# Clinical Value of Growth Differentiation Factor 15 Detection in the Diagnosis of Early Liver Cancer Based on Data Mining

Authors: Hongyan Guo; Qingfeng Liu
Journal: BioMed Research International
 (2022)

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## Abstract

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P<0.01), there was a significant difference (P<0.01) compared with adjacent tissue (P<0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients.


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## Body

## 1. Introduction

Data mining is mainly used to classify and predict data to derive useful rules and criteria. Although the method of data mining is widely used in the study of the experience of famous and old Chinese medicine, there are still many problems, such as incomplete original clinical data, lack of standardization of Chinese medicine terminology, and vague data processing. So far, there is no unified liver cancer assessment index based on syndrome differentiation and treatment. Therefore, in the face of the experience of many famous doctors in liver cancer treatment, how to formulate a repeatable and feasible liver cancer diagnosis and treatment plan according to the existing TCM theory to guide clinical diagnosis and treatment and make full use of the absolute advantages of the liver. TCM treatment of liver cancer is an urgent problem that needs to be solved. Hepatocellular carcinoma is a relatively common malignant tumor, and it is also a malignant tumor with a high mortality rate. Every year, 10,000 people die of liver cancer. Surgical resection and metastasis are still common clinical problems in the treatment of liver cancer.Liver disease is one of the normal threatening cancers in China with a high occurrence. Hepatocellular carcinoma is a common malignant tumor in humans, and the cellular mechanism of its occurrence is still unclear. Because the early symptoms of liver cancer are not obvious or lack of typicality, patients are easy to ignore, resulting in a late stage of the disease at the first diagnosis of many patients. It is fundamentally packed in the center and late stages, which is the primary justification for the high mortality of liver disease. Top to bottom investigation of the component of event and improvement of liver disease and the quest for more viable finding and treatment techniques are significant issues that should be settled. Liver cancer research needs to be solved urgently.The innovation of this paper: (1) this paper combines data mining with early liver cancer diagnosis, and introduces the theory and related methods of data mining in detail. (2) In the face of the expression of GDF-15 in early hepatocellular carcinoma, this paper explores the clinical value of GDF-15 by analyzing the difference of GDF-15 expression between patients and normal people and the difference in serum GDF-15 content between patients and normal people.


## 2. Related Work

Data mining technology is a new technology that came into being in the 1980s but matured in the late 1990s and has gradually been widely used in many fields. Many scholars have explored the use of data mining for disease diagnosis. Xu et al. take a more extensive viewpoint on security issues connected with information mining and studies different strategies that assist with safeguarding touchy data. He momentarily presents the fundamentals of related research points, audits best in class techniques, and presents a few primer thoughts for future exploration headings. However, he did not give specific measures for the problem [1]. Chaurasia and Pal examine the exhibition of various characterization methods. In the investigations, they thought about three arrangement procedures in Weka programming, and the correlation results showed that the succession insignificant enhancement has higher expectation exactness than IBK and BF Tree techniques; that is to say, 96.2%. However, his process is more cumbersome [2]. Yan and Zheng develop a “universe” of in excess of 18,000 major signs from fiscal summaries and utilizations a bootstrap way to deal with survey the effect of information mining on key based inconsistencies. Their proof shows that major based abnormalities, remembering newfound irregularities for his exploration, cannot be ascribed to arbitrary possibility, and that mispricing can more readily clarify them. However, they are not based on reality [3]. Hong et al. proposed another technique to develop a flood responsiveness map in Poyang County, Jiangxi Province, China by carrying out fluffy load of proof and information mining strategies. The oddity of the proposed technique is the utilization of fluffy WFOE with a double reason. The subsequent flood awareness guides can assist specialists and neighborhood states with forming flood control methodologies. However, its application scope is limited [4]. Carneiro et al. depict the turn of events and sending of an extortion location framework in an enormous e-retailer, investigates a mix of manual and computerized order, gives knowledge into the total advancement cycle, and looks at changed AI draws near. However, their accuracy needs to be improved [5]. Parvizimosaed et al. present a new multistage SEMS design for ideal energy the board in MGs thinking about different dubious assets. For the treatment of vulnerability, he utilizes stochastic planning strategies, including the mean and difference of energy costs, applied to the advancement cycle. However, his performance is lower [6]. Huang et al. mean to give an effective technique to figure harsh approximations of fluffy ideas in powerful fluffy choice frameworks (FDS) where items and highlights change all the while. His trial results show that the proposed dynamic calculation is fundamentally more proficient than the static calculation and the mix of the two reference increase calculations. However, his data is less [7]. To manage the elements of preparing tests and further develop the forecast exactness, Wu and Peng proposed an information mining technique made out of K-implies bunching and packing brain organization (NN) for momentary WPF. Recreation results show that it can accomplish better expectation precision contrasted with different baselines and existing transient WPF techniques. However, their time limit is longer [8]. These methods improve the efficiency of data mining to a certain extent and promote the development of this field, but these methods are more complicated to implement.


