CN108647493B - Individualized prognosis evaluation method for renal clear cell carcinoma - Google Patents

Individualized prognosis evaluation method for renal clear cell carcinoma Download PDF

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CN108647493B
CN108647493B CN201810440933.0A CN201810440933A CN108647493B CN 108647493 B CN108647493 B CN 108647493B CN 201810440933 A CN201810440933 A CN 201810440933A CN 108647493 B CN108647493 B CN 108647493B
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李文兴
李功华
黄京飞
赵旭东
代绍兴
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Kunming Institute of Zoology of CAS
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Abstract

The invention discloses a kidney clear cell carcinoma individualized prognosis evaluation method based on polygene expression profile, which comprises the following steps: obtaining a renal clear cell carcinoma prognosis risk gene list and gene weight; constructing a prognosis evaluation model by using a tumor tissue transcriptome and survival data of a renal clear cell carcinoma patient; calculating a risk score for the patient based on the gene expression profile of the tumor tissue of the renal clear cell carcinoma patient; the annual probability of survival of the patient is calculated based on the risk score of the patient. The annual survival probability of renal clear cell carcinoma patients obtained by the method of the invention is highly consistent with the actual annual survival rate (linear correlation R)20.999 and P8.21E-74). The method is proved to have high prediction accuracy and to be highly consistent with the actual survival state. Meanwhile, for each tumor patient, the invention can provide a survival probability curve specific to the patient.

Description

Individualized prognosis evaluation method for renal clear cell carcinoma
Technical Field
The invention belongs to the field of biotechnology and medicine, and particularly relates to a renal clear cell carcinoma personalized prognosis evaluation method based on a polygene expression profile.
Background
Clear cell carcinoma of the kidney is a renal cell carcinoma, accounting for 60% -70% of renal cancers. The etiology of renal clear cell carcinoma is unknown, and mainly affects the male population 60-70 years old. Patients with renal clear cell carcinoma often have a better prognosis. Global Burden of Disease (GBD) data shows that the number of people with kidney cancer worldwide in 2016 reaches 130 ten thousand, wherein the number of people with kidney cancer in china is 16.6 ten thousand. The number of deaths of kidney cancer patients worldwide in 2016 is 13.2 ten thousand, accounting for 0.24% of the total deaths. The number of deaths in 2016 in China is 1.6 ten thousand, accounting for 0.17% of the total deaths. Statistics show that the prevalence and the mortality of renal clear cell carcinoma increase rapidly and the prevalence and the mortality of Chinese increase slowly from 1990 to 2016.
The currently internationally accepted method of staging tumors is the TNM staging system, which is a method of classifying malignant tumors proposed by the American Joint Committee on Cancer (AJCC). The National Cancer Institute (NCI) describes the staging of TNM as: t refers to the size and extent of the major tumor, which is often referred to as the primary tumor. N refers to the number of nearby lymph nodes with cancer. M refers to whether the cancer has metastasized, i.e., spread from the primary tumor to other parts of the body. Malignant tumors can be roughly classified into stage I, stage II, stage III and stage IV according to the above indexes, wherein higher stage indicates higher malignancy of the tumor. The TNM staging system is helpful for the treatment and prognosis evaluation of tumor patients. However, due to the occurrence mechanism of tumors and the difference of in vivo microenvironment among different individuals, the survival time of different patients varies greatly, and the TNM staging system does not well reflect the prognosis of the patients. Studies have found that patients diagnosed with stage I may have a shorter survival (1-2 years), whereas patients diagnosed with stage IV may have a longer survival (5 years and beyond). Thus, TNM staging systems may be more likely to describe the average level of a population of cancer patients, with less suitability for personalized diagnosis and treatment. On the other hand, for patients diagnosed in the late stage (stage III and IV), it is difficult to select a certain treatment scheme for the patients and medical staff, resulting in premature death of many tumor patients who can survive for a long time due to over-medical treatment or medical misappropriation; while other patients who should be treated appropriately may have prolonged survival, giving up treatment or improper treatment may also lead to premature death of the tumor patient.
