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DERL3 functions as a tumor suppressor in gastric cancer
Journal Pre-proof DERL3 functions as a tumor suppressor in gastric cancer Yongyuan Li, Hongjie Liu, Hekai Chen, Jianping Shao, Feng Su, Shupeng Zhang, Xuejun Cai, Xianghui He
PII:
S1476-9271(19)30650-4
DOI:
https://doi.org/10.1016/j.compbiolchem.2019.107172
Reference:
CBAC 107172
To appear in:
Computational Biology and Chemistry
Received Date:
25 July 2019
Revised Date:
13 November 2019
Accepted Date:
21 November 2019
Please cite this article as: Li Y, Liu H, Chen H, Shao J, Su F, Zhang S, Cai X, He X, DERL3 functions as a tumor suppressor in gastric cancer, Computational Biology and Chemistry (2019), doi: https://doi.org/10.1016/j.compbiolchem.2019.107172
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
DERL3 functions as a tumor suppressor in gastric cancer Short title: DERL3 suppresses gastric cancer
Yongyuan Li1,2,#, Hongjie Liu3,#, Hekai Chen2, Jianping Shao2, Feng Su2, Shupeng Zhang2, Xuejun Cai2, Xianghui He1,*
1
Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, 300052,
2Department
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China. of General Surgery, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
3
Department of Radiology, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
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# These authors contributed equally to this work.
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*Correspondence to:
Xianghui He, Department of General Surgery, Tianjin Medical University General Hospital, Tianjin,
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300052, China. E-mail: [email protected].
Yongyuan Li: [email protected]; Hongjie Liu: [email protected]; Hekai Chen: xyz181 [email protected]; Jianping Shao: [email protected]; Feng Su: [email protected]; Shupeng [email protected];
Xuejun
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Zhang:
Cai:
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[email protected].
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Graphical Abstract
1
[email protected];
Xianghui
He:
Highlights
Differentially expressed genes and differentially methylated genes were determined, and a comprehensive analysis of STAD pathogenesis was conducted.
DERL3 is low expressed in tumors. However, high expression of DERL3 suppresses the invasion, migration, and proliferation of gastric cancer cells.
DERL3 was screened and validated as a candidate diagnostic marker gene of STAD.
DERL3 is a potential molecular target for the treatment of STAD.
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Abstract Background: Gastric cancer is a common malignant tumor in the clinic with a high mortality rate,
ranking the first among malignant tumors of the digestive system. Early gastric cancer exhibits no specific clinical symptoms and signs, and most of the patients were diagnosed as advanced gastric
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cancer. The prognosis is poor, and the 5-year overall survival rate is still lower than 30%, seriously threatening people’s life and health. However, the pathogenesis of gastric cancer is still unclear.
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Methods: This study aimed to identify methylated differentially expressed genes in gastric cancer and to study the cellular functions and pathways that may be involved in its regulation, as well as
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the biological functions of key methylated differentially expressed genes. The gene expression data set and methylation data set of gastric cancer genes based on TCGA were analyzed to identify prognostic methylated genes.
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Results: This study showed that the methylation of the DERL3 promoter was correlated with the clinical analysis of tumors. Further studies were conducted on genes co-expressed with DERL3,
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whose functions and pathways to inhibit gastric cancer were adaptive immune response, T cell activation, immune response-regulating pathway, cell surface on molecules, and natural killer cell-
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mediated cytotoxicity. Finally, cell proliferation assay, cell scratch assay, and cell invasion assay confirmed that DERL3 as a tumor suppressor gene inhibited the malignant evolution of gastric cancer.
Conclusions: The analysis of key methylated differentially expressed genes helped elucidate the epigenetic regulation mechanism in the development of gastric cancer. DERL3, as a methylation biomarker, has a predictive and prognostic value in the accurate diagnosis and treatment of gastric cancer and provides potential targets for the precision treatment of gastric cancer. 2
Trial Registration: Not applicable
List of abbreviation: DMGs: differentially methylated genes, DEGs: differentially expressed genes, Hypo-HGs: hypomethylated, highly exprocessed genes, Hyper-LGs: hypermethylated, lowly exprocessed genes, PPI: protein–protein interaction, TCGA: the cancer genome atlas, STAD: stomach adenocarcinoma
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Key words: DERL3; prognosis; functional network analysis; methylation; gastric cancer
Background
Gastric cancer is a common malignancy worldwide[1-3]. Although the incidence and mortality
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of gastric cancer have decreased in recent years, among all malignant tumors, gastric cancer still ranks the fifth in incidence and the third in mortality. At present, surgery is still the main method
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for the treatment of early gastric cancer, but for advanced gastric cancer, more comprehensive treatment is needed, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy[4].
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Advanced gastric cancer is mainly treated with drugs, but the treatment scheme is limited, and the curative effect needs to be improved urgently. The pathogenesis of gastric cancer has not been
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completely clarified, which cannot meet the need for the effective prevention and treatment of gastric cancer in the clinic. Therefore, exploring the pathogenesis of gastric cancer and finding potential therapeutic targets are of great significance in the early diagnosis of gastric cancer and the
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development of individualized treatment programs. Genetic changes (e.g., mutation, recombination, and copy number changes) and epigenetic
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changes (e.g., DNA methylation) play a key role in the development of cancer[5]. The hypermethylation of CpG island leads to the transcriptional silencing of tumor suppressor genes, whereas the hypomethylation of oncogenes leads to the activation of oncogenes[6-9]. Abnormal demethylation is common in gastric cancer tissues. The methylation rate of homeodomain-only protein X (HOPX)-β in gastric cancer tissues is 84%, whereas that in corresponding normal tissues is only 10%. Therefore, the abnormal methylation level of this gene is highly correlated with the occurrence of gastric cancer. In addition, MGMT, a DNA mismatch 3
repair gene in gastric cancer tissues, is often inactivated because of high methylation, making MGMT protein unable to be expressed. The deletion of MGMT protein will make some key genes (such as p53, protooncogene, and K-Ras) undergo G-C mutation to A-T and cannot be repaired, resulting in the loss of normal physiological functions of the corresponding genes. The anticancer effect of tumor suppressor gene ZIC1 is manifested because it renders gastric cancer cells stagnate in the S phase, and the hypermethylation of this gene promoter region downregulates the expression of this gene, thus promoting the occurrence of gastric cancer. Proteins encoded by the DERL3 (derlin-3) gene belong to the Derlin family and are present in
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the endoplasmic reticulum (ER)[10, 11]. Proteins that unfold or misfold in the ER must be refolded or degraded to maintain ER homeostasis[11]. This protein is involved in the degradation of misfolded glycoprotein in the ER. Several variable splicing transcriptional variants encoding different isotypes act on this gene. DERL3 is a tumor suppressor gene. Its loss of function may lead
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to problems in protein folding and degradation, which may lead to gastric cancer and promote the metastasis of tumor cells.
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In this study, high-throughput methylation spectra from a large number of patients were used to study the altered DNA methylation patterns between STAD samples and adjacent tissue samples,
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and to identify specific DNA methylation sites as potential biomarkers. Using The Cancer Genome Atlas (TCGA) data set, we identified the methylated differentially expressed genes related to gastric
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cancer, including hypermethylated, low-expressed genes, hypomethylated, and high-expressed genes[12, 13]. We analyzed the gene list and found that DERL3 played an important role. We further analyzed the correlation between DERL3 expression level and methylation level and the
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clinicopathology of gastric cancer. Third, we analyzed the co-expression of DERL3 in gastric cancer, and we used the functional annotation tool to study the gene ontology and KEGG pathway of
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DERL3 co-expression[14]. Finally, DERL3 was proved to be a tumor suppressor gene of gastric cancer by cell proliferation assay, cell scratch assay, and cell invasion assay[15-17]. These analyses provide comprehensive biological information for the study of methylation of gastric cancer, contribute to the understanding of the occurrence and development of gastric cancer, and further reveal the biological function of DERL3.
