CN108733980A - A kind of glioblastoma personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum - Google Patents
A kind of glioblastoma personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum Download PDFInfo
- Publication number
- CN108733980A CN108733980A CN201810440854.XA CN201810440854A CN108733980A CN 108733980 A CN108733980 A CN 108733980A CN 201810440854 A CN201810440854 A CN 201810440854A CN 108733980 A CN108733980 A CN 108733980A
- Authority
- CN
- China
- Prior art keywords
- gene
- patient
- glioblastoma
- survival
- risk
- 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
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Abstract
The invention discloses a kind of glioblastoma personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum, include the following steps：Obtain glioblastoma prognostic risk list of genes and gene weights；Prognosis evaluation model is built using glioblastoma specimens transcript profile and Survival data；The risk score of patient is calculated according to the gene expression profile of glioblastoma specimens；The annual survival probability of patient is calculated according to the risk score of patient.The annual survival probability of glioblastoma patient and the practical annual survival highly consistent (linear correlation R of ratio that the method for the present invention obtains2=0.963, P value=1.38E-13).It confirms that this method has very high forecasting accuracy, is identical with practical survival condition.Meanwhile for each tumor patient, the present invention can provide the distinctive survival probability curve of the patient.
Description
Technical field
The invention belongs to biotechnologys and medical domain, specifically, being related to a kind of based on multi-gene expression characteristic spectrum
Glioblastoma personalization prognostic evaluation methods.
Background technology
Glioblastoma, be it is a kind of it is most common be also the most aggressive cancer of the brain.Symptom at the beginning is not obvious, and is suffered from
The symptom etc. that person may have a headache, personality change, feel nausea, have similar apoplexy.Symptom when deterioration is then come very quick,
It may lead to patient's unconsciousness.Global disease burden (Global Burden of Disease, GBD) data show, 2016
Number of the year whole world with brain and nervous system cancer reaches 780,000, wherein Chinese number of patients is 260,000.The whole world in 2016
The death toll of cancer of the brain patient is 230,000, accounts for the 0.42% of total death toll.China's Died Patients number in 2016 is 5.9 ten thousand, is accounted for
The 0.61% of total death toll.Statistical result showed, global glioblastoma illness rate and death from nineteen ninety by 2016
Rate sustainable growth, Chinese illness rate and the death rate increase relatively slow.
General neoplasm staging method is TNM stage system in the world at present, which is american cancer joint committee member
A kind of malignant tumour sorting technique that meeting (American Joint Committee on Cancer, AJCC) proposes.State of the U.S.
Family's Cancer Institute (National Cancer Institute, NCI) is described as TNM stage：T refers to the big of primary tumor
Small and range, primary tumor are commonly known as primary tumor.N refers to the number with lymph node near cancer.M refers to cancer
It is no transferred, i.e., other positions of body are diffused into from primary tumor.Malignant tumour can substantially be divided according to the above index
For I phases, II phases, III phases and IV phases, wherein by stages higher indicate that the grade malignancy of tumour is higher.TNM stage system suffers from tumour
The treatment of person and prognosis evaluation have certain help.But due to the mechanism of tumour in Different Individual and internal microenvironment
Difference causes the life span difference of different patients huge, and TNM stage system cannot reflect the prognosis shape of patient well
Condition.The study found that life cycle (1-2) that may be only shorter for certain patients for being diagnosed as the I phases, however some are examined
Longer life cycle (5 years or more) may be had for the patient of IV phases by breaking.Therefore, TNM stage system may be more likely to retouch
The average level for stating a cancer patient group, it is poor to personalized diagnosing and treating applicability.On the other hand, for diagnosis
For the patient of late period (III phases, IV phases), certain therapeutic scheme can be caused to select to patient and medical worker difficult, caused very
The more tumor patient that can be survived for a long time originally death in advance due to over-treatment or malpractice；And other should be into
The patient that the appropriate treatment of row can extend existence also results in tumor patient and shifts to an earlier date death due to abandoning treatment or malpractice.