## 3. Methods of Studying the Clinical Value of Growth Differentiation Factor 15

### 3.1. Use of Data Mining Methods

So far, there are no strict data mining standards. Data mining refers to the process of algorithmically searching for information hidden in a large amount of data. Broadly speaking, data mining refers to discovering something valuable to users from a huge database or data system. Discover relationships that have not been discovered or confirmed before, and draw conclusions worthy of user understanding [9, 10].The premise of data mining is to clarify the target problem and work, and to clarify the purpose of data extraction. Based on the explicit extraction reasons, information disclosure is done through the key step of information mining. There are many handling stages all through the information extraction process. The process of how to derive useful patterns from raw data and then further acquire knowledge is shown in Figure1.Figure 1
Basic process of data mining.As shown in Figure1, first wash and clean the data samples, then convert the data, and mine the converted data to obtain knowledge data. As per the essential course of information mining, we can determine a standard information mining framework, which comprises of the accompanying modules. Data mining needs to go through the collection and integration of information and data conversion after data reduction and cleaning, and then implement mining and finally carry out pattern evaluation and knowledge expression. The specific process is shown in Figure 2:Figure 2
Typical structure of a data mining system.


### 3.2. Artificial Neural Network

Among various data mining techniques, artificial neural network is one of the most commonly used methods [11]. Figure 3 shows a data mining process based on an artificial neural network approach. Artificial neural networks calculate and analyze problems by artificially simulating biological brains. Before describing artificial neural networks in detail, we need a brief description of the biological cerebral cortical nervous system. The sensory system of the human cerebral cortex is composed of countless neurons, the number of which is about 1011. These neurons communicate and send processing information with other neurons through synapses. Generally, each neuron has many synapses that connect to other neurons, the number of which is about 104.
(1)
Artificial neuron modelFigure 3
Data mining process based on artificial neural network.Figure4 shows the basic design of the pseudo-neuron model that the researchers built by removing biological neurons [12]. The neuron of the artificial neural network is a biological model established by biological nerve cells as the imitation object. ANN models are generated by applying neural system formulations to the field of artificial intelligence.Figure 4
Artificial neuron model.Compared with the processing unit in the ANN, the biological neuron is similar to it, and it also plays the role of transmitting the signal. The neurons of the artificial neural network also have their own input and output functions, and they also have a threshold to control the output of the signal.Thresholds or biases can be used as input, increasing the input dimension by one dimension. The relationship can be expressed mathematically, namely(1)ui=∑a=1pwiaxa,(2)vi=neti=ui−θi,(3)yi=φvi.(2)
Artificial neural network modelDepending on the method, artificial neural network models can be classified in many different ways. Common ones are the type of network topology according to the topology type of network connection and the type of information flow within the network (as shown in Figure5). The type of network information flow can be based on different directions of internal information transmission [10, 13]. It is divided into neurofeedback network (x) and neurofeedback network (y) (as displayed in Figure 6).
(3)
BP neural networkFigure 5
Schematic diagram of hierarchical network structure.Figure 6
Schematic diagram of feedback neural network.Common ANN models in data mining include BP neural network, RBF neural network, and Hybrid neural network. The BP neural network is described [14, 15]. The basic algorithm principle of BP neural network is that the signal propagates forward in the network, and through the comparison with the expected output, the generated error is propagated back. The center thought of BP calculation is to utilize preparing tests and assessment markers to prepare the organization.Its structure diagram is shown in Figure7.Figure 7
Topological structure diagram of BP neural network.As shown in Figure7, the input value is denoted as x1,x2,Λ,xn, the weight value is denoted as wab and wac, and the predicted value is denoted as y1,y2,Λ,yn. The organization addresses a capacity planning relationship, which has n free factors and m ward factors.The following will specifically analyze the steps of the BP neural network algorithm:Network initialization determines the number of nodes at each layer, that is,n nodes, d nodes, and m nodes. Then, at that point, the loads and association limits are introduced, and finally the function of the neural network activation neuron fx and the learning rate η are given [16].Compute the output of the hidden layer as(4)Hb=f∑a=1nwabxa−ib,b=1,2,Λ,d.In the formula, the hidden layer activation function is denoted asf, also, the quantity of hubs in the secret layer is meant as d. There are many expressions for the excitation function, the expressions we chose are