Materials and methods 4
1. Data sources DNA Methylation data of 397 samples (395 STAD samples and 2 matched adjacent normal tumor tissue samples) generated by Illumina Human Methylation 450k Array were obtained from TCGA. RNA-seq gene expression data of 407 STAD samples generated by Illumina HiSeq were obtained from TCGA. Samples of STAD clinical information, including tumor stage, survival status, and time, were obtained from the GDC data portal (https://portal.gdc.cancer.gov/)[18, 19]. 2. Data preprocessing and screening of differentially methylated regions DNA methylation data were obtained from TGCA. After preprocessing data and quality control,
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annotatable probes were retained for further analysis. R software was used to preprocess and standardize DNA methylation data, and the subset quantiles in array standardization were filtered
by probes, color offset correction and background subtraction, and subset quantile normalization.
The edgeR packages of Bioconductor analysis tools were applied to detect the DEGs, using FDR <
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0.05 and |logFC| ≥ 1 as cut-off criteria[20, 21]. Finally, 367 differentially methylated genes were
obtained, including 181 hypermethylated genes and 186 hypomethylated genes. Differences in gene
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analysis revealed 11,472 differentially expressed genes, including 7876 upregulated and 3596
3. MethSurv analysis
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downregulated genes.
MethSurv is a web tool used to analyze DERL3 methylation survival. MethSurv USES
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methylated data (-values) and patient clinical data (age, gender, staging, survival, etc.) from the GDAC Firehose database. Annotations of CpG loci are based on Illumina microchip annotation files. CpG loci in MethSurv can be classified in two ways: gene centric regions and relative CpG island
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regions.
4. UALCAN analysis
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UALCAN is an interactive web portal that USES TCGA clinical data from 31 cancer types and
level 3 RNA-seq data. The target gene expression data can be analyzed in depth. Various correlations between DERL3 expression level and methylation degree and gastric cancer grade or other clinical pathological characteristics were analyzed by using UALCAN to determine the relative expression level of DERL3 in tumor and normal samples, as well as the clinical case information based on gastric cancer. Open access to UALCAN is available on http://ualcan.path.uab.edu[22]. 5. LinkedOmics analysis 5
LinkedOmics is a web-based platform for the development of a tumor research database (http://www.linkedomics.org/login.php)[14]. The data path can be used to analyze 32 TCGA cancerrelated cubes. In this study, LinkedOmics was used to analyze the co-expressed genes of DERL3 and the functions and pathways involved in the co-expressed genes. The LinkFinder module of LinkedOmics was used to study the differentially expressed genes related to DERL3 in the TCGA STAD group. The results of co-expressed genes were expressed by a volcano map, a heat map, or a scatter map. The genes co-expressed with DERL3 were statistically analyzed using Pearson correlation coefficient. Further extraction of genes with high co-expression correlation of DERL3
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was performed to calculate the person correlation coefficient and p value. The LinkInterpreter module in LinkedOmics was used to analyze the GO and KEGG of differentially expressed genes. The threshold criterion is FDR <0.05. 6. DERL3 cloning and MGC-803 cell culture
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Primers for DERL3 gene transcription were designed, and the pIRES2-eGFP vector (BD
Biosciences Clontech) was selected as the cloning vector. MGC-803 cells were cultured in RPMI-
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1640 medium containing 10% fetal bovine serum. During passage, the cells on the wall of the culture flask were blown down with a straw at 800 RPM and then centrifuged for 5 min. The culture medium
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was discarded, and 6 mL of RPMI-1640 culture medium containing 10% FBS was added, beaten, and then mixed. Depending on density, the cells were cultured in a 1:2 or 1:3 flask. Cells were
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cultured to logarithmic growth stage in an incubator at 37℃ and 5% CO2 saturated humidity for subsequent experiments.
7. Cell proliferation experiment
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Cell viability and proliferation were determined by MTT assay. A total of 800 cells were inoculated on a 96-well microdilution plate, and MTT was added for 7 consecutive days, with a
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final concentration of 5 mg/mL. At 37 ℃ and 5% CO2 for 3 h after incubation, every hole and determined by MTT solution (5 mg/mL with PBS) 20 μL. Each hole, plus 150μL DMSO oscillation for 10 minutes, made the crystal fully dissolved. The absorbance was measured on a microplate[16, 23]. 8. Transwell experiment DERL3-transfected cells and control cells were adjusted to 5×105/mL with serum-free RPMI1640. RPMI-1640 (650 μL) containing 10% fetal bovine serum was added to the lower chamber, 6
and 100 μL of the cell suspension was added to the upper chamber. The cells were incubated for 24 h, the upper chamber was removed, and the cells were fixed with precooled methanol for 20 min. Then, the cells were stained with 0.1% crystal violet for 15 min, washed with PBS for three times, photographed, and then counted. 9. Wound healing experiment MGC-803 cells transfected with DERL3 were digested and inoculated in 24-well plates. Photos were taken at 0, 24, and 48 h. The relative migration distances of cells in the control and DERL3 transfection groups were calculated, and the mean values of three replications were obtained.
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10. Statistical analysis All experimental results were repeated three times. Student's t test or one-way ANOVA test was
used to analyze the differences between groups. The threshold of significance level was set at
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p<0.05[24].
Results
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Identification of methylated differentially expressed genes in gastric cancer and analysis of DERL3 protein– protein interaction (PPI) network and survival
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We first pretreated the downloaded STAD expression profile data and methylation data, removed the batch effect, and used limma package to analyze the differentially expressed genes. Thresholds
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were set at p<0.05 and | logFC |>1. In the data set of methylation analysis, 367 differentially methylated genes were obtained, including 181 hypermethylated genes and 186 hypomethylated genes (Figures 1A & 1B). In the data set of RNA-seq expression profile, 11,472 differentially
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expressed genes were detected, including 3596 downregulated genes and 7876 upregulated genes. DEGs are shown in volcano maps and heat maps (Figures 1C & 1D).
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Figure 2A shows the detailed flow chart of this study. Venn analysis was performed on
hypermethylated, significantly downregulated genes and hypomethylated, significantly upregulated genes. By comparison, 7 UGs-Hypo genes and 21 DGs-Hyper genes were obtained (Table 1 & Table 2). The PPI network construction of all differentially methylated genes is shown in Figure 2B. A PPI interaction network was built by uploading genes to the STRING network tool. We obtained highly methylated low-expressed genes according to the list of high and low methylated expressed genes and PPI. We focused on highly methylated low-expressed genes and determined their 7
correlation. Figure 2C shows that the methylation level of DERL3 significantly negatively correlated with its expression level. Survival analysis showed that DERL3 was a good prognostic factor for gastric cancer (Figure 2D). The overall survival time of patients with high DERL3 expression was significantly higher than that of patients with low DERL3 expression (p=0.015). CpG island analysis of DERL3 We screened the differentially methylated regions in the genome of gastric cancer and further analyzed the relationship between the CpG island region of DERL3 and clinical cases. We used the relationship between the position of DERL3 methylation region and the position of each component
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of the gene to observe where the DERL3 methylation region is mainly distributed in the gene. Figure 3A shows that most of the methylated regions of DERL3 are located in the first exon of the gene,
5'UTR, TSS200, TSS1500, and the genome (Figure 3A). The median methylation level of
methylation sites in each differentially methylated region was used to modify the methylation level
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of the differentially methylation region, and the samples were further divided into hypermethylated
and hypomethylated groups according to the median methylation level of the differentially
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methylated region in each sample. The Kaplan–Meier method was used to analyze the prognostic differences between the two groups based on the prognostic information of the patients, and the
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differentially methylated regions with prognostic differences were finally screened out. Analysis results showed that the hypermethylation of cg02293096 negatively correlated with the prognosis
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of patients with gastric cancer (Figure 3B) and correlated with the patient's age and clinical stage (Figures 3C & 3D).
