A reagent for tumor diagnosis using red blood cell protein

Abstract

The application discloses a reagent for tumor diagnosis by using red blood cell protein. The reagent for detecting protein markers in red blood cells is applied to the preparation of a tumor auxiliary diagnosis reagent. The protein markers in the red blood cells are selected from any one of the following: (1) any one or more of proteins in BAG6, EIF2S3 and AQP1; (2) any one or more of proteins in SMIM1, QDPR, ANXA7, CAPNS1 and HAGH; (3) any one or more of proteins in LAMP2, PSA7, HBD and SODM. The tumor is selected from lung cancer, liver cancer and breast cancer. The application provides a new target for tumor auxiliary diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of medical diagnostics and relates to a reagent for tumor diagnosis using erythrocyte proteins. Background Technology

[0002] Liquid biopsy is a method for diagnosing and monitoring tumors in patients by detecting CTCs, ctDNA, and other related indicators in the blood. This technology overcomes the challenges of clinical sampling, meets the needs of high-frequency patient monitoring, and has the advantage of lower cost compared to puncture biopsy. Therefore, its development has progressed rapidly. In the future, it is expected to be applied in areas such as early tumor screening, dynamic monitoring of cancer patients, and personalized medication guidance, with a broad market prospect. Currently, the main indicators of liquid biopsy are circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomes isolated from the blood. However, the currently used liquid biopsy subjects, whether ctDNA, CTCs, or exosomes, all have drawbacks such as trace amounts, difficulty in isolation, and high isolation costs. Furthermore, ctDNA, CTCs, and tumor-derived exosomes are only released from tumor tissue into the bloodstream when the tumor grows to a certain stage. Therefore, ctDNA, CTCs, and tumor-derived exosomes currently only have significant application value in screening for gene mutations in advanced tumors.

[0003] Red blood cells, the most abundant component of blood, were long thought to play only a role in oxygen and carbon dioxide transport. However, subsequent studies have shown that red blood cells also have crucial functions in tumor development and progression. For example, 1) due to the incomplete vascularization within tumors, red blood cells can cross blood vessels to enter the tumor and directly contact tumor cells through proteins such as galectin-4, thereby participating in the regulation of tumor cell growth; 2) red blood cells participate in the regulation of tumor immunity through various means, including clearing circulating immune complexes, promoting phagocytosis by phagocytes to reduce the immunogenicity around the tumor, and regulating the ability of lymphocytes to adhere to cancer cells to weaken the killing effect of lymphocytes on cancer cells. Studies have shown that during tumor development and progression, red blood cells are not only trained by tumor cells (tumor-educated RBCs) to transport more oxygen to tumor cells, but they can also absorb proteins, DNA, and RNA released from tumor cells at the tumor site, promoting tumor occurrence and development.

[0004] This invention identifies a group of erythrocyte proteins that can be used for tumor diagnosis using proteomics and protein quantification techniques. Summary of the Invention

[0005] The purpose of this invention is to provide the application of reagents for detecting protein markers in erythrocytes in the preparation of auxiliary diagnostic reagents for tumors.

[0006] Another objective of this invention is to provide a tumor-aided diagnostic reagent.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] Application of reagents for detecting protein markers in erythrocytes in the preparation of auxiliary diagnostic reagents for tumors; wherein the protein markers in erythrocytes are selected from any one of the following:

[0009] (1) Any one or more proteins among BAG6, EIF2S3, and AQP1;

[0010] (2) Any one or more proteins from SMIM1, QDPR, ANXA7, CAPNS1, and HAGH;

[0011] (3) Any one or more of LAMP2, PSA7, HBD, and SODM.

[0012] As a preferred embodiment of the present invention, the tumor is selected from lung cancer, liver cancer, or breast cancer.

[0013] Specifically, the application of reagents for detecting one or more proteins in erythrocytes, such as BAG6, EIF2S3, and AQP1, in the preparation of auxiliary diagnostic reagents for lung cancer.

