Research on the Correlation between the Blood Lipid Metabolism

Level and Lung Cancer based on LIS Database

Yong Feng

, Juan Feng

and Yuxin Li

Qingdao West Coast New District People's Hospital, Qingdao, Shandong, China

Keywords: LIS Database, Health Data Analysis, Lung Cancer, Blood Lipid Metabolism.

Abstract: In recent years, the incidence of lung cancer has gradually increased, ranking second in malignant tumors,

and the mortality rate ranks first. Laboratory Information Management System (LIS) is a set of laboratory

information management system specially designed for hospital laboratory. LIS can network experimental

instruments and computers. LIS realizes intelligent, automated and standardized management of

complicated operation processes such as specimen scanning and registration, experimental data access,

report review, printing and distribution, and experimental data statistical analysis. The hospital will analyze

a large amount of test data every day, generate test reports, and store them in the LIS database through the

network. In this study, 1710 lung cancer patients were selected as the experimental group, and 1123 healthy

people who visited a doctor during the same period were selected as the control group. In this study, the LIS

database was used to analyze the correlation between the two groups of blood lipid metabolism levels. The

study found that the metabolic levels of serum total cholesterol (CHO), triglyceride (TG), high-density

lipoprotein (HDL-C) and apolipoprotein AI (ApoAI) in patients with lung cancer were significantly lower

than those in the physical examination population. There was no significant difference in low density

lipoprotein (LDL) and apolipoprotein B (ApoB) between the two groups. In this study, the LIS database was

used to retrieve medical and health data, and the LIS data was fully utilized to verify the correlation between

human blood lipids and lung cancer metabolic levels. This study has confirmed that the metabolic levels of

human serum total cholesterol (CHO), triglyceride (TG), high-density lipoprotein (HDL-C) and

apolipoprotein AI (ApoAI) can be used as important evaluation indicators for lung cancer.

1 INTRODUCTION

The incidence of lung cancer has risen to second

place in cancer, mortality ranks first, a serious

impact on people 's lives and health(Siegel 2019).

Studies have found that the metabolism of blood

lipids is related to the occurrence and prognosis of

tumors(

Lauby-Secretan 2016

). Abnormal lipid

metabolism can be used as a reference for the

diagnosis of tumor(

Xenoulis 2011, Kitahara 2011, Ahn

2009

). Excessive proliferation of cancer cells can

cause abnormal metabolism of blood lipids(

Pavlova

2016, Mancini 2018,

Sung 2019). Studies at home and

abroad have shown that abnormal lipid metabolism

was accompanied by changes in lipoprotein

metabolism in patients with malignant tumors.

Changes in lipid metabolism were associated with a

https://orcid.org/0000-0002-4515-560X

https://orcid.org/0000-0002-7321-7732

https://orcid.org/0000-0002-7793-6451

variety of malignant tumors(

Baenke 2013, Fiorenza

2000, Guan 2020

). In this study, the original health

data were collected by LIS database, and the data

were analyzed by SPSS statistical software package.

The indicators of dyslipidemia were expressed as ⎯x

± s. The analysis of variance was used to compare

the dyslipidemia between the lung cancer group and

the normal physical examination group. In this

study, the LIS database was used to extract medical

health data, which can effectively reduce the

statistical error and improve the accuracy of the

experimental results. This study shows that the

decrease of blood lipid metabolism can be used as an

important evaluation index for the deterioration of

lung cancer patients, and provides reference for

clinicians to observe the development of lung cancer

patients.

278

Feng, Y., Feng, J. and Li, Y.

Research on the Correlation between the Blood Lipid Metabolism Level and Lung Cancer based on LIS Database.

DOI: 10.5220/0011292500003438

In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 278-283

ISBN: 978-989-758-596-8

2 MATERIALS AND METHODS

2.1 Research Object

With the help of LIS database, 1710 patients with

pulmonary malignant tumors treated in the hospital

from January 2019 to June 2021 were selected as the

experimental group.

2.2 Standard Constrain

There were 1168 males and 542 females in the

experimental group; they were 24–92 years old with

an average age of ( 63.79 ± 9.10 ) years old. (1)

Patients diagnosed as pulmonary malignant tumor

by pathological examination; (2) no history of

surgery, radiotherapy and chemotherapy; (3)

Patients had the results of fasting blood lipid

examination during the morning of hospitalization;

(4) Complete clinical data of patients.

2.3 Method

In the lung malignant tumor group, 3ml of fasting

peripheral venous blood was taken in the early

morning of hospitalization and delivered to the

laboratory Beckman AU5800 automatic biochemical

analyzer for unified testing. Reference standards for

each test item: total cholesterol: 0-5.17mmol/L,

triglycerides: 0-2.3mmol/L, high-density lipoprotein

cholesterol: 1.29-1.55mmol/L, low-density

lipoprotein cholesterol: 0-3.37 mmol/L,

Apolipoprotein AI: 1.0-1.6g/L, Apolipoprotein B:

0.6-1.1g/L.

2.4 Statistical Analysis

The SPSS statistical software package was used to

analyze the research data. The measurement data

was represented by ⎯x±s parallel analysis of

variance or t test, and the count data was represented

by n(%) parallel X

test. P<0.05 indicated that the

difference was statistically significant.

3 RESULT

3.1 Abnormal Distribution of Blood

Lipids

689 of CHO cases were abnormal in the

experimental group, accounting for 40.29%, and 685

cases in the control group were abnormal,

accounting for 61.00%. The difference between the

two groups was statistically significant (P<0.05).

141 of TG cases in the experimental group are

abnormal, accounting for 8.25%, and 201 cases in

the control group were abnormal, accounting for

17.90%. The difference between the two groups was

statistically significant (P<0.05).

845 of HDL-C cases were abnormal in the

experimental group, accounting for 46.69%, and 292

cases in the control group were abnormal,

accounting for 23.88%. The difference between the

two groups was statistically significant (P<0.05).

276 of ApoAI cases in the experimental group

were abnormally decreased, accounting for 16.14% ,

345 cases were abnormally elevated, accounting for

20.18%, 13 cases in the control group were

abnormally decreased, accounting for 1.16%, and

348 cases were abnormally elevated, accounting for

30.99%. The difference between abnormal increase

and abnormal decrease between the two groups was

statistically significant (P<0.05).

81 of ApoB cases in the experimental group were

abnormally decreased, accounting for 4.74% , 657

cases were abnormally elevated, accounting for

38.42%, 78 cases in the control group were

abnormally decreased, accounting for 6.95%, and

443 cases were abnormally elevated, accounting for

39.45%. The difference between abnormal increase

and abnormal decrease between the two groups was

statistically significant (P<0.05). See Table 1.

Table 1: Distribution of dyslipidemia.

Lung cancer Control

Chi-squ P value

Abnormality(n) Proportion(%) Abnormality(n) Proportion(%)

Cho 689 40.29 685 61.00 116.3 <0.0001

TG 141 8.25 201 17.90 59.50 <0.0001

HDL-C 845 46.69 292 23.88 162.0 <0.0001

LDL 448 26.20 273 24.31 1.28 0.259

ApoAI

(reduction)

276 16.14 13 1.16

182.3 <0.0001

ApoAI

(ascension)

345 20.18 348 30.99

ApoB

(reduction)

81 4.74 78 6.95

7.35 <0.05

ApoB

(ascension)

657 38.42 443 39.45

Research on the Correlation between the Blood Lipid Metabolism Level and Lung Cancer based on LIS Database

279

3.2 Comparison of Abnormal Blood

Lipid Metabolism in Different Age

Groups

In the experimental group, 206 cases (29.90%) were

under 60 years old, 271 cases (39.33%) were 60 to

69 years old, 212 cases (30.77%) were over 70 years

old, and the control group was 60. There were 343

cases (50.0 7%) under the age of, 209 cases

(30.51%) between 60 and 69 years old, and 133

cases (19.42%) over 70 years old. The difference in

total cholesterol metabolism abnormalities between

the two groups of different age groups was

statistically significant (P<0.05).

In the experimental group, 33 cases (23.40%)

were under 60 years old, 51 cases (36.17%) were 60

to 69 years old, 57 cases were over 70 years old

(40.43%); control group was 60 101 cases (50.25%)

were under the age of, 72 cases (35.82%) were 60 to

69 years old, and 28 cases (13.93%) were over 70

years old. There was a statistically significant

difference in triglyceride metabolism between the

two groups of different age groups (P<0.05).

In the experimental group, there were 133 cases

(29.69%) under 60 years old, 168 cases (37.50%)

between 60 and 69 years old, and 147 cases

(32.81%) over 70 years old in the experimental

group; There were 150 cases (54.95%) under 60

years old, 79 cases (28.94%) between 60 and 69

years old, and 44 cases (16.12%) over 70 years old.

There was a statistically significant difference in the

abnormality of low-density lipoprotein metabolism

between the two groups at different ages (P<0.05).

The experimental group was abnormally

elevated, 96 cases (27.83%) under 60 years old, 125

cases (36.23%) between 60 and 69 years old, 124

cases (35.94%) over 70 years old; 170 cases

(48.85%) under 60 years old in the control group, 60

101 cases (29.02%) were 69 years old and 77 cases

(22.13%) were over 70 years old. There was a

statistically significant difference in the abnormal

metabolism of apolipoprotein AI between the two

groups of different ages (P＜0.05).

The experimental group was abnormally

elevated, with 197 cases (29.98%) under 60 years

old, 257 cases (39.12%) between 60 and 69 years

old, 203 cases (30.90%) over 70 years old; 233 cases

(52.60%) under 60 years old in the control group, 60

138 cases (31.15%) were 69 years old and 72 cases

(16.25%) were over 70 years old. The difference in

abnormal metabolism of apolipoprotein B between

the two groups of different ages was statistically

significant (P<0.05). See Table 2.

Table 2: Distribution of dyslipidemia.

≤60(Age,n,%) 61-69(Age,n,%) ≥70(Age,n,%)

Chi-squ

P-value

Lung cancer Control Lung cancer Control Lung cancer Control

Cho

206

（29.90%）

343

（50.07%）

271

（39.33%）

209

（30.51%）

212

（30.77%）

133

（19.42%）

60.27 <0.0001

33（23.40%）

101

（50.25%）

51（36.17%） 72（35.82%） 57（40.43%） 28（13.93%）

38.25 <0.0001

HDL-C

300

（35.50%）

117

（40.07%）

356

（42.13%）

104

（35.62%）

189

（22.37%）

71（24.32%）

3.869 0.145

LDL

133

（29.69%）

150

（54.95%）

168

（37.50%）

79（28.94%）

147

（32.81%）

44（16.12%）

49.05 <0.0001

ApoAI

(reduction)

93（34.07%） 7（

53.85%）

124

（45.42%）

4（30.77%） 56（20.51%） 2（15.38%）

2.145 0.342

ApoAI

(ascension)

96（27.83%）

170

（48.85%）

125

（36.23%）

101

（29.02%）

124

（35.94%）

77（22.13%）

34.11 <0.0001

ApoB

(reduction)

22（27.16%） 27（34.62%） 39（48.15%） 25（32.05%） 20（24.69%） 26（33.33%）

4.300 0.117

ApoB

(ascension)

197

（29.98%）

233

（52.60%）

257

（39.12%）

138

（31.15%）

203

（30.90%）

72（16.25%）

61.98 <0.0001

3.3 Comparison of the Results of Blood

Lipid Determination between the

Two Groups

The metabolic levels of CHO (5.03±1.20) mmol/L,

TG (1.40±1.04) mmol/L, HDL-C (1.32±0.33)

mmol/L, ApoAI (1.32±0.29) g/L of the experimental

group and the control group CHO (5.52±1.14)

mmol/L, TG (1.75±1.26) mmol/L, HDL-C

(1.52±0.34) mmol/L, ApoAI (1.48±0.20) g/L have

statistically significant differences (all P<0.05 ) was

shown in Table 3 and Figure 1.

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

280

Table 3: Comparison of blood lipid determination results between the two groups (mmol/L, g/L).

Group n Cho TG HDL-C LDL ApoAI ApoB

Lung cancer 1710 5.03±1.20 1.40±1.04 1.32±0.33 2.88±0.86 1.32±0.29 0.99±0.25

Control 1123 5.52±1.14 1.75±1.26 1.52±0.34 2.87±0.80 1.48±0.20 1.00±0.26

t value 10.77 7.88 15.72 0.10 15.60 0.92

P value <0.0001 <0.0001 <0.0001 0.92 <0.0001 0.36

Figure 1: Comparison of the classification of blood lipid results between the two groups.

4 DISCUSSION

Lung malignant tumor was the cancer with the

highest mortality rate among malignant tumors. In

recent years, research on the metabolism of lung

malignant tumors has always been the focus of

everyone's attention. On the basis that the baseline

data was not statistically significant, this study used

the LIS database to retrieve medical data of 1,710

patients with lung malignant tumors, and compared

them with 1,123 health examiners in the same

period.

