Journal of Immunology Research

Journal of Immunology Research / 2021 / Article
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Extracellular Matrix and Immune Niches in Human Disease 2021

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Research Article | Open Access

Volume 2021 |Article ID 5574176 | https://doi.org/10.1155/2021/5574176

Panpan Zhang, Tong Su, Shu Zhang, "Comprehensive Analysis of Prognostic Value of MEX3A and Its Relationship with Immune Infiltrates in Ovarian Cancer", Journal of Immunology Research, vol. 2021, Article ID 5574176, 21 pages, 2021. https://doi.org/10.1155/2021/5574176

Comprehensive Analysis of Prognostic Value of MEX3A and Its Relationship with Immune Infiltrates in Ovarian Cancer

Academic Editor: Jian Song
Received13 Feb 2021
Revised03 May 2021
Accepted20 May 2021
Published04 Jun 2021

Abstract

MEX3A is a critical RNA-binding ubiquitin ligase that is upregulated in various types of cancer. However, the correlations of MEX3A with prognosis and its molecular mechanism in ovarian cancer (OC) remain unclear. The expression level, prognostic values, and the genetic variations of MEX3A were analyzed via Gene Expression Profiling Interactive Analysis (GEPIA) Oncomine, Kaplan–Meier plotter, and cBioPortal. We used the LinkedOmics database to investigate the functions of MEX3A coexpressed genes and performed visualizing gene interaction network analysis on the GeneMANIA website. The correlations between MEX3A and cancer immune infiltration were analyzed by the Tumor Immune Estimation Resource (TIMER) site and the TISIDB database. Furthermore, in vitro analysis was performed to evaluate the biological functions of MEX3A in OC cells. Our study showed that the expression of the MEX3A in OC was higher than in normal tissues; it had the greatest prognostic value in OC, and strong physical interaction with PABPC1, LAMTOR2, KHDRBS2, and IGF2BP2, which indicated the association between MEX3A and immune infiltration. We also found that MEX3A was negatively related to infiltrating levels of several types of immune cells, including macrophages, neutrophils, dendritic cells (DCs), B cells, and CD8+ T cells. Additionally, in vitro experiments demonstrated that MEX3A promotes proliferation and migration in OC cells. Taken together, MEX3A might influence the biological functions of OC cells by regulating the immune infiltration in the microenvironment as a prognostic biomarker and a potential therapeutic target.

1. Introduction

Ovarian cancer (OC) is a common gynecological malignancy with high mortality. More than 70% of patients with OC are diagnosed with advanced-stage cancer (III and IV) [1]. Although the development of surgery and chemotherapy in ovarian cancer has been advanced in recent decades, the benefits of traditional treatment are limited [2]. Recently, immunotherapy has offered a novel and promising therapeutic strategy. Still, immunotherapy, which has been developing rapidly resulting in major breakthroughs in many areas, cannot achieve a good treatment effect because of a special tumor immune microenvironment [3]. Like many other solid tumors, OC is immunogenic, and the imbalance between immune activation and immune suppression can lead to tumorigenesis and cancer progression. Thus, it is necessary to select and identify reliable immune-related biomarkers and novel targets for immunotherapy strategies necessary to diagnose OC early.

MEX3A is an important component of the Mex3 family, which has a conserved region of about 70 amino acids, including MEX3A, MEX3B, MEX3C, and MEX3D [4]. MEX3A is a kind of RNA-binding protein (RBPs), which has the highly conserved RNA-binding domain and a C-terminal RING finger domain that are involved in posttranscriptional regulatory mechanisms [5]. Recently, MEX3A has been reported as a novel biomarker promoting proliferation and migration in various cancers such as pancreatic ductal adenocarcinoma (PDA), liver cancer, and colorectal cancer [68]; yet, its role in OC is still unclear.

In this study, we investigated the mRNA expression, mutation patterns, and prognosis value of MEX3A in OC for the first time based on large database analyses including Oncomine, GEPIA, cBioPortal, PrognoScan, and the Kaplan–Meier plotter. We also explored the function of the coexpression genes with MEX3A to clarify the potential mechanism in OC by GO and KEGG. In addition, we revealed the potential relationship between the expression of MEX3A and immune infiltration in the OC microenvironment via TIMER and TISIDB. We have further demonstrated that MEX3A enhanced tumor proliferation and migration in vitro. Collectively, our findings revealed the important role of MEX3A and provided a novel target and a valuable insight into the underlying mechanism between MEX3A and tumor-immune interactions in OC.

2. Materials and Methods

2.1. Oncomine Analysis

The MEX3A mRNA expression level was analyzed in OC by the Oncomine platform (http://www.oncomine.org/), a publicly accessible, online cancer microarray database with 715 data sets and 86,733 samples that allow for a powerful genome-wide expression analysis [9]. We selected avalue of 0.01 and a fold change of 2 as the threshold, and ranked genes in the top 10% as significant.

2.2. GEPIA Analysis

Gene Expression Profiling Interactive Analysis (GEPIA) is an interactive web used to analyze the RNA sequencing expression, including The Cancer Genome Atlas (TCGA) tumor sample information and Genotype-Tissue Expression (GTEx) normal sample information. GEPIA provides a series of key interactive and customizable functions by using a standard processing pipeline (http://gepia.cancer-pku.cn) [10].

2.3. cBioPortal Analysis

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides an online resource to explore, visualize, and analyze complex cancer genomics and clinical profile data from TCGA [11]. In this study, the cBioPortal was used to access genetic variations in MEX3A (amplifications, deep deletions, and missense mutations), DNA copy number alterations, and mRNA expression -scores (RNA Seq V2 RSEM). The tab OncoPrint shows an overview of genetic alterations for each sample in MEX3A. Besides, coexpression datasets were analyzed according to the online instructions of cBioPortal, and the R package was used for further enrichment analysis.