[0014] Application of reagents for detecting one or more proteins of SMIM1, QDPR, ANXA7, CAPNS1, and HAGH in erythrocytes in the preparation of auxiliary diagnostic reagents for liver cancer.

[0015] Application of reagents for detecting one or more proteins of LAMP2, PSA7, HBD, and SODM in erythrocytes in the preparation of auxiliary diagnostic reagents for breast cancer.

[0016] A tumor-associated diagnostic kit comprising reagents for detecting protein markers in erythrocytes; wherein the erythrocyte protein markers are selected from any one of the following:

[0017] (1) Any one or more proteins among BAG6, EIF2S3, and AQP1;

[0018] (2) Any one or more proteins from SMIM1, QDPR, ANXA7, CAPNS1, and HAGH;

[0019] (3) Any one or more of LAMP2, PSA7, HBD, and SODM.

[0020] As a preferred embodiment of the present invention, the tumor is selected from lung cancer, liver cancer, or breast cancer.

[0021] Specifically, a lung cancer auxiliary diagnostic kit includes reagents for detecting protein markers in erythrocytes; the erythrocyte protein markers are selected from any one or more proteins among BAG6, EIF2S3, and AQP1.

[0022] A liver cancer auxiliary diagnostic kit includes reagents for detecting protein markers in erythrocytes; the erythrocyte protein markers are selected from any one or more proteins selected from SMIM1, QDPR, ANXA7, CAPNS1, and HAGH.

[0023] A breast cancer auxiliary diagnostic kit includes reagents for detecting protein markers in erythrocytes; the erythrocyte protein markers are selected from any one or more proteins among LAMP2, PSA7, HBD, and SODM.

[0024] Beneficial effects:

[0025] This invention provides a variety of tumor auxiliary diagnostic biomarkers. The ROC curve AUC shows that reagents for detecting these biomarkers have ideal diagnostic value for related tumor diagnosis and can be used to prepare tumor auxiliary diagnostic reagents.

[0026] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be described in detail here. Attached Figure Description

[0027] Figure 1 Proteomics analysis was used to detect differentially expressed proteins in erythrocytes of lung cancer patients. (A) Principal component analysis (PCA) showed that proteins in erythrocytes could significantly distinguish lung cancer patients (LUAD, marked in blue) from healthy individuals (HD, marked in red). (B) A heatmap showed significant differences in different proteins between erythrocytes of lung cancer patients and healthy individuals. Red indicates proteins with high abundance, and blue indicates proteins with low abundance. (C) Volcano plot analysis was used to analyze differentially expressed proteins between erythrocytes of healthy individuals and lung cancer patients. Red dots represent proteins that increased in erythrocytes of lung cancer patients (fold change greater than 2, p-value less than 0.05), blue dots represent proteins that decreased in erythrocytes of lung cancer patients (fold change less than 0.5, p-value less than 0.05), and black dots represent proteins that did not change in erythrocytes of lung cancer patients and healthy individuals (fold change between 2 and 0.5, p-value greater than or equal to 0.05).

[0028] Figure 2The protein levels of BAG6 (A), EIF2S3 (B), and AQP1 (C) in erythrocytes of lung cancer patients (Tumor), patients with benign pulmonary nodules, and healthy individuals (NOR+BPN).

[0029] Figure 3 ROC curves of BAG6, EIF2S3, and AQP1 proteins in erythrocytes distinguishing between lung cancer patients (Tumor) and patients with benign pulmonary nodules and healthy individuals (NOR+BPN). (AC) ROC curves of BAG6, EIF2S3, and AQP1 proteins in erythrocytes individually distinguishing between lung cancer patients (Tumor) and patients with benign pulmonary nodules and healthy individuals (NOR+BPN). (D) ROC curves of BAG6 and EIF2S3, BAG6 and AQP1, and EIF2S3 and AQP1 in pairs, and BAG6, EIF2S3, and AQP1 in all three combinations, distinguishing between lung cancer patients (Tumor) and patients with benign pulmonary nodules and healthy individuals (NOR+BPN), using binary logistic regression analysis.

