CN113517023A - Sex-related liver cancer prognosis marker factor and screening method thereof - Google Patents
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Abstract
The invention relates to sex-related liver cancer prognosis marker factors and a screening method thereof, belonging to the technical field of medicine. The invention adopts R language software to carry out statistical analysis, and adopts methods of variance analysis, random forest, principal component analysis and the like to screen the symbolic factors. A large number of liver cancer patient data samples are selected, linear regression equation coefficients between main components and all factors are obtained by means of extraction based on main component analysis total life cycle difference reasons, a head function is called, prediction is carried out, multiple regression analysis is carried out, a random forest function is called, and the like, the liver cancer data samples are obtained.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to sex-related liver cancer prognosis marker sex factors and a screening method thereof.
Background
The liver is the largest digestive gland of the human body and mainly participates in a plurality of processes of digestion, metabolism, excretion, detoxification, immunity and the like of the human body. Also, liver cancer is called a metabolic factory of the human body, and substances absorbed from the stomach and intestines almost enter the liver, and are synthesized, decomposed, transported and stored in the liver. After various causes of liver cancer damage act on the liver, liver cells and liver dysfunction can be caused to different degrees.
Liver cancer is one of common malignant tumors in China, and meanwhile, the incidence rate and the fatality rate of the liver cancer are high, the fatality rate of the liver cancer is the third place of the malignant tumors all over the world, the course of treatment of the liver cancer is short, the treatment is difficult, and the liver cancer seriously threatens the life health of human beings. With the development of medical level, measures for treating liver cancer are diversified, the existing treatment is gradually developed to be mainly combined treatment and is not single treatment any more, but the treatment effect of patients is greatly different, and the survival rate is only 7% within 5 years, so the treatment form of liver cancer is not optimistic. The survival rate of patients is influenced by various factors, the prognostic factors of the patients with different sexes are different in research, and if the research can be carried out in a targeted manner in the treatment process, the method has important significance for controlling the rapid development of liver cancer so as to improve the survival period of the patients and improve the prognosis of the liver cancer.
Disclosure of Invention
In order to solve the problems in the prior art, the invention designs a screening method of liver cancer prognosis marker factors related to gender, finds out the marker factors for the prognosis of liver cancer patients with different genders, and provides targeted diagnosis and treatment for the liver cancer patients with different genders in the aspect of prognosis clinically.
The hallmark factors of liver cancer prognosis are tumor diameter, presence or absence of lymph node metastasis and child.pugh score for female patients, and tumor diameter, child.pugh score, AFP and presence or absence of cirrhosis for male patients.
The application discloses a screening method of sex-related liver cancer prognosis marker sex factors, which uses R language software to carry out statistical analysis and adopts methods such as variance analysis, random forest, principal component analysis and the like, and the specific screening method comprises the following steps: step1, selecting statistical survival data of liver cancer patients with enough sample quantity, confirming a plurality of factors possibly related to the total survival cycle of the patients, and respectively counting the factor conditions related to the total survival cycle of male patients and female patients;
step2, statistical analysis is carried out on the collected patient samples by adopting R language software, a lithotripsy graph is obtained by extracting the general life cycle difference reason based on principal component analysis, and principal components needing to be reserved are obtained for analysis; extracting the reason of the overall life cycle difference based on the random forest, obtaining a goodness-of-fit value by calling a random forest function, and analyzing the importance of each factor;
and 3, predicting, removing factors with low correlation by utilizing multivariate regression analysis, calling a random forest function to the remaining factors to obtain a goodness-of-fit value, screening out prognostic marker factors of male and female liver cancer patients, and obtaining a final importance factor ranking result.
Further, the factors identified in step1 that are relevant to the overall life cycle of the patient are sex, age, AFP, tumor diameter, cirrhosis, HBV infection, lymph node metastasis, portal cancer emboli and child.
