Prognostic and Immunological Role of FCER1G in multiple cancers

doi:10.21203/rs.3.rs-1527691/v1

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Prognostic and Immunological Role of FCER1G in multiple cancers

https://doi.org/10.21203/rs.3.rs-1527691/v1

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The high-affinity IgE receptor gamma subunit gene (FCER1G) plays a pivotal role in allergic inflammatory signaling, which is closely associated with allergic reactions. Previous studies have shown that FCER1G is an innate immunity gene and is involved in the development of many diseases. However, the role of FCER1G in pan-cancer has not been fully studied. This study aimed to explore the prognostic value of FCER1G in pan-cancer and investigate the relationship between FCER1G expression and immune infiltration. We analyzed FCER1G mRNA expression in the Oncomine, TIMER, and GEPIA databases. We used GEPIA, PrognoScan, and Kaplan-Meier Plotter to analyze the prognostic value of FCER1G. We explored the correlations between FCER1G expression and immune infiltration in TIMER databases. Cytoscape was used to perform pathway enrichment and functional enrichment analyses. Spearman method was used to determine the statistical significance.FCER1G can act as a prognostic marker for pan-cancer, FCER1G expression can affect patients' prognosis and correlate with immune cells infiltration. In kidney renal clear cell carcinoma and thyroid carcinoma, FCER1G is closely associated with different immune cells infiltration states and tumor purity, and immune infiltration can interact with FCER1G-mediated activities both in tumor cells and immune cells. FCER1G can be used to predict the efficacy of immunological checkpoint therapy among these types of tumors patients. Our study also provides an important basis for the clinical use of FCER1G to assess the patient immune status and the selection of individualized immunotherapy options.

FCER1G

pan-cancer

prognosis

immune infiltration

tumor microenvironment

The emergence of new treatment methods has changed cancer treatment considerably. The use of new anticancer drugs with improved efficacy and therapeutic effects has prolonged the survival of cancer patients. However, the efficacy of anticancer drugs is lower in progressive cancer patients than in early-stage cancer patients. Additionally, many cancers are resistant to anti-cancer drugs, including the recently developed immune checkpoint inhibitors; thus the choice of treatment methods and drugs is limited.

In addition to cancer cells, the tumor tissue is composed of vascular endothelial cells, fibroblasts, and immune cells, similar to normal visceral organs. However, the tumor tissue environment differs from that of a normal tissue, forming a tumor tissue-specific environment known as the tumor microenvironment (TME). Various cellular components in the TME facilitate immune evasion in tumors. As a novel anticancer treatment, immunotherapy targeting interactions between infiltrating immune cells and tumor cells has been developed recently. However, only a few patients respond satisfactorily to this treatment. Therefore, there is an urgent need to identify new targets.

The High-affinity IgE receptor gamma subunit gene (FCER1G), also named FcRγ, is a protein coding gene located on chromosome 1q23.3 [1]. As a component of the high-affinity immunoglobulin E (IgE) receptor, FCER1G mediates allergic inflammatory signaling in mast cells [2], selectively mediates interleukin 4/IL4 production by basophils, and priming T-cells toward effector T-helper 2 subset [3,4]. FCER1G associates with pattern recognition receptors CLEC4D and CLEC4E to form a functional signaling complex in myeloid cells, and binds to mycobacterial trehalose 6,6'-dimycolate (TDM) to lead phosphorylation of ITAM, triggering activation of SYK, CARD9, and NF-kappa-B, consequently driving the maturation of antigen-presenting cells and shaping antigen-specific priming of T-cells toward effector T-helper 1 and T-helper 17 cell subtypes. It is also involved in integrin alpha-2/ITGA2-mediated platelet activation. Previous studies have suggested that FCER1G is an innate immunity-related gene involved in the development of eczema, meningioma, and childhood leukemia [5-7]. However, the role of FCER1G in cancer remains unclear. Only a few studies explore the function of FCER1G in tumorgenesis before. Pauline et al. found that FCER1G plays a key role in squamous carcinogenesi, which is essential for the chronic inflammatory process [8]. Yutaka et al. reported that FCER1G activates apoptotic signaling pathways to induce cell death [9]. Chen et al. suggested that FCER1G affects clear cell renal cell carcinoma progression via immune-related pathways and targeting it may improve patient prognosis [10]. Therefore, FCER1G expression may affect patient survival through the above-mentioned pathways, and the role of FCER1G in tumorigenesis and cancer development warrants further studies. Accordingly, we aimed to explore whether FCER1G expression could serve as a prognostic biomarker for several cancers as well as for patient immune status assessment.