[0030] Figure 4 Proteomics analysis was used to detect differentially expressed proteins in erythrocytes of hepatocellular carcinoma (HCC) patients. (A) Principal component analysis (PCA) showed that proteins in erythrocytes could significantly distinguish HCC patients (marked in red) from healthy individuals (HC, marked in blue). (B) A heatmap showed significant differences in different proteins between HCC patients and healthy individuals. Red indicates proteins with high abundance, and blue indicates proteins with low abundance. (C) Volcano plot analysis was used to analyze differentially expressed proteins between healthy individuals and HCC patients. Red dots represent proteins that increased in HCC patients' erythrocytes (fold change greater than 2, p-value less than 0.05), blue dots represent proteins that decreased in HCC patients' erythrocytes (fold change less than 0.5, p-value less than 0.05), and black dots represent proteins that did not change in either HCC patients' or healthy individuals' erythrocytes (fold change between 2 and 0.5, p-value greater than or equal to 0.05).

[0031] Figure 5 The protein levels of SMIM1(A), QDPR(B), ANXA7(C), CAPNS1(D), and HAGH(E) in erythrocytes from patients with liver cancer (HCC), patients with hepatitis (LC), and healthy individuals (HC).

[0032] Figure 6 ROC curves of ANXA7 protein in erythrocytes distinguishing between liver cancer patients and normal individuals (A), and between liver cancer patients and hepatitis patients (B).

[0033] Figure 7Proteomics analysis was used to detect differentially expressed proteins in erythrocytes of breast cancer patients. (A) Principal component analysis (PCA) showed that proteins in erythrocytes could significantly distinguish breast cancer patients (BC, marked in red) from healthy individuals (HC, marked in blue). (B) A heatmap showed significant differences in different proteins between erythrocytes of breast cancer patients and healthy individuals. Red indicates proteins with high abundance, and blue indicates proteins with low abundance. (C) Volcano plot analysis was used to analyze differentially expressed proteins between erythrocytes of healthy individuals and breast cancer patients. Red dots represent proteins that increased in erythrocytes of breast cancer patients (fold change greater than 2, p-value less than 0.05), blue dots represent proteins that decreased in erythrocytes of breast cancer patients (fold change less than 0.5, p-value less than 0.05), and black dots represent proteins that did not change in erythrocytes of both breast cancer patients and healthy individuals (fold change between 2 and 0.5, p-value greater than or equal to 0.05).

[0034] Figure 8 ROC curves for differentiating breast cancer patients from normal individuals when LAMP2 and HBD protein in erythrocytes are used in combination. Detailed Implementation

[0035] Example 1: Proteomics detection of differentially expressed proteins in erythrocytes of lung cancer patients

[0036] 1) Collect 10ml of anticoagulated blood from 3 lung cancer patients and 3 healthy volunteers, centrifuge at 300G for 20min at 4 degrees Celsius, discard the upper plasma layer, and retain the lower blood cell layer.

[0037] 2) Add an equal volume of PBS buffer (Solarbio) to the bottom layer of blood cells, mix well, and slowly add dropwise to a 15ml centrifuge tube containing 5ml of lymphocyte separation medium (Solarbio). Centrifuge at 800G for 30min at 4°C. The cells at the bottom layer are red blood cells. Carefully aspirate the bottom layer of red blood cells using a pipette, then resuspend the red blood cells in 10ml of PBS and centrifuge at 300G for 10min at 4°C. Discard the supernatant and retain the pellet (red blood cells).

[0038] 3) Add 1 ml of erythrocyte protein extraction buffer (40 mM HEPES (Sigma-Aldrich, St. Louis, MO, USA), 2% Triton-x100 (Sigma-Aldrich, St. Louis, MO, USA), 200 Mm NaCl, 40 mM MgCl2, 20 mM EGTA (Sigma-Aldrich, St. Louis, MO, USA), 80 mM β-glycerophosphate (Sigma-Aldrich, St. Louis, MO, USA)) to the erythrocyte pellet, incubate on ice for 30 min, centrifuge at 12000 G at 4 degrees Celsius for 30 min, discard the pellet, retain the supernatant and perform high-throughput proteomics sequencing.

