Background
The morbidity of renal cell carcinoma (RCC) is second only to prostate cancer and bladder cancer [
1] among urinary system malignancies, and it has a high mortality and recurrence rate. It is estimated that RCC is the seventh most common cancer in men and the ninth in women, with 48,780 newly diagnosed cases and 27,300 new cases of RCC-related mortality in the United States [
2]. Overall, the worldwide incidence has increased by 2% per year during the last 2 decades [
3]. Clear cell RCC is the most frequent renal cell carcinoma (ccRCC), accounting for approximately 80–90% of all kidney cancers [
4]. Surgery is still the most curative treatment for localized RCC, and there are therapeutic approaches as alternatives to surgery, including embolization, ablative therapies, targeted therapies, immunotherapy and adjuvant therapy [
5]. However, the treatment results are not satisfactory, and the mortality rates are stubbornly high. Therefore, the identification of biomarkers for ccRCC can improve its prognostic systems, which urgently needs to be addressed.
N6-methyladenosine (m6A), an epigenetic modification, is the most prevalent methylation in eukaryotic mRNAs and was first discovered in 1974 [
6,
7]. With the rapid development of high-throughput sequencing technology and further research, m6A was found to exist in various types of RNAs [
8]. Subsequently, m6A was discovered to be involved in various aspects of RNA metabolism, including pre-mRNA splicing, 3′ end processing, nuclear export, translation regulation, mRNA decay, and noncoding RNA (ncRNA) processing [
9]. Recent reports have shown that it is closely related to the regulation of gene expression at the posttranscriptional level, biological development, and human diseases, especially tumorigenesis and progression [
10].
Meanwhile, m6A modification marks a new direction for oncotherapy [
11], and the differential expression of m6A regulators in different types of tumours can significantly affect the prognosis of patients [
12]. The regulation of m6A modification is a dynamic and reversible process. M6A methyltransferases are called “Writers”, such as methyltransferase-like protein 3/14 (METTL3/14), wt1-associated protein (WTAP), and vir-likem6A methyltransferase associated protein (VIRMA). Fat mass and obesity-associated protein (FTO) and alkylation repair homologue 5 (ALKBH5) can remove the m6A mark and induce demethylation, called Erasers”. All kinds of Readers can bind to the m6A modification site in RNA and thereby have different effects on targeted mRNAs, including the YT521-B homology (YTH) domain family, heterogeneous nuclear ribonucleoproteins (HNRNPs) and insulin-like growth factor 2 mRNA-binding proteins (IGF2BPs, including IGF2BP1/2/3). Previous studies have mentioned the influence of the expression and impact of m6A-related genes in ccRCC [
13,
14]. We collected 29 m6A-related genes, and the EIF3A gene was selected to study its role in ccRCC.
EIF3A, a “writer”, is the largest subunit of EIF3, which is a critical factor in translation initiation. EIF3A can bind with the 5′UTR to promote the translation of cap-independent mRNAs [
15]. Evidence suggests that EIF3A is a proto-oncogene involved in tumorigenesis and metastasis in the lung [
16], colon [
17], stomach [
18] and urinary bladder [
19]. The expression of EIF3A can influence cancer cell growth, and the malignant phenotype of cancer cells can be reversed by knocking down EIF3A [
20].
A previous study used whole exome sequencing to identify mortality-related somatic mutations in ccRCC, and subsequent validation of the results showed that only SIPA1L2 and EIF3A were associated with the ccRCC prognosis out of 138 prioritized genes, which can better evaluate the impact on ccRCC patient mortality. [
21]. EIF3A can affect the resistance to some anticancer drugs, whether knocked down or overexpressed [
22]. In general, high expression of EIF3A being associated with better survival is not consistent with what we recognize as a proto-oncogene, and its mechanisms of action in cancer tumorigenesis and prognosis remain unknown.
Hence, we investigated the correlation between EIF3A expression and clinical and pathological characteristics and the prognostic value of EIF3A. Gene set enrichment analysis (GSEA) and GEPIA were undertaken. Furthermore, the Linkedomics and STRING datasets were utilized to analyse coexpression and visualized via Cytoscape. Finally, the relationship of EIF3A expression and infiltration of immune cells and marker gene expression in ccRCC was researched by Tumour Immune Estimation Resource (TIMER).
Materials and methods
Dataset acquisition
The RNA-seq transcriptome data and clinicopathological information from 539 ccRCC samples and 72 normal samples were retrieved from the TCGA database (
https://portal.gdc.cancer.gov/). A total of 29 m6A-related genes were selected (METTL3, METTL14, METTL13, WTAP, RBM15, RBM15B, ZC3H13, NSun2, MTCH2, CBLL1, ALKBH3, FTO, ALKBH5, YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, HNRNPA2B1, HNRNPC, HNRNPG, LRPPRC, FMR1, IGF2BP1, IGF2BP2, IGF2BP3, EIF3A, NKAP, and KIAA1429). The RNA-seq data underwent normalization.