Further, the general life cycle difference reason extraction in the step2 based on principal component analysis is used for obtaining a lithotripsy, which shows that only two principal components need to be reserved; performing principal component analysis to obtain the load, the principal component common factor variance and the component uniqueness of the two principal components on each factor, and the characteristic value, the variance ratio and the cumulative variance ratio of the two principal components; and then, performing principal component rotation, and calling a head function to obtain a linear regression equation coefficient between the principal component and the original variable.
Further, the obtained female patient liver cancer prognosis marker factors and the importance ranking thereof are as follows: the tumor diameter is larger than the existence of lymph node metastasis and larger than the child.Pugh score, and the liver cancer prognosis marker factors and the importance ranking of the marker factors are that the tumor diameter is larger than the child.Pugh score and larger than AFP and the existence of cirrhosis.
Compared with the prior art, the sex-related liver cancer prognosis marker sex factor and the screening method thereof which are designed by the invention have the advantages that: through the statistical analysis means of R language software, the marker factors of liver cancer prognosis related to gender are accurately screened, wherein for female patients, the marker factors of liver cancer prognosis are tumor diameter, whether lymph node metastasis exists and child. The screening method adopted by the invention is more scientific and reliable, and the diagnosis and research of the marker factors are pertinently carried out in the treatment process, thereby being beneficial to controlling the rapid development of the liver cancer, further improving the survival time of patients and improving the prognosis of the liver cancer.
Drawings
FIG. 1 is a female patient statistical chart
FIG. 2 is a lithotripsy of a female patient
FIG. 3 shows the results of principal component analysis of female patients
FIG. 4 shows the results of a rotational analysis of the principal components of a female patient
FIG. 5 is a linear regression equation coefficient result for female patients
FIG. 6 is the result of importance analysis of each factor of female patients
FIG. 7 is an image of the importance analysis result of each factor of female patient
FIG. 8 and FIG. 9 the results of multiple regression analysis of female patients
FIG. 10 and FIG. 11 are the results of the significance analysis of the remaining three factors of the female patient
FIG. 12 and FIG. 13 are the results of multivariate regression analysis of the remaining three factors of female patients
FIG. 14 Male patient statistics
FIG. 15 lithotripsy of male patient
FIG. 16 analysis results of principal Components of Male patient
FIG. 17 shows the results of spin analysis of principal components of male patients
FIG. 18 Linear regression equation coefficients results for female patients
FIG. 19 and FIG. 20 show the results of importance analysis of factors of male patients
FIG. 21 and FIG. 22 show the results of multivariate regression analysis of male patients
FIG. 23 and FIG. 24 analysis results of importance of remaining four factors of male patients
FIG. 25, FIG. 26 the results of the four-factor multiple regression analysis of the remaining Male patients
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. The technical solutions in the embodiments of the present invention are clearly and completely described, and the described embodiments are only some embodiments, but not all embodiments of the present invention. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative effort belong to the protection scope of the present invention.
The invention selects the complete survival data of 120 tumor patients, selects 9 factors possibly related to the total survival period of the patients to analyze, and comprises sex, age, AFP, tumor diameter, whether cirrhosis exists, whether HBV infection exists, whether lymph node metastasis exists, whether portal cancer embolism exists and child. Statistical analysis is carried out by R language software, and methods such as variance analysis, random forest, principal component analysis and the like are adopted.
The factors of 9 of sex, age, AFP, tumor diameter, liver cirrhosis, HBV infection, lymph node metastasis, portal cancer embolus, child. pugh score of 120 patients (female 22, male 98) were set to x1-x9, respectively, and overall survival was set to y. The statistical analysis is carried out on the male part and the female part respectively.
Example 1
Screening of female patient liver cancer prognosis marker factors
Step 1: the presence of liver cirrhosis, HBV infection, lymph node metastasis and portal cancer thrombus was counted in all 22 female patients. The statistical results are shown in fig. 1.
Step 2: extracting the reasons of the overall life cycle difference based on principal component analysis to obtain a lithotripsy graph, as shown in fig. 2: it is illustrated that only two principal components need to be retained.