The study found that the abnormal distribution of

blood lipids was compared. Among them, 689 cases

(40.29%) in the experimental group of serum total

cholesterol (CHO) were abnormal, 685 cases

(61.00%) in the control group were abnormal, and

141 cases in the experimental group were

triglycerides (TG) ( 8.25%) abnormality, control

group 201 cases (17.90%) abnormality;

ancer

Cho(mmoL/L)

t=10.77

P<0.0001

ng c

ancer

ontrol grou

TG(mmoL/L

）

t=7.88

P<0.0001

ng cancer

ontr

HDL-C(mmoL/L)

t=15.72

P<0.0001

gro

rol

LDL(mmoL/L)

t=0.10

P=0.92

g c

grou

ApoA1(g/L)

t=15.60

P<0.0001

ung canc

ontrol grou

ApoB(g/L)

t=0.92

P=0.36

Research on the Correlation between the Blood Lipid Metabolism Level and Lung Cancer based on LIS Database

281

experimental group HDL-C (HDL-C) 845 cases

(46.69%) abnormality, control group 292 cases

(23.88%) abnormality; experimental group

apolipoprotein AI (ApoAI) 276 cases (16.14%) were

abnormally decreased, 345 cases (20.18%) were

abnormally increased, 13 cases (1.16%) were

abnormally decreased in the control group, and 348

cases (30.99%) were abnormally increased; the

experimental group apolipoprotein B ( ApoB)

abnormally decreased in 81 cases (4.74%),

abnormally increased in 657 cases (38.42%),

abnormally decreased in 78 cases (6.95%) in the

control group, and abnormally increased in 443 cases

(39.45%). There were 448 cases (26.20%) abnormal

low-density lipoprotein (LDL) in the experimental

group and 273 cases (24.31%) in the control group.

The difference between the two groups was not

statistically significant (P>0.05).

In the determination results of the two groups of

blood lipids, the experimental group's CHO

(5.03±1.20) mmol/L, TG (1.40±1.04) mmol/L,

HDL-C (1.32±0.33) mmol/L, ApoAI (1.32±0.29) g

/L Metabolic level was different from the control

group CHO (5.52±1.14) mmol/L, TG (1.75±1.26)

mmol/L, HDL-C (1.52±0.34) mmol/L, ApoAI

(1.48±0.20) g/L Statistical significance (P<0.05).

Blood lipids participate in the formation of

human cell membranes, and play an important role

in the body's inflammatory response and signal

transduction. During the occurrence and

development of lung malignant tumors, the rapid

growth of cells causes abnormal metabolism of

blood lipids. The metabolic changes of blood lipids

will further affect the cellular metabolism of lung

malignant tumors. This study found that the

metabolic levels of CHO, TG, HDL-C, and ApoAI

in patients with lung malignant tumors were

significantly lower than those of normal healthy

people, and the metabolic levels of LDL and ApoB

were no different from those of normal healthy

people. The reason for this result was the abnormal

proliferation of cells in patients with lung malignant

tumors, which require a large amount of blood lipids

to synthesize cell membranes. As the degree of

malignancy increases, the consumption of blood

lipids increases, and the metabolic level of blood

lipids decreases.

5 CONCLUSIONS

The use of LIS database to retrieve a large number of

patients' health data for correlation analysis of blood

lipids and lung malignancies was an important

research method. In this study, a large amount of

health data was collected, and with the help of SPSS

statistical software, the chi-square test was used to

analyze the correlation between the patient's blood

lipid metabolism and lung malignant tumors. The

metabolism of CHO, TG, LDL, HDL-C, ApoAI, and

ApoB in patients with lung malignant tumors and

healthy people in the same period was selected as

the evaluation criteria. Studies have found that the

metabolic levels of CHO, TG, HDL-C, and ApoAI

in patients with lung malignant tumors were

significantly lower than those of healthy people on

physical examination, confirming that the metabolic

levels of CHO, TG, HDL-C, and ApoAI play a role

in the occurrence and development of lung

malignant tumors. The metabolic levels of LDL and

ApoB are not statistically significant between

patients with lung malignant tumors and healthy

people.

Although this study has achieved some

preliminary results, it also has the shortcomings and

limitations of applying retrospective data and single-

institution analysis. In the next stage, this study will

further use LIS database to carry out prospective and

specific index research to further confirm the

research conclusions and provide a new diagnostic

basis for the occurrence and development of lung

malignant tumors

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