CN117070635A - Application of biomarker combination in preparation of kit for predicting transparent renal cell carcinoma - Google Patents
Application of biomarker combination in preparation of kit for predicting transparent renal cell carcinoma Download PDFInfo
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- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
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- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
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- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
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- C—CHEMISTRY; METALLURGY
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
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Abstract
The application discloses application of a biomarker combination in preparation of a kit for predicting transparent renal cell carcinoma. In particular, the application of a biomarker combination in preparing a kit for predicting and/or diagnosing transparent renal cell carcinoma is disclosed, wherein the biomarker combination consists of 42 biomarkers, and the kit comprises a reagent for detecting the expression level of the biomarkers in the biomarker combination. The biomarker combination has the advantages of high sensitivity and high specificity in predicting early-stage transparent renal cell carcinoma risk, provides favorable technical support for predicting the occurrence and development of transparent renal cell carcinoma, has wide scientific research value and provides great convenience for early-stage clinical diagnosis, intervention treatment and the like.
Description
Technical Field
The application belongs to the field of biomedical technology and diagnosis, and particularly relates to application of a biomarker combination in preparation of a kit for predicting transparent renal cell carcinoma.
Background
Renal cancer is one of the diseases with the fastest growing morbidity and mortality, and is also one of malignant tumors that seriously jeopardize human health. Renal clear cell carcinoma is the most common pathological type, accounting for more than about 80% of all renal cancers.
Clinically, the pathological changes can be found early through B ultrasonic, CT examination, biopsy pathological examination and the like, and the pathological changes can be found early through periodic self-examination. However, these methods have the disadvantages of excessively high false positive rate and relatively late discovery time. Therefore, a high-sensitivity and high-accuracy diagnostic method is urgently needed to achieve early cancer screening.
Proteomics plays a major role in revealing complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis, and tolerance to therapy. Proteomics tumor diagnosis has the advantages of high sensitivity, strong specificity and clear background mechanism, and is increasingly applied to tumor detection in recent years. Moreover, the study of these tumor markers is often based on a certain amount of experimental data, with relatively limited numbers of cancer types and sample sizes involved. In recent years, as proteomics continues to develop, the body fluid proteomics has grown in size. Therefore, by collecting body fluid proteome data and utilizing a big data analysis method, a tumor risk model with wide application range and high accuracy is found, thereby being beneficial to realizing early diagnosis and having important clinical significance for early diagnosis and early treatment of patients.
Disclosure of Invention
The application aims to solve the technical problem that the prior art lacks a biomarker capable of accurately predicting the risk of transparent renal cell carcinoma in an early stage, and provides application of a biomarker combination in preparation of a kit for predicting transparent renal cell carcinoma. The biomarker combination has the advantages of high sensitivity and high specificity in predicting early-stage transparent renal cell carcinoma risk, provides favorable technical support for predicting the occurrence and development of transparent renal cell carcinoma, has wide scientific research value and provides great convenience for early-stage clinical diagnosis, intervention treatment and the like.
The application solves the technical problems through the following technical proposal.
The first aspect of the application provides the use of a biomarker combination for the manufacture of a kit for predicting and/or diagnosing transparent renal cell carcinoma;
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIBP and UFC 1.
In a second aspect, the application provides a reagent for detecting a biomarker combination consisting of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING, SYNM, TOM1, TRIBP and UFC 1.
In some embodiments of the application, the agent is used to detect the expression level of the biomarker combination; the expression level is protein expression level and/or mRNA transcription level.
In some preferred embodiments of the application, the agent is a biomolecular agent that specifically binds to the biomarker, or specifically hybridizes to a nucleic acid encoding the biomarker.
In some embodiments of the application, the biomolecular reagent is selected from the group consisting of a primer, a probe, and an antibody.
In some embodiments of the application, the agent is an agent for genomic, transcriptomic, and/or proteomic sequencing.
In a third aspect, the application provides the use of a reagent for detecting a biomarker combination in the manufacture of a kit for predicting and/or diagnosing transparent renal cell carcinoma;
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIBP and UFC 1.
In some embodiments of the application, the agent is as described in the second aspect.
In a fourth aspect the application provides a kit comprising a reagent according to the second aspect and a biomarker combination, wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYTONM, M1, TROBP and UFC 1.
