A biomarker combination, a reagent containing the same, and its application

Abstract

The present invention discloses a biomarker combination, a reagent containing the same, and its use. The biomarkers included in the biomarker combination are detailed herein. Experiments have shown that the protein molecular markers provided herein exhibit significant variations in expression levels in clinical samples of esophageal cancer patients with different treatment responses. Therefore, the protein molecular markers provided herein can be used as a model for predicting the different responses of esophageal cancer patients to platinum-based combination paclitaxel therapy. They exhibit high sensitivity and specificity, providing advantageous technical support for predicting the likelihood of esophageal cancer patients receiving platinum-based combination paclitaxel therapy.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology and diagnosis, and specifically relates to a biomarker combination, a reagent containing the biomarker, and applications thereof. The application specifically relates to a prediction method and system for platinum-based combined paclitaxel-based treatment of esophageal cancer. Background Art

[0002] Esophageal cancer (EC) is a common and fatal cancer with a poor prognosis and a high mortality rate. It is the eighth most common cancer and the sixth leading cause of cancer-related death worldwide. Early detection of esophageal cancer is low, and cure rates are low in late stages, with a five-year survival rate of less than 19%. Surgery is the preferred treatment for esophageal cancer patients. For some patients who are unable to undergo surgery, medication is the only option to improve quality of life or even maintain survival. Despite significant progress in recent years with the development of first-line treatments, second-line therapies, and targeted immunosuppressants such as pembrolizumab (Keytruda), drug tolerance remains a challenge, and the overall prognosis for esophageal cancer treatment remains poor. Clinically, treatment responses to tumors vary significantly from person to person, but there is limited evidence to support personalized drug selection. Biomarkers to guide personalized precision medicine are urgently needed to alleviate drug tolerance issues.

[0003] Therefore, in order to achieve personalized treatment of esophageal cancer, it is necessary to identify subtypes based on molecular genetic and pathological characteristics, and discover and apply corresponding target genes. In addition, in esophageal cancer research, results have been reported that the prognosis of esophageal cancer can be classified according to the subtype of esophageal cancer. Currently, many research patents have emerged that are based on the gene expression levels of the genome and transcriptome to achieve early screening of esophageal cancer, such as esophageal cancer prognostic markers and their applications (Patent No. CN106701992A) and esophageal cancer diagnosis and treatment markers (Patent No. CN105886627B). However, in clinical practice, the first-line treatment for esophageal cancer patients is a platinum-based combined paclitaxel treatment regimen; however, not all patients can benefit from this treatment regimen, and cancer treatment tolerance remains an urgent problem to be solved; there is still a lack of methods to screen those who benefit from different treatment regimens to achieve personalized treatment for cancer patients.

[0004] Proteomics plays a significant role in revealing the complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis, and treatment resistance. Proteomic tumor diagnostics, with its advantages of high sensitivity, strong specificity, and clear underlying mechanisms, has been increasingly used in tumor detection in recent years. Furthermore, the research on these tumor markers is often based on a limited amount of experimental data, involving relatively limited cancer types and sample sizes. Therefore, by collecting proteomic data and utilizing big data analysis methods to establish models that predict treatment effectiveness, it is possible to achieve personalized chemotherapy and have important clinical significance in recommending appropriate treatment options for patients. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a biomarker combination, a reagent containing the same, and applications thereof, in particular, a method and system for predicting platinum-based combined with paclitaxel treatment of esophageal cancer.

[0006] Specifically, the present invention solves the above technical problems through the following technical solutions.

