CN108733980B - Glioblastoma multiforme personalized prognosis evaluation method based on polygene expression profile - Google Patents

Glioblastoma multiforme personalized prognosis evaluation method based on polygene expression profile Download PDF

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CN108733980B
CN108733980B CN201810440854.XA CN201810440854A CN108733980B CN 108733980 B CN108733980 B CN 108733980B CN 201810440854 A CN201810440854 A CN 201810440854A CN 108733980 B CN108733980 B CN 108733980B
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赵旭东
李文兴
李功华
黄京飞
代绍兴
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Kunming Institute of Zoology of CAS
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Abstract

The invention discloses a glioblastoma multiforme personalized prognosis evaluation method based on a polygene expression profile, which comprises the following steps: obtaining a glioblastoma prognosis risk gene list and gene weight; constructing a prognosis evaluation model by using a tumor tissue transcriptome and survival data of a glioblastoma patient; calculating a risk score for the patient based on the gene expression profile of the tumor tissue of the patient with glioblastoma; the annual probability of survival of the patient is calculated based on the risk score of the patient. The annual survival probability of glioblastoma patients obtained by the method of the invention is highly consistent with the actual annual survival rate (linear correlation R)20.963, P-1.38E-13). 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

Glioblastoma multiforme personalized prognosis evaluation method based on polygene expression profile
Technical Field
The invention belongs to the field of biotechnology and medicine, and particularly relates to a glioblastoma multiforme personalized prognosis evaluation method based on a polygene expression profile.
Background
Glioblastoma, a most common and most aggressive cancer of the brain. The symptoms are initially insignificant and the patient may experience headache, personality changes, nausea, symptoms like stroke, etc. Worsening symptoms then come very quickly, possibly leading to patient confusion. Global Burden of Disease (GBD) data shows that 78 million people with brain and nervous system cancer worldwide in 2016, with 26 million people in china. The number of deaths of brain cancer patients worldwide in 2016 is 23 million, accounting for 0.42% of the total deaths. The number of deaths in 2016 in China is 5.9 thousands, accounting for 0.61% of the total deaths. Statistics show that the global glioblastoma prevalence and mortality rate continues to increase from 1990 to 2016, with a slower increase in chinese prevalence and mortality rate.
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 this, the invention provides a method for assessing individualized prognosis of glioblastoma based on polygene expression profiles, which can accurately predict the annual survival probability of patients.
In order to solve the technical problems, the invention discloses a glioblastoma multiforme personalized prognosis evaluation method based on a polygene expression profile,
the method comprises the following steps:
step 1, obtaining a glioblastoma prognosis risk gene list and gene weight;
step 2, constructing a prognosis evaluation model by using a tumor tissue transcriptome and survival data of a glioblastoma patient;
step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the patient with the glioblastoma;
and 4, calculating the annual survival probability of the patient according to the risk score of the patient.
Optionally, the obtaining of the glioblastoma prognosis risk gene list and the gene weights in step 1 are specifically performed according to the following steps:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and tissues beside cancer of a glioblastoma patient from a Genomic Data common Data Portal database to obtain a gene expression profile FPKM value of the tumor tissues of the glioblastoma patient, 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 using a V tableNote that the i-th gene is denoted 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, namely the risk ratio of each 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; defining P value less than 0.05, screening survival related gene with significance, 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 of the ith geneiThe calculation formula is as follows:
Figure GDA0003275621590000031
thus, the weight of each gene is obtained, and the list of the glioblastoma prognosis risk genes and the gene weight are finally obtained.
Optionally, the list of glioblastoma prognosis risk genes and the gene weights are shown in the following table:
Figure GDA0003275621590000032
Figure GDA0003275621590000041
Figure GDA0003275621590000051
Figure GDA0003275621590000061
Figure GDA0003275621590000071
optionally, the step 2 of constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the glioblastoma multiforme patient is specifically performed according to 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 GDA0003275621590000072
wherein i represents a gene number, j represents a sample number, m represents the total number of samples, and VijExpressing the expression value of the ith gene in the jth sample;
step 2.2, all glioblastoma patient samples are ranked from low to high risk score, and the average risk score is calculated for each 50 samples using the sliding window model
Figure GDA0003275621590000073
The calculation formula is as follows:
Figure GDA0003275621590000074
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 GDA0003275621590000081
where k > 0 is a shape parameter and λ > 0 is a distributed ratio parameter;
step 2.4, calculate for every 50 samples
Figure GDA0003275621590000082
Corresponding kjAnd λj(ii) a Empirically, kjIs a relatively fixed number with the mean being:
Figure GDA0003275621590000083
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 GDA0003275621590000084
the functional relationship of (A) is as follows:
Figure GDA0003275621590000085
wherein λ isjAnd (3) a proportion parameter representing the Weibull distribution from the j sample to the j +49 sample, e is the base of a natural logarithm, alpha and beta are parameters of a function, and the logarithm is taken on the formula:
Figure GDA0003275621590000086
where log λiAnd
Figure GDA0003275621590000087
solving for linear relation through linear fitting;
according to average risk score
Figure GDA0003275621590000088
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure GDA0003275621590000089
will be provided with
Figure GDA0003275621590000091
Substituting the function to obtain a predicted lambdaj′，λj' for the expected distribution parameter calculated with this function, λ is calculatedjAnd λj' correlation to obtain a correlation coefficient R20.983, P6.44E-65.