In a fifth aspect, the application provides a method for detecting transparent renal cell carcinoma for non-diagnostic purposes, the method comprising detecting the expression level of a biomarker combination in a test sample;
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIOBP, and UFC 1;
the expression level is protein expression level and/or mRNA transcription level.
In the application, the non-diagnostic purpose is the purpose of scientific research and pathological data statistics, and the applicable scene comprises verification of whether an animal model is successfully constructed, in-vitro efficacy experiments, epidemiological statistics of tumors and the like.
A sixth aspect of the present application provides a prediction system for risk of transparent renal cell carcinoma, the prediction system comprising a detection module and an analysis and judgment module; the detection module detects the expression level of the biomarker combination in the sample to be detected and transmits the expression level data to the analysis and judgment module; the analysis judging module compares the expression level data with a preset expression level threshold value, processes the expression level data through Firmiana software, presets a machine learning algorithm based on a generalized linear regression model, constructs a prediction model, predicts the probability of the sample suffering from transparent renal cell carcinoma and the probability of the sample not suffering from transparent renal cell carcinoma respectively, judges whether the expression level data accords with preset judging conditions or not, predicts the risk of the sample suffering from transparent renal cell carcinoma, and outputs a prediction result; the judging condition is that the probability of getting the transparent renal cell carcinoma is larger than or equal to the probability of not getting the transparent renal cell carcinoma;
outputting a prediction result of "having a risk of transparent renal cell carcinoma" when the expression level data satisfies the judgment condition; when the expression level data does not meet the judgment condition, namely the probability of suffering from transparent renal cell carcinoma is smaller than the probability of not suffering from transparent renal cell carcinoma, outputting a prediction result of 'no risk of having transparent renal cell carcinoma';
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIOBP, and UFC 1;
the expression level is protein expression level and/or mRNA transcription level.
In some embodiments of the present application, the prediction system is configured to process the expression level data by Firmiana software after the receiving or inputting is completed, and preset as a machine learning algorithm based on a generalized linear regression model, so as to construct the prediction system.
In a preferred embodiment of the present application, the parameters of the generalized linear regression model are: and screening the markers by adopting a backward regression method, and carrying out model training and prediction function prediction by utilizing the train function of the R packet Caret. Preferably, the R-package of the generalized linear regression model includes: model=train (formula, data=train_data, method= "glm", family= 'binominal') (formula: model formula, input molecular combination; train_data: training set); prediction code: prediction (prediction. Model: training set derived predictive model, test_data: internal or external validation set).
In some embodiments of the application, the sample to be tested is a plasma sample.
In some embodiments of the application, the prediction system further comprises a data collection module for collecting expression level data of the biomarker combinations in the test sample.
In some embodiments of the application, the predictive system is a system for predicting early stages of clear renal cell carcinoma.
In some embodiments of the present application, in the analysis and judgment module, the training set parameter is set to 80%, and the verification set parameter is set to 20%.
A seventh aspect of the application provides a computer readable storage medium storing a computer program which, when executed by a processor, performs the functions of the prediction system according to the sixth aspect of the application or performs the steps of the method according to the fifth aspect of the application.
An eighth aspect of the application provides an electronic device comprising a memory storing a computer program for executing the computer program to perform the functions of the prediction system according to the sixth aspect of the application or to perform the steps of the method according to the fifth aspect of the application.
According to the application, through the body fluid protein molecules for screening the transparent renal cell carcinoma, a tumor risk model is established, so that the early diagnosis of the transparent renal cell carcinoma is facilitated.
The application obtains a group of biomarkers capable of predicting the risk of transparent renal cell carcinoma by screening a body fluid proteome, and the screening method comprises the following steps:
(1) Collecting body fluid samples of healthy people and patients with transparent renal cell carcinoma;
(2) Preparing body fluid sample proteins of healthy people and patients with transparent renal cell carcinoma;
(3) Detecting the protein molecule expression level in body fluid samples of healthy people and patients with transparent renal cell carcinoma;
(4) Finding out the protein group molecules with high body fluid specificity of tumor patients, and constructing a classifier to distinguish.
On the basis of conforming to the common knowledge in the field, the above preferred conditions can be arbitrarily combined to obtain the preferred examples of the application.