Analyses of the association between EIF3A expression and clinical, pathological characteristics
Logistic regression analyses and independent sample t-tests were utilized to analyse the correlation between EIF3A expression and the clinical and pathological characteristics of ccRCC. According to the median EIF3A expression, patients were divided into a high-expression group and a low-expression group. Then, we assessed the survival difference between the groups by the Kaplan–Meier (K–M) method and the log-rank test. Based on the receiver operating characteristic curves (ROCs) and the area under the curve (AUC), we evaluated the specificity and sensitivity of EIF3A.
Immunohistochemistry (IHC) and colon, pancreatic cancer tissue and human ccRCC tissue arrays
Human ccRCC tumour tissue arrays were purchased from Shanghai Superchip (Biochip Lot No. XT15-050, CGt No. HKidE180Su02, website address:
http://www.superchip.com.cn/biology/tissue.html, Shanghai, China), and 150 ccRCC and 30 corresponding nontumour tissues were purchased from BioChip (Shanghai, China). Pancreatic and colon cancers and their adjacent tissues were paraffin embedded tissues, and we sectioned them (The study protocol was approved by the ethics committee of The First Affiliated Hospital of An Hui Medical University and a written informed consent was obtained from all participants involved in this study). The tissue array sections and paraffin embedded tissues were dehydrated and subjected to peroxidase blocking by H
2O
2. Then, heat-mediated antigen retrieval was performed using citrate buffer. After treating the tissue arrays with 5% BSA for 20 min at room temperature, anti-EIF3A antibody was added and incubated at room temperature for 1 h. After washing with PBS, the sections were subjected to indirect immunohistochemistry using HRP-labelled goat antirabbit IgG (Thermo Scientific). Next, DAB substrate (ab80437, Abcam) was added and incubated for 1–10 min. The tissue array sections were counterstained with haematoxylin. Images were taken with a microscope. The mean proportion of stained cells per specimen was determined semiquantitatively and scored as follows: 0 for staining 0–1%, 1 for 1–25%, 2 for 26–50%, 3 for 51–75%, and 4 for > 75% of the examined cells. The staining intensity was graded as follows: 0, negative staining; 1, weak staining; 2, moderate staining; and 3, strong staining. The histological score (H-score) for each specimen was computed by the formula: H-score = Proportion score × Intensity score. Overall scores of < 6 and ≥ 6 were defined as negative and positive, respectively [
23].
Deparaffinize and rehydrate: incubate sections in 2 changes of xylene, 15 min each. Dehydrate in 2 changes of pure ethanol for 5 min, followed by dehydrate in gradient ethanol of 85% and 75% ethanol, respectively, 5 min each. Wash in distilled water. Antigen retrieval: immerse the slides in EDTA antigen retrieval buffer (pH 8.0) and maintain at a sub-boiling temperature for 8 min, standing for 8 min and then followed by another sub-boiling temperature for 7 min. Be sure to prevent buffer solution evaporate. Let air cooling. Wash three times with PBS (pH 7.4) in a Rocker device, 5 min each. Use the right antigen retrieval buffer and heat extent according to tissue characteristics. Circle and Serum blocking: eliminate obvious liquid, mark the objective tissue with liquid blocker pen. Add 3% BSA to cover the marked tissue to block non-specific binding for 30 min. Cover objective area with 10% donkey serum (for the case of primary antibody originated from goat) or 3% BSA (for the case of primary antibody originated from others). Primary antibody: throw away the blocking solution slightly. Incubate slides with primary antibody (diluted with PBS appropriately) overnight at 4 ℃, placed in a wet box containing a little water. Secondary antibody: wash slides three times with PBS (pH 7.4) in a Rocker device, 5 min each. Then throw away liquid slightly. Cover objective tissue with secondary antibody (appropriately respond to primary antibody in species), incubate at room temperature for 50 min in dark condition. DAPI counterstain in nucleus: wash three times with PBS (pH 7.4) in a Rocker device, 5 min each. Then incubate with DAPI solution at room temperature for 10 min, kept in dark place. Spontaneous fluorescence quenching: wash three times with PBS (pH 7.4) in a Rocker device, 5 min each. Add spontaneous fluorescence quenching reagent to incubate for 5 min. Wash in running tap water for 10 min. Throw away liquid slightly, then cover slip with anti-fade mounting medium. Microscopy detection and collect images by Fluorescent Microscopy. DAPI glows blue by UV excitation wavelength 330–380 nm and emission wavelength 420 nm; FITC glows green by excitation wavelength 465–495 nm and emission wavelength 515–555 nm; CY3 glows red by excitation wavelength 510–560 nm and emission wavelength 590 nm.