At present, there are reports that can be used to evaluate prognosis of tumor patients by using gene expression profiles. However, most reports only use a single or a plurality of genes, only classify one population, and only qualitatively classify the survival time of individuals (such as two indexes of good prognosis and poor prognosis). Therefore, there is a need to build more elaborate personalized tumor prognosis evaluation models to evaluate the survival time of patients to select appropriate treatment regimens.
Disclosure of Invention
In view of the above, the invention provides a renal clear cell carcinoma personalized prognosis evaluation method based on a polygene expression profile, which can accurately predict the annual survival probability of a patient.
In order to solve the technical problems, the invention discloses a renal clear cell carcinoma personalized prognosis evaluation method based on a polygene expression profile, which comprises the following steps:
step 1, obtaining a renal clear cell carcinoma prognosis risk gene list and gene weight;
step 2, constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the renal clear cell carcinoma patient;
step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the patient with renal clear cell carcinoma;
And 4, calculating the annual survival probability of the patient according to the risk score of the patient.
Optionally, the obtaining of the renal clear cell carcinoma prognosis risk gene list and the gene weights in step 1 specifically include:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and para-carcinoma tissues of patients with renal clear cell carcinoma from a Genomic Data common Data Portal database to obtain a tumor tissue gene expression profile FPKM value of the patients with renal clear cell carcinoma, and carrying out logarithmic conversion;
step 1.2, setting the total number of samples as m, dividing all samples into three groups according to the three quantiles of the gene expression values of the samples, wherein the gene expression values refer to the FPKM values obtained in the step 1.1 and are represented by V, and the ith gene is marked as ViCalculating the survival risk of the third grouping compared with the first grouping by using a Cox proportion risk model to obtain a risk ratio HRi and a P value of the ith gene; defining P value less than 0.05, screening survival risk gene with significance, and marking as N1(ii) a In addition, calculating the correlation between each gene and the survival days of the patient to obtain the correlation coefficient r and the P value of each gene; definition P value <0.05 significance, screening significant survival related gene, and marking as N 2(ii) a Defining the intersection of the survival risk gene and the survival related gene as a prognosis risk gene, and marking as N, then:
N＝N1∩N2 (1)
step 1.3, calculating the weight W of the ith gene according to the risk ratio HRi of the ith geneiThe calculation formula is as follows:
Figure GDA0003280409780000031
thus, the weight of each gene is obtained, and the list of the renal clear cell carcinoma prognosis risk genes and the gene weight are finally obtained.
Optionally, the list of renal clear cell carcinoma prognosis risk genes and the gene weights are shown in the following table:
Figure GDA0003280409780000032
Figure GDA0003280409780000041
Figure GDA0003280409780000051
Figure GDA0003280409780000061
Figure GDA0003280409780000071
Figure GDA0003280409780000081
optionally, the step 2 of constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the renal clear cell carcinoma patient includes:
step 2.1, defining the gene expression value as V, and calculating the risk score S of the ith patient according to the expression value and the weight of the ith gene in the jth samplej(ii) a The calculation formula is as follows:
Figure GDA0003280409780000091
wherein j represents a sample number, VijExpressing the expression value of the ith gene in the jth sample;
step 2.2, all renal clear cell carcinoma patient samples are ranked from low to high risk score, and an average risk score is calculated for each 50 samples using a sliding window model
Figure GDA0003280409780000092
The calculation formula is as follows:
Figure GDA0003280409780000093
where j +49 represents the last 50 samples counted from sample j;
Step 2.3, performing curve fitting on the survival data of 50 samples by using Weibull distribution, wherein the probability density function of the Weibull distribution is as follows:
Figure GDA0003280409780000094
where k > 0 is a shape parameter and λ > 0 is a distributed ratio parameter;
step 2.4, calculate for every 50 samples
Figure GDA0003280409780000095
Corresponding kjAnd λj(ii) a Empirically, kjIs a relatively fixed number with the mean being:
