CN113517023B - Liver cancer prognosis marker factor related to sex and screening method - Google Patents
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Abstract
The invention relates to a liver cancer prognosis marker factor related to sex 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 such as variance analysis, random forest, principal component analysis and the like to screen the landmark factors. Selecting a large number of liver cancer patient data samples, extracting the total survival time difference reasons based on principal component analysis, calling head functions to obtain linear regression equation coefficients between the principal components and various factors, carrying out prediction, multiple regression analysis, calling random forest functions and the like to obtain the liver cancer patient prognosis, wherein the factors related to female liver cancer patient prognosis include tumor diameter, lymph node metastasis and child. Pugh score, and the factors related to male liver cancer patient prognosis include AFP, tumor diameter, cirrhosis and child. Pugh score.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a liver cancer prognosis marker factor related to gender and a screening method thereof.
Background
The liver is the largest digestive gland of human body and mainly participates in a plurality of processes of human body digestion, metabolism, excretion, detoxification, immunity and the like. Liver cancer is also known as the metabolic factory of the human body, and substances from gastrointestinal absorption almost enter the liver where they are synthesized, decomposed, transported and stored. Various causes of liver cancer damage can cause liver cell damage and liver dysfunction to different degrees after acting on the liver.
Liver cancer is one of common malignant tumors in China, meanwhile, the incidence rate and the death rate of the disease are high, the death rate of the disease is the third malignant tumor worldwide, the course of treatment of the disease is short, the treatment is difficult, and the disease causes serious threat to the life health of human beings. Along with the development of medical level, the measures for treating liver cancer are diversified, the current treatment is gradually developed to be mainly combined treatment and no single treatment is needed, but the treatment effects of patients are huge, the survival rate is only 7% within 5 years, so that the treatment form of liver cancer is not optimistic. The survival rate of patients is affected by various factors, and the prognosis factors of patients with different sexes are found to be different in the research, so that if the patients can be subjected to targeted diagnosis and research in the treatment process, the method has important significance in controlling the rapid development of liver cancer, further improving the survival time of the patients and improving 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 the marker factors of liver cancer patients with different sexes for prognosis, and provides targeted diagnosis and treatment for clinically liver cancer patients with different sexes in prognosis.
For female patients, the marker factors for liver cancer prognosis are tumor diameter, presence or absence of lymph node metastasis and child. Pugh score, and for male patients, the marker factors for liver cancer prognosis are tumor diameter, child. Pugh score, AFP and presence or absence of cirrhosis.
The screening method of liver cancer prognosis marker factors related to gender disclosed by the application uses R language software for statistical analysis, adopts methods such as analysis of variance, random forest, principal component analysis and the like, and comprises the following specific screening methods: step1, selecting statistical survival data of liver cancer patients with enough sample numbers, and confirming a plurality of factors possibly related to the total survival period of the patients, and respectively counting the factor conditions related to the total survival period of male patients and female patients;
step2, carrying out statistical analysis on the acquired patient samples by adopting R language software, extracting based on total lifetime difference reasons of principal component analysis, obtaining a lithotripsy map, and obtaining principal components to be reserved for analysis; based on the total lifetime difference reason extraction of the random forest, obtaining a fitting goodness value by calling a random forest function, and analyzing the importance of each factor;
and 3, predicting, namely removing factors with low correlation by utilizing multiple regression analysis, calling a random forest function on the remaining factors to obtain a fitting goodness value, screening out prognosis marker factors of male and female liver cancer patients, and obtaining a final importance factor sequencing result.
Further, factors related to the overall survival of the patient identified in step1 are sex, age, AFP, tumor diameter, cirrhosis, HBV infection, lymph node metastasis, portal cancer embolism and child. Pugh score.
Further, step2 extracts based on the total lifetime difference cause of the principal component analysis to obtain a lithotripsy map, which shows that only two principal components need to be reserved; carrying out principal component analysis to obtain the load of the two principal components on each factor, the principal component common factor variance, the component uniqueness, and the characteristic values, the variance proportion and the accumulated variance proportion of the two principal components; and then, rotating the principal component, and calling the head function to obtain the coefficient of a linear regression equation between the principal component and the original variable.