[0007] The present invention provides a biomarker combination comprising the following markers:

[0008] <h2 style=";text-align:left;direction:ltr">ABCB8、ACADSB、ACE、ACOX1、ACP1、ADAM15、AEBP1、ALDH1B1、ALOX15B、ANGPT L2、ANK3、ANP32B、AP1B1、APCS、APOM、APPL1、ARHGDIB、BRCC3、C10orf76、C1 1orf68、C19orf70、C1S、C2orf54、C3orf58、C4BPA、C5orf51、C6orf132、C8G 、CD2BP2、CD63、CDC26、CDCP1、CES1、CETN2、CLPB、CLU、CNPY4、COA3、COL10A 1, CPA3, CPSF3L, CRMP1, CTHRC1, CTSB, CTSZ, DCAKD, DDX54, DHCR7, DHRS9, DPYSL3, DPYSL4, EEA1, EFEMP1, EVPL, F10, F2, FABP6, FBLN1, FBN1, FTH1, GBP 3、GEMIN5、GINS2、GK、GNAI1、GOSR2、GSTM3、H1FX、HDAC1、HIF1AN、HOOK1、HP SE、IGF2BP1、IGF2BP2、IGFALS、IRF2BP2、ITGA5、ITGB2、KBTBD11、KHNYN、KN G1、KRAS、LAMC1、LPCAT2、LPP、LRRC47、LSM4、LUM、LXN、LYZ、MAP1B、MCM5、ME D27、MMP1、MMP2、MMP8、MOB2、MPG、MPHOSPH10、MREG、MRPS18C、MTA3、MXRA7、 NAA20、NACA2、NBN、NCOR1、NDUFA11、NNMT、NOL9、NUDT2、OSBPL2、PADI3、PAK 4、PARP9、PARVA、PDIA5、PFN2、PHACTR4、PLG、POLR2D、PPM1F、PPP2R1B、PPP2 R5C、PRELP、PRPF3、QPCTL、RABL6、RBL1、RPAP3、SAMD9、SERPINA5、SERPINC1 、SERPIND1、SERPINE2、SERPINF1、SHROOM3、SIRT3、SLC12A7、SLC1A3、SLTM、 SMAD2、SMARCD2、SPIN1、SRSF4、STARD7、TAGLN、TALDO1、TFCP2、TIMP3、TMSB 4X、TNIP2、TNS1、TRIM24、TUBB1、UBA7、UBE2I、UBE2M、UGDH、UROD、VTN、WASL、WDFY1 and YES1.

[0009] The present invention also provides a reagent for detecting the expression level of the above-mentioned biomarker combination, wherein the reagent comprises a biomolecule that specifically hybridizes with the above-mentioned biomarker combination;

[0010] Preferably, the biomolecules include one or more selected from primers, probes and antibodies.

[0011] The present invention also provides the use of the above-mentioned biomarker combination or the above-mentioned reagent in preparing a product for predicting the response to platinum-based combined with paclitaxel therapy for esophageal cancer.

[0012] The present invention also provides a kit comprising the biomarker combination or the reagent as described above.

[0013] The present invention also provides use of the above-mentioned kit in preparing a product for predicting the response to platinum-based combined with paclitaxel-based treatment of esophageal cancer.

[0014] The present invention also provides a system for predicting the response of esophageal cancer to platinum-based combined with paclitaxel therapy, the system comprising:

[0015] a data processing module, configured to calculate the biomarker combination data received or input from the patient to obtain a calculation result; and

[0016] The judgment and output module is used to judge whether the calculation result meets the preset judgment conditions and output the prediction result;

[0017] Optionally, in the judgment and output module, when the calculation result meets the judgment condition, the output prediction result is "response to treatment"; when the calculation result does not meet the judgment condition, the output prediction result is "not responding to treatment";

[0018] Optionally, in the data processing module, the data is the expression level information of the patient's biomarker combination as described above;

[0019] Optionally, the expression level information of the biomarker is obtained by sequencing method;

[0020] Optionally, the judgment and output module is preferably a GLM generalized linear model;

[0021] Optionally, in the judgment and output module, the judgment includes the performance of the expression information of the biomarker combination as described above in the model, that is, the sensitive prediction probability and the non-sensitive prediction probability;

[0022] Optionally, the judgment condition is a comparison of the sensitive prediction probability and the non-sensitive prediction probability: 1) if the sensitive prediction probability is greater than the non-sensitive prediction probability, and the sensitive prediction probability is greater than 0.8, the treatment regimen is recommended to the patient; 2) if the non-sensitive prediction probability is greater than the sensitive prediction probability, and the non-sensitive prediction probability is greater than 0.8, the treatment regimen is not recommended; 3) if the sensitive prediction probability or the non-sensitive prediction probability is less than or equal to 0.8, the treatment regimen is not recommended.