Figure GDA0003280409780000096
wherein k isjThe shape parameter of the Weibull distribution from the jth sample to the j +49 th sample;
proportional parameter lambdajIs widely varied, defining lambdajAnd
Figure GDA0003280409780000101
the functional relationship of (A) is as follows:
Figure GDA0003280409780000102
wherein λ isjA proportion parameter representing the Weibull distribution of the survival curves from the jth sample to the j +49 th sample;
where e is the base of the natural logarithm, α, β are parameters of the function, and the logarithm is taken to the above formula:
Figure GDA0003280409780000103
where log λjAnd
Figure GDA0003280409780000104
solving for linear relation through linear fitting;
according to average risk score
Figure GDA0003280409780000105
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure GDA0003280409780000106
will be provided with
Figure GDA0003280409780000107
Substituting the function to obtain a predicted lambdaj′，λj' for the expected distribution parameter calculated with this function, λ is calculatediAnd λj' correlation to obtain a correlation coefficient R20.883, and 6.50E-100.
Optionally, the calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the renal clear cell carcinoma patient in the step 3 specifically comprises:
Obtaining FPKM value of ith gene expression profile of tumor tissue of renal clear cell carcinoma patient, and recording as: vl(ii) a The weight corresponding to the ith gene is recorded as: wi(ii) a The patient risk score was scored as: s; the calculation formula is as follows:
Figure GDA0003280409780000108
wherein i is the gene number and n is the gene number.
Optionally, the step 4 of calculating the annual survival probability of the patient according to the risk score of the patient specifically includes: substituting the risk score S of a patient into the cumulative distribution function of the Weibull distribution yields a survival probability function for that patient as:
Figure GDA0003280409780000111
wherein t is time, alpha, beta, S,
Figure GDA0003280409780000112
Are all fixed parameters.
Compared with the prior art, the invention can obtain the following technical effects:
1) and (2) continuously: the invention can predict the survival probability of tumor patients in continuous time. For example, the probability of survival of the patient per month, the probability of survival of the patient per year, etc. may be given. The current clinical typing method can only give a qualitative judgment.
2) More accurate: compared with the traditional TNM staging, the individual prognosis evaluation method for the renal clear cell carcinoma based on the polygene expression profile can more accurately reflect the survival state of the patient.
3) Personalization: for each tumor patient, the invention can give a survival probability curve specific to the patient, which is not possessed by a general tumor prognosis evaluation model.
Of course, it is not necessary for any one product in which the invention is practiced to achieve all of the above-described technical effects simultaneously.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a comparison of the predicted average annual survival probability and the actual annual survival probability of the present invention;
FIG. 2 is a correlation of TNM tumor stage and patient survival time according to the present invention;
FIG. 3 is a curve fitted to the Weibull distribution parameter scale according to the present invention for the average risk score;
FIG. 4 is a plot of the fitted residuals of the mean risk scores of the present invention with the Weibull distribution parameter scale;
FIG. 5 shows the result of the prognosis evaluation of the personalized renal clear cell carcinoma according to the present invention.
Detailed Description
The following embodiments are described in detail with reference to the accompanying drawings, so that how to implement the technical features of the present invention to solve the technical problems and achieve the technical effects can be fully understood and implemented.
The invention discloses a kidney clear cell carcinoma individualized prognosis evaluation method based on polygene expression profile, which comprises the following steps:
Step 1, obtaining a renal clear cell carcinoma prognosis risk gene list and gene weight, specifically:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and para-carcinoma tissues of patients with renal clear cell carcinoma from a Genomic Data common Data Portal database to obtain a tumor tissue gene expression profile FPKM (gene expression profiling for renal clear cell carcinoma patients) (FPKM)Fragments Per Kilobase of transcript per Mill fragments mapped) values, log transformed (log 2).