The reagents and materials used in the present application are commercially available.
The application has the positive progress effects that:
the application has the beneficial effects that:
the biological marker combination is used for risk prediction and detection of the transparent renal cell carcinoma, has the advantages of high sensitivity and high specificity, provides favorable technical support for predicting occurrence and development of the transparent renal cell carcinoma, has wide scientific research value, and provides great convenience for early clinical diagnosis, intervention treatment and the like.
Drawings
Fig. 1 is a schematic view of the area under the ROC curve.
FIG. 2 shows the predicted results of marker combinations in the validation set, including the prediction accuracy, sensitivity and specificity results.
Fig. 3 is a schematic diagram of the structure of a system for predicting risk of transparent renal cell carcinoma.
Fig. 4 is a schematic structural diagram of an electronic device.
Detailed Description
The application is further illustrated by means of the following examples, which are not intended to limit the scope of the application. The experimental methods, in which specific conditions are not noted in the following examples, were selected according to conventional methods and conditions, or according to the commercial specifications.
Examples include 200 normal populations and 130 human plasma samples of transparent renal cell carcinoma. The design and practice of this study has been approved and supervised ethically and written informed consent has been obtained for all patients.
Example 1 screening and validation of combinations of biomarkers for predicting risk of clear renal cell carcinoma
1.1 Separation of plasma
Collecting whole blood sample, mixing in EDTA anticoagulant tube, centrifuging at 4deg.C for 10 min with 1,600Xg, centrifuging, collecting supernatant (blood plasma) in new EP tube, centrifuging at 16,000Xg for 10 min to remove cell debris, packaging blood plasma in centrifuge tube, and freezing at-80deg.C for use.
1.2 Plasma sample pretreatment
To 2. Mu.L of plasma sample was added 100. Mu.L of 50mM ammonium bicarbonate, vortexed and mixed for 1min, the sample was incubated at 95℃for 4 min to thermally denature the protein, cooled to room temperature, 2. Mu.g of pancreatic protease (Trypsin) was added to the system, 18℃was shaken for h at 37℃and then 10. Mu.L of aqueous ammonia was added to the system to stop the enzymatic hydrolysis. Desalting the peptide sample after enzymolysis, pumping, and freezing at-80 ℃ until mass spectrum detection.
1.3 Mass spectrometric detection of ASD plasma samples
The peptide sample was tested using a Orbitrap Fusion Lumos three-in-one high resolution mass spectrometry system (Thermo Fisher Scientific, rockford, USA) in tandem with a high performance liquid chromatography system (EASY-nLC 1200,Thermo Fisher) and mass spectrometry data was obtained for the whole protein corresponding to the peptide sample. The specific operation is as follows:
the nano-flow liquid chromatography is adopted, and the chromatographic column is a self-made C18 chromatographic column (150 μm ID multiplied by 8 cm,1.9 μm/120A filler). The temperature of the column temperature box is 60 ℃. The dry powder peptide was reconstituted with loading buffer (0.1% formic acid in water), separated by column chromatography, eluted with 600 nL/min linear 6-30% mobile phase B (ACN and 0.1% formic acid), and combined with mass spectrometry detection means for data independent acquisition (Data Independent Acquisition, DIA) using a 10 min liquid phase gradient. The DIA mass spectrometry detection parameters were set as follows: the ion mode is positive ions; the resolution of the primary mass spectrum is 30K, the maximum injection time is 20 ms, the AGC Target is 3e6, and the scanning range is 300-1400 m/z; the secondary scanning resolution is 15K, 30 variable isolation windows are acquired, and the collision energy is 27%. The liquid chromatography tandem mass spectrometry system uses Xcalibur software control for data acquisition.
1.4 Data analysis
All data were processed using Firmiana (V1.0). The Firmiana is a workflow based on Galaxy system, and consists of a plurality of functional modules such as a user login interface, raw data, identification and quantification, data analysis, knowledge mining and the like. DIA data was searched against the UniProt human protein database (updated at 2019.12.17, 20406 entries) using DIANN (v 12.1). The mass difference of the parent ion was 20 ppm and the mass difference of the daughter ion was 50 mmu. At most two leaky sites are allowed. The search engine sets cysteine carbamoyl methylation as the fixed modification and N-acetylation and oxidation of methionine as the variable modification. The parent ion charge range is set to +2, +3, and +4. The error discovery rate (False Discovery Rate, FDR) was set to 1%. The results of the DIA data were incorporated into the reference library using SpectraST software. A total of 327 libraries were used as reference libraries.
