CN115011690A - Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model - Google Patents

Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model Download PDF

Info

Publication number
CN115011690A
CN115011690A CN202210516206.4A CN202210516206A CN115011690A CN 115011690 A CN115011690 A CN 115011690A CN 202210516206 A CN202210516206 A CN 202210516206A CN 115011690 A CN115011690 A CN 115011690A
Authority
CN
China
Prior art keywords
gene
colorectal cancer
marker gene
prediction
predictive model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210516206.4A
Other languages
Chinese (zh)
Inventor
顾晋
雷福明
黄文生
高兆亚
顾国利
汪欣
叶盛威
孟庆凯
刘海义
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shougang Hospital Co ltd
Original Assignee
Shougang Hospital Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shougang Hospital Co ltd filed Critical Shougang Hospital Co ltd
Priority to CN202210516206.4A priority Critical patent/CN115011690A/en
Publication of CN115011690A publication Critical patent/CN115011690A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • 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
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/50—Mutagenesis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • C—CHEMISTRY; METALLURGY
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Abstract

The embodiments of the present application provide a marker gene for post-operative recurrence prediction of stage ii/iii colorectal cancer, including CUL9, PCDHA12, HECTD3, DCX, SMARCA2, FAM193A, AATK, and SORCS 2. The embodiments of the application also provide a first prediction model for post-operative recurrence of stage ii/iii colorectal cancer, the first prediction model being constructed based on the marker gene as described, the first prediction model being a marker gene-based ROC analysis prediction model. The technical scheme provided by the application can predict the recurrence of the II/III stage colorectal cancer postoperative patient at least to a certain extent, so as to carry out early treatment.