Step 1.2, assuming the total number of samples as m, all samples were scored as V for the ith gene based on their gene expression values (FPKM value obtained in step 1.1, denoted by V)i) The third quantile is divided into three groups, the survival risk of the third group compared with the first group is calculated by using a Cox proportional risk model, and the risk ratio HRi and the P value of the ith gene are obtained. Defining P value less than 0.05, screening survival risk gene with significance, and marking as N1. In addition, the correlation between each gene and the survival days of the patient is calculated, and the correlation coefficient r and the P value of each gene are obtained. Defining P value less than 0.05, screening survival related gene with significance, and marking as N2. Defining the intersection of the survival risk gene and the survival related gene as a prognosis risk gene, and marking as N, then:
N＝N1∩N2 (1)
Step 1.3, calculating the weight W of the ith gene according to the risk ratio HRi of the ith geneiThe calculation formula is as follows:
Figure GDA0003280409780000121
thus, the weight of each gene is calculated.
The list of the risk genes and the gene weights of the prognosis renal clear cell carcinoma are shown in table 1.
TABLE 1 Gene names and weights
Figure GDA0003280409780000122
Figure GDA0003280409780000131
Figure GDA0003280409780000141
Figure GDA0003280409780000151
Figure GDA0003280409780000161
Figure GDA0003280409780000171
Figure GDA0003280409780000181
Step 2, constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the renal clear cell carcinoma patient, which specifically comprises the following steps:
step 2.1, defining the gene expression value as V, and calculating the risk score S of the ith patient according to the expression value and the weight of the ith gene in the jth samplej(ii) a The calculation formula is as follows:
Figure GDA0003280409780000182
wherein j represents a sample number, VijExpressing the expression value of the ith gene in the jth sample;
step 2.2, all renal clear cell carcinoma patient samples were ranked from low to high risk score using a sliding window model (Kang HJ et alre.2011; 478(7370): 483-489.) calculate the average risk score for each 50 samples
Figure GDA0003280409780000183
The calculation formula is as follows:
Figure GDA0003280409780000184
where j +49 represents the last 50 samples counted from sample j.
Step 2.3, performing curve fitting on the survival data of 50 samples by using Weibull distribution, wherein the probability density function of the Weibull distribution is as follows:
Figure GDA0003280409780000185
Where k > 0 is the shape (shape) parameter and λ > 0 is the scale of distribution (scale) parameter.
Step 2.4, calculate for every 50 samples
Figure GDA0003280409780000191
Corresponding kjAnd λj. Empirically, kjIs a relatively fixed number with the mean being:
Figure GDA0003280409780000192
wherein k isjThe shape parameter of the Weibull distribution from the jth sample to the j +49 sample survival curve is the same as k in the above, wherein j is added to refer to a specific group of samples;
proportional parameter lambdajIs widely varied, defining lambdajAnd
Figure GDA0003280409780000193
the functional relationship of (A) is as follows:
Figure GDA0003280409780000194
wherein λ isjA proportion parameter representing the Weibull distribution of the survival curves from the jth sample to the j +49 th sample;
where e is the base of the natural logarithm, α, β are parameters of the function, and the logarithm is taken to obtain the formula:
Figure GDA0003280409780000195
where log λjAnd
Figure GDA0003280409780000196
the linear relationship can be solved by linear fitting.
FIG. 3 shows the average risk score
Figure GDA0003280409780000197
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure GDA0003280409780000198
will be provided with
Figure GDA0003280409780000199
Substituting the function to obtain a predicted lambdaj′(λj' is an expected distribution parameter calculated using the function), lambda is calculatedjAnd λj' correlation can be given by a correlation coefficient R20.883, and 6.50E-100.