Expression and methylation level of DERL3 in STAD
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Subgroup analysis was performed on the clinicopathological features of gastric cancer (STAD) in TCGA, mRNA expression level and methylation level of DERL3. The analysis was performed
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using the UALCAN website. Results showed that the transcription level of DERL3 was significantly lower than that of normal gastric tissue. In the subgroup analysis based on gender, age, race, disease stage, and tumor grade, the transcription level of DERL3 in STAD patients was correlated with the clinical cases of patients (Figure 4), and the expression of DERL3 may be a potential diagnostic indicator of STAD. In addition, DERL3 methylation levels were associated with individual cancer stages, race, gender, age, and tumor grade in patients with gastric cancer (Figure 5). Therefore, DERL3 may also be a potential drug target. 8
Enrichment analysis of DERL3 functional networks in STAD To study how DERL3 acts as a tumor suppressor gene, we first analyzed the co-expression of genes with DERL3. To analyze the function of co-expressed genes with DERL3, we took all coexpressed genes as the target gene set and used their enrichment function to study their biological functions. MRNA sequencing data of STAD patients in TCGA were selected, and LinkedOmics was used to analyze the co-expressed genes of DERL3. Figure 6A shows the results of the volcano map analysis of co-expressed genes with DERL3. Positive co-genes are represented by red dots, whereas dark green dots show genes negatively correlated with DERL3 (the threshold is set as FDR <0.01).
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Figures 6B and 6C respectively show 50 genes with significant positive co-expression with DERL3 and 50 genes with significant negative co-expression with DERL3. The results suggest that DERL3
has a broad effect on the transcriptome of gastric cancer. The top three genes co-expressed with DERL3 were further extracted for analysis (Figure 6D). The results showed a strong positive
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correlation between DERL3 expression and TNFRSF17, ADAM6, and PIM2 expression, with
Pearson correlation coefficients of 0.840, 0.831, and 0.812, respectively. GO term analysis of GSEA
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indicated that the differentially expressed genes related to DERL3 were mainly enriched in adaptive immune response, T cell activation, immune response-regulating pathway, and leukocyte
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differentiation. The differentially expressed genes related to DERL3 were mainly located inside the membrane, receptor complex, secretory granule membrane, endocytic vesicle, and extracellular
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matrix (Figures 6E–6G). KEGG pathway analysis revealed that the DERL3 co-expressing gene was mainly enriched in cell adhesion molecules (CAMs), natural killer cell-mediated cytotoxicity, and cytokine–cytokine receptor interaction (Figure 6H).
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DERL3 inhibited the malignant progression of gastric cancer To further verify the importance of DERL3, we first verified the expression level of DERL3 in
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normal gastric cells and gastric cancer cells. Experimental results showed that DERL3 expression in gastric cancer was lower than that in normal gastric cells (Figure 7A). DERL3 transfection was performed on the gastric cancer cell line MGC-803. After DERL3 treatment, MMT results showed that DERL3 inhibited the proliferation of MGC-803 cells (Figure 7B). The scratch test results showed that the migration ability of cells in the DERL3 treatment group was significantly lower than that in the control group (Figure 7C). Transwell results showed that the number of cell migration in the DERL3 treatment group was significantly lower than that in the control group 9
(Figure 7D). The results indicate that DERL3 inhibits malignant progression in gastric cancer cells.
Discussion Tumors result from a combination of genetics and epigenetics. Epigenetics of gastric cancer is characterized by DNA methylation and histone modification[5, 25]. Methylation of several tumor suppressor genes leads to their loss of function, which plays a very important role in the occurrence, development, and metastasis of gastric cancer. Elevated methylation in a region of a tumor suppressor gene decreases gene expression, which consequently increases cancer risk. By contrast,
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a decrease in DNA methylation in the regulation region of the oncogene may also lead to cancer and other diseases.
Epigenetic changes in the progression of carcinoma in situ and early gastric cancer provide the
possibility of using DNA methylation as a clinical marker and illustrate the importance of epigenetic
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changes in tumors. The DNA methylation expression profile can be used as a potential clinical tool for the characterization of tumor microenvironment and cell types, as well as for the evaluation of
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tumor immune response and the improvement of the diagnosis and treatment of gastric cancer and other cancers.
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DNA methylation is a major epigenetic mechanism and an important regulator of gene expression, which can inhibit the binding of transcription factors or inhibit the recruitment of proteins. Previous
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studies have shown that DNA methylation in epigenetic gene regulation plays a critical role in normal development and cellular function. Previous studies have also shown significant differences in DNA methylation profiles between cancer and normal tissues. An increasing number of reports
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on DNA methylation disorders in cancer cells indicated that abnormal methylation changes leading to inappropriate gene expression are key and early events in the pathogenesis of most human cancers
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and contribute to tumorigenesis. In addition, they can be easily detected in the plasma or serum of cancer patients, highlighting the potential of DNA methylation as a new molecular marker of cancer diagnosis and prognosis. Hypermethylation is the methylation of unmethylated sites in normal tissues[26-28]. The high methylation of tumor suppressor genes and DNA repair gene promoter region CpG island leads to the silencing or downregulation of the transcription expression of these genes, resulting in the inactivation of tumor suppressor genes and the increase in gene damage, which eventually lead to 10
the occurrence of gastric cancer[6]. DERL3 methylation may occur in early gastric cancer, and DERL3 expression is decreased in gastric cancer with high DERL3 methylation. The epigenetic silencing of DERL3 have already been described in human cancer, it was reported that the overexpression of SLC2A1 mediated by DERL3 epigenetic deletion promoted the Warburg effect in colon cancer cells [11]. In addition to DERL3, other genes affecting the ERAD pathway can also undergo epigenetic inactivation in human cancer, such as SVIPA[29]. Study on TCGA gastric cancer results showed that DERL3 methylation-induced DERL3 expression positively correlated with tumor stage and grade, and negatively correlated with patient prognosis. Therefore, DERL3
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methylation is an early event in the progression of gastric cancer, and DERL3 expression reduction may be associated with the progression and metastasis of gastric cancer.
GO term analysis revealed that the main molecular functions of the DERL3 co-expression genes were adaptive immune response, T cell activation, immune response-regulating pathway, and
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leukocyte differentiation[30]. KEGG enrichment analysis showed that the co-expressed genes functioned in CAMs, natural killer cell-mediated cytotoxicity, and cytokine–cytokine receptor
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interaction. Therefore, DERL3 may also be associated with immunomodulatory function[31]. Conclusion
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In this study, the DNA methylation spectra of a large number of STAD samples were comprehensively analyzed to study the altered DNA methylation patterns in STAD. The comparison
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of DNA methylation spectra between STAD samples and adjacent tissue samples revealed abnormal DNA methylation changes in STAD samples. In addition, we investigated the role of DERL3 as a tumor suppressor gene for gastric cancer. DERL3 was less expressed in gastric cancer than in normal
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gastric tissue. Cell function experiments confirmed that DERL3 overexpression inhibited the proliferation, invasion, and migration of gastric cancer cells. Therefore, DERL3 may have a
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potential predictive and prognostic value as a methylation-based biomarker for the accurate diagnosis and treatment of gastric cancer.
