B, KRT10, EPO , LCAT, KRT2, APOC4, EZR, GPLD1, MPO, COLEC10, CLEC3B, VEGF, FGB, LAMP2, PLK1, IGHV3-11, TNFSF15, APOL1, APOC3, SERPINA4, PRSS1, MUC1, IL-4, TNC, K RT1, FCGBP, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFB P3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGKV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LC N2, IGHG2, PSAP, CTSG, IGLV1-36, SIGLEC1, SERPINA6, KPNA2, PIP, ERVW-1, MMRN1, BMP1, FABP3, SERPINF1, TXN, FETUB, LRG1, NFE2L2, LAMP1, IL-5, HCRT , AGER, SAA, PRTN3, WBSCR22, IL10RB, IGKV6-21, HMGA2, MPC1, IL-8, IGHV3-20, APOA1, FGF12, TGFB3, FGF22, GSK3B, GAPDH, PSMB8, RNASE3, SMAD4, SELL, CPB2, MS4A1, IGLV3-1, CDH2, APOB, H4-16, LDHB, MSMB, IGLV2-23, PON3, IL-12-p70, AZGP1, C4B, CPN2, F12, CD8A, HSPA8, AHSG, IL11, PEBP4, PROM1, APOD,At least two of PTGDS, LUM, HSPG2, KHSRP, ALB, KRT5, CORO2A, LCP1, APOE, FBLN1, IGHE, C8G, LY6E, FGG, PROZ, IGKV3-7, CD22, ABI3BP, CRH, IGHA1, LBP, APOC2, PTPN1, IGKV1-17, CCL8, SWAP70, IGHV4-30-2, IGKV3-11, CACNA2D1, TNXB, P2RX7, PI16, SLC30A10, PTH, RALGAPB, IL31, APOM, BPIFB1, ABHD2, TSHR, CD5L, PROC, Cyfra21-1, SCC, or IL-6.

[0074] Preferably, the biomarker is IGFBP-1, CD86, RBP1, ENO2, VWF, LRRFIP1, CCND1, PRDX1, CAT, TAGLN2, BLVRB, CA1, PRDX6, HBB, PRDX2, ACTB, ARF3, HBA2, CA2, CNDP1, DCD, HBD, AK1, C2, MFAP4, DBH, HGF, PPIA, C9, ACTA2, HBG2, SERPINA5, IGLV5-45, APOC1, ENPP2, ITIH3, YWHAB, CHGA, ELANE, AMY1B, KRT10, EPO, LCAT, KRT2, APOC4, EZR, GPLD1, MPO, COLEC10, CLEC3B, VEGF, FGB, LAMP2, PLK1, IGHV3-11, TNFSF15, APOL1, APOC3, SERPINA4, PRSS1, MUC1, IL-4, TNC, KRT1, FCGBP, I FNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KR T9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGKV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, IGHG2, PS AP, CTSG, IGLV1-36, SIGLEC1, SERPINA6, KPNA2, PIP, ERVW-1, MMRN1, BMP1, FABP3, SERPINF1, TXN, FETUB, LRG1, NFE2L2, LAMP1, IL-5, HCRT, AGER, SAA, P RTN3, WBSCR22, IL10RB, IGKV6-21, HMGA2, MPC1, IL-8, IGHV3-20, APOA1, FGF12, TGFB3, FGF22, GSK3B, GAPDH, PSMB8, RNASE3, SMAD4, SELL, CPB2, MS4A1, IGLV3-1, CDH2, APOB, H4-16, LDHB, MSMB, IGLV2-23, PON3, IL-12-p70, AZGP1, C4B, CPN2, F12, CD8A, HSPA8, AHSG, IL11, PEBP4, PROM1, APOD, PTGDS, LUM,A combination of HSPG2, KHSRP, ALB, KRT5, CORO2A, LCP1, APOE, FBLN1, IGHE, C8G, LY6E, FGG, PROZ, IGKV3-7, CD22, ABI3BP, CRH, IGHA1, LBP, APOC2, PTPN1, IGKV1-17, CCL8, SWAP70, IGHV4-30-2, IGKV3-11, CACNA2D1, TNXB, P2RX7, PI16, SLC30A10, PTH, RALGAPB, IL31, APOM, BPIFB1, ABHD2, TSHR, CD5L, PROC, Cyfra21-1, SCC, and IL-6.