Optionally, the step 3 of calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the glioblastoma patient is specifically performed according to the following steps: FPKM values were obtained for the ith gene expression profile of glioblastoma patient tumor tissue and are reported 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 GDA0003275621590000092
wherein i is the gene number and n is the number of genes listed in Table 1.
Optionally, the calculating the annual survival probability of the patient according to the risk score of the patient in the step 4 is specifically performed according to the following steps: substituting the patient's risk score of 5 into the cumulative distribution function of the Weibull distribution yields the patient's survival probability function as:
Figure GDA0003275621590000093
wherein t is time, alpha, beta, S,
Figure GDA0003275621590000094
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 glioblastoma multiforme 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 curve fitted to the Weibull distribution parameter scale according to the present invention for the average risk score;
FIG. 3 is a plot of the fitted residuals of the mean risk scores of the present invention with the Weibull distribution parameter scale;
FIG. 4 shows the result of the prognosis evaluation of the personalized glioblastoma of the 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 glioblastoma multiforme personalized prognosis evaluation method based on a polygene expression profile, which comprises the following steps:
step 1, obtaining a glioblastoma prognosis risk gene list and gene weight, and specifically implementing the following steps:
step 1.1, downloading the transcriptome Data and clinical Data of the tumor tissue and the adjacent tissues of the glioblastoma patient from the Genomic Data common Data Portal database to obtain the tumor tissue gene expression profile FPKM (Fragments Per Kilobase of transcript per Mill fragments mapped) values, log transformed (log 2).
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 ViAnd calculating the survival risk of the third grouping compared with the first grouping by using a Cox proportional risk model, and obtaining the risk ratio HRi and a P value of the ith gene, namely obtaining the risk ratio of each gene. 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 of the ith geneiThe calculation formula is as follows:
Figure GDA0003275621590000111
thus, the weight of each gene was obtained, and the list of genes and the weights of genes for the prognosis risk of glioblastoma were shown in table 1.
TABLE 1 Gene names and weights
Figure GDA0003275621590000112
Figure GDA0003275621590000121
Figure GDA0003275621590000131
Figure GDA0003275621590000141
Figure GDA0003275621590000151
Step 2, constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the patient with the glioblastoma, and specifically performing 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 GDA0003275621590000152
wherein i represents a gene number, j represents a sample number, m represents the total number of samples, and VijExpressing the expression value of the ith gene in the jth sample;
step 2.2, all glioblastoma patient samples are ranked from low to high risk score and the average risk score is calculated for each 50 samples using a sliding window model (Kang HJ et al, spread-temporal translation of the human brain in Nature.2011; 478 (7370): 483-
Figure GDA0003275621590000155
The calculation formula is as follows:
Figure GDA0003275621590000153
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 GDA0003275621590000154
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 GDA0003275621590000161
Corresponding kjAnd λj. Empirically, kjIs a relatively fixed number with the mean being:
Figure GDA0003275621590000162
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 GDA0003275621590000163
the functional relationship of (A) is as follows:
Figure GDA0003275621590000164
wherein λ isjThe proportion parameter of the Weibull distribution of the survival curve from the jth sample to the (j + 49) th sample is expressed, e is the base of the natural logarithm, alpha and beta are parameters of the function, and the logarithm of the formula can be takenObtaining:
Figure GDA00032756215900001610
where log λjAnd
Figure GDA0003275621590000165
the linear relationship can be solved by linear fitting.
FIG. 2 shows the average risk score
Figure GDA0003275621590000166
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure GDA0003275621590000167
will be provided with
Figure GDA0003275621590000168
Substituting the function to obtain a predicted lambdaj′，λj' for the expected distribution parameter calculated with this function, λ is calculatedjAnd λj' correlation can be given by a correlation coefficient R20.983, P6.44E-65.
By analyzing the fitted residual map and the Q-Q map (FIG. 3), it was shown that the model achieved significance, i.e., the average risk score
Figure GDA0003275621590000169
With Weibull distribution parameter lambdajIs trusted.
