CN108647493A - A kind of clear cell carcinoma of kidney personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum - Google Patents
A kind of clear cell carcinoma of kidney personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum Download PDFInfo
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Abstract
The invention discloses a kind of clear cell carcinoma of kidney personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum, include the following steps：Obtain clear cell carcinoma of kidney prognostic risk list of genes and gene weights；Prognosis evaluation model is built using clear cell carcinoma of kidney specimens transcript profile and Survival data；The risk score of patient is calculated according to the gene expression profile of clear cell carcinoma of kidney specimens；The annual survival probability of patient is calculated according to the risk score of patient.The annual survival probability of clear cell carcinoma of kidney patient and the practical annual survival highly consistent (linear correlation R of ratio that the method for the present invention obtains2=0.999, P value=8.21E 74).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
Clear cell carcinoma of kidney personalization prognostic evaluation methods.
Background technology
Clear cell carcinoma of kidney is a kind of clear-cell carcinoma, accounts for the 60%~70% of kidney.The cause of disease of clear cell carcinoma of kidney is unknown,
The main males for influencing 60-70 Sui.Clear cell carcinoma of kidney patient usually has preferable prognosis.Global disease burden
(Global Burden of Disease, GBD) data show that number of the whole world with kidney reached 1,300,000 in 2016, wherein
Chinese number of patients is 16.6 ten thousand.The death toll of global patients with renal cell carcinoma in 2016 is 13.2 ten thousand, accounts for total death toll
0.24%.China's Died Patients number in 2016 is 1.6 ten thousand, accounts for the 0.17% of total death toll.Statistical result showed, from nineteen ninety
By 2016, global clear cell carcinoma of kidney illness rate and the death rate increased very fast, and Chinese illness rate and the death rate slowly increase.
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 clear cell carcinoma of kidney 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 kidney hyaline cells based on multi-gene expression characteristic spectrum
Cancer personalization prognostic evaluation methods, include the following steps：
Step 1 obtains clear cell carcinoma of kidney prognostic risk list of genes and gene weights；
Step 2 builds prognosis evaluation model using clear cell carcinoma of kidney specimens transcript profile and Survival data；
Step 3, the risk score that patient is calculated according to the gene expression profile of clear cell carcinoma of kidney specimens；
Step 4 calculates the annual survival probability of patient according to the risk score of patient.
Optionally, the acquisition clear cell carcinoma of kidney prognostic risk list of genes in the step 1 is specially with gene weights：
Step 1.1 downloads clear cell carcinoma of kidney trouble from Genomic Data Commons Data Portal databases
Person's tumor tissues and cancer beside organism's transcript profile data and clinical data obtain clear cell carcinoma of kidney 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 the Hazard ratio HR of i-th of geneiWith P values；It is fixed
Adopted P values<0.05 has conspicuousness, screens the survival risk gene with conspicuousness, is denoted as n1；In addition, calculate each gene with
The correlation of survival of patients number of days obtains the correlation coefficient r and P values of each gene；Define P values<0.05 has conspicuousness, screening
Existence related gene with conspicuousness, is denoted as n2；The intersection of survival risk gene and existence related gene is defined as prognosis
Risk genes are denoted as n, then have：
N=n1∩n2 (1)
Step 1.3, according to the Hazard ratio HR of i-th of geneiCalculate the weight W of i-th of genei, calculation formula is：
Obtain the weight of each gene, finally obtained clear cell carcinoma of kidney prognostic risk list of genes and base in this way
Because of weight.
Optionally, the clear cell carcinoma of kidney prognostic risk list of genes and gene weights are as shown in the table：
Optionally, being built using clear cell carcinoma of kidney specimens transcript profile and Survival data in the step 2
Prognosis evaluation model, specially：
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, j indicates sample number, VijIndicate expression value of i-th of gene in j-th of sample；
Step 2.2 sorts all clear cell carcinoma of kidney 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, λjIndicate the scale parameter that j-th of sample is distributed to+49 sample survivorship curve Weibull of jth；
Wherein e is the bottom of natural logrithm, and 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=0.883, P value=6.50E-100.
Optionally, patient is calculated according to the gene expression profile of clear cell carcinoma of kidney specimens in the step 3
Risk score be specially：
The FPKM numerical value for obtaining i-th of gene expression profile of clear cell carcinoma of kidney specimens, is denoted as：Vi；I-th
The corresponding weight 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 gene number.