Currently, having been reported that proposition can carry out prognosis evaluation using gene expression profile to tumor patient.But it is most
Report only using single or several genes, can only classify to a group, can only be carried out qualitatively to the individual survival phase
It divides (such as good prognosis, poor prognosis two indices).Therefore, it is necessary to establish finer personalized tumor prognosis evaluation model to comment
The life span of patient is estimated to select suitable therapeutic scheme.
Invention content
In view of this, the present invention provides a kind of glioblastoma personalization prognosis based on multi-gene expression characteristic spectrum
Appraisal procedure, being capable of the annual survival probability of Accurate Prediction patient.
In order to solve the above-mentioned technical problem, the invention discloses a kind of glioblasts based on multi-gene expression characteristic spectrum
Tumor personalization prognostic evaluation methods,
Include the following steps：
Step 1 obtains glioblastoma prognostic risk list of genes and gene weights；
Step 2 builds prognosis evaluation model using glioblastoma specimens transcript profile and Survival data；
Step 3, the risk score that patient is calculated according to the gene expression profile of glioblastoma specimens；
Step 4 calculates the annual survival probability of patient according to the risk score of patient.
Optionally, the acquisition glioblastoma prognostic risk list of genes in the step 1 is specifically pressed with gene weights
Implement according to following steps：
Step 1.1 downloads glioblastoma trouble from Genomic Data Commons Data Portal databases
Person's tumor tissues and cancer beside organism's transcript profile data and clinical data obtain glioblastoma specimens gene table
Up to spectrum FPKM numerical value, Logarithm conversion is carried out；
Step 1.2 sets total number of samples as m, and all samples are divided into three groups according to the tertile of its gene expression values,
In, gene expression values refer to the FPKM numerical value obtained in step 1.1, are indicated with V, and V is denoted as to i-th of genei, utilize Cox ratios
Example risk model calculates survival risk of the third grouping compared to the first grouping, obtains Hazard ratio HRi and the P value of i-th of gene, i.e.,
Obtain the Hazard ratio of each gene；Define P values<0.05 has conspicuousness, screens the survival risk gene with conspicuousness, note
For n1；In addition, calculating the correlation of each gene and survival of patients number of days, the correlation coefficient r and P values of each gene are obtained；It is fixed
Adopted P values<0.05 has conspicuousness, screens the existence related gene with conspicuousness, is denoted as n2；By survival risk gene and existence
The intersection of related gene is defined as prognostic risk gene, is denoted as n, then has：
N=n1∩n2 (1)
Step 1.3, the weight W that i-th of gene is calculated according to the Hazard ratio of i-th of genei, calculation formula is：
Thus obtain the weight of each gene, finally obtained glioblastoma prognostic risk list of genes and base
Because of weight.
Optionally, the glioblastoma prognostic risk list of genes and gene weights are as shown in the table：
Optionally, it is built using glioblastoma specimens transcript profile and Survival data in the step 2 pre-
Assessment models are specifically implemented according to the following steps afterwards：
Step 2.1, definition gene expression values are V, according to expression value and weight meter of i-th of gene in j-th of sample
Calculate the risk score S of i-th of patientj；Calculation formula is：
Wherein, i indicates that gene number, j indicate that sample number, m indicate total sample number, VijIndicate i-th of gene at j-th
Expression value in sample；
Step 2.2 sorts all glioblastoma clinical samples according to risk score from low to high, uses sliding window
Mouth mold type calculates average risk score to every 50 samplesCalculation formula is：
Wherein j+49 indicates rear 50 samples started counting up from sample j；
Step 2.3 carries out curve fitting to the Survival data of 50 samples using Weibull distributions,
Weibull distribution probability density function be：
Wherein k > 0 are form parameters, and λ > 0 are the scale parameters of distribution；
Step 2.4 calculates every 50 samplesCorresponding kjAnd λj；Rule of thumb, kjIt is relatively-stationary for one
Numerical value, mean value are：
Wherein, kjFor j-th of sample to the form parameter of+49 sample survivorship curve Weibull distributions of jth；
Scale parameter λjVariation range it is larger, define λjWithFunctional relation be：
Wherein, λjJ-th of sample is indicated to the scale parameter of+49 sample survivorship curve Weibull distributions of jth, e is certainly
The bottom of right logarithm, α, β are the parameter of function, take logarithm to obtain above formula：
Wherein log λjWithFor linear relationship, solved by linear fit；
According to average risk scoreWith Weibull distributed constants λjMatched curve, the functional relation obtained is：
It willSubstitute into the λ that the function obtains predictionj', λj' it is with the calculated expected distributed constant of the function, calculating λjWith
λj' correlation obtain coefficient R2=0.983, P value=6.44E-65.