(5)fx=11+e−x.Ascertain the result of the result layer.(6)Oc=∑a=1dHbwac−jn,c=1,2,Λ,m.Error calculation as follows:(7)ec=yc−Oc,c=1,2,Λ,m.Update weights as follows: as per the expectation blunder of the brain network e, update the network connection weightswab and wac as
(8)wab=wab+ηHa1−Hbxa∑c=1mwacec,a=1,2,Λ,n;b=1,2,Λ,d,(9)wac=wac+ηHbec,b=1,2,Λ,d;c=1,2,Λ,m.where η is the learning pace of the brain organization.Update edge as follows: according to the forecast mistakee of the organization, update the edges i and j of the secret layer and result layer of the organization as follows:
(10)ib=ib+ηHb1−Hb∑c=1mwacec,b=1,2,Λ,d,(11)jc=jc+ec,c=1,2,Λ,mDetermines whether the algorithm repeats end condition is satisfied, and if not, returns to the output step of computing the hidden level.(4)
Support vector machineBP neural network is good at solving the problem of pattern classification and nonlinear mapping. Support Vector Machine (SVM) is a general-purpose feedforward neural network that can also be used for tasks such as pattern classification and nonlinear regression. As far as the standard linearly separable classification is concerned, the idea of the SVM algorithm is to create an optimal classification hyperplane (Optimal Hyperplane) and maximize the distance between the two classes of samples located farthest close to both sides. Different from BP neural network which is based on some biological principle, the theoretical basis of SVM is statistical learning theory [17, 18].Support vector machines have many advantages, such as versatility and simple calculation. The key to building the SVM learning algorithm is to establish the conceptual entry region of the inner product Kernel between the “support vector”xa and the x vector extracted from the sample. It is difficult to implement for large-scale training samples, and it is difficult to solve multiclassification problems.The design of the help vector machine is displayed in Figure8:Figure 8
Architecture of support vector machine.The following describes the C-SVC model in detail.Given a known training set(12)S=x1,y1,Λ,xa,ya∈X×Yt,where:
(13)x1=X=Rn,a=1,2,Λ,t,(14)ya=Y=1,−1,a=1,2,Λ,t.x
a is the feature vector.Construct and solve the optimization problem(15)minα12∑a=1b∑b=1tyaybαaαbKxa,xb−∑b=1tαbs.t.∑a=1tyaαa=0,0≤αa≤C,a=1,Λ,t.Get the optimal solution(16)α∗=α1∗,Λ,αt∗S.Pick a positive component0<αb∗<C of α∗ and calculate the threshold accordingly as follows:
(17)jc=jc+ec,c=1,2,Λ,m.According tofx being 1 or 1, determine its class attribution.Different algorithms are distinct in different aspects and efficiencies. Therefore, it is important to choose a suitable calculation as per the various information attributes and the last motivation behind order, and get an arrangement model with extraordinary execution as far as exactness, existence intricacy, and grouping speed.The neural network algorithm has high precision and strong learning ability, and can be used for feature extraction, but because the learning process is long and opaque, it cannot detect and monitor the process. Decision tree classification algorithms are easy to understand and fast to run, but are prone to overfitting and struggle with missing data. Pure Bayesian classification algorithms are fast, long-lasting, and straightforward to interpret results, but their classifiers require sufficient database support and tuning thresholds.Through the comparative analysis of the algorithms, it can be seen that when faced with different problems, it is necessary to choose the mining algorithm to solve the problem. Each classification algorithm has its own unique advantages and limitations. At the same time, for the same classification problem, there are many different classification algorithms to solve it.


## 3.1. Use of Data Mining Methods

So far, there are no strict data mining standards. Data mining refers to the process of algorithmically searching for information hidden in a large amount of data. Broadly speaking, data mining refers to discovering something valuable to users from a huge database or data system. Discover relationships that have not been discovered or confirmed before, and draw conclusions worthy of user understanding [9, 10].The premise of data mining is to clarify the target problem and work, and to clarify the purpose of data extraction. Based on the explicit extraction reasons, information disclosure is done through the key step of information mining. There are many handling stages all through the information extraction process. The process of how to derive useful patterns from raw data and then further acquire knowledge is shown in Figure1.Figure 1
Basic process of data mining.As shown in Figure1, first wash and clean the data samples, then convert the data, and mine the converted data to obtain knowledge data. As per the essential course of information mining, we can determine a standard information mining framework, which comprises of the accompanying modules. Data mining needs to go through the collection and integration of information and data conversion after data reduction and cleaning, and then implement mining and finally carry out pattern evaluation and knowledge expression. The specific process is shown in Figure 2:Figure 2
Typical structure of a data mining system.