Optionally, the annual survival probability of patient is calculated according to the risk score of patient in the step 4, specially：
The cumulative distribution function that the risk score S of patient is brought into Weibull distributions show that the survival probability function of the patient is：
Wherein t is the time,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 clear cell carcinoma of kidney 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 correlation of TNM neoplasm stagings and survival of patients time of the present invention；
Fig. 3 is the matched curve of average risk score of the present invention and Weibull distributed constants scale；
Fig. 4 is the regression criterion figure of average risk score of the present invention and Weibull distributed constants scale；
Fig. 5 is personalized clear cell carcinoma of kidney 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 clear cell carcinoma of kidney personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum,
Include the following steps：
Step 1 obtains clear cell carcinoma of kidney prognostic risk list of genes and gene weights, specially：
Step 1.1 downloads clear cell carcinoma of kidney trouble from Genomic Data Commons Data Portal databases
Person's tumor tissues and cancer beside organism's transcript profile data and clinical data obtain clear cell carcinoma of kidney specimens gene table
Up to spectrum FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) numbers
Value carries out Logarithm conversion (log2).
Step 1.2 sets total number of samples as m, by all samples according to its gene expression values (FPKM obtained in step 1.1
Numerical value is indicated with V, and V is denoted as to i-th of genei) tertile be divided into three groups, utilize Cox proportional hazard models calculate third
Grouping obtains the Hazard ratio HR of i-th of gene compared to the survival risk of the first groupingiWith P values.Define P values<0.05 has significantly
Property, the survival risk gene with conspicuousness is screened, n is denoted as1.In addition, it is related to survival of patients number of days to calculate each gene
Property, obtain the correlation coefficient r and P values of each gene.Define P values<0.05 has conspicuousness, screens the existence phase with conspicuousness
Correlation gene is denoted as n2.The intersection of survival risk gene and existence related gene is defined as prognostic risk gene, is denoted as n, then
Have：
N=n1∩n2 (1)
Step 1.3, according to the Hazard ratio HR of i-th of geneiCalculate the weight W of i-th of genei, calculation formula is：
The weight of each gene is thus calculated.
Finally obtained clear cell carcinoma of kidney prognostic risk list of genes is shown in Table 1 with gene weights.
1 Gene Name of table and weight
Step 2 builds prognosis evaluation model, tool using clear cell carcinoma of kidney specimens transcript profile and Survival data
Body is：
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, j indicates sample number, VijIndicate expression value of i-th of gene in j-th of sample；
Step 2.2 sorts all clear cell carcinoma of kidney 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, kjIt is the form parameter that j-th of sample is distributed to+49 sample survivorship curve Weibull of jth, with above
In k meanings it is identical, refer to specific a group sample plus j here；
Scale parameter λjVariation range it is larger, define λjWithFunctional relation be：
Wherein, λjIndicate the scale parameter that j-th of sample is distributed to+49 sample survivorship curve Weibull of jth；
Wherein e is the bottom of natural logrithm, and 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 3 average risk scoreWith Weibull distributed constants λjMatched curve, the functional relation obtained
For：
It willSubstitute into the λ that the function obtains predictionj′(λj' be with the calculated expected distributed constant of the function), calculate λj
With λj' correlation can obtain coefficient R2=0.883, P value=6.50E-100.
Scheme (Fig. 4) 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 clear cell carcinoma of kidney specimens, specifically
For：
Obtain clear cell carcinoma of kidney specimens i-th of gene expression profile FPKM numerical value (should include all or
Listed gene in most of table 1), it is denoted as：Vi(i numbers for gene)；The corresponding weight of i-th of gene is denoted as in table 1：Wi(i is
Gene is numbered)；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, specially：The risk of patient is obtained
Divide the cumulative distribution function that S brings Weibull distributions into that can show that the survival probability function of the patient is：
Wherein t is the time,It is preset parameter.
It is illustrated in figure 5 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) 0 expression patient is still survived.Red put marks corresponding number of days and survival probability when patient is survived on curve, in figure
The corresponding survival probability of patient is 0.64 or so.