[0075] The reagents for detecting biomarkers include reagents for detecting the presence or expression level of biomarkers.

[0076] The esophageal cancer includes esophageal squamous cell carcinoma and / or esophageal adenocarcinoma.

[0077] In a fourth aspect of the present invention, a biomarker for diagnosis and / or prognosis assessment of esophageal cancer is provided, wherein the biomarker comprises at least two of SCC, IL-6, VEGF or IL-8.

[0078] Preferably, the biomarker further comprises one or more of Cyfra21-1, HGF or IGFBP-1.

[0079] In a specific embodiment of the present invention, the biomarkers are SCC, VEGF, IL-6 and IL-8.

[0080] In a specific embodiment of the present invention, the biomarkers are SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8.

[0081] In a specific embodiment of the present invention, the biomarker is selected from IGFBP-1, CD86, RBP1, ENO2, VWF, LRRFIP1, CCND1, PRDX1, CAT, TAGLN2, BLVRB, CA1, PRDX6, HBB, PRDX2, ACTB, ARF3, HBA2, CA2, CNDP1, DCD, HBD, AK1, C2, MFAP4, DBH, HGF, PPIA, C9, ACTA2, HBG2, SERPINA5, IGLV5-45, APOC1, ENPP2, ITIH3, YWHAB, CHGA, ELANE, AMY1B, KRT10, EPO , LCAT, KRT2, APOC4, EZR, GPLD1, MPO, COLEC10, CLEC3B, VEGF, FGB, LAMP2, PLK1, IGHV3-11, TNFSF15, APOL1, APOC3, SERPINA4, PRSS1, MUC1, IL-4, TNC, K RT1, FCGBP, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFB P3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGKV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LC N2, IGHG2, PSAP, CTSG, IGLV1-36, SIGLEC1, SERPINA6, KPNA2, PIP, ERVW-1, MMRN1, BMP1, FABP3, SERPINF1, TXN, FETUB, LRG1, NFE2L2, LAMP1, IL-5, HCRT , AGER, SAA, PRTN3, WBSCR22, IL10RB, IGKV6-21, HMGA2, MPC1, IL-8, IGHV3-20, APOA1, FGF12, TGFB3, FGF22, GSK3B, GAPDH, PSMB8, RNASE3, SMAD4, SELL, CPB2, MS4A1, IGLV3-1, CDH2, APOB, H4-16, LDHB, MSMB, IGLV2-23, PON3, IL-12-p70, AZGP1, C4B, CPN2, F12, CD8A, HSPA8, AHSG, IL11, PEBP4, PROM1, APOD,At least two of PTGDS, LUM, HSPG2, KHSRP, ALB, KRT5, CORO2A, LCP1, APOE, FBLN1, IGHE, C8G, LY6E, FGG, PROZ, IGKV3-7, CD22, ABI3BP, CRH, IGHA1, LBP, APOC2, PTPN1, IGKV1-17, CCL8, SWAP70, IGHV4-30-2, IGKV3-11, CACNA2D1, TNXB, P2RX7, PI16, SLC30A10, PTH, RALGAPB, IL31, APOM, BPIFB1, ABHD2, TSHR, CD5L, PROC, Cyfra21-1, SCC, or IL-6.