In this study, we analyzed the expression of FCER1G and depicted the prognostic landscape of FCER1G in pan-cancer using public bioinformatics databases like ONCOMINE, PrognoScan, GEPIA, and Kaplan-Meier Plotter databases. We also explored the potential relationships of FCER1G expression with tumor‐infiltrating immune cells and immune molecules using the GEPIA and TIMER databases. The results from this study indicated that FCER1G affects the prognosis of tumor patients through interaction with infiltrating immune cells.

mRNA and protein expression levels of FCER1G in multiple tumors

The Oncomine online database was used to analyze the mRNA expression of FCER1G in different types of solid tumors compared with adjacent normal tissues. The results showed that FCER1G expression was upregulated in bladder, CNS, breast, esophageal, head and neck, renal, lymph system cancer, and some other cancers (mainly germ cell tumors) tissues compared with normal control tissues in the Oncomine database. On the other hand, the expression of FCER1G was found lower only in colorectal cancer, lung cancer, and myeloma (P＜0.0001, fold change: 2) (Figure 1A).

Moreover, we used the TIMER online database to examine the mRNA expression of FCER1G in tumors and paracancerous tissues from TCGA across diverse cancer types. Wilcoxon test was used to evaluate the statistical differential expressions. The results revealed that FCER1G expression was significantly higher in BRCA (breast invasive carcinoma), ESCA (esophageal carcinoma), GBM (glioblastoma multiforme), HNSC (head and neck cancer), KIRC (kidney renal clear cell carcinoma), KIRP (kidney renal papillary cell carcinoma), STAD (stomach adenocarcinoma), THCA (thyroid carcinoma), and UCEC (uterine corpus endometrial carcinoma) than in adjacent normal tissues. On the other hand, compared to the normal tissues, FCER1G expression was lower in colorectal cancer, lung cancer, liver hepatocellular carcinoma, pancreatic adenocarcinoma, and skin cutaneous melanoma (*: p-value < 0.05; **: p-value <0.01; ***: p-value <0.001) (Figure 1B).

In addition, the GEPIA database was used to analyze the mRNA expression of FCER1G in different types of tumors and matched normal tissues extracted from TCGA normal and GTEx data. The results showed that the expression of FCER1G was significantly higher in CESC (cervical and endocervical cancer), ESCA, GBM, HNSC, KIRC, KIRP, LAML (acute myeloid leukemia), LGG (lower grade glioma), OV (ovarian serous cystadenocarcinoma), PAAD (pancreatic adenocarcinoma), SKCM (skin cutaneous melanoma), STAD, and TGCT (testicular germ cell tumors) than in the paired normal samples, lower expression in LUAD (lung adenocarcinoma), and LUSC (lung squamous cell carcinoma) (|log2FC| > 1.0, P < 0.01) (Figure 1C).

We further analyzed the protein expression of FCER1G in various tumors using The Human Protein Atlas database. This database uses immunohistochemical technology to provide the distribution and expression of human proteins in the tissues and cells of tumors and normal tissues. The results showed that FCER1G protein expression was upregulated in renal carcinoma, thyroid carcinoma, and pancreatic carcinoma tissues compared with normal tissues (Figure 1D-F).

Prognostic value of FCER1G in diverse cancer types

In the part of survival analysis, we first used the GEPIA database to analyze the overall prognostic value of FCER1G in pan-cancer data from TCGA. In general, the high expression of FCER1G was detrimental across all tumor types (overall survival (OS): total number=3011, HR=1.1, Log rank P=0.0051; disease-free survival (DFS): total number=4750, HR=1.2, Log rank P=2.1e-06) (Figure 2A).

Next, we explored the correlation of FCER1G expression with the prognosis in diverse types of tumors in PrognoScan. The results showed that increased FCER1G expression was associated with poor OS and RFS in lung cancer (OS: total number=204, HR=2.85, Cox P=0.001285; relapse-free survival (RFS): total number=204, HR=2.46, Cox P=0.000177), head and neck cancer (total number=28, HR=2.17, Cox P=0.027855) and breast cancer (total number=159, HR=2.06, Cox P=0.026952) (Figure 2B-D).