Declarations Ethics approval and consent to participate Not applicable 11
Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed in the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding
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The study was supported by the Science and Technology Project of Health and Family Planning Commission of Tianjin Binhai New District (grant no. 2018BWKZ004). Author Contributions
X.H. designed the experiments. Y.L., H.L., H.C., J.S. and F.S. performed experiments and data
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analysis. S.Z and X.C. performed data analysis. Y.L. wrote the manuscript, with contributions from all authors. All authors have read and approved the final manuscript.
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Acknowledgements
Authors’ information 1Department
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Not applicable
of General Surgery, Tianjin Medical University General Hospital, Tianjin, 300052,
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China.
of General Surgery, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
3Department
of Radiology, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
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2Department
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Pan, Oncotarget, 7 (2016) 37132-37144. [26] X. Chen, Y. Yang, J. Liu, B. Li, Y. Xu, C. Li, Q. Xu, G. Liu, Y. Chen, J. Ying, S. Duan, Oncotarget, 8 (2017) 8105-8119. [27] H. Hu, X. Chen, C. Wang, Y. Jiang, J. Li, X. Ying, Y. Yang, B. Li, C. Zhou, J. Zhong, D. Wu, J. Ying, S. Duan, Oncotarget, 8 (2017) 84054-84065. [28] N. Sayar, G. Karahan, O. Konu, B. Bozkurt, O. Bozdogan, I.G. Yulug, Clin Epigenetics, 7 (2015) 104. [29] P. Llinas-Arias, M. Rossello-Tortella, P. Lopez-Serra, M. Perez-Salvia, F. Setien, S. Marin, J.P. Munoz, A. Junza, J. Capellades, M.E. Calleja-Cervantes, H.J. Ferreira, M.C. de Moura, M. Srbic, A. MartinezCardus, C. de la Torre, A. Villanueva, M. Cascante, O. Yanes, A. Zorzano, C. Moutinho, M. Esteller, JCI Insight, 5 (2019). [30] D.W. Huang, B.T. Sherman, Q. Tan, J. Kir, D. Liu, D. Bryant, Y. Guo, R. Stephens, M.W. Baseler, H.C. Lane, R.A. Lempicki, Nucleic Acids Res, 35 (2007) W169-175. [31] Z.S. Mustafin, S.A. Lashin, Y.G. Matushkin, K.V. Gunbin, D.A. Afonnikov, BMC Bioinformatics, 18
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(2017) 1427.
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Figure Legends
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Figure 1. Analysis of differentially methylated genes and differentially expressed genes. (A) Volcano plots of analysis of differentially methylation genes. (B) Cluster analysis of differential methylation genes. (C) Volcano plots of differential expression analyses. (D) Cluster analysis of differential methylation genes. Red dots represent upregulated genes, and green dots represent downregulated genes. The criteria for selection of DEGs and DMGs were set as fold change ≥ 2 and p < 0.05.
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correlated with the good prognosis of gastric cancer patients.
Figure 3. Survival analysis of DNA methylation data based on DERL3. (A) Correlation analysis between different DERL3 CpG islands and clinical cases of gastric cancer. (B) Hypermethylation of cg02293096 was associated with poor prognosis in gastric cancer. (C) Violin diagram of correlation between methylation level of cg02293096 and age of gastric cancer patients. (D) Violin diagram of correlation analysis between methylation level of cg02293096 and clinical staging of 17
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gastric cancer patients.
Figure 4. DERL3 transcription in subgroups of patients with STAD, stratified based on stages, race, gender, age, tumor grade, Helicobacter pylori infection, and other criteria. (A) Boxplot of 18
the relative expression of DERL3 in normal and STAD samples. (B) Boxplot of DERL3 relative expression in normal individuals or STAD patients at stage 1, 2, 3, or 4. (C) Boxplot showing DERL3 relative expression in normal or white, African American, or Asian STAD patients of any race. (D) Boxplot of DERL3 relative expression in normal individuals, including gender, male or female STAD patients. (E) Boxplot of the relative expression of DERL3 in normal persons of any age or in STAD patients aged 21–40, 41–60, 61–80, or 81–100 years. (F) Boxplot results of the relative expression of DERL3 in normal individuals or in STAD patients with grade 1, 2, 3, or 4 tumors. (G) Boxplot of DERL3 relative expression in normal individuals or patients with different
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Helicobacter infection status. (H) Boxplot showing the relative expression of DERL3 in normal individuals or histologic subtypes of STAD patients. Data are mean ± SE. *, p < 0.05; **, p < 0.01;
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***, p < 0.001.
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Figure 5. Correlation analysis of DERL3 promoter methylation level with stage, race, gender, age, and tumor grade in STAD patients. (A) Boxplot of the relative promoter methylation level of DERL3 in normal and STAD samples. (B) Boxplot of promoter methylation level of DERL3 in normal individuals or STAD patients at stage 1, 2, 3, or 4. (C) Boxplot showing promoter methylation level of DERL3 in normal or white, African American, or Asian STAD patients of any race. (D) Boxplot of promoter methylation level of DERL3 in normal individuals, including gender, 20
male or female STAD patients. (E) Boxplot of the promoter methylation level of DERL3 in normal persons of any age or in STAD patients aged 21–40, 41–60, 61–80, or 81–100 years. (F) Boxplot results of the promoter methylation level of DERL3 in normal individuals or in STAD patients with
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grade 1, 2, 3, or 4 tumors. Data are mean ± SE. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 6. Genes differentially expressed in correlation with DERL3 in STAD. (A) Correlation analysis between DERL3 and STAD differentially expressed genes. (B–C) heat map analysis of genes with positive and negative correlation with DERL3 (top 50). Green for negatively correlated 21
genes and red for positively correlated genes. (D) Correlation analysis of the expression of DERL3, ADAM6, TNFRSF17, and PIM2 genes. Scatter plot showed the correlation between DERL3 expression and the expression of other genes, and Pearson correlation coefficient was used for statistical analysis. (E–H) GSEA was used to analyze STAD cell components, biological processes, molecular functions, and KEGG pathways. The GO and KEGG signaling pathways that co-
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expressed with DERL3 were analyzed.
Figure 7. DERL3 inhibited the malignant progression of gastric cancer. (A) DERL3 is low expressed in gastric cancer cells. (B) Overexpression of DERL3 inhibited the proliferation of MGC803 cells. (C) DERL3 suppresses the migration of MGC-803 cells. (D) DERL3 inhibits the invasion ability of MGC-803 cells. Data are mean ± SE. *, p < 0.05; **, p < 0.01. 22
Table 1. The Hyper-LGs genes in gastric cancer. PValue
FDR
DERL3
-2.441
3.86E-28
4.29E-26
BARX1
-2.315
1.04E-16
3.01E-15
NPY
-2.132
4.99E-08
4.01E-07
JAM2
-1.899
4.06E-22
2.39E-20
GDF7
-1.885
2.87E-16
7.85E-15
IRF4
-1.871
8.56E-16
2.21E-14
PRDM8
-1.845
1.90E-19
7.91E-18
GSTM2
-1.823
1.36E-29
1.81E-27
TBL1Y
-1.785
5.83E-05
0.000240055
GPR25
-1.735
3.42E-11
4.71E-10
ZNF471
-1.682
5.60E-16
1.49E-14
CCL21
-1.678
5.86E-09
5.60E-08
KCNA3
-1.566
2.39E-10
2.85E-09
TMEM220
-1.444
2.74E-18
9.86E-17
POU3F1
-1.366
2.05E-06
1.19E-05
EIF1AY
-1.261
0.00314374
0.007903512
RPS4Y1
-1.170
0.008408348
0.018359574
ZNF582
-1.158
5.89E-10
6.62E-09
CCNA1
-1.116
0.000137575
0.00051557
LTC4S
-1.098
1.88E-07
1.35E-06
DDX3Y
-1.019
0.010619593
0.022426372
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Table 2. The Hypo-HGs genes in gastric cancer.