The identified peptide fragment quantification results are recorded as the average of the peak areas of chromatographic fragment ions in all reference spectra libraries. Protein quantification was performed using the unlabeled absolute intensity-based quantification (Intensity Based Absolute Auantification, iBAQ) method. We calculated the peak area values as part of the corresponding proteins. Total Fraction (FOT) is used to represent normalized abundance of a particular protein in a sample. FOT is defined as the iBAQ of the protein divided by the total iBAQ of all identified proteins in the sample. A protein having at least one proprietary peptide (unique peptide) and 1% FDR is selected.
The Firmiana selected in this embodiment is preset as a machine learning algorithm based on a generalized linear regression model, and a prediction model is constructed to predict the probability of the sample suffering from transparent renal cell carcinoma and the probability of the sample not suffering from transparent renal cell carcinoma, respectively. The parameters of the generalized linear regression model are as follows: and screening the markers by adopting a backward regression method, and carrying out model training and prediction function prediction by utilizing the train function of the R packet Caret. The R-package of the generalized linear regression model includes: model=train (formula, data=train_data, method= "glm", family= 'binominal') (formula: model formula, input molecular combination; train_data: training set); prediction code: prediction (prediction. Model: training set derived predictive model, test_data: internal or external validation set).
Experiments found that there was a significant change in the expression level of a portion of the protein in body fluid samples of tumor patients and healthy persons, calculation AUC (Area Under the ROC Curve) was performed on the relative expression level ROC curves (Receiver Operating Curve) of 42 protein molecular markers (ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, nm, m1, TRIOBP and UFC 1) in plasma samples of patients with clear kidney cell cancer, wherein the training set comprises 108 positive cases, 168 negative cases, auc=0.99, diagnostic sensitivity 100.00%, specificity 100.00% (see fig. 1), and the internal validation set comprises the remaining 22 positive cases, 32 negative cases, diagnostic sensitivity 95%, and specificity 97% (see fig. 2). Analytical methods are described in Karimollah Hajian-Tilaki, receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation, caspian J Intern Med2013; 4 (2): 627-635. Substituting the expression level of the biomarker into a model for an unknown sample to obtain a transparent renal cell carcinoma risk prediction of the sample and outputting a result, wherein when the probability of suffering from the transparent renal cell carcinoma is greater than or equal to the probability of not suffering from the transparent renal cell carcinoma, the result of the prediction is 'having the risk of the transparent renal cell carcinoma'; when the probability of having transparent renal cell carcinoma is smaller than the probability of opaque renal cell carcinoma, the prediction result is output as "no risk of having transparent renal cell carcinoma". The matrix information for the expression levels of each biomarker is shown in table 1.
From the above results, it can be seen that the use of 42 protein molecular markers (ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIBP and UFC 1) in combination in the plasma of tumor patients can be used to predict tumor risk.
Table 1 matrix information of expression levels of markers
Example 2 System for predicting the risk of clear renal cell carcinoma
System 61 for predicting risk of clear renal cell carcinoma: the data processing module 52 and the judging and outputting module 53 further include a data collecting module 51 (fig. 3).
The data collection module 51 is used to collect the expression level data of the biomarker combinations in patient transparent renal cell carcinoma tissue samples and transmit them to the data processing module.
The data processing module 52 is configured to analyze the expression level data of the received or input biomarker combinations according to the data analysis method described in example 1 to obtain a calculation result. Wherein the expression level data of the biomarker combinations can be collected by the data collection module 51, and the expression level data of the biomarker combinations can also be obtained from other sources.
