CN115074439A - Group of NK/T cell lymphoma prognosis related genes, genome prognosis model and application thereof - Google Patents
Group of NK/T cell lymphoma prognosis related genes, genome prognosis model and application thereof Download PDFInfo
- Publication number
- CN115074439A CN115074439A CN202210660332.7A CN202210660332A CN115074439A CN 115074439 A CN115074439 A CN 115074439A CN 202210660332 A CN202210660332 A CN 202210660332A CN 115074439 A CN115074439 A CN 115074439A
- Authority
- CN
- China
- Prior art keywords
- prognosis
- cell lymphoma
- model
- genome
- genes
- 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.)
- Granted
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
- G16B40/20—Supervised data analysis
-
- 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
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Genetics & Genomics (AREA)
- Biophysics (AREA)
- Public Health (AREA)
- Biotechnology (AREA)
- Analytical Chemistry (AREA)
- Data Mining & Analysis (AREA)
- Organic Chemistry (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Zoology (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Wood Science & Technology (AREA)
- Molecular Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Immunology (AREA)
- Epidemiology (AREA)
- Oncology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Hospice & Palliative Care (AREA)
- Bioethics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Biochemistry (AREA)
- Microbiology (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Primary Health Care (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a group of NK/T cell lymphoma prognosis related genes, which comprise: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1. The invention also provides a genome prognosis model of the NK/T cell lymphoma, a method for constructing the genome prognosis model of the NK/T cell lymphoma, application of the NK/T cell lymphoma prognosis related gene to construction of the genome prognosis model and a detection kit of the NK/T cell lymphoma prognosis related gene. The invention can be used for accurately predicting the prognosis of NK/T cell lymphoma patients, and can combine and improve the existing NK/T cell lymphoma prognosis scoring system to ensure that the system has better evaluation effect.
Description
Technical Field
The invention relates to the field of gene mutation detection, in particular to a group of genes related to NK/T cell lymphoma prognosis, a genome prognosis model constructed by the genes and application of the genome prognosis model.
Background
NK/T cell lymphoma is a non-hodgkin lymphoma, and NK cells and cytotoxic T cells derived from lymphocytes often invade the lymphoid organs around the nose, often occurring in young men. There are currently a number of prognostic scoring systems that have been applied to NK/T cell lymphomas. Among them, the International Prognostic score (IPI), the Prognostic Index of NK/T Cell Lymphoma (PINK) and the improved PINK-E of Lymphoma were well-validated, and a series of corresponding treatments were proposed for the high-risk and low-risk groups of NK/T Cell Lymphoma patients. The risk factors included in these prognostic scoring systems are primarily clinical data for patients, including advanced age, advanced patients, extranodal involvement greater than 1, and non-nasal types.
However, currently a subset of patients lack sufficiently effective risk stratification to guide clinical treatment, and therefore risk stratification and prognostic assessment for NK/T cell lymphoma patients still needs further improvement.
With the increasing convenience of the next-generation sequencing technology and the increasing promotion of genetic analysis means, some recent researches have revealed potential driving injuries related to the risk of NK/T cell lymphoma, including the genetic polymorphisms of single nucleotide sites of several genes, namely JAK-STAT/NF-kB/MAPK pathway, epigenetic modification molecules, RNA helicase and HLA-DPB1, IL18RAP and HLA-DRB 1. These studies lay a solid foundation for the establishment and perfection of genomic prognosis models.
Disclosure of Invention
The invention aims to solve the technical problems and provides a technical scheme capable of realizing more accurate and effective prognosis judgment on NK/T cell lymphoma patients.
To achieve the above objects, the present invention provides a set of NK/T cell lymphoma prognosis-related genes comprising: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1.
In another aspect, the present invention provides a genomic prognosis model of NK/T cell lymphoma, wherein the genomic prognosis model is constructed based on the following group of genes of interest: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1.
In another aspect, the present invention also provides a method for constructing a genomic prognostic model of NK/T cell lymphoma, comprising the steps of:
step one, performing genome sequencing on an NK/T cell lymphoma tumor sample, and analyzing to obtain all genes with somatic mutation;
step two, combining clinical data of a patient corresponding to a sequencing sample, screening a group of prognosis related genes which are obviously and frequently associated with NK/T cell lymphoma tumor prognosis from all genes with mutation by using LASSO Cox regression, wherein the group of prognosis related genes comprises BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B and NRG 1;
thirdly, constructing the genome prognosis model by using the group of prognosis related genes, analyzing a survival curve, and evaluating the survival rate of NK/T cell lymphoma patients and the efficiency of the genome prognosis model;
step four, carrying out correlation analysis on the genome prognosis model and risk factors contained in the existing NK/T cell lymphoma prognosis scoring system, and checking the independence of the genome prognosis model;
and step five, comparing the genome prognosis model with other risk factors by using multivariate Cox regression analysis to determine the quality of the prognosis effect of the NK/T cell lymphoma.
The method for constructing the genome prognosis model according to the present invention can be used for non-disease diagnosis purposes as well as for disease diagnosis purposes.
Preferably, in the method according to the present invention, in the second step, when the NK/T cell lymphoma patient has a mutation in any of 13 genes of the genomic prognosis model, the patient is assigned a risk score of 1, and as the mutant type group, the patient without the mutation is the wild type group.
Preferably, the method according to the invention, wherein in step four, the existing NK/T cell lymphoma prognosis scoring system incorporated in the analysis comprises, but is not limited to IPI, PINK or PINK-E.
Preferably, the method according to the present invention, wherein after the step five, the method further comprises the step of combining the genomic prognosis model into an existing NK/T cell lymphoma prognosis scoring system to obtain an improved NK/T cell lymphoma prognosis scoring system.
In another aspect, the invention also provides the use of the NK/T cell lymphoma prognosis related gene provided by the invention for constructing a NK/T cell lymphoma genome prognosis model.
In another aspect, the invention also provides the use of the NK/T cell lymphoma prognosis related gene according to the invention for combining and improving the existing NK/T cell lymphoma prognosis scoring system.
In another aspect, the present invention provides a kit for detecting a gene involved in prognosis of NK/T cell lymphoma, wherein the kit comprises a probe for capturing a target gene, and the target gene comprises: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1.
The invention has the following beneficial effects:
1. the novel genome prognosis model constructed by the 13 genes with somatic mutation has obvious prediction value on prognosis of progression-free survival (PFS) and total survival (OS) of NK/T cell lymphoma patients, so that accurate and effective prognosis judgment on NK/T cell lymphoma patients can be realized, poor treatment prognosis can be improved in a targeted manner, the medical level is improved, and the life quality is improved.
2. The genome prognosis model can be used for improving the existing NK/T cell lymphoma prognosis scoring system, has a better prediction effect, further improves the risk stratification and prognosis evaluation of NK/T cell lymphoma patients, and has a good clinical popularization prospect.
Drawings
FIG. 1 shows the specific somatic mutation of 13 genes used for constructing a genomic prognosis model in 100 samples of NK/T cell lymphoma tumors in which mutations were identified (black indicates that the gene in the sample has a mutation, and white indicates no mutation), and the percentage indicated on the right side of the graph is the frequency of the mutation of the corresponding gene in 100 samples.
FIG. 2 is a genome prognostic model that divides NK/T cell lymphoma patients in training set into wild type and mutant type groups, and survival curve analysis shows the difference between the progression-free survival PFS (upper panel, A) and total survival OS (lower panel, B) between the two groups of patients.
FIG. 3 is a genomic prognostic model that divides confirmed concentrated NK/T cell lymphoma patients into wild type and mutant groups, and survival curve analysis shows the difference in progression-free survival PFS (upper panel, A) and total survival OS (lower panel, B) between the two groups of patients.
FIG. 4 is a heat map of the correlation between 8 risk factors, including the genomic prognosis model, and 9 factors, including the genomic prognosis model and the sample national source, with only significant Cram er's V correlation coefficients (P <0.05) shown in the figure, with coefficient magnitudes corresponding to the colors in the right legend.
FIG. 5 is a graph showing the survival rate of PFS without progression of existing IPI (A), PINK (B) and PINK-E (C) prognostic scoring systems and their respective improved IPI-G (D), PINK-G (E) and PINK-E-G (F) prognostic scoring systems after being added to a genomic prognostic model, wherein the larger the value of Harrell's C index (C-index), the higher the efficiency of the prognostic model.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood by those skilled in the art that the examples are only for the understanding of the present invention and should not be construed as the specific limitations of the present invention; unless otherwise specified, the sequencing and analysis methods used in the following examples are conventional methods.
As used herein, "prediction" or "prognosis" refers to predicting the course or outcome of a patient's condition and does not mean that the course or outcome of the patient's condition can be predicted with 100% accuracy. "predicting" or "prognosis" refers to determining whether a certain process or outcome is more likely than not, and does not mean determining the likelihood of the certain process or outcome occurring by comparison to a situation in which the certain process or outcome does not occur. As used herein, a particular process or outcome is more likely to be observed in a patient identified as a mutant by a genomic prognostic model than in a person not displaying that characteristic.
A total of 260 NK/T cell lymphoma tumor samples from the central tumor prevention and treatment center of university of zhongshan and total singapore hospital were collected for genomic sequencing data consisting of 50 whole genome sequencing data and 210 targeted capture sequencing data. All genes were analyzed for the presence of somatic mutations in NK/T cell lymphomas.
In combination with 212 NK/T cell lymphoma patients with corresponding survival data as a training set, LASSOCox regression was used to screen 13 mutated genes, BCOR (NCBI Gene ID: 54880), JAK3(NCBI Gene ID: 3718), KRAS (NCBI Gene ID: 3845), MYH11(NCBI Gene ID: 4629), DCC (NCBI Gene ID: 1630), ITK (NCBI Gene ID: 3702), NOTCH1(NCBI Gene ID: 4851), FAS (NCBI Gene ID: 355), RET (NCBI Gene ID: 5979), BIRC3(NCBI Gene ID: 330), MLLT1(NCBI Gene ID: 4298), LRP1B (NCGene ID: 53353), and NRG1(NCBI ID: 3084), which are associated with high prognosis frequency of NK/T cell lymphoma patients. As shown in FIG. 1, the specific somatic mutation of the 13 genes in 100 samples of NK/T cell lymphoma tumors in which mutations were identified (black indicates that the gene in the sample has a mutation, and white indicates no mutation), and the percentage indicated on the right side of the figure is the frequency of the mutation of the corresponding gene in 100 samples.
A genome prognosis model for predicting the prognosis of NK/T cell lymphoma was constructed by using the 13 objective genes, and when a patient suffering from NK/T cell lymphoma had a mutation of any of the 13 objective genes, the patient was assigned a risk score of 1, and the patient was used as a mutant group, and the patient without the mutation was used as a wild-type group. In other words, when there is a mutation of any gene among the 13 target genes in NK/T cell lymphoma patients, the number of mutations of any gene is 1, and therefore, the number of patients with the mutation type is 1, and the number of patients with the wild type (non-mutation type) is 0, and these patients are grouped.
Statistical tests show that the risk grouping of NK/T cell lymphoma patients by applying the genomic prognosis model can remarkably predict the prognosis of PFS and OS of the patients (PFS and OS: P <0.0001, logarithmic rank test; FIG. 2), and the duplication can be realized in an independent verification set (PFS: P: 0.041, OS: P: 0.011; FIG. 3), which indicates that the genomic prognosis model has high prognosis efficacy.
The existing NK/T cell lymphoma prognosis scoring system mainly comprises IPI, PINK and PINK-E, and the included risk factors comprise advanced age, patients with advanced stage, ECOG physical performance score larger than 1, increased lactate dehydrogenase, extranodal involvement part number larger than 1, non-nasal type and high copy number of serum EB Virus (Epstein-Barr Virus, EBV). And (3) carrying out correlation analysis on the genome prognosis model and the risk factors to calculate a Cram er's V correlation coefficient of the genome prognosis model and the risk factors so as to test whether the genome prognosis model has independence.
Correlation analysis shows that the genome prognosis model is weakly correlated with other risk factors (
FIG. 4), which shows the independence of the genomic prognostic model of the present invention, can be used for risk stratification and prognostic evaluation of NK/T cell lymphoma patients together with other risk factors.
In addition, the multivariate Cox regression analysis compares the efficacy of the genomic prognosis model and other risk factors in predicting the prognosis of NK/T cell lymphoma patients, and the genomic prognosis model has the greatest risk ratio compared with other risk factors (table 1), indicating that the genomic prognosis model of the present invention is the most significantly correlated risk factor with the prognosis of PFS and OS in NK/T cell lymphoma patients.
TABLE 1 Cox regression model test of various risk factors
Risk ratio, 95% confidence interval and significance as univariate/multivariate for NK/T cell lymphoma prognosis
In addition, the genomic prognosis model can be combined with the existing NK/T cell lymphoma prognosis scoring system.
And calculating the sum of the risk score of the genome prognosis model and the score result of the existing NK/T cell lymphoma prognosis scoring system (IPI, PINK and PINK-E) as the improved scoring system (IPI-G, PINK-G and PINK-E-G) for predicting the prognosis of the NK/T cell lymphoma patient.
Through the statistical test of Harrell's C index, compared with the prognosis effects of the original IPI, PINK and PINK-E (respectively corresponding to A, B, C in FIG. 5), the prognosis effects of IPI-G, PINK-G and PINK-E-G (respectively corresponding to A, B, C in FIG. 5) after the genome prognosis model is combined by each scoring system are remarkably improved (P is shown in the figure 5)<0.001,χ 2 Test), which indicates that the genomic prognostic model of the present invention can be used to improve the current NK/T cell lymphoma prognostic scoring system to achieve more accurate prognostic assessment.
The present invention can also provide a kit for detecting a gene involved in the prognosis of NK/T cell lymphoma using the 13 target genes, wherein the kit contains a probe for capturing the target genes, and the target genes include: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1. Thereby, prognostic evaluation of NK/T cell lymphoma is achieved.
It should be noted that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art should also make changes, modifications, additions or substitutions within the spirit and scope of the present invention.
Claims (9)
1. A group of NK/T cell lymphoma prognosis related genes is characterized by comprising: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1.
2. A genomic prognosis model of NK/T cell lymphoma, wherein the genomic prognosis model is constructed based on the following group of genes of interest: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1.
3. A method for constructing a genomic prognostic model of NK/T cell lymphoma, characterized by comprising the steps of:
step one, performing genome sequencing on an NK/T cell lymphoma tumor sample, and analyzing to obtain all genes with somatic mutation;
step two, screening a group of prognosis-related genes which are obviously and frequently associated with the prognosis of NK/T cell lymphoma tumor from all genes with mutation by combining clinical data of a patient corresponding to a sequencing sample by using LASSOCox regression, wherein the group of prognosis-related genes comprises BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B and NRG 1;
thirdly, constructing the genome prognosis model by using the group of prognosis related genes, analyzing a survival curve, and evaluating the survival rate of NK/T cell lymphoma patients and the efficiency of the genome prognosis model;
step four, carrying out correlation analysis on the genome prognosis model and risk factors contained in the existing NK/T cell lymphoma prognosis scoring system, and checking the independence of the genome prognosis model;
and step five, comparing the genome prognosis model with other risk factors by using multivariate Cox regression analysis to determine the quality of the prognosis effect of the NK/T cell lymphoma.
4. The method according to claim 3, wherein in the second step, when the NK/T cell lymphoma patient has a mutation in any one of 13 genes in the genomic prognosis model, the patient is assigned a risk score of 1, and as the mutant type group, the patient without the mutation is the wild type group.
5. The method according to claim 3, wherein in step four, the existing NK/T cell lymphoma prognostic scoring system under analysis includes, but is not limited to IPI, PINK or PINK-E.
6. The method of claim 3, wherein after step five, the method further comprises the step of combining the genomic prognosis model into an existing NK/T cell lymphoma prognosis scoring system to obtain an improved NK/T cell lymphoma prognosis scoring system.
7. Use of the NK/T cell lymphoma prognosis related gene according to claim 1 for constructing a genome prognosis model of NK/T cell lymphoma.
8. Use of the NK/T cell lymphoma prognosis related gene according to claim 1 for binding to and improving the existing NK/T cell lymphoma prognosis scoring system.
9. A kit for detecting genes relevant to NK/T cell lymphoma prognosis, which is characterized in that the kit contains a probe for capturing target genes, and the target genes comprise: BCOR, JAK3, KRAS, MYH11, DCC, ITK, NOTCH1, FAS, RET, BIRC3, MLLT1, LRP1B, and NRG 1.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210660332.7A CN115074439B (en) | 2022-06-13 | 2022-06-13 | Group of NK/T cell lymphoma prognosis related genes, genome prognosis model and application thereof |
| PCT/CN2022/105036 WO2023240725A1 (en) | 2022-06-13 | 2022-07-12 | A set of nk/t cell lymphoma prognosis-related genes, genomic prognostic model and use thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210660332.7A CN115074439B (en) | 2022-06-13 | 2022-06-13 | Group of NK/T cell lymphoma prognosis related genes, genome prognosis model and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115074439A true CN115074439A (en) | 2022-09-20 |
| CN115074439B CN115074439B (en) | 2023-02-28 |
Family
ID=83250578
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210660332.7A Active CN115074439B (en) | 2022-06-13 | 2022-06-13 | Group of NK/T cell lymphoma prognosis related genes, genome prognosis model and application thereof |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115074439B (en) |
| WO (1) | WO2023240725A1 (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015186774A1 (en) * | 2014-06-05 | 2015-12-10 | 公益財団法人がん研究会 | Nk/t cell lymphoma examining method |
| CN109385666A (en) * | 2017-08-02 | 2019-02-26 | 深圳华大基因股份有限公司 | Lymthoma gene trap chip and its application |
| CN110499364A (en) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | A kind of probe groups and its kit and application for detecting the full exon of extended pattern hereditary disease |
| CN111850116A (en) * | 2019-06-06 | 2020-10-30 | 上海交通大学医学院附属瑞金医院 | Gene mutation site group of NK/T cell lymphoma, targeted sequencing kit and application |
| CN112410424A (en) * | 2020-11-04 | 2021-02-26 | 上海交通大学医学院附属瑞金医院 | Detection kit and database building method for NK/T cell lymphoma related genes |
-
2022
- 2022-06-13 CN CN202210660332.7A patent/CN115074439B/en active Active
- 2022-07-12 WO PCT/CN2022/105036 patent/WO2023240725A1/en unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015186774A1 (en) * | 2014-06-05 | 2015-12-10 | 公益財団法人がん研究会 | Nk/t cell lymphoma examining method |
| CN109385666A (en) * | 2017-08-02 | 2019-02-26 | 深圳华大基因股份有限公司 | Lymthoma gene trap chip and its application |
| CN111850116A (en) * | 2019-06-06 | 2020-10-30 | 上海交通大学医学院附属瑞金医院 | Gene mutation site group of NK/T cell lymphoma, targeted sequencing kit and application |
