Comprehensive bioinformatics analysis of the mRNA profile of PLCE1 knockdown in esophageal squamous cell carcinoma.

The authors previously reported that Phospholipase C epsilon 1 (PLCE1) exacerbated esophageal squamous cell carcinoma (ESCC), however, the underlying mechanism remains to be fully elucidated. The present study aimed to identify key differentially expressed genes (DEGs) and signaling pathways regulated by PLCE1 in ESCC. EC9706 and Eca109 cell lines were transfected with the specific small interfering (si) RNA of PLCE1, reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) and western blotting were performed to detect the expression levels of PLCE1, and subsequently, mRNA array and multiple bioinformatics analysis were conducted. RT‑qPCR was used to verify gene expression array results. The findings of the present study indicated that PLCE1 mRNA and protein expression were significantly suppressed (P<0.05) in the PLCE1 siRNA‑transfected cells. In addition, a total of 223 DEGs with >2‑fold alterations were screened between the PLCE1 siRNA‑treated cells, including 168 upregulated and 53 downregulated DEGs. In particular, inflammation or immune‑associated molecules, including Toll‑like receptor (TLR)‑4 interleukin‑6, ‑8 and chemokine C‑X‑C motif ligand 2 were significantly increased following PLCE1 knockdown. Furthermore, Gene Ontology enrichment revealed terms associated with cell proliferation, differentiation, apoptosis, signal transduction, invasion and metastasis, which may potentially be associated with PLCE1 function. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated 46 pathways were disturbed by DEGs, including focal adhesion, mitogen activated protein kinase, TLR, p53 and janus kinase/signal transducer and activator of transcription signaling pathways. The RT‑qPCR results for validation of the selected DEGs were consistent with that of the microarray data. Overall, the results of the multiple bioinformatic analysis contributes to a systematic understanding of the roles of PLCE1 in ESCC.

Introduction

Esophageal squamous cell carcinoma (ESCC), which ranks the 6th in mortality and the 7th in morbidity in China, is one of the most prevalent malignant neoplasm around the world (1). Low intake of fruits and vegetables, poor nutritional status, smoking, and drinking all contribute to the etiology of ESCC (2). Despite improvements in ESCC treatments, the five-year survival rate of patients with ESCC is still low, because of poor understanding of the molecular pathogenesis of ESCC and infrequent early-stage diagnosis (3,4). Although studies have indicated that some oncogenes and tumor suppressor genes are involved in the progression of esophageal cancer including Phospholipase C epsilon 1 (PLCE1), the molecular mechanisms are still poorly known. Therefore, it is imperative to study the functioning of genes that could be involved in the mechanism of the occurrence and development of ESCC.

Recently, a novel ESCC susceptibility loci located in chromosome10q23 in PLCE1 gene was identified by three genome-wide association studies (GWAS) in Chinese Han populations (5–7). Accumulated evidences have showed PLCE1 plays crucial roles in types of several cancers, such as head and neck (8), bladder (9–11), gastric (12,13), skin (14), prostate (15). Our previous studies revealed increased of PLCE1 expression was significantly associated with lymph node metastasis and advanced TNM stage of Kazakh ESCC, which implicated that PLCE1 could be a novel biomarker for patients of Kazakh ESCC (16). Afterwards, we have stably knockdown PLCE1 by siRNA in ESCC cell lines Eca109 and EC9706, knockdown of PLCE1 resulted in an increase of the apoptosis, a decrease in cell proliferation as well as migration and invasion, and an inhibition of the formation of lamellipodia and filopodia of F-actin in vitro, the effect of PLCE1 on cell migration, invasiveness, and motility is correlated with epithelial-mesenchymal transition (EMT) and cytoskeleton dynamics (4). These results suggested that PLCE1 might play important roles in oncogenesis and progression of ESCC. Therefore, further research must be carried out to understand the exact role of PLCE1 in ESCC. To better understand the molecular mechanism of PLCE1, the mRNA expression profile of PLCE1 knockdown was analyzed by the Affymetrix Gene Chip Human genome arrays. Nevertheless, a comprehensive analysis of the knockdown of PLCE1 has not been reported for microarray-based techniques in previous studies.