By analyzing the fitted residual map and the Q-Q map (FIG. 4), it was shown that the model achieved significance, i.e., the average risk score
Figure GDA00032804097800001910
With Weibull distribution parameter lambdajIs in a functional relationship ofThe letter is as follows.
Step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the renal clear cell carcinoma patient, which comprises the following specific steps:
FPKM values (including all or most of the genes listed in table 1) were obtained for the ith gene expression profile of tumor tissue from renal clear cell carcinoma patients and are reported as: vi(i is the gene number); the weights corresponding to the ith gene in table 1 are given as: wi(i is the gene number); the patient risk score was scored as: s; the calculation formula is as follows:
Figure GDA0003280409780000201
wherein i is the gene number and n is the number of genes listed in Table 1.
Step 4, calculating the annual survival probability of the patient according to the risk score of the patient, specifically: substituting the risk score S of a patient into the cumulative distribution function of the Weibull distribution yields a probability of survival function for that patient as:
Figure GDA0003280409780000202
wherein t is time, alpha, beta, S,
Figure GDA0003280409780000203
Are all fixed parameters.
FIG. 5 shows the survival probability curve for a patient, with days on the abscissa and probability of survival on the ordinate. The annual survival probability of the patient is indicated below the curve. The black box in the upper right corner indicates the actual number of days the patient survived, and Status (Status)0 indicates that the patient was still alive. The red dots on the curve mark the number of days and probability of survival for the patient, and the probability of survival for the patient is around 0.64.
The invention utilizes TCGA-KIRC transcriptome and clinical data to carry out personalized survival prediction on all renal clear cell carcinoma patients, and utilizes a cross validation method to carry out survival prediction on all renal clear cell carcinoma patientsThe results obtained were verified. The results show that the annual survival probability of the renal clear cell carcinoma patients obtained by the renal clear cell carcinoma individualized prognosis evaluation method adopting the multi-gene expression profile is highly consistent with the actual annual survival rate (linear correlation R)20.999, P-value 8.21E-74, fig. 1). The method is proved to have high prediction accuracy and to be highly consistent with the actual survival state.
As shown in fig. 2, TNM staging has a lower correlation with survival time for renal clear cell carcinoma patients. Fig. 1 and fig. 2 can be compared to obtain the individual prognosis evaluation method of renal clear cell carcinoma based on multi-gene expression profile, which can reflect the survival status of the patient more accurately than the traditional TNM stage.
As shown in FIG. 5, for each tumor patient, the present invention can give a survival probability curve (FIG. 5) specific to the patient, which is not available in general tumor prognosis evaluation models.
While the foregoing description shows and describes several preferred embodiments of the invention, it is to be understood, as noted above, that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (2)

1. A method for personalized prognosis of renal clear cell carcinoma, comprising the steps of:
step 1, obtaining a renal clear cell carcinoma prognosis risk gene list and gene weight;
step 2, constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the renal clear cell carcinoma patient;
step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the patient with renal clear cell carcinoma;
step 4, calculating the annual survival probability of the patient according to the risk score of the patient;
the obtaining of the renal clear cell carcinoma prognosis risk gene list and the gene weight in the step 1 are specifically as follows:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and para-carcinoma tissues of patients with renal clear cell carcinoma from a Genomic Data common Data Portal database to obtain a tumor tissue gene expression profile FPKM value of the patients with renal clear cell carcinoma, and carrying out logarithmic conversion;