Optionally, calculate patient's according to the gene expression profile of glioblastoma specimens in the step 3
Risk score is specifically implemented according to the following steps：Obtain i-th of gene expression profile of glioblastoma specimens
FPKM numerical value, is denoted as：Vi；The corresponding weight of i-th of gene is denoted as：Wi；Patient risk's score is denoted as：S；Calculation formula is：
Wherein i numbers for gene, and n is the gene number listed in table 1.
Optionally, in the step 4 according to the risk score of patient calculate the annual survival probability of patient specifically according to
Lower step is implemented：The cumulative distribution function that the risk score S of patient is brought into Weibull distributions obtains the survival probability of the patient
Function is：
Wherein t be the time, α, β, S,It is preset parameter.
Compared with prior art, the present invention can be obtained including following technique effect：
1) continuous：The present invention can predict the survival probability of tumor patient continuous time.Such as patient's every month can be provided
Survival probability, the annual survival probability etc. of patient.And the classifying method that clinic uses at present can only provide one and qualitatively sentence
It is disconnected.
2) more accurate：The present invention is based on the glioblastoma personalization prognostic evaluation methods phases of multi-gene expression characteristic spectrum
Than the survival condition that traditional TNM stage can more accurately reflect patient.
3) personalized：For each tumor patient, the present invention can provide the distinctive survival probability curve of the patient, this is
Not available for general tumor prognosis evaluation model.
Certainly, it implements any of the products of the present invention it is not absolutely required to while reaching all the above technique effect.
Description of the drawings
Attached drawing described herein is used to provide further understanding of the present invention, and constitutes the part of the present invention, this hair
Bright illustrative embodiments and their description are not constituted improper limitations of the present invention for explaining the present invention.In the accompanying drawings：
Fig. 1 is that the present invention predicts that survival probability is compared with practical annual survival probability every year on average；
Fig. 2 is the matched curve of average risk score of the present invention and Weibull distributed constants scale；
Fig. 3 is the regression criterion figure of average risk score of the present invention and Weibull distributed constants scale；
Fig. 4 is personalized glioblastoma prognosis evaluation result of the invention.
Specific implementation mode
Carry out the embodiment that the present invention will be described in detail below in conjunction with embodiment, thereby to the present invention how application technology hand
Section solves technical problem and reaches the realization process of technical effect to fully understand and implement.
The invention discloses a kind of glioblastoma personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum,
Include the following steps：
Step 1 obtains glioblastoma prognostic risk list of genes and gene weights, specifically real according to the following steps
It applies：
Step 1.1 downloads glioblastoma trouble from Genomic Data Commons Data Portal databases
Person's tumor tissues and cancer beside organism's transcript profile data and clinical data obtain glioblastoma specimens gene table
Up to spectrum FPKM (Fragments Per Kilobase of transcript per MIllion fragments mapped) number
Value carries out Logarithm conversion (log2).
Step 1.2 sets total number of samples as m, and all samples are divided into three groups according to the tertile of its gene expression values,
In, gene expression values refer to the FPKM numerical value obtained in step 1.1, are indicated with V, and V is denoted as to i-th of genei, utilize Cox ratios
Example risk model calculates survival risk of the third grouping compared to the first grouping, obtains Hazard ratio HRi and the P value of i-th of gene, i.e.,
It can obtain the Hazard ratio of each gene.Define P values<0.05 has conspicuousness, screens the survival risk gene with conspicuousness,
It is denoted as n1.In addition, calculating the correlation of each gene and survival of patients number of days, the correlation coefficient r and P values of each gene are obtained.
Define P values<0.05 has conspicuousness, screens the existence related gene with conspicuousness, is denoted as n2.By survival risk gene and life
The intersection for depositing related gene is defined as prognostic risk gene, is denoted as n, then has：
n-n1∩n2 (1)
Step 1.3, the weight W that i-th of gene is calculated according to the Hazard ratio of i-th of genei, calculation formula is：
Thus obtain the weight of each gene, finally obtained glioblastoma prognostic risk list of genes and base
Because weight is shown in Table 1.
