CN108363907A - A kind of adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum - Google Patents
A kind of adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum Download PDFInfo
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Abstract
The invention discloses a kind of adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum, include the following steps：Obtain adenocarcinoma of lung prognostic risk list of genes and gene weights；Prognosis evaluation model is built using patients with lung adenocarcinoma tumor tissues transcript profile and Survival data；The risk score of patient is calculated according to the gene expression profile of patients with lung adenocarcinoma tumor tissues；The annual survival probability of patient is calculated according to the risk score of patient.The annual survival probability of patients with lung adenocarcinoma and the practical annual survival highly consistent (linear correlation R of ratio that the method for the present invention obtains2=0.994, P value=2.86E 43).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
Adenocarcinoma of lung personalization prognostic evaluation methods.
Background technology
Adenocarcinoma of lung accounts for about the 40% of all patients with lung cancer.Lung cancer is the highest tumour of global incidence, and leads to male
The first cause of cancer mortality.The incidence of lung cancer is only second to breast cancer in women population.Global disease burden (Global
Burden of Disease, GBD) data show, number of the whole world with trachea-bronchial epithelial cell or lung cancer is more than 280 within 2016
Ten thousand, wherein Chinese number of patients is up to 1,000,000.Death toll of the whole world with above-mentioned cancer is 1,700,000 within 2016, accounts for total death
The 3.12% of number.China's Died Patients number in 2016 is 590,000, accounts for the 6.11% of total death toll.Statistical result showed, from
Nineteen ninety global trachea-bronchial epithelial cell and lung cancer illness rate and death rate sustainable growth, Chinese illness rate and the death rate by 2016
Also sustainable growth and growth trend and global growth trend are relatively uniform.
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 adenocarcinoma of lung personalization prognosis evaluation side based on multi-gene expression characteristic spectrum
Method, 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 adenocarcinoma of lung individual characteies based on multi-gene expression characteristic spectrum
Change prognostic evaluation methods,
Include the following steps：
Step 1 obtains adenocarcinoma of lung prognostic risk list of genes and gene weights；
Step 2 builds prognosis evaluation model using patients with lung adenocarcinoma tumor tissues transcript profile and Survival data；
Step 3, the risk score that patient is calculated according to the gene expression profile of patients with lung adenocarcinoma tumor tissues；
Step 4 calculates the annual survival probability of patient according to the risk score of patient.
Optionally, the acquisition adenocarcinoma of lung prognostic risk list of genes in the step 1 with gene weights specifically according to following
Step is implemented：
Step 1.1 downloads patients with lung adenocarcinoma tumour from Genomic Data Commons Data Portal databases
Tissue and cancer beside organism's transcript profile data and clinical data obtain patients with lung adenocarcinoma tumor tissues gene expression profile FPKM numbers
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,
In, gene expression values are the FPKM numerical value obtained in step 1.1, are indicated with V, V is denoted as to i-th of genei, utilize Cox ratios
Risk model calculates survival risk of the third grouping compared to the first grouping, obtains Hazard ratio HR and the P value of each 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 each gene is calculated according to the Hazard ratio of i-th of genei, calculation formula is：
Wherein i indicates gene number, HRiIndicate the Hazard ratio of i-th of gene；
The weight of each gene is thus calculated；Finally obtained adenocarcinoma of lung prognostic risk list of genes and gene
Weight.
Optionally, the adenocarcinoma of lung prognostic risk list of genes specifically see the table below with gene weights：
Optionally, being commented using patients with lung adenocarcinoma tumor tissues transcript profile and Survival data structure prognosis in the step 2
Estimate model to be specifically 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 gene number, VijIndicate expression value of i-th of gene in j-th of sample；
Step 2.2 sorts all patients with lung adenocarcinoma samples according to risk score from low to high, uses sliding window model
Average risk score is calculated 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 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, 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=0.943, P value=2.96E-97.
Optionally, the risk that patient is calculated according to the gene expression profile of patients with lung adenocarcinoma tumor tissues in the step 3
Score is specifically implemented according to the following steps：
The FPKM numerical value for obtaining the gene expression profile of patients with lung adenocarcinoma tumor tissues, is denoted as：Vi, wherein i compiles for gene
Number；The corresponding weight of i-th gene is denoted as：Wi, wherein i numbers for gene；Patient risk's score is denoted as：S；Calculation formula is：
Wherein i numbers for gene, and n is gene number.
Optionally, in the step 4 according to the risk score of patient calculate the annual survival probability of patient specifically according to
Following steps are implemented：The cumulative distribution function that the risk score S of patient is brought into Weibull distributions show that the survival of the patient is general
Rate 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：It can predict the survival probability of tumor patient continuous time.Such as the existence of patient's every month can be provided
The annual survival probability etc. of probability, patient.And the clinical classifying method used can only provide a qualitatively judgement at present.
2) more accurate：Adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum divide compared to tradition TNM
Phase can more accurately reflect the survival condition of 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 adenocarcinoma of lung 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 adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum, including with
Lower step：
Step 1 obtains adenocarcinoma of lung prognostic risk list of genes and gene weights；
Step 1.1 downloads patients with lung adenocarcinoma tumour from Genomic Data Commons Data Portal databases
Tissue and cancer beside organism's transcript profile data and clinical data obtain patients with lung adenocarcinoma tumor tissues gene expression profile FPKM
(Fragments Per Kilobase of transcript per MIllion fragments mapped) numerical value, it 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 are the FPKM numerical value obtained in step 1.1, are indicated with V, V is denoted as to i-th of genei, utilize Cox ratios
Risk model calculates survival risk of the third grouping compared to the first grouping, obtains Hazard ratio HR and the P value of each 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 each gene is calculated according to the Hazard ratio of i-th of genei, calculation formula is：
Wherein i indicates gene number, HRiIndicate the Hazard ratio of i-th of gene.