[0039] Test results

[0040] Bioinformatics analysis of erythrocyte proteome sequencing results revealed significant differences between proteins in erythrocytes from lung cancer patients and those from healthy individuals (Table 1). Figure 1 As shown in Figure A, PCA (principal component analysis) analysis revealed that proteins in erythrocytes significantly distinguished lung cancer patients (LUAD, marked in blue) from healthy individuals (HD, marked in red). The heatmap also showed significant differences in the composition of different proteins in the erythrocytes of lung cancer patients and healthy individuals. Figure 1 B). Volcano plot analysis showed that, compared to healthy individuals, certain proteins were significantly elevated in the red blood cells of lung cancer patients ( Figure 1 C, red dots), such as BAG6; some proteins showed significant decreases (Figure 1C, blue dots), such as PRG3 and ATL3.

[0041] Table 1. Differentially expressed proteins in erythrocytes of lung cancer patients and healthy individuals screened by proteomics (fold change greater than 1.5 or less than 0.6, p-value less than 0.05).

[0042]

[0043] Example 2 Quantitative detection of proteins in erythrocytes as diagnostic markers for early lung cancer

[0044] 1) To further verify that erythrocyte protein can be used as a biomarker for lung cancer diagnosis, we collected 1 ml of anticoagulated blood from 154 patients with early-stage lung cancer, 44 patients with benign pulmonary nodules, and 50 healthy individuals. After separating erythrocytes using the same method as in Example 1, erythrocyte protein was extracted using erythrocyte protein extraction solution.

[0045] 2) Purchase BAG6 antibody (proteintech, Cat No: 26417-1-AP), EIF2S3 antibody (proteintech, Cat No: 11162-1-AP), and AQP1 antibody (proteintech, Cat No: 20333-1-AP). Dilute each of these three antibodies to 2000 μg / mL with coating diluent, then add 100 μL to each well of a 96-well plate. Incubate at 37°C for 4 hours, then discard the solution and wash three times with PBS containing 5% fetal bovine serum. Next, add 200 μL of PBS containing 5% fetal bovine serum to each well and block at 37°C for 30 min. After blocking, wash three times with 100 μL of PBS containing 5% fetal bovine serum to each well.

[0046] 3) Add 80 μL of PBS solution containing 5% fetal bovine serum and 20 μL of erythrocyte protein extraction solution to each well. Incubate at 37°C for 1 hour, then discard the solution and wash three times with PBS solution containing 5% fetal bovine serum.

[0047] 4) Purchase BAG6 antibody (proteintech, Cat No: 66661-1-Ig), EIF2S3 antibody (ThermoFisher, Cat No: H00001968-M01), and AQP1 antibody (proteintech, Cat No: 66805-1-Ig). Dilute the above three antibodies to 2000ug / ml with coating diluent, and then add them to the corresponding 96-well empty plate detection wells in step 3. Incubate at 37°C for 1 hour, then discard the solution and wash three times with PBS solution containing 5% fetal bovine serum.

[0048] 5) Purchase horseradish peroxidase-conjugated anti-mouse IgG enzyme-labeled secondary antibody (Anti-Mouse IgG, HRP-Linked Antibody, Biovision, Cat No: 6402-05). Dilute the antibody to 2000 ug / ml with coating diluent, then add it to the 96-well empty plate. Incubate at 37°C for 1 hour, then discard the solution and wash three times with PBS solution containing 5% fetal bovine serum.

[0049] 6) Add 100 μL of TMB-hydrogen peroxide urea solution to each well, incubate at 37°C in the dark for 5 minutes, then add 50 μL of stop solution to each well for color development. Within 20 minutes, read the absorbance at 450 nm using a spectrophotometer. After zeroing the instrument based on the blank wells (without erythrocyte protein extraction solution), calculate the content of the corresponding protein in the erythrocytes of each sample using a standard curve.

[0050] Test results

[0051] After extracting proteins from the isolated red blood cells, the levels of BAG6, EIF2S3, and AQP1 proteins in the red blood cells of each sample were detected using a double-antibody sandwich assay. For example... Figure 2 As shown in AC, the levels of BAG6, EIF2S3, and AQP1 proteins in erythrocytes of lung cancer patients (Tumor) were significantly higher than those in patients with benign pulmonary nodules and healthy individuals (NOR+BPN). Further analysis using receiver operating characteristic (ROC) curves revealed that BAG6, EIF2S3, and AQP1 proteins in erythrocytes could effectively distinguish lung cancer patients (Tumor) from patients with benign pulmonary nodules and healthy individuals (NOR+BPN), with areas under the curves of 0.9839, 0.9793, and 0.6699, respectively. Figure 3 AC). Through binary logistic regression analysis, the areas under the curve (AUC) for distinguishing between lung cancer patients (Tumor), benign lung nodule patients, and healthy individuals (NOR+BPN) when BAG6 was paired with EIF2S3, BAG6 with AQP1, and EIF2S3 with AQP1, and when BAG6, EIF2S3, and AQP1 were combined in three ways, were 0.9943, 0.9850, 0.9832, and 0.9956, respectively. Figure 3 D).

[0052] Example 3: Proteomics detection of differentially expressed proteins in erythrocytes of liver cancer patients

[0053] 10 ml of anticoagulated blood was collected from 3 hepatocellular carcinoma patients and 3 healthy volunteers. The blood was centrifuged at 300 G for 20 min at 4 degrees Celsius, discarding the supernatant plasma and retaining the sub-cell layer. High-throughput proteomics sequencing was performed using the same protocol as in Example 1.

[0054] Test results

[0055] Bioinformatics analysis of erythrocyte proteome sequencing results revealed significant differences between proteins in erythrocytes from liver cancer patients and those from healthy individuals (Table 2). Figure 4 As shown in Figure A, PCA (principal component analysis) analysis revealed that proteins in erythrocytes significantly distinguished hepatocellular carcinoma (HCC, marked in red) from healthy individuals (HC, marked in blue). The heatmap also showed significant differences in the composition of different proteins in the erythrocytes of HCC patients and healthy individuals. Figure 4 B). Volcano plot analysis showed that, compared to healthy individuals, certain proteins were significantly elevated in the red blood cells of liver cancer patients ( Figure 4 C, red dots); some proteins showed a significant decrease (Figure 4C, blue dots).

[0056] Table 2. Differentially expressed proteins in erythrocytes of liver cancer patients and healthy individuals screened by proteomics (fold change greater than 1.5 or less than 0.6, p-value less than 0.05).

[0057] Genes Change ratio (liver cancer vs. healthy individuals) p-value SMIM1 130.2218466 0.006729 QDPR 3.226924727 0.000104 ANXA7 8.810087524 3.05E-07 CAPNS1 1.803012303 0.001008 HAGH 1.624573998 0.001872 GPHN 0.344643063 1.46E-05 STAM 0.479323476 0.003127 NR2C2AP 0.417333289 2.65E-05 MAPK1IP1L 0.417145957 0.000346 CHORDC1 0.273951179 0.000527 CSNK2A1 0.085896129 0.041635 LPIN2 0.302171112 0.001139 AP2M1 0.187402304 2.88E-08 RIC8A 0.3701349 0.023588 CLIC2 0.415355008 0.020685 AIDA 0.103268674 4.45E-07

[0058] Example 4 Quantitative detection of proteins in erythrocytes as diagnostic markers for liver cancer

[0059] 7) To further verify that erythrocyte proteins can be used as... liver cancer For diagnostic biomarkers, we collected data from 50 cases. liver cancer One milliliter of anticoagulated blood from patients, 40 hepatitis patients, and 50 healthy individuals was used to separate red blood cells using the same method as in Example 1. Red blood cell proteins were then extracted using red blood cell protein extraction buffer. The content of the corresponding protein in the red blood cells of each sample was calculated using the same testing method as in Example 2.