Step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the patient with the glioblastoma, and specifically implementing the following 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 glioblastoma 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 a groupDue to numbering); the patient risk score was scored as: s; the calculation formula is as follows:
Figure GDA0003275621590000171
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, and specifically implementing the following steps: 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 GDA0003275621590000172
wherein t is time, alpha, beta, S,
Figure GDA0003275621590000173
Are all fixed parameters.
FIG. 4 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 that the patient survived, and Status (Status)1 indicates that the patient has died. The red dots on the curve (Death dots) mark the number of days and probability of survival for the patient at Death, and the probability of survival for the patient at Death in the graph is around 0.13.
According to the invention, by utilizing TCGA-GBM transcriptome and clinical data, personalized survival prediction is carried out on all glioblastoma patients, and the obtained results are verified by utilizing a cross-validation method. The results show that the annual survival probability of the glioblastoma patients obtained by the glioblastoma personalized prognosis evaluation method adopting the multi-gene expression profile is highly consistent with the actual annual survival rate (linear correlation R)20.963, P-value 1.38E-13, 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. 1, the predicted average annual survival probability is highly consistent with the actual annual survival probability. Compared with the traditional TNM staging, the individual prognosis evaluation method for the glioblastoma based on the polygene expression profile can more accurately reflect the survival state of the patient.
As shown in FIG. 4, the present 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. 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.
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. The method for assessing individualized prognosis of glioblastoma based on multi-gene expression profiles is characterized by comprising the following steps:
step 1, obtaining a glioblastoma prognosis risk gene list and gene weight;
step 2, constructing a prognosis evaluation model by using a tumor tissue transcriptome and survival data of a glioblastoma patient;
step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the patient with the glioblastoma;
step 4, calculating the annual survival probability of the patient according to the risk score of the patient;
the obtaining of the glioblastoma cell prognosis risk gene list and the gene weight in the step 1 are specifically implemented according to the following steps:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and tissues beside cancer of a glioblastoma patient from a Genomic Data common Data Portal database to obtain a gene expression profile FPKM value of the tumor tissues of the glioblastoma patient, 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, namely the risk ratio of each 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 of the ith geneiThe calculation formula is as follows:
Figure FDA0003275621580000021
thus, the weight of each gene is obtained, and the finally obtained glioblastoma prognosis risk gene list and the gene weight are obtained;
the list of glioblastoma cell prognosis risk genes and the gene weights are shown in the following table:
Figure FDA0003275621580000022
Figure FDA0003275621580000031
Figure FDA0003275621580000041
Figure FDA0003275621580000051
in the step 2, the construction of a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the glioblastoma multiforme patient is specifically implemented according to 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 FDA0003275621580000061
wherein i represents a gene number, j represents a sample number, m represents the total number of samples, and VijExpressing the expression value of the ith gene in the jth sample;
step 2.2, all glioblastoma patient samples are ranked from low to high risk score, and the average risk score is calculated for each 50 samples using the sliding window model
Figure FDA0003275621580000062
The calculation formula is as follows:
Figure FDA0003275621580000063
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 FDA0003275621580000064
where k > 0 is a shape parameter and λ > is a distributed ratio parameter;
step 2.4, calculate for every 50 samples
Figure FDA0003275621580000065
Corresponding kjAnd λj；kjIs a fixed number with the mean:
Figure FDA0003275621580000066
wherein k isjThe shape parameter of the Weibull distribution from the jth sample to the j +49 th sample;
definition of lambdajAnd
Figure FDA0003275621580000067
the functional relationship of (A) is as follows:
Figure FDA0003275621580000071
wherein λ isjAnd (3) a proportion parameter representing the Weibull distribution from the j sample to the j +49 sample, e is the base of a natural logarithm, alpha and beta are parameters of a function, and the logarithm is taken on the formula:
Figure FDA0003275621580000072
where log λjAnd
Figure FDA0003275621580000073
solving for linear relation through linear fitting;
according to average risk score
Figure FDA0003275621580000074
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure FDA0003275621580000075
will be provided with
Figure FDA0003275621580000076
Substituting the function to obtain a predicted lambdaj′，λj' for the expected distribution parameter calculated with this function, λ is calculatedjAnd λj' correlation to obtain a correlation coefficient R20.983, P value 6.44E-65;
in the step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the patient with glioblastoma concretely comprises the following steps: FPKM values were obtained for the ith gene expression profile of glioblastoma patient tumor tissue and are reported 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 FDA0003275621580000077
wherein i is the gene number and n is the number of genes listed in the table.
2. The prognostic assessment method according to claim 1, wherein the calculation of the annual survival probability of the patient based on the risk score of the patient in step 4 is carried out in particular according to the following steps: 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 FDA0003275621580000081
wherein t is time, alpha, beta, S,
Figure FDA0003275621580000082
Are all fixed parameters.
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