1 Gene Name of table and weight
Step 2 builds prognosis evaluation model, tool using glioblastoma specimens transcript profile and Survival data
Body is implemented according to the following steps：
Step 2.1, definition gene expression values are V, according to expression value and weight meter of i-th of gene in j-th of sample
Calculate the risk score S of i-th of patienti；Calculation formula is：
Wherein, i indicates that gene number, j indicate that sample number, m indicate total sample number, VijIndicate i-th of gene at j-th
Expression value in sample；
Step 2.2 sorts all glioblastoma clinical samples according to risk score from low to high, uses sliding window
Mouth mold type (Kang HJ et al.Spatio-temporal transcriptome of the human
brain.Nature.2011；478(7370):Average risk score 483-489.) is calculated to every 50 samplesCalculation formula
For：
Wherein j+49 indicates rear 50 samples started counting up from sample j.
Step 2.3 carries out curve fitting to the Survival data of 50 samples using Weibull distributions, Weibull distributions
Probability density function is：
Wherein k > 0 are shape (shape) parameters, and λ > 0 are ratio (scale) parameters of distribution.
Step 2.4 calculates every 50 samplesCorresponding kjAnd λj.Rule of thumb, kjIt is relatively-stationary for one
Numerical value, mean value are：
Wherein, kjFor j-th of sample to the form parameter of+49 sample survivorship curve Weibull distributions of jth；
Scale parameter λjVariation range it is larger, define λjWithFunctional relation be：
Wherein, λjJ-th of sample is indicated to the scale parameter of+49 sample survivorship curve Weibull distributions of jth, e is certainly
The bottom of right logarithm, α, β are the parameter of function, take logarithm that can obtain above formula：
Wherein log λjWithFor linear relationship, can be solved by linear fit.
It is illustrated in figure 2 average risk scoreWith Weibull distributed constants λjMatched curve, the functional relation obtained
For：
It willSubstitute into the λ that the function obtains predictionj', λj' it is with the calculated expected distributed constant of the function, calculating λjWith
λj' correlation can obtain coefficient R2=0.983, P value=6.44E-65.
Scheme (Fig. 3) by analyzing regression criterion figure and Q-Q, shows that the model reaches conspicuousness, i.e. average risk score
With Weibull distributed constants λjFunctional relation be believable.
Step 3, the risk score that patient is calculated according to the gene expression profile of glioblastoma specimens, specifically
Implement according to the following steps：Obtaining the FPKM numerical value of i-th of gene expression profile of glioblastoma specimens (should wrap
Containing listed gene in wholly or largely table 1), it is denoted as：Vi(i numbers for gene)；The corresponding weight note of i-th of gene in table 1
For：Wi(i numbers for gene)；Patient risk's score is denoted as：S；Calculation formula is：
Wherein i numbers for gene, and n is the gene number listed in table 1.
Step 4 calculates the annual survival probability of patient according to the risk score of patient, is specifically implemented according to the following steps：
The risk score S of patient is brought into the cumulative distribution function of Weibull distributions can show that the survival probability function of the patient is：
Wherein t be the time, α, β, S,It is preset parameter.
It is illustrated in figure 4 the survival probability curve of a patient, abscissa is number of days in figure, and ordinate is survival probability.
The annual survival probability of patient just marks under the curve.The practical number of days of patient's survival, state are marked in the black box of the upper right corner
(Status) 1 indicate that patient is dead.It corresponding number of days and is deposited when red point marks death (at Death points) on curve
Probability living, in figure when death corresponding survival probability 0.13 or so.
The present invention utilizes TCGA-GBM transcript profiles and clinical data, we have carried out all glioblastoma patients a
The Prediction of survival of property, and obtained result is verified using the method for cross validation.As a result display uses polygenes
The existence that the glioblastoma patient that obtains of glioblastoma personalization prognostic evaluation methods of expression characteristic spectrum is annual is general
Rate and the practical annual survival highly consistent (linear correlation R of ratio2=0.963, P value=1.38E-13, Fig. 1).Confirm the party
Method has very high forecasting accuracy, is identical with practical survival condition.