## 3.2. Artificial Neural Network

Among various data mining techniques, artificial neural network is one of the most commonly used methods [11]. Figure 3 shows a data mining process based on an artificial neural network approach. Artificial neural networks calculate and analyze problems by artificially simulating biological brains. Before describing artificial neural networks in detail, we need a brief description of the biological cerebral cortical nervous system. The sensory system of the human cerebral cortex is composed of countless neurons, the number of which is about 1011. These neurons communicate and send processing information with other neurons through synapses. Generally, each neuron has many synapses that connect to other neurons, the number of which is about 104.
(1)
Artificial neuron modelFigure 3
Data mining process based on artificial neural network.Figure4 shows the basic design of the pseudo-neuron model that the researchers built by removing biological neurons [12]. The neuron of the artificial neural network is a biological model established by biological nerve cells as the imitation object. ANN models are generated by applying neural system formulations to the field of artificial intelligence.Figure 4
Artificial neuron model.Compared with the processing unit in the ANN, the biological neuron is similar to it, and it also plays the role of transmitting the signal. The neurons of the artificial neural network also have their own input and output functions, and they also have a threshold to control the output of the signal.Thresholds or biases can be used as input, increasing the input dimension by one dimension. The relationship can be expressed mathematically, namely(1)ui=∑a=1pwiaxa,(2)vi=neti=ui−θi,(3)yi=φvi.(2)
Artificial neural network modelDepending on the method, artificial neural network models can be classified in many different ways. Common ones are the type of network topology according to the topology type of network connection and the type of information flow within the network (as shown in Figure5). The type of network information flow can be based on different directions of internal information transmission [10, 13]. It is divided into neurofeedback network (x) and neurofeedback network (y) (as displayed in Figure 6).
(3)
BP neural networkFigure 5
Schematic diagram of hierarchical network structure.Figure 6
Schematic diagram of feedback neural network.Common ANN models in data mining include BP neural network, RBF neural network, and Hybrid neural network. The BP neural network is described [14, 15]. The basic algorithm principle of BP neural network is that the signal propagates forward in the network, and through the comparison with the expected output, the generated error is propagated back. The center thought of BP calculation is to utilize preparing tests and assessment markers to prepare the organization.Its structure diagram is shown in Figure7.Figure 7
Topological structure diagram of BP neural network.As shown in Figure7, the input value is denoted as x1,x2,Λ,xn, the weight value is denoted as wab and wac, and the predicted value is denoted as y1,y2,Λ,yn. The organization addresses a capacity planning relationship, which has n free factors and m ward factors.The following will specifically analyze the steps of the BP neural network algorithm:Network initialization determines the number of nodes at each layer, that is,n nodes, d nodes, and m nodes. Then, at that point, the loads and association limits are introduced, and finally the function of the neural network activation neuron fx and the learning rate η are given [16].Compute the output of the hidden layer as(4)Hb=f∑a=1nwabxa−ib,b=1,2,Λ,d.In the formula, the hidden layer activation function is denoted asf, also, the quantity of hubs in the secret layer is meant as d. There are many expressions for the excitation function, the expressions we chose are
(5)fx=11+e−x.Ascertain the result of the result layer.(6)Oc=∑a=1dHbwac−jn,c=1,2,Λ,m.Error calculation as follows:(7)ec=yc−Oc,c=1,2,Λ,m.Update weights as follows: as per the expectation blunder of the brain network e, update the network connection weightswab and wac as
(8)wab=wab+ηHa1−Hbxa∑c=1mwacec,a=1,2,Λ,n;b=1,2,Λ,d,(9)wac=wac+ηHbec,b=1,2,Λ,d;c=1,2,Λ,m.where η is the learning pace of the brain organization.Update edge as follows: according to the forecast mistakee of the organization, update the edges i and j of the secret layer and result layer of the organization as follows:
(10)ib=ib+ηHb1−Hb∑c=1mwacec,b=1,2,Λ,d,(11)jc=jc+ec,c=1,2,Λ,mDetermines whether the algorithm repeats end condition is satisfied, and if not, returns to the output step of computing the hidden level.(4)
Support vector machineBP neural network is good at solving the problem of pattern classification and nonlinear mapping. Support Vector Machine (SVM) is a general-purpose feedforward neural network that can also be used for tasks such as pattern classification and nonlinear regression. As far as the standard linearly separable classification is concerned, the idea of the SVM algorithm is to create an optimal classification hyperplane (Optimal Hyperplane) and maximize the distance between the two classes of samples located farthest close to both sides. Different from BP neural network which is based on some biological principle, the theoretical basis of SVM is statistical learning theory [17, 18].Support vector machines have many advantages, such as versatility and simple calculation. The key to building the SVM learning algorithm is to establish the conceptual entry region of the inner product Kernel between the “support vector”xa and the x vector extracted from the sample. It is difficult to implement for large-scale training samples, and it is difficult to solve multiclassification problems.The design of the help vector machine is displayed in Figure8:Figure 8
Architecture of support vector machine.The following describes the C-SVC model in detail.Given a known training set(12)S=x1,y1,Λ,xa,ya∈X×Yt,where:
(13)x1=X=Rn,a=1,2,Λ,t,(14)ya=Y=1,−1,a=1,2,Λ,t.x
a is the feature vector.Construct and solve the optimization problem(15)minα12∑a=1b∑b=1tyaybαaαbKxa,xb−∑b=1tαbs.t.∑a=1tyaαa=0,0≤αa≤C,a=1,Λ,t.Get the optimal solution(16)α∗=α1∗,Λ,αt∗S.Pick a positive component0<αb∗<C of α∗ and calculate the threshold accordingly as follows:
(17)jc=jc+ec,c=1,2,Λ,m.According tofx being 1 or 1, determine its class attribution.Different algorithms are distinct in different aspects and efficiencies. Therefore, it is important to choose a suitable calculation as per the various information attributes and the last motivation behind order, and get an arrangement model with extraordinary execution as far as exactness, existence intricacy, and grouping speed.The neural network algorithm has high precision and strong learning ability, and can be used for feature extraction, but because the learning process is long and opaque, it cannot detect and monitor the process. Decision tree classification algorithms are easy to understand and fast to run, but are prone to overfitting and struggle with missing data. Pure Bayesian classification algorithms are fast, long-lasting, and straightforward to interpret results, but their classifiers require sufficient database support and tuning thresholds.Through the comparative analysis of the algorithms, it can be seen that when faced with different problems, it is necessary to choose the mining algorithm to solve the problem. Each classification algorithm has its own unique advantages and limitations. At the same time, for the same classification problem, there are many different classification algorithms to solve it.


## 4. Experiment and Analysis of GDF-15 Detection in the Diagnosis of Early Liver Cancer

### 4.1. Biological Characteristics of GDF-15

Growth differentiation factor is called macrophage inhibitory factor. It is activated by a specific hydrolase into a biologically active dimer. After activation, the Smad-dependent signaling pathway and the protein-independent Smad pathway are activated through autocrine or parathyroid effects, and finally exert biological effects [19]. Figure 9 shows the various expressions of GDF-15 in tissues.Figure 9
Multiple expression of GDF-15 in tissues.At present, relevant studies believe that the targets of GDF-15 are mainly Smad2 and 3, and also have a mild effect on Smad1, 5, and 8 [20]. Among the pathways that inhibit ventricular remodeling, cardiac hypertrophy can be inhibited by the Smad2/3 pathway or the Smad1 pathway. GDF-15 is a stress cytokine. In the basal physiological state, its secretion is highly tissue-specific, and is only expressed at high levels in the placenta and prostate, especially in the second and third trimesters of pregnancy, while weakly expressed in most tissue cells (such as the heart). Most investigations have shown that GDF15 articulation is expanded in an assortment of growths. Cell studies have likewise observed that GDF15 can repress the development of disease cells and advance the apoptosis of malignant growth cells.However, when the body is in a variety of different pathological states, it will be abundantly expressed in cardiomyocytes, and the serum concentration will also increase rapidly, thereby inhibiting apoptosis. Studies have shown that it plays a role in protecting the myocardium through biological effects such as improved myocardial remodeling and anti-inflammatory effects such as abdominal hypertrophy. GDF-15 affects the production of inflammatory factors by inhibiting leukocyte infiltration and macrophage activation, inhibiting excessive inflammatory responses [21].


### 4.2. Experiment Preparation

#### 4.2.1. Organizational Source

In this experiment, we chose a sum of 40 patients with beginning phase liver disease who went through careful resection in a clinic from January 2020 to December 2021. This experiment has obtained the consent of all patients, and all their case information is strictly confidential. We collected samples from carcinogenic tissues and serum from early-stage liver cancer patients, respectively, as the content of this experimental study. After the samples are taken, they are placed in a vacuum space with a certain temperature, and they are kept warm. Among the 40 patients, there were 18 females and 22 males. Most of them were 43 years old and between 27 and 56 years old. The age span showed that the incidence of liver cancer was not greatly affected by age. Both tumors were one, and the first time they had cancer, the tumor size was less than 2.5 cm in diameter. The data and samples were used as the experimental group of this experiment. At the same time, we also selected 40 normal liver blood and in vivo tissues as the control group for this experimental study.


#### 4.2.2. Method

The motivation behind this study is to notice the statement of GFD-15 in disease tissue and serum of patients with beginning phase liver malignant growth, and to investigate its conceivable system of activity to investigate the clinical meaning of GFD-15 in the determination of beginning phase liver malignant growth.


