Use of a biomarker in the preparation of a product for diagnosing and / or prognosticating esophageal cancer

Abstract

The present invention belongs to the field of biomedical technologies, and particularly relates to the use of a biomarker or a reagent for detecting a biomarker in the preparation of a product for diagnosing and / or prognostically evaluating esophageal cancer. The biomarker includes one or more of SCC, Cyfra21-1, HGF, VEGF, IL-6, IL-8 or IGFBP-1. It has been experimentally verified that the biomarker of the present application has significantly high sensitivity and specificity in the diagnosis and prognostic evaluation of esophageal cancer, and has very good application and research value in the biomedical field.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly to the application of biomarkers in the preparation of products for diagnosing and / or prognostically evaluating esophageal cancer. Background Art

[0002] Esophageal cancer (EC) is one of the major malignant tumors threatening the lives and health of residents. According to histological subtypes, esophageal cancer is divided into esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Currently, the clinical diagnosis of EAC usually includes medical history and physical examination, endoscopy, histopathological examination, and imaging examination. Although esophageal endoscopy is the gold standard for detecting and diagnosing esophageal cancer at present, its invasive method is not conducive to clinical application and popularization. The currently clinically used serum tumor markers such as squamous cell carcinoma antigen (SCCA), carcinoembryonic antigen (CEA), and Cyfra21-1 have insufficient 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 the diagnostic performance.

[0003] DIA-MS (Data-Independent Acquisition Mass Spectrometry) technology is a mass spectrometry analysis 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 for ion selection in a mass spectrometer so as to measure all ions in the sample 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 technology 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 the expression levels of proteins by methods such as fluorescence, chemiluminescence, or mass spectrometry. This technology uses multiple different antibodies immobilized on a microarray chip to simultaneously detect multiple target proteins. 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 microarrays can simultaneously detect and quantify a large number of protein molecules, obtaining the expression levels of multiple proteins in a sample at one time. Antibody microarrays can detect low expression levels of proteins and have good sensitivity for detecting rare or small amounts of proteins. By using specific antibodies, antibody microarrays can identify and detect target proteins with high specificity. Antibody microarrays can simultaneously compare different samples, helping to study differential protein expression and interactions. Antibody arrays have excellent performance and are suitable for knowledge-based bioanalysis, overcoming the sensitivity problems associated with non-targeted proteomic techniques. Antibody arrays are particularly useful for serological analysis because most TAPs are low-abundance cellular effluents such as hormones, cytokines, chemokines, intracellular signaling molecules, and post-translational modifications. Antibody arrays have been applied to find diagnostic signatures for bladder cancer. Their applications in prostate cancer, ovarian cancer, colorectal cancer, etc. have also been confirmed. Antibody microarray technology has a wide range of applications in proteomics research, which can be used to discover and validate biomarkers, understand disease development mechanisms, study signal pathways and protein interaction networks, etc. It plays an important role in understanding the changes in the 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 comprehensively explore the proteomic profile in the plasma of EC patients, combining the existing conditions in our laboratory, we attempted to combine the novel custom antibody chip technology and data-independent acquisition (DIA) mass spectrometry detection technology to establish a serum proteome research platform with deep coverage to find more efficient diagnostic and prognostic biomarkers related to EC. The establishment of this platform aims to find more efficient and accurate diagnostic and prognostic biomarkers related to EC, opening up a new way for the individualized precision diagnosis and treatment research of EC. Summary of the Invention

[0007] The inventors have determined 213 differential genes or proteins related to esophageal cancer through creative labor. By combining statistics and bioinformatics, the systematic changes in the expression levels of differential genes or proteins in esophageal cancer patients can be comprehensively and systematically analyzed, and the mutual relationships between differential genes and proteins and clinical physiological and biochemical indicators can be revealed, thus 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. The sensitivity and specificity are further improved after combined diagnosis.

[0008] In a first aspect of the present invention, there is provided an application of a biomarker or a reagent for detecting a biomarker in the preparation of a product for diagnosing and / or prognosticating esophageal cancer.

[0009] The biomarker includes at least two of SCC, IL-6, VEGF or IL-8. Preferably, the biomarker further includes 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, KRT1, FCGBP, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGLV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, 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, IGLV6-21, 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, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGLV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, 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, IGLKV6-21, 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 combined use of SCC, IL-6, VEGF and IL-8 as biomarkers in the preparation of a product for diagnosing and / or prognosticating esophageal cancer.