[0023] Preferably, the system further comprises a sample extraction module, which extracts sample data and transmits it to the marker combination verification module.

[0024] The present invention also provides a device including a machine learning model, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the functions of the system as described above can be realized.

[0025] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the functions of the system described above can be realized.

[0026] The present invention also provides the use of the above system in preparing a product for predicting the response of esophageal cancer to platinum-based combined paclitaxel therapy;

[0027] Preferably, the product is a chip, a kit, a test paper or a high-throughput sequencing platform.

[0028] The present invention provides a method for predicting treatment response based on clinical sample proteome, the method comprising the following steps:

[0029] (1) Collect clinical samples such as paraffin-embedded tumor tissue sections from patients with esophageal cancer who have different responses to platinum-based combined paclitaxel therapy before treatment;

[0030] (2) Protein preparation from clinical samples of patients with esophageal cancer;

[0031] (3) Detecting protein molecule expression levels in samples from esophageal cancer patients;

[0032] (4) Find the highly expressed proteomic molecules in esophageal cancer patients with different treatment responses and construct a classifier to distinguish them.

[0033] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0034] The reagents and raw materials used in the present invention are commercially available.

[0035] The positive progress effect of the present invention is:

[0036] The above-mentioned protein molecular marker provided by the present invention has been found through experiments to have significant changes in expression levels in clinical samples of esophageal cancer patients with different treatment responses. Therefore, the protein molecular marker provided by the present invention can be used as a model for predicting the different responses of esophageal cancer patients to platinum-based combined paclitaxel treatment. It has the advantages of high sensitivity and high specificity, and provides favorable technical support for predicting the acceptance of esophageal cancer patients for platinum-based combined paclitaxel treatment.

[0037] The development of a corresponding prediction device based on protein molecular markers of clinical samples of esophageal cancer patients with different responses to platinum-based combined paclitaxel treatment has broad scientific research value and can provide personalized treatment for esophageal cancer patients and recommend whether the treatment plan is suitable for the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Displays the prediction accuracy, sensitivity, and specificity of the training set.

[0039] Figure 2 Prediction accuracy, sensitivity, and specificity of the internal validation set are shown. DETAILED DESCRIPTION

[0040] The present invention is further illustrated by way of examples below, but the present invention is not limited to the scope of the examples. Experimental methods in the following examples where specific conditions are not specified were performed according to conventional methods and conditions, or selected according to the product specifications.

[0041] Example 1 Pretreatment of clinical samples before esophageal cancer treatment

[0042] Clinical specimens were formalin-fixed, paraffin-embedded tissue. Sample pretreatment: 3-10 μm thick sections were macroscopically dissected from paraffin blocks, dewaxed with xylene, washed with ethanol, and air-dried to obtain white slides. 3 μm thick sections were stained with hematoxylin-eosin to assess tumor cell content and delineate tumor areas under a tumor microscope. Tumor samples were collected from 10 μm thick sections into centrifuge tubes and frozen at -80°C until further use.