2.4. LinkedOmics

LinkedOmics (http://www.linkedomics.orglogin.php) is a publicly available web tool used to provide multiomics data of 32 TCGA cancer types [12]. We used the linkInterpreter module to derive biological insights into coexpressed gene enrichment by using Pearson’s correlation coefficient. These genes were presented in volcano plots and heat maps.

2.5. Functional Enrichment Analysis

To further explore the functions of MEX3A, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed in R statistical computing environment.

2.6. GeneMANIA

GeneMANIA (http://www.genemania.org) is a flexible, friendly web interface that is used for visualizing gene interaction networks and evaluating gene function [13]. It enables analysis of gene lists and prioritizes the marked genes for functional assays associated with MEX3A. The sources of the edge of the network, which represent the following bioinformatics methods, namely, physical interaction, coexpression, colocation, genetic interaction, and website prediction, were set.

2.7. TIMER Database Analysis

To obtain the MEX3A expression and correlation between MEX3A and immunity cells in TCGA datasets, an online analytical tool called “Tumor Immune Estimation Resource (TIMER)” was used. TIMER is an online dataset used for evaluating the relationship between clinical associations, mutation, SCNA, and infiltration of different immune cells (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells) in diverse cancer types [14]. The survival module also showed the Kaplan–Meier plotter and provided the multivariable Cox regression analysis of clinical factors (age, stage, and tumor purity). Once all conditions were defined, TIMER outputs revealed the Cox regression results, including hazard ratios (HR), 95% confidence intervals (CI), and statistical significance () automatically.

2.8. TISIDB Analysis

TISIDB (http://cis.hku.hk/TISIDB) is a user-friendly web portal, which contains a summary of 988 immune-related antitumor genes for 30 TCGA cancer types [15]. The associations between gene expression and immune features, including lymphocytes, immunomodulators, subtypes, and chemokines, were calculated by high-throughput data analysis. In this research, we used the TISIDB web to analyze the correlations between MEX3A expression and clinical stages, lymphocytes, and subtype immunomodulators in OC.

2.9. Kaplan–Meier Plotter Analysis

The Kaplan–Meier plotter (http://www.kmplot.com) is a common tool for biomarkers used to assess survival and prognosis, which includes gene expression data and survival information of 1,816 clinical tissue samples from OC patients [16]. The overall survival (OS) and progression-free survival (PFS) of OC patients were determined by dividing two groups (high vs. low expression) of patients by median. In addition, we further investigated OS and PFS of different histological subtypes (endometrioid and serous) in MEX3A by using the Kaplan–Meier method. These data were evaluated with a hazard ratio (HR), 95% confidence intervals (CI), and log-rank value.

2.10. PrognoScan Database Analysis

The relationship between MEX3A expression and prognosis in OC was analyzed by the PrognoScan database (http://www.abren.net/PrognoScan/), such as OS and PFS [17]. The threshold was adjusted to a Cox value < 0.05 or corrected value < 0.5.

2.11. Cell Culture and Transfection

The ES2 cells were obtained from the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China) and cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; HyClone; GE Healthcare Life Sciences) with 10% FBS at 37°C and 5% CO2. ES2 cells (to 70% confluence) were seeded on 6-well plates transfected with MEX3A siRNAs designed and synthesized by GenePharma (Shanghai, China), which were transfected into the cells using Lipofectamine 2000 (Invitrogen; Thermo Fisher Scientific) according to the protocols for the interference expression of MEX3A; the cells were cultured for 48 h or 72 h for further assays.

2.12. Cell Counting Kit-8 (CCK-8) Assay and Colony Formation Assays

Transfected cells were seeded on a 96-well plate at a density of cells/well. The CCK-8 solution (10 μl; Dojindo Laboratories, Kumamoto, Japan) was then added to each well of the plate. The plate was incubated for 2 h in the incubator, and the absorbance at each wavelength of 450 nm was measured using an automatic enzyme-linked immune detector.

For the colony formation assay, transfected cells were seeded into 6-well plates. One week later, the cells were fixed with 4% paraformaldehyde and stained with 0.5% (/) crystal violet. Then, cell clones were photographed and counted. These experiments were performed in triplicate.

2.13. 5-Ethynyl-2-Deoxyuridine (EdU) Staining Assay

Collected cells were seeded on 24-well plates at a density of cells/well and incubated for 24 h. According to the protocol of the EdU Kit (BeyoClick™ EDU Cell Proliferation Kit with Alexa Fluor 488; Beyotime, Shanghai, China), after transfection, EdU was added 1 : 1,000 in the cell medium for 2 h at 37°C. Cells were fixed with 4% paraformaldehyde for 15 min and treated with 0.3% Triton-X for 10 min at room temperature. Then, the cells were incubated for 30 min with a Click reaction cocktail in the dark. Nuclei were stained with Hoechst 33342 for 10 min. Photographs were taken in three randomly selected fields with an Olympus (Tokyo, Japan) microscope to analyze proliferation rates. Each experiment was performed at least three times.

2.14. Transwell Assay and Wound Healing Assay

Cells ( cells/well) were incubated in 100 μl culture medium and seeded on the Transwell inserts (Corning Glass Works; Corning, NY, USA) with 8 μm pores to determine the migration ability of the cells. A 600 μl culture medium was added to the lower chamber. After 48 h, the inserts were fixed with 95% ethanol, and 0.5% (/) crystal violet was used for staining. Migrated cells were counted in five nonoverlapping locations.