As shown in Figure 1, survival probability and practical annual survival probability are highly consistent every year on average for present invention prediction.Based on more
The glioblastoma personalization prognostic evaluation methods of allelic expression spectrum, which compare traditional TNM stage, more accurately to be reflected
The survival condition of patient.
As shown in figure 4, the present invention can predict the survival probability of tumor patient continuous time.Such as can to provide patient each
The annual survival probability etc. of the survival probability of the moon, patient.And the clinical classifying method used can only provide one qualitatively at present
Judge.For each tumor patient, the present invention can provide the distinctive survival probability curve of the patient, this is general tumor prognosis
Not available for assessment models.
Above description has shown and described several preferred embodiments of invention, but as previously described, it should be understood that invention is not
It is confined to form disclosed herein, is not to be taken as excluding other embodiments, and can be used for various other combinations, modification
And environment, and can be carried out by the above teachings or related fields of technology or knowledge in the scope of the invention is set forth herein
Change.And changes and modifications made by those skilled in the art do not depart from the spirit and scope of invention, then should all be weighed appended by invention
In the protection domain that profit requires.
Claims (6)
1. a kind of glioblastoma personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum, which is characterized in that packet
Include following steps：
Step 1 obtains glioblastoma prognostic risk list of genes and gene weights；
Step 2 builds prognosis evaluation model using glioblastoma specimens transcript profile and Survival data；
Step 3, the risk score that patient is calculated according to the gene expression profile of glioblastoma specimens；
Step 4 calculates the annual survival probability of patient according to the risk score of patient.
2. prognostic evaluation methods according to claim 1, which is characterized in that the acquisition glioblast in the step 1
Tumor prognostic risk list of genes is specifically implemented according to the following steps with gene weights：
Step 1.1, download glioblastoma patient is swollen from Genomic Data Commons Data Portal databases
Tumor tissue and cancer beside organism's transcript profile data and clinical data obtain glioblastoma specimens gene expression profile
FPKM numerical value carries out Logarithm conversion；
Step 1.2 sets total number of samples as m, and all samples are divided into three groups according to the tertile of its gene expression values, wherein
Gene expression values refer to the FPKM numerical value obtained in step 1.1, are indicated with V, and V is denoted as to i-th of genei, utilize Cox ratio wind
Dangerous model calculates survival risk of the third grouping compared to the first grouping, obtains Hazard ratio HRi and the P value of i-th of gene to get going out
The Hazard ratio of each gene；Define P values<0.05 has conspicuousness, screens the survival risk gene with conspicuousness, is denoted as
n1；In addition, calculating the correlation of each gene and survival of patients number of days, the correlation coefficient r and P values of each gene are obtained；Define P
Value<0.05 has conspicuousness, screens the existence related gene with conspicuousness, is denoted as n2；By survival risk gene and existence phase
The intersection of correlation gene is defined as prognostic risk gene, is denoted as n, then has：
N=n1∩n2 (1)
Step 1.3, the weight W that i-th of gene is calculated according to the Hazard ratio of i-th of genei, calculation formula is：
The weight of each gene is thus obtained, finally obtained glioblastoma prognostic risk list of genes is weighed with gene
Weight.