[0082] Preferably, the biomarker is IGFBP-1, CD86, RBP1, ENO2, VWF, LRRFIP1, CCND1, PRDX1, CAT, TAGLN2, BLVRB, CA1, PRDX6, HBB, PRDX2, ACTB, ARF3, HBA2, CA2, CNDP1, DCD, HBD, AK1, C2, MFAP4, DBH, HGF, PPIA, C9, ACTA2, HBG2, SERPINA5, IGLV5-45, APOC1, ENPP2, ITIH3, YWHAB, CHGA, ELANE, AMY1B, KRT10, EPO, LCAT, KRT2, APOC4, EZR, GPLD1, MPO, COLEC10, CLEC3B, VEGF, FGB, LAMP2, PLK1, IGHV3-11, TNFSF15, APOL1, APOC3, SERPINA4, PRSS1, MUC1, IL-4, TNC, KRT1, FCGBP, I FNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KR T9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGKV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, IGHG2, PS AP, CTSG, IGLV1-36, SIGLEC1, SERPINA6, KPNA2, PIP, ERVW-1, MMRN1, BMP1, FABP3, SERPINF1, TXN, FETUB, LRG1, NFE2L2, LAMP1, IL-5, HCRT, AGER, SAA, P RTN3, WBSCR22, IL10RB, IGKV6-21, HMGA2, MPC1, IL-8, IGHV3-20, APOA1, FGF12, TGFB3, FGF22, GSK3B, GAPDH, PSMB8, RNASE3, SMAD4, SELL, CPB2, MS4A1, IGLV3-1, CDH2, APOB, H4-16, LDHB, MSMB, IGLV2-23, PON3, IL-12-p70, AZGP1, C4B, CPN2, F12, CD8A, HSPA8, AHSG, IL11, PEBP4, PROM1, APOD, PTGDS, LUM,A combination of HSPG2, KHSRP, ALB, KRT5, CORO2A, LCP1, APOE, FBLN1, IGHE, C8G, LY6E, FGG, PROZ, IGKV3-7, CD22, ABI3BP, CRH, IGHA1, LBP, APOC2, PTPN1, IGKV1-17, CCL8, SWAP70, IGHV4-30-2, IGKV3-11, CACNA2D1, TNXB, P2RX7, PI16, SLC30A10, PTH, RALGAPB, IL31, APOM, BPIFB1, ABHD2, TSHR, CD5L, PROC, Cyfra21-1, SCC, and IL-6.

[0083] The term "diagnosis" as used herein refers to finding out whether a patient has had a disease or condition in the past, at the time of diagnosis, or in the future, or to finding out the progression of a disease or its possible future progression.

[0084] The "prognosis assessment" mentioned in the present invention refers to the assessment of a patient's response to treatment and the risk of future illness.

[0085] The "subject" described in the present invention can be a human or a non-human mammal, and the non-human mammal can be a wild animal, a zoo animal, an economic animal, a pet, an experimental animal, etc. Preferably, the non-human mammal includes but is not limited to pigs, cattle, sheep, horses, donkeys, foxes, raccoon dogs, minks, camels, dogs, cats, rabbits, mice (such as rats, mice, guinea pigs, hamsters, gerbils, chinchillas, squirrels) or monkeys, etc.

[0086] The "expression level" and "expression amount" described in the present invention are interchangeable.

[0087] The "and / or" described in the present invention includes all combinations of items connected by the term, and each combination should be deemed to have been listed separately in the present application. For example, "A and / or B" includes "A", "B" and "A and B"; another example is "A, B and / or C", which includes "A", "B", "C", "A and B", "A and C", "B and C" and "A and B and C".

[0088] The terms “include” or “comprising” described in the present invention are open-ended descriptions, which include the specified components or steps described, and other specified components or steps that will not be substantially affected.

[0089] The "method" or "application" of the present invention may be for diagnostic purposes or non-diagnostic purposes.

[0090] The full names corresponding to the abbreviations of this application are shown in Table 1.