[0060] Test results

[0061] After extracting proteins from the isolated red blood cells, the levels of SMIM1, QDPR, ANXA7, CAPNS1, and HAGH proteins in the red blood cells of each sample were detected using a double-antibody sandwich assay. Figure 5 As shown in the AE, the levels of SMIM1, QDPR, ANXA7, CAPNS1, and HAGH proteins in erythrocytes of hepatocellular carcinoma (HCC) patients were significantly higher than those in hepatitis patients (LC) and healthy individuals (HC). Further analysis using receiver operating characteristic (ROC) curves revealed that SMIM1, QDPR, ANXA7, CAPNS1, and HAGH proteins, used alone or in combination, effectively distinguished HCC patients from LC and HC patients, as shown in Table 3. According to the ROC curve analysis, ANXA7 had the largest area under the curve (AUC) in distinguishing HCC patients from LC and HC patients, at 0.7961 and 0.9013, respectively. Figure 6 AB).

[0062] Table 3. Area under the ROC curve for SMIM1, QDPR, ANXA7, CAPNS1, HAGH proteins and their combinations in erythrocytes when distinguishing between hepatocellular carcinoma patients and normal individuals, and between hepatocellular carcinoma patients and hepatitis patients.

[0063] Genes AUC p-value SMIM1 Liver cancer vs. healthy people 0.516 0.3432 to 0.6888 Liver cancer vs. hepatitis patients 0.7 0.5533 to 0.8467 QDPR Liver cancer vs. healthy people 0.836 0.7130 to 0.9590 Liver cancer vs. hepatitis patients 0.7314 0.5833 to 0.8795 ANXA7 Liver cancer vs. healthy people 0.9013 0.8081 to 0.9946 Liver cancer vs. hepatitis patients 0.7961 0.6639 to 0.9283 CAPNS1 Liver cancer vs. healthy people 0.8293 0.7169 to 0.9418 Liver cancer vs. hepatitis patients 0.7863 0.6571 to 0.9154 HAGH Liver cancer vs. healthy people 0.7267 0.5875 to 0.8659 Liver cancer vs. hepatitis patients 0.7078 0.5595 to 0.8561 QDPR+ANXA7 Liver cancer vs. healthy people 0.9013 0.8081 to 0.9946 Liver cancer vs. hepatitis patients 0.7961 0.6639 to 0.9283 QDPR+CAPNS1 Liver cancer vs. healthy people 0.9013 0.8122 to 0.9904 Liver cancer vs. hepatitis patients 0.7863 0.6571 to 0.9154 CAPNS1+ANXA7 Liver cancer vs. healthy people 0.9013 0.8081 to 0.9946 Liver cancer vs. hepatitis patients 0.7961 0.6639 to 0.9283 QDPR+ANXA7+CAPNS1 Liver cancer vs. healthy people 0.9013 0.8081 to 0.9946 Liver cancer vs. hepatitis patients 0.7961 0.6639 to 0.9283

[0064] Example 5: Proteomics detection of differentially expressed proteins in erythrocytes of breast cancer patients

[0065] 1) Collect 10 ml of anticoagulated blood from 3 breast cancer patients and 3 healthy volunteers, centrifuge at 300 G for 20 min at 4 degrees Celsius, discard the upper plasma layer, and retain the lower blood cell layer. Perform high-throughput proteomics sequencing using the same protocol as in Example 1.

[0066] Test results

[0067] Bioinformatics analysis of erythrocyte proteome sequencing results revealed significant differences between proteins in erythrocytes from breast cancer patients and those from healthy individuals (Table 4). Figure 7 As shown in Figure A, PCA (principal component analysis) analysis revealed that proteins in erythrocytes significantly distinguished breast cancer patients (BC, marked in red) from healthy individuals (HC, marked in blue). The heatmap also showed significant differences in the composition of different proteins in the erythrocytes of breast cancer patients and healthy individuals. Figure 7 B). Volcano plot analysis showed that, compared to healthy individuals, certain proteins were significantly elevated in the red blood cells of breast cancer patients ( Figure 7 C, red dots); some proteins showed a significant decrease (Figure 7C, blue dots).