The present invention utilizes TCGA-KIRC transcript profiles and clinical data, and individual character has been carried out to all clear cell carcinoma of kidney patients
The Prediction of survival of change, and obtained result is verified using the method for cross validation.As a result display uses polygenes table
The annual survival probability of the clear cell carcinoma of kidney patient that is obtained up to the clear cell carcinoma of kidney personalization prognostic evaluation methods of characteristic spectrum
With the practical annual survival highly consistent (linear correlation R of ratio2=0.999, P value=8.21E-74, Fig. 1).Confirm this method
With very high forecasting accuracy, it is identical with practical survival condition.
As shown in Fig. 2, TNM stage and the life span of clear cell carcinoma of kidney patient have lower correlation.Fig. 1 and figure
2 compared to can relatively obtain the clear cell carcinoma of kidney personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum compared to tradition
TNM stage can more accurately reflect the survival condition of patient.
As shown in figure 5, for each tumor patient, the present invention can provide the distinctive survival probability curve of the patient (figure
5), this is not available for general tumor prognosis evaluation model.
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 clear cell carcinoma of kidney personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum, which is characterized in that packet
Include following steps：
Step 1 obtains clear cell carcinoma of kidney prognostic risk list of genes and gene weights；
Step 2 builds prognosis evaluation model using clear cell carcinoma of kidney specimens transcript profile and Survival data；
Step 3, the risk score that patient is calculated according to the gene expression profile of clear cell carcinoma of kidney 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 kidney hyaline cell in the step 1
Cancer prognostic risk list of genes is specially with gene weights：
Step 1.1, download clear cell carcinoma of kidney patient is swollen from Genomic Data Commons Data Portal databases
Tumor tissue and cancer beside organism's transcript profile data and clinical data obtain clear cell carcinoma of kidney 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；Define P
Value<0.05 has conspicuousness, screens the survival risk gene with conspicuousness, is denoted as n1；In addition, calculating each gene and patient
The correlation of survival day obtains the correlation coefficient r and P values of each gene；Define P values<0.05 there is conspicuousness, screening to have
The existence related gene of conspicuousness, is denoted as n2；The intersection of survival risk gene and existence 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 HRi of i-th of genei, calculation formula is：
The weight of each gene is obtained in this way, and finally obtained clear cell carcinoma of kidney prognostic risk list of genes is weighed with gene
Weight.
3. prognostic evaluation methods according to claim 2, which is characterized in that the clear cell carcinoma of kidney 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 kidney hyaline cell in the step 2
Cancer specimens transcript profile and Survival data build prognosis evaluation model, specially：
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, j indicates sample number, VijIndicate expression value of i-th of gene in j-th of sample；
Step 2.2 sorts all clear cell carcinoma of kidney 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, λjIndicate the scale parameter that j-th of sample is distributed to+49 sample survivorship curve Weibull of jth；
Wherein e is the bottom of natural logrithm, and 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=0.883, P value=6.50E-100.
5. prognostic evaluation methods according to claim 1, which is characterized in that in the step 3 according to kidney hyaline cell
The risk score that the gene expression profiles of cancer specimens calculates patient is specially：
The FPKM numerical value for obtaining i-th of gene expression profile of clear cell carcinoma of kidney specimens, is denoted as：Vi；I-th of gene
Corresponding weight is denoted as：Wi；Patient risk's score is denoted as：S；Calculation formula is：
Wherein i numbers for gene, and n is gene number.
6. prognostic evaluation methods according to claim 1, which is characterized in that the risk according to patient in the step 4
Score calculates the annual survival probability of patient, specially：The risk score S of patient is brought into the cumulative distribution of Weibull distributions
Function show that the survival probability function of the patient is：
Wherein t be the time, α, β, S,It is preset parameter.
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| CN111944898A (en) * | 2020-08-04 | 2020-11-17 | 佛山科学技术学院 | Characteristic mRNA expression profile combination and renal clear cell carcinoma early prediction method |
| CN111944899A (en) * | 2020-08-04 | 2020-11-17 | 佛山科学技术学院 | Feature miRNA expression profile combination and renal papillary cell carcinoma early prediction method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109887600A (en) * | 2019-04-16 | 2019-06-14 | 上海理工大学 | A kind of analysis method of pair of non-small cell lung cancer prognosis Survival |
| CN111944898A (en) * | 2020-08-04 | 2020-11-17 | 佛山科学技术学院 | Characteristic mRNA expression profile combination and renal clear cell carcinoma early prediction method |
| CN111944899A (en) * | 2020-08-04 | 2020-11-17 | 佛山科学技术学院 | Feature miRNA expression profile combination and renal papillary cell carcinoma early prediction method |
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