### 4.3. Experimental Results and Analysis

The declaration of GDF-15 in early hepatocellular carcinoma was fundamentally not the same as that in typical liver tissue (P<0.01). Contrasted and the neighboring tissue, there is additionally an exceptionally huge distinction (P<0.01), and the particular exploratory information are displayed in Table 1. The outcomes showed that the articulation in early hepatocellular carcinoma tissue was fundamentally higher than that in paracancerous tissue and typical liver tissue. The articulation qualities were steady with AFP.Table 1
Differences in GDF-15 expression between patients and normal subjects (ng/mL).
Group
n
GDF-15
AFP
Normal tissue
40
0.52
±
0.49
3.67
±
2.81
Liver cancer tissue
40
2.50
±
0.56
∗
∗
##
19.40
±
5.18
∗
∗
##
Adjacent tissue
40
1.31
±
0.35
∗
11.19
±
3.59
∗
∗
Note: contrasted and typical tissue,∗P<0.05, ∗∗P<0.01; compared with neighboring tissue, #P<0.05, ##P<0.01.It can likewise be seen from Table2 that the substance of GDF-15 in the serum of patients with early liver disease was (22.47±7.49) IU/L, and the difference was very significant compared with normal people (P<0.01). Its expression is similar to that of AFP, and they are higher than normal.Table 2
Differences in serum GDF-15 levels between patients and normal subjects (IU/L).
Detection index
n
GDF-15
AFP
Patients with liver cancer
40
22.47
±
7.49
∗
∗
31.53
±
8.85
∗
∗
Normal person
40
3.01
±
0.54
3.44
±
1.92
Note: compared with normal people,∗P<0.05, ∗∗P<0.01.To additionally break down the viability of serum GDF-15 in diagnosing beginning phase liver disease, we drew the beneficiary working trademark bend (ROC bend) of GDF-15. The curve indicated that the value of GDF-15 for diagnosing liver cancer was as follows: 65.7% sensitivity and 89.6% specificity, as shown in Figure10(a).Figure 10
ROC curve analysis.
(a)
ROC curve analysis of serum GDF-15 in the diagnosis of early hepatocellular carcinoma
(b)
ROC curve analysis of serum AFP in the diagnosis of early liver cancerIn the wake of portraying the ROC bend of serum AFP and early liver disease, the worth of serum GDF-15 for diagnosing lymph hub metastasis of gallbladder malignant growth was as per the following: 65.7%, specificity 89.6%, as shown in Figure10(b).Compared with normal liver tissue, the expression levels of GDF-15 and AFP in primary liver cancer tissue were significantly different (P<0.01), indicating that the expression in primary liver cancer tissue was significantly higher than that in normal liver tissue. The statement of GDF-15 and alpha-fetoprotein in serum of patients with liver disease was steady and higher than that of ordinary individuals (P<0.01). Whether it becomes a more sensitive early diagnostic marker for liver cancer needs further research to confirm.As indicated by the consequences of this review, the articulation level of GDF-15 is vital in essential begin. As the results of this review show, joint levels of GDF-15 are important for the underlying pathogenesis of hepatic malignant proliferation (measured in cancers <2.5 cm). Not exactly the same as the expected liver tissue (P<0.01), there is a large difference between the tissue and adjacent tissues (P<0.01), indicating that the joints of early primary liver malignancies are basically higher than those of the affected tissue and normal liver organize. This is because the level of GDF-15 in early hepatocellular carcinoma tissue is higher than that in normal tissue. On the other hand, the serum GDF-15 level in patients with primary liver disease was (22.47±7.49) IU/L, which was fundamentally different from the typical baseline group (P<0.01). Using AFP, it was possible to predict the joint mass of GDF-15 in diseased tissue and serum of patients in the early stages of malignant liver proliferation, which was higher than usual.


## 4.1. Biological Characteristics of GDF-15

Growth differentiation factor is called macrophage inhibitory factor. It is activated by a specific hydrolase into a biologically active dimer. After activation, the Smad-dependent signaling pathway and the protein-independent Smad pathway are activated through autocrine or parathyroid effects, and finally exert biological effects [19]. Figure 9 shows the various expressions of GDF-15 in tissues.Figure 9
Multiple expression of GDF-15 in tissues.At present, relevant studies believe that the targets of GDF-15 are mainly Smad2 and 3, and also have a mild effect on Smad1, 5, and 8 [20]. Among the pathways that inhibit ventricular remodeling, cardiac hypertrophy can be inhibited by the Smad2/3 pathway or the Smad1 pathway. GDF-15 is a stress cytokine. In the basal physiological state, its secretion is highly tissue-specific, and is only expressed at high levels in the placenta and prostate, especially in the second and third trimesters of pregnancy, while weakly expressed in most tissue cells (such as the heart). Most investigations have shown that GDF15 articulation is expanded in an assortment of growths. Cell studies have likewise observed that GDF15 can repress the development of disease cells and advance the apoptosis of malignant growth cells.However, when the body is in a variety of different pathological states, it will be abundantly expressed in cardiomyocytes, and the serum concentration will also increase rapidly, thereby inhibiting apoptosis. Studies have shown that it plays a role in protecting the myocardium through biological effects such as improved myocardial remodeling and anti-inflammatory effects such as abdominal hypertrophy. GDF-15 affects the production of inflammatory factors by inhibiting leukocyte infiltration and macrophage activation, inhibiting excessive inflammatory responses [21].


