CN108630317B - Liver cancer personalized prognosis evaluation method based on polygene expression profile - Google Patents

Liver cancer personalized prognosis evaluation method based on polygene expression profile Download PDF

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CN108630317B
CN108630317B CN201810440101.9A CN201810440101A CN108630317B CN 108630317 B CN108630317 B CN 108630317B CN 201810440101 A CN201810440101 A CN 201810440101A CN 108630317 B CN108630317 B CN 108630317B
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李文兴
李功华
黄京飞
赵旭东
代绍兴
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Abstract

The invention discloses a gene expression method based on polygeneThe liver cancer personalized prognosis evaluation method of the characteristic spectrum comprises the following steps: acquiring a liver cancer prognosis risk gene list and gene weight; constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the liver cancer patient; calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the liver cancer patient; the annual probability of survival of the patient is calculated based on the risk score of the patient. The annual survival probability of liver cancer patients obtained by the method of the invention is highly consistent with the actual annual survival rate (linear correlation R)20.992, P-6.18E-37). 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

Liver cancer 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 liver cancer personalized prognosis evaluation method based on a polygene expression profile.
Background
Liver cancer is a highly malignant tumor in China and all over the world, and the morbidity and mortality in developing countries such as China are generally higher than in developed countries. The incidence and mortality of liver cancer in men are higher than those in women worldwide. Global Burden of Disease (GBD) data shows that the number of people with liver cancer worldwide in 2016 reaches 100 ten thousand, and the number of people with liver cancer in china reaches 67 ten thousand. The number of deaths of the liver cancer patients worldwide in 2016 is 83 thousands, accounting for 1.52% of the total deaths. The number of deaths in 2016 in China exceeds 42 thousands, accounting for 4.42% of the total deaths. Statistics show that the prevalence and the mortality of liver cancer are continuously increased globally from 1990 to 2016, the prevalence and the mortality of China are also continuously increased, and the increasing trend is relatively consistent with the global increasing trend. Data show that the number of patients with liver cancer in China and all over the world is rapidly increasing in recent years.
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 liver cancer 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 liver cancer personalized prognosis evaluation method based on a polygene expression profile,
the method comprises the following steps:
step 1, acquiring a risk gene list and gene weight for liver cancer prognosis;
step 2, constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the liver cancer patient;
step 3, calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the liver cancer patient;
and 4, calculating the annual survival probability of the patient according to the risk score of the patient.
Optionally, the obtaining of the liver cancer prognosis risk gene list and the gene weight in step 1 specifically include:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and tissues beside a liver cancer patient from a Genomic Data common Data Portal database to obtain a gene expression profile FPKM value of the tumor tissues of the liver cancer patient, and carrying out logarithmic conversion;
step 1.2, setting the total number of samples as m, dividing all the samples into three groups according to the tertile number of the gene i expression value for each gene i obtained in the step 1.1, calculating the survival risk of the third group compared with the first group by using a Cox proportional risk model, and obtaining the risk ratio HRi and the 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 of the ith geneiThe calculation formula is as follows:
Figure GDA0001701082710000031
thus, the weight of each gene is calculated; finally obtaining a liver cancer prognosis risk gene list and gene weight.
Optionally, the list of liver cancer prognosis risk genes and the gene weights are shown in the following table:
Figure GDA0001701082710000032
Figure GDA0001701082710000041
Figure GDA0001701082710000051
Figure GDA0001701082710000061
optionally, the step 2 of constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the liver cancer patient specifically comprises:
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 genej(ii) a The calculation formula is as follows:
Figure GDA0001701082710000062
j represents a sample number, VijExpressing the expression value of the ith gene in the jth sample;
step 2.2, all liver cancer patient samples are sorted from low to high according to risk scores, and a sliding window model is used for every 50Calculate the average Risk score for each sample
Figure GDA0001701082710000071
The calculation formula is as follows:
Figure GDA0001701082710000072
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 GDA0001701082710000073
where k > 0 is a shape parameter and λ > 0 is a distributed ratio parameter;
step 2.4, calculate for every 50 samples
Figure GDA0001701082710000074
Corresponding kjAnd λj(ii) a Empirically, kjIs a relatively fixed number with the mean being:
Figure GDA0001701082710000075
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 GDA0001701082710000076
the functional relationship of (A) is as follows:
Figure GDA0001701082710000077
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 GDA0001701082710000081
where log λjAnd
Figure GDA0001701082710000082
solving for linear relation through linear fitting;
according to average risk score
Figure GDA0001701082710000083
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure GDA0001701082710000084
will be provided with
Figure GDA0001701082710000085
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 R2P-value is 0.900, 8.27E-51.
Optionally, the calculating the risk score of the patient according to the gene expression profile of the tumor tissue of the liver cancer patient in step 3 specifically comprises:
