High Expression of eIF4E Regulates Immune Cell Inltration and Leads to Poor Prognosis in Breast Cancer

Background: The expression and activation of Eukaryotic translation initiation factor 4E (eIF4E) is related to tumor transformation and genesis, but the functional role and the mechanism whereby it drives immune inltration in breast cancer remain uncertain. Methods: Multiple databases were used for assessing the expression and clinic-pathological features of eIF4E, after which ImmuCellAI and TIMER database were used to explore the relationship between eIF4E and immune inltration of breast cancer. Finally, the genes co-expressed with eIF4E were identied by LinkedOmics. These genes were analyzed protein interaction network and TF-miRNA interaction network by Networkanalyst, and enriched by GO and KEGG. The key genes of immune markers were functionally annotated. Results: High expression of eIF4E was associated with poor overall survival (OS) and relapse-free survival (RFS) in breast cancer samples from multiple databases. It is notable that the expression of eIF4E is positively correlated with the inltration of CD8 + T cells, macrophages, neutrophils and dendritic cells. The expression of eIF4E is closely related to many immune markers in breast cancer. Functional analysis of co-expressed genes showed that they were involved in the biological process of human immune response. GO analysis showed that co-expressed immune marker genes were involved in human immune response, adaptive immune response, macrophage activation, extracellular structure organization and regulation of DNA metabolism process. KEGG enrichment showed that it was involved in inammatory bowel disease, cell adhesion molecule pathway, JAK-STAT signal pathway, T cell receptor signal pathway and so on. Conclusions: These results suggest that high expression of eIF4E regulates immune cell inltration, especially promotes macrophage M2 polarization by JAK / STAT6 and PI3K / AKT pathway, which is associated with poor prognosis cancer It cancer


Background
Breast cancer is the most common malignancy in women worldwide and is curable in 70-80% of patients with early, non-metastatic disease. Advanced breast cancer with distal organ metastases is considered incurable with currently available treatments (1).In 2019, about 316,700 new BC cases will be diagnosed in women in the United States, an annual increase of nearly 0.3%. Data from China show that the incidence of BC will be also increasing every year (272,400 cases in 2015 and 367,900 cases in 2018) (2).Breast cancer is considered as composed of at least four different clinically relevant molecular subtypes: luminal A, luminal B, Her2-Enriched, and basal-like type (3).
EIF4E is one of the essential constituent factors of protein initiation factor complex (eukaryotic translation initiation factor4, eIF4F) in eukaryotic protein translation initiation stage. mRNA caps containing 7-methylguanosine are recognized and bound in the early stages of protein synthesis, and ribosome binding is promoted by inducing the release of the secondary structure of mRNA. As a protooncogene, its expression and activation are related to tumor transformation and genesis. Selective translation regulation of mRNA transcripts usually occurs at the beginning, which can be achieved by speci c RNA binding proteins, microRNA and regulating the activity of 5'cap binding eIF4E (4,5). Previous studies have shown that patients with high expression of eIF4E are more likely to relapse and have higher mortality than minimal expression of eIF4E in patients with TNBC (6).
Here, we used Oncomine, PrognoScan and Kaplan-Meier to evaluate the relationship between eIF4E and prognosis. We further considered the relationship between eIF4E and tumor immune cell in ltration using tumor immunoassay resources (ImmuCellAI and TIMER). Our results provide new insights into the functional role of eIF4E in breast cancer. Thus highlighting the potential mechanism of eIF4E affecting the interaction between immune cells and tumors.

Oncomine database analysis
The Oncomine database compiled 86,733 samples and 715 gene expression data sets into a single comprehensive database designed to facilitate data mining efforts (7). We therefore used this database to assess the association between eIF4E expression and prognostic outcome in various cancer types (https://www.oncom ine.org/resource/login.html).

PrognoScan database analysis
The PrognoScan database is designed to facilitate meta-analyses of gene prognostic value by comparing the relationship between gene expression and relevant outcome including overall survival (OS) in a wide range of published cancer microarray data sets (8). We therefore used this database to assess the relationship between eIF4E expression and patient outcome (http://www.abren.net/PrognoScan/).

Kaplan-Meier plotter analysis
The Kaplan-Meier plotter offers a means of readily exploring the impact of a wide array of genes on patient survival in 21 different types of cancer, with large sample sizes for the breast (n=6,234), ovarian (n=2,190), lung (n=3,452) and gastric (n=1,440) cancer cohorts (9).We therefore used this database to explore the association between eIF4E expression and outcome in patients with gastric, breast, ovarian and lung cancer (http://kmplot.com/analysis/).