## 4.2. Experiment Preparation

### 4.2.1. Organizational Source

In this experiment, we chose a sum of 40 patients with beginning phase liver disease who went through careful resection in a clinic from January 2020 to December 2021. This experiment has obtained the consent of all patients, and all their case information is strictly confidential. We collected samples from carcinogenic tissues and serum from early-stage liver cancer patients, respectively, as the content of this experimental study. After the samples are taken, they are placed in a vacuum space with a certain temperature, and they are kept warm. Among the 40 patients, there were 18 females and 22 males. Most of them were 43 years old and between 27 and 56 years old. The age span showed that the incidence of liver cancer was not greatly affected by age. Both tumors were one, and the first time they had cancer, the tumor size was less than 2.5 cm in diameter. The data and samples were used as the experimental group of this experiment. At the same time, we also selected 40 normal liver blood and in vivo tissues as the control group for this experimental study.


### 4.2.2. Method

The motivation behind this study is to notice the statement of GFD-15 in disease tissue and serum of patients with beginning phase liver malignant growth, and to investigate its conceivable system of activity to investigate the clinical meaning of GFD-15 in the determination of beginning phase liver malignant growth.


## 4.2.1. Organizational Source

In this experiment, we chose a sum of 40 patients with beginning phase liver disease who went through careful resection in a clinic from January 2020 to December 2021. This experiment has obtained the consent of all patients, and all their case information is strictly confidential. We collected samples from carcinogenic tissues and serum from early-stage liver cancer patients, respectively, as the content of this experimental study. After the samples are taken, they are placed in a vacuum space with a certain temperature, and they are kept warm. Among the 40 patients, there were 18 females and 22 males. Most of them were 43 years old and between 27 and 56 years old. The age span showed that the incidence of liver cancer was not greatly affected by age. Both tumors were one, and the first time they had cancer, the tumor size was less than 2.5 cm in diameter. The data and samples were used as the experimental group of this experiment. At the same time, we also selected 40 normal liver blood and in vivo tissues as the control group for this experimental study.


## 4.2.2. Method

The motivation behind this study is to notice the statement of GFD-15 in disease tissue and serum of patients with beginning phase liver malignant growth, and to investigate its conceivable system of activity to investigate the clinical meaning of GFD-15 in the determination of beginning phase liver malignant growth.