In addition, we used Kaplan-Meier Plotter, which is sourced from databases that include GEO, EGA, and TCGA, to investigate the relationships between FCER1G expression and the prognostic role in a variety of tumors. The results revealed that FCER1G played a protective role in CESC (OS: total number=304, HR=0.59, 95% CI from 0.37 to 0.94, Log rank P=0.025), THCA (OS: total number=502, HR=0.33, 95% CI from 0.12 to 0.88, Log rank P=0.02) and UCEC (OS: total number=542, HR=0.54, 95% CI from 0.34 to 0.86, Log rank P=0.0087) (Figure 3A-C). Moreover, high FCER1G expression was associated with poor OS in ESCA (OS: total number=81, HR=2.89, 95% CI from 1.13 to 7.42, Log rank P=0.021), KIRC (OS: total number=530, HR=1.8, 95% CI from 1.33 to 2.44, Log rank P=0.00012), LIHC (OS: total number=370, HR=1.69, 95% CI from 1.17 to 2.42, Log rank P=0.0042) and PAAD (OS: total number=177, HR=1.66, 95% CI from 1.07 to 2.57, Log rank P=0.023) (Figure 3D-G).

Gene functional enrichment and pathway enrichment analysis of FCER1G

We obtained sixty mRNAs co‐expressed with FCER1G in eight types of tumors (BRCA, CESC, ESCA, HNSC, KIRC, PAAD, THCA, UCEC) based on the correlation between FCER1G and OS by GEPIA. KEGG and GO analysis were visualized using ClueGO and CluePedia apps on Cytoscape software to analyze the gene pathway enrichment and functional enrichment. The results showed that everal genes were enriched in various immune-related pathways in BRCA, CESC, ESCA, HNSC, KIRC, PAAD, THCA, and UCEC, such as negative regulation of leukocyte activation, regulation of mononuclear cell migration, immune receptor activity, and macrophage differentiation (P ≤ 0.05) (Figure 4A-C).

FCER1G expression in a stratified KIRC population

We next chose KIRC to investigate the correlation between FCER1G expression and clinical features. The results showed that FCER1G was detrimental in KIRC patients with characteristics of White (n=459, HR=1.36, 95% CI from 1.86 to 2.55, P=8.5e-05) and those with characteristics of stage 1 (n=265, HR=1.83, 95% CI from 1 to 3.35, P=0.045), stage 4 (n=82, HR=2.18, 95% CI from 1.28 to 3.71, P=0.0032), and low mutation burden (n=164, HR=3.79, 95% CI from 1.70 to 8.42, P=4.6e-04) for OS. Both female (n=186, HR=2.19, 95% CI from 1.32 to 3.63, P=0.0018) and male (n=344, HR=1.76, 95% CI from 1.18 to 2.62, P=0.0048) experienced poor OS in KIRC patients. Contrastingly, FCER1G expression exerted a protective effect on the OS of KIRC patients exhibiting only stage 3 characteristics (n=123, HR=0.27, 95% CI from 0.47 to 0.83, P=0.0075) (Figure 5).

Contradictory results for KIRC and THCA in correlations between FCER1G and immune cells infiltration

The results above revealed that FCER1G can play a prognostic role in multiple kinds of tumors. Since the tumor immune microenvironment could also have an important impact on the survival prognosis of tumor patients, we then used the TIMER database to explore the correlation of FCER1G expression with tumor purity and the degree of infiltration of CD8+T cells, CD4+ T cells, B cells, dendritic cells (DCs), neutrophils, and macrophages in the above ten types of tumors. The results showed that FCER1G expression in 9 of these 10 types of tumors,except THCA, was significantly correlated with tumor purity. Additionally, there was a significant correlation between FCER1G expression and the degree of infiltration of CD8+T cells, CD4+ T cells, B cells, DCs, neutrophils, and macrophages in CESC, UCEC, LIHC, and KIRC. Based on the survival analysis results, we selected KIRC as the representative tumor with poor prognosis and THCA as the tumor with favorable prognosis. The results showed that high expression of FCER1G in KIRC was significantly positively correlated with the degree of infiltration of CD8+T cells (R=0.266, P=6.95E-09), CD4+ T cells (R=0.119, P=2.68E-02), B cells (R=0.355, P=4.01E-15), DCs (R=0.721, P=3.20E-75), neutrophils (R=0.684, P=8.65E-65), and macrophages (R=0.267, P=6.14E-09) (Figure 6A). However, for THCA, high FCER1G expression was negatively correlated with the degree of infiltration of CD8+T cells (R=-0.496, P=1.05E-31), CD4+ T cells (R=-0.304, P=6.45E-12), and B cells (R=-0.333, P=3.85E-14), while there was no significant correlation with DCs (R=-0.164, P=7.97E-02), neutrophils (R=0.138, P=0.141), and macrophages (R=-0.023, P=0.609) (Figure 6B). In addition, we found a significant negative correlation between FCER1G expression and tumor purity in KIRC (R=-0.324, P=9.26E-13) but no significant correlation in THCA (R=0.128, P=0.169) (Figure 6). The results further suggested that FCER1G expression could influence patient survival prognosis by affecting immune cells infiltration levels.