The judging and outputting module 53 is configured to judge whether the calculated result meets a preset judging condition, that is, the risk probability of suffering from transparent renal cell carcinoma is greater than or equal to the risk prediction probability of not suffering from transparent renal cell carcinoma, so as to predict the risk of transparent renal cell carcinoma, and output a prediction result; wherein, in the judging and outputting module, when the expression level data satisfies the judging condition that the risk probability of suffering from transparent renal cell carcinoma is greater than or equal to the risk prediction probability of not suffering from transparent renal cell carcinoma, outputting a prediction result as "having a risk of transparent renal cell carcinoma"; and outputting a prediction result as 'no risk of transparent renal cell carcinoma' when the expression level data does not meet the judgment condition and the risk probability of the transparent renal cell carcinoma is smaller than the risk prediction probability of the non-transparent renal cell carcinoma.
Example 3 electronic device
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (e.g., may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the method for predicting risk of transparent renal cell carcinoma in embodiment 1 of the present application when executing the computer program.
Fig. 4 shows a schematic diagram of a hardware structure of the present embodiment, where the electronic device specifically includes:
at least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
the bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 further includes a program having a set (at least one) of program modules 924 and/or program means 925, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as the data analysis method of embodiment 1 of the present application, by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Embodiment 4 computer-readable storage Medium
An embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of predicting risk of transparent renal cell carcinoma in embodiment 1 of the present application.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the application may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the method for predicting risk of transparent renal cell carcinoma in embodiment 1 of the present application, when said program product is run on the terminal device.
Wherein the program code for carrying out the application may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
Finally, the above embodiments are only for illustrating the technical solution of the present application, and are not limiting.
Biomarker name: (reference may be made to NCBI or genegards database)
ADPRS：ADP-ribosylserine hydrolase，Gene ID: 54936
AKR1C1：aldo-keto reductase family 1 member C1，Gene ID: 1645
ANKHD1：ankyrin repeat and KH domain containing 1，Gene ID: 54882
ANKRD17：ankyrin repeat domain 17，Gene ID: 26057
B3GAT3：beta-1,3-glucuronyltransferase 3，Gene ID: 26229
BHMT2：betaine--homocysteine S-methyltransferase 2，Gene ID: 23743
BLVRB：biliverdin reductase B，Gene ID: 645
CA1：carbonic anhydrase 1，Gene ID: 759
CAT：catalase，Gene ID: 847
CCNT1：cyclin T1，Gene ID: 904
CDC37：cell division cycle 37，Gene ID: 11140
CLIC1：chloride intracellular channel 1，Gene ID: 1192
COPS5：COP9 signalosome subunit 5，Gene ID: 10987
CSTF1：cleavage stimulation factor subunit 1，Gene ID: 1477
DDT：D-dopachrome tautomerase，Gene ID: 1652
EIF5A2：eukaryotic translation initiation factor 5A2，Gene ID: 56648
EIF5AL1：eukaryotic translation initiation factor 5A like 1，Gene ID: 143244
GCLC：glutamate-cysteine ligase catalytic subunit，Gene ID: 2729
GFER：growth factor, augmenter of liver regeneration，Gene ID: 2671
GLO1：glyoxalase I，Gene ID: 2739
HBB：hemoglobin subunit beta，Gene ID: 3043
HBE1：hemoglobin subunit epsilon 1，Gene ID: 3046
HPRT1：hypoxanthine phosphoribosyltransferase 1，Gene ID: 3251
MACF1：microtubule actin crosslinking factor 1，Gene ID: 23499
MSL1：MSL complex subunit 1，Gene ID: 339287
MTPN：myotrophin，Gene ID: 136319
PACSIN2：protein kinase C and casein kinase substrate in neurons 2，Gene ID: 11252
PBLD：phenazine biosynthesis like protein domain containing，Gene ID: 64081
PGLS：6-phosphogluconolactonase，Gene ID: 25796
PLBD2：phospholipase B domain containing 2，Gene ID: 196463
PSMB8：proteasome 20S subunit beta 8，Gene ID: 5696
PSMD6：proteasome 26S subunit, non-ATPase 6，Gene ID: 9861
RPS23：ribosomal protein S23，Gene ID: 6228
S100A4：S100 calcium binding protein A4，Gene ID: 6275
SLC2A1：solute carrier family 2 member 1，Gene ID: 6513
SMARCA5：SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 5，Gene ID: 8467
SMC1A：structural maintenance of chromosomes 1A，Gene ID: 8243
STING1：stimulator of interferon response cGAMP interactor 1，Gene ID: 340061
SYNM：synemin，Gene ID: 23336
TOM1：target of myb1 membrane trafficking protein，Gene ID: 10043
TRIOBP：TRIO and F-actin binding protein，Gene ID: 11078
UFC1：ubiquitin-fold modifier conjugating enzyme 1，Gene ID: 51506。
Claims (15)
1. Use of a biomarker combination in the preparation of a kit for predicting and/or diagnosing transparent renal cell carcinoma;
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIBP and UFC 1.