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

[0016] The biomarkers include genes or proteins.

[0017] The reagent for detecting the biomarker can be an antibody, such as 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 kit, a test strip, a membrane strip or a device. The device can be selected from liquid phase or mass spectrometry. The chip can include an antibody chip, such as a liquid phase chip (Multiplex bead-based immunoassay by flow cytometry).

[0020] Preferably, the product includes a reagent for detecting the presence or absence or expression level of the biomarker in a test sample.

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

[0022] Preferably, the method for detecting the biomarker in the test sample includes 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, absence or expression level of biomarkers in a sample. More preferably, it further includes the step of comparing the detection result with a threshold value.

[0024] Preferably, the threshold value is obtained from previous experiments, that is, the threshold value is determined by experiments and data analysis of the differences in biomarkers between esophageal cancer patients and healthy people.

[0025] When the expression level of the biomarker described in the present application is different or significantly different from the threshold value, it is diagnosed as having the disease. For example:

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

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

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

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

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

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

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

[0033] When the expression level of the biomarker described in the present application is different or significantly different from the threshold value, a good prognosis or the risk of recurrence is determined. For example:

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

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

[0036] C) The expression level of VEGF higher than or significantly higher than the threshold value indicates a high risk of recurrence; and / or,

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

[0038] Or,

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

[0040] B) The expression level of IL-6 being lower than the threshold or significantly lower than the threshold indicates 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 being 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] In a second aspect of the present invention, there is provided a method for diagnosing and / or prognosticating esophageal cancer, the method comprising detecting biomarkers in a sample of a subject.

[0045] The biomarkers include at least two of SCC, IL-6, VEGF or IL-8. Preferably, the biomarkers further 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 includes detecting the presence or content of the biomarker, such as the expression level of the protein.

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

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

[0051] When the expression level of the biomarker described in the present application has a difference or a significant difference from the threshold, it is diagnosed as the disease. For example:

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

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

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

[0059] When there is a difference or significant difference between the expression level of the biomarker described in the present application and the threshold, the prognosis is determined to be good or the risk of re-disease is determined, for example:

[0060] A) The expression level of SCC being higher than the threshold or significantly higher than the threshold indicates a high risk of re-disease; and / or,

[0061] B) The expression level of IL-6 being higher than the threshold or significantly higher than the threshold indicates a high risk of re-disease; and / or,

[0062] C) The expression level of VEGF being higher than the threshold or significantly higher than the threshold indicates a high risk of re-disease; and / or,

[0063] D) The expression level of IL-8 being lower than the threshold or significantly lower than the threshold indicates a high risk of re-disease.

[0064] Or,

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

[0066] B) The expression level of IL-6 being lower than the threshold or significantly lower than the threshold indicates 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 being 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 chip), ELISA.

[0070] In a third aspect of the present invention, there is provided a chip, kit, test strip, membrane strip or device for esophageal cancer diagnosis and / or prognosis assessment, wherein the chip, kit, test strip, membrane strip or device contains reagents for detecting biomarkers, and the biomarkers include at least two of SCC, IL-6, VEGF or IL-8. Preferably, the biomarkers further include 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, KRT1, FCGBP, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGLV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, 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, IGLV6-21, 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 biomarkers are 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, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGLV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, 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, IGLV6-21, 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 reagent for detecting a biomarker includes a reagent for detecting the presence or absence or the expression level of the biomarker.

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

[0077] In a fourth aspect of the present invention, a biomarker for diagnosing and / or prognosticating esophageal cancer is provided, and the biomarker includes at least two of SCC, IL-6, VEGF, or IL-8.

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

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

[0080] In a specific embodiment of the present invention, the biomarker is 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, KRT1, FCGBP, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGLV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, 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, IGLV6-21, 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 biomarkers are 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, IFNA2, TYR, FN1, APOA2, SERPINF2, KRT14, ATRN, TYRP1, TBX21, RNLS, IGFALS, HRNR, ANG, GPBP1, AFM, HGFAC, IGHV1-3, S100A12, WNT5A, IGFBP3, KRT6A, KRT9, CRP, AR, YWHAZ, BRME1, ALDOB, SAA2, CDH13, MMRN2, MUC5AC, SPP2, PLXDC2, H3F3AP6, CCR7, IGLV2D-24, IGLC3, MENT, OIT3, IGHV3-74, LCN2, 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, IGLKV6-21, 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] As used herein, "diagnosis" refers to ascertaining whether a patient has had, has, or will have a disease or disorder, or to ascertaining the progression or potential future progression of a disease.