Description

Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model
Technical Field
The application relates to the technical field of medicine, in particular to a marker gene and a prediction model for postoperative recurrence prediction of stage II/III colorectal cancer.
Background
Colorectal cancer is one of the most common malignancies in the world. Colorectal cancer is high fifth in incidence of cancer in men and fourth in women in china. The II/III stage colorectal cancer is mainly treated by surgical operation, however, the prognosis of patients after the operation is greatly different, part of patients have relapse or metastasis within 2 years, and part of patients do not relapse within 5 years after the operation.
Therefore, there is a need for a technical solution for prediction of postoperative recurrence of stage ii/iii colorectal cancer for early treatment of patients.
Disclosure of Invention
The embodiment of the application provides a marker gene and a prediction model for predicting postoperative recurrence of colorectal cancer in II/III stage, so that the recurrence prediction of patients can be performed at least to a certain extent, and early treatment can be performed.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to one aspect of the present application, there is provided a marker gene for post-operative recurrence prediction of stage ii/iii colorectal cancer, the marker gene comprising CUL9, PCDHA12, HECTD3, DCX, SMARCA2, FAM193A, AATK, and SORCS 2.
According to one aspect of the present application, there is provided a first predictive model for post-operative recurrence of stage ii/iii colorectal cancer, the first predictive model being constructed based on the marker gene of claim 1, the first predictive model being a marker gene-based ROC analysis predictive model.
According to one aspect of the present application, there is provided a second predictive model for post-operative recurrence of stage ii/iii colorectal cancer, the second predictive model being constructed based on the marker genes of claim 1, the second predictive model being a marker gene cluster analysis-based predictive model.
In some embodiments of the present application, the second predictive model is constructed based on a first genome comprising the following marker genes: CUL9, DCX, AATK, and SORCS 2.
According to one aspect of the present application, there is provided a third predictive model for post-operative recurrence of stage ii/iii colorectal cancer, the third predictive model being constructed based on the marker gene of claim 1, the third predictive model being a marker gene-based linear discriminant predictive model.
In some embodiments of the present application, the third predictive model is constructed based on a second genome, the first genome comprising the following marker genes: CUL9, HECTD3, DCX, and AATK.
According to one aspect of the present application, there is provided a method of analyzing a marker gene for prediction of postoperative recurrence of stage ii/iii colorectal cancer, the method comprising: acquiring a gene mutation data set of colorectal cancer tumor tissue of stage II/III, wherein the gene mutation data set comprises the tumor tissue gene mutation condition of a patient who does not relapse within 5 years after operation and the tumor tissue gene mutation condition of the patient who relapses within 2 years after operation; determining differential genes with different mutation frequencies according to the gene mutation data set, and calculating the statistical P value of each differential gene; arranging the differential genes according to the statistical P values of the differential genes, and taking the differential genes with preset quantity before screening as candidate marker genes; according to the mutation condition of each candidate marker gene, scoring the tumor tissue gene mutation condition of each patient in the gene mutation data set, wherein the value of the mutation existing in the gene is assigned to 1, and the value of the mutation nonexistence in the gene is assigned to 0, so as to obtain the final score of the tumor tissue gene mutation condition of each patient; and (3) evaluating the prediction performance of each candidate marker gene through ROC analysis according to the final score of the tumor tissue gene mutation condition of each patient, and screening to obtain the target marker gene.
Based on the scheme, the application has at least the following advantages or progress effects:
the application provides a marker gene and prediction model for II/III stage colorectal cancer postoperative recurrence prediction, through screening a plurality of marker genes to construct the prediction model based on the marker gene, can carry out effectual postoperative recurrence risk prediction to II/III stage colorectal cancer patient, help colorectal cancer patient postoperative adjunctie therapy's individuation to formulate.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 shows a marker gene ROC plot in one embodiment of the present application;
FIG. 2 shows a graph of a first genome-based cluster analysis prediction model in an embodiment of the present application;
FIG. 3 illustrates a histogram of a second genome-based linear discriminative prediction model in one embodiment of the present application;
FIG. 4 shows a survival analysis graph in one embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
It is noted that the terms first, second and the like in the description and claims of the present application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the objects so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than those illustrated or described herein.
Example 1: screening II/III stage colorectal cancer postoperative recurrence marker gene and ROC prediction model based on marker gene
60 colorectal cancer patients were collected from hospitals such as the first Steel Hospital of Beijing university, who had been pathologically diagnosed, including 30 patients who had relapsed within 2 years after surgery and 30 patients who had not relapsed within 5 years after surgery. Patient postoperative cancer tissue samples and paracancerous samples were subjected to whole exome sequencing with approval from the hospital ethics committee. Samples from 23 patients with recurrence within 2 years after surgery and 24 patients with no recurrence within 5 years after surgery were passed quality control. The somatic mutation map of each patient is obtained through the biogenic analysis, and genes with significant difference in mutation frequencies in two groups are screened by combining clinical and pathological information to construct a recurrence risk prediction model. The specific process is as follows:
exome sequencing:
extracting DNA of colorectal cancer tissues and tissues beside the colorectal cancer, sending the samples to a Zhen and Biometrics company for whole exon sequencing, wherein a whole exon capturing chip is Twist Human core exon, and a sequencer is Illumina NovaSeq 6000.
Processing raw data:
performing quality control filtration on off-line original sequencing data, removing low-quality sequences, and performing genome alignment on all sequencing data by using sequence alignment software BWA and a human reference genome (Hg19) as a template. Identification of SNV mutations using VarDict, FreeBayes to pool heterozygous mutations, and following annotation by ANNOVAR, somatic mutations were screened for following criteria: (i) allele frequency > 5%, (ii) not located in intergenic or intronic regions, (iii) non-synonymous mutations, (iv) support sequence number > -5, (v) population frequency in the outer Aggregation consortium (ExAC) and Genome Aggregation Database (gnomAD) databases < 0.2%.
Postoperative recurrence risk related genes and prediction models:
and (3) counting the gene mutation frequencies of the colorectal cancer patients relapsed within 2 years after the operation and the colorectal cancer patients not relapsed within 5 years after the operation by adopting Fisher accurate test to obtain a gene list with significant difference between the two groups of mutation frequencies, wherein the statistical significance is considered when the P value is less than 0.05.
And (3) according to the statistical P value of each difference gene, arranging from small to large, selecting top50 genes as a candidate marker gene set, scoring each patient according to the mutation condition of the genes, wherein the value of the mutation existing in the genes is 1, the value of the mutation not existing in the genes is 0, and the final score of each patient is the sum of all the gene mutation scores existing in the patient.
The prediction performance of these marker genes was evaluated using ROC analysis, and the optimal 8 genes were iteratively subtracted and screened for establishing a model for risk of recurrence prediction.
Finally, 8 marker genes were screened: CUL9, PCDHA12, HECTD3, DCX, SMARCA2, FAM193A, AATK, and SORCS 2. Patients in this model scored greater than or equal to 1 for low risk of relapse and patients scored less than 1 for high risk of relapse.
Referring to fig. 1, fig. 1 shows a ROC plot of marker genes in one embodiment of the present application, as shown in fig. 1, the area under the curve (AUC) value is 0.973, p is 0.000, 95% CI is 0.928-1.000, the accuracy is 95.74%, and the positive predictive value is 100%.
Detailed results can be shown in table 1:
TABLE 1
Model prediction of recurrence Model prediction of non-recurrence Total up to
Relapse within 2 years of clinical use 21 2 23
The disease does not relapse within 5 years in clinic 0 24 24
Total up to 21 26 47
Example 2: first genome-based cluster analysis prediction model
Based on the data in example 1 and the screened 8 marker genes related to the postoperative recurrence risk of colorectal cancer, clustering analysis is used for postoperative recurrence risk stratification in an iterative deletion mode, and finally a postoperative recurrence risk prediction model based on clustering analysis of a first group of genes is established, wherein the first group of genes comprises CUL9, DCX, AATK and SORCS2, please refer to FIG. 2, and FIG. 2 shows a graph of the clustering analysis prediction model based on the first group of genes in an embodiment of the present application. The overall accuracy of the model was 95.74% with a positive predictive value of 100%.
Example 3: linear discriminant prediction model based on second genome
Based on the data in example 1 and the screened 8 colorectal cancer postoperative recurrence risk related marker genes, performing postoperative recurrence risk stratification by linear discriminant analysis in an iterative deletion mode, and finally establishing a postoperative recurrence risk prediction model based on cluster analysis of a second group of genes, wherein the second genome comprises CUL9, HECTD3, DCX and AATK and the linear discriminant analysis postoperative recurrence risk prediction model. Referring to fig. 3, fig. 3 shows a histogram of a second genome-based linear discriminant prediction model in an embodiment of the present application, where the abscissa is the score and the ordinate is the frequency, the overall accuracy of the model is 91.49% and the positive predictive value is 91.30%.
Example 4: validation of predictive reliability of marker genes provided herein in public databases
Data acquisition:
the genomic data and clinical pathology data of colorectal cancer in the items of colon TCGA and Pan cancer Atlas are selected by using a cBioPotal website platform, and 357 colorectal cancer patients in II/III stage are counted, wherein 92 (26%) patients have at least one genetic variation in the 8 genes.
Survival analysis:
survival analysis was performed in conjunction with clinical data from these patients. Referring to fig. 4, fig. 4 is a graph of survival analysis in one embodiment of the present application, in fig. 4, the abscissa is time to survival without recurrence and the ordinate is proportion of survival without recurrence.
As can be seen from fig. 4, 147 patients had recurrence and survival data, 38 patients had any mutation in these 8 genes and were in the low risk group, and 109 patients had no mutation in these 8 genes and were in the high risk group. The relapse-free survival in the low-risk group was significantly higher than that in the high-risk group (p 0.049, Logrank Test).
To sum up, the marker gene and the prediction model provided by the application can predict postoperative recurrence risk of colorectal cancer patients in stage II/III to a certain extent, and can help postoperative adjuvant therapy of colorectal cancer patients to be formulated individually.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (7)