Further, the obtained female patient liver cancer prognosis marker factors and the importance sequences thereof are as follows: tumor diameter > presence or absence of lymph node metastasis > child. Pugh score, and the prognosis marker factor of liver cancer in male patients and its importance are ranked as tumor diameter > child. Pugh score > AFP > presence or absence of liver cirrhosis.
Compared with the prior art, the liver cancer prognosis marker factor related to sex and designed by the invention has the advantages that: and accurately screening out liver cancer prognosis marker factors related to gender through an R language software statistical analysis means, wherein for female patients, the marker factors of liver cancer prognosis are tumor diameter, presence or absence of lymph node metastasis and child. Pugh score, and for male patients, the marker factors of liver cancer prognosis are tumor diameter, child. Pugh score, AFP and presence or absence of liver cirrhosis. The screening method adopted by the invention is more scientific and reliable, and the marker factors are subjected to diagnosis and research in the treatment process in a targeted way, so that the method is beneficial to controlling the rapid development of liver cancer, further improving the survival time of patients and improving the prognosis of the liver cancer.
Drawings
FIG. 1 is a statistical chart of female patients
FIG. 2 is a lithotripsy view of female patients
FIG. 3 shows the results of principal component analysis in female patients
FIG. 4 is a result of principal component rotation analysis of female patients
FIG. 5 is a linear regression equation coefficient result for female patients
FIG. 6 is a graph showing the results of analysis of the importance of factors in female patients
Multiple regression analysis results for female patients of FIGS. 7 and 8
Results of importance analysis of the remaining three factors of the female patients of FIGS. 9 and 10
Results of multiple regression analysis of the remaining three factors for female patients of FIGS. 11 and 12
FIG. 13 statistical map of male patients
FIG. 14 Male patient lithotripsy view
FIG. 15 results of principal component analysis of Male patient
FIG. 16 is a principal component rotation analysis result of a male patient
FIG. 17 results of coefficients of the linear regression equation for male patients
FIGS. 18 and 19 show results of analysis of importance of factors in male patients
FIGS. 20 and 21 show the results of multiple regression analysis of male patients
Results of importance analysis of the remaining four factors of the male patients of FIGS. 22 and 23
FIG. 24 and FIG. 25 results of a four-factor multiple regression analysis of the male patient remaining
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific examples. 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, which can be made by those skilled in the art based on the embodiments of the invention disclosed herein without departing from the scope of the invention.
The invention selects the complete survival data of 120 tumor patients, and selects 9 factors possibly related to the total survival time of the patients for analysis, including gender, age, AFP, tumor diameter, whether liver cirrhosis exists, whether HBV infection exists, whether lymph node metastasis exists or not, whether portal cancer embolism exists or not and child. Statistical analysis is performed by using R language software, and methods such as variance analysis, random forest analysis, principal component analysis and the like are adopted.
Sex, age, AFP, tumor diameter, cirrhosis, HBV infection, lymph node metastasis, portal cancer embolism, child. Pugh score 9 factors of 120 patients (22 female patients, 98 male patients) were set to x1 to x9, respectively, and total survival was set to y. The method is divided into two parts of male and female for statistical analysis.
Example 1
Female patient liver cancer prognosis marker factor screening
Step1: all 22 female patients were counted for cirrhosis, HBV infection, lymph node metastasis, portal cancer embolism. The statistical results are shown in fig. 1.
Step2: total lifetime difference cause extraction based on principal component analysis, and a lithotripsy map is obtained, 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 are shown in FIG. 3,
first part in fig. 3:
PC1, PC2: the load of each principal component on each observation variable, i.e., the correlation coefficient of the observation variable with the principal component.
h2: principal component common factor variance, i.e., the variance solution of the principal component to each variable.
u2: component uniqueness, i.e. the proportion of variance that cannot be interpreted by the principal component.