3. prognostic evaluation methods according to claim 2, which is characterized in that the glioblastoma prognostic risk base
Because list is as shown in the table with gene weights：
4. prognostic evaluation methods according to claim 1, which is characterized in that utilize glioblastoma in the step 2
Specimens transcript profile and Survival data structure prognosis evaluation model are specifically implemented according to the following steps：
Step 2.1, definition gene expression values are V, according to expression value and weight calculation i-th of i-th of gene in j-th of sample
The risk score S of a patientj；Calculation formula is：
Wherein, i indicates that gene number, j indicate that sample number, m indicate total sample number, VijIndicate i-th of gene in j-th of sample
In expression value；
Step 2.2 sorts all glioblastoma clinical samples according to risk score from low to high, uses sliding window mouth mold
Type calculates average risk score to every 50 samplesCalculation formula is：
Wherein j+49 indicates rear 50 samples started counting up from sample j；
Step 2.3 carries out curve fitting to the Survival data of 50 samples using Weibull distributions, the probability of Weibull distributions
Density function is：
Wherein k > 0 are form parameters, and λ > 0 are the scale parameters of distribution；
Step 2.4 calculates every 50 samplesCorresponding kjAnd λj；Rule of thumb, kjFor a relatively-stationary number
Value, mean value are：
Wherein, kjFor j-th of sample to the form parameter of+49 sample survivorship curve Weibull distributions of jth；
Scale parameter λjVariation range it is larger, define λjWithFunctional relation be：
Wherein, λjJ-th of sample is indicated to the scale parameter of+49 sample survivorship curve Weibull distributions of jth, e is nature pair
Several bottoms, α, β are the parameter of function, take logarithm to obtain above formula：
Wherein log λjWithFor linear relationship, solved by linear fit；
According to average risk scoreWith Weibull distributed constants λjMatched curve, the functional relation obtained is：
It willSubstitute into the λ that the function obtains predictionj', λj' it is with the calculated expected distributed constant of the function, calculating λjWith λj'
Correlation obtains coefficient R2=0.983, P value=6.44E-65.
5. prognostic evaluation methods according to claim 1, which is characterized in that according to glioblastoma in the step 3
The risk score that the gene expression profile of specimens calculates patient is specifically implemented according to the following steps：Obtain glioblast
The FPKM numerical value of i-th of gene expression profile of tumor specimens, is denoted as：Vi；The corresponding weight of i-th of gene is denoted as：Wi；
Patient risk's score is denoted as：S；Calculation formula is：
Wherein i numbers for gene, and n is the gene number listed in table 1.
6. prognostic evaluation methods according to claim 1, which is characterized in that obtained according to the risk of patient in the step 4
The survival probability for dividing calculating patient annual is specifically implemented according to the following steps：Bring the risk score S of patient into Weibull distributions
Cumulative distribution function show that the survival probability function of the patient is：
Wherein t be the time, α, β, S,It is preset parameter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810440854.XA CN108733980B (en) | 2018-05-09 | 2018-05-09 | Glioblastoma multiforme personalized prognosis evaluation method based on polygene expression profile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810440854.XA CN108733980B (en) | 2018-05-09 | 2018-05-09 | Glioblastoma multiforme personalized prognosis evaluation method based on polygene expression profile |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108733980A true CN108733980A (en) | 2018-11-02 |
| CN108733980B CN108733980B (en) | 2022-01-18 |
Family
ID=63937144
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810440854.XA Active CN108733980B (en) | 2018-05-09 | 2018-05-09 | Glioblastoma multiforme personalized prognosis evaluation method based on polygene expression profile |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108733980B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109411012A (en) * | 2018-11-13 | 2019-03-01 | 中国人民解放军总医院 | Glioblastoma risk stratification model and its application assessed in clinical prognosis |
| CN109913549A (en) * | 2019-03-07 | 2019-06-21 | 北京师范大学 | Glioma molecule parting and application based on CDC20 gene co-expressing network |
| CN110197701A (en) * | 2019-04-22 | 2019-09-03 | 福建医科大学附属第一医院 | A kind of novel Huppert's disease nomogram construction method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101688240A (en) * | 2007-04-10 | 2010-03-31 | 国立台湾大学 | By survival expection after microRNA prediction cancer patients's the treatment |
| CN106295244A (en) * | 2015-06-05 | 2017-01-04 | 上海交通大学医学院附属瑞金医院 | The screening technique of diagnosing tumor mark, the breast carcinoma Lung metastases related gene obtained in this way and application thereof |