[0068] Table 4. Differentially expressed proteins in erythrocytes of breast cancer patients and healthy individuals screened by proteomics (fold change greater than 1.5 or less than 0.6, p-value less than 0.05).

[0069] Change ratio (breast cancer vs. healthy individuals) p-value PSA7 2.27106 0.012353 HBD 2.826871 0.008921 LAMP2 3.201762 0.019953 SODM 2.99694 0.042083 FN3K 0.228073 0.063343 ADDA 0.208526 0.008828 UBP5 0.408356 0.020154 ALBU 0.160317 0.047152

[0070] Example 6 Quantitative detection of proteins in erythrocytes as diagnostic markers for early lung cancer

[0071] 1) To further verify that erythrocyte proteins can serve as biomarkers for lung cancer diagnosis, we collected 1 ml of anticoagulated blood from 50 breast cancer patients and 50 healthy individuals. After separating erythrocytes using the same method as in Example 2, erythrocyte proteins were extracted using erythrocyte protein extraction buffer. The content of the corresponding protein in the erythrocytes of each sample was calculated using the same testing method as in Example 2.

[0072] Test results

[0073] After extracting proteins from the isolated red blood cells, the levels of LAMP2, PSA7, HBD, and SODM proteins in each sample were detected using a double-antibody sandwich assay. The levels of LAMP2, HBD, and SODM proteins in the red blood cells of breast cancer patients (BC) were significantly higher than those in healthy individuals (HC), while PSA7 showed no significant difference between the two groups. Further analysis using receiver operating characteristic (ROC) curves revealed that LAMP2, HBD, and SODM proteins in red blood cells could effectively distinguish between breast cancer patients (BC) and healthy individuals (HC), with areas under the curves of 0.7299, 0.7321, and 0.7438, respectively (Table 5). Through binary logistic regression analysis, the areas under the curve for distinguishing breast cancer patients (BC) from healthy individuals (HC) when LAMP2 is combined with HBD, LAMP2 with SODM, HBD with SODM in pairs, and LAMP2, HBD with SODM in triplicate are 0.9198, 0.7299, 0.9012, and 0.9198, respectively (Table 5).

[0074] ROC curve analysis showed that the combination of LAMP2 and HBD had the largest area under the curve (AUC) of 0.9198 when distinguishing between breast cancer patients (BC) and normal individuals (HC). Figure 8 ).

[0075] Table 5. Area under the ROC curve for LAMP2, PSA7, HBD, SODM proteins and their combinations in erythrocytes when distinguishing between breast cancer patients and normal individuals.

[0076] Genes AUC p-value LAMP2 Breast cancer vs. healthy people 0.7299 0.5647 to 0.8951 HBD Breast cancer vs. healthy people 0.7321 0.6114 to 0.8528 SODM Breast cancer vs. healthy people 0.7438 0.6527 to 0.8350 LAMP2+HBD Breast cancer vs. healthy people 0.9198 0.8341 to 1.000 LAMP2+SODM Breast cancer vs. healthy people 0.7299 0.5647 to 0.8951 HBD+SODM Breast cancer vs. healthy people 0.9012 0.8052 to 0.9973 LAMP2+HBD+SODM Breast cancer vs. healthy people 0.9198 0.8341 to 1.000 .

Claims

1. Application of reagents for detecting protein markers in erythrocytes in the preparation of auxiliary diagnostic reagents for lung cancer; wherein the protein markers in erythrocytes are any one of BAG6 and EIF2S3, or multiple proteins of BAG6, EIF2S3 or AQP1.

2. A lung cancer auxiliary diagnostic kit, characterized in that, The reagent includes a reagent for detecting protein markers in erythrocytes; the protein markers in erythrocytes are any one of BAG6 and EIF2S3, or multiple proteins of BAG6, EIF2S3 or AQP1.

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