To analyze wound healing, we seeded transfected cells on 6-well plates. When the cell density reached 80-100%, we scraped cells at the bottom of the wells using a sterile 200 μl pipette tip to form a linear gap and culture treated cells with FBS-free DMEM. After 24 h, images of the wells were taken with an inverted fluorescence microscope. All assays were repeated at least three times.

2.15. Quantitative Real-Time PCR

Total RNA was extracted using the TRIzol Reagent (Invitrogen; Thermo Fisher Scientific). According to the manufacturer’s instructions, the concentration of total RNA was measured using Thermo Fisher Scientific NanoDrop ND-100. cDNA was synthesized using the SYBR PrimeScript RT-PCR Kit (Takara Bio, Inc., Japan). Real-time PCR was carried out using a Thermal Cycler Dice™ Real-Time system Tp800 (Takara Bio, Inc.). The primer sequences designed for MEX3A and β-actin are as follows (5-3): MEX3A, forward, TGGAGAACTAGGATGTTTCGGG, and reverse, GAGGCAGAGTTGATCGAGAGC; and β-actin, forward, CATGTACGTTGCTATCCAGGC, and reverse, CTCCTTAATGTCACGCACGAT. The mRNA expression of the target gene was analyzed using the method.

2.16. Western Blotting

Total protein was obtained from cells using ice-cold RIPA buffer mixed with protease inhibitor cocktails (Roche), and concentration was assayed by a BCA assay. Fifty micrograms of denatured protein was separated by 10% SDS-PAGE and transferred onto PVDF membranes. After blocking with 5% skimmed milk for 1 h at room temperature, the membranes were incubated with antibodies against MEX3A (1 : 1000; ab79046; Abcam) overnight at 4°C, followed by incubation with a secondary antibody (1 : 3,000; #A0208; Beyotime, Beijing, China) at room temperature for 1 h. The ECL detection kit was used to detect protein signals.

2.17. Immunohistochemical (IHC) Staining

MEX3A expression was assessed by IHC assay, using previously described protocol [18]. Anti-MEX3A antibody (ab79046; Abcam) was used at a 1 : 50 dilution at 4°C overnight. Rabbit immunoglobulin G (1 : 1000; ab6721; Abcam) was used as a negative control. Aperio Scanning System (Aperio Group, LLC) was employed to scan the slides, and Aperio Image Scope software (version 10.2.2.2317, Aperio Technologies) was used for further quantitative analysis.

2.18. Statistical Analyses

Survival analysis was analyzed using the Kaplan–Meier method. GO enrichment analysis and KEGG enrichment analysis were performed under an R computing environment. Statistical analyses were performed using GraphPad Prism 7.0 (GraphPad Software, La Jolla, CA, USA). Comparisons were performed by a two-tailed Student’s -test. values < 0.05 were considered statistically significant. Data were expressed as (SD).

3. Result

3.1. High Expression Level and Prognostic Value of MEX3A in Ovarian Cancer by Bioinformatics Analyses

Firstly, to determine differences in MEX3A expression in tumor and normal tissues, the MEX3A mRNA levels in different tumors and normal tissues of various cancer types were analyzed using the Oncomine database. The database, which had a total of 241 unique samples for MEX3A, and a total of 22 cancers, including brain and CNS cancer, breast cancer, colorectal cancer, and ovarian cancer, showed that MEX3A mRNA levels were significantly upregulated in various cancers, and MEX3A expression in OC was high on top 5 (Figure S1a). GEPIA analysis also revealed similar results (Figure S1b).

Next, we found that MEX3A expression in OC significantly increased between 426 cases of OC and 88 cases of normal ovarian tissues via GEPIA (Figure 1(a)). In order to clarify the results, the expression differences in OC tissues (40 samples from Renji Hospital) and normal ovarian tissues (25 samples from Renji Hospital) were also validated by IHC staining (Figure 1(c)).

In addition, we investigated whether MEX3A was associated with prognosis in OC patients by using the Kaplan–Meier plotter and PrognoScan. The Kaplan–Meier plotter and PrognoScan databases showed that OC patients with high MEX3A expression experienced poor OS and PFS (Figures 1(b) and 1(d), Table 1). In order to explore the prognostic value of different histologies, the database revealed that higher MEX3A expression was correlated with shorter OS and PFS both in patients with endometrioid and serous cancers (Figures S1c–e). Collectively, MEX3A can be considered as an independent prognostic biomarker linked to a poor survival rate in OC.


DatasetEndpointProbe IDNumberCorrected valueCOX valueIn (HR) HR (95% CI-low CI-up)

GSE9891Overall survival226346_at2780.0136330.0071410.271.31 (1.08-1.60)
GSE9891Overall survival227512_at2780.1146670.0226130.251.28 (1.04-1.59)
GSE17260Overall survivalA_24_P8574041100.0049340.031824-0.1.360.72 (0.53-0.97)
GSE17260Overall survivalA_32_P960361100.0019220.097382-1.230.78 (0.58-1.05)
GSE17260Progression-free survivalA_32_P960361100.0444300.338936-0.110.90 (0.72-1.12)