Correlations between FCER1G expression and immune molecules

We used the TIMER database examining immune molecules to further investigate the correlations between FCER1G expression and immune cells infiltration. The results showed that FCER1G expression was significantly correlated with 45 out of 49 immune molecules in KIRC and 30 out of 49 immune molecules in THCA after tumor purity adjustments. FCER1G expression differently correlated with B cells, CD8+ T cells, Tfh, Th1and Th9 infiltration in KIRC and THCA. In addition, FCER1G expression was significantly associated with immunological checkpoint molecule expression including PD-1 (PDCD1), CTLA4, PD-L1 (CD274), LAG3, TIM3 (HAVCR2), and TIGIT both in KIRC and THCA (Table 1).

We further examined the immunological checkpoint molecules in various kinds of tumors to assess the predictive effect of FCER1G expression on the efficacy of tumor immunotherapy. A significant strong positive correlation (partial correlation>0.5) or a median correlation (partial correlation>0.3) was found between FCER1G with PD‐1 (PDCD1), PD‐L1 (CD274), CTLA4, LAG3, TIM3 (HAVCR2), and TIGIT molecules in BRCA, CESC, ESCA, HNSC, PAAD, THCA, and UCEC (Figure 7A-D, F-H). In KIRC, there was a strong positive correlation with PD‐1, CTLA4, LAG3, and TIGIT, while a certain correlation with PD‐L1 and TIM3 (Figure 7E).

FCER1G is a key molecule involved in allergic reactions [2]. Previous studies have confirmed that FCER1G was also widely involved in a variety of immune response signaling pathways in a variety of cell types, containing the ITAM whose activation triggers phagocytosis, oxidative bursts, cytokine release, antibody-dependent cell-mediated cytotoxicity (ADCC), and degranulation [12]. Although FCER1G has not been completely studied in tumor immunology, several researches have been conducted. Previous studies suggested that the expression of FCER1G is closely related to squamous carcinogenesis, tumor cell apoptosis, and the progression of clear cell renal cell carcinoma (ccRCC) [8–10]. Therefore, it is reasonable to speculate that FCER1G expression may affect patient survival through the above pathways. Although previous studies proposed some possible pathways for FCER1G expression to influence tumor development and patient survival, the role of FCER1G in other important aspects of tumor progression has not been well studied. Given solid tumors exhibit similar characteristics to tissues damage caused by autoimmune dysfunction, such as angiogenesis, immune cell infiltration, and tissue remodeling, we hypothesized that similar immune-mediated regulatory pathways may have been involved in the development of solid tumors.

In this study, we analyzed the expression of FCER1G in various kinds of tumors and adjacent normal tissues, and the correlation between FCER1G expression and endpoints (OS, DFS). The results were consistent with Chen’s finding that FCER1G expression was significantly higher and was associated with poor tumor prognosis in KIRC. Similar results were also found in BRCA, ESCA, HNSC, and PAAD. A significantly higher expression of FCER1G with a better prognosis was found in THCA, CESC, and UCEC. We further verified the protein expression of FCER1G in various tumor species and normal tissues in The Human Protein Atlas database. The results showed that FCER1G protein was up-regulated in renal adenocarcinoma tissues and thyroid gland adenocarcinoma tissues compared with the corresponding normal tissues. KEGG and GO analysis in these types of tumors suggested that mRNAs co-expressed with FCER1G were mainly enriched in immune‐related pathways and immune‐related functions, indicating that FCER1G may be related to immunity. The tumor microenvironment (TME) consists of many immune cells, stromal cells, and extracellular matrix in addition to tumor cells, which can be generally divided into an immune microenvironment of immune cells and a non-immune microenvironment of fibroblasts [13]. In the tumor immune microenvironment, infiltrating immune cells

(such as B lymphocytes, T lymphocytes, natural killer cells, Tumor-associated macrophages, etc.) account for a large proportion. Previous studies have suggested that the TME has dual effects on the tumor cells. It is generally believed that immune cells as an integral component of antitumor strategies. However, recent evidence has also shown that immune cells in the TME interact closely with the transformed cells to promote oncogenesis and tumor development [14, 15]. In based on these studies, as a novel anticancer treatment, immunotherapy targeting interactions between the infiltrating immune cells and tumor cells has been developed in recent years. However, only a limited percentage of patients respond well to current immunotherapy. Therefore, there is an urgent need to explore new potential targets.