1. A marker gene for prediction of postoperative recurrence of stage II/III colorectal cancer, comprising CUL9, PCDHA12, HECTD3, DCX, SMARCA2, FAM193A, AATK, and SORCS 2.
2. A first predictive model for post-operative recurrence of stage ii/iii colorectal cancer, the first predictive model being constructed based on the marker gene of claim 1, wherein the first predictive model is a marker gene-based ROC analysis predictive model.
3. A second predictive model for post-operative recurrence of stage ii/iii colorectal cancer, the second predictive model constructed based on the marker gene of claim 1, wherein the second predictive model is a marker gene cluster analysis-based predictive model.
4. The second prediction model of claim 3, wherein the second prediction model is constructed based on a first genome comprising the following marker genes: CUL9, DCX, AATK, and SORCS 2.
5. A third predictive model for post-operative recurrence of stage ii/iii colorectal cancer, the third predictive model being constructed based on the marker gene of claim 1, wherein the third predictive model is a marker gene-based linear discriminant predictive model.
6. The third prediction model of claim 5, wherein the third prediction model is constructed based on a second genome, and the first genome comprises the following marker genes: CUL9, HECTD3, DCX, and AATK.
7. A method for analyzing a marker gene for prediction of postoperative recurrence of stage ii/iii colorectal cancer, the method comprising:
acquiring a gene mutation data set of colorectal cancer tumor tissue at the II/III stage, wherein the gene mutation data set comprises the gene mutation condition of the tumor tissue of a patient who does not relapse within 5 years after operation and the gene mutation condition of the tumor tissue of the patient who relapses within 2 years after operation;
determining differential genes with different mutation frequencies according to the gene mutation data set, and calculating the statistical P value of each differential gene;
arranging the differential genes according to the statistical P values of the differential genes, and taking the differential genes with preset quantity before screening as candidate marker genes;
according to the mutation condition of each candidate marker gene, scoring the tumor tissue gene mutation condition of each patient in the gene mutation data set, wherein the value of the mutation existing in the gene is assigned to 1, and the value of the mutation nonexistence in the gene is assigned to 0, so as to obtain the final score of the tumor tissue gene mutation condition of each patient;
and (3) evaluating the prediction performance of each candidate marker gene through ROC analysis according to the final score of the tumor tissue gene mutation condition of each patient, and screening to obtain the target marker gene.
CN202210516206.4A 2022-05-12 2022-05-12 Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model Pending CN115011690A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210516206.4A CN115011690A (en) 2022-05-12 2022-05-12 Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210516206.4A CN115011690A (en) 2022-05-12 2022-05-12 Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model