| CN106407689A (en) * | 2016-09-27 | 2017-02-15 | 牟合（上海）生物科技有限公司 | Stomach cancer prognostic marker screening and classifying method based on gene expression profile |
| US20180100199A1 (en) * | 2016-10-06 | 2018-04-12 | Gwangju Institute Of Science And Technology | Method for determining prognosis of breast cancer patient by using gene expression data |
-
2018
- 2018-05-09 CN CN201810440854.XA patent/CN108733980B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101688240A (en) * | 2007-04-10 | 2010-03-31 | 国立台湾大学 | By survival expection after microRNA prediction cancer patients's the treatment |
| CN106295244A (en) * | 2015-06-05 | 2017-01-04 | 上海交通大学医学院附属瑞金医院 | The screening technique of diagnosing tumor mark, the breast carcinoma Lung metastases related gene obtained in this way and application thereof |
| CN106407689A (en) * | 2016-09-27 | 2017-02-15 | 牟合（上海）生物科技有限公司 | Stomach cancer prognostic marker screening and classifying method based on gene expression profile |
| US20180100199A1 (en) * | 2016-10-06 | 2018-04-12 | Gwangju Institute Of Science And Technology | Method for determining prognosis of breast cancer patient by using gene expression data |
Non-Patent Citations (3)
| Title |
|---|
| CHRISTOS SOTIRIOU等: "Breast cancer classification and prognosis based on gene expression profiles from a population-based study", 《PNAS》 * |
| 李晓青等: "基于多基因表达水平的乳腺癌预后预测研究进展", 《中国肿瘤临床》 * |
| 贾红英等: "神经胶质瘤术后预后因素的研究", 《中华流行病学杂志》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109411012A (en) * | 2018-11-13 | 2019-03-01 | 中国人民解放军总医院 | Glioblastoma risk stratification model and its application assessed in clinical prognosis |
| CN109913549A (en) * | 2019-03-07 | 2019-06-21 | 北京师范大学 | Glioma molecule parting and application based on CDC20 gene co-expressing network |
| CN110197701A (en) * | 2019-04-22 | 2019-09-03 | 福建医科大学附属第一医院 | A kind of novel Huppert's disease nomogram construction method |
| CN110197701B (en) * | 2019-04-22 | 2021-08-10 | 福建医科大学附属第一医院 | Novel multiple myeloma nomogram construction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108733980B (en) | 2022-01-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108363907A (en) | A kind of adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| CN108630317A (en) | A kind of liver cancer personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| CN108648826A (en) | A kind of cancer of pancreas personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| CN108470111A (en) | A kind of gastric cancer personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| CN108733980A (en) | A kind of glioblastoma personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| Orbe et al. | Comparing proportional hazards and accelerated failure time models for survival analysis | |
| McCormack et al. | Changes and tracking of mammographic density in relation to Pike's model of breast tissue aging: a UK longitudinal study | |
| Yusoff et al. | Psychometric properties of the Malay Version of the hospital anxiety and depression scale: a study of husbands of breast cancer patients in Kuala Lumpur, Malaysia | |
| CN108611416A (en) | A kind of cervical carcinoma personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| CN108647493A (en) | A kind of clear cell carcinoma of kidney personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| CN107561280B (en) | A kind of kit for predicting breast cancer relapse | |
| CN108320806A (en) | A kind of colon cancer personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum | |
| CN108531597A (en) | A kind of detection kit for oral squamous cell carcinomas early diagnosis | |
| Foy et al. | A smoking‐based carcinogenesis model for lung cancer risk prediction | |
| CN113658696A (en) | Prediction system for jointly predicting gastric cancer prognosis based on patient age, nutritional indexes, tumor stages and tumor markers | |
| CN109358185A (en) | Rubber compounding performance prediction model and prediction technique based on extreme learning machine | |
| Vander Poorten et al. | Prognostic scoring for malignant salivary gland neoplasms | |
| CN108220445A (en) | A kind of evaluation triple negative breast cancer methods of risk assessment | |
| CN107818329A (en) | A kind of MASS SPECTRAL DATA ANALYSIS method | |
| Manton et al. | Strategies for analysing ecological health data: models of the biological risk of individuals | |
| CN111733252A (en) | Characteristic miRNA expression profile combination and early gastric cancer prediction method | |
| Ingram et al. | Risk grouping versus risk continuum in patients with clinically localized prostate cancer: a taxometric test | |
| Miceli et al. | Revising a prognostic index developed for classification purposes: an application to gastric cancer data | |
| CN109875522A (en) | A method of prediction prostate biopsy and root value criterion pathological score consistency | |
| Krishna et al. | LOSS TO FOLLOW-UP WEIGHTED ESTIMATION METHOD FOR SURVIVAL ANALYSIS |
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 |