3.2. MEX3A Expression Is Correlated with Immune Infiltration Level in OC

To better understand the underlying mechanism of MEX3A in OC, we further investigated the relationships between MEX3A and the immune system. Tumor-infiltrating immune cells (TIICs) are an important part of the tumor microenvironment and which are independent predictors of cancer survival. It is unclear whether targeting MEX3A could influence the recruitment numbers of TIICs to impact the prognosis of cancers. Through TIMER analysis, we found that most immune cells were negatively correlated with MEX3A expression (Figure 2(a)). MEX3A expression had a negative correlation with B cells, CD8+ T cells, neutrophils and dendritic cells (DCs), and macrophages. However, the expression of MEX3A had weak associations with CD4+ T cells in OC. Subsequently, we used the TISIDB database to further analyze the relationship between MEX3A expression and immune regulation. Figures 2(b) and 2(c) show the correlation between MEX3A expression and TILs, which corresponded to the results reported above. Immunomodulators can be further divided into immunoinhibitors, immunostimulators, and major histocompatibility complex (MHC) molecules. Furthermore, we assessed the correlation between MEX3A expression and diverse immunomodulators. Figures 2(d) and 2(e) indicate the correlations between MEX3A expression and immunostimulators, and the greatest correlations include C10orf54 (Spearman’s: , ), TNFRSF18 (Spearman’s: , ), TNFRSF14 (Spearman’s: , ), and TNFRSF13C (Spearman’s: , ). Figures 2(f) and 2(g) indicate correlations between MEX3A levels and immunoinhibitors, where the strongest include IL10RB (Spearman’s: , ), IDO1 (Spearman’s: , ), VTCN1 (Spearman’s: , ), and HAVCR2 (Spearman’s: , ). Correlations between MEX3A expression and MHC molecules were also explored, and the greatest correlations include B2M (Spearman’s: , ), HLA-DMA (Spearman’s: , ), HLA-DPA1 (Spearman’s: , ), and HLA-DPB1 (Spearman’s: , ) (Figures 3(h) and 3(i)). Therefore, MEX3A may be involved in negative immune regulation.

3.3. Enrichment Analysis of Coexpression Genes Correlated with MEX3A in OC

Next, we analyzed mRNA sequencing data from OC patients in TCGA by using the function module of LinkedOmics. As shown in the volcano plot (Figure 2(a)), 2596 genes (dark red dots) showed significant positive correlations with MEX3A, and 3050 genes (dark green dots) showed significant negative correlations (). The 50 significant gene sets (such as ACTBL2, C12orf43, CCDC56, CCL27, CPNE8, FAM78B, KCTD17, and LAMB4) positively and negatively correlated with MEX3A are shown in the heat map (Figures 2(b) and 2(c)). These results indicated an important influence of MEX3A on the transcriptome level. Besides, GO term analysis showed that high expressed genes in correlation with MEX3A were mainly located in the chromatin centrosome and nuclear chromosome part, where they mostly participated in mRNA processing, covalent chromatin modification, and histone modification. Poor expressed genes were mainly located in the endosome membrane, secretory granule membrane, and side of the membrane and were involved in immune-related processing, including neutrophil and T cell activation and regulation of lymphocyte activation (Figures 4(d) and 4(e)). KEGG pathway analysis showed the most important enrichment in the herpes simplex virus 1 infection of high expressed genes and cytokine-cytokine receptor interaction of poor expressed genes (Figures 4(f) and 4(g)). These data pointed out that MEX3A might promote tumor progression by regulating immune cell response in the tumor microenvironment.

3.4. Genomic Alterations of MEX3A in OC

Based on the above analysis, MEX3A is closely related to tumor immunology. In order to better understand the potential immune mechanism of MEX3A in cancer, genetic variations of MEX3A retrieved from the TCGA database (489 cases, Nature 2011) were analyzed by using the cBioPortal database. The results showed mRNA expression changes in 60 cases (16%), amplification in 38 cases (10%), a mutation in 1 case (0.3%), and multiple alterations in 19 cases (5%), in which amplification was the most common type (Figure 5(a)). Further, the expression of 771 genes was positively related to MEX3A and was increased with the amplification of MEX3A. Among these genes, LAMTOR2 had the most frequent alterations (Table 2). LAMTOR2 is essential for macrophage and dendritic cell (DC) homeostasis via mediating immune responses [19, 20]. Significantly enriched GO analysis showed that these genes encoded proteins that were mainly localized to the cornified envelope (Figures 5(b) and 5(c)). They were primarily involved in immunoglobulin binding, IgG binding, and RAGE reporter binding.