2. A reagent for detecting a biomarker combination, characterized in that the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIOBP and UFC 1.
3. The reagent of claim 2, wherein the reagent is used to detect the expression level of the biomarker combination;
the expression level is protein expression level and/or mRNA transcription level.
4. The agent of claim 3, wherein the agent is a biomolecular agent that specifically binds to the biomarker, or specifically hybridizes to a nucleic acid encoding the biomarker.
5. The reagent of claim 4, wherein the biomolecular reagent is selected from the group consisting of a primer, a probe and an antibody.
6. The reagent of claim 3, wherein the reagent is a reagent for genomic, transcriptome, and/or proteomic sequencing.
7. Use of a reagent for detecting a biomarker combination in the preparation of a kit for predicting and/or diagnosing transparent renal cell carcinoma;
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIBP and UFC 1.
8. The use according to claim 7, wherein the agent is as claimed in any one of claims 3 to 6.
9. A kit comprising the reagent of any one of claims 2-6 and a biomarker combination, wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, nm, TOM1, TRIOBP, and C1.
10. A method for detecting transparent renal cell carcinoma for non-diagnostic purposes, comprising detecting the expression level of a biomarker combination in a test sample;
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIOBP, and UFC 1;
the expression level is protein expression level and/or mRNA transcription level.
11. A prediction system for risk of transparent renal cell carcinoma, which is characterized by comprising a detection module and an analysis and judgment module; the detection module detects the expression level of the biomarker combination in the sample to be detected and transmits the expression level data to the analysis and judgment module; the analysis judging module processes the expression level data through Firmiana software, presets a machine learning algorithm based on a generalized linear regression model, constructs a prediction model, predicts the probability of the sample suffering from transparent renal cell carcinoma and the probability of the sample not suffering from transparent renal cell carcinoma respectively, judges whether the expression level data accords with preset judging conditions or not so as to predict the risk of the sample suffering from transparent renal cell carcinoma, and outputs a prediction result; the judging condition is that the probability of getting the transparent renal cell carcinoma is larger than or equal to the probability of not getting the transparent renal cell carcinoma;
outputting a prediction result as having a risk of transparent renal cell carcinoma when the expression level data satisfies the judgment condition; when the expression level data does not meet the judgment condition, namely the probability of suffering from transparent renal cell carcinoma is smaller than the probability of not suffering from transparent renal cell carcinoma, outputting a prediction result to be that the risk of not suffering from transparent renal cell carcinoma is not met;
wherein the biomarker combination consists of ADPRS, AKR1C1, ANKHD1, ANKRD17, B3GAT3, BHMT2, BLVRB, CA1, CAT, CCNT1, CDC37, CLIC1, COPS5, CSTF1, DDT, EIF5A2, EIF5AL1, GCLC, GFER, GLO1, HBB, HBE1, HPRT1, MACF1, MSL1, MTPN, PACSIN2, PBLD, PGLS, PLBD2, PSMB8, PSMD6, RPS23, S100A4, SLC2A1, SMARCA5, SMC1A, STING1, SYNM, TOM1, TRIOBP, and UFC 1;
the expression level is protein expression level and/or mRNA transcription level.
12. The predictive system of claim 11 wherein the sample to be tested is a human plasma sample.
13. The prediction system of claim 11 or 12 further comprising a data collection module for collecting expression level data of the biomarker combinations in the test sample.
14. A computer readable storage medium storing a computer program, which, when executed by a processor, performs the functions of the prediction system of any one of claims 11-13, or performs the steps of the method of claim 10.
15. An electronic device comprising a memory storing a computer program and a processor, wherein the processor is configured to execute the computer program to implement the functionality of the prediction system of any of claims 11-13 or to implement the steps of the method of claim 10.
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