Univariate and multivariate cox hazard regression analyses
The independent prognostic factors were identified by univariate and multivariate Cox hazard regression, and ROC curves and AUC values of these eight factors were calculated, including EIF3A expression level, grade, T stage, N stage, M stage, age and sex.
Nomogram predict survival probabilities and risk score
To predict 1-year, 3-year, and 5-year survival rates, we carried out visualization of the correlation between OS and various factors through the R “rms” package. The risk score (RS) was estimated by using the formula: Risk score = coefficient1 × EIF3A + coefficient N × clinical characteristics N. By means of the Kaplan–Meier (K–M) method, the log-rank test and ROC analyses, we determined whether the survival differences between two groups based on the median of the risk score were significant.
Gene set enrichment analysis (GSEA)
GSEA is a computational method that compares the concordant differences between two groups (high expression and low expression) [
24]. In the present study, a hallmark gene set was used to explore the potential mechanism and discover significant critical biological pathways of EIF3A expression in ccRCC. In general, it was considered to be significant when gene sets had a false discovery rate (FDR) < 0.25, absolute value of the normalized enrichment score (NES) ≥ 1.0, and normalized P < 0.05.
Analyses of coexpressed genes
We used the LinkedOmics database to screen out genes that were coexpressed with EIF3A in ccRCC by Pearson’s correlation, and the results are presented as heatmaps and volcano plots [
25]. To undertake functional annotations for coexpressed genes, the Gene Ontology (GO) database and KEGG database were used in Metascape, and the results are shown as bubble charts. Then, by means of the STRING database, we established the potential protein–protein interactions (PPIs) of coexpressed genes. PPI pairs were extracted with a minimum interaction score of 0.4, and the PPI network was visualized by Cytoscape 3.7.2. CytoHubba Plugin was used to identify the top 10 core genes in the gene interaction network and PPI network according to the degree score of each gene node.
Immune cells infiltration
The relationship between EIF3A expression in ccRCC and the infiltration of immune cells, including B cells, CD4 + T cells, CD8 + T cells, macrophages, neutrophils, and dendritic cells (DCs), and tumour purity was analysed by using the Timer “Gene” module. In addition, the correlation between the expression of differential MMUNE cells and marker genes was analysed by correlation modules.
Statistical analysis
All statistics and data were analysed by using SPSS 23.0 (IBM, Chicago, USA), R 4.05 (
https://www.rproject.org/) and GraphPad Prism 8.0 (San Diego, CA, USA). The correlation between two different genes was analysed by the Pearson correlation method. To assess the associations between clinicopathological parameters and EIF3A, we used the chi-square test and logistic regression. We evaluated the diagnostic efficacy of EIF3A expression and RS by using Kaplan–Meier plotter and the log-rank test. Cox regression analysis was used to evaluate factors associated with overall survival (OS) and disease-free survival (DFS). The R statistical packages were used to draw the nomogram. All statistical results with P < 0.05 were statistically significant.
Discussion
Recently, an increasing number of studies have focused on m6A interactions in cancer [
30]. A potential role of m6A methylation in tumorigenesis and progression has been well documented [
31]. Clear cell renal cell carcinoma (ccRCC) is the most common histological type of RCC, and several M6a-related genes have been shown to be associated with OS and/or DFS in ccRCC [
32‐
34]. EIF3A is a highly conserved gene that may also be involved in the regulation of cellular, physiological, and pathological processes, not only in cancer [
35]. However, EIF3A, as a “reader”, has hardly been mentioned in ccRCC. The expression of EIF3A is different from that of other genes, being expressed at a low level in normal tissues, increases significantly in the presence of cancer, and decreases again in high-grade tumours [
35]. EIF3A may be essential for the maintenance of the malignant status of cells and thus affects the prognosis [
21]. Hence, we systematically investigated the prognostic significance of EIF3A in ccRCC.
In this study, we investigated the relationship between EIF3A expression, clinicopathological parameters and patient survival outcomes based on the TCGA database and ccRCC tissue array. The results revealed that the correlation between EIF3A and ZC3H13 was the highest among 29 m6A-related genes; 16 genes were upregulated and 13 genes were downregulated in ccRCC. These discrepant EIF3A expression levels in different cancers are the result of different underlying mechanisms with distinct biological properties, and it has been demonstrated that high expression of EIF3A is associated with cell proliferation, colony formation, wound healing, migration and invasion in lung, urinary bladder and pancreatic cancer cells [
17,
19,
20]. In clear cell renal cell carcinoma, EIF3A expression is lower in the tumour tissue. Additionally, high EIF3A expression was significantly associated with better pathologic stage, histological grade, T stage, and M stage. At the protein expression level, the IHC results revealed that EIF3A staining was weaker in ccRCC tissues than in normal tissues (Fig.