TIMER database analysis
Page 4/19 TIMER (https://cistrome.shinyapps.io/timer/) is a database designed for the analysis of immune cell in ltrates in multiple cancers. This database employs pathological examination-validated statistical methodology in order to estimate tumor immune in ltration by neutrophils, macrophages, dendritic cells, B cells and CD4 + /CD8 + T cells (10). We initially employed this database to assess differences in eIF4E expression levels in particular tumor types using the TIMER database, and we then explored the association between this eIF4E expression and the degree of in ltration by particular immune cell subsets. Then Kaplan-Meier curve analysis and multi-factor COX proportional hazard model were carried out to explore the effect of immune cell in ltration on the survival rate of breast cancer patients. Finally, the relationship between the expression of eIF4E and the expression of speci c immune in ltrating cell subsets was evaluated.
2.5 GEPIA database analysis GEPIA (http://gepia.cancer-pku.cn/index.html) is an online database that can be used for standardized TCGA and GTEx dataset analysis of tumor samples and control samples (11). GEPIA database was used to evaluate the relationship between the expression of eIF4E and the prognosis of patients, as well as the subgroup analysis of clinical pathological features.

ImmuCellAI database analysis
The ImmuCellAI tool can accurately predict the abundance of 24 kinds of immune cells in the sample, including 18 kinds of T cell subtypes (12), based on the expression data of RNA-Seq or microarray. We used the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) data set GSE109169 to analyze the difference of gene expression between breast cancer tissues and normal tissues adjacent to the cancer, and estimated the immunized cell in ltration abundance by using ImmuCellAI data bank (http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/).

LinkedOmics database analysis
The LinkedOmics database (http://www.linkedomics.org/login.php) is a web-based platform for analyzing 32 TCGA cancer-associated multi-dimensional datasets (13). EIF4E co-expression was analyzed statistically using Pearson's correlation coe cient, presenting in volcano plots, heat maps, or scatter plots. The functional module of LinkedOmics analyzed gene ontological biological process (GO_BP), KEGG pathway, kinase target enrichment, miRNA target enrichment and transcription factor target enrichment by gene set enrichment analysis(GSEA).

Networkanalyst database analysis
Network interpreting gene expression was used by NetworkAnalyst 3.0 tool (https://www.network analyst.ca/) (14), which integrates cell-type or tissue speci c protein-protein interaction(PPI) networks, gene regulatory networks, and gene co-expression networks.

Enrichment analysis of GO and KEGG pathways
The data base (DAVID v.6.8) and the data base(DAVID.ncifcrf.gov/) were used to identify the enrichment analysis (15). The biological process of GO and the enrichment analysis of KEGG pathway were carried out on the key genes of co-expression and immune marker gene cross, and the visualization was made by Cytoscape v3.7.2 software(16) and R4.0.2 language. The P value adjusted by FDR was statistically signi cant.

Human protein Altas database
The expression of eIF4E and related immune markers in breast cancer was veri ed by HPA database (https://www.proteinatlas.org/). The protein expression in 44 major human tissues and some tumor tissues was expressed by the immunohistochemical method (17).

Statistical analysis
Prognoscan, Kaplan-Meier plotter, TIMER and GEPIA databases were used for generating survival plots in respective analyses, with data including either HR and P-values or P-values derived from a log-rank test.
Data from the Oncomine database are presented with information regarding ranking, fold-change and Pvalues. Spearman's correlation analyses were used to gauge the degree of correlation between particular variables, with the following r values being used to judge the strength of correlation: 0.00-0.19 'very weak',0.20-0.39 'weak',0.40-0.59 'moderate',0.60-0.79 'strong',0.80-1.0 'very strong'. P <0.05 was the signi cance threshold.