[0091] Table 1: Abbreviation and full name correspondence table

[0092]

[0093] The beneficial effects of the present invention are as follows: (1) The present invention combines antibody chip technology with data-independent acquisition mode mass spectrometry detection technology to identify proteins in the serum of esophageal cancer patients and healthy controls, and screens out 7 biomarkers for diagnosis and / or prognosis evaluation of esophageal cancer by combining biostatistical methods. (2) The obtained biomarkers are verified by liquid phase chip, and the results are consistent with the results of the combination of antibody chip technology and data-independent acquisition mode mass spectrometry detection technology. (3) The Youden index was used to clarify the sensitivity and specificity of the biomarkers, and the AUC value was confirmed by the ROC curve. All biomarkers had high specificity and sensitivity for the diagnosis of esophageal cancer. When four biomarkers (SCC, IL-6, VEGF, and IL-8) were used in combination to diagnose esophageal cancer, the specificity was as high as 89.1%, and the AUC value reached 0.720. Seven biomarkers (SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6, and IL-8) were further used for combined diagnosis. The specificity for the diagnosis of esophageal cancer was 78.2%, the sensitivity was 69.4%, and the AUC value was 0.785. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 : Flow chart of sample serum testing.

[0095] Figure 2 : Spearman correlation coefficient diagram of mass spectrometry standards. The numbers in the boxes represent the pairwise Spearman correlation coefficients of the samples.

[0096] Figure 3 : Schematic diagram of antibody chip results.

[0097] Figure 4 :Schematic diagram of correlation within the same array of antibody chip.

[0098] Figure 5 : Schematic diagram of the correlation between different arrays of antibody chips.

[0099] Figure 6 : Volcano plot of differentially expressed proteins associated with esophageal adenocarcinoma.

[0100] Figure 7 :Esophageal adenocarcinoma antibody chip combined with mass spectrometry detection of biomarkers can distinguish between healthy and esophageal adenocarcinoma groups.

[0101] Figure 8 : Heatmap of biomarker expression in the validation cohort.

[0102] Fig. 9 : Expression concentrations of biomarkers in the validation cohort.

[0103] Fig.10 : Analysis of the expression level of SCC and the prognosis survival rate of esophageal cancer.

[0104] Fig.11 :Analysis of IL-6 expression level and prognosis survival rate of esophageal cancer.

[0105] Fig.12 :Analysis of VEGF expression level and prognosis survival rate of esophageal cancer.

[0106] Fig.13 :Analysis of IL-8 expression level and prognosis survival rate of esophageal cancer. DETAILED DESCRIPTION

[0107] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0108] The samples and experimental methods involved in the embodiments are as follows:

[0109] 1. Sample collection

[0110] Two clinical cohorts were collected in this application. The discovery cohort included 53 healthy controls (HC) collected from Sun Yat-sen University Cancer Center and 50 EAC patients' preoperative sera obtained from Sun Yat-sen University Cancer Center and Peking University Cancer Hospital. The validation cohort was collected from 55 healthy controls (HC) and 51 EAC patients' preoperative sera obtained from Sun Yat-sen University Cancer Center and Peking University Cancer Hospital, as well as 158 ESCC patients' preoperative sera collected during the same period.

[0111] 2. Experimental Design

[0112] By combining the novel customizable antibody chip technology with the data-independent acquisition (DIA) mass spectrometry detection technology (DIA-MS), a deep coverage serum proteome research platform was established, which can realize the detection of large-scale proteomes in serum, and the detection protein concentration range can reach 10 orders of magnitude. Then, the inter-sample data were standardized by quantile normalization and Quantile method. The standardized DIA-MS data and antibody chip data were respectively identified by SAM statistical method (p value < 0.05) to find the differential proteins between healthy controls and EAC patients in the cohort. In the validation cohort, the candidate markers were validated by liquid phase chip, and the Mann-Whitney U test was performed by GraphPad Prism10.0 software for inter-group comparison. Binary Logistic regression analysis was performed using SPSS26.0 for marker joint analysis. Prognostic analysis was performed by finding the cut off value using the surv_cutpoint function in the Survminor package in R studio to draw the KM curve.