step 1.2, setting the total number of samples as m, dividing all samples into three groups according to the three quantiles of the gene expression values of the samples, wherein the gene expression values refer to the FPKM values obtained in the step 1.1 and are represented by V, and the ith gene is marked as ViCalculating the survival risk of the third grouping compared with the first grouping by using a Cox proportion risk model to obtain a risk ratio HRi and a P value of the ith gene; defining P value <0.05 significant, screening significant survival risk gene, and marking as N1(ii) a In addition, calculating the correlation between each gene and the survival days of the patient to obtain the correlation coefficient r and the P value of each gene; defining P value<0.05 significance, screening significant survival related gene, and marking as N2(ii) a Defining the intersection of the survival risk gene and the survival related gene as a prognosis risk gene, and marking as N, then:
N＝N1∩N2 (1)
step 1.3, calculating the weight W of the ith gene according to the risk ratio HRi of the ith geneiThe calculation formula is as follows:
Figure FDA0003280409770000021
thus obtaining the weight of each gene, and finally obtaining a renal clear cell carcinoma prognosis risk gene list and the gene weight;
the list of risk genes and the gene weights for prognosis of renal clear cell carcinoma are shown in the following table:
Figure FDA0003280409770000022
Figure FDA0003280409770000031
Figure FDA0003280409770000041
Figure FDA0003280409770000051
Figure FDA0003280409770000061
Figure FDA0003280409770000071
in the step 2, a prognosis evaluation model is constructed by using the tumor tissue transcriptome and survival data of the renal clear cell carcinoma patient, and the method specifically comprises the following steps:
step 2.1, defining the gene expression value as V, and calculating the risk score S of the ith patient according to the expression value and the weight of the ith gene in the jth samplej(ii) a The calculation formula is as follows:
Figure FDA0003280409770000072
wherein j represents a sample number, VijExpressing the expression value of the ith gene in the jth sample;
Step 2.2, all renal clear cell carcinoma patient samples are ranked from low to high according to risk score, and the average risk is calculated for each 50 samples using a sliding window modelScore of
Figure FDA0003280409770000073
The calculation formula is as follows:
Figure FDA0003280409770000074
where j +49 represents the last 50 samples counted from sample j;
step 2.3, performing curve fitting on the survival data of 50 samples by using Weibull distribution, wherein the probability density function of the Weibull distribution is as follows:
Figure FDA0003280409770000081
where k > 0 is a shape parameter and λ > 0 is a distributed ratio parameter;
step 2.4, calculate for every 50 samples
Figure FDA0003280409770000082
Corresponding kjAnd λj；kjIs a fixed number with the mean:
Figure FDA0003280409770000083
wherein k isjThe shape parameter of the Weibull distribution from the jth sample to the j +49 th sample;
definition of lambdajAnd
Figure FDA0003280409770000084
the functional relationship of (A) is as follows:
Figure FDA0003280409770000085
wherein λ isjA proportion parameter representing the Weibull distribution of the survival curves from the jth sample to the j +49 th sample;
where e is the base of the natural logarithm, α, β are parameters of the function, and the logarithm is taken to the above formula:
Figure FDA0003280409770000086
where log λjAnd
Figure FDA0003280409770000087
solving for linear relation through linear fitting;
according to average risk score
Figure FDA0003280409770000088
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure FDA0003280409770000089
will be provided with
Figure FDA0003280409770000091
Substituting the function to obtain a predicted lambdaj′，λj' for the expected distribution parameter calculated with this function, λ is calculated jAnd λj' correlation to obtain a correlation coefficient R20.883, P value 6.50E-100;
the step 3 of calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the renal clear cell carcinoma patient specifically comprises the following steps:
obtaining FPKM value of ith gene expression profile of tumor tissue of renal clear cell carcinoma patient, and recording as: vi(ii) a The weight corresponding to the ith gene is recorded as: wi(ii) a The patient risk score was scored as: s; the calculation formula is as follows:
Figure FDA0003280409770000092
wherein i is the gene number and n is the gene number.
2. The prognostic assessment method according to claim 1, wherein the step 4 of calculating the annual survival probability of the patient based on the risk score of the patient comprises: substituting the risk score S of a patient into the cumulative distribution function of the Weibull distribution yields a survival probability function for that patient as:
Figure FDA0003280409770000093
wherein t is time, alpha, beta, S,
Figure FDA0003280409770000094
Are all fixed parameters.
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