Expression of eIF4E in different tumors and normal tissues
We rst analyzed the expression of eIF4E in a variety of tumors and normal tissues using Oncomine database, and found that the expression of eIF4E in brain cancer, breast cancer, cervical cancer, colorectal cancer, gastric cancer, head and neck tumor, kidney cancer, lung cancer, lymphoma, ovarian cancer, pancreatic cancer, sarcoma and other tumors was higher than that in normal tissues (P <0.001) ( Figure   1A). We further used the TIMER database to evaluate the differential expression of eIF4E in speci c tumor types ( Figure 1B). The results showed that the expression of eIF4E in invasive breast carcinoma, endometrial carcinoma, cholangiocarcinoma, colonic adenocarcinoma, hepatocellular carcinoma, gastric adenocarcinoma, lung squamous cell carcinoma, lung adenocarcinoma and esophageal carcinoma was signi cantly higher than that in normal controls. The expression of eIF4E in thyroid carcinoma, renal papillary cell carcinoma and renal clear cell carcinoma was signi cantly lower than that in normal control groups. The expression in metastatic skin melanoma was higher than that in skin melanoma (P <0.05).
Further subgroup analysis of multiple clinic-pathological features of TCGA-Breast invasive carcinoma samples in the UALCAN database consistently showed an increase in the transcriptional level of eIF4E.
According to the analysis of sample type, age, subtype of breast cancer, disease stage, lymph node metastasis and TP53 mutation, the expression of eIF4E in breast cancer patients was signi cantly higher than that in normal controls, and the expression of eIF4E in patients aged 61 to 80 was signi cantly higher than that in patients aged 41 to 60, with statistical difference (P =0.037399). In all subtypes of breast cancer, the expression of eIF4E was signi cantly higher than that of normal subjects, and the expression of Luminal type was signi cantly higher than that of Triple negative type (P <0.01), and the expression levels of stage1, stage2 and stage3 in different tumor stages were signi cantly higher than those in normal group. Lymph node metastasis showed that the expression level of N2 was the highest and signi cantly different from that of N0 (P =0.0127978) and N3 (P =0.0169045). TP53 mutation analysis showed that the expression level of TP53 non-mutated group was higher than that of the mutant group (P =0.024296) ( Figure 2). Therefore, according to the expression differences of breast cancer subtypes, tumor stages and lymph node metastasis, the expression of eIF4E can be used as a potential diagnostic index in BRCA.

Relationship between eIF4E expression and prognosis of patients with different tumors
Next, we used the PrognoScan database to explore the relationship between the expression of eIF4E and the prognosis of tumor patients. We found that breast and colorectal cancers were signi cantly associated with the expression of eIF4E ( Figure 3A Figure 3C-G). We further used GEPIA database to evaluate the relationship between the expression of eIF4E and the prognosis of patients, and analyzed 33 tumor types. It was found that the prognosis of high expression of eIF4E was poor in breast cancer, brain low-grade glioma, lung adenocarcinoma, cervical squamous cell carcinoma and adenocarcinoma, hepatocellular carcinoma and lung squamous cell carcinoma, while the low expression of eIF4E in renal clear cell carcinoma, hepatic clear cell carcinoma and colorectal cancer had poor prognosis(Supplement gure1A-I).These results clearly showed that in many tumor types, the expression of eIF4E was signi cantly correlated with poor prognosis, and the high expression of eIF4E in different databases was signi cantly correlated with poor prognosis of breast cancer patients.

Relationship between eIF4E expression and in ltration of immune cells in breast cancer
Gene expression data set GSE109169, related to breast cancer was searched from the comprehensive gene expression database (GEO) to analyze the difference of gene expression between breast cancer and adjacent normal tissues (Supplementary table1).The abundance of immune cell in ltration was calculated by ImmuCellAI database. It was found that in 18 kinds of T cells and other 6 types of immune cells, the in ltration levels of macrophages, nTreg cells, Th1, B cells, CD8 + T cells and γδT cells in breast cancer tissues were signi cantly higher than those in adjacent normal tissues. In ltration levels of Th17 cells, Tfh, NKT cells, monocytes, neutrophils and CD4 + T cells in tumor tissues were lower than those in normal tissues ( Figure 4). This showed that there are a signi cant difference in immune cell in ltration between breast cancer patients and adjacent normal tissues, and different levels of immune cell in ltration have potential effects on the occurrence, development and survival of breast cancer patients.
Since we found that the expression of eIF4E was related to the poor prognosis of patients with breast cancer, we further drew a Kaplan-Meier map using the TIMER database to explore the relationship between immune cell in ltration and the expression of eIF4E to explore its potential mechanism in breast cancer. In breast cancer, eIF4E expression was signi cantly correlated with tumor purity (r =0.134 P =2.21e-05), CD8 + T cells(r =0.268 P =1.51e-17), macrophages (r =0.237 P =5.07e-14), neutrophils(r =0.161 P =6.14e-07) and dendritic cells (r =0.067 P =3.94e-02) ( Figure 5A).
In order to further study the relationship between immune cell in ltration and eIF4E expression in BRCA, we further used TIMER database to generate Kaplan-Meier map. We found that the in ltration of CD8 + T cells (P =0.006), CD4 + T cells (P =0.006), neutrophils (P =0.007) and dendritic cells (P =0.004) was signi cantly correlated with the prognosis of BRCA ( Figure 5B). In addition, the multivariate hazard model was used to evaluate the effect of eIF4E expression in the presence of different immune cell in ltration. The OS risk of eIF4E was 1.482 times higher (P <0.05) ( Figure 5C). This suggested that eIF4E played an important role in regulating immune cell in ltration in breast cancer.

Evaluation of the correlation between eIF4E and the expression of immune markers
Next, we used TIMER databases to further explore the relationship between the expression of eIF4E and the level of immune cell in ltration in breast cancer. We evaluated the correlation between eIF4E  Table 1).The expression of eIF4E in breast cancer was positively correlated with the expression of monocytes, TAM, M1 macrophages, M2 macrophages, Neutrophils, Th1, Th2, Tfh, Th17 and Treg markers, and negatively correlated with CD8+T cells, B cells, dendritic cells and Exhaustion T cell markers. Figure 6A showed the scatter diagram of TAM, M2 macrophages, Th1, Th2, Th17, Treg and Exhaustion T cell markers. Cor, R value of Spearman's correlation; None, correlation without adjustment. Purity, correlation adjusted by purity. *P < .01; **P < .001;***P < .0001. Abbreviations: TAM, tumour-correlated macrophage; Tfh, follicular helper T cell; Th, T helper cell; Treg, regulatory T cell.
In addition, the protein expression level of eIF4E can be discovered by using clinical samples from HPA database. The immunohistochemical images showed that eIF4E shows moderate staining in breast cancer ( Figure 6B). At the same time, we veri ed the expression level of eIF4E signi cantly related immune cell markers in the same breast cancer patients, including TAM markers (IL10), M2 macrophage markers (CD163), Th1 (STAT1), Th2 (GATA3, STAT6), Th17 (STAT3) and Treg markers (STAT5B), in which GATA3, STAT3 and STAT5B were moderately stained and the others were weakly positive. The difference of expression of immune markers in tumor tissues of patients with breast cancer was further discussed.

Analysis of co-expression genes of eIF4E in breast cancer
In order to gure out the biological signi cance of eIF4E in BRCA, the functional module of LinkedOmics was used to check the co-expression pattern of eIF4E in the BRCA cohort. As showed in gure 7A, 5315 genes (dark red dots) were signi cantly positively correlated with eIF4E while 8395 genes (dark green dots) were negatively correlated. The heat map showed the rst 50 important genes positively and negatively correlated with PRPF3 ( Figure. Gene ontology (GO) terminology annotations made through gene set enrichment analysis (GSEA) showed that genes co-expressed by eIF4E were mainly involved in chromosome segregation, RNA localization and DNA replication while extracellular structure organization, human immune response and protein localization to endoplasmic reticulum were inhibited (Figure7D, supplementary table3). KEGG enrichment showed that it was mainly concentrated in ubiquitin-mediated proteolysis, RNA transport, cell cycle and other signal pathways, while ribosome, glycosaminoglycan biosynthesis, cell adhesion molecules and other signal pathways were inhibited (Figure7E, supplementary table4).
In addition, co-expressed network of protein-protein interactions by Differential Net was constructed based on breast-speci c data collected from eIF4E database ( Figure.8A, supplementary table5) (19). Extracellular heat shock protein 90α (HSP90AA1) has been widely reported promoting tumor cell movement and tumor metastasis in many tumors. It has been observed that extracellular heat shock protein 90α can promote EMT and the migration of breast cancer cells in breast cancer (20). YWHAZ binds and stabilizes key proteins involved in signal transduction, cell proliferation and apoptosis (21). Studies have further shown that YWHAZ is involved in drug resistance in breast cancer (22).
Finally, the TF (transcription factor)-mi RNA regulatory interaction of eIF4E co-expressed genes was constructed based on RegNetwork database ( Figure.8B, supplementary table6). The top three TF were upstream stimulating factor1 (USF1), CCCTC binding factor (CTCF) and transcription factor YY1. USF1 related studies have shown that USF1 can transcriptionally up-regulate the expression of FAK in lung cancer, thus activating the FAK signal pathway and promoting cell migration (23). USF1 is involved in the transcription of many proteins and plays an important role as a regulator in many diseases, including tumors (24).Studies have shown that CTCF expression is involved in tumorigenesis (25) and can be used as a transcription factor, to control gene expression by binding to the transcriptional initiation site(TSSs) of many genes (26).Some studies have shown that the binding and overexpression of transcription factor YY1 with BRCA1 promoter inhibits the proliferation and focus formation of nude mice cells and inhibits the growth of MDA-MB-231 tumor. In addition, tissue microarray detected that there was a positive correlation between the expression of YY1 and BRCA1 in human breast cancer (27, 28).