[0084] As used herein, "prognosis assessment" refers to evaluating a patient's response to treatment and the future risk of developing a disease.

[0085] As used herein, "subject" can be a human or non-human mammal, and the non-human mammal can be a wild animal, zoo animal, economic animal, pet, laboratory animal, etc. Preferably, the non-human mammals include, but are not limited to, pigs, cows, sheep, horses, donkeys, foxes, raccoon dogs, minks, camels, dogs, cats, rabbits, rats (such as rats, mice, guinea pigs, hamsters, gerbils, chinchillas, squirrels) or monkeys, etc.

[0086] As used herein, "expression level" and "expression amount" can be used interchangeably.

[0087] As used herein, "and / or" includes all combinations of the items connected by this term, and each combination should be considered separately listed in this application. For example, "A and / or B" includes "A", "B", and "A and B"; and for another example, "A, B and / or C" includes "A", "B", "C", "A and B", "A and C", "B and C", and "A and B and C".

[0088] As used herein, "comprising" or "including" is an open-ended description, containing the specified components or steps, as well as other specified components or steps that do not materially affect.

[0089] As used herein, "method" or "application" can be for diagnostic purposes or for non-diagnostic purposes.

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

[0091] Table 1: Correspondence Table of Abbreviations and Full Names

[0092]

[0093] Advantages of the present invention: (1) By combining the antibody chip technology and the data-independent acquisition mode mass spectrometry detection technology, proteins in the sera of esophageal cancer patients and healthy controls were identified, and 7 biomarkers for diagnosing and / or prognosticating esophageal cancer were screened out by combining biostatistical methods. (2) The obtained biomarkers were verified by liquid chip, and the results were consistent with those obtained by combining the antibody chip technology and the data-independent acquisition mode mass spectrometry detection technology. (3) Using the Youden index, the sensitivity and specificity of the biomarkers were determined, and the AUC value was confirmed by the ROC curve. Each biomarker had high specificity and sensitivity for diagnosing esophageal cancer. When 4 biomarkers (SCC, IL-6, VEGF, and IL-8) were used for the combined diagnosis of esophageal cancer, the specificity was as high as 89.1%, and the AUC value reached 0.720. Further, when 7 biomarkers (SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6, and IL-8) were used for the combined diagnosis, the diagnostic specificity for esophageal cancer was 78.2%, the sensitivity was 69.4%, and the AUC value was 0.785. Description of the Drawings

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

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

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

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

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

[0099] Figure 6 : Volcano plot of differential proteins related to esophageal adenocarcinoma.

[0100] Figure 7 : The combined detection of biomarkers by antibody chip and mass spectrometry for esophageal adenocarcinoma can distinguish the healthy group from the esophageal adenocarcinoma group.

[0101] Figure 8 : Expression heat map of biomarkers in the validation cohort.

[0102] Figure 9 : Expression concentration of biomarkers in the validation cohort.

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

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

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

[0106] Figure 13 : Analysis chart of the expression level of IL-8 and the prognosis survival rate of esophageal cancer. Specific implementation manners

[0107] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only partial embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

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

[0109] 1. Sample collection

[0110] A total of 2 clinical cohorts were collected in this application. The discovery cohort included 53 healthy controls (HC) collected from the Sun Yat-sen University Cancer Center and preoperative sera of 50 EAC patients obtained from the Sun Yat-sen University Cancer Center and Peking University Cancer Hospital. The validation cohort was from 55 healthy controls (HC) collected and preoperative sera of 51 EAC patients obtained from the Sun Yat-sen University Cancer Center and Peking University Cancer Hospital, as well as preoperative sera of 158 ESCC patients collected during the same period.