[0113] 1) DIA-MS analysis

[0114] Taking the discovery cohort as an example, the serum sample testing process is as follows Figure 1 As shown, the serum sample was centrifuged at 10000 rpm for 3 min, 2 μL of serum supernatant was added to a 1.5 mL centrifuge tube, 100 μL of 6 mol / L urea lysis buffer was used to denature serum protein, and 1 μL 1mol / L DTT was reduced in a 37°C water bath for 60min; then, alkylation treatment was performed with 10μL 500mmol / L IAA for 45min at 25°C in the dark, the solution was transferred to a 0.5mL-30K ultrafiltration tube, and centrifuged and washed three times with 200μL 50mmol / L NH4HCO3 at 12000g to remove excess alkylation reagent and precipitate the alkylated protein in the ultrafiltration tube; next, enzymatic hydrolysis was performed with 0.04mg / mL trypsin at 37°C for 16h, centrifuged at 12000g at room temperature for 15min, the filtrate was collected in a centrifuge tube, 200μL pure water was added to the ultrafiltration tube, centrifuged at 12000g for 15min, and all the filtrate was collected; the collected peptide solution was vacuum-dried for 4h and dissolved in 20μL 0.1% formic acid; finally, the peptide concentration was determined using a DS-11 spectrophotometer, and the absorbance was set to A280nm.

[0115] Data-dependent acquisition analysis (DIA) was performed using an Orbitrap Fusion Lumos Tribrid mass spectrometer. Briefly, 3 μg of mixed peptides were loaded onto a C18 trap column (100 μm × 2 cm, self-packed) with 12 μL of solvent A (0.1% formic acid) at a maximum pressure of 280 bar on an EASY-nLCTM 1200 liquid chromatography system, and then injected at a flow rate of 600 nL / min. C18 analytical column (150 μm×250 mm, 1.9 μm). The gradient for 120 minutes was set as follows: 7%-15% solvent B (80% CAN, 0.1% formic acid) from 0 to 15 minutes, 15%-30% solvent B from 15 to 90 minutes, 30%-50% solvent B from 90 to 115 minutes, 50%-95% solvent B from 115 to 117 minutes, and 95% solvent B from 117 to 120 minutes.

[0116] The DIA acquisition protocol for the QE-HF mass spectrometer included 45 variable windows with a scan range of 350–1400 m / z, an overlap of 1 Da, and precursor ion isolation windows set as follows: 374–412, 412–436.5, 436.5–457, 457–471.5, 471.5–483.5, 481.5–484.5, 494.5–507, 507–520.5, 520.5–533.5, 533.5–554.5, 554.5–563.5, 563.5–573.5, 583.5–593.5, 593.5–604.5. 04-615, 615-626, 626-636, 636-646, 646-657, 675-668.5, 668.5-680, 680-691, 691-702, 702-714, 714-726.5, 726.5-739.5, 73 9.5-753, 753-767, 781-796, 796-812, 812-828.5, 828.5-846.5, 835.5-964, 964-998, 998-1040.5, 1040.5-1101, 1101-1269m / z. The DIA parameters were: NCE of 28, total cycle time of 3.6 s, resolution of 30000, AGC of 1e6, and maximum ion injection time of 45 ms.

[0117] The DIA data were analyzed using Spectronaut Pulsar 17 software, and the default settings were used unless otherwise specified. The raw DIA data were searched in the above-mentioned multi-disease spectral library using Spectronaut Pulsar 17.

[0118] 2) Antibody chip analysis

[0119] Preparation of antibody chip: The antibody chip is made by the National Center for Protein Science (Beijing). The specific antibodies of the proteins used as candidate markers are fixed on the chip substrate. The serum to be tested passes through the chip surface, and the proteins in the serum bind to the antibodies to detect the expression level of the proteins in the serum. The prepared antibody chip is stored at -20℃ for standby use. The detection indicators include 940 proteins.