2d). Moreover, the overall survival was related to the clinical stage, grade, M stage, age and EIF3A expression (Table
3). Univariate and multivariate Cox regression analyses demonstrated that EIF3A expression was associated with a poor prognosis in patients with renal cancer. Interestingly, EIF3A was relatively downregulated in ccRCC and negatively correlated with the degree of malignancy of the tumour. For patient prognosis, analysis of EIF3A in Kaplan–Meier analyses with log-rank tests indicated that decreased EIF3A expression was related to an unfavourable prognosis in ccRCC (OS and DFS). Therefore, survival analysis of our own microarray samples was performed. Furthermore, the AUC for RS was 0.6202, a convincing prognostic value for overall survival of ccRCC patients.
Overall, DNA synthesis decreased by approximately 50% when antisense cDNA was used to reduce EIF3A expression [
20]. In another study, inhibition of EIF3A expression increased epidermal growth factor (EGF) stimulation of DNA synthesis [
36]. Multiple studies have shown that low EIF3A subunit expression reduces ribonucleotide-reductase M2 [
20] expression and stimulates p27kip1 synthesis [
20] and N-myc downstream regulated gene-1 (NDRG1) [
37]. The conclusions of the above studies suggest that YTHDF2 plays a dual and complex role in tumour cells. Therefore, GSEA was also conducted to explore how EIF3A participates in ccRCC pathogenesis, and the results revealed that the pathways with strong correlations included renal cell carcinoma, endometrial cancer, adherens junctions, rab guanyl nucleotide exchange factor activity, response to hepatocyte growth factor, and response to hepatocyte growth factor.
The results of coexpression analyses revealed that there was a strong positive correlation between EIF3A expression and LTV1 expression. LTV1 is one of many assembly factors (AFs) and it is involved in assembling the small (40S) ribosomal subunit [
38,
39]. Because of the increasing demand for protein synthesis, the ribosomal assembly pathway is upregulated in all cancers [
40,
41]. In one study, it was shown that LTV1 was substoichiometric in breast cancer cells, producing reduced RPS10 and RACK1 ribosomes [
42]. Furthermore, knockdown of LTV1 attenuated SR-3029-induced apoptosis in MDA-MB-231 breast cancer cells [
43] and restored drug sensitivity [
43,
44]. Therefore, we compared the differential LTV1 expression in ccRCC tissues and paracancerous tissues, finding that LTV1 expression was significantly higher than that in nontumour tissues. In view of the results of the survival analysis, high expression of LTV1 was associated with poor survival outcomes. We hypothesized that LTV1 and EIF3A could jointly promote the tumorigenesis of clear cell renal cell carcinoma and significantly affect the prognosis.
Another important finding of this study is that EIF3A is associated with the degree of immune invasion in various tissues. The expression of EIF3A was correlated with various immune cells to different degrees, among which its expression was moderately positively correlated with macrophages and neutrophils and weakly positively correlated with the B cells, CD8 + , CD4 + , DCs and neutrophils. We found that the correlation between EIF3A and M1/M2 macrophage markers, including PTGS2, IRF5, CD163, VSIG4 and MS4A4A, and markers of M1 macrophages was stronger than that of markers of M2 macrophages. Moreover, EIF3A was related to TAM markers, which suggested a potential regulatory role of YTHDF2 in TAM polarization. This study also found that the two most closely positively related markers were STAT3 (markers of Th17) and STAT5B (markers of Tregs), which indicated that EIF3A could activate and stimulate Tregs and Th17 cells. In addition to these two T cells, there are multiple markers of other T cells associated with EIF3A expression, including Th1, Th2 and Tfh cells. Moreover, Tim-3, a key gene in T cell exhaustion, was positively correlated with the expression of EIF3A, but negative correlations were found for T cell exhaustion markers, including PD-1, LAG-3 and GZMB, which demonstrated that the potency of EIF3A to induce infiltration of T cell exhaustion may not unidirectionally promote or suppress T cell-mediated immunity. Therefore, it is reasonable to surmise that EIF3A has an important role in regulating immune cell recruitment and activation in ccRCC.
Taken together, we identified for the first time genetic alterations in EIF3A in ccRCC and found a clear relationship between alterations leading to an increase in EIF3A levels and worse clinical characteristics, including survival. EIF3A is a crucial regulator of m6A modifications, tumorigenesis and progression. The results of this study may provide a potential direction and new insights into the pathogenesis of M6A-related genes in ccRCC, which are conducive to the development of new targeted drugs. Our results call for further experimental studies for validation and to clarify the mechanism by which EIF3A affects ccRCC.
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