PValue 4.30E-11 3.52E-13 3.91E-05 1.53E-11 6.99E-06 0.000118219 6.15E-13
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Hypo-HGs genes Genes logFC SCML1 1.316 MATN3 2.686 CXCL3 1.401 CXCL1 2.690 FAM84B 1.045 ZNF479 3.188 HIST1H3A 2.367
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logFC
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Genes
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Hyper-LGs genes
FDR 5.84E-10 6.51E-12 0.000168015 2.24E-10 3.59E-05 0.000450467 1.09E-11
23
PII:
S1476-9271(19)30650-4
DOI:
https://doi.org/10.1016/j.compbiolchem.2019.107172
Reference:
CBAC 107172
To appear in:
Computational Biology and Chemistry
Received Date:
25 July 2019
Revised Date:
13 November 2019
Accepted Date:
21 November 2019
Please cite this article as: Li Y, Liu H, Chen H, Shao J, Su F, Zhang S, Cai X, He X, DERL3 functions as a tumor suppressor in gastric cancer, Computational Biology and Chemistry (2019), doi: https://doi.org/10.1016/j.compbiolchem.2019.107172
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
DERL3 functions as a tumor suppressor in gastric cancer Short title: DERL3 suppresses gastric cancer
Yongyuan Li1,2,#, Hongjie Liu3,#, Hekai Chen2, Jianping Shao2, Feng Su2, Shupeng Zhang2, Xuejun Cai2, Xianghui He1,*
1
Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, 300052,
2Department
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China. of General Surgery, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
3
Department of Radiology, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
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# These authors contributed equally to this work.
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*Correspondence to:
Xianghui He, Department of General Surgery, Tianjin Medical University General Hospital, Tianjin,
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300052, China. E-mail: [email protected].
Yongyuan Li: [email protected]; Hongjie Liu: [email protected]; Hekai Chen: xyz181 [email protected]; Jianping Shao: [email protected]; Feng Su: [email protected]; Shupeng [email protected];
Xuejun
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Zhang:
Cai:
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[email protected].
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Graphical Abstract
1
[email protected];
Xianghui
He:
Highlights
Differentially expressed genes and differentially methylated genes were determined, and a comprehensive analysis of STAD pathogenesis was conducted.
DERL3 is low expressed in tumors. However, high expression of DERL3 suppresses the invasion, migration, and proliferation of gastric cancer cells.
DERL3 was screened and validated as a candidate diagnostic marker gene of STAD.
DERL3 is a potential molecular target for the treatment of STAD.
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Abstract Background: Gastric cancer is a common malignant tumor in the clinic with a high mortality rate,
ranking the first among malignant tumors of the digestive system. Early gastric cancer exhibits no specific clinical symptoms and signs, and most of the patients were diagnosed as advanced gastric
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cancer. The prognosis is poor, and the 5-year overall survival rate is still lower than 30%, seriously threatening people’s life and health. However, the pathogenesis of gastric cancer is still unclear.
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Methods: This study aimed to identify methylated differentially expressed genes in gastric cancer and to study the cellular functions and pathways that may be involved in its regulation, as well as
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the biological functions of key methylated differentially expressed genes. The gene expression data set and methylation data set of gastric cancer genes based on TCGA were analyzed to identify prognostic methylated genes.
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Results: This study showed that the methylation of the DERL3 promoter was correlated with the clinical analysis of tumors. Further studies were conducted on genes co-expressed with DERL3,
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whose functions and pathways to inhibit gastric cancer were adaptive immune response, T cell activation, immune response-regulating pathway, cell surface on molecules, and natural killer cell-
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mediated cytotoxicity. Finally, cell proliferation assay, cell scratch assay, and cell invasion assay confirmed that DERL3 as a tumor suppressor gene inhibited the malignant evolution of gastric cancer.
Conclusions: The analysis of key methylated differentially expressed genes helped elucidate the epigenetic regulation mechanism in the development of gastric cancer. DERL3, as a methylation biomarker, has a predictive and prognostic value in the accurate diagnosis and treatment of gastric cancer and provides potential targets for the precision treatment of gastric cancer. 2
Trial Registration: Not applicable
List of abbreviation: DMGs: differentially methylated genes, DEGs: differentially expressed genes, Hypo-HGs: hypomethylated, highly exprocessed genes, Hyper-LGs: hypermethylated, lowly exprocessed genes, PPI: protein–protein interaction, TCGA: the cancer genome atlas, STAD: stomach adenocarcinoma
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Key words: DERL3; prognosis; functional network analysis; methylation; gastric cancer
Background
Gastric cancer is a common malignancy worldwide[1-3]. Although the incidence and mortality
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of gastric cancer have decreased in recent years, among all malignant tumors, gastric cancer still ranks the fifth in incidence and the third in mortality. At present, surgery is still the main method
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for the treatment of early gastric cancer, but for advanced gastric cancer, more comprehensive treatment is needed, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy[4].
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Advanced gastric cancer is mainly treated with drugs, but the treatment scheme is limited, and the curative effect needs to be improved urgently. The pathogenesis of gastric cancer has not been
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completely clarified, which cannot meet the need for the effective prevention and treatment of gastric cancer in the clinic. Therefore, exploring the pathogenesis of gastric cancer and finding potential therapeutic targets are of great significance in the early diagnosis of gastric cancer and the
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development of individualized treatment programs. Genetic changes (e.g., mutation, recombination, and copy number changes) and epigenetic
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changes (e.g., DNA methylation) play a key role in the development of cancer[5]. The hypermethylation of CpG island leads to the transcriptional silencing of tumor suppressor genes, whereas the hypomethylation of oncogenes leads to the activation of oncogenes[6-9]. Abnormal demethylation is common in gastric cancer tissues. The methylation rate of homeodomain-only protein X (HOPX)-β in gastric cancer tissues is 84%, whereas that in corresponding normal tissues is only 10%. Therefore, the abnormal methylation level of this gene is highly correlated with the occurrence of gastric cancer. In addition, MGMT, a DNA mismatch 3
repair gene in gastric cancer tissues, is often inactivated because of high methylation, making MGMT protein unable to be expressed. The deletion of MGMT protein will make some key genes (such as p53, protooncogene, and K-Ras) undergo G-C mutation to A-T and cannot be repaired, resulting in the loss of normal physiological functions of the corresponding genes. The anticancer effect of tumor suppressor gene ZIC1 is manifested because it renders gastric cancer cells stagnate in the S phase, and the hypermethylation of this gene promoter region downregulates the expression of this gene, thus promoting the occurrence of gastric cancer. Proteins encoded by the DERL3 (derlin-3) gene belong to the Derlin family and are present in
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the endoplasmic reticulum (ER)[10, 11]. Proteins that unfold or misfold in the ER must be refolded or degraded to maintain ER homeostasis[11]. This protein is involved in the degradation of misfolded glycoprotein in the ER. Several variable splicing transcriptional variants encoding different isotypes act on this gene. DERL3 is a tumor suppressor gene. Its loss of function may lead
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to problems in protein folding and degradation, which may lead to gastric cancer and promote the metastasis of tumor cells.