Publications (1)

Publication Number Publication Date
CN115011690A true CN115011690A (en) 2022-09-06

Family

ID=83069070

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210516206.4A Pending CN115011690A (en) 2022-05-12 2022-05-12 Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model

Country Status (1)

Country Link
CN (1) CN115011690A (en)

Similar Documents

Publication Publication Date Title
AU2019229273B2 (en) Ultra-sensitive detection of circulating tumor DNA through genome-wide integration
US11621083B2 (en) Cancer evolution detection and diagnostic
Zhu et al. Immune gene expression profiling reveals heterogeneity in luminal breast tumors
JP2022521492A (en) An integrated machine learning framework for estimating homologous recombination defects
KR20210003094A (en) System and method for detection of residual disease
CN109411015B (en) Tumor mutation load detection device based on circulating tumor DNA and storage medium
CN117402950A (en) Method for determining tissue and/or cell type leading to the production of cell-free DNA and method for identifying diseases or disorders using the same
CN107586852B (en) Gastric cancer peritoneal metastasis prediction model based on 22 genes and application thereof
TWI679280B (en) Non-invasive detection of bladder cancer and method for monitoring its recurrence
KR102029393B1 (en) Circulating Tumor DNA Detection Method Using Sample comprising Cell free DNA and Uses thereof
CN112086129A (en) Method and system for predicting cfDNA of tumor tissue
US20210358626A1 (en) Systems and methods for cancer condition determination using autoencoders
JP2015089364A (en) Cancer diagnostic method by multiplex somatic mutation, development method of cancer pharmaceutical, and cancer diagnostic device
CN110408706A (en) It is a kind of assess recurrent nasopharyngeal carcinoma biomarker and its application
CN116312800A (en) Lung cancer characteristic identification method, device and storage medium based on circulating RNA whole transcriptome sequencing in blood plasma
CN115424728A (en) Method for constructing tumor malignant cell gene prognosis risk model
CN115011690A (en) Marker gene for II/III stage colorectal cancer postoperative recurrence prediction and prediction model
CN113362897A (en) Tumor marker screening method based on nucleosome distribution characteristics and application
US11535896B2 (en) Method for analysing cell-free nucleic acids
CN116403648B (en) Small cell lung cancer immune novel typing method established based on multidimensional analysis
EP4318493A1 (en) Artificial-intelligence-based method for detecting tumor-derived mutation of cell-free dna, and method for early diagnosis of cancer, using same
WO2024020036A1 (en) Dynamically selecting sequencing subregions for cancer classification
CN117721200A (en) Kit for detecting TP53 gene mutation sensitive to laryngeal squamous cell carcinoma radiotherapy and application of kit
CN113362884A (en) Tumor marker screening method based on single base substitution characteristics and application
CN116904594A (en) Marker for prognosis layering of IDH mutant astrocytoma and application thereof

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination