Overexpression of APOA2 is Associated with Progression and Poor Prognosis in Gastric Cancer

DOI: https://doi.org/10.21203/rs.3.rs-1361258/v1

Abstract

Background: Gastric cancer is a malignant tumor originating from gastric mucosal epithelium, and more than 90% of them are gastric adenomas. At present, surgical treatment is the main method to cure gastric cancer. We were able to find through bioinformatics that many genes are aberrantly expressed in gastric cancer. APOA2, a gene in the apolipoprotein A family, is the major apolipoprotein of high-density lipoprotein (HDL). APOA2 is composed of 77 amino acid residues of two polypeptide chains and exists in the plasma dimer form. APAO2 is closely related to obesity, atherosclerosis, diabetes, and hyperlipidemia, and can also be used as a diagnostic marker for diseases such as pancreatic cancer and liver cancer.

Objective: To investigate the expression and mechanism of APOA2 in gastric cancer cells, and to find out miRNAs that target and regulate APOA2.

Methods: The clinical data of gastric cancer were downloaded using GCTA database, the relationship between APOA2 and the occurrence and development of gastric cancer was analyzed using bioinformatics, and the clinical samples were verified. Its potential miRNA regulatory mechanism was also sought. The correlation between APOA2 expression and clinicopathological parameters in clinical cases with different stage grades was statistically analyzed with t-test and chi-square test. The log-rank test was used to predict and find the relationship between APOA2 expression and overall survival (OS) and relapse-free survival (RFS). Gene set enrichment analysis (GSEA) was performed to screen KEGG pathways associated with APOA2 expression during gastric carcinogenesis. MiRNA expression data of gastric cancer were downloaded from the TCGA database, their differential expression was analyzed, and miRNAs targeting APOA2 expression were screened, along with their relationship with gastric cancer progression and prognosis. Paraffin sections of gastric cancer tissues and normal gastric tissues were collected in 5 cases each, and the expression of APOA2 in these samples was verified by PCR.

Results: APOA2 expression was significantly different between gastric cancer tumor samples and normal tissue samples by analysis of TCGA database samples (p < 0.001). Cox regression analysis showed that upregulation of APOA2 expression was associated with overall survival (OS) and recurrence-free survival (RFS) in gastric cancer. In addition, miR-27b-3p targeted up-regulation of APOA2 and resulted in decreased OS and RFS. APOA2 was found to be significantly enriched in the "DNA replication", "cell cycle" pathways by GSEA analysis.

Conclusion: APOA2 is involved in the whole process of gastric cancer development and is regulated by miR-27b-3p, and affects the overall survival and recurrence-free survival of patients, which mainly promotes the progression of gastric cancer by affecting the "DNA replication" and "cell cycle" of cancer cells. APOA2 is expected to be a promising prognostic biomarker and candidate therapeutic target for gastric cancer. Keywords: APOA2, gastric cancer, miR-27b-3p

Introduction

Gastric cancer is a common digestive tract tumor, which is one of the most common cancer in the world since less than one century ago. With the improvement of people's quality of life, the incidence of gastric cancer has decreased significantly, but it is still the second common cancer causing death[1]. There are many risk factors for gastric cancer, such as geographical environment, diet, alcohol consumption, smoking, Helicobacter pylori infection, family history, EVB virus infection, etc.[2], Helicobacter pylori is the greatest cause. Studies have found that the infection rate of Helicobacter pylori of gastric cancer in high incidence areas in China is more than 60%, and it through damaging the gastric microenvironment to induce gastric epithelial transformation. At present, the diagnosis of gastric cancer mainly depends on the medical history, physical examination and laboratory tests. Early gastric cancer through screening the anti-Helicobacter pylori IgG[3]. For positive patients, endoscopic examination is difficult due to no symptoms, endoscopic examination only shows subtle changes[4], it is difficult to timely detect, and it often progresses to advanced stage before treatment.

The treatment of gastric cancer is mainly based on surgery. For patients, who with potentially serious diseases, preoperative chemoradiotherapy is given. Different surgical method are selected according to different conditions during intraoperative[5]. When gastric cancer progresses to advanced stage, chemoradiotherapy and molecular targeted therapy are the main methods[6]. Immunotherapy is a new method for the treatment of gastric cancer, resistant tumor activity by activating human autoimmune cells[7]. However, these therapies can only prolong the patient's life and cannot completely cure gastric cancer. Therefore, it is important to find biomarkers and effective therapeutic targets for gastric cancer using high-throughput detection, bioinformatics analysis and clinical sample validation for the early diagnosis and treatment of gastric cancer. Our group previously downloaded the transcriptomic data of gastric cancer from the TCGA database and performed bioinformatics analysis to discover valuable genes. Through the validation of clinical samples and literature search, it was found that APOA2 was closely related to the occurrence and development of gastric cancer and was worthy of in-depth study.

APOA2 is a type of apolipoprotein A family gene, which is a fat metabolism-related gene that is highly expressed in HDL and has a major role in transporting cholesterol to the liver for metabolism. Differential expression of its genotype is a potential mechanism affecting obesity[8], and some genes in the APOA2 promoter are associated with insulin resistance, which leads to the development of obesity[9]. Studies have found that APOA2 expression is decreased in pregnant women with gestational diabetes and plays a role in inflammation[10]. In addition, the content of APOA2 in serum is related to the cognitive function of elderly men and can be used as a biomarker of senile dementia[11]. APOA2 is also associated with cancer, and it can be used as a diagnostic marker for pancreatic cancer[12] and lung cancer[13], also it as an indicator in the combined marker of colorectal cancer[14], and it as a prognostic indicator for metastatic renal cancer[15]. We through bioinformatics found APOA2 be differentially expressed in prostate cancer, liver cancer, cholangiocarcinoma and it can be used as a candidate gene for biomarkers[16, 17]. These findings indicate that APOA2 plays an important role in tumor development. The expression level of APOA2 in gastric cancer and its prognostic and therapeutic role for gastric cancer patients remain unclear. In this paper, we investigate the relationship between APOA2 expression levels and clinical TNM stage, prognosis by bioinformatics analysis. In addition, we also analyzed the biological function of APOA2 in gastric cancer and the influence of APOA2 differential expression by miRNA regulation.

MiRNAs are a class of non-coding single-stranded RNA molecules of approximately 22 nucleotides in length encoded by endogenous genes that bind to the mRNA3 ′ -UTR to regulate genes at the transcriptional level. MiRNAs have been demonstrated to play a key role in tumor progression and treatment[18]. Analysis of transcriptome data in precancerous gastric cancer revealed significantly changed miR-27b-3p, and consulting the literature revealed that miR-27b-3p regulates many genes affecting the progression of various tumors, for example, regulating ATG10 affecting colorectal chemoresistance[19], regulating YAP1 inhibits glioma development[20], and regulating NRF2 inhibits esophageal cancer development[21]. In gastric cancer, the regulatory mechanism of miR-27b-3p has not been reported. Bioinformatics analysis revealed that APOA2 is a target gene of miR-27b-3p. In this paper, we used bioinformatics analysis and clinical gastric cancer sample validation to deeply study the relationship between APOA2 and the occurrence and development of gastric cancer and its potential regulatory mechanism.

Methods

1. Database Data Download and Analysis

The expression levels of the target gene APOA2 in normal and tumor tissues in various parts of the human body are searched from the GEPIA database. RNA-Seq, Counts, and FPKM are used as keywords for data search from the TCGA-STAD database, with a total of 373 gastric cancer (STAD) cases and 32 normal controls. Downloaded data are integrated using the R software package edgeR with a criterion of p < 0.05, logFC > 1. APOA2 differential expression was compared between normal and tumor tissues and output in txt file form. The logFC is used as a screening index to select the differentially express gene APOA2 base on whether there are no literature reports. The R software package is used to analyze the expression of APOA2 in STAD TMN, pathological stage grade and different survival status. The survival rates of highly and lowly express APOA2 in different TMN, pathological stage grades are also compared.

2. MiRNA predicted to regulate APOA2

STAD miRNA expression data are downloaded from the TCGA database and compared with normal tissues to analyze their differential expression and select the up-regulated miRNAs. MiRNA regulating APOA2 gene are found from DIANA-microT and miRwalk databases, respectively, and data are downloaded in txt format. The merging set of two database miRNAs is taken, and then the intersection is taken with the up-regulated miRNAs in STAD. The resulting miRNAs are considered as potential miRNAs regulating APOA2 in STAD.

3. Gene Enrichment Analysis

To explore for which pathways that APOA2 gene expression will influence STAD progression, we performed GSEA analysis by using TCGA-STAD data. The required data files are downloaded from the KEGG database (c2.cp.kegg.v7.1.symbols.gmt ), and annotation files (annotating Ensemble IDs to symbol). NES (normalized enrichment fraction) is calculated to analyze the p-value, FDR (false discovery rate) of the gene set. The smaller the absolute value of NES, the smaller the FDR value, and the smaller the p value, the better the enrichment. The gene set under this pathway is significant when the p-value is less than 0.05 and the FDR is less than 0.25.

4. statistical analysis

Receiver operating characteristic curve (ROC) is used to verify the diagnostic value of APOA2 up-regulation for STAD. Calculating AUC area, AUC area epilepsy, the greater the diagnostic value for STAD. The t-test is used to validate APOA2 expression in different clinical stage and grades, and the chi-square test is used to assess the correlation between APOA2 expression and clinicopathological parameters. The log-rank test is used to compare low APOA2 expression with high APOA2 expression in overall survival (OS) and recurrence-free survival (RFS). Cox analysis is used to analyze the age, gender, and TNM grade of OS and RFS, and their hazard ratios (HR) and 95% confidence intervals (Cl) are analyzed. The linear graph models of OS and RFS are constructed. Statistical analysis is performed using R4.1.0 version. A value of p < 0.05 is considered to indicate a significant result.

5. qPCR

Five cases of gastric adenocarcinoma and five cases of normal gastric tissue wax block sections were collected from the Department of Pathology of the First Affiliated Hospital of Kunming Medical University, and informed consent and consent were obtained from the Department of Pathology of the First Affiliated Hospital of Kunming Medical University for the use of these pathological wax block sections. Place the pathological wax block into a centrifuge tube, add 1ml xylene into each tube to dissolve the wax in the wax block section, place it on the centrifuge at 4℃, 12000r for 10 min, discard the supernatant, and take the underlying tissue. Then add RNA lysate to react for 10 minutes, centrifuge for 10 minutes and then take the supernatant into a new centrifuge tube, add 300 ul of chloroform (trichloromethane) into the tube, invert and shake for 15 seconds, allow to stand on ice for 15 minutes, and divide the liquid in the tube into three layers: the upper water sample is the RNA layer, the middle layer is the protein layer, and the lower layer is the impurity layer such as salt. Place them on a centrifuge at 4℃, 12000r for 15 min. Take 500 ul of supernatant and add 750 ul of isopropanol into a new EP tube, invert and mix well for 15 s, and allow to stand at -20℃ overnight. Take out the centrifuge tube, and place it on a centrifuge at 4℃, 12000r for 10 min. After centrifugation, discard the supernatant, retain white RNA precipitation, add 1ml of 80% ice-cold ethanol to the centrifuge tube, invert and mix well, place it on the centrifuge at 4℃ for 12000r for 10 min, remove the supernatant, and place it on the filter paper for drying for about 10 min. Add 20 ul of DEPC water to the centrifuge tube to dissolve the RNA and place it in the centrifuge point at 3000 rpm (about 8 seconds).2.5 ul of the solution was used to measure the RNA concentration with a microplate reader, and the sample volume as well as the amount of water loaded were calculated based on the measured RNA concentration. The reverse transcription system was 20 ul (Table 1). According to Table 1, the sample loading and water loading were added into the centrifuge tube, and then placed into the PCR instrument for reaction at 65℃ for 5 min. After the completion of reaction, the total amount of buffer, enzyme and mix was added into the centrifuge tube and stored at 37℃, 15 min, 98℃, 5 min and 4℃.After the reaction time, the centrifuge tube was taken out and stored at − 20 ° C. The cDNA sequence of APOA2 was searched and primers (Table 2) were designed to amplify with a fluorescence-based assay by quantitative PCR. The mixture for amplification (Table 3) was prepared as required. PCR conditions were 95 ° for 3 min and 95 ° for 10 sec, followed by 39 cycles at 54 ° for 15 sec and 72 ° for 20 sec. APOA2 expression was compared between gastric adenocarcinoma tissues and normal gastric tissues.

 

Teble1 Reverse transcription system

Reagent

Volume

5×RT buffer

4µl

RT Enzyme Mix

1µl

Primer Mix

1µl

RNA

3000/RNAConcentration

RNase-free water

Up to 20µl

Teble2 Primer Sequence

 

Sequence (5'->3')

Length

Tm

GC%

Self complementarity

Self 3' complementarity

product length

 

Forward primer

GGAGCTTTGGTTCGGAGACA

20.00

59.97

55.00

4.00

3.00

 

 

 

 

202

Reverse primer

TAACCAGTTCCGTTCCAGCC

20.00

59.96

55.00

4.00

1.00

Teble3 Amplification system

Name

Amount used

SybrGreen qPCR mastermix

10ul

PCR Forward Primer

0.6ul

PCR Reverse Primer

0.6ul

cDNA

1ul

ddH2O

7.8ul

Total

20ul

6. Ethics approval and consent to participate

The study was conducted in accordance with the Institutional Research Ethics guidelines and ethical principle involving human participation (i.e., Helsinki Declaration).The Department of Pathology of the First Affiliated Hospital of Kunming Medical University agreed and approved our use of pathological wax blocks. All patients were informed of the purpose of the pathological wax block, and all patient-related information was kept confidential.

Results

1. APOA2 is up-regulated in STAD tissues

In the GEPIA database, all tumor samples and corresponding normal tissue expression profiles, we find APOA2 expression is significantly up-regulated in STAD tumor tissues. Through analysis of TCGA database samples, APOA2 expression is found to be significantly elevated (p < 0.05) in tumor samples (n = 373) compared to normal tissue samples (n = 32) (Figure1A, B). Clinically, STAD is diagnosed base on the expression of APOA2, and its ROC curve (AUC = 0.69, p < 0.05) also confirms that APOA2 is up-regulated in gastric cancer tissues (Figure1C)

2. APOA2 overexpression is correlated with STAD clinical stage grade

The OS threshold of 373 STAD patients is used as the cut-off point of APOA2 expression, which is divided into the group with high APOA2 expression and low APOA2 expression, and the clinical significance of APOA2 expression is analyzed according to the corresponding indicators (Table 4). We respectively analyze age, gender, family history, TNM stage, histological grade, residual tumor, survival status, and disease status and found that there is no significant difference between high and low APOA2 expression in these parameters (p > 0.05). We further analyzed the clinical parameters significantly associated with APOA2 overexpression and found that in TNM stage, there were significant differences in APOA2 overexpression between TNM stage I and TNM stage II, TNM stage I and TNM stage III, TNM stage I and TNM stage IV, TNM stage II and TNM stage III, and TNM stage III and TNM stage IV. In histological grade, G1 grade versus G2 grade, G2 grade versus G3 grade, there is a significant difference in APOA2 overexpression (p < 0.05).We confirm that APOA2 overexpression is significantly different in TNM stage, histological grade, survival status, and disease status (Figure 2).

Table 4 Relationship between APOA2 expression and clinical parameters in patients with TCGA hepatocellular carcinoma

APOA2 Expression

 

Total

High

Low

P-value

 

(N=338)

(N=143)         

(N=195)

 

Age (year)

 

 

 

 

< 65

143 (42.3%)

57 (39.9%)

86 (44.1%)

0.686

≥ 65

192 (56.8%)

85 (59.4%)

107 (54.9%)

 

Unknown

3 (0.9%)

1 (0.7%)

2 (1.0%)

 

Gender

 

 

 

 

Male

217 (64.2%)

87 (60.8%)

130 (66.7%)

0.323

Female

121 (35.8%)

56 (39.2%)

65 (33.3%)

 

Family history of cancer

 

 

 

 

NO

257 (76.0%)

106 (74.1%)

151 (77.4%)

0.439

YES

15 (4.4%)

5 (3.5%)

10 (5.1%)

 

Unknown

66 (19.5%)

32 (22.4%)

34 (17.4%)

 

TNM stage

 

 

 

 

I

48 (14.2%)

27 (18.9%)

21 (10.8%)

0.193

II

109 (32.2%)

43 (30.1%)

66 (33.8%)

 

III

147 (43.5%)

58 (40.6%)

89 (45.6%)

 

IV

34 (10.1%)

15 (10.5%)

19 (9.7%)

 

Histologic grade

 

 

 

 

G1–G2

127 (37.6%)

54 (37.8%)

73 (37.4%)

0.899

G3–G4

203 (60.1%)

85 (59.4%)

118 (60.5%)

 

Unknown

8 (2.4%)

4 (2.8%)

4 (2.1%)

 

Residual tumor

 

 

 

 

R0

282 (83.4%)

113 (79.0%)

169 (86.7%)

0.0973

R1-R2

28 (8.3%)

13 (9.1%)

15 (7.7%)

 

Unknown

28 (8.3%)

17 (11.9%)

11 (5.6%)

 

Living status

 

 

 

 

Alive

204 (60.4%)

81 (56.6%)

123 (63.1%)

0.279

Dead

134 (39.6%)

62 (43.4%)

72 (36.9%)

 

Disease status

 

 

 

 

NO

173 (51.2%)

66 (46.2%)

107 (54.9%)

0.23

YES

37 (10.9%)

19 (13.3%)

18 (9.2%)

 

Unknown

128 (37.9%)

58 (40.6%)

70 (35.9%)

 

3.APOA2 overexpression predicts OS and RFS in STAD

To evaluate the prognostic value of APOA2 expression in STAD, the OS threshold is used as a cut-off point for APOA2 expression and divide into groups with high APOA2 expression and low APOA2 expression. We found that patients with high APOA2 expression have significantly lower OS and RFS survival within 6 years than those with low APOA2 expression (p < 0.05) (Figure 3). We use Cox regression analysis to further investigate the relationship between APOA2 expression in each index and OS, RFS. In univariate analysis, TNM stage, residual tumor, and age are correlated with OS (p < 0.05); histological grade is correlated with RFS (p < 0.05). To further exclude the influence of other factors, multivariate analysis find that APOA2 overexpression is an independent indicator in OS and RFS. APOA2 overexpression has a regulatory effect on other indicators in gastric cancer (Tables 5 and Tables 6). In TNM stage subunits, TNM stage I compare with TNM II, and TNM stage III compare with TNM stage IV, it is found that the OS and RFS survival rates of patients with high MEGEA6 expression are significantly lower than those of patients with low APOA2 expression within 6 years (p < 0.05) (Figure 4).

Table 5 , Cox proportional hazards regression model analysis of overall survival rate

 

Univariate analysis

Multivariate analysis

Variables

HR (95% CI)

p

HR (95% CI)

p

Age (≥ 65 vs. < 65)

1.6(1.12,2.29)

0.010

1.65(1.14,2.38)

0.008

Gender (Female vs. Male)

0.76(0.53,1.1)

0.151

-

-

Family history of cancer (YES vs. NO)

1.05(0.51,2.17)

0.885

-

-

TNM stage (II vs. I)

1.55(0.78,3.07)

0.211

1.53(0.77,3.05)

0.227

TNM stage (III vs. I)

2.42(1.28,4.58)

0.007

2.36(1.23,4.49)

0.009

TNM stage (IV vs. I)

4.08(1.98,8.43)

<0.001

3.62(1.67,7.83)

0.001

Histologic grade (G3–G4 vs. G1–G2)

1.4(0.97,2.02)

0.068

-

-

Residual tumor (R1–R2 vs. R0)

3.12(1.92,5.07)

<0.001

2.26(1.32,3.87)

0.003

APOA2 (high vs. low)

1.02(0.98,1.06)

0.324

-

-

Table 6, Cox proportional hazards regression model analysis of relapse-free survival

 

Univariate analysis

Multivariate analysis

Variables

HR (95% CI)

p

HR (95% CI)

p

Age (≥ 65 vs. < 65)

1.13(0.59,2.16)

0.717

-

-

Gender (Female vs. Male)

0.55(0.27,1.15)

0.112

-

-

Family history of cancer (YES vs. NO)

0.88(0.21,3.68)

0.860

-

-

TNM stage (II vs. I)

2.03(0.72,5.7)

0.179

-

-

TNM stage (III vs. I)

1.57(0.57,4.33)

0.387

-

-

Histologic grade (G3–G4 vs. G1–G2)

2.18(1.03,4.63)

0.043

2.18(1.03,4.63)

0.043

Residual tumor (R1–R2 vs. R0)

1.12(0.15,8.22)

0.913

-

-

APOA2 (high vs. low)

1.08(1,1.17)

0.052

-

-

4. Validation of the prognostic value of APOA2 in gastric cancer

To validate the prognostic value of APOA2 in gastric cancer, we use APOA2 expression and TNM stage as independent indicators by multivariate analysis and constructed nomogram prognostic analysis to predict the 1-year, 3-year, and 5-year survival rates of OS and RFS. By constructing a line graph, we find that the survival correction values predict by the prognostic analysis of gene-clinical TNM staging nomogram are more consistent with the actual survival observation values. Confirm that by multivariate analysis, we can predict the prognosis and survival of gastric cancer patients more accurately (Figure 5).

5. APOA2 is upregulated in STAD by miR-27b-3p downregulation

We explore the mechanism by which APOA2 is upregulated in STAD from a genetic perspective. We evaluate the relationship between APOA2 expression and APOA2 methylation,and found that APOA2 expression is negatively correlated with APOA2 methylation (p < 0.05), illustrating that methylation can inhibit APOA2 expression. We in turn explore potential miRNAs capable of regulating MAGEA in STAD and select miR-27b-3p as a miRNA regulating APOA2. Through the GCTA database, we find that miR-27b-3p is significantly down-regulated (p < 0.05) in gastric cancer tissues (n = 434) compare with normal tissues (n = 41), and miR-27b-3p is negatively correlated with APOA2 (p < 0.05). Analysis of the relationship between miR-27b-3p down-regulation and OS, RFS reveal that low miR-27b-3p expression causes high APOA2 expression which decreases OS and RFS. We find that miR-27b-3p downregulation is significantly associated with decreased OS and RFS in STAD within 4 years (p < 0.05) (Figure 6).

6.APOA2 Enrichment Analysis

We select the top 50 differentially expressed genes with high enrichment in gastric cancer according to the degree of enrichment and we construct a PPI network map and find that APOA2 is directly associated with two genes, AFP and ALB. The matascape database is used to perform GO enrichment analysis of APOA2 to investigate the role of APOA2 in molecular function (MF), cellular component (CC), and biological process (BP) in STAD. We find that APOA2 is significantly enriched in endoplasmic reticulum lumen in CC, steroid metabolic process; negative regulation of catabolic process; negative regulation of hydrolase activity in BP (p < 0.05) (Figure 7). We also use GSEA to investigate the role of APOA2 upregulation in KEGG pathways and find that APOA2 is significantly enriched in "DNA replication", "cell cycle" (p < 0.05) (Figure 8).

7.APOA2 Expression Results

We obtained the Cq values of APOA2 and β-actin in gastric cancer tissues and normal gastric tissues by qPCR experiments, and the smaller the Cq, the higher the expression of this gene in the samples. We used 2- ∆∆ Ct as a parameter for gene expression and found that APOA2 expression was significantly higher in gastric cancer tissues than in normal gastric tissues by independent samples t-test (p < 0.05) (Figure 9).

Discussion

In recent years, bioinformatics and Internet technology have been rapidly developed, and we can download the information we need and conduct in-depth study through the tumor database, which will also promote the, early understanding and diagnosis of tumors. Previously we mined differentially expressed genes in gastric cancer in the database and performed in-depth bioinformatics analysis and validation of clinical samples to obtain whether the gene of interest, APOA2, can be used as a tumor marker. APOA2 has been found to be differentially expressed in a variety of cancers, for example, in pancreatic cancer[12], lung cancer[13], and so on.However, differential expression in gastric cancer has not been reported. We find that APOA2 is overexpressed in gastric cancer compared with normal gastric tissues. It is find that APOA2 overexpression can better diagnose gastric cancer by ROC curve evaluation. The findings suggest that APOA2 may be a biomarker for the diagnosis of gastric cancer.And APOA2 overexpression is significant in clinical parameters of gastric cancer.APOA2 overexpression is correlated with TNM stage and histological grade, which indicate that APOA2 overexpression may have a close relationship with gastric cancer progression.By multivariate analysis, we find that APOA2 overexpression is not conducive to OS and RFS in patients with gastric cancer, and the nomogram prognostic model base on APOA2 expression and TNM stage could accurately predict OS and RFS in patients with gastric cancer, which suggest that APOA2 may be used as a prognostic indicator in clinical practice.In addition we further explore the mechanism of APOA2 upregulation in gastric cancer from a genetic point of view.First, we find that APOA2 is located on chromosome 1 q23, and that chromosomal amplification of this segment is often find on some sarcomas[22], so APOA2 overexpression is also often find in some malignancies.DNA hypomethylation has been found to promote chromosomal instability in cancer[23].APOA2 hypomethylation is found to promote APOA2 expression in gastric cancer by analysis.We analyze whether APOA2 has a function in gastric cancer from biological functions, and GO enrichment analysis revealed that APOA2 is significantly enriched in "endoplasmic reticulum lumen", "steroid metabolic process", "negative regulation of catabolic process", and "negative regulation of hydrolase activity". In KEGG enrichment, APOA2 is associated with "DNA replication", "cell cycle" pathways. DNA replication plays an important role in the development and progression of gastric cancer by affecting genomic instability[24]; cell cycle arrest and apoptosis play an important role in gastric cancer, and many genes affect gastric cancer through the "cell cycle" clear pathway[25, 26]. APOA2 may affect gastric cancer progression in part through these pathways.We found that APOA2 has a reciprocal relationship with AFP and ALB genes. AFP is closely related to the occurrence of a variety of tumors. Clinically, it is used as a serum marker of primary liver cancer. Through the analysis of multiple clinical cases, it is found that AFP also has important diagnostic value and prognostic value for gastric cancer[27]. At present, there is no literature reporting the relationship between APOA2 and AFP, whether they have an upstream and downstream relationship is still unclear, but APOA2 may be used as a satellite gene of AFP, in clinical practice, AFP, CEA and other markers combine with APOA2 detection, to improve the diagnosis and prognosis of gastric cancer.