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In this study, high-throughput methylation spectra from a large number of patients were used to study the altered DNA methylation patterns between STAD samples and adjacent tissue samples,
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and to identify specific DNA methylation sites as potential biomarkers. Using The Cancer Genome Atlas (TCGA) data set, we identified the methylated differentially expressed genes related to gastric
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cancer, including hypermethylated, low-expressed genes, hypomethylated, and high-expressed genes[12, 13]. We analyzed the gene list and found that DERL3 played an important role. We further analyzed the correlation between DERL3 expression level and methylation level and the
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clinicopathology of gastric cancer. Third, we analyzed the co-expression of DERL3 in gastric cancer, and we used the functional annotation tool to study the gene ontology and KEGG pathway of
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DERL3 co-expression[14]. Finally, DERL3 was proved to be a tumor suppressor gene of gastric cancer by cell proliferation assay, cell scratch assay, and cell invasion assay[15-17]. These analyses provide comprehensive biological information for the study of methylation of gastric cancer, contribute to the understanding of the occurrence and development of gastric cancer, and further reveal the biological function of DERL3.
Materials and methods 4
1. Data sources DNA Methylation data of 397 samples (395 STAD samples and 2 matched adjacent normal tumor tissue samples) generated by Illumina Human Methylation 450k Array were obtained from TCGA. RNA-seq gene expression data of 407 STAD samples generated by Illumina HiSeq were obtained from TCGA. Samples of STAD clinical information, including tumor stage, survival status, and time, were obtained from the GDC data portal (https://portal.gdc.cancer.gov/)[18, 19]. 2. Data preprocessing and screening of differentially methylated regions DNA methylation data were obtained from TGCA. After preprocessing data and quality control,
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annotatable probes were retained for further analysis. R software was used to preprocess and standardize DNA methylation data, and the subset quantiles in array standardization were filtered
by probes, color offset correction and background subtraction, and subset quantile normalization.
The edgeR packages of Bioconductor analysis tools were applied to detect the DEGs, using FDR <
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0.05 and |logFC| ≥ 1 as cut-off criteria[20, 21]. Finally, 367 differentially methylated genes were
obtained, including 181 hypermethylated genes and 186 hypomethylated genes. Differences in gene
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analysis revealed 11,472 differentially expressed genes, including 7876 upregulated and 3596
3. MethSurv analysis
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downregulated genes.
MethSurv is a web tool used to analyze DERL3 methylation survival. MethSurv USES
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methylated data (-values) and patient clinical data (age, gender, staging, survival, etc.) from the GDAC Firehose database. Annotations of CpG loci are based on Illumina microchip annotation files. CpG loci in MethSurv can be classified in two ways: gene centric regions and relative CpG island
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regions.
4. UALCAN analysis
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UALCAN is an interactive web portal that USES TCGA clinical data from 31 cancer types and
level 3 RNA-seq data. The target gene expression data can be analyzed in depth. Various correlations between DERL3 expression level and methylation degree and gastric cancer grade or other clinical pathological characteristics were analyzed by using UALCAN to determine the relative expression level of DERL3 in tumor and normal samples, as well as the clinical case information based on gastric cancer. Open access to UALCAN is available on http://ualcan.path.uab.edu[22]. 5. LinkedOmics analysis 5
LinkedOmics is a web-based platform for the development of a tumor research database (http://www.linkedomics.org/login.php)[14]. The data path can be used to analyze 32 TCGA cancerrelated cubes. In this study, LinkedOmics was used to analyze the co-expressed genes of DERL3 and the functions and pathways involved in the co-expressed genes. The LinkFinder module of LinkedOmics was used to study the differentially expressed genes related to DERL3 in the TCGA STAD group. The results of co-expressed genes were expressed by a volcano map, a heat map, or a scatter map. The genes co-expressed with DERL3 were statistically analyzed using Pearson correlation coefficient. Further extraction of genes with high co-expression correlation of DERL3
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was performed to calculate the person correlation coefficient and p value. The LinkInterpreter module in LinkedOmics was used to analyze the GO and KEGG of differentially expressed genes. The threshold criterion is FDR <0.05. 6. DERL3 cloning and MGC-803 cell culture
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Primers for DERL3 gene transcription were designed, and the pIRES2-eGFP vector (BD
Biosciences Clontech) was selected as the cloning vector. MGC-803 cells were cultured in RPMI-
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1640 medium containing 10% fetal bovine serum. During passage, the cells on the wall of the culture flask were blown down with a straw at 800 RPM and then centrifuged for 5 min. The culture medium
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was discarded, and 6 mL of RPMI-1640 culture medium containing 10% FBS was added, beaten, and then mixed. Depending on density, the cells were cultured in a 1:2 or 1:3 flask. Cells were
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cultured to logarithmic growth stage in an incubator at 37℃ and 5% CO2 saturated humidity for subsequent experiments.
7. Cell proliferation experiment
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Cell viability and proliferation were determined by MTT assay. A total of 800 cells were inoculated on a 96-well microdilution plate, and MTT was added for 7 consecutive days, with a
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final concentration of 5 mg/mL. At 37 ℃ and 5% CO2 for 3 h after incubation, every hole and determined by MTT solution (5 mg/mL with PBS) 20 μL. Each hole, plus 150μL DMSO oscillation for 10 minutes, made the crystal fully dissolved. The absorbance was measured on a microplate[16, 23]. 8. Transwell experiment DERL3-transfected cells and control cells were adjusted to 5×105/mL with serum-free RPMI1640. RPMI-1640 (650 μL) containing 10% fetal bovine serum was added to the lower chamber, 6
and 100 μL of the cell suspension was added to the upper chamber. The cells were incubated for 24 h, the upper chamber was removed, and the cells were fixed with precooled methanol for 20 min. Then, the cells were stained with 0.1% crystal violet for 15 min, washed with PBS for three times, photographed, and then counted. 9. Wound healing experiment MGC-803 cells transfected with DERL3 were digested and inoculated in 24-well plates. Photos were taken at 0, 24, and 48 h. The relative migration distances of cells in the control and DERL3 transfection groups were calculated, and the mean values of three replications were obtained.
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10. Statistical analysis All experimental results were repeated three times. Student's t test or one-way ANOVA test was
used to analyze the differences between groups. The threshold of significance level was set at
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p<0.05[24].
Results
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Identification of methylated differentially expressed genes in gastric cancer and analysis of DERL3 protein– protein interaction (PPI) network and survival
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We first pretreated the downloaded STAD expression profile data and methylation data, removed the batch effect, and used limma package to analyze the differentially expressed genes. Thresholds
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were set at p<0.05 and | logFC |>1. In the data set of methylation analysis, 367 differentially methylated genes were obtained, including 181 hypermethylated genes and 186 hypomethylated genes (Figures 1A & 1B). In the data set of RNA-seq expression profile, 11,472 differentially
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expressed genes were detected, including 3596 downregulated genes and 7876 upregulated genes. DEGs are shown in volcano maps and heat maps (Figures 1C & 1D).