The principal component analysis was performed, and the results obtained are shown in FIG. 3,
first part in fig. 3:
PC1, PC 2: the load of each principal component on each observed variable, i.e., the correlation coefficient of the observed variable with the principal component.
h 2: the principal component commonality factor variance, i.e., the degree of variance interpretation of the principal component for each variable.
u 2: component uniqueness, i.e., the proportion of the variance that cannot be explained by the principal component.
A second part: two main components will be explained
SS loading: characteristic value of two principal components
Porting Var: proportion of variance, i.e. degree of interpretation of data by each principal component
Cumulant Var: the variance ratio is accumulated.
Then, the principal component rotation is performed, and the analysis result is shown in FIG. 4,
and calling a head function to obtain linear regression equation coefficients between the principal components and the original variables. The results are shown in figure 5 of the drawings,
namely, it is
RC1＝0.331967769x2+0.002301612x3+0.065787514x4+0.341201996x5+0.256129375 x6+0.307579902x7
RC2＝-0.31021926x2+0.34902973x3+0.23255103x4-0.03138882x5+0.04034265x6-0. 09537888x7
RC1 was primarily x2 age, x5 had cirrhosis, x6 had HBV infection, x7 had lymph node metastasis effect on overall survival, RC2 was primarily x3AFP and x4 tumor diameter effect on overall survival.
Based on the extraction of the total life cycle difference reason of the random forest, the goodness of fit is 30.75 by calling a random forest function, the results and images obtained by analyzing the importance of each factor are shown in figures 6 and 7,
the sequence of importance of the factors of the female patient can thus be found to be: x4> x9> x8> x3> x7> x6> x5> x2, i.e. tumor diameter > child.
Step 3: the results of prediction and multiple regression analysis are shown in FIGS. 8 and 9,
the results show that the p values of x2, x3, x5, x6 and x8 are far more than 0.05 required by statistical significance, the p values cannot pass t test, and the p values need to be removed from a regression model, and x4, x7 and x9 remain;
calling random forest functions for x4, x7 and x9, wherein the goodness of fit is 38.61,
the significance results obtained are shown in fig. 10 and 11:
the sequence of the importance of the remaining three factors can be obtained through the graphical results as follows: x4> x9> x7, and tumor diameter > child.
The results of the multiple regression analysis are shown in FIGS. 12 and 13,
the p values of the three variables in the graph are all less than 0.05, so y is related to all three factors. I.e., overall survival of female tumors, was related to tumor diameter, presence or absence of lymph node metastasis and child.
Example 2
Screening of liver cancer prognosis marker factors for male patients
Analysis was performed on male patients who had a total of 98.
Step 1: the results of statistical comparison of the number of patients with liver cirrhosis, HBV infection, lymph node metastasis and portal thrombus are shown in FIG. 14.
Step 2: the general life cycle difference reason based on principal component analysis was extracted to obtain a lithotripsy map, as shown in fig. 15.
FIG. 15 illustrates that only two principal components need to be retained and then principal component analysis is performed, resulting in the analysis results shown in FIG. 16.
First part in fig. 16:
PC1, PC 2: the load of each principal component on each observed variable, i.e., the correlation coefficient of the observed variable with the principal component.
h 2: the principal component commonality factor variance, i.e., the degree of variance interpretation of the principal component for each variable.
u 2: component uniqueness, i.e., the proportion of the variance that cannot be explained by the principal component.
A second part: two main components will be explained
SS loading: characteristic value of two principal components
Porting Var: proportion of variance, i.e. degree of interpretation of data by each principal component
Cumulant Var: the variance ratio is accumulated.
The principal component rotation was performed, and the analysis results obtained are shown in FIG. 17,
the head function is called to obtain the coefficients of the linear regression equation between the inverse principal component and the original variable, and the result is shown in figure 18,
namely, it is
RC1＝-0.18515561x2+0.20818351x3+0.23548719x4+0.09684601x5+0.06244058 x6+0.21921164x7
RC2＝0.760313825x2-0.006271847x3+0.066226063x4+0.313416066x5+0.354369959 x6-0.095319739x7
RC1 primarily accounts for the effects of AFP, tumor diameter, and the presence or absence of lymph node metastasis on overall survival, and RC2 primarily accounts for the effects of age, the presence or absence of cirrhosis, and the presence or absence of HBV infection on overall survival.
Based on the total life cycle difference extraction of the random forest, the goodness of fit obtained by calling a random forest function is 67.81, the importance of each factor is analyzed, the obtained analysis result and the image are shown in figures 19 and 20,
the sequence of the importance of the factors can be found from fig. 20 as follows: x4> x3> x9> x8> x7> x5> x6> x2, i.e. tumor diameter > AFP > child.
Step 3: the prediction of prediction was performed, and the results obtained by the multiple regression analysis are shown in FIGS. 21 and 22,
the results show that the p-values of x2, x6, x7, x8 are much greater than 0.05 required for statistical significance, fail t-test, need to be rejected in the regression model, and the remaining x3, x4, x5, x9, i.e. AFP, tumor diameter, presence or absence of cirrhosis, child.
And calling random forest functions for x3, x4, x5 and x9, wherein the goodness of fit is 66.23.
The obtained importance ranking results are shown in figure 23 and figure 24,
from the figure we can see that the sequence of importance is: x4> x9> x3> x 5.
The results of the multiple regression analysis are shown in FIGS. 25 and 26
The results show that the p-values of all four variables are less than 0.05, so y is correlated to these four factors. That is, the overall survival of male tumors is related to four factors, AFP, tumor diameter, presence or absence of cirrhosis, and child.
The above description is only for the preferred embodiment of the present invention, and should not be taken as limiting the scope of the invention, which is defined by the appended claims and the description of the invention.
Claims (5)
1. The sex-related marker factors for liver cancer prognosis include tumor diameter, presence or absence of lymph node metastasis and child.Pugh score for female patients, and tumor diameter, child.Pugh score, AFP and presence or absence of cirrhosis for male patients.
2. The method of screening for prognostic markers for liver cancer associated with gender according to claim 1, wherein the screening method comprises: step1, selecting statistical survival data of liver cancer patients with enough sample quantity, confirming a plurality of factors possibly related to the total survival cycle of the patients, and respectively counting the factor conditions related to the total survival cycle of male patients and female patients;
step2, statistical analysis is carried out on the collected patient samples by adopting R language software, a lithotripsy graph is obtained by extracting the general life cycle difference reason based on principal component analysis, and principal components needing to be reserved are obtained for analysis; extracting the reason of the overall life cycle difference based on the random forest, obtaining a goodness-of-fit value by calling a random forest function, and analyzing the importance of each factor;
and 3, predicting, removing factors with low correlation by utilizing multivariate regression analysis, calling a random forest function to the remaining factors to obtain a goodness-of-fit value, screening out prognostic marker factors of male and female liver cancer patients, and obtaining an importance factor ranking result.
3. The method of claim 2, wherein the factors related to the overall life cycle of the patient identified in step1 are sex, age, AFP, tumor diameter, liver cirrhosis, HBV infection, lymph node metastasis, portal thrombosis, and child.
4. The method for screening sex-related liver cancer prognostic marker factors according to claim 3, wherein the step2 is based on the total survival time difference cause extraction of principal component analysis to obtain a lithotripsy map indicating that only two principal components need to be retained; performing principal component analysis to obtain the load, the principal component common factor variance and the component uniqueness of the two principal components on each factor, and the characteristic value, the variance ratio and the cumulative variance ratio of the two principal components; and then, performing principal component rotation, calling a head function to obtain a linear regression equation coefficient between the principal component and the original variable, and obtaining the importance sequence of each factor.
5. The method of claim 4, wherein the prognostic marker factors for liver cancer in female patients are selected from the group consisting of: the tumor diameter is larger than lymph node metastasis is larger than child.Pugh score, and the liver cancer prognosis marker factors and the importance ranking of the marker factors are that the tumor diameter is larger than child.Pugh score and AFP is larger than liver cirrhosis.
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