| CN110499364A (en) * | 2019-07-30 | 2019-11-26 | 北京凯昂医学诊断技术有限公司 | A kind of probe groups and its kit and application for detecting the full exon of extended pattern hereditary disease |
| CN112410424A (en) * | 2020-11-04 | 2021-02-26 | 上海交通大学医学院附属瑞金医院 | Detection kit and database building method for NK/T cell lymphoma related genes |
Non-Patent Citations (3)
| Title |
|---|
| QIONG LI: "Plasma circulating tumor DNA assessment reveals KMT2D as a potential poor prognostic factor in extranodal NK/T-cell lymphoma", 《BIOMARKER RESEARCH》 * |
| YANJIE ZHANG: "Frequent Mutations in Natural Killer/T Cell Lymphoma", 《CELLULAR PHYSIOLOGY AND BIOCHEMISTRY》 * |
| 李信阳: "NK_T细胞淋巴瘤相关基因异常的研究进展", 《现代肿瘤医学》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023240725A1 (en) | 2023-12-21 |
| CN115074439B (en) | 2023-02-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112133365B (en) | Gene set for evaluating tumor microenvironment, scoring model and application of gene set | |
| CN106980763B (en) | Screening method of cancer driver gene based on gene mutation frequency | |
| CN107423534B (en) | Method and system for detecting genome copy number variation | |
| CN111278993A (en) | Somatic cell mononucleotide variants from cell-free nucleic acids and applications for minimal residual lesion monitoring | |
| US20200199656A1 (en) | Cell-free dna end characteristics | |
| CN108624650B (en) | Method for judging whether solid tumor is suitable for immunotherapy and detection kit | |
| CN109585017B (en) | Risk prediction algorithm model and device for age-related macular degeneration | |
| WO2023071877A1 (en) | Prediction model, and evaluation system and method for postoperative recurrence risk of urolithiasis | |
| CN114807377A (en) | Application of bladder cancer prognosis survival time marker, evaluation device and computer readable medium | |
| CN116206681A (en) | Method for evaluating prognostic gene pair value of immune infiltration cell model | |
| CN112037863B (en) | Early NSCLC prognosis prediction system | |
| CN115074439B (en) | Group of NK/T cell lymphoma prognosis related genes, genome prognosis model and application thereof | |
| CN116364179A (en) | Colorectal cancer prognosis marker screening system and method and colorectal cancer prognosis risk assessment system | |
| CN106119406B (en) | Genotyping diagnostic kit for multiple granulomatous vasculitis and arteriolositis and using method thereof | |
| CN116469552A (en) | Method and system for breast cancer polygene genetic risk assessment | |
| CN115148364A (en) | Device and computer-readable storage medium for predicting prognosis of DLBCL naive patients based on peripheral blood ctDNA levels | |
| CN114085910A (en) | Diffuse large B cell lymphoma prognosis model based on MYC/BCL2 dual expression and immune microenvironment, application and prognosis method | |
| WO2023011723A1 (en) | Identification of somatic variants | |
| EP3055425B1 (en) | Predicting increased risk for cancer | |
| Wan et al. | Potential clinical impact of metagenomic next-generation sequencing of plasma for cervical spine injury with sepsis in intensive care unit: a retrospective study | |
| KR20240067867A (en) | Determination of lymphocyte abundance in mixed samples | |
| CN108913760B (en) | Method for evaluating and quantifying relevance between single nucleotide polymorphism and specific traits | |
| CN116343902A (en) | Method and system for complex disease polygenic genetic risk assessment | |
| CN116694749A (en) | SNP markers related to radioactive brain injury caused by intensity modulated radiotherapy and application thereof | |
| Ramasubramanian et al. | EVALUATING T-CELL AGE-RELATED IMMUNE PHENOTYPES IN THE CONTEXT OF BIOLOGICAL AGING IN HEALTH AND RETIREMENT STUDY |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |