[go: up one dir, main page]

CN109988708A - A kind of system for carrying out parting to the patient with colorectal cancer - Google Patents

A kind of system for carrying out parting to the patient with colorectal cancer Download PDF

Info

Publication number
CN109988708A
CN109988708A CN201910106934.6A CN201910106934A CN109988708A CN 109988708 A CN109988708 A CN 109988708A CN 201910106934 A CN201910106934 A CN 201910106934A CN 109988708 A CN109988708 A CN 109988708A
Authority
CN
China
Prior art keywords
listed
genes
patient
gene
similarity
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
Application number
CN201910106934.6A
Other languages
Chinese (zh)
Other versions
CN109988708B (en
Inventor
孙恬
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.)
Carbon Logic Biotechnology Foshan Co ltd
Original Assignee
Carbon Logic Biotechnology (zhongshan) 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 Carbon Logic Biotechnology (zhongshan) Co Ltd filed Critical Carbon Logic Biotechnology (zhongshan) Co Ltd
Priority to CN201910106934.6A priority Critical patent/CN109988708B/en
Publication of CN109988708A publication Critical patent/CN109988708A/en
Application granted granted Critical
Publication of CN109988708B publication Critical patent/CN109988708B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • 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

Landscapes

  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Organic Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Engineering & Computer Science (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Zoology (AREA)
  • Genetics & Genomics (AREA)
  • Wood Science & Technology (AREA)
  • Physics & Mathematics (AREA)
  • Biotechnology (AREA)
  • Microbiology (AREA)
  • Molecular Biology (AREA)
  • Hospice & Palliative Care (AREA)
  • Biophysics (AREA)
  • Oncology (AREA)
  • Biochemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

The present invention provides a kind of systems for carrying out parting to the patient with colorectal cancer, carrying out parting to the patient with colorectal cancer using present system can show that significant performance, parting be in patient 5 years of high risk of recurrence type and low risk of recurrence type recurrence-free survival rate have it is dramatically different.

Description

A kind of system for carrying out parting to the patient with colorectal cancer
Technical field
The present invention relates to a kind of systems for carrying out parting to the patient with colorectal cancer.
Background technique
Colorectal cancer is the third-largest common cancer (Ferlay et al., 2013) in the world.The about new disease of one third Example is diagnosed as 2 phase diseases, and 2 phase colorectal cancer patients, 5 years survival rates are about 60-80% after surgery alone excision, therefore, only There are about 25% 2 phase colorectal cancer patients to may require that adjuvant treatment (Labianca et al., 2013).Have after determining operation 2 phase colorectal cancer patients of high risk recurrence can be used to select to have benefited from the patient subgroups of post-operative adjuvant therapy, but It is clinically widely applied patient clinical pathological parameter at present, for example, lymph node numbers, histological grade is tumor-infiltrated Deep, adjacent organs is invaded, can not Accurate Prediction prognosis and accurate selection there is the patient of high risk of recurrence to receive adjuvant treatment. Therefore present invention offer can be to the classifying method of the tumor sample of colorectal cancer patients, so that it is determined that the high recurrence wind The patient of danger and the patient of low risk of recurrence.
Summary of the invention
The purpose of the present invention is to provide a kind of systems for carrying out parting to the patient with colorectal cancer, use System of the invention carries out parting to the patient with colorectal cancer, is the trouble with high risk of recurrence by the patient classification The patient of person and low risk of recurrence, the patient subgroups of high risk of recurrence are the patients that should be treated after surgery.
To achieve the above object, the technical solution taken: a kind of for dividing the patient with colorectal cancer The system of type, including
Data input module, the first biomarker in the tissue samples of the patient for colorectal cancer will to be suffered from Rna expression level with 5 years in have described in the tissue samples of the patient of palindromia and low cell cycle events the first biology The tissue samples of first similarity value and the patient with colorectal cancer between the rna expression level of marker Described in the first biomarker rna expression level with 5 years in first described in the tissue samples of patient that do not recur of disease Second similarity value input model computing module between the rna expression level of biomarker, first biomarker Including at least three gene listed in table 1；For biological by the tissue samples of the patient with colorectal cancer second The rna expression level of marker with 5 years in have institute in the tissue samples of the patient of palindromia and non-low cell cycle events State the third similarity value between the rna expression level of the second biomarker and the patient with colorectal cancer Tissue samples described in the second biomarker rna expression level with 5 years in the tissue samples of patient that do not recur of disease Described in the second biomarker rna expression level between the 4th similarity input model computing module, described second Biomarker includes at least three gene listed in table 2；
Model computation module, including parting model, for similar according to first similarity value, second similarity value, third Property value, the 4th similarity and parting model calculate the patient with colorectal cancer the first parting score value and second point Type score value；The first parting score value=first similarity value-second similarity value, the second parting score value=the Three the-the four similarities of similarity；
As a result output module, for being obtained according to the first parting score value and the second parting of the patient with colorectal cancer Score value determines patient with poor prognosis or has good prognosis, when the first parting score of the patient with colorectal cancer Value >=first similarity threshold value, or/and with colorectal cancer patient the second parting score value >=second similarity threshold value When, patient classification is high risk of recurrence, has poor prognosis；As the first parting score value < of the patient with colorectal cancer First similarity threshold value, and when the second parting score value < second similarity threshold value of the patient with colorectal cancer, patient It is classified as low risk of recurrence, there is good prognosis.
Preferably, first similarity threshold value=0.155, second similarity threshold value=0.076.
The present invention provides a kind of methods for carrying out parting to the patient with colorectal cancer, including following step It is rapid:
(1) the RNA sample extracted from the tumor sample of the patient is provided, the tumor sample includes that colorectal cancer is thin Born of the same parents；
Determine that the rna expression of biomarker in the RNA sample is horizontal, wherein the biomarker includes in table 1 At least three gene listed；
Determine the rna expression level of the biomarker with 5 years in have palindromia and low cell cycle events Similarity a between the rna expression level of the biomarker of patient；
It determines the rna expression level of the biomarker and the biology of the patient of palindromia is not marked in 5 years Similarity b between the rna expression level of will object；
Determine the difference e of above-mentioned similarity a and similarity b；
(2) determine that the rna expression of biomarker in the RNA sample is horizontal, wherein the biomarker includes table At least three gene listed in 2；
It determines the rna expression level of the biomarker and has palindromia and non-low cell cycle events in 5 years Patient the biomarker rna expression level between similarity c；
It determines the rna expression level of the biomarker and the biology of the patient of palindromia is not marked in 5 years Similarity d between the rna expression level of will object；
Determine the difference f of above-mentioned similarity c and similarity d；
(3) if the difference e is higher than similarity threshold or/and the difference f higher than similarity threshold, by the trouble Person is classified as with poor prognosis；If the difference e is lower than the similarity threshold, and the difference f is lower than the phase It is with good prognosis by the patient classification like property threshold value.
The present invention provides a kind of systems for carrying out parting to the patient with colorectal cancer, including
Data input module, third biomarker in the tissue samples of the patient for colorectal cancer will to be suffered from Rna expression level with 5 years in have third described in the tissue samples of the patient of palindromia and low cell cycle events biological The tissue samples of the 5th similarity and the patient with colorectal cancer between the rna expression level of marker Described in third biomarker rna expression level with 5 years in third described in the tissue samples of patient that do not recur of disease The 6th similarity input model computing module between the rna expression level of biomarker, the third biomarker Including at least three gene listed in table 3；For biological by the in the tissue samples of the patient with colorectal cancer the 4th The rna expression level of marker with 5 years in have institute in the tissue samples of the patient of palindromia and non-low cell cycle events State the 7th similarity between the rna expression level of the 4th biomarker and the patient with colorectal cancer Tissue samples described in the 4th biomarker rna expression level with 5 years in the tissue samples of patient that do not recur of disease Described in the 4th biomarker rna expression level between the 8th similarity input model computing module, the described 4th Biomarker includes at least three gene listed in table 4；
Model computation module, including parting model, for according to the 5th similarity, the 6th similarity, the 7th similar Property value, the 8th similarity and parting model calculate the patient with colorectal cancer third parting score value and the 4th point Type score value；The third parting score value=the 5th the-the six similarity of similarity, tetrad segregation types score value=the Seven the-the eight similarities of similarity；
As a result output module, for being obtained according to the third parting score value and tetrad segregation types of the patient with colorectal cancer Score value determines patient with poor prognosis or has good prognosis, when the third parting score of the patient with colorectal cancer Value >=third similarity threshold value, or/and with colorectal cancer patient tetrad segregation types score value >=the 4th similarity threshold When, patient classification is high risk of recurrence, has poor prognosis；As the third parting score value < of the patient with colorectal cancer Third similarity threshold value, and when tetrad segregation types score value four similarity threshold of < of the patient with colorectal cancer, patient It is classified as low risk of recurrence, there is good prognosis.
Preferably, third similarity threshold value=0.198, the 4th similarity threshold=- 0.003.
The present invention provides a kind of methods for carrying out parting to the patient with colorectal cancer, using stablizing cell Core gene spectrum, comprising the following steps:
(1) the RNA sample extracted from the tumor sample of the patient is provided, the tumor sample includes that colorectal cancer is thin Born of the same parents；
Determine that the rna expression of biomarker in the RNA sample is horizontal；Wherein, the biomarker includes in table 3 At least three gene listed；
Determine the rna expression level of the biomarker with 5 years in have palindromia and low cell cycle events Similarity a between the rna expression level of the biomarker of patient；
It determines the rna expression level of the biomarker and the biology of the patient of palindromia is not marked in 5 years Similarity b between the rna expression level of will object；
Determine the difference e of above-mentioned similarity a and similarity b；
(2) determine that the rna expression of biomarker in the RNA sample is horizontal；Wherein, the biomarker includes table At least three gene listed in 4；
It determines the rna expression level of the biomarker and has palindromia and non-low cell cycle events in 5 years Patient the biomarker rna expression level between similarity c；
It determines the rna expression level of the biomarker and the biology of the patient of palindromia is not marked in 5 years Similarity d between the rna expression level of will object；
Determine the difference f of above-mentioned similarity c and similarity d；
(3) if the difference e is higher than similarity threshold or/and the difference f higher than similarity threshold, by the trouble Person is classified as with poor prognosis；If the difference e is lower than the similarity threshold, and the difference f is lower than the phase It is with good prognosis by the patient classification like property threshold value.
Preferably, the colorectal cancer includes the TNM II phase cancer according to TNM stage system.
Preferably, the tissue samples include the expression of colorectal cancer cell or the nucleic acid from colorectal cancer cell The clinically relevant sample of product.
Preferably, the measuring method of the rna expression level includes blot hybridization, quantitative PCR, RNAseq sequencing and micro- battle array Column analysis；Measure the first similarity value, second similarity value, third similarity value, the 4th similarity, the 5th similitude Value, the 6th similarity, the 7th similarity or the 8th similarity method include but be not limited to Euclidean distance, it is graceful Hatton's distance, Chebyshev's distance, Minkowski Distance standardize Euclidean distance, mahalanobis distance, included angle cosine, Hamming distance From, Jie Kade distance, related coefficient or comentropy.Preferably, the first similarity value, second similarity value, are measured Three similarities, the 4th similarity, the 5th similarity, the 6th similarity, the 7th similarity or the 8th similitude The method of value is Pearson correlation coefficient.
The method of the system deviation at least three genes that normalization table 1 is listed includes but is not limited to, gene CPM method is sequenced in chip fRMA method, genetic chip RMA method, RNAseq, and FPKM method is sequenced in RNAseq.
The method of the system deviation at least three genes that normalization table 2 is listed includes but is not limited to, gene CPM method is sequenced in chip fRMA method, genetic chip RMA method, RNAseq, and FPKM method is sequenced in RNAseq.
The method of the system deviation at least three genes that normalization table 3 is listed includes but is not limited to, gene CPM method is sequenced in chip fRMA method, genetic chip RMA method, RNAseq, and FPKM method is sequenced in RNAseq.
The method of the system deviation at least three genes that normalization table 4 is listed includes but is not limited to, gene CPM method is sequenced in chip fRMA method, genetic chip RMA method, RNAseq, and FPKM method is sequenced in RNAseq.
Preferably, having the patient of palindromia and low cell cycle events in described 5 years is to have disease in follow-up 5 years It recurs and the patient of low cell cycle events, the patient that disease does not recur in described 5 years is that disease does not recur in follow-up 5 years Patient.
Threshold value is can to distinguish the RNA sample of the patient with high cancer relapse risk and with low cancer relapse risk Any number of the RNA sample of patient.
Preferably, first biomarker includes at least four gene listed in table 1；It is highly preferred that described first Biomarker includes at least five gene listed in table 1；It is highly preferred that first biomarker includes listing in table 1 At least six gene；It is highly preferred that first biomarker includes at least seven gene listed in table 1；It is highly preferred that First biomarker includes at least eight gene listed in table 1；It is highly preferred that first biomarker includes At least nine gene listed in table 1；It is highly preferred that first biomarker includes at least ten base listed in table 1 Cause；It is highly preferred that first biomarker includes at least 15 genes listed in table 1；It is highly preferred that described first is raw Object marker includes at least 20 genes listed in table 1；It is highly preferred that first biomarker includes listing in table 1 At least 25 genes；It is highly preferred that first biomarker includes at least 30 genes listed in table 1；More preferably Ground, first biomarker include at least 40 genes listed in table 1；It is highly preferred that first biomarker Including at least 50 genes listed in table 1；It is highly preferred that first biomarker includes at least 60 listed in table 1 A gene；It is highly preferred that first biomarker includes at least 70 genes listed in table 1；It is highly preferred that described One biomarker includes at least 80 genes listed in table 1；It is highly preferred that first biomarker includes in table 1 At least 90 genes listed；It is highly preferred that first biomarker includes at least 100 genes listed in table 1；More Preferably, first biomarker includes at least 150 genes listed in table 1；It is highly preferred that the first biology mark Will object includes at least 200 genes listed in table 1；Most preferably, first biomarker includes the institute listed in table 1 There is gene；
Second biomarker includes at least four gene listed in table 2；It is highly preferred that the second biology mark Will object includes at least five gene listed in table 2；It is highly preferred that second biomarker includes listing at least in table 2 6 genes；It is highly preferred that second biomarker includes at least seven gene listed in table 2；It is highly preferred that described Two biomarkers include at least eight gene listed in table 2；It is highly preferred that second biomarker includes arranging in table 2 At least nine gene out；It is highly preferred that second biomarker includes at least ten gene listed in table 2；More preferably Ground, second biomarker include at least 15 genes listed in table 2；It is highly preferred that second biomarker Including at least 20 genes listed in table 2；It is highly preferred that second biomarker includes at least 25 listed in table 2 A gene；It is highly preferred that second biomarker includes at least 30 genes listed in table 2；It is highly preferred that described Two biomarkers include at least 40 genes listed in table 2；It is highly preferred that second biomarker includes in table 2 At least 50 genes listed；It is highly preferred that second biomarker includes at least 60 genes listed in table 2；More Preferably, second biomarker includes at least 70 genes listed in table 2；It is highly preferred that the second biology mark Will object includes at least 80 genes listed in table 2；It is highly preferred that second biomarker include listed in table 2 to Few 90 genes；Most preferably, second biomarker includes all genes listed in table 2.
Preferably, the third biomarker includes at least four gene listed in table 3；It is highly preferred that the third Biomarker includes at least five gene listed in table 3；It is highly preferred that the third biomarker includes listing in table 3 At least six gene；It is highly preferred that the third biomarker includes at least seven gene listed in table 3；It is highly preferred that The third biomarker includes at least eight gene listed in table 3；It is highly preferred that the third biomarker includes At least nine gene listed in table 3；It is highly preferred that the third biomarker includes at least ten base listed in table 3 Cause；It is highly preferred that the third biomarker includes at least 15 genes listed in table 3；It is highly preferred that the third is raw Object marker includes at least 20 genes listed in table 3；It is highly preferred that the third biomarker includes listing in table 3 At least 25 genes；It is highly preferred that the third biomarker includes at least 30 genes listed in table 3；More preferably Ground, the third biomarker include at least 40 genes listed in table 3；It is highly preferred that the third biomarker Including at least 50 genes listed in table 3；It is highly preferred that the third biomarker includes at least 60 listed in table 3 A gene；It is highly preferred that the third biomarker includes at least 70 genes listed in table 3；It is highly preferred that described Three biomarkers include at least 80 genes listed in table 3；It is highly preferred that the third biomarker includes in table 3 At least 90 genes listed；It is highly preferred that the third biomarker includes at least 100 genes listed in table 3；Most Preferably, the third biomarker includes all genes listed in table 3；
4th biomarker includes at least four gene listed in table 4；It is highly preferred that the 4th biology mark Will object includes at least five gene listed in table 4；It is highly preferred that the 4th biomarker includes listing at least in table 4 6 genes；It is highly preferred that the 4th biomarker includes at least seven gene listed in table 4；It is highly preferred that described Four biomarkers include at least eight gene listed in table 4；It is highly preferred that the 4th biomarker includes arranging in table 4 At least nine gene out；It is highly preferred that the 4th biomarker includes at least ten gene listed in table 4；More preferably Ground, the 4th biomarker include at least 15 genes listed in table 4；It is highly preferred that the 4th biomarker Including at least 20 genes listed in table 4；It is highly preferred that the 4th biomarker includes at least 25 listed in table 4 A gene；It is highly preferred that the 4th biomarker includes at least 30 genes listed in table 4；Most preferably, described Four biomarkers include all genes listed in table 4.
Preferably, increase the stability of final parting score value using model superposition.Tables 1 and 2 gene is according to gene function 8 groups can be divided into: (1) cell division gene (1 gene function of table, cell cycle), (2) DNA-repair gene (1 gene function of table Can, dnarepair), (3) epithelium mesenchyma transformed gene (1 gene function of table, emt), (4) cell metastatic gene (1 gene of table Function, movement), (5) T cell has correlation gene (1 gene function of table, tcell), and (6) have recurrence and low cell cycle events Most significant preceding 60 genes (1 gene function of table, top) are counted, (7) Wnt Information Conduction (2 gene function of table, wnt) (8) has Recurrence and non-low cell cycle cycle events count most significant preceding 60 genes (2 gene function of table, top).This 8 groups of genes, Every group of gene is all used alone nearest mass center classification method and calculates separately parting score value, finally uses K- neighbour homing method (Hechenbichler and Schliep, 2004) this 8 parting score value of Model Fusion become 2 final parting score value. The method of model superposition includes but is not limited to, K- neighbour's Return Law, nearest mass center classification method, neural network.For each Sample, if the final parting score value of the 1st of sample is more than that first similarity threshold value or the 2nd final parting score value are super Second similarity threshold value is crossed, is considered as high risk of recurrence sample, otherwise is low risk of recurrence sample.
Present invention provides the methods to patient's designated treatment with colorectal cancer, including system according to the present invention The patient classification is the patient of patient and low risk of recurrence with high risk of recurrence by system, if the patient is classified as With high risk of recurrence, receive adjuvant treatment.Chemotherapy compound include but be not limited to oxaliplatin, tetrahydrofolate formate, 5FU, capecitabine replace anti-, 1 inhibitor of topoisomerase according to vertical.Antybody therapy includes but is not limited to bevacizumab, western appropriate Former times monoclonal antibody, PD-1 inhibitor Ou Diwo, TGFBeta acceptor inhibitor.
The present invention provides the RNA at least three genes listed at least three genes listed in table 1, detection table 1 The RNA table at least three genes listed at least three genes or detection table 2 listed in the reagent of expression, table 2 Up to horizontal reagent in preparation for the application in the reagent or kit to patient's progress parting with colorectal cancer.
The present invention provides the combination of at least three genes listed in table 1 and at least three genes listed in table 2 or At least three genes listed in the reagent and detection table 2 of the rna expression level at least three genes listed in person's detection table 1 Rna expression level reagent combination preparation for colorectal cancer patient carry out parting reagent or reagent Application in box.
The present invention provides the RNA at least three genes listed at least three genes listed in table 3, detection table 3 The RNA table at least three genes listed at least three genes or detection table 4 listed in the reagent of expression, table 4 Up to horizontal reagent in preparation for the application in the reagent or kit to patient's progress parting with colorectal cancer.
The present invention provides the combination of at least three genes listed in table 3 and at least three genes listed in table 4 or At least three genes listed in the reagent and detection table 4 of the rna expression level at least three genes listed in person's detection table 3 Rna expression level reagent combination preparation for colorectal cancer patient carry out parting reagent or reagent Application in box.
The utility model has the advantages that
The present invention provides a kind of systems for carrying out parting to the patient with colorectal cancer, are using the present invention It unites and parting is carried out to the patient with colorectal cancer can show that significant performance, parting is high risk of recurrence type and low multiple There is dramatically different recurrence-free survival rate in hair state of risk 5 years, and genotyping result is better than medical handbook recommendation used at present Risk assessment clinical parameter.
Detailed description of the invention
Fig. 1 is the flow chart for taking turns supervision machine learning method repeatedly and gene function generation classifying method using 4.
Fig. 2 is to be recurred in the 1st group of 416 II phase Patients with Colorectal Cancer using gene in Tables 1 and 2 and present system The survivorship curve of time, high risk of recurrence type and in low risk of recurrence type 5 years recurrence-free survival rate have it is dramatically different.
Fig. 3 is to be recurred in the 2nd group of 41 II phase Patients with Colorectal Cancer using gene in Tables 1 and 2 and present system The survivorship curve of time, high risk of recurrence type and in low risk of recurrence type 5 years recurrence-free survival rate have it is dramatically different.
Fig. 4 be using in table 3 and table 4 stabilization cell core gene profile and present system tie in the 1st group of 416 II phases The survivorship curve of recurrence time in Patients With Rectal Carcinoma, high risk of recurrence type and there is recurrence-free survival rate in low risk of recurrence type 5 years It is dramatically different.
Fig. 5 be using in table 3 and table 4 stabilization cell core gene profile and present system tie in the 2nd group of 41 II phases The survivorship curve of recurrence time in Patients With Rectal Carcinoma, high risk of recurrence type and there is recurrence-free survival rate in low risk of recurrence type 5 years It is dramatically different.
Fig. 6 is the risk of recurrence ratio of any 3 to 50 gene constructed classifying methods and present system in table 1.
Fig. 7 is the risk of recurrence ratio of any 3 to 50 gene constructed classifying methods and present system in table 2.
Fig. 8 is the risk of recurrence ratio of any 3 to 50 gene constructed classifying methods and present system in table 3.
Fig. 9 is the risk of recurrence ratio of any 3 to 50 gene constructed classifying methods and present system in table 4.
Specific embodiment
Embodiment 1: the generation of classification system of the present invention and method has the patient of palindromia and low cell cycle events With the selection method for having the patient of palindromia and non-low cell cycle events
Using II phase colorectal cancer tumor specimen, (n=416,84 high risks of recurrence have record cancer in follow-up period for this research The patient specimen of recurrence, 332 low risks of recurrence do not record the patient specimen of cancer return in follow-up period).Clinical data includes The TNM stage of tumour, the follow-up of patient survival and recurrence 5 years.The gene expression data of tumor specimen uses fRMA method normalizing Change data.Also other conventional normalization programs can be used.
Using the gene expression data of all genes, supervision machine learning method (such as Fig. 1) repeatedly is taken turns using 4.It is each Wheel, a part of high risk of recurrence sample and all low risk of recurrence samples compare.In this 4 wheel repeatedly supervision machine study, Every high risk of recurrence sample used of taking turns is different from, and every selection criteria for taking turns the high risk of recurrence sample used is listed in Fig. 1 In.
1st wheel is used to sort out characteristic similar high risk of recurrence sample with the 3rd wheel supervision machine study, and statistical method is to make Use p-value < 0.05 of the cox proportional hazards regression. gene of all samples of epicycle as system The wheel of meter standard the 2nd and the 4th wheel supervision machine study are used to the genome that selection method finally uses, and are come using 3 kinds of SSs Select gene: (1) gene uses the Cox of all samples than p-value < 0.05 of Risk mode in epicycle；(2) gene is in epicycle Difference at least 1.2 is planted using the middle position of all low risk of recurrence samples using the median and epicycle of all high risk of recurrence samples Times；(3) for gene in 200 10 folding cross validations of the epicycle using all samples, each cross validation all records the Cox of gene The p-value of ratio wind wind mode, gene have 180 times or more p-values.cox < 0.05.The base selected by statistical method Because using GO terms to mark their function and subcellular localization (The Gene Ontology Consortium, 2017).