The weight of each gene is thus calculated；Finally obtained adenocarcinoma of lung prognostic risk list of genes and gene
Weight is shown in Table 1.
1 Gene Name of table and weight
Step 2 builds prognosis evaluation model using patients with lung adenocarcinoma tumor tissues transcript profile and Survival data；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 i indicates that gene number, j indicate patient code, VijIndicate expression value of i-th of gene in j-th of sample；
Step 2.2 sorts all patients with lung adenocarcinoma samples according to risk score from low to high, uses sliding window model
Average risk score is calculated 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 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, 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' it is with the calculated expected distributed constant of the function, calculating λjWith
λj' correlation can obtain coefficient R2=0.943, P value=2.96E-97.
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 patients with lung adenocarcinoma tumor tissues；Specially：
The FPKM numerical value for obtaining the gene expression profile of patients with lung adenocarcinoma tumor tissues (should be comprising in wholly or largely table 1
Listed gene), 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 numbers for gene)；Suffer from
Person's risk 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 score S of patient is brought into the cumulative distribution function of Weibull distributions can show that the survival of the patient is general
Rate function is：
Wherein t be the time, α, β, S,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) 1 indicate that patient is dead.Red point (i.e. Death) marks corresponding number of days and survival when death on curve
Probability, in figure when death corresponding survival probability 0.22 or so.
In conclusion the present invention utilizes TCGA-LUAD transcript profiles and clinical data, all patients with lung adenocarcinoma are carried out 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 annual survival probability of the patients with lung adenocarcinoma that obtains of adenocarcinoma of lung personalization prognostic evaluation methods of expression characteristic spectrum with it is practical annual
Survival highly consistent (the linear correlation R of ratio2=0.994, P value=2.86E-43, Fig. 1).It is very high to confirm that this method has
Forecasting accuracy is identical with practical survival condition.
As shown in Fig. 2, TNM stage and the life span of patients with lung adenocarcinoma only have lower negative correlation.Fig. 1 and Fig. 2 phases
Comparing can show that the adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum can compared to traditional TNM stage
More accurately reflect the survival condition of patient.
As shown in figure 5, 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 adenocarcinoma of lung personalization prognostic evaluation methods based on multi-gene expression characteristic spectrum, which is characterized in that including following
Step：
Step 1 obtains adenocarcinoma of lung prognostic risk list of genes and gene weights；
Step 2 builds prognosis evaluation model using patients with lung adenocarcinoma tumor tissues transcript profile and Survival data；
Step 3, the risk score that patient is calculated according to the gene expression profile of patients with lung adenocarcinoma tumor tissues；
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 adenocarcinoma of lung prognosis in the step 1
Risk genes list is specifically implemented according to the following steps with gene weights：
Step 1.1 downloads patients with lung adenocarcinoma tumor tissues from Genomic Data Commons Data Portal databases
With cancer beside organism's transcript profile data and clinical data, patients with lung adenocarcinoma tumor tissues gene expression profile FPKM numerical value is obtained, into
Row 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 are the FPKM numerical value obtained in step 1.1, are indicated with V, V is denoted as to i-th of genei, utilize Cox Proportional hazards
Model calculates survival risk of the third grouping compared to the first grouping, obtains Hazard ratio HR and the P value of each gene；Define P values<
0.05 has conspicuousness, screens the survival risk gene with conspicuousness, is denoted as n1；It is given birth to patient in addition, calculating each gene
The correlation for depositing number of days obtains the correlation coefficient r and P values of each gene；Define P values<0.05 there is conspicuousness, screening to have aobvious
The existence related gene of work property, is denoted as n2；The intersection of survival risk gene and existence related gene is defined as prognostic risk base
Cause is denoted as n, then has：
N=n1∩n2 (1)
Step 1.3, the weight W that each gene is calculated according to the Hazard ratio of i-th of genei, calculation formula is：
Wherein i indicates gene number, HRiIndicate the Hazard ratio of i-th of gene；
The weight of each gene is thus calculated；Finally obtained adenocarcinoma of lung prognostic risk list of genes is weighed with gene
Weight.
3. prognostic evaluation methods according to claim 1, which is characterized in that the adenocarcinoma of lung prognostic risk list of genes
It specifically see the table below with gene weights：
4. prognostic evaluation methods according to claim 1, which is characterized in that utilize patients with lung adenocarcinoma in the step 2
Tumor tissues 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 gene number, VijIndicate expression value of i-th of gene in j-th of sample；
Step 2.2 sorts all patients with lung adenocarcinoma samples according to risk score from low to high, using sliding window model to every
50 samples calculate average risk scoreCalculation 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 shape (shape) parameters, and λ ＞ 0 are ratio (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=0.943, P value=2.96E-97.
5. prognostic evaluation methods according to claim 1, which is characterized in that in the step 3 according to patients with lung adenocarcinoma
The risk score that the gene expression profile of tumor tissues calculates patient is specifically implemented according to the following steps：
The FPKM numerical value for obtaining the gene expression profile of patients with lung adenocarcinoma tumor tissues, is denoted as：Vi, wherein i numbers for gene；I-th
The corresponding weight of gene is denoted as：Wi, wherein 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.
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 and is specifically implemented according to the following steps：Bring the risk score S of patient into Weibull points
The cumulative distribution function of cloth 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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