## 4.3. Experimental Results and Analysis

The declaration of GDF-15 in early hepatocellular carcinoma was fundamentally not the same as that in typical liver tissue (P<0.01). Contrasted and the neighboring tissue, there is additionally an exceptionally huge distinction (P<0.01), and the particular exploratory information are displayed in Table 1. The outcomes showed that the articulation in early hepatocellular carcinoma tissue was fundamentally higher than that in paracancerous tissue and typical liver tissue. The articulation qualities were steady with AFP.Table 1
Differences in GDF-15 expression between patients and normal subjects (ng/mL).
Group
n
GDF-15
AFP
Normal tissue
40
0.52
±
0.49
3.67
±
2.81
Liver cancer tissue
40
2.50
±
0.56
∗
∗
##
19.40
±
5.18
∗
∗
##
Adjacent tissue
40
1.31
±
0.35
∗
11.19
±
3.59
∗
∗
Note: contrasted and typical tissue,∗P<0.05, ∗∗P<0.01; compared with neighboring tissue, #P<0.05, ##P<0.01.It can likewise be seen from Table2 that the substance of GDF-15 in the serum of patients with early liver disease was (22.47±7.49) IU/L, and the difference was very significant compared with normal people (P<0.01). Its expression is similar to that of AFP, and they are higher than normal.Table 2
Differences in serum GDF-15 levels between patients and normal subjects (IU/L).
Detection index
n
GDF-15
AFP
Patients with liver cancer
40
22.47
±
7.49
∗
∗
31.53
±
8.85
∗
∗
Normal person
40
3.01
±
0.54
3.44
±
1.92
Note: compared with normal people,∗P<0.05, ∗∗P<0.01.To additionally break down the viability of serum GDF-15 in diagnosing beginning phase liver disease, we drew the beneficiary working trademark bend (ROC bend) of GDF-15. The curve indicated that the value of GDF-15 for diagnosing liver cancer was as follows: 65.7% sensitivity and 89.6% specificity, as shown in Figure10(a).Figure 10
ROC curve analysis.
(a)
ROC curve analysis of serum GDF-15 in the diagnosis of early hepatocellular carcinoma
(b)
ROC curve analysis of serum AFP in the diagnosis of early liver cancerIn the wake of portraying the ROC bend of serum AFP and early liver disease, the worth of serum GDF-15 for diagnosing lymph hub metastasis of gallbladder malignant growth was as per the following: 65.7%, specificity 89.6%, as shown in Figure10(b).Compared with normal liver tissue, the expression levels of GDF-15 and AFP in primary liver cancer tissue were significantly different (P<0.01), indicating that the expression in primary liver cancer tissue was significantly higher than that in normal liver tissue. The statement of GDF-15 and alpha-fetoprotein in serum of patients with liver disease was steady and higher than that of ordinary individuals (P<0.01). Whether it becomes a more sensitive early diagnostic marker for liver cancer needs further research to confirm.As indicated by the consequences of this review, the articulation level of GDF-15 is vital in essential begin. As the results of this review show, joint levels of GDF-15 are important for the underlying pathogenesis of hepatic malignant proliferation (measured in cancers <2.5 cm). Not exactly the same as the expected liver tissue (P<0.01), there is a large difference between the tissue and adjacent tissues (P<0.01), indicating that the joints of early primary liver malignancies are basically higher than those of the affected tissue and normal liver organize. This is because the level of GDF-15 in early hepatocellular carcinoma tissue is higher than that in normal tissue. On the other hand, the serum GDF-15 level in patients with primary liver disease was (22.47±7.49) IU/L, which was fundamentally different from the typical baseline group (P<0.01). Using AFP, it was possible to predict the joint mass of GDF-15 in diseased tissue and serum of patients in the early stages of malignant liver proliferation, which was higher than usual.


## 5. Discussion

First of all, through the study of the relevant knowledge points of the literature works, this paper has initially mastered the relevant basic knowledge. This paper examines how to concentrate on the clinical worth of development separation recognition the conclusion of early liver disease in light of information mining examination. This paper expounds the concept of data mining and related technical algorithms, and focuses on the BP neural network. This paper explores the algorithm application in support vector machine, also, examines the clinical worth of GDF-15 in the determination of early liver malignant growth through tests.Now, people have begun to explore the molecular mechanisms of early-stage tumors, and it is difficult to find sensitive and specific early diagnostic markers and therapeutic targets for young people. Taking primary liver cancer with a high degree of malignancy as an example, although its mechanism is still unclear, AFP is the most dependable serum marker for liver malignant growth, with an awareness of around 60%-70% and a particularity of around 90%.In this paper, the experimental analysis shows that: GDF-15 assumes a critical part in an assortment of diseases. GDF-15 was essentially communicated in early hepatocellular carcinoma tissues and serum. These outcomes recommend that GDF-15 can be utilized as a serum growth marker for foreseeing beginning phase liver disease and may give proof to the forecast of liver malignant growth repeat. We surmise that if the declaration of GDF-15 is misleadingly down-managed, it might hinder the event and advancement of liver disease, along these lines giving another objective to the designated treatment of liver malignant growth. In any case, its particular organic component and whether the designated downregulation of GDF-15 can be applied to clinical treatment actually require further exploration.


## 6. Conclusion

In recent years, GDF-15 has received more attention as a novel biomarker. GDF-15 is a novel cytokine that plays a unique role in the occurrence, development, and chemotherapy resistance of cancer. Its tumor growth-promoting effect in nearly all types of cancer may be a fusion point of cancer features. However, studies establishing reference intervals for expression concentrations in healthy individuals and cancer patients, and prospective large-scale studies in different cancers are still lacking. These studies promote the use of GDF-15 as a biomarker for early diagnosis and prognosis of early HCC. Furthermore, studies of signaling pathways and biological mechanisms in various cancer models (animals and cell lines) suggest that GDF-15 may be a potential target for future intervention in early-stage liver cancer. Most data support an oncogenic role of GDF-15 in carcinogenesis, and some conflicting data suggest the need for further studies, but do not affect the oncogenic role of GDF-15. At present, many research results show that GDF-15 can be used as a target for cancer targeted therapy, but its specific molecular biological mechanism is still unclear and needs further research by researchers. By reviewing the experimental process of this paper, if the selection of the experimental objects is more detailed, the results may be more accurate. Due to time constraints, this paper has not conducted research to provide reference.



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