Cross analysis of eIF4E co-expression genes and immune marker genes
We showed that eIF4E co-expression gene GO analysis was involved in human immune-related biological processes, KEGG enrichment also showed that it was involved in the cell adhesion molecule pathway, which related to the expression of cytokines. In order to further explore the relationship between eIF4E coexpression genes and immune in ltration, we made a cross-analysis of 13710 co-expression genes and 30 immune marker genes signi cantly related to eIF4E. The results showed that there were 18 overlapping genes ( Figure 9A). The interaction of these key genes was analyzed by Cytoscape software and GO analysis. The results showed that the key genes were mainly involved in the human immune response, the adaptive immune response, macrophage activation, extracellular structure organization and regulation of DNA metabolic process ( Figure 9C). KEGG analysis showed that these key genes were  Figure 9B). These results suggested that eIF4E co-expression genes were involved in the regulation of tumor immunity and provided strong evidence that eIF4E was an important regulator of immune in ltration in breast cancer.

Discussion
The activity of eIF4E is regulated at several levels, including PI3K (phosphatidylinositol 3-kinase) / AKT (also known as protein kinase B, PKB) / mTOR (mechanical / mammalian target of rapamycin) and RAS (rat sarcoma) / MAPK (mitogen activated protein kinase) / MNK (MAPK interacting kinase (29). EIF4E plays an important role in the whole process of tumor evolution. On one hand, eIF4E over expression changes cell morphology, enhances cell proliferation, and induces cell transformation, tumor formation and metastasis; on the other hand, eIF4E regulates the translation of tumor-related mRNA in many ways, including cell mitosis, activation of proto-oncogenes, angiogenesis, enhancement of autocrine, cell survival, invasion and communication with extracellular environment. Related studies have shown that eIF4E contains the original hypoxia response (HRE), in hypoxia microenvironment HIF1α up-regulates eIF4E, to promote selective mRNA cap-dependent translation (30).In order to better understand the potential function and regulatory network of eIF4E in breast cancer, we conducted a bioinformatics analysis of public data to guide future research on breast cancer.
Analysis of different tumor-related databases con rmed that the levels of eIF4E mRNA in breast cancer were signi cantly higher than those in normal breast tissues (Figure 1). In addition, high expression of eIF4E was signi cantly associated with poor survival and disease-free status in multiple cohorts.
Therefore, our results suggested that eIF4E was up-regulated in many breast cancer cases, which was worthy of further clinical veri cation as a potential diagnostic and prognostic marker.
In order to explore the signaling network controlling the abnormal expression of eIF4E, we draw eIF4E coexpression network. Our results indicate that eIF4E was involved in the biological functions of chromosome segregation, RNA localization and DNA replication, while extracellular structure organization, human immune response and protein localization to endoplasmic reticulum were inhibited. The main pathways involved include ubiquitin-mediated proteolysis, cell cycle, RNA transport, ribosome, Glycosaminoglycan biosynthesis and cell adhesion molecule pathway. These ndings were related to the carcinogenic molecules pathway of breast cancer, which gave a reasonable explanation for the negative correlation between the expression of EIF4E and the 5-year survival rate of patients with breast cancer, and suggested that eIF4E was related to immune response, and gave clues to regulate tumor microenvironment and immune response.
We further found that the expression of eIF4E was related to the expression of several different markers of immune cell subsets in tumors, which highlighted the possible role of eIF4E in tumor immune interaction in breast cancer, making it a valuable biomarker worthy of further study. We found that the expression of eIF4E was positively correlated with the degree of CD8 + T and macrophage in ltration in breast cancer, and weakly positively correlated with the degree of DC and neutrophil in ltration ( Figure  5A). In addition, the correlation between the expression of eIF4E and some immunological marker genes strongly suggested that eIF4E can control the in ltration and interaction of immune cells in the tumor microenvironment in breast cancer. We observed a positive correlation between eIF4E and TAM, M2 macrophage markers (including CCL2, CD68, IL10, CD163, VSIG4 and MS4A4A) ( Table 1), indicating that eIF4E has a role in regulating TAM polarization. TAM is transformed into M2 to enhance tumor angiogenesis in advanced tumors (31). Previous studies have demonstrated that IFN-γ can regulate the metabolism and translation of human macrophages by targeting kinases mTORC1 and MNK, both of which act on the selective regulators of translation initiation eIF4E (5). Because of the correlation between the expression of eIF4E and macrophage related genes, we inferred that the expression of eIF4E was involved in macrophage in ltration. We further found that eIF4E levels in breast cancer were associated with markers of Treg cell and Exhaustion T cell (FOXP3, CCR8, STAT5B, PD-1, LAG3 and TIM-3) (Table 1). Related studies have shown that Tim-3 promotes liver cancer through NF -κ B/IL-6/STAT3 axis(32); FOXP3 is essential to maintain Treg inhibition function (33). This suggested that eIF4E might inhibit T-cell-mediated immunity by promoting Treg response. In addition, eIF4E expression was considered to be associated with the expression of multiple T cell subsets (Th1, Th2, Tfh, and Th17) in breast cancer. This might suggest the ability of eIF4E to regulate T cell response in breast cancer. As an immune marker of Th17, STAT3 expression was signi cantly positively correlated with eIF4E ( Figure 6).
Related studies have shown that some signal molecules are involved in M2 polarization of macrophages, such as PI3K/ AKT-ERK signal, STAT3, HIF1α, STAT6 and so on. M2 macrophages promote the progression of breast cancer through ERK/STAT3 regulation (34). Consistent studies have shown that eIF4E was regulated by the PI3K/ AKT /mTOR pathway to promote the translation of cytokines (35).
The overlapping gene function and pathway enrichment of co-expressed genes and immune marker genes signi cantly related to eIF4E also showed that they were involved in tumor-related pathways such as cell adhesion molecule pathway, JAK-STAT signal pathway, immune-related biological processes such as adaptive immune response, macrophage activation, extracellular structure and regulation of DNA metabolism. Taken together, these results highlighted the potential ability of eIF4E to regulate the recruitment and activation of immune cells in breast cancer.
Our results showed that the expression of eIF4E was positively correlated with STAT4 and STAT6, suggesting that eIF4E has a potential role in promoting M2 polarization of macrophages. Macrophage polarization is a complex process of multi factor interaction, which is regulated by a variety of intracellular signaling molecules and pathways, including JAK-STAT and PI3K/AKT signaling pathways.
M2 macrophages play an important role in angiogenesis, secretion of anti-in ammatory factors, tissue repair and wound healing. Studies have shown that M2 macrophages can promote tumor progression (36). STAT6 is the main signal of IL-4-mediated signaling pathway, and JAK / STAT6 pathway is the main pathway from the cell membrane to the nucleus, which ultimately regulates the gene expression in the nucleus and mediates the differentiation and maturation of M2 cells (37).Th2 can activate macrophages by secreting IL-4 and / or IL-13 through signal transducers and activators of transcription 6 (STAT6) signal, which makes them have completely different characteristics from the classical activation (38,39).
At the same time, PI3K / AKT pathway regulates the survival, migration and proliferation of macrophages, but also coordinates the response of macrophages to different metabolic and in ammatory signals (40). PI3K activation has been reported as an essential step toward M2 activation of macrophages in response to surfactant protein A or IL-4. AKT activation is necessary for M2 activation, because AKT inhibition eliminates the up-regulation of M2 genes (41). EIF4E is downstream of PI3K / AKT pathway, which further suggests that eIF4E has potential promoting effect on M2 polarization of macrophages.

Conclusions
In conclusion, high expression of eIF4E regulates immune cell in ltration, especially promotes macrophage M2 polarization by JAK / STAT6 and PI3K / AKT pathway, which is associated with poor prognosis of breast cancer patients. EIF4E is a valuable prognostic biomarker and may be used as a potential therapeutic target in the future. These ndings need further research on breast cancer genomics and function.