We revealed the prognostic value of FCER1G in pan-cancer in our study. High expression of FCER1G correlated with a better prognosis in thyroid, cervical, and uterine cancers, with a poor prognosis in breast, head and neck, esophageal, kidney, liver, and pancreatic cancers. After further analysis of the clinical and pathological features of the KIRC population, we found that an increased FCER1G expression could specifically impact the prognosis of KIRC patients with the following characteristics: White, stage 1, stage 4, and low mutation burden, which was partially consistent with Chen’s finding that FCER1G expression was significantly associated with poor tumor prognosis in 4 stages in KIRC. Therefore, our findings provide supporting evidence for the use of FCER1G as a prognostic marker for pan-cancer, which may benefit mechanism research in the future.

As shown in Fig. 6, we found a significant correlation of FCER1G expression in tumors with immune cells infiltration. Especially in KIRC and THCA, FCER1G was closely associated with different immune cells infiltration states, as yet another important finding in our study. The results showed different patterns of KIRC and THCA in the correlation between FCER1G expression and immune infiltration, especially in the infiltration level of B cells, CD4 + T cells, and CD8 + T cells, which was positively related with FCER1G expression in KIRC and negatively related with FCER1G expression in THCA. In addition, macrophages, neutrophils, and DCs were also significantly positively related to FCER1G expression in KIRC, while they did not correlate with FCER1G expression in THCA (Fig. 6). As important antigen presenting cells (APCs), B cells and macrophages were significantly positively correlated with FCER1G expression in KIRC; however, in THCA, B cells were negatively correlated with FCER1G, and macrophages had no significant correlation with FCER1G expression. Interestingly, we have found that FCER1G expression level is significantly positively related to the tumor purity in KIRC, which suggested its comparative enrichment in tumor cells. Nevertheless, the FCER1G expression had no significant correlation with the tumor purity in THCA, indicating that FCER1G is equally expressed in the tumor microenvironment and tumor cells. Such different enrichment patterns in different immune cells and immune molecules showed the heterogeneity of the tumors when recruiting APCs to the TME, and indicated that FCER1G plays a pivotal role in the regulation and recruitment of immune infiltrating cells in cancers.

Moreover, after adjustment for tumor purity, our study also provided evidence that the immune infiltration levels of helper 1 (Th1) cells, helper 2 (Th2) cells, helper 9 (Th9) cells, and helper 17 (Th17) cells are significantly correlated with the FCER1G expression level in cancers. Except for the interactions among the immune cells, the tumor cells can also influence the immune cells by producing cytokines. IFNγ, IL-12, and Tbet promote the differentiation of Th1 cells, and Th1 cells could activate the cytolysis and other effector functions of other immune cells such as macrophages, B cells, and CD8 + cytotoxic T lymphocytes (CTL). Th1 cells also play a crucial role in activating CTL to target and kill tumors, and also enhances the survival and memory of CTL. The Th9 cells, mainly secrete the cytokine IL-9 acting on other cells to play anti-tumor immune effects, and can also secrete IL-21, IL-3 and act on other cells in different ways, including DCs, CTL, NK, and mast cells. Yong Lu et al. reported that Th9 cells are a unique subset of CD4 + T cells, and found that Th9 cells could eradicate advanced tumors using mouse melanoma model [16]. Also, YL A et al. reported that pSTAT5 activation could upregulate the expression of FCER1G and FcRg-related receptors on APCs, enhance allergen sensitization and allergic inflammation in patients with allergic diseases. The upregulation of FCER1G expression could also induce TH2/TH17 polarization [17]. Besides, Tjota M Y et al. found that the upregulation of FcRg-related receptors on DCs could promote IL-33 production and induce Th2-mediated allergic inflammation [18]; and tumors usually appear as a Th2 type immune response. These interactions between tumor cells and infiltrating immune cells help explain the findings from this study indicating that TH1, TH2, TH9, and TH17 cells have a positive correlation with FCER1G expression in KIRC and that the high expression of FCER1G is associated with a worse KIRC patient prognosis. It is reasonable to speculate that immune infiltration can interact with FCER1G-mediated activities both in tumor cells and immune cells.