In 2nd wheel supervision machine study, 65 used have the cell cycle events of the II phase patient cancer cell of recurrence The cell cycle events gene table of II phase patient cancer cell that is not recurred relative to 332 used of gene expression dose Weakened up to level, there are relatively low cell cycle events, there is palindromia and low cell cycle events in referred to as 5 years Patient.As shown in table 1,6 groups of genes are selected in this wheel: (1) cell division gene (cell cycle), (2) DNA repair base Cause (dnarepair), (3) epithelium mesenchyma transformed gene (emt), (4) cell metastatic gene (movement), (5) T cell have Correlation gene (tcell), (6) have recurrence and low cell cycle cycle events to count most significant preceding 60 genes (top).In order to increase Adding method stability, cell division genome and DNA-repair gene group only include the gene of Codocyte nucleoprotein.
In 4th wheel supervision machine study, 17 used have the cell cycle events of the II phase patient cancer cell of recurrence The cell cycle events gene table of II phase patient cancer cell that is not recurred relative to 332 used of gene expression dose It is increased slightly up to level, without relatively low cell cycle events, has palindromia in referred to as 5 years and the non-low cell cycle is living Dynamic patient.As shown in table 2,2 groups of genes are selected in this wheel: (7) Wnt Information Conduction (wnt) (8) has recurrence and non-low thin Most significant preceding 60 genes (top) of born of the same parents' cycle activity statistics.
In order to increase the stability of final parting score value, model superposition is used.To this 8 groups of genes, every group of gene is all Nearest mass center classification method is used alone and calculates separately parting score value.Use K- neighbour regression algorithm (Hechenbichler and Schliep, 2004) this 8 parting score value of Model Fusion become 2 final parting score value.With optimal sensitivity and spy The opposite sex is the optimal classification thresholds that standard determines similar plant.For each sample, if the final parting score value of the 1st of sample It is more than second similarity threshold value 0.076 more than first similarity threshold value 0.155 or the 2nd final parting score value, is considered It is high risk of recurrence sample, otherwise is low risk of recurrence sample.
Embodiment 2: the Performance Evaluation of classification system of the present invention and method
657 determining genes be used to construct colorectal cancer prognosis classification, as shown in Table 1 and Table 2.For each II phase Rectal cancer patient, tumor specimen can be divided into 2 classes: high risk of recurrence type and low risk of recurrence type.It is straight in the 1st group of 416 II phases Patients with bowel cancer sample verifying in (n=416,84 high risks of recurrence follow-up period have record cancer return patient specimen, 332 A low risk of recurrence does not record the patient specimen of cancer return in follow-up period), the recurrence of this group of patient and follow up time have Definite record.Survivorship curve is analysis shows parting is that recurrence-free survival rate is 97.8% in patient 5 years of low risk of recurrence type [95%CI, 95.7%-1], parting be in patient 5 years of high risk of recurrence type recurrence-free survival rate be 57.7% [95%CI, 50.7%-65.7%].Compare low risk of recurrence type, the risk of recurrence of high risk of recurrence type than be up to HR=23.3 (p-value < 0.0001) (such as Fig. 2).
The significant related gene of table 1
The significant related gene of table 2
(n=41,10 high risks of recurrence have note in follow-up period in the 2nd group of 41 II phase rectal cancer patient samples verifying The patient specimen of cancer return is recorded, 31 low risks of recurrence do not record the patient specimen of cancer return in follow-up period).This group disease The recurrence of people has definite record, and follow up time is the expected value in 10 years.Survivorship curve is analysis shows parting is low recurrence wind Recurrence-free survival rate is 93.8% [95%CI, 82.6%-1] in patient 5 years of dangerous type, and parting is the patient of high risk of recurrence type Recurrence-free survival rate is 50.5% [95%CI, 32.0%-79.8%] in 5 years.Compare low risk of recurrence type, high risk of recurrence type Risk of recurrence than be up to HR=11.3 (p-value=0.00371) (such as Fig. 3).
Parting is carried out using system and method for the present invention, and significant performance, high risk of recurrence are all shown in two groups of patients Type and recurrence-free survival rate has dramatically different in low risk of recurrence type 5 years, and result is better than the wind that medical handbook is recommended at present Danger evaluation clinical parameter.
Embodiment 3: the Performance Evaluation of classification system of the present invention and method, using stablizing cell core gene profile
Use 250 stable cell core gene profiles, including 150 cell division genes, 50 DNA-repair genes and system It counts most significant preceding 50 genes and be used to construct colorectal cancer prognosis classification as shown in Table 3 and Table 4.It is straight for each II phase Patients with bowel cancer, tumor specimen can be divided into 2 classes: high risk of recurrence type and low risk of recurrence type.In the 1st group of 416 II phase rectum In the verifying of cancer patient's sample, the recurrence of this group of patient and follow up time have definite record.Survivorship curve is analysis shows parting is Recurrence-free survival rate is 93.5% [95%CI, 89.7%-97.5%] in patient 5 years of low risk of recurrence type, and parting is high multiple Sending out recurrence-free survival rate in patient 5 years of state of risk is 65.1% [95%CI, 58.7%-72.3%].Compare low risk of recurrence Type, the risk of recurrence of high risk of recurrence type is than being up to HR=6.15 (p-value < 0.0001) (Fig. 4).
The significant related gene of table 3
The significant related gene of table 4
In the 2nd group of 41 II phase rectal cancer patient samples verifying, the recurrence of this group of patient has definite record, follow up time For the expected value in 10 years.Survivorship curve is analysis shows parting is recurrence-free survival rate in patient 5 years of low risk of recurrence type It is 87.8% [95%CI, 73.4%-1], parting is that recurrence-free survival rate is 57.2% in patient 5 years of high risk of recurrence type [95%CI, 38.5%-85.0%].Low risk of recurrence type is compared, the risk of recurrence of high risk of recurrence type is than being up to HR=4.38 (p-value=0.0415) (Fig. 5)
It is all aobvious in two groups of patients using classification system of the present invention and method using 250 stable cell core gene profiles Significant performance is shown, high risk of recurrence type and recurrence-free survival rate has dramatically different, and result in low risk of recurrence type 5 years The risk assessment clinical parameter recommended better than medical handbook used at present.
Embodiment 4: the result of classification system and minimum 3 genes of method
Using gene dosage since 2 genes, increase by 1 every time, and terminate with 50 genes.For each selected Gene dosage (from 2 to 50) carries out 200 wheel simulations.In the simulation of every wheel, the group of selected genes quantity is selected from genome It closes, i.e., the combination of 200 times any 2 genes, the combination of 200 any 3 genes, the combination of 200 any 4 genes, 200 The combination of secondary any 5 genes, until the combination of 200 any 50 genes.Combination is simulated to every wheel of each gene dosage, The scoring and the high risk of recurrence type in patient specimen that calculate classifying method and system compare the recurrence wind of low risk of recurrence type Dangerous ratio.The risk of recurrence ratio of each selected genes number is the average risk of recurrence ratio of 200 wheels simulation.The result shows that minimum only need Want the combination of any 3 genes.
As can be seen from FIG. 6, the gene constructed classifying method in any 3 to 50 tables 1 can achieve risk of recurrence ratio
As can be seen from FIG. 7, the gene constructed classifying method in any 3 to 50 tables 2 can achieve risk of recurrence ratio
As can be seen from FIG. 8, the gene constructed classifying method in any 3 to 50 tables 3 can achieve risk of recurrence ratio
As can be seen from FIG. 9, the gene constructed classifying method in any 3 to 50 tables 4 can achieve risk of recurrence ratio.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention rather than protects to the present invention The limitation of range is protected, although the invention is described in detail with reference to the preferred embodiments, those skilled in the art should Understand, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the essence of technical solution of the present invention And range.

Claims (10)

1. a kind of system for carrying out parting to the patient with colorectal cancer, which is characterized in that input mould including data Block, in the tissue samples of the patient for colorectal cancer will to be suffered from the rna expression level of the first biomarker with 5 years in There is the rna expression of the first biomarker described in the tissue samples of the patient of palindromia and low cell cycle events horizontal Between first similarity value and the patient with colorectal cancer tissue samples described in the first biomarker Rna expression level with 5 years in the first biomarker described in the tissue samples of patient that do not recur of disease rna expression Second similarity value input model computing module between level, first biomarker include at least 3 listed in table 1 A gene；For the rna expression of the second biomarker in the tissue samples of the patient with colorectal cancer is horizontal With 5 years in have the second biomarker described in the tissue samples of the patient of palindromia and non-low cell cycle events Third similarity value between rna expression level and the described in the tissue samples of the patient with colorectal cancer The rna expression level of two biomarkers with 5 years in the second biological marker described in the tissue samples of patient that do not recur of disease The 4th similarity input model computing module between the rna expression level of object, second biomarker includes in table 2 At least three gene listed；
Model computation module, including parting model, for according to first similarity value, second similarity value, third similarity value, 4th similarity and parting model calculate the first parting score value of the patient with colorectal cancer and the second parting obtains Score value；The first parting score value=first similarity value-second similarity value, the second parting score value=third phase Like four similarity of property value-the；
As a result output module, for the first parting score value and the second parting score value according to the patient with colorectal cancer Determine patient have poor prognosis or have good prognosis, when suffer from colorectal cancer patient the first parting score value >= First similarity threshold value, or/and with colorectal cancer patient the second parting score value >=second similarity threshold value when, suffer from Person is classified as high risk of recurrence, has poor prognosis；As the first parting score value < first of the patient with colorectal cancer Similarity threshold, and when the second parting score value < second similarity threshold value of the patient with colorectal cancer, patient classification For low risk of recurrence, there is good prognosis.
2. a kind of system for carrying out parting to the patient with colorectal cancer, which is characterized in that input mould including data Block, in the tissue samples of the patient for colorectal cancer will to be suffered from the rna expression level of third biomarker with 5 years in There is the rna expression of third biomarker described in the tissue samples of the patient of palindromia and low cell cycle events horizontal Between the 5th similarity and the patient with colorectal cancer tissue samples described in third biomarker Rna expression level with 5 years in third biomarker described in the tissue samples of patient that do not recur of disease rna expression The 6th similarity input model computing module between level, the third biomarker include at least 3 listed in table 3 A gene；For the rna expression of the 4th biomarker in the tissue samples of the patient with colorectal cancer is horizontal With 5 years in have the 4th biomarker described in the tissue samples of the patient of palindromia and non-low cell cycle events The 7th similarity between rna expression level and the described in the tissue samples of the patient with colorectal cancer The rna expression level of four biomarkers with 5 years in the 4th biological marker described in the tissue samples of patient that do not recur of disease The 8th similarity input model computing module between the rna expression level of object, the 4th biomarker includes in table 4 At least three gene listed；
Model computation module, including parting model, for according to the 5th similarity, the 6th similarity, the 7th similarity, 8th similarity and parting model calculate the third parting score value of the patient with colorectal cancer and tetrad segregation types obtain Score value；The third parting score value=the 5th the-the six similarity of similarity, tetrad segregation types score value=the 7th phase Like eight similarity of property value-the；
As a result output module, for the third parting score value and tetrad segregation types score value according to the patient with colorectal cancer Determine patient have poor prognosis or have good prognosis, when suffer from colorectal cancer patient third parting score value >= Third similarity threshold value, or/and with colorectal cancer patient tetrad segregation types score value >=four similarity threshold when, suffer from Person is classified as high risk of recurrence, has poor prognosis；When the third parting score value < third of the patient with colorectal cancer Similarity threshold, and when tetrad segregation types score value four similarity threshold of < of the patient with colorectal cancer, patient classification For low risk of recurrence, there is good prognosis.
3. system according to claim 1 or 2, which is characterized in that the colorectal cancer includes according to TNM stage system TNM II phase cancer；The tissue samples include the expression of colorectal cancer cell or the nucleic acid from colorectal cancer cell The clinically relevant sample of product.
4. system according to claim 1 or 2, which is characterized in that the measuring method of the rna expression level includes trace Hybridization, quantitative PCR, RNAseq sequencing and microarray analysis；Measure the first similarity value, second similarity value, third phase Like property value, the 4th similarity, the 5th similarity, the 6th similarity, the 7th similarity or the 8th similarity Method includes Euclidean distance, manhatton distance, Chebyshev's distance, Minkowski Distance, standardizes Euclidean distance, geneva away from From, included angle cosine, Hamming distance, Jie Kade distance, related coefficient or comentropy.
5. system according to claim 1, which is characterized in that first biomarker include listed in table 1 to It is listed at least six gene or table 1 listed at least five gene or table 1 listed in few 4 genes or table 1 It is listed at least nine gene or table 1 listed at least eight gene or table 1 listed at least seven gene or table 1 At least ten gene or table 1 at least 20 genes or table 1 listed at least 15 genes or table 1 listed At least 40 genes for being listed at least 30 genes or table 1 listed at least 25 genes or table 1 listed or At least 70 genes listed at least 60 genes or table 1 listed at least 50 genes or table 1 listed in table 1 At least 100 listed at least 90 genes or table 1 listed at least 80 genes or table 1 either listed in table 1 It is listed at least 200 genes or table 1 listed at least 150 genes or table 1 listed in a gene or table 1 All genes；
Second biomarker include at least five gene listed at least four gene or table 2 listed in table 2 or At least eight gene listed at least seven gene or table 2 listed at least six gene or table 2 listed in person's table 2 At least 15 listed at least ten gene or table 2 listed at least nine gene or table 2 either listed in table 2 It is listed at least at least 25 genes or the table 2 listed at least 20 genes or table 2 listed in gene or table 2 It is listed at least 50 genes or table 2 listed at least 40 genes or table 2 listed in 30 genes or table 2 It is arranged at least 80 genes or the table 2 listed at least 70 genes or table 2 listed at least 60 genes or table 2 All genes listed at least 90 genes or table 2 out.
6. system according to claim 2, which is characterized in that the third biomarker include listed in table 3 to It is listed at least six gene or table 3 listed at least five gene or table 3 listed in few 4 genes or table 3 It is listed at least nine gene or table 3 listed at least eight gene or table 3 listed at least seven gene or table 3 At least ten gene or table 3 at least 20 genes or table 3 listed at least 15 genes or table 3 listed At least 40 genes for being listed at least 30 genes or table 3 listed at least 25 genes or table 3 listed or At least 70 genes listed at least 60 genes or table 3 listed at least 50 genes or table 3 listed in table 3 At least 100 listed at least 90 genes or table 3 listed at least 80 genes or table 3 either listed in table 3 All genes listed in a gene or table 3；
4th biomarker include at least five gene listed at least four gene or table 4 listed in table 4 or At least eight gene listed at least seven gene or table 4 listed at least six gene or table 4 listed in person's table 4 At least 15 listed at least ten gene or table 4 listed at least nine gene or table 4 either listed in table 4 It is listed at least at least 25 genes or the table 4 listed at least 20 genes or table 4 listed in gene or table 4 All genes listed in 30 genes or table 4.
7. the reagent of the rna expression level at least three genes listed at least three genes listed in table 1, detection table 1, The reagent of the rna expression level at least three genes listed at least three genes or detection table 2 listed in table 2 is being made The application being ready for use in the reagent or kit for carrying out parting to the patient with colorectal cancer.
8. being listed in the combination at least three genes listed at least three genes and table 2 listed in table 1 or detection table 1 At least three genes rna expression level reagent and detection table 2 in the rna expression of at least three genes listed it is horizontal The combination of reagent is in preparation for the application in the reagent or kit to patient's progress parting with colorectal cancer.
9. the reagent of the rna expression level at least three genes listed at least three genes listed in table 3, detection table 3, The reagent of the rna expression level at least three genes listed at least three genes or detection table 4 listed in table 4 is being made The application being ready for use in the reagent or kit for carrying out parting to the patient with colorectal cancer.
10. being arranged in the combination at least three genes listed at least three genes and table 4 listed in table 3 or detection table 3 The rna expression at least three genes listed in the reagent and detection table 4 of the rna expression level of at least three genes out is horizontal Reagent combination preparation for colorectal cancer patient carry out parting reagent or kit in application.
CN201910106934.6A 2019-02-01 2019-02-01 System for typing a patient suffering from colorectal cancer Active CN109988708B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910106934.6A CN109988708B (en) 2019-02-01 2019-02-01 System for typing a patient suffering from colorectal cancer