[0043] Example 2 Protein and peptide extraction from clinical samples

[0044] Collect equal amounts of FFPE tissue in EP tubes, add lysis buffer (0.1M Tris-HCL pH8.0, 0.1M DTT, 1mM PMSF), and then grind with a grinding rod for 3 minutes; add sodium dodecyl sulfate (SDS) to a final concentration of 4%, shake at 99°C and 1800rpm for 2-2.5 hours; centrifuge at 12,000g for 5 minutes, collect the supernatant in an EP tube, add 4 volumes of acetone, and place at -20°C for 4 hours or overnight; centrifuge at 12,000g for 1 minute at 4°C, discard the supernatant and retain the precipitate, wash the precipitate three times with cold acetone, and dry the protein precipitate in a clean typhoon; re-dissolve the protein precipitate with 8M Urea and 50mM NH4HCO3 and add it to a FASP tube, and repeatedly centrifuge with 50mM NH4HCO3 to remove Urea; 50μL 50mM NH4HCO3 was added to the FASP tube and incubated at 37°C for 18-20 hours for enzymatic hydrolysis. Peptides were collected by centrifugation at 12,800g for 15 minutes. To increase the yield of peptides, the peptides were washed twice with 200μL MS water and collected together. The peptides were vacuum-dried at 60°C to obtain the peptides required for mass spectrometry detection.

[0045] Example 3 Mass spectrometry detection of clinical samples

[0046] The peptide samples were detected using a Q-Exactive HF-X hybrid quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific, Rockford, IL, USA) and a high-performance liquid chromatography system (EASY nLC 1200, Thermo Fisher). The specific operation was as follows:

[0047] The dried peptide sample was redissolved in solvent A (0.1% formic acid in water) and loaded onto a trap column (100 μm × 2 cm; particle size, 3 μm; pore size, ), and then separated on an analytical column (150 μm × 12 cm, particle size, 1.9 μm; pore size, ), with a gradient of 5-35% mobile phase B (80% acetonitrile and 0.1% formic acid) at a flow rate of 600 nL / min for a total elution time of 75 minutes. MS analysis was performed on the QE-HFX using a full scan (300-1400 m / z, resolution = 12000) with a maximum ion number allowed into the ion trap (automatic gain control target, AGC target) of 3E+06 ions, followed by high-energy collision-induced dissociation (isolation window of 1.6 m / z, collision energy of 27%, AGC target of 5E+04 ions, maximum injection time of 30 ms, and dynamic exclusion set to 18 seconds). Data acquisition was controlled by the liquid chromatography-tandem mass spectrometry system using Xcalibur software (Thermo Scientific).

[0048] Example 4 Data Analysis

[0049] All data were processed using Firmiana. Firmiana is a workflow based on the Galaxy system, consisting of multiple modules, including a user login interface, raw data, identification and quantification, data analysis, and knowledge mining. Raw files were searched against the Refseq protein database maintained by the National Center for Biotechnology Information (NCBI). Trypsin was selected as the proteolytic enzyme, with a maximum of two missed cleavage sites allowed. Fixed modifications were carbamidomethyl (C), and dynamic modifications were protein acetyl (protein N-term) and oxidation (M). The primary search mass tolerance was 20 ppm, and the primary search peptide tolerance was 0.5 Da. False discovery rates (FDRs) for peptide spectrum matches (PSMs) and proteins were both less than 1%. Quantification of identified peptides was calculated as the average of the chromatographic fragment ion peak areas across all reference libraries. Protein quantification was performed using the label-free intensity-based absolute quantification (iBAQ) method. Peak area values ​​were calculated as a fraction of the corresponding protein. The fraction of total (FOT) was used to represent the normalized abundance of a specific protein in the sample. The FOT was defined as the iBAQ of a protein divided by the total iBAQ of all identified proteins in the sample. Proteins with at least one unique peptide and a 1% FDR were selected for further analysis.