obtaining FPKM value of the gene expression profile of the tumor tissue of the liver cancer patient, and recording as: viWherein i is the gene number; 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 GDA0001701082710000086
wherein i is the gene number and n is the gene number.
Optionally, the probability of survival of the patient per year is calculated according to the risk score of the patient in step 4, specifically: 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 GDA0001701082710000087
wherein t is time, alpha, beta, S,
Figure GDA0001701082710000088
Are all fixed parameters.
Compared with the prior art, the invention can obtain the following technical effects:
1) and (2) continuously: can predict the survival probability of the liver cancer patient 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 liver cancer personalized prognosis evaluation method based on the multi-gene 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 personalized liver cancer prognosis evaluation 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 liver cancer personalized prognosis evaluation method based on a polygene expression profile, which comprises the following steps:
step 1, obtaining a liver cancer prognosis risk gene list and gene weights, specifically:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and tissues beside liver cancer patients from a Genomic Data common Data Portal database to obtain a tumor tissue gene expression profile FPKM (gene expression profiling for liver cancer patients) (FPKM)Fragments Per Kilobase of transcript per Mill fragments mapped) values, log transformed (log 2).
And 1.2, setting the total number of samples as m, dividing all the samples into three groups according to the tertile number of the gene i expression value for each gene i obtained in the step 1.1, and calculating the survival risk of the third group compared with the first group by using a Cox proportional risk model to obtain the risk ratio HRi and the P value of the ith gene. Defining P value<0.05 significant, screening significant survival risk gene, 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<0.05 significance, screening significant survival related gene, and marking as n2. Intersecting the survival risk gene and the survival-related geneThe definition of the prognostic risk gene is marked as n, and 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 GDA0001701082710000101
thus, the weight of each gene is calculated.
The list of the finally obtained liver cancer prognosis risk genes and the gene weights are shown in table 1.
TABLE 1 Gene names and weights
Figure GDA0001701082710000102
Figure GDA0001701082710000111
Figure GDA0001701082710000121
Figure GDA0001701082710000131
Step 2, constructing a prognosis evaluation model by using the tumor tissue transcriptome and survival data of the liver cancer patient;
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 genej(ii) a The calculation formula is as follows:
Figure GDA0001701082710000141
j represents a sample number, VijExpressing the expression value of the ith gene in the jth sample;
step 2.2, all liver cancer patient samples are ranked from low to high according to risk score, and an average risk score is calculated for each 50 samples using a sliding window model (Kang HJ et al
Figure GDA0001701082710000142
The calculation formula is as follows:
Figure GDA0001701082710000143
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 GDA0001701082710000144
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 GDA0001701082710000145
Corresponding kjAnd λj. Empirically, kjIs a relatively fixed number with the mean being:
Figure GDA0001701082710000146
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 GDA0001701082710000147
the functional relationship of (A) is as follows:
Figure GDA0001701082710000151
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 GDA0001701082710000152
where log λjAnd
Figure GDA0001701082710000153
the linear relationship can be solved by linear fitting.
FIG. 3 shows the average risk score
Figure GDA0001701082710000154
With Weibull distribution parameter lambdajThe obtained function relationship is as follows:
Figure GDA0001701082710000155
will be provided with
Figure GDA0001701082710000156
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 R2P-value is 0.900, 8.27E-51.
By analyzing the fitted residual map and the Q-Q map (FIG. 4), it was shown that the model achieved significance, i.e., mean riskIs divided into
Figure GDA0001701082710000157
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 liver cancer patient, which comprises the following steps:
the FPKM values for the gene expression profiles (including all or most of the genes listed in table 1) obtained for the tumor tissues of patients with liver cancer were recorded 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 GDA0001701082710000158
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 GDA0001701082710000161
wherein t is time, alpha, beta, S,
Figure GDA0001701082710000162
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 that the patient survived, and Status (Status)1 indicates that the patient has died. The red dots (i.e., the Death dots) on the curve 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 0.22.
In conclusion, the invention utilizes TCGA-LIHC transcriptome and clinical data to perform personalized survival prediction on all liver cancer patients, and utilizes a cross validation method to validate the obtained results. The result shows that the annual survival probability of the liver cancer patient obtained by the liver cancer personalized prognosis evaluation method adopting the multi-gene expression profile is highly consistent with the actual annual survival rate (linear correlation R)20.992, P-value 6.18E-37, 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 did not correlate with survival time of liver cancer patients (P value greater than 0.05). Compared with fig. 2, fig. 1 can obtain the liver cancer personalized prognosis evaluation method based on the polygene expression profile, and compared with the traditional TNM staging, the survival status of the patient can be reflected more accurately.
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 (1)

1. A gene combination for personalized prognosis evaluation of liver cancer, which comprises:
Figure FDA0003279412620000011
Figure FDA0003279412620000021
Figure FDA0003279412620000031
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