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910106934.6A CN109988708B (en) 2019-02-01 2019-02-01 System for typing a patient suffering from colorectal cancer

Publications (2)

Publication Number Publication Date
CN109988708A true CN109988708A (en) 2019-07-09
CN109988708B CN109988708B (en) 2022-12-09

Family

ID=67129810

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910106934.6A Active CN109988708B (en) 2019-02-01 2019-02-01 System for typing a patient suffering from colorectal cancer

Country Status (1)

Country Link
CN (1) CN109988708B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112062827A (en) * 2020-09-16 2020-12-11 军事科学院军事医学研究院生物医学分析中心 Application of CEP55 protein in regulation of cilia de-assembly and preparation of cilia-related disease model
CN113436741A (en) * 2021-07-16 2021-09-24 四川大学华西医院 Lung cancer recurrence prediction method based on tissue specific enhancer region DNA methylation
CN113774130A (en) * 2020-06-09 2021-12-10 碳逻辑生物科技(香港)有限公司 Biomarkers and methods for selecting a group of chemotherapy responsive patients and uses thereof
CN116870139A (en) * 2023-09-06 2023-10-13 暨南大学附属第一医院（广州华侨医院） Application of LASP1 protein in the preparation of drugs for the treatment and repair of spinal cord injury

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009002175A1 (en) * 2007-06-28 2008-12-31 Agendia B.V. A method of typing a sample comprising colorectal cancer cells
CN101896819A (en) * 2007-10-15 2010-11-24 剑桥癌症诊断有限公司 Diagnostic, predictive and prognostic tests for cancer
US20100304410A1 (en) * 2007-09-28 2010-12-02 Royal College Of Surgeons In Ireland Method of assessing colorectal cancer status in an individual
US20110319285A1 (en) * 2008-12-24 2011-12-29 Paul Roepman Methods and means for typing a sample comprising colorectal cancer cells
US8105777B1 (en) * 2008-02-13 2012-01-31 Nederlands Kanker Instituut Methods for diagnosis and/or prognosis of colon cancer
US20140031251A1 (en) * 2010-11-03 2014-01-30 H. Lee Moffitt Cancer Center And Research Institute, Inc. Methods of classifying human subjects with regard to cancer prognosis
AU2015202173A1 (en) * 2007-10-05 2015-05-14 Pacific Edge Limited Proliferation signature and prognosis for gastrointestinal cancer
EP3009842A2 (en) * 2014-09-26 2016-04-20 Sysmex Corporation Method for supporting diagnosis of risk of colorectal cancer recurrence, program and computer system
CN109938695A (en) * 2019-03-08 2019-06-28 度特斯(大连)实业有限公司 A kind of human body diseases Risk Forecast Method and equipment based on heterogeneous degree index