In addition, miRNAs could regulate the expression of genes, and we screen miRNAs that can potentially regulate APOA2. Compare with normal gastric tissues, miR-27b-3p is down-regulated in gastric cancer (p < 0.05), and miR-27b-3p is negatively correlated with APOA2 expression, indicating that miR-27b-3p down-regulation can up-regulate APOA2 expression, and miR-27b-3p has a binding site in the coding region (CDS) fragment of APOA2, which suggest that miR-27b-3p can be used as an upstream modulator of APOA2.It has been documented that miR-27b-3p can target regulatory genes to inhibit glioma[20], esophageal cancer[21], colorectal cancer[28], and endometrial cancer[29].Some studies have found that miR-27b-3p agonist alone can inhibit gastric cancer growth[30], while APOA2 regulation by miR-27b-3p may be another potential mechanism to inhibit gastric cancer growth.As describein the review, both APOA2 hypomethylation and miR-27b-3p down-regulation can upregulate APOA2 in gastric cancer.

In summary, APOA2 is confirmed to be differentially expressed in gastric cancer, and it has some significance as a clinical indicator and is regulated by specific miRNAs, but its exact mechanism in gastric cancer still needs to be continuously explored.

Declarations

1.Ethical

The study was conducted in accordance with the Institutional Research Ethics guidelines and ethical principle involving human participation (i.e., Helsinki Declaration).The Department of Pathology of the First Affiliated Hospital of Kunming Medical University agreed and approved our use of pathological wax blocks. All patients were informed of the purpose of the pathological wax block, and all patient-related information was kept confidential.Informed consent obtained from all the participants included in the study. All experimental protocols were approved by The Department of Pathology of the First Affiliated Hospital of Kunming Medical University.

2.Consent to participate:

Not applicable.

3.Consent to publish

Not applicable.

4.Availability of data and materials

Informed consent was obtained for all data used in the article. All raw data used have been uploaded to DYRAD (Fig. https://datadryad.org/stash ), my dataset has been assigned a unique digital object identifier (DOI): doi:10.5061/dryad.1c59zw3x6.

5.Competing interests

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

6.Funding

 No funding

7.Authors' contribution

Machicheng Bao completed bioinformatics data mining, data analysis, mapping, article writing, and completed PCR experiments with Jing Xu, and Dr.Jia Liu provided article ideas, experimental samples, and financial support. All authors reviewed the manuscript

8.Acknowledgement

Not applicable.