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Figure 2A shows the detailed flow chart of this study. Venn analysis was performed on
hypermethylated, significantly downregulated genes and hypomethylated, significantly upregulated genes. By comparison, 7 UGs-Hypo genes and 21 DGs-Hyper genes were obtained (Table 1 & Table 2). The PPI network construction of all differentially methylated genes is shown in Figure 2B. A PPI interaction network was built by uploading genes to the STRING network tool. We obtained highly methylated low-expressed genes according to the list of high and low methylated expressed genes and PPI. We focused on highly methylated low-expressed genes and determined their 7
correlation. Figure 2C shows that the methylation level of DERL3 significantly negatively correlated with its expression level. Survival analysis showed that DERL3 was a good prognostic factor for gastric cancer (Figure 2D). The overall survival time of patients with high DERL3 expression was significantly higher than that of patients with low DERL3 expression (p=0.015). CpG island analysis of DERL3 We screened the differentially methylated regions in the genome of gastric cancer and further analyzed the relationship between the CpG island region of DERL3 and clinical cases. We used the relationship between the position of DERL3 methylation region and the position of each component
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of the gene to observe where the DERL3 methylation region is mainly distributed in the gene. Figure 3A shows that most of the methylated regions of DERL3 are located in the first exon of the gene,
5'UTR, TSS200, TSS1500, and the genome (Figure 3A). The median methylation level of
methylation sites in each differentially methylated region was used to modify the methylation level
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of the differentially methylation region, and the samples were further divided into hypermethylated
and hypomethylated groups according to the median methylation level of the differentially
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methylated region in each sample. The Kaplan–Meier method was used to analyze the prognostic differences between the two groups based on the prognostic information of the patients, and the
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differentially methylated regions with prognostic differences were finally screened out. Analysis results showed that the hypermethylation of cg02293096 negatively correlated with the prognosis
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of patients with gastric cancer (Figure 3B) and correlated with the patient's age and clinical stage (Figures 3C & 3D).
Expression and methylation level of DERL3 in STAD
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Subgroup analysis was performed on the clinicopathological features of gastric cancer (STAD) in TCGA, mRNA expression level and methylation level of DERL3. The analysis was performed
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using the UALCAN website. Results showed that the transcription level of DERL3 was significantly lower than that of normal gastric tissue. In the subgroup analysis based on gender, age, race, disease stage, and tumor grade, the transcription level of DERL3 in STAD patients was correlated with the clinical cases of patients (Figure 4), and the expression of DERL3 may be a potential diagnostic indicator of STAD. In addition, DERL3 methylation levels were associated with individual cancer stages, race, gender, age, and tumor grade in patients with gastric cancer (Figure 5). Therefore, DERL3 may also be a potential drug target. 8
Enrichment analysis of DERL3 functional networks in STAD To study how DERL3 acts as a tumor suppressor gene, we first analyzed the co-expression of genes with DERL3. To analyze the function of co-expressed genes with DERL3, we took all coexpressed genes as the target gene set and used their enrichment function to study their biological functions. MRNA sequencing data of STAD patients in TCGA were selected, and LinkedOmics was used to analyze the co-expressed genes of DERL3. Figure 6A shows the results of the volcano map analysis of co-expressed genes with DERL3. Positive co-genes are represented by red dots, whereas dark green dots show genes negatively correlated with DERL3 (the threshold is set as FDR <0.01).
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Figures 6B and 6C respectively show 50 genes with significant positive co-expression with DERL3 and 50 genes with significant negative co-expression with DERL3. The results suggest that DERL3
has a broad effect on the transcriptome of gastric cancer. The top three genes co-expressed with DERL3 were further extracted for analysis (Figure 6D). The results showed a strong positive
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correlation between DERL3 expression and TNFRSF17, ADAM6, and PIM2 expression, with
Pearson correlation coefficients of 0.840, 0.831, and 0.812, respectively. GO term analysis of GSEA
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indicated that the differentially expressed genes related to DERL3 were mainly enriched in adaptive immune response, T cell activation, immune response-regulating pathway, and leukocyte
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differentiation. The differentially expressed genes related to DERL3 were mainly located inside the membrane, receptor complex, secretory granule membrane, endocytic vesicle, and extracellular
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matrix (Figures 6E–6G). KEGG pathway analysis revealed that the DERL3 co-expressing gene was mainly enriched in cell adhesion molecules (CAMs), natural killer cell-mediated cytotoxicity, and cytokine–cytokine receptor interaction (Figure 6H).
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DERL3 inhibited the malignant progression of gastric cancer To further verify the importance of DERL3, we first verified the expression level of DERL3 in
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normal gastric cells and gastric cancer cells. Experimental results showed that DERL3 expression in gastric cancer was lower than that in normal gastric cells (Figure 7A). DERL3 transfection was performed on the gastric cancer cell line MGC-803. After DERL3 treatment, MMT results showed that DERL3 inhibited the proliferation of MGC-803 cells (Figure 7B). The scratch test results showed that the migration ability of cells in the DERL3 treatment group was significantly lower than that in the control group (Figure 7C). Transwell results showed that the number of cell migration in the DERL3 treatment group was significantly lower than that in the control group 9
(Figure 7D). The results indicate that DERL3 inhibits malignant progression in gastric cancer cells.
Discussion Tumors result from a combination of genetics and epigenetics. Epigenetics of gastric cancer is characterized by DNA methylation and histone modification[5, 25]. Methylation of several tumor suppressor genes leads to their loss of function, which plays a very important role in the occurrence, development, and metastasis of gastric cancer. Elevated methylation in a region of a tumor suppressor gene decreases gene expression, which consequently increases cancer risk. By contrast,
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a decrease in DNA methylation in the regulation region of the oncogene may also lead to cancer and other diseases.
Epigenetic changes in the progression of carcinoma in situ and early gastric cancer provide the
possibility of using DNA methylation as a clinical marker and illustrate the importance of epigenetic
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changes in tumors. The DNA methylation expression profile can be used as a potential clinical tool for the characterization of tumor microenvironment and cell types, as well as for the evaluation of
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tumor immune response and the improvement of the diagnosis and treatment of gastric cancer and other cancers.
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DNA methylation is a major epigenetic mechanism and an important regulator of gene expression, which can inhibit the binding of transcription factors or inhibit the recruitment of proteins. Previous
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studies have shown that DNA methylation in epigenetic gene regulation plays a critical role in normal development and cellular function. Previous studies have also shown significant differences in DNA methylation profiles between cancer and normal tissues. An increasing number of reports
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on DNA methylation disorders in cancer cells indicated that abnormal methylation changes leading to inappropriate gene expression are key and early events in the pathogenesis of most human cancers
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and contribute to tumorigenesis. In addition, they can be easily detected in the plasma or serum of cancer patients, highlighting the potential of DNA methylation as a new molecular marker of cancer diagnosis and prognosis. Hypermethylation is the methylation of unmethylated sites in normal tissues[26-28]. The high methylation of tumor suppressor genes and DNA repair gene promoter region CpG island leads to the silencing or downregulation of the transcription expression of these genes, resulting in the inactivation of tumor suppressor genes and the increase in gene damage, which eventually lead to 10
the occurrence of gastric cancer[6]. DERL3 methylation may occur in early gastric cancer, and DERL3 expression is decreased in gastric cancer with high DERL3 methylation. The epigenetic silencing of DERL3 have already been described in human cancer, it was reported that the overexpression of SLC2A1 mediated by DERL3 epigenetic deletion promoted the Warburg effect in colon cancer cells [11]. In addition to DERL3, other genes affecting the ERAD pathway can also undergo epigenetic inactivation in human cancer, such as SVIPA[29]. Study on TCGA gastric cancer results showed that DERL3 methylation-induced DERL3 expression positively correlated with tumor stage and grade, and negatively correlated with patient prognosis. Therefore, DERL3
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methylation is an early event in the progression of gastric cancer, and DERL3 expression reduction may be associated with the progression and metastasis of gastric cancer.