[0111] 2. Experimental design

[0112] By combining the novel customizable antibody microarray technology with data-independent acquisition (DIA) mass spectrometry detection technology (DIA-MS), a serum proteome research platform with deep coverage was established, which can achieve the detection of large-scale proteomes in serum, and the detectable protein concentration range can reach 10 orders of magnitude. Then, the data between samples were standardized by quantile normalization and the Quantile method. The standardized DIA-MS data and antibody microarray data were respectively analyzed by the SAM statistical method (p value < 0.05) to identify differential proteins between healthy controls and EAC patients in the discovery cohort. In the validation cohort, the candidate biomarkers were verified by liquid-phase microarray, and the Mann-Whitney U test was performed using GraphPad Prism 10.0 software for between-group comparison. The combined analysis of biomarkers was performed by binary Logistic regression analysis using SPSS 26.0. The prognostic analysis was carried out by finding the cut-off value using the surv_cutpoint function in the Survminor package in R studio for KM curve plotting.

[0113] 1) DIA-MS analysis

[0114] Taking the discovery cohort as an example, the detection process of serum samples is as Figure 1 shown. The serum samples were centrifuged at 10,000 rpm for 3 min, 2 μL of serum supernatant was added to a 1.5 mL centrifuge tube, and the serum proteins were denatured with 100 μL of 6 mol / L urea lysis solution. Then, 1 μL of 1 mol / L DTT was added and reduced in a 37 °C water bath for 60 min. Subsequently, under light-shielded conditions at 25 °C, 10 μL of 500 mmol / L IAA was used for alkylation treatment for 45 min. The solution was transferred to a 0.5 mL - 30K ultrafiltration tube, and washed three times by centrifugation at 12,000 g with 200 μL of 50 mmol / L NH4HCO3 to remove the excess alkylating reagent, causing the alkylated proteins to precipitate in the ultrafiltration tube. Next, 0.04 mg / mL trypsin was used for enzymatic digestion at 37 °C for 16 h, centrifuged at 12,000 g at room temperature for 15 min, and the filtrate was collected in a centrifuge tube. Another 200 μL of pure water was added to the ultrafiltration tube and centrifuged at 12,000 g for 15 min to collect all the filtrate. After vacuum drying the collected peptide solution for 4 h, it was dissolved in 20 μL of 0.1% formic acid. Finally, the peptide concentration was measured using a DS-11 spectrophotometer with the absorbance set at A280 nm.

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

[0116] The DIA acquisition protocol for the QE-HF mass spectrometer included 45 variable windows with a scanning range of 350 - 1400 m / z, 1 Da overlap, and the parent ion isolation windows were 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, 604 - 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, 739.5 - 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 - 1269 m / z. The DIA parameters were: NCE was 28, the total cycle time was 3.6 s, the resolution was 30000, AGC was 1e6, and the maximum ion injection time was 45 ms.

[0117] The DIA data was analyzed using Spectronaut Pulsar 17 software, and default settings were used if not otherwise specified. The raw DIA data was searched in the above multi-disease spectral library by Spectronaut Pulsar 17.

[0118] 2) Antibody microarray analysis

[0119] Preparation of antibody microarray: The antibody microarray was spotted by the National Protein Science Center (Beijing). Specific antibodies of proteins serving as candidate markers were immobilized on the chip substrate. The serum to be tested passed through the chip surface, and the proteins in the serum bound to their antibodies, and the expression levels of the proteins in the serum were detected. The prepared antibody microarray was stored at -20 °C for standby. The detection indexes included 940 proteins.

[0120] First, protein biotinylation of the serum was performed: 10 μl of serum sample was taken and added to 90 μl of phosphate buffered saline (PBS), and then 1 μl of 20 g / L biotinylation reagent was added. The reaction was carried out at room temperature for 1 h. 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 min, the effluent was collected and 500 μl of 5% skim milk was added and mixed for standby. The antibody microarray was taken out from the -20 °C refrigerator and equilibrated at room temperature for 20 min. A plastic fence was added and blocked with 5% skim milk for 1 h. It was washed 3 times with PBST containing 2% Tween, 10 min each time. The biotin-labeled serum protein sample was added and incubated for 2 h. It was washed 3 times with PBST, 10 min each time. 2 μg / ml Streptavidin-Cy3 solution was added and incubated for 1 h; it was washed 3 times with PBST and 3 times with deionized water, 10 min each time. The plastic fence was removed and the chip was dried. Finally, the chip was scanned by Genepix4300A with a wavelength of 532 nm and a scanning resolution of 10 μm. GenepixPro7 software was used to extract the fluorescence signal values of the chip.