[0050] The 164 protein molecular markers (ABCB8, ACADSB, ACE, ACOX1, ACP1, ADAM15, AEBP1, ALDH1B1, ALOX15B, ANGPTL2, ANK3, ANP32B, AP1B1, APCS, APOM, APPL1, ARHGDIB, BRCC3, C10orf76, C11orf68, C19orf70, C1S, C2orf54, C3orf58, C4BPA, C5orf51, C6orf132, C8G, CD2BP2, CD63, CDC26, CDCP1, CES1, etc.) were detected in the clinical samples of esophageal cancer patients from Zhongshan Hospital of Fudan University. 1. CETN2, CLPB, CLU, CNPY4, COA3, COL10A1, CPA3, CPSF3L, CRMP1, CTHRC1, CTSB, CTSZ, DCAKD, DDX54, DHCR7, DHRS9, DPYSL3, DPYSL4, EEA1, EFEMP1, EVP L, F10, F2, FABP6, FBLN1, FBN1, FTH1, GBP3, GEMIN5, GINS2, GK, GNAI1, GOSR2, GSTM3, H1FX, HDAC1, HIF1AN, HOOK1, HPSE, IGF2BP1, IGF2BP2, IGFALS, IRF 2BP2, ITGA5, ITGB2, KBTBD11, KHNYN, KNG1, KRAS, LAMC1, LPCAT2, LPP, LRRC47, LSM4, LUM, LXN, LYZ, MAP1B, MCM5, MED27, MMP1, MMP2, MMP8, MOB2, MPG, M PHOSPH10, MREG, MRPS18C, MTA3, MXRA7, NAA20, NACA2, NBN, NCOR1, NDUFA11, NNMT, NOL9, NUDT2, OSBPL2, PADI3, PAK4, PARP9, PARVA, PDIA5, PFN2, PHACT R4, PLG, POLR2D, PPM1F, PPP2R1B, PPP2R5C, PRELP, PRPF3, QPCTL, RABL6, RBL1, RPAP3, SAMD9, SERPINA5, SERPINC1, SERPIND1, SERPINE2, SERPINF1, SHR OOM3, SIRT3, SLC12A7, SLC1A3, SLTM, SMAD2, SMARCD2, SPIN1, SRSF4, STARD7, TAGLN, TALDO1, TFCP2, TIMP3, TMSB4X, TNIP2, TNS1, TRIM24, TUBB1, UBA7,The relative expression levels of UBE2I, UBE2M, UGDH, UROD, VTN, WASL, WDFY1, and YES1 were used to calculate the prediction accuracy, sensitivity, and specificity. A training set of 60 cases was included with a prediction accuracy of 100%, a diagnostic sensitivity of 100%, and a specificity of 100% (see, Figure 1 ), the remaining 15 cases were used as internal validation set, with a prediction accuracy of 100%, a diagnostic sensitivity of 100%, and a specificity of 100% (see Figure 2 For patients with esophageal cancer to be treated, based on the expression levels of protein molecular markers, the output results of different responses of the patients to the treatment with platinum combined with paclitaxel are obtained, so as to recommend or not recommend the treatment plan to the patients (see Table 1).

[0051] The data is based on a GLM model. In the R language environment, the predict function is used, such as predict(glm.model,test_data,type="prob") . Here, glm.model represents the marker prediction model for the treatment regimen, test_data represents the proteomic expression of the marker combination measured for the patient to be treated, and type="prob" represents the predicted probability of sensitivity or insensitivity. As described above, the marker combination expression values ​​are input into the model, and the predict function is used to output the probability of the patient being sensitive or insensitive to conventional chemotherapy combined with targeted therapy. The output is shown in the table. The sensitivity and insensitivity prediction probabilities for the treatment regimen are used to determine whether to use the treatment regimen. This patent recommends the following reference values: If the sensitivity prediction probability is greater than the insensitivity prediction probability and the sensitivity prediction probability is greater than 0.8, the treatment regimen is recommended; if the insensitivity prediction probability is greater than the sensitivity prediction probability and the insensitivity prediction probability is greater than 0.8, the treatment regimen is not recommended; if either the sensitivity prediction probability or the insensitivity prediction probability is less than or equal to 0.8, the treatment regimen is not recommended.

[0052] Table 1

[0053]

[0054] The above results show that 164 protein molecular markers (ABCB8, ACADSB, ACE, ACOX1, ACP1, ADAM15, AEBP1, ALDH1B1, ALOX15B, ANGPTL2, ANK3, ANP32B, AP1B1, APCS, APOM, APPL1, ARHGDIB, BRCC3, C10orf76, C11orf68, C19orf70, C1S, C2orf54, C3orf58, C4BPA, C5orf51, C6orf132, C8G, CD2BP2, CD63, CDC26, CDCP1, CES1, etc.) in clinical samples of esophageal cancer patients were significantly up-regulated. , CETN2, CLPB, CLU, CNPY4, COA3, COL10A1, CPA3, CPSF3L, CRMP1, CTHRC1, CTSB, CTSZ, DCAKD, DDX54, DHCR7, DHRS9, DPYSL3, DPYSL4, EEA1, EFEMP1, EVPL , F10, F2, FABP6, FBLN1, FBN1, FTH1, GBP3, GEMIN5, GINS2, GK, GNAI1, GOSR2, GSTM3, H1FX, HDAC1, HIF1AN, HOOK1, HPSE, IGF2BP1, IGF2BP2, IGFALS, IRF2 BP2, ITGA5, ITGB2, KBTBD11, KHNYN, KNG1, KRAS, LAMC1, LPCAT2, LPP, LRRC47, LSM4, LUM, LXN, LYZ, MAP1B, MCM5, MED27, MMP1, MMP2, MMP8, MOB2, MPG, MP HOSPH10, MREG, MRPS18C, MTA3, MXRA7, NAA20, NACA2, NBN, NCOR1, NDUFA11, NNMT, NOL9, NUDT2, OSBPL2, PADI3, PAK4, PARP9, PARVA, PDIA5, PFN2, PHACTR 4. PLG, POLR2D, PPM1F, PPP2R1B, PPP2R5C, PRELP, PRPF3, QPCTL, RABL6, RBL1, RPAP3, SAMD9, SERPINA5, SERPINC1, SERPIND1, SERPINE2, SERPINF1, SHR OOM3, SIRT3, SLC12A7, SLC1A3, SLTM, SMAD2, SMARCD2, SPIN1, SRSF4, STARD7, TAGLN, TALDO1, TFCP2, TIMP3, TMSB4X, TNIP2, TNS1, TRIM24, TUBB1, UBA7,UBE2I, UBE2M, UGDH, UROD, VTN, WASL, WDFY1, YES1) can be used to predict whether esophageal cancer patients are suitable for platinum combined with paclitaxel treatment.

[0055] Finally, the above specific implementation method is only used to illustrate the technical solution of the present invention, rather than to limit it.

Claims

1. Use of a reagent for detecting the expression level of a biomarker combination protein in the preparation of a product for predicting the response of esophageal cancer patients to platinum-based combined paclitaxel therapy, characterized in that: The biomarker combination is: <h2 style=";text-align:left;direction:ltr">ABCB8、ACADSB、ACE、ACOX1、ACP1、ADAM15、AEBP1、ALDH1B1、ALOX15B、ANGPT L2、ANK3、ANP32B、AP1B1、APCS、APOM、APPL1、ARHGDIB、BRCC3、C10orf76、C1 1orf68、C19orf70、C1S、C2orf54、C3orf58、C4BPA、C5orf51、C6orf132、C8G 、CD2BP2、CD63、CDC26、CDCP1、CES1、CETN2、CLPB、CLU、CNPY4、COA3、COL10A 1, CPA3, CPSF3L, CRMP1, CTHRC1, CTSB, CTSZ, DCAKD, DDX54, DHCR7, DHRS9, DPYSL3, DPYSL4, EEA1, EFEMP1, EVPL, F10, F2, FABP6, FBLN1, FBN1, FTH1, GBP 3、GEMIN5、GINS2、GK、GNAI1、GOSR2、GSTM3、H1FX、HDAC1、HIF1AN、HOOK1、HP SE、IGF2BP1、IGF2BP2、IGFALS、IRF2BP2、ITGA5、ITGB2、KBTBD11、KHNYN、KN G1、KRAS、LAMC1、LPCAT2、LPP、LRRC47、LSM4、LUM、LXN、LYZ、MAP1B、MCM5、ME D27、MMP1、MMP2、MMP8、MOB2、MPG、MPHOSPH10、MREG、MRPS18C、MTA3、MXRA7、 NAA20、NACA2、NBN、NCOR1、NDUFA11、NNMT、NOL9、NUDT2、OSBPL2、PADI3、PAK 4、PARP9、PARVA、PDIA5、PFN2、PHACTR4、PLG、POLR2D、PPM1F、PPP2R1B、PPP2 R5C、PRELP、PRPF3、QPCTL、RABL6、RBL1、RPAP3、SAMD9、SERPINA5、SERPINC1 、SERPIND1、SERPINE2、SERPINF1、SHROOM3、SIRT3、SLC12A7、SLC1A3、SLTM、 SMAD2、SMARCD2、SPIN1、SRSF4、STARD7、TAGLN、TALDO1、TFCP2、TIMP3、TMSB 4X、TNIP2、TNS1、TRIM24、TUBB1、UBA7、UBE2I、UBE2M、UGDH、UROD、VTN、WASL、WDFY1 and YES1.