In this study, we explored the functional roles of PLCE1 on downstream genes and signaling pathways in ESCC. Hundreds of differentially expressed genes (DEGs), especially inflammation or immune-related genes, were identified in PLCE1-siRNA-treated cells compared with the control cells. Based on Gene Ontology enrichment and KEGG pathway analysis, we found that PLCE1 knockdown could impact a number of genes involved in proliferation, apoptosis, invasion and metastasis of tumor cells, et al and pathways including MAPK, Toll-like receptor, p53, Focal adhesion, et al. To the best of our knowledge, this is the first paper to perform a systematic analysis of how PLCE1 can influence DEGs and signal pathways in ESCC by microarray-based techniques.

Materials and methods

Cell culture and transfections

Esophageal cancer cell lines (Eca109 and EC9706) were purchased from Institute of Biochemistry and cell biology, Chinese Academy of Sciences (Shanghai, China) Cells was cultured in Dulbecco's modified Eagle's medium (DMEM) or RPMI-1640 (Gibco, Grand Island, NY, USA) containing 10% fetal bovine serum (FBS) at 37°C under 5% CO2. The transfection of RNA was in the case of the final concentration of 5 nM with HiPerFect transfection reagents (Qiagen, Hilden, Germany) in serum-free conditions. The target sequences for PLCE1-siRNA is 5′-AGCGUUGGUCCAUGCUUAATT-3′.

The DEGs

Raw data were normalized by MAS 3.0 algorithm. The main criterion for selection of DEGs between the two groups of samples being compared: i) q-value ≤ 5% (t-test); ii) fold change ≥2 and ≤0.5; given that there are three types of call values of each detected probe, at lowest one group among the values of three repetition should be non-A values. The expression level of the appropriate gene was the tallest number of P signs, for those genes including a lot of probes. A scatter plot was visualized to evaluate variations from chip to chip.

Gene ontology and pathway enrichment analyses

Molecular annotation system (MAS3.0) was used for gene ontology (GO) enrichment analysis. The ‘conditional’ option was designed to correct, while ‘p value Cutoff’ was designed to 0.05. For those GO terms including at least one DEG, a false discovery rate (FDR) was performed by the Benjamini-Hochberg method. FDR was set to 0.1 (cutoff: 0.1).

Pathway enrichment analysis was used to perform via the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/.html) database. The KEGG maps of biological functions associated with DEGs were identified. The P-value of the enrichment of DEGs in different pathways was reckoned by the super-geometric test, the significance threshold of false discovery rate (FDR) was also designed to 0.1.

Verification for gene expression using quantitative real-time polymerase chain reaction (qRT-PCR). Total RNA was extracted from ESCC cell lines with mRNA Extraction kit (Qiagen) according to the standard protocol. The cDNA of mRNA was synthesized with One Step PrimeScript mRNA cDNA Synthesis kit (Qiagen). All qRT-PCR data was standardized to β-actin expression. An ABI Prism 7500 Sequence Detection System (Applied Biosystems, Foster City, CA, USA) was used to detect the response. Amplification occurred at an initial denaturation cycle of 95°C for 5 min followed by 40 cycles of 95°C for 10 sec, 60°C for 30 sec. All experiments were performed in triplicate at least three times. The 2-ΔΔCq method was used to quantify the results. The primers used have been listed in Table I.

Table I.

The primers used for Q-RT-PCR.

Gene name	Primers	Annealing temperature (°C)	The length of product (bp)
PLCE1	F:GAGCTGCAATCGAAGTCTGG	60	192
	R:AAGGCCTTCTGTGAGTCCTC
IL-1a	F:ACGACTGGGTTTCAATCAGG	60	142
	R:CTGCATGACTCGCCTTATCA
IL-1b	F:CCAGGATGAGGACCTGAGAA	60	149
	R:CGAGGCATTTCTGTTGTTCA
CXCl1	F:CCCAAACCGAAGTCATAGCC	58	109
	R:GATTTGTCACTGTTCAGCATCTTT
CXCL2	F:CGAAGTCATAGCCACACTCAAG	58	116
	R:CTTCTGGTCAGTTGGATTTGC
CCL2	F:GCACTCTCGCCTCCAGCATGA	50	121
	R:CAGCAGGTGACTGGGGCATTGA
CCL20	F:TGCTGTACCAAGAGTTTGCTC	58	217
	R:CGCACACAGACAACTTTTTCTTT
β-actin	F:TGAAGTGTGACGTGGACATCCGC	60	356
	R:GCCAATCTCATCTTGTTTTCTGCGC

F, forward; R, reverse.

Western blot analysis

Transfected cells were lysed using RIPA buffer supplemented with Protease Inhibitor (PMSF). Protein was separated by SDS-PAGE, transferred to the PVDF membrane, and prevented by blocking with 0.1% Tween-20 in TBST including 5% nonfat milk for 2 h at room temperature, then, membranes were incubated at 4°C overnight with primary antibodies against PLCE1 (1:200; rabbit. no. sc-368932; Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA) and β-Actin (1:500; rabbit. no. PR-0255; ZSGB-BIO, Beijing, China), and then incubated with secondary antibodies (1:10,000; rabbit. no. zf-0311; ZSGB-BIO) for 2 h at 37°C. After washing, membranes were exposed in the darkroom. Finally, the objective bands were analyzed with the ECL procedure.

Statistics

Statistical analysis was carried out by Graphpad Prism 5.0e Software. Results were expressed as mean ± standard deviation comparison between two groups. Differences between means were investigated by paired t-test within two groups. P<0.05 was considered to indicate a statistically significant difference.

Results

Si-PLCE1 inhibited PLCE1 mRNA and protein expression

To determine the oncogenic function of PLCE1, si-PLCE1 was used to interfere with PLCE1 expression in Eca109 and EC9706 cells. The endogenous expression of PLCE1 was detected by qRT-PCR and western blot analysis after transfection. The expression of PLCE1 at both mRNA and protein levels was significantly reduced in the PLCE1 knockdown cells, compared with control groups (Fig. 1). These results indicated that the expression levels of PLCE1 mRNA and protein were markedly silenced after transfection in Eca109 and EC9706 cells.

Figure 1.

Effects of si-PLCE1 on the expression of PLCE1 in ECa109 and EC9706 cells. (A) PLCE1 mRNA expression was examined by qRT-PCR. (*P<0.05, **P<0.01) *P<0.05. (B) Expression of PLCE1 protein was detected by western blotting. β-actin acted as the control.

Analysis of the DEGs

To explore the influence of PLCE1 on downstream genes, the mRNA expression profile was analyzed using the Affymetrix GeneChip Human genome 3, IVT arrays which contain 49294 genes in Eca109 and EC9706 transfected with PLCE1 siRNA compared to control groups.

Results compared with control groups, a total of 223 DEGs were identified by greater than 2.0-fold, of these, 168 DEGs were robustly upregulated and 55 DEGs were robustly downregulated. Primitively, the significantly increased genes were identified, such as DUSP5, IL8, IER3, IL6 and so on, while significantly downregulated genes including TMEM30A, SNTB2, TMEM106B and so on (Table II). Interestingly, the expression of inflammatory factors was significantly increased after PLCE1 gene silencing, such as IL-8, IL-6, CXCR4 and so on. Contrary to popular belief, inflammation was regarded as a promoter of tumor formation and metastasis. The results of our study suggested that PLCE1 might inhibit inflammatory factors expression. A two-dimensional rectangular coordinate plane was shown a scatter plot (Fig. 2). Collectively, PLCE1 might have a significant impact on expressions of PLCE1-modulated downstream genes. Accordingly, a lot more researches are needed to figure out the possible relationship between these genes and PLCE1.

Table II.

Differentially expressed genes in PLCE1 siRNA-Treated esophageal squamous cell carcinoma cells, compared with control cells.

A, Upregulated genes
Gene symbol	Fold change	Gene symbol	Fold change	Gene symbol	Fold change
IL8	22.624	HSPA5	3.007	TNRC6A	2.27
CXCL2	10.224	SAT1	3	IFI16	2.26
IFI27	10.111	CLDN1	2.966	HSPA5	2.26
IFIT2	8.896	PRIC285	2.929	CXCR4	2.252
CXCL2	8.1833	PLSCR1	2.925	SOD2	2.249
OASL	7.9428	PHLDA1	2.908	JUN	2.246
11762907_at	7.457	RELB	2.908	DUSP6	2.242
11760093_at	7.012	NFKBIZ	2.879	FNDC3A	2.241
IFIT1	6.821	NT5E	2.808	GDF15	2.238
DDIT3	6.788	STC2	2.802	SAMD9	2.232
DDX58	6.5573	OAS1	2.734	HIF1A	2.23
DDIT3	6.071	FNDC3A	2.715	FNDC3A	2.225
IFIT3	5.934	SERPINE2	2.691	TNRC6A	2.214
IL6	5.899	F3	2.663	ZC3HAV1	2.214
TRAC	5.221	ZC3H12C	2.663	IFITM1	2.202
GDF15	5.007	SAT1	2.613	PLSCR1	2.198
PTGS2	5.005	ADM	2.61	ADRB2	2.197
ISG20	4.985	IRF7	2.596	CDKN1A	2.194
TNFRSF21	4.789	ABCD3	2.592	SPINK1	2.193
IRF9	4.719	OAS2	2.59	CLEC2B	2.181
11761908_at	4.698	ADM	2.562	PMAIP1	2.157
GDF15	4.418	FNDC3A	2.522	ZC3H12C	2.156
PTGS2	4.31	ITGA2	2.506	FGF2	2.143
C5orf41	4.295	IRF1	2.503	SIPA1L2	2.136
ISG15	4.122	PARP9	2.503	FICD	2.129
GDF15	4.091	PLSCR1	2.501	DUSP6	2.114
TNFRSF21	4.05	DNAJB9	2.5	HSPA5	2.112
STC2	4.024	ICAM1	2.495	RSPO3	2.108
DUSP5	4.003	MAFF	2.482	ANGPTL4	2.107
TNFAIP3	3.996	FAIM3	2.475	SAMHD1	2.105
SERPINE2	3.81	SAA1/SAA2	2.47	PPP1R15A	2.103
PLSCR1	3.805	STX11	2.454	B3GNT5	2.086
RND3	3.796	CDKN1A	2.441	FNDC3B	2.078
TNFAIP3	3.743	DUSP6	2.44	USP18	2.076
AMIGO2	3.664	ICAM1	2.427	RND3	2.069
PTGS2	3.533	CXCR4	2.422	FNDC3B	2.068
SERPINE2	3.492	STAT1	2.415	ELL2	2.062
HSPA5	3.483	PMAIP1	2.407	JAG1	2.049
IFI6	3.462	PTGER4	2.386	MCL1	2.048
FOS	3.427	HERC5	2.385	TLR4	2.048
FOSL1	3.419	FOSL1	2.378	DRAM1	2.044
HSPA5	3.417	BIRC3	2.347	OAS1	2.043
SAMD9L	3.409	BIRC3	2.346	SIPA1L2	2.038
ATF3	3.387	FGF2	2.346	OTUD1	2.037
C5orf41	3.231	PLSCR1	2.345	GFPT1	2.032
FOSL1	3.196	VEGFC	2.336	OSMR	2.026
HIVEP2	3.148	OAS1	2.335	GADD45A	2.023
BIRC3	3.131	JUN	2.33	ETV4	2.021
CLDN1	3.128	HRASLS2	2.317	PLAUR	2.019
CPA4	3.123	ZFP36	2.316	JUN	2.016
FOSL1	3.12	ABCD3	2.297	IRF9	2.014
IRAK2	3.089	IFITM1	2.294	DUSP4	2.011
IER3	3.081	OAS1	2.293	SERPINE1	2.009
GABBR1	3.052	CXCR4	2.293	KRT34	2.005
RHEBL1	3.032	SAMHD1	2.281	SLC6A6	2.0008
SOD2	3.013	DUSP6	2.271	HERPUD1	2.0004
B, Downregulated genes
Gene symbol	Fold change	Gene symbol	Fold change	Gene symbol	Fold change
TMEM30A	0.255	C5orf53	0.418	SEPP1	0.468
TMEM30A	0.259	SUV39H1	0.42	NEURL1B	0.474
SNTB2	0.311	NCRNA00201	0.426	MOBKL2B	0.476
C21orf45	0.345	GNA11	0.43	LOXL1	0.477
IGSF8	0.348	VEPH1	0.439	SUV39H1	0.478
STYX	0.357	CAV1	0.446	CHST13	0.48
C21orf45	0.36	C8orf83	0.453	PSKH1	0.481
STYX	0.371	MOBKL2B	0.454	TMEM106B	0.482
DAZAP1	0.374	CLIC3	0.455	ZNF362	0.483
C7orf41	0.385	METTL7A	0.456	BMP8B	0.483
FAM107B	0.398	SORBS2	0.458	SGOL2	0.486
CCNF	0.399	LMO1	0.459	TMEM106B	0.486
FAM107B	0.403	CBX5	0.46	FAM47E	0.488
HNRNPU	0.404	SORBS2	0.463	CBX5	0.49
SNTB2	0.406	CBX5	0.463	RNF34	0.491
GNA11	0.408	SREK1IP1	0.464	AMY1A	0.492
ALPI	0.409	SAMD11	0.465	EXD3	0.494
DPH3	0.41	MXD3	0.468	PABPC4L	0.495
				SUV39H1	0.497

Figure 2.

RNA array analyses of si-PLCE1-treated cells ECa109 and EC9706 cells compared to control cells. The log-log scatter-plot for the microarray results, red plots represent upregulated genes, while green plots represent downregulated genes.

GO enrichment analyses

In order to have a command of the functional categories of that DEGs involved in and to find out which GO terms can be strongly enriched or significantly associated with our selected genes, GO term enrichment analysis was performed by MAS3.0. As is shown in Fig. 3, these DEGs were sorted and categorized into 26 different functional categories by PLCE1 knockdown in ECa109 and EC9706 cells. While 442, 178 and 94 significant GO terms for these DEGs were identified in Biological Process, Molecular Function and Cellular Component.

Figure 3.

Functional classification of genes differentially expressed in PLCE1 knockdown cells. GO ontology software was used to analyze the list of differentially expressed genes classified according to biological process, molecular function and cellular component. These pie charts represent the distribution of the genes induced and repressed by PLCE1 knockdown, pie sections are proportional to the number of genes.

Remarkably, among the Biological Process, predominant terms were identified as follows. Firstly, the downregulated DEGs involved in cell division (GO:0051301), as well as upregulated DEG linked to cell cycle arrest (GO:0007050) and negative regulation of cell proliferation (GO:0008285) were represented, which revealed the inhibition of the PLCE1-mediated cell might block cell proliferation via these DEGs. Secondly, upregulated DEGs were enriched in anti-apoptosis (GO:0006916), negative regulation of apoptosis (GO:0043066), indicating theses terms might have direct or immediate adverse effect on apoptosis induction in PLCE1-siRNA-treated Eca109 and EC9706 cells. Thirdly, upregulated genes were also enriched in cell-cell signaling (GO:0007267), cell adhesion (GO:0007155), cell motility (GO:0006928), implying the terms might play a negative role in regulation of metastasis and invasion of PLCE1 knockdown cells. Fourthly, we also found that many DEGs were significant enrichment in regulation of transcription, DNA-dependent (GO:0006355), signal transduction (GO:0007165), G-protein coupled receptor protein signaling pathway (GO:0007186), cell surface receptor linked signal transduction (GO:0007166), which suggested these changes were likely to participate in the oncogenesis and tumor development of ESCC. To our interest, a significant proportion of clusters was enriched in ‘response to virus (GO:0009615)’, ‘immune response (GO:0006955)’, ‘chemotaxis (GO:0006935)’ and ‘inflammatory response (GO:0006954)’ in the biological process category, suggesting that the changes related to these terms might take place during the genesis of ESCC.

Besides, a few terms connected to cytoskeleton organization were identified from Molecular Function and Cellular Component, including actin binding (GO:0003779), actin cytoskeleton (GO:0015629), focal adhesion (GO:0005925), integrin complex (GO:0008305), indicated that PLCE1 might exert significant influence in the metastasis, invasion and mobility of ESCC through the functions of genes implicated in these terms.

KEGG pathway enrichment analyses

To find out how the DEGs from PLCE1 knockdown influence process of cell biological function, KEGG pathway enrichment analysis was carried out to identify relevant pathways. Based on the KEGG pathway analyses, these DEGs were enriched in 46 pathways which changed significantly (q-value <0.05), containing MAPK, Toll-like receptor, p53, JAK-STAT signaling pathway, small cell lung cancer, renal cell carcinoma, bladder cancer, prostate cancer, cytokine-cytokine receptor interaction, focal adhesion, ECM-receptor interaction concluding consist of ITGA2, Starch and sucrose metabolism consisting of AMY1A, AMY1B, AMY1B, AMY1C, AMY2A, AMY2B, AMY2B, glutamate metabolism covering GFPT1, Nitrogen metabolism extending to NT5E and so on (Table III). The majority of these pathways were involved in cell biological function, including proliferation, invasiveness and apoptosis. Nevertheless, compared with the other signaling pathway, the majority of DEGs were significantly enriched in MAPK, JAK-STAT, P53, TLRs signaling pathways and the Focal adhesion. We should therefore concentrate our interest on the genes associated with the above pathways. The DEGs involved in the above signaling pathways are represented. There were alterations involved in the focal adhesion including 5 upregulated and 1 downregulated DEGs. Among the MAPK signaling pathways -related genes, expression changes mainly occurred in 10 genes, including DUSP6, RELB, FGF2, FOS, DUSP4, DDIT3 (GADD153), DUSP5, and JUN between the PLCE1 siRNA and control group. The data of gene chip shown 4 upregulated genes (PMAIPI, SERAIPI (PAI-1), CDKN1A (P21), GADD45) in P53 signaling pathway, deregulated with a >2-fold difference following PLCE1 siRNA transfection in Eca109 and EC9706 cells. There were 7 upregulated genes (STAT1, FOS, TLR4, IL6, IL-8) with >2-fold changes between the treatment and un-treatment cell involved in TLRs signaling pathways. Thus, our results indicated a strong interaction between PLCE1 and these pathways that DEGs were involved in.

Table III.

KEGG pathway analysis of DEGs in response to PLCE1 knockdown.

Pathway	Count	P-value	Gene
MAPK signaling pathway	10	2.03E-09	DUSP6; RELB; FGF2; FGF13; FOS; DUSP4; DDIT3; GADD45A; DUSP5; JUN
Toll-like receptor signaling pathway	7	7.46E-09	STAT1; FOS; TLR4; IL6; JUN; IL8; IRF7
Starch and sucrose metabolism	5	2.07E-07	AMY1A; AMY1A; AMY1B; AMY1C; AMY2A; AMY2A; AMY2B
Cytokine-cytokine receptor interaction	7	4.52E-06	CXCR4; TNFRSF21; OSMR; CXCL2; IL6; IL8; VEGFC
Focal adhesion	6	1.33E-05	BIRC3; ITGA2; CAV1; PAK3; JUN; VEGFC
p53 signaling pathway	4	2.78E-05	PMAIP1; SERPINE1; CDKN1A; GADD45A
Renal cell carcinoma	4	3.11E-05	HIF1A; PAK3; JUN; VEGFC
Bladder cancer	3	0.00016	CDKN1A; IL8; VEGFC
Jak-STAT signaling pathway	4	0.00063	STAT1; OSMR; IRF9; IL6
Complement and coagulation cascades	3	0.0007	PLAUR; SERPINE1; F3
Melanoma	3	0.00076	FGF2; FGF13; CDKN1A
B cell receptor signaling pathway	3	0.00089	FOS; IFITM1; JUN
Small cell lung cancer	3	0.00132	BIRC3; PTGS2; ITGA2
ErbB signaling pathway	3	0.00137	CDKN1A; PAK3; JUN
Regulation of actin cytoskeleton	4	0.00221	FGF2; FGF13; ITGA2; PAK3
T cell receptor signaling pathway	3	0.0026	FOS; PAK3; JUN
Leukocyte transendothelial migration	3	0.00349	CXCR4; CLDN1; ICAM1
Cell adhesion molecules (CAMs)	3	0.00465	CLDN1; ICAM1; CNTN1
mTOR signaling pathway	2	0.00751	HIF1A; VEGFC
Pathogenic Escherichia coli infection - EHEC	2	0.00837	CLDN1; TLR4
Pathogenic Escherichia coli infection - EPEC	2	0.00837	CLDN1; TLR4
Epithelial cell signaling in Helicobacter pylori infection	2	0.01328	JUN; IL8
Pancreatic cancer	2	0.01438	STAT1; VEGFC
Colorectal cancer	2	0.01874	FOS; JUN
Hematopoietic cell lineage	2	0.02002	ITGA2; IL6
Apoptosis	2	0.02089	BIRC3; IRAK2
Neuroactive ligand-receptor interaction	3	0.02635	GABBR1; PTGER4; ADRB2
GnRH signaling pathway	2	0.02939	GNA11; JUN

KEGG, Kyoto encyclopedia of genes and genomes; DEG, differentially expressed genes.

Confirmation of genes associated with inflammatory factors by qRT-PCR after PLCE1-siRNA transfection

To approve or confute the results of our mRNA microarray analysis, qRT-PCR analysis was performed to evaluate mRNA levels of six pivotal DEGs selected in EC9706 and ECa109 transfected with PLCE1-siRNA. As expected, the results were consistent with the results of microarray findings that implicated involvement of IL-1a, IL-1b, CXCL-1, CXCL-2, CCl-2, CCL-20 genes. Compared with control group, by Q-RT-PCR analysis, all 6 genes were upregulated by PLCE1-siRNA treatment of EC9706 and ECa109 cells (Fig. 4). The results were corresponded to that of microarray data, which bore out the reliability of the results.

Figure 4.

Q-RT-PCR validation for inflammatory factors. The relative expression of IL-1a, IL-1b, CXCL-1, CXCL-2, CCL-20 was significantly higher in PLCE1-siRNA-treated EC9706 and ECa109 cells than untreated control cell, respectively. (*P<0.05, **P<0.01).

Discussion

ESCC, which is the fourth leading cause of cancer death, is one of the most aggressively malignant tumors in China (17). It was reported that PLCE1 was a susceptibility gene in ESCC. Although PLCE1 has been extensively studied, it was not consistent with its role in cancer. Previous findings demonstrated PLCE1 has been identified as a tumor-suppressor gene of colorectal tumor (18). However, PLCE1 has also acted as a novel oncogene in a variety of human tumors, such as bladder cancer (9) and head and neck cancer (8). Hence, PLCE1 promoting or inhibiting the occurrence and development of tumor is still to be further studied. Our previous studies showed increased expression of PLCE1 could be used as a potential biomarker for the early diagnosis and poor prognosis of ESCC (16). Furthermore, we also found PLCE1 knockdown effectively inhibited cell growth/proliferation and increased apoptosis as well as reduced the invasion and migration in ESCC (4). Our results demonstrated that PLCE1 could play a tumor-oncogenic function in ESCC. Thus, it would be of great use to have systematic understanding of the functions of PLCE1. Here we presented a comprehensive analysis of mRNA profile of PLCE1 knockdown in ESCC cells by multiple bioinformatic analyses.

In the present study, a total of 223 DEGs, including 168 upregulated and 55 downregulated DEGs, were identified. In order to have a command of the functions of these DEGs, the DEGs were analyzed by GO enrichment tools. In the present study, these upregulated DEGs were significantly enriched in cell cycle arrest (PPP1R15A, DDIT3, CDKN1A, GADD45A, IL8), negative regulation of cell proliferation (PTGS2, FGF2, IL6, CDKN1A, IFITM1, IL8), apoptosis (FGF2, CXCR4, TNFRSF21, PPP1R15A, RNF34, GADD45A, TNFAIP3, IER3, PHLDA1), cell-cell signaling (STC2, FGF2, ADM, GDF15, ISG15, IL8), cell adhesion (CLDN1, AMIGO2, RND3, ITGA2, F3), these results supported such an assumption that oncogenic PLCE1 was involved in esophageal proliferation, apoptosis and metastasis. Besides that, KEGG pathway enrichment analysis was carried out to identify relevant pathways, these DEGs were identified significantly enriched in 46 pathways. Among them, DEGs are closely linked to a variety of different signaling networks, including the MAPK, p53, JAK-STAT, Toll-like receptor signaling pathways. A following discussion focuses mainly on the alteration of genes involved primarily in MAPK, p53, Toll-like receptor signaling pathways.

In the MAPK signaling pathway, several genes from the MAPK signaling pathway are altered in cells of PLCE1 downregulation, including 10 upregulated genes (DUSP6, DUSP4, DUSP5, RELB, FGF2, FGF13, FOS, DDIT3, GADD45A, JUN). Mitogen-activated protein kinase (MAPK) pathways, which has extracellular regulated kinases 1 and 2 (ERK1/2), c-Jun-N-terminal and p38, regulate many cellular functions including cell proliferation, differentiation, migration and apoptosis. Study showed that CDC25 homology domain of PLCE exhibited GEF activity toward Rap1, transiting from the Rap1.GDP-bound state to the Rap1.GTP-bound state facilitated by GEF, leading to the accumulation of the formation of Rap1GTP, thereby, which in turn stimulates B-Raf/MEK/ERK pathways within the cell, in addition, activation of Rap2B stimulates PLCE1, then activating H-Ras results in cascade response corresponding to MAPK, finally MAPK was activated, and promoting cell proliferation. In addition, our previous study has shown that ERKl protein expression was significantly increased in the ESCC tissues (19), thus it can be seen that PLCE1 might promote the incidence of esophageal carcinoma indirectly via MAPK/ERK1/2 pathways. Fibroblast growth factor 2 (FGF2) activated a series of intracellular signal transduction pathways by tyrosine kinase receptors (FGFR) to form a complete pathway: FGF2/MAPK/ERK1/2 (20,21), which finally induces various cellular activities, including mitosis, differentiation, proliferation, and cell migration (22,23). However, DUSP6, DUSP5 and DUSP4, belonging to dual specificity phosphatase (DUSP) family, limits over-activation of the FGFs/MAPK/ERKl/2 signal transduction pathways in a negative feedback manner (24). This implies that the PLCE1 siRNA-induced apoptosis and proliferative inhibition in ESCC are related to DUSPs/FGFs/MAPK/ERKl/2 signal pathways. These findings provide a novel mentality to study the pathogenesis of ESCC in MAPK signaling pathway.

As known, p53 is one of the most important tumor suppressor genes in cancer-associated diseases and plays a crucial role in regulating cell cycle arrest, apoptosis and senescence (25), recent study also revealed p53 is involved in PLCE1-knockdown induced apoptosis in Esophageal Cancer cells (26). Our results demonstrated 2 key upregulated components CDKN1A (P21) and GADD45A involved in the p53 signaling pathway were upregulated in PLCE1 siRNA treated cells. Following the downstream target of p53, p21 and GADD45A are activated and block the cell cycle at the G1 and G2 phases (27,28). This result suggests that PLCE1 inhibition may lead to an increase in p53 function by upregulating CDKN1A (P21) and GADD45A, and that a new PLCE1/P53/P21 pathway is involved in PLCE1-knockdown induced apoptosis and cell cycle arrest in ESCC. In addition, cellular senescence is primarily regulated by p53/p21 and p16/pRb pathways, where the p53/p21 pathway mediates the replicative senescence and plays crucial roles in DNA damage response (29). Therefore, PLCE1 suppression might have an effect on the p53/p21 signaling pathway through p53 and p21 upregulation, leading to proliferation inhibition and inducing cell senescence and in Eca109 and EC9706 cells.

In this study, we have shown that inflammation or immune-related molecules (TLR4, IL-8, IL-6, CXCL2) were significantly increased after PLCE1 suppression, and the results by qRT-PCR analysis were in agreement with that of microarray data. However, these results did not agree with recent reports in which PLCE was shown to enhance tumor development in many human cancers (30–32), by increasing the production of inflammatory cytokines in local tissues, obviously, our results suggested that PLCE1 might be involved a yet unknown signaling pathway linking tumor promotion and cytokines. Interestingly, it was reported that the expression of cytokines IL-1a, IL-6, and IL-8 initiated and supported cellular senescence (33). Similar report also showed that IL-1a activated tumor-suppressive pathways to maintain senescent growth (34). Nevertheless, cellular senescence restricts unlimited cell proliferation and plays an essential role in tumor suppression. Therefore, we investigate whether PLCE1 could enhance carcinogenesis and progression of ESCC via loss of cytokines-supported senescence. In addition, it was well recognized that toll-like receptor 4 (TLR4), an intensively studied member in the TLR family, results in transcriptional activation of pro-inflammatory genes including cytokines (IL-1, IL-6, CXCL-2) (35). Importantly, TLR4 activation inhibited hepatocellular carcinogenesis due to immune networks (IL-1a/b, IL-6, CXCL-2) triggering cellular senescence (36). These results taken together suggested PLCE1 could decrease cellular senescence via inhibition of the TLR4 signaling pathway, which contributed to the development of ESCC. In future studies, it will be worthwhile to determine whether PLCE1 has a role in esophageal carcinoma by loss of TLR4-mediated immunity supporting senescence.

In summary, the comprehensive understanding the role of PLCE1 in ESCC was obtained by bioinformatics workflow of mRNA profile after PLCE1 knockdown by multiple bioinformatics analysis. A total of 223 DEGs, 168 DEGs upregulated and 55 downregulated DEGs, were identified in PLCE1-siRNA-treated cells compared with the control cells. Alterations genes in the Focal adhesion, MAPK, P53 and TLRs signaling pathways may be important during PLCE1 siRNA-induced apoptosis, and proliferation, invasive and migrant inhibition of Eca109 and EC9706 cells. These findings provide information useful for combination targeted therapy in PLCE1-relevant neoplasm. We need to characterize DEGs and identify the exact molecular mechanism PLCE1 involved in proliferation, apoptosis, invasion and metastasis.