[0120] First, the serum was biotinylated: 10 μl serum sample was added to 90 μl phosphate buffered saline (PBS), and then 1 μl of 20 g / L biotinylation reagent was added. The reaction was allowed to react at room temperature for 1 hour. The labeled reactant was added to the center of the gel bed of the micro separation column Bio-Spin. After centrifugation at 1000 g for 4 minutes, the effluent was collected and 500 μl of 5% skim milk was added. The mixture was mixed for later use. The antibody chip was taken out of the -20°C refrigerator, equilibrated at room temperature for 20 minutes, and then a gel fence was added. The chip was blocked with 5% skim milk for 1 hour. The chip was washed with PBST containing 2% Tween for 3 times, 10 minutes each time, and the biotinylated serum protein sample was added. The sample was incubated for 2 hours. The sample was washed with PBST for 3 times, 10 minutes each time, and a 2 μg / ml Streptavidin-Cy3 solution was added for incubation for 1 hour. The sample was washed with PBST for 3 times, 10 minutes each time, and a 2 μg / ml Streptavidin-Cy3 solution was added for incubation for 1 hour. The sample was washed with PBST for 3 times, and then washed with deionized water for 3 times, 10 minutes each time. The gel fence was removed and the chip was dried. Finally, Genepix4300A was used to scan the chip, with a wavelength of 532nm and a scanning resolution of 10μm. GenepixPro7 software was used to extract the chip fluorescence signal value.

[0121] Chip data processing: The foreground median value minus the background median value is taken as the signal value of the target, and the average of the two repeated points of each target protein is taken as the fluorescence signal value of the inflammatory factor. The Quantile method is used for standardization to eliminate the differences between different chips.

[0122] 3) Liquid phase chip analysis

[0123] After the magnetic fluorescent microspheres were successfully linked to the capture antibody, the serum dilution, standard solution and blank control PBS-TBN solution were added to the reaction plate at 50 μL / well, incubated on a plate shaker at room temperature in the dark for 2 h, and washed 3 times with 100 μL PBS-TBN solution; the biotin-labeled detection antibody solution was added to the reaction plate at 50 μL / well, gently pipetted and mixed, incubated at room temperature in the dark for 1 h, and washed 3 times with PBS-TBN; the SAPE solution was added to the corresponding wells at 50 μL / well, incubated at room temperature in the dark for 0.5 h; the washing steps were repeated, the microspheres were resuspended in 200 μL PBS-TBN solution, the microspheres were transferred to the flow tube, and detected by the EasyCell flow cytometer.

[0124] Example 1: Screening for esophageal cancer biomarkers

[0125] Serum samples from HC (53 cases) and EAC patients (50 cases) from the discovery cohort of 103 patients were analyzed by DIA-MS. To evaluate the reproducibility of DIA-MS, the same trypsin-digested human HEK293T cell lysate was analyzed at different time points throughout the mass spectrometry experiment, and the inter-assay correlation coefficient (r) reached 0.95-1.00 ( Figure 2 ). A total of 940 proteins were quantified by the antibody chip ( Figure 3 ), and good correlation between different arrays and within the same array ( Figure 4 and Figure 5 ), the antibody chip platform has high stability.

[0126] In the discovery cohort, the standardized DIA-MS data and antibody chip data were analyzed by SAM statistics (p < 0.05). After removing the duplication of the two methods to detect proteins, 213 differentially expressed proteins were obtained (Table 2), of which 67 were upregulated in EAC samples and 146 were downregulated in EAC samples ( Figure 6 ).