References

  1. Karimi, P., et al., Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomarkers Prev, 2014. 23(5): p. 700-13.
  2. Machlowska, J., et al., Gastric Cancer: Epidemiology, Risk Factors, Classification, Genomic Characteristics and Treatment Strategies. Int J Mol Sci, 2020. 21(11).
  3. Yoon, H. and N. Kim, Diagnosis and management of high risk group for gastric cancer. Gut Liver, 2015. 9(1): p. 5-17.
  4. Pasechnikov, V., et al., Gastric cancer: prevention, screening and early diagnosis. World J Gastroenterol, 2014. 20(38): p. 13842-62.
  5. Joshi, S.S. and B.D. Badgwell, Current treatment and recent progress in gastric cancer. CA Cancer J Clin, 2021. 71(3): p. 264-279.
  6. Song, Z., et al., Progress in the treatment of advanced gastric cancer. Tumour Biol, 2017. 39(7): p. 1010428317714626.
  7. Kawazoe, A., et al., Current status of immunotherapy for advanced gastric cancer. Jpn J Clin Oncol, 2021. 51(1): p. 20-27.
  8. Lai, C.Q., et al., Epigenomics and metabolomics reveal the mechanism of the APOA2-saturated fat intake interaction affecting obesity. Am J Clin Nutr, 2018. 108(1): p. 188-200.
  9. Zaki, M.E., K.S. Amr, and M. Abdel-Hamid, Evaluating the association of APOA2 polymorphism with insulin resistance in adolescents. Meta Gene, 2014. 2: p. 366-73.
  10. Ramanjaneya, M., et al., apoA2 correlates to gestational age with decreased apolipoproteins A2, C1, C3 and E in gestational diabetes. BMJ Open Diabetes Res Care, 2021. 9(1).
  11. Song, F., et al., Plasma apolipoprotein levels are associated with cognitive status and decline in a community cohort of older individuals. PLoS One, 2012. 7(6): p. e34078.
  12. Honda, K., et al., CA19-9 and apolipoprotein-A2 isoforms as detection markers for pancreatic cancer: a prospective evaluation. Int J Cancer, 2019. 144(8): p. 1877-1887.
  13. Yoon, H.I., et al., Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer. J Cancer Prev, 2016. 21(3): p. 187-193.
  14. Voronova, V., et al., Diagnostic Value of Combinatorial Markers in Colorectal Carcinoma. Front Oncol, 2020. 10: p. 832.
  15. Vermaat, J.S., et al., Validation of serum amyloid α as an independent biomarker for progression-free and overall survival in metastatic renal cell cancer patients. Eur Urol, 2012. 62(4): p. 685-95.
  16. Kang, X., et al., Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis. Medicine (Baltimore), 2020. 99(50): p. e23563.
  17. Lima, T., et al., Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways. Med Oncol, 2021. 38(1): p. 9.
  18. Hanahan, D. and R.A. Weinberg, Hallmarks of cancer: the next generation. Cell, 2011. 144(5): p. 646-74.
  19. Sun, W., et al., The c-Myc/miR-27b-3p/ATG10 regulatory axis regulates chemoresistance in colorectal cancer. Theranostics, 2020. 10(5): p. 1981-1996.
  20. Miao, W., et al., MiR-27b-3p suppresses glioma development via targeting YAP1. Biochem Cell Biol, 2020. 98(4): p. 466-473.
  21. Han, M., et al., MiR-27b-3p exerts tumor suppressor effects in esophageal squamous cell carcinoma by targeting Nrf2. Hum Cell, 2020. 33(3): p. 641-651.
  22. Kresse, S.H., et al., Mapping and characterization of the amplicon near APOA2 in 1q23 in human sarcomas by FISH and array CGH. Mol Cancer, 2005. 4: p. 39.
  23. Eden, A., et al., Chromosomal instability and tumors promoted by DNA hypomethylation. Science, 2003. 300(5618): p. 455.
  24. Yin, Y., et al., The DNA Endonuclease Mus81 Regulates ZEB1 Expression and Serves as a Target of BET4 Inhibitors in Gastric Cancer. Mol Cancer Ther, 2019. 18(8): p. 1439-1450.
  25. Zhang, L., et al., LncRNA CASC11 promoted gastric cancer cell proliferation, migration and invasion in vitro by regulating cell cycle pathway. Cell Cycle, 2018. 17(15): p. 1886-1900.
  26. Zhang, W., K. Liao, and D. Liu, MiRNA-12129 Suppresses Cell Proliferation and Block Cell Cycle Progression by Targeting SIRT1 in GASTRIC Cancer. Technol Cancer Res Treat, 2020. 19: p. 1533033820928144.
  27. Liu, D., et al., The clinicopathological features and prognosis of serum AFP positive gastric cancer: a report of 16 cases. Int J Clin Exp Pathol, 2020. 13(9): p. 2439-2446.
  28. Yang, X., et al., MiR-27b-3p promotes migration and invasion in colorectal cancer cells by targeting HOXA10. Biosci Rep, 2019. 39(12).
  29. Liu, L., et al., miR-27b-3p/MARCH7 regulates invasion and metastasis of endometrial cancer cells through Snail-mediated pathway. Acta Biochim Biophys Sin (Shanghai), 2019. 51(5): p. 492-500.
  30. Cui, Y., et al., Combined targeting of vascular endothelial growth factor C (VEGFC) and P65 using miR-27b-3p agomir and lipoteichoic acid in the treatment of gastric cancer. J Gastrointest Oncol, 2021. 12(1): p. 121-132.