2. Use of a reagent for detecting the expression level of a biomarker combination protein in the preparation of a kit for predicting the response of esophageal cancer patients to platinum-based combined paclitaxel therapy, characterized in that: The biomarker combination is: <h2 style=";text-align:left;direction:ltr">ABCB8、ACADSB、ACE、ACOX1、ACP1、ADAM15、AEBP1、ALDH1B1、ALOX15B、ANGPT L2、ANK3、ANP32B、AP1B1、APCS、APOM、APPL1、ARHGDIB、BRCC3、C10orf76、C1 1orf68、C19orf70、C1S、C2orf54、C3orf58、C4BPA、C5orf51、C6orf132、C8G 、CD2BP2、CD63、CDC26、CDCP1、CES1、CETN2、CLPB、CLU、CNPY4、COA3、COL10A 1, CPA3, CPSF3L, CRMP1, CTHRC1, CTSB, CTSZ, DCAKD, DDX54, DHCR7, DHRS9, DPYSL3, DPYSL4, EEA1, EFEMP1, EVPL, F10, F2, FABP6, FBLN1, FBN1, FTH1, GBP 3、GEMIN5、GINS2、GK、GNAI1、GOSR2、GSTM3、H1FX、HDAC1、HIF1AN、HOOK1、HP SE、IGF2BP1、IGF2BP2、IGFALS、IRF2BP2、ITGA5、ITGB2、KBTBD11、KHNYN、KN G1、KRAS、LAMC1、LPCAT2、LPP、LRRC47、LSM4、LUM、LXN、LYZ、MAP1B、MCM5、ME D27、MMP1、MMP2、MMP8、MOB2、MPG、MPHOSPH10、MREG、MRPS18C、MTA3、MXRA7、 NAA20、NACA2、NBN、NCOR1、NDUFA11、NNMT、NOL9、NUDT2、OSBPL2、PADI3、PAK 4、PARP9、PARVA、PDIA5、PFN2、PHACTR4、PLG、POLR2D、PPM1F、PPP2R1B、PPP2 R5C、PRELP、PRPF3、QPCTL、RABL6、RBL1、RPAP3、SAMD9、SERPINA5、SERPINC1 、SERPIND1、SERPINE2、SERPINF1、SHROOM3、SIRT3、SLC12A7、SLC1A3、SLTM、 SMAD2、SMARCD2、SPIN1、SRSF4、STARD7、TAGLN、TALDO1、TFCP2、TIMP3、TMSB 4X、TNIP2、TNS1、TRIM24、TUBB1、UBA7、UBE2I、UBE2M、UGDH、UROD、VTN、WASL、WDFY1 and YES1.

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