[0127] Table 2: 213 diagnostic markers for esophageal adenocarcinoma

[0128]

[0129]

[0130] According to the difference in the differential proteins in the discovery cohort, seven proteins with significant expression differences were used as biomarkers for esophageal cancer, namely SCC, HGF, Cyfra21-1, VEGF, IL-6, IL-8 and IGFBP-1. Among them, SCC, HGF, Cyfra21-1, IL-6, VEGF and IGFBP-1 showed an increasing trend in patients with esophageal adenocarcinoma, and IL-8 showed a decreasing trend in patients with esophageal adenocarcinoma ( Figure 7 ).

[0131] Example 2: Further validation of biomarkers

[0132] To further validate the biomarkers, the liquid phase chip method was used in another independent clinical validation cohort including 55 HC and 51 EAC. It was found that the biomarkers SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8 were significantly different between esophageal cancer patients and healthy controls ( Figure 8 ), and the trend was consistent with the DIA-MS data and antibody chip data in the discovery cohort. For example, the differences in SCC, HGF, IGFBP-1, and IL-8 between esophageal cancer patients and healthy controls were shown in Fig. 9 , confirming the correctness of the previous findings.

[0133] In addition, SCC, IL-6, VEGF, and IL-8 were found to be highly correlated with prognosis in the serum of 158 ESCC patients in the validation cohort (see Figure 10-13 ).

[0134] Example 3: Diagnostic effect evaluation

[0135] In order to evaluate the diagnostic value of the biomarkers, the Youden index was further used in the validation cohort (ESCC group: 55 healthy controls, 158 ESCC patients; EAC group: 55 healthy controls, 51 EAC patients; EC group: 55 healthy controls, 158 ESCC patients and 51 EAC patients, a total of 209 EC patients) to clarify the sensitivity and specificity, and the AUC value was confirmed by the ROC curve (see Table 3). At the same time, a joint analysis of 4 biomarkers (SCC, IL-6, VEGF, IL-8) or 7 markers (SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6 and IL-8) was performed to form a biomarker combination for the diagnosis of EC as a comprehensive biomarker panel. This biomarker panel can diagnose EAC and ESCC, and can not only serve as a universal biomarker combination for EC, but also predict the prognosis of patients.

[0136] Table 3: Summary of diagnostic value of biomarkers

[0137]

[0138] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

Claims

1. Use of a biomarker or a reagent for detecting a biomarker in the preparation of a product for diagnosing and / or evaluating the prognosis of esophageal cancer, characterized in that: The biomarkers include at least two of SCC, IL-6, VEGF or IL-8.

2. The use according to claim 1, characterized in that: The biomarkers also include one or more of Cyfra21-1, HGF or IGFBP-1.

3. The use according to claim 1 or 2, characterized in that: The products include chips, test kits, test strips, membrane strips or devices.

4. The use according to any one of claims 1 to 3, characterized in that: The product includes reagents for detecting the presence or expression level of biomarkers in a sample.

5. The use according to any one of claims 1 to 4, characterized in that: The diagnosis and / or prognosis assessment of esophageal cancer includes detecting the presence or expression level of biomarkers in a sample.

6. The use according to claim 5, characterized in that: The method for detecting biomarkers in a sample includes one or more of mass spectrometry, liquid chromatography, antibody chip (preferably liquid phase chip) or ELISA.

7. The use according to any one of claims 4 to 6, characterized in that: The sample is a plasma sample or a serum sample.

8. The use according to any one of claims 1 to 7, characterized in that: The esophageal cancer includes esophageal squamous cell carcinoma and / or esophageal adenocarcinoma.

9. A chip, a kit, a test paper, a membrane strip or a device for diagnosing and / or evaluating the prognosis of esophageal cancer, characterized in that: The chip, kit, test paper, membrane strip or device comprises a reagent for detecting a biomarker, wherein the biomarker comprises at least two of SCC, IL-6, VEGF or IL-8; Preferably, the biomarker further comprises one or more of Cyfra21-1, HGF or IGFBP-1.

10. The chip, kit, test paper, membrane strip or device according to claim 9, characterized in that: The reagents for detecting biomarkers include reagents for detecting the presence or expression level of biomarkers.

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