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009002175A1 (en) * 2007-06-28 2008-12-31 Agendia B.V. A method of typing a sample comprising colorectal cancer cells
US20100304410A1 (en) * 2007-09-28 2010-12-02 Royal College Of Surgeons In Ireland Method of assessing colorectal cancer status in an individual
AU2015202173A1 (en) * 2007-10-05 2015-05-14 Pacific Edge Limited Proliferation signature and prognosis for gastrointestinal cancer
CN101896819A (en) * 2007-10-15 2010-11-24 剑桥癌症诊断有限公司 Diagnostic, predictive and prognostic tests for cancer
US8105777B1 (en) * 2008-02-13 2012-01-31 Nederlands Kanker Instituut Methods for diagnosis and/or prognosis of colon cancer
US20110319285A1 (en) * 2008-12-24 2011-12-29 Paul Roepman Methods and means for typing a sample comprising colorectal cancer cells
CN102325902A (en) * 2008-12-24 2012-01-18 阿詹迪亚有限公司 Method and device for typing samples comprising colorectal cancer cells
US20140031251A1 (en) * 2010-11-03 2014-01-30 H. Lee Moffitt Cancer Center And Research Institute, Inc. Methods of classifying human subjects with regard to cancer prognosis
EP3009842A2 (en) * 2014-09-26 2016-04-20 Sysmex Corporation Method for supporting diagnosis of risk of colorectal cancer recurrence, program and computer system
CN109938695A (en) * 2019-03-08 2019-06-28 度特斯(大连)实业有限公司 A kind of human body diseases Risk Forecast Method and equipment based on heterogeneous degree index

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
仲召阳: "多肿瘤标志物蛋白质芯片检测系统对结直肠癌诊断的临床意义", 《重庆医学》 *
孙建海等主编: "《临床肿瘤诊断与治疗》", 31 March 2014, 科学技术文献出版社 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113774130A (en) * 2020-06-09 2021-12-10 碳逻辑生物科技(香港)有限公司 Biomarkers and methods for selecting a group of chemotherapy responsive patients and uses thereof
CN112062827A (en) * 2020-09-16 2020-12-11 军事科学院军事医学研究院生物医学分析中心 Application of CEP55 protein in regulation of cilia de-assembly and preparation of cilia-related disease model
CN112062827B (en) * 2020-09-16 2022-10-21 中国人民解放军军事科学院军事医学研究院 Application of CEP55 protein in regulating ciliary disassembly and preparing cilia-related disease models
CN113436741A (en) * 2021-07-16 2021-09-24 四川大学华西医院 Lung cancer recurrence prediction method based on tissue specific enhancer region DNA methylation
CN113436741B (en) * 2021-07-16 2023-02-28 四川大学华西医院 A method for predicting lung cancer recurrence based on DNA methylation in tissue-specific enhancer regions
CN116870139A (en) * 2023-09-06 2023-10-13 暨南大学附属第一医院（广州华侨医院） Application of LASP1 protein in the preparation of drugs for the treatment and repair of spinal cord injury

Also Published As

Publication number Publication date
CN109988708B (en) 2022-12-09

Similar Documents

Publication Publication Date Title
Xu et al. Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma
CN109988708A (en) A kind of system for carrying out parting to the patient with colorectal cancer
CN109689891A (en) The method of segment group spectrum analysis for cell-free nucleic acid
US20230242992A1 (en) Methods of predicting cancer progression
CN107885973A (en) A kind of clinical research methods of DNA-repair gene in colorectal cancer
CN110010195A (en) A kind of method and device detecting single nucleotide mutation
CN109929934B (en) Application of immune-related genes in colorectal cancer prognosis kits and systems
Zhou et al. Identification of telomere-associated gene signatures to predict prognosis and drug sensitivity in glioma
Zhang et al. Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection
CN105243300A (en) Approximation spectral clustering algorithm based method for predicting cancer metastasis and recurrence
Li et al. Establishment of a novel combined nomogram for predicting the risk of progression related to castration resistance in patients with prostate cancer
CN120319306A (en) Method for constructing a prognostic model for colorectal cancer based on genes related to fatty acid metabolism
Xu et al. An effective seven-CpG-based signature to predict survival in renal clear cell carcinoma by integrating DNA methylation and gene expression
Liang et al. Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay
Wang et al. Classification of muscle invasive bladder cancer to predict prognosis of patients treated with immunotherapy
CN116312814B (en) A method, device, apparatus and kit for constructing a molecular typing model for lung adenocarcinoma
US12555648B2 (en) Method for acquiring intracellular deterministic events and electronic apparatus
Hu et al. A novel Cuprotosis-related signature predicts the prognosis and selects personal treatments for melanoma based on bioinformatics analysis
US20220293212A1 (en) Method for automatically predicting treatment management factor characteristics of disease and electronic apparatus
Pan et al. Explainable machine learning model identified potential biomarkers in liver cancer survival prediction
Špendl et al. Large scale gene set ranking for survival-related gene sets
CN119799840B (en) Microbial marker composition for predicting prognosis of hepatocellular carcinoma and application thereof
Zheng et al. Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma
Jiang et al. Identification of Cancer Development Related Pathways Based on Co-Expression Analyses
CN121137143A (en) Application of metabolic cell death-related genes as detection targets in the preparation of products for the diagnosis or auxiliary diagnosis of liver ischemia-reperfusion injury.

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
CB03 Change of inventor or designer information

Inventor after: Sun Tian

Inventor after: Wu Rujia

Inventor before: Sun Tian

CB03 Change of inventor or designer information
GR01 Patent grant
GR01 Patent grant
CP03 Change of name, title or address

Address after: A133, Building B, No. 1 Huanzhen East Road South, Beijiao Community, Beijiao Town, Shunde District, Foshan City, Guangdong Province, 528000 (Residence Application)

Patentee after: Carbon Logic Biotechnology (Foshan) Co.,Ltd.

Address before: 528,400 Card 502, Floor 5, Shumao Building, No. 6, Xiangxing Road, Torch Development Zone, Zhongshan City, Guangdong Province

Patentee before: Carbon Logic Biotechnology (Zhongshan) Co.,Ltd.

CP03 Change of name, title or address