A second part: description is made for two principal components
SS loading: characteristic values of two principal components
Porthole Var: variance ratio, i.e. the degree of interpretation of the data by the principal components
Cumulative Var: the variance ratio is accumulated.
Then the principal component rotation is performed, the analysis result is shown in figure 4,
and calling the head function to obtain the coefficient of the linear regression equation between the principal component and the original variable. The results are shown in FIG. 5, i.e
RC1＝0.331967769x2+0.002301612x3+0.065787514x4+0.341201996x5+0.256129375x6+0.307579902x7
RC2＝-0.31021926x2+0.34902973x3+0.23255103x4-0.03138882x5+0.04034265x6-0.09537888x7
RC1 is mainly the effect of x2 age, x5 with cirrhosis, x6 with HBV infection, x7 with lymph node metastasis on total survival, RC2 is mainly the effect of x3AFP and x4 tumor diameter on total survival.
Based on the total lifetime difference reason extraction of the random forest, the fitting goodness is 30.75 by calling the random forest function, the result and the image obtained by analyzing the importance of each factor are shown in figure 6,
the order of importance of the factors that can be derived for female patients is therefore: x4> x9> x8> x3> x7> x6> x5> x2, i.e. tumor diameter > child. Pugh score > presence of portal cancer plug > AFP > presence of lymph node metastasis > presence of HBV infection > presence of cirrhosis > age.
Step3: prediction was performed, and the results obtained by multiple regression analysis are shown in FIGS. 7 and 8,
the result shows that the p value of x2, x3, x5, x6 and x8 is far greater than 0.05 required by statistical significance, the t test cannot be passed, the residual x4, x7 and x9 are needed to be removed from the regression model;
for x4, x7, x9, a random forest function was called, the goodness of fit was 38.61,
the importance results obtained are shown in fig. 9 and 10:
the sequence of the importance of the three remaining factors can be obtained through the graphical result: x4> x9> x7, tumor diameter > child. Pugh score > whether there is lymph node metastasis.
The multiple regression analysis was performed, and the results obtained are shown in FIGS. 11 and 12,
the p values of the three variables in the graph are all smaller than 0.05, so that y has a correlation with all three factors. I.e. the total survival of a female tumor is related to tumor diameter, presence or absence of lymph node metastasis and child. Pugh score.
Example 2
Screening of liver cancer prognosis marker factors of male patients
The analysis was performed on male patients, and total 98 patients were found.
Step1: the results of the statistical comparison of the number of people with or without cirrhosis, HBV infection, lymph node metastasis, portal cancer embolism and portal cancer embolism in all male patients are shown in fig. 13.
Step2: and extracting total lifetime difference reasons based on principal component analysis to obtain a lithotripsy map, as shown in fig. 14.
Fig. 14 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. 15.
First part in fig. 15:
PC1, PC2: the load of each principal component on each observation variable, i.e., the correlation coefficient of the observation variable with the principal component.
h2: principal component common factor variance, i.e., the variance solution of the principal component to each variable.
u2: component uniqueness, i.e. the proportion of variance that cannot be interpreted by the principal component.
A second part: description is made for two principal components
SS loading: characteristic values of two principal components
Porthole Var: variance ratio, i.e. the degree of interpretation of the data by the principal components
Cumulative Var: the variance ratio is accumulated.
The principal component rotation was performed, and the analysis results obtained are shown in FIG. 16,
invoking head function to invert the coefficients of the linear regression equation between the principal component and the original variable, the result is shown in FIG. 17, i.e
RC1＝-0.18515561x2+0.20818351x3+0.23548719x4+0.09684601x5+0.06244058x6+0.21921164x7
RC2＝0.760313825x2-0.006271847x3+0.066226063x4+0.313416066x5+0.354369959x6-0.095319739x7
RC1 is mainly AFP, tumor diameter and influence of the presence or absence of lymph node metastasis on total survival, RC2 is mainly age, whether there is liver cirrhosis, whether there is HBV infection on total survival.
Based on the total lifetime difference reason extraction of random forests, the fitting goodness is 67.81 by calling random forest functions, the importance of each factor is analyzed, the obtained analysis results and images are shown in figures 18 and 19,
the order in which the importance of each factor can be obtained from fig. 19 is: x4> x3> x9> x8> x7> x5> x6> x2, i.e. tumor diameter > AFP > child. Pugh score > presence or absence of portal cancer plug > presence or absence of lymph node metastasis > presence or absence of liver cirrhosis > presence or absence of HBV infection > age.
Step3: the prediction was performed, and the results obtained by the multiple regression analysis are shown in FIG. 20 and FIG. 21,
the results show that the p values of x2, x6, x7, x8 are far greater than 0.05 required for statistical significance, cannot pass t-test, need to be rejected in regression models, and the remaining x3, x4, x5, x9, i.e. AFP, tumor diameter, whether there is cirrhosis, child.
The random forest function was called for x3, x4, x5, x9, with a goodness of fit of 66.23.
The obtained importance ranking result is shown in figure 22 and figure 23,
from the figure we can learn the order of importance: x4> x9> x3> x5.
The results of the multiple regression analysis are shown in FIG. 24 and FIG. 25
The results show that the p-values of the four variables are all less than 0.05, so y is related to these four factors. That is, the total survival of the male tumor is related to four factors, AFP, tumor diameter, presence or absence of cirrhosis and child.
The foregoing is merely illustrative of the present invention and is not intended to limit the scope of the invention, i.e., all such modifications and variations are within the scope of the invention as defined in the appended claims and their equivalents.
Claims (4)
1. A screening method of liver cancer prognosis marker factors related to gender, which is characterized by comprising the following steps: step1, selecting statistical survival data of liver cancer patients with enough sample numbers, and confirming a plurality of factors possibly related to the total survival period of the patients, and respectively counting the factor conditions related to the total survival period of male patients and female patients;
step2, carrying out statistical analysis on the acquired patient samples by adopting R language software, extracting based on total lifetime difference reasons of principal component analysis, obtaining a lithotripsy map, and obtaining principal components to be reserved for analysis; based on the total lifetime difference reason extraction of the random forest, obtaining a fitting goodness value by calling a random forest function, and analyzing the importance of each factor;
and 3, predicting, namely removing factors with low correlation by utilizing multiple regression analysis, calling a random forest function on the remaining factors to obtain a fitting goodness value, and screening out prognosis marker factors of male and female liver cancer patients to obtain an importance factor sequencing result.
2. The method for screening for a sex-related liver cancer prognostic marker according to claim 1, wherein the factors related to the total life cycle of the patient confirmed in step1 are sex, age, AFP, tumor diameter, liver cirrhosis, HBV infection, lymph node metastasis, portal cancer embolism and child. Pugh score.
3. The screening method of liver cancer prognosis marker factor related to sex according to claim 2, wherein step2 extracts based on the total lifetime difference cause of principal component analysis to obtain a lithotripsy map indicating that only two principal components need to be retained; carrying out principal component analysis to obtain the load of the two principal components on each factor, the principal component common factor variance, the component uniqueness, and the characteristic values, the variance proportion and the accumulated variance proportion of the two principal components; and then, rotating the main component, calling a head function to obtain a linear regression equation coefficient between the main component and the original variable, extracting based on the total lifetime difference reason of the random forest, obtaining a fitting goodness by calling the random forest function, analyzing the importance of each factor, and obtaining the sequence of the importance of each factor.
4. The method for screening a sex-related liver cancer prognostic marker according to claim 3, wherein the obtained female patient liver cancer prognostic marker and the ranking of importance thereof are: tumor diameter > with lymph node metastasis > child. Pugh score, and the prognosis marker factor of liver cancer in male patients and its importance are ranked as tumor diameter > child. Pugh score > AFP > with or without cirrhosis.
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