Furthermore, we analyzed the association of FCER1G with immunostimulatory molecules and immunosuppressive molecules to completely demonstrate the relationship between FCER1G and immunity. In recent years, tumor immunotherapy has gradually become the most prominent means of tumor treatment, one of which is to inhibit the tumor immunological checkpoint protein. Checkpoint proteins such as CTLA-4, PD-1 are considered an important part of the immune system, expressed on different immune cells, and can activate or inhibit the immune response [19]. Blocking the PD-1 and CTLA-4 checkpoint proteins has been shown to exhibit a persistent therapeutic response in a variety of malignancies. However, no obvious benefit was seen in many tumor patients who receive immunological checkpoint block treatment. The dynamic immune microenvironment, tumor infiltrating status, immune biomarkers, and other checkpoint molecules all affect the efficacy of immunotherapy, and checkpoint protein expression status alone is incomplete in determining which patients should be accepted immunological checkpoint blockade therapy [20]. Therefore, exploring new immune biomarkers is essential for clinically personalized immunotherapy. We found that FCER1G was significantly associated with immunological checkpoint molecules including CTLA4, PD-1, PD‐L1, LAG3, TIM3, and TIGIT in various kinds of tumors with better reactivity against immunological checkpoint blockade therapy. Therefore, it is reasonable to speculate that FCER1G can be used to predict the efficacy of immunological checkpoint therapy among patients with these types of tumors.

However, our study still has some limitations. First, we only collected the microarray and sequencing data analyzing from tumor tissue information, a higher resolution in the cell-level analysis such as single-cell RNA sequencing should be encouraged. Second, our study only executed a bioinformatics analysis of FCER1G expression and patient survival using different databases, further studies with in vivo/in vitro experiments should be performed. Third, although the expression of FCER1G was found associated with immune cells infiltration and survival in patients with cancers, we have not demonstrated that FCER1G affects patient survival through immune infiltration, and further mechanism research is still needed in the future.

In summary, FCER1G can act as a prognostic marker for pan-cancer, FCER1G expression can affect patients' prognosis and correlate with immune cells infiltration. In KIRC and THCA, FCER1G is closely associated with different immune cell infiltration states and tumor purity, and immune infiltration can interact with FCER1G-mediated activities both in tumor cells and immune cells. FCER1G can be used to predict the efficacy of immunological checkpoint therapy among these types of tumors patients. Our study also provides an important basis for the clinical use of FCER1G to assess the patient immune status and the selection of individualized immunotherapy options.

Expression levels of FCER1G in different types of cancers

We analyzed FCER1G mRNA expression in the Oncomine database to evaluate the expression of FCER1G in pan-cancer. Oncomine is an online database that contains 86733 samples from 715 datasets. We further examined FCER1G mRNA expression in different tumor tissues and adjacent normal tissues in TIMER (the tumor immune estimation resource). In addition, we also analyzed the expression of FCER1G mRNA in various tumors and normal tissues using the different expression patterns by GEPIA (the gene expression profiling interactive analysis). The Human Protein Atlas database was used to analyze FCER1G expression at the protein level in various tumors.

Survival analysis in different types of cancers

We used GEPIA, PrognoScan, and Kaplan-Meier Plotter to analyze the association between FCER1G expression and patient survival across all cancers. GEPIA database collects tumor and normal samples information from the TCGA and GTEx gene expression data online. First, we explored the overall impact of FCER1G expression on pan-cancer OS, DFS using GEPIA. Additionally, we retrieved and analyzed the association between FCER1G expression and prognosis in all available microarray datasets in PrognoScan, including OS, RFS, etc. The threshold Cox P value < 0.05 was set. Later, we analyzed the correlation of FCER1G expression and OS using Kaplan-Meier Plotter, an online database containing information on 21 kinds of tumors and their related genes, in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), thyroid carcinoma (THCA), uterine corpus endometrial carcinoma (UCEC), esophageal carcinoma (ESCA), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and pancreatic adenocarcinoma (PAAD). Log-rank P values and hazard ratios (HRs) with 95% confidence intervals were calculated.

Correlations between FCER1G expression and immune cells

The tumor immune estimation resource (TIMER) is a database containing 32 tumor types and 10897 samples from TCGA (The Cancer Genome Atlas). It could examine the immune cells infiltration and the association between different genes and immune infiltration status, to explore the tumor immunological, clinical, and genomic features [11]. We used the TIMER database to analyze the correlation between FCER1G expression and infiltration of six immune cell types—CD8+T cells, CD4+ T cells, B cells, dendritic cells (DCs), neutrophils, and