GO term analysis revealed that the main molecular functions of the DERL3 co-expression genes were adaptive immune response, T cell activation, immune response-regulating pathway, and
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leukocyte differentiation[30]. KEGG enrichment analysis showed that the co-expressed genes functioned in CAMs, natural killer cell-mediated cytotoxicity, and cytokine–cytokine receptor
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interaction. Therefore, DERL3 may also be associated with immunomodulatory function[31]. Conclusion
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In this study, the DNA methylation spectra of a large number of STAD samples were comprehensively analyzed to study the altered DNA methylation patterns in STAD. The comparison
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of DNA methylation spectra between STAD samples and adjacent tissue samples revealed abnormal DNA methylation changes in STAD samples. In addition, we investigated the role of DERL3 as a tumor suppressor gene for gastric cancer. DERL3 was less expressed in gastric cancer than in normal
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gastric tissue. Cell function experiments confirmed that DERL3 overexpression inhibited the proliferation, invasion, and migration of gastric cancer cells. Therefore, DERL3 may have a
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potential predictive and prognostic value as a methylation-based biomarker for the accurate diagnosis and treatment of gastric cancer.
Declarations Ethics approval and consent to participate Not applicable 11
Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed in the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding
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The study was supported by the Science and Technology Project of Health and Family Planning Commission of Tianjin Binhai New District (grant no. 2018BWKZ004). Author Contributions
X.H. designed the experiments. Y.L., H.L., H.C., J.S. and F.S. performed experiments and data
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analysis. S.Z and X.C. performed data analysis. Y.L. wrote the manuscript, with contributions from all authors. All authors have read and approved the final manuscript.
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Acknowledgements
Authors’ information 1Department
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Not applicable
of General Surgery, Tianjin Medical University General Hospital, Tianjin, 300052,
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China.
of General Surgery, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
3Department
of Radiology, Tianjin Fifth Central Hospital, Tianjin, 300450, China.
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2Department
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Figure Legends
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Figure 1. Analysis of differentially methylated genes and differentially expressed genes. (A) Volcano plots of analysis of differentially methylation genes. (B) Cluster analysis of differential methylation genes. (C) Volcano plots of differential expression analyses. (D) Cluster analysis of differential methylation genes. Red dots represent upregulated genes, and green dots represent downregulated genes. The criteria for selection of DEGs and DMGs were set as fold change ≥ 2 and p < 0.05.
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correlated with the good prognosis of gastric cancer patients.
Figure 3. Survival analysis of DNA methylation data based on DERL3. (A) Correlation analysis between different DERL3 CpG islands and clinical cases of gastric cancer. (B) Hypermethylation of cg02293096 was associated with poor prognosis in gastric cancer. (C) Violin diagram of correlation between methylation level of cg02293096 and age of gastric cancer patients. (D) Violin diagram of correlation analysis between methylation level of cg02293096 and clinical staging of 17
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gastric cancer patients.
Figure 4. DERL3 transcription in subgroups of patients with STAD, stratified based on stages, race, gender, age, tumor grade, Helicobacter pylori infection, and other criteria. (A) Boxplot of 18
the relative expression of DERL3 in normal and STAD samples. (B) Boxplot of DERL3 relative expression in normal individuals or STAD patients at stage 1, 2, 3, or 4. (C) Boxplot showing DERL3 relative expression in normal or white, African American, or Asian STAD patients of any race. (D) Boxplot of DERL3 relative expression in normal individuals, including gender, male or female STAD patients. (E) Boxplot of the relative expression of DERL3 in normal persons of any age or in STAD patients aged 21–40, 41–60, 61–80, or 81–100 years. (F) Boxplot results of the relative expression of DERL3 in normal individuals or in STAD patients with grade 1, 2, 3, or 4 tumors. (G) Boxplot of DERL3 relative expression in normal individuals or patients with different
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Helicobacter infection status. (H) Boxplot showing the relative expression of DERL3 in normal individuals or histologic subtypes of STAD patients. Data are mean ± SE. *, p < 0.05; **, p < 0.01;
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***, p < 0.001.
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Figure 5. Correlation analysis of DERL3 promoter methylation level with stage, race, gender, age, and tumor grade in STAD patients. (A) Boxplot of the relative promoter methylation level of DERL3 in normal and STAD samples. (B) Boxplot of promoter methylation level of DERL3 in normal individuals or STAD patients at stage 1, 2, 3, or 4. (C) Boxplot showing promoter methylation level of DERL3 in normal or white, African American, or Asian STAD patients of any race. (D) Boxplot of promoter methylation level of DERL3 in normal individuals, including gender, 20
male or female STAD patients. (E) Boxplot of the promoter methylation level of DERL3 in normal persons of any age or in STAD patients aged 21–40, 41–60, 61–80, or 81–100 years. (F) Boxplot results of the promoter methylation level of DERL3 in normal individuals or in STAD patients with
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grade 1, 2, 3, or 4 tumors. Data are mean ± SE. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 6. Genes differentially expressed in correlation with DERL3 in STAD. (A) Correlation analysis between DERL3 and STAD differentially expressed genes. (B–C) heat map analysis of genes with positive and negative correlation with DERL3 (top 50). Green for negatively correlated 21
genes and red for positively correlated genes. (D) Correlation analysis of the expression of DERL3, ADAM6, TNFRSF17, and PIM2 genes. Scatter plot showed the correlation between DERL3 expression and the expression of other genes, and Pearson correlation coefficient was used for statistical analysis. (E–H) GSEA was used to analyze STAD cell components, biological processes, molecular functions, and KEGG pathways. The GO and KEGG signaling pathways that co-
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expressed with DERL3 were analyzed.
Figure 7. DERL3 inhibited the malignant progression of gastric cancer. (A) DERL3 is low expressed in gastric cancer cells. (B) Overexpression of DERL3 inhibited the proliferation of MGC803 cells. (C) DERL3 suppresses the migration of MGC-803 cells. (D) DERL3 inhibits the invasion ability of MGC-803 cells. Data are mean ± SE. *, p < 0.05; **, p < 0.01. 22
Table 1. The Hyper-LGs genes in gastric cancer. PValue
FDR
DERL3
-2.441
3.86E-28
4.29E-26
BARX1
-2.315
1.04E-16
3.01E-15
NPY
-2.132
4.99E-08
4.01E-07
JAM2
-1.899
4.06E-22
2.39E-20
GDF7
-1.885
2.87E-16
7.85E-15
IRF4
-1.871
8.56E-16
2.21E-14
PRDM8
-1.845
1.90E-19
7.91E-18
GSTM2
-1.823
1.36E-29
1.81E-27
TBL1Y
-1.785
5.83E-05
0.000240055
GPR25
-1.735
3.42E-11
4.71E-10
ZNF471
-1.682
5.60E-16
1.49E-14
CCL21
-1.678
5.86E-09
5.60E-08
KCNA3
-1.566
2.39E-10
2.85E-09
TMEM220
-1.444
2.74E-18
9.86E-17
POU3F1
-1.366
2.05E-06
1.19E-05
EIF1AY
-1.261
0.00314374
0.007903512
RPS4Y1
-1.170
0.008408348
0.018359574
ZNF582
-1.158
5.89E-10
6.62E-09
CCNA1
-1.116
0.000137575
0.00051557
LTC4S
-1.098
1.88E-07
1.35E-06
DDX3Y
-1.019
0.010619593
0.022426372
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Table 2. The Hypo-HGs genes in gastric cancer.
PValue 4.30E-11 3.52E-13 3.91E-05 1.53E-11 6.99E-06 0.000118219 6.15E-13
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Hypo-HGs genes Genes logFC SCML1 1.316 MATN3 2.686 CXCL3 1.401 CXCL1 2.690 FAM84B 1.045 ZNF479 3.188 HIST1H3A 2.367
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logFC
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Genes
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Hyper-LGs genes
FDR 5.84E-10 6.51E-12 0.000168015 2.24E-10 3.59E-05 0.000450467 1.09E-11
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