[0121] Chip data processing: The median value of the foreground was subtracted from the median value of the background as the signal value of this target, and the average value of 2 duplicate points of each target protein was used as the fluorescence signal value of this inflammatory factor. The Quantile method was used for normalization to eliminate the differences between different chips.

[0122] 3) Liquid chip analysis

[0123] After the magnetic fluorescent microspheres were successfully conjugated with 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 at room temperature in the dark for 2 h on a plate shaker, and washed 3 times with 100 μL of 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 step was repeated, the microspheres were resuspended in 200 μL of PBS-TBN solution, the microspheres were transferred to a flow tube, and detected by an EasyCell flow cytometer.

[0124] Example 1: Screening of esophageal cancer biomarkers

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

[0126] In the discovery cohort, the standardized DIA-MS data and antibody microarray data were analyzed by SAM statistics (p < 0.05). After removing the duplicates of the proteins detected by the two methods, 213 differentially expressed proteins were obtained as shown in Table 2, of which 67 were up-regulated in EAC samples and 146 were down-regulated in EAC samples ( Figure 6 ).

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

[0128]

[0129]

[0130] According to the degree of difference of the differential proteins in the discovery cohort, 7 proteins with significant differential expression 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 upward trend in esophageal adenocarcinoma patients, and IL-8 showed a downward trend in esophageal adenocarcinoma patients (Figure 7 )。

[0131] Example 2: Further verification of biomarkers

[0132] To further verify the biomarkers, the liquid chip method was used for verification in another independent clinical validation cohort containing 55 HC and 51 EAC. It was found that SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6, and IL-8 among the biomarkers 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. Exemplarily, the differential results of SCC, HGF, IGFBP-1, and IL-8 between esophageal cancer patients and healthy controls are shown in Figure 9 , confirming the correctness of the previous findings.

[0133] In addition, it was also found that SCC, IL-6, VEGF, and IL-8 were highly correlated with prognosis in the sera of 158 ESCC patients in the validation cohort (see Figures 10 - 13 ).

[0134] Example 3: Evaluation of diagnostic efficacy

[0135] To evaluate the diagnostic value of the biomarkers, the Youden index was further used for the validation cohort (ESCC group: 55 healthy controls, 158 ESCC patients; EAC group: 55 healthy controls, 51 EAC patients; EC group: 55 healthy controls, 209 EC patients including 158 ESCC patients and 51 EAC 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 combined analysis was performed on 4 biomarkers (SCC, IL-6, VEGF, IL-8) or 7 biomarkers (SCC, Cyfra21-1, HGF, IGFBP-1, VEGF, IL-6, and IL-8) to form a biomarker combination for diagnosing EC, serving as a comprehensive biomarker panel. This biomarker panel can diagnose EAC and ESCC, and can not only be used as a general biomarker combination for EC, but also predict the prognosis of patients.

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

[0137]

[0138] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, and these simple modifications all fall within 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 prognostically evaluating esophageal cancer, characterized in that, The biomarkers described above include SCC, IL-6, VEGF, and IL-8.

2. The application according to claim 1, characterized in that, The biomarkers described above further include one or more of Cyfra21-1, HGF, or IGFBP-1.

3. The application according to claim 1, wherein The biomarkers described above include SCC, IL-6, VEGF, IL-8, Cyfra21-1, HGF, and IGFBP-1.

4. The application according to claim 1, wherein The products described above include chips, kits, test strips, membrane strips, or devices.

5. The application according to claim 1, characterized in that, The products described above include reagents for detecting the presence, absence, or expression level of biomarkers in a sample.

6. The application according to claim 1, characterized in that, The diagnosis and / or prognosis assessment of esophageal cancer described above includes detecting the presence, absence, or expression level of biomarkers in a sample.

7. The application according to claim 6, wherein The methods for detecting biomarkers in a sample described above include one or more of mass spectrometry, liquid chromatography, antibody chips, or ELISA.

8. The application according to claim 7, wherein The antibody chips described above include liquid chips.

9. The application according to claim 5 or 6, characterized in that, The sample is a plasma sample or a serum sample.

10. The application according to any one of claims 1-4, characterized in that, The esophageal cancer described above includes esophageal squamous cell carcinoma and / or esophageal adenocarcinoma.

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