GeneLogFCentrezID

LAMTOR2>1028956
RAB25>1057111
UBQLN47.956893
LMNA7.864000
SSR26.96746
ARHGEF26.319181
RXFP46.27339403
KHDC45.8622889
SEMA4A7.7364218
SLC25A447.689673
SCARNA46.19677771
SNORA80E6.19677823
SYT116.1423208
RIT15.736016
PMF16.6411243
PMF1-BGLAP6.64100527963
GON4L5.6854856
BGLAP6.48632
PAQR65.979957
SMG55.923381
CCT35.847203
GLMP5.84112770
TMEM795.8484283
VHLL5.84391104
ASH1L-AS14.95645676
DAP34.957818
MSTO14.9555154
YY1AP14.9555249
MSTO2P4.73100129405
TSACC5.73128229
ASH1L4.1355870
C1ORF615.67NA
RHBG5.6757127
POU5F1P44.16645682
LRRC716.98149499
TRIM464.280128
DPM34.1454344
EFNA14.141942
GBA4.142629
GBAP14.142630
KRTCAP24.14200185
MTX14.144580
MUC14.144582
SLC50A14.1455974
THBS34.147059
ARHGEF116.99826
HDGF5.983068
INSRR5.983645
ISG20L25.9881875
MRPL245.9879590
NTRK15.984914
PEAR15.98375033
PRCC5.985546
RRNAD15.9851093
SH2D2A5.989047
FAM189B3.9910712
SCAMP33.9910067
CLK23.791196
FDPS3.792224
HCN33.7957657
PKLR3.795313
RUSC13.7923623
RUSC1-AS13.79284618
IQGAP35.39128239
MEF2D5.394209
CRABP25.91382
CYCSP526.82360155
ETV36.822117
ETV3L6.82440695
EFNA44.191945
ZBTB7B4.1951043
ADAM154.028751
DCST14.02149095
DCST1-AS14.02100505666
DCST24.02127579
EFNA34.021944
BCAN5.3163827
GPATCH45.3154865
HAPLN25.3160484
NAXE5.31128240
NES5.3110763
TTC245.31164118
FCRL46.6483417
FCRL56.6483416
CKS1B4.051163
FLAD14.0580308
LENEP4.0555891
ADAR4.58103
KCNN33.883782
PBXIP13.8857326
PMVK3.8810654
PYGO23.8890780
SHC13.886464
CD5L6.54922
FCRL16.54115350
FCRL26.5479368
FCRL36.54115352
KIRREL16.5455243
CHRNB24.491141
SHE4.23126669
TDRD104.23126668
UBE2Q14.2355585
CREB3L43.9148327
CRTC23.9200186
DENND4B3.99909
JTB3.910899
RAB133.95872
RPS273.96232
SLC39A13.927173
NUP210L3.6591181
CD1A6.43909
CD1B6.43910
CD1C6.43911
CD1D6.43912
CD1E6.43913
LINC017046.43646268
OR10K26.43391107
OR10T26.43128360
IL6R4.013570
C1ORF1893.73NA
C1ORF433.73NA
HAX13.7310456
TPM33.737170
UBAP2L3.739898
OR10K15.43391109
OR10R25.43343406
GATAD2B3.2857459
SLC27A33.2811000
PSMD42.955710
AQP103.6589872
ATP8B23.6557198
C2CD4D3.05100191040
C2CD4D-AS13.05100132111
LINGO43.05339398
MRPL93.0565005
OAZ33.0551686
RORC3.056097
TDRKH3.0511022
THEM53.05284486
INTS33.1765123
SNAPIN3.1723557
LYSMD12.87388695
PIP5K1A2.878394
SCNM12.8779005
TMOD42.8729765
TNFAIP8L22.8779626
TNFAIP8L2-SCNM12.87100534012
VPS722.876944
MNDA4.844332
LCE2B3.4926239
LCE2C3.49353140
LCE2D3.49353141
THEM42.96117145
SEMA6C2.810500
ILF23.073608
NPR13.074881
OR10X15.31128367
OR10Z15.31128368
OR6K65.31128371
OR6N15.31128372
OR6N25.3181442
OR6P15.31128366
OR6Y15.31391112
TCHH3.27062
TCHHL13.2126637
CGN2.8857530
PLEKHO12.8851177
VPS452.8811311
ZNF6872.8857592
LCE3A3.36353142
LCE3B3.36353143
LCE3C3.36353144
LCE3D3.3684648
LCE3E3.36353145
HORMAD12.684072
CHTOP2.9826097
ANP32E2.881611
APH1A2.851107
C1ORF542.8NA
CA142.823632
CIART2.8148523
MRPS212.854460
POGZ2.823126
NBPF18P3.09441908
RPTN3.09126638
S100A103.096281
S100A113.096282
ANXA92.668416
MINDY12.6655793
PRUNE12.6658497
GOLPH3L2.5455204
CRCT13.2354544
LCE5A3.23254910
CELF32.8911189
RIIAD12.89284485
S100A12.896271
S100A132.896284
S100A142.8957402
TUFT12.897286
C1ORF683.41NA
LCE2A3.41353139
LCE4A3.41199834
ACKR14.732532
AIM24.739447
CADM34.7357863
CADM3-AS14.73100131825
FCER1A4.732205
IFI164.733428
OR10J34.73441911
OR6K24.7381448
OR6K34.73391114
PYHIN14.73149628
SPTA14.736708
GABPB22.6126626
OTUD7B2.8156957
PI4KB2.815298
PSMB42.815692
RFX52.815993
S100A22.816273
SELENBP12.818991
SNX272.8181609
CRNN3.1249860
PRPF32.669129
ADAMTSL42.5454507
ADAMTSL4-AS12.54574406
MCL12.544170
KPRP3.27448834
LCE1F3.27353137
S100A162.73140576
BNIPL2.59149428
C1ORF562.59NA
CDC42SE12.5956882
CERS22.5929956
MLLT112.5910962
FLG3.012312
FLG-AS13.01339400
FLG23.01388698
CTSS2.481520
ENSA2.482029
LCE1A3.14353131
LCE1B3.14353132
LCE1C3.14353133
LCE1D3.14353134
LCE1E3.14353135
LCE6A3.14448835
SPRR2C3.146702
MTMR112.8110903
SF3B42.8110262
ARNT2.53405
CTSK2.531513
ECM12.531893
FALEC2.53100874054
RPRD22.5323248
SETDB12.539869
TARS22.5380222
HRNR2.91388697
S100A32.736274
S100A42.736275
S100A52.736276
S100A62.736277
OR10J14.626476
OR10J54.6127385
LOC1019280093.03101928009
SMCP3.034184
SPRR2G3.036706
SV2A2.589900
PGLYRP42.9257115
S100A122.926283
S100A82.926279
S100A92.926280
SPRR2A3.056700
SPRR2B3.056701
SPRR2E3.056704
SPRR2F3.056705
APCS4.19325
S100A72.826278
S100A7A2.82338324
S100A7L22.82645922
LELP12.93149018
LOR2.934014
PGLYRP32.93114771
PRR92.93574414
SPRR2D2.936703
NOTCH22.444853
DUSP233.8754935
LMOD13.5125802
TIMM17A3.5110440
SPRR1B2.836699
SPRR42.83163778
SRGAP2D2.28100996712
IVL2.733713
SPRR1A2.736698
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LY94.054063
ACP62.4251205
ANKRD20A12P2.42100874392
ANKRD34A2.42284615
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BCL92.42607
BOLA12.4251027
CD1602.4211126
CHD1L2.429557
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FAM72B2.42653820
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FMO52.422330
GJA52.422702
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GPR89A2.42653519
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HIST2H2AA32.428337
HIST2H2AB2.42317772
HIST2H2AC2.428338
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HIST2H3A2.42333932
HIST2H3D2.42653604
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HJV2.42148738
ITGA102.428515
LINC006232.42728855
LINC006242.42100289211
LINC008692.4257234
LINC011382.42388685
LINC025912.42388692
LIX1L2.42128077
LOC1027237692.42102723769
LOC6535132.42653513
LOC7289892.42728989
LSP1P52.42645166
NBPF102.42100132406
NBPF112.42200030
NBPF122.42149013
NBPF13P2.42644861
NBPF142.4225832
NBPF152.42284565
NBPF17P2.42401967
NBPF202.42100288142
NBPF25P2.42101929780
NBPF82.42728841
NBPF92.42400818
NOTCH2NLA2.42388677
NUDT172.42200035
PDE4DIP2.429659
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PDZK12.425174
PDZK1P12.42100034743
PEX11B2.428799
PFN1P22.42767846
PIAS32.4210401
POLR3C2.4210623
POLR3GL2.4284265
PPIAL4A2.42653505
PPIAL4D2.42645142
PPIAL4E2.42730262
PPIAL4G2.42644591
PRKAB22.425565
RBM8A2.429939
RNF1152.4227246
SEC22B2.429554
SRGAP2-AS12.42100873165
TXNIP2.4210628
ELF33.311999
PIK3C2B3.315287
CFAP453.625790
CRP3.61401
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TAGLN23.68407
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FAM72C2.12554282
ADAMTS44.319507
APOA24.31336
ARHGAP304.31257106
B4GALT34.318703
CFAP1264.31257177
DEDD4.319191
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HSPA64.313310
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LOC1019283724.31101928372
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NIT14.314817
NR1I34.319970
PCP4L14.31654790
PFDN24.315202
PPOX4.315498
RPL31P114.31641311
SDHC4.316391
TOMM40L4.3184134
TSTD14.31100131187
UFC14.3151506
USF14.317391
USP214.3127005
ATP1A23.14477
RNPEP3.146051
LINC011333.38100505633
PPP1R15B3.3884919
CD483.73962
SLAMF73.7357823
ATP1A43.19480
IGSF83.1993185
KCNJ103.193766
KCNJ93.193765
MDM43.194194
PIGM3.1993183
ATF63.922926
CD2443.951744
ITLN13.955600
LOC1019284043.9101928404
OLFML2B3.925903
RGS43.95999
RGS53.98490
SHISA43.47149345
SLAMF13.476504
LINC011424.14284688
METTL11B4.14149281
CASQ13.24844
CENPL3.2491687
DARS23.2455157
DCAF83.2450717
GAS53.2460674
GAS5-AS13.24100506046
IPO93.2455705
LINC006283.24127841
PEA153.248682
RC3H13.24149041
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SNORD443.2426806
SNORD473.2426802
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SNORD763.24692196
SNORD773.24692197
SNORD783.24692198
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SNORD803.2426774
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ZBTB373.2484614
C1ORF2263.58NA
CCDC1903.58339512
LOC1004222123.58100422212
NUF23.5883540
SH2D1B3.58117157
SPATA463.58284680
UAP13.586675
UHMK13.58127933
LAMC22.523918
NMNAT22.5223057
ASTN13.05460
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CD843.318832
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ETNK22.8855224
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CNTN23.096900
DSTYK3.0925778
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KLHL203.0927252
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RBBP53.095929
TMEM813.09388730
ATP1B13.95481
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CCDC1813.9557821
LINC006263.9579100
LINC009703.95101978719
METTL183.9592342
NME73.9529922
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SELL3.956402
SELP3.956403
SLC19A23.9510560
ARL8A2.73127829
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C1ORF2202.73NA
CSRP12.731465
GLUL2.732752
GPR37L12.739283
LINC002722.73388719
LINC003032.73284573
LINC013442.73400799
NAV12.7389796
PTPN72.735778
RPS10P72.73376693
SOAT12.736646
TEDDM12.73127670
TEX352.7384066
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NUCKS12.964710
OCLM2.910896
ODR42.954953
PBX12.95087
PDC2.95132
PRG42.910216
RAB292.98934
RNU6-72P2.9100873775
SLC41A12.9254428
TPR2.97175
ARPC52.5910092
LINC016862.59284648
NPL2.5980896
PHLDA32.5923612
RABGAP1L2.599910
RASAL22.599462
RGS162.596004
RGS82.5985397
RGSL12.59353299
RNASEL2.596041
TSEN152.59116461
FMO13.142326
FMO43.142329
LMX1A3.144009
TOP1P13.147151
ANKRD36BP13.5484832
CD2473.54919
CREG13.548804
DPT3.541805
DUSP273.5492235
F53.542153
FAM78B3.54149297
FMO9P3.54116123
GPA333.5410223
ILDR23.54387597
LINC013633.54101928484
LOC1005059183.54100505918
MAEL3.5484944
POGK3.5457645
POU2F13.545451
SFT2D23.54375035
TADA13.54117143
TBX193.549095
TIPRL3.54261726
XCL13.546375
XCL23.546846
BRINP22.7357795
CRYZL2P2.73730102
GS1-279B7.12.73100288079
IVNS1ABP2.7310625
LINC017412.73101928778
RASAL2-AS12.73100302401
RNU6-79P2.73100873779
SWT12.7354823
TRMT1L2.7381627
ZBED62.73100381270
APOBEC42.47403314
DHX92.471660
LHX42.4789884
LHX4-AS12.47100527964
SHCBP1L2.4781626
ERO1B1.9756605
ANKRD452.92339416
BRINP32.92339479
COPA2.921314
EEF1AKNMT2.9251603
GPR522.929293
LEMD12.9293273
LEMD1-AS12.92284576
LINC017202.92440704
LOC1005057162.92100505716
MYOC2.924653
NCSTN2.9223385
NHLH12.924807
PEX192.925824
PLA2G4A2.925321
PRRC2C2.9223215
RXRG2.926258
SCARNA32.92677679
SUMO1P32.92474338
TEX502.92730159
TMCC22.929911
VAMP42.928674
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ADORA12.58134
AXDND12.58126859
CHI3L12.581116
CHIT12.581118
FAM163A2.58148753
LAX12.5854900
LINC013502.58101929093
MYBPH2.584608
MYOG2.584656
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PPP1CB2.585500
RNF22.586045
SEC16B2.5889866
TDRD52.58163589
TOR1AIP12.5826092
TOR1AIP22.58163590
COLGALT22.3523127
RGL12.3523179
SMG7-AS12.35284649
ADCY103.2155811
GPR1613.2123432
MPC23.2125874
MPZL13.219019
RCSD13.2192241
SLC4A1AP3.2122950
SUPT7L3.219913
SUGCT>1079783
MTR1.994548
C1ORF532.73NA
CDK182.735129
COP12.7364326
LHX92.7356956
LOC1005057952.73100505795
NEK72.73140609
PPP1R12B2.734660
PTGS22.735743
SLC9C22.73284525
NCF22.244688
PLEKHG21.5664857
ZFP361.567538
BTG22.447832
CACNA1E2.44777
CEP3502.449857
CTSE2.441510
EDEM32.4480267
FLJ238672.44NA
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HMCN12.4483872
LINC011362.44730227
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PAPPA22.4460676
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MT1HL11.92645745
LRRC522.95440699
MRPL332.959553
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GMFG1.529535
MED291.5255588
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ACTN21.8588
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GPR137B1.857107
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LGALS81.853964
LGALS8-AS11.85100287902
NID11.854811
RYR21.856262
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GLRX22.5651022
RO602.566738
SLC45A32.5685414
SRGAP22.5623380
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IER52.3151278
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MR12.313140
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NCCRP11.47342897
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SYCN1.47342898

3.5. Construction of a Gene-Gene Interaction Network

To further explore the potential mechanism of MEX3A in promoting OC progression, we constructed a gene-gene interaction network by using the GeneMANIA database. Their functions were also analyzed. MEX3A were surrounded by 20 nodes representing genes that were greatly correlated with the family in terms of physical interactions, coexpression, prediction, colocalization, pathway, genetic interactions, and shared protein domains. From the results (Figure 3), we found that PABPC1, a kind of shuttling protein from the cytoplasm to nucleus in most eukaryotes, was correlated with MEX3A for physical interactions. PABPC1 is important for protein translation initiation and decay by binding to regulatory proteins [21]. In addition, KHDRBS2 was associated with IGF2BP2 and MEX3A in terms of shared protein domains. KHDRBS2 is also an RNA-binding protein that is tyrosine phosphorylated by Src during mitosis [22]. IGF2BP2 was colocalized with STRA6. Further functional analysis revealed that most proteins were greatly correlated with skeletal system development and genitalia development.

3.6. MEX3A Promoted Ovarian Cell Proliferation, Migration, and Invasion In Vitro

To further evaluate the biological functions of MEX3A on ovarian cancer, the expression of MEX3A in different cell lines was tested, and in vitro studies were performed (Figure 6(a)). The ES2 cell line was chosen for further study. We silenced MEX3A expression by siRNA, and a nontargeting siRNA was used as a control. The efficiency was evaluated by Western blotting and RT-PCR (Figures 6(b) and 6(c)). We first studied its influence on OC growth by using CCK8 assay, clone formation assay, and EdU assay. Compared with the normal control group, MEX3A knockdown partly suppressed the proliferation of OC cells (, Figure 6(d)). Similarly, the colony number was significantly smaller than that of the control group (, Figure 6(e)). EdU is a thymidine nucleoside analogue, which is involved in DNA replication when targeting proliferating cells. The proliferation activity of ES2 cells can be analyzed with the number of red/blue fluorescence spots. Figure 6(f) shows that compared with the control group, knockdown of MEX3A significantly inhibits the EdU uptake rate, which also indicates suppressed proliferation ability. Next, we assessed the role of MEX3A knockdown on the migration ability of OC. Transwell assay and wound healing assay were performed, and the results showed that MEX3A knockdown significantly inhibited cell migration in ES2 cells in comparison to the control group (Figures 6(g) and 6(h)). Collectively, these results indicated that MEX3A could promote the proliferation and migration in OC cells.

4. Discussion

OC is usually detected during the late stages; thus, few patients are eligible for timely treatment. Identifying sensitive and specific biomarkers for improving diagnosis and accurately evaluating prognosis continues to be an important research focus. In this study, we explored a novel gene—MEX3A—which is an RNA-binding protein or an E3 ubiquitin ligase acting posttranscriptional regulation, associated with the diagnosis and prognosis of OC. MEX3A has important roles in biological processes. Its expression is associated with intestinal homeostasis by regulating intestinal differentiation and promoting high expression of intestinal stem cell markers (LGR5, BMI1, and MSI1) [23, 24]. Moreover, a few studies have evaluated the effect of MEX3A on tumors. Abnormal activation of MEX3A can promote tumor cell proliferation, metastasis, and migration in gastric cancer and pancreatic ductal adenocarcinoma, breast cancer, and osteosarcoma [6, 2527]. For example, MEX3A may act as a tumor promoter for breast cancer by regulating PIK3CA. Also, MEX3A could combine RIG-I to promote its ubiquitylation and proteasome-dependent degradation, which is beneficial for tumorigenesis [28].

However, the mechanisms of the MEX3A function have yet to be elucidated in OC. To the best of our knowledge, this is the first study that reported the role of MEX3A in OC through bioinformatics analysis of public sequencing data to guide future research in OC.

First, the results of the prognostic analysis showed that upregulation of MEX3A mRNA expression had the greatest correlation with poor OS and PFS in OC patients. In addition, we performed a series of in vitro experiments, which proved the inhibition of OC development by MEX3A knockdown. Hence, we speculate that MEX3A is extremely important as a prognostic indicator in OC patients and can be used as a predictor of tumor proliferation and metastasis. These results are consistent with bladder cancer, lung adenocarcinoma, and glioma [2931]. Liang et al. found that MEX3A could enhance the instability of LAMA2 mRNA to promote lung adenocarcinoma metastasis by the PI3K/AKT pathway. In addition, they reported that MEX3A exerted its ubiquitination role to induce glioma tumorigenesis.

To explore the specific mechanisms of MEX3A in OC, a comprehensive bioinformatic analysis of MEX3A has been performed. Copy number variations (CNVs) have major genomic implications in human diseases, especially cancer, which can lead to phenotypic differences [30]. We found that the major CNV type of MEX3A was amplification, which was associated with shorter survival. Besides, neighboring gene networks close to MEX3A generally showed different degrees of amplification in OC. The genes coexpressed with MEX3A were subjected to functional and pathway enrichment analyses, and the results indicated that they were mainly involved in the immune response processes during tumorigenesis and progression of ovarian cancer.

We also constructed a gene-gene interaction network. The results suggested that MEX3A interacted intensively with other genes, such as PABPC1 and LAMTOR2. PABPC1 has been reported to bind the poly(A) tails of mRNAs, regulating the stability and biofunction of lncRNAs, which have critical roles in OC progression [31, 32]. PABPC1 could promote the binding of hnRNPLL (a plasma cell-specific RBP) to the immunoglobulin mRNA and regulate switching from mIgH to sIgH in plasma cells [33]. Yu et al. reported that PABPC1 could involve innate immune surveillance by regulating the activity of NK cells [34]. LAMTOR2, a regulator/LAMTOR complex member, activates AKT/mTOR to regulate dendritic cell homeostasis [20]. They implied that MEX3A might have an essential role in immunity by combining with PABPC1. Therefore, these results suggested that MEX3A and its related genes together regulate OC progression by a complex regulatory network.

Another important aspect of this study was that MEX3A expression was related to immune infiltration in OC. Our results demonstrated a moderate to a strong relationship between MEX3A expression level and infiltration level of macrophages, neutrophils, dendritic cells (DCs), B cells, and CD8+ T cells. Furthermore, immune cell activation and immunomodulators have been known for reducing mortality rates in patients with OC. In our study, we assessed the correlation between MEX3A and the immune system via the TIMER and TISIDB database, finding that MEX3A had the greatest correlation with lymphocytes (such as B cells, CD8+ T cells, neutrophils, and dendritic cells (DCs) and macrophages), immune inhibitors (such as IL10RB, IDO1, VTCN1, and HAVCR2), immunostimulators (such as C10orf54, TNFRSF18, TNFRSF14, and TNFRSF13C), and MHC molecules (such as B2M HLA-DMA HLA-DPA1, and HLA-DPB1). Therefore, MEX3A, which is associated with these immune-related genes, may provide a new target in immune therapy for OC.

The present study has several limitations. First, most results on the transcriptional level may reflect some aspects of immune infiltration. Also, reported findings need to be confirmed with larger clinical samples and experimental data using molecular biology techniques. Finally, we plan to further deepen our understanding of the underlying mechanism of immunomodulators related to MEX3A in our future work.

In conclusion, this study demonstrated that high MEX3A expression was correlated with poor prognosis and increased immune infiltration levels in macrophages, neutrophils, dendritic cells (DCs), B cells, and CD8+ T cells in OC. Our study provides a novel insight into the potential role of MEX3A as a cancer biomarker from the perspective of tumor immunology.

Data Availability

Previously reported [RNA-Seq] TCGA data were used to support this study and are available at GEPIA (doi: 10.1093/nar/gkx247), cBioPortal (doi: 10.1126/scisignal.2004088), LinkedOmics (doi: 10.1093/nar/gkx1090), and the Kaplan–Meier plotter (doi: 10.1530/ERC-11-0329). These prior studies (and datasets) are cited at relevant places within the text as references [911, 15]. There is no research data used to support this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Authors’ Contributions

Panpan Zhang and Tong Su contributed equally to this work.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (ID 81672564 to Shu Zhang).

Supplementary Materials

Figure S1: the mRNA expression levels of MEX3A in various cancers and prognostic values of MEX3A in ovarian cancer in the Kaplan–Meier plotter. (a) The expression of MEX3A in various cancers from Oncomine. The threshold was designed with the following parameters: and . The color intensity (red or blue) is directly proportional to the significance level of upregulation or downregulation. (b) The MEX3A expression levels in different tumor types from the TCGA database were determined by GEPIA. (c, d) Prognostic significance of MEX3A in serous ovarian carcinoma. (e, f) Prognostic significance of MEX3A in endometrioid carcinoma. , , and . (Supplementary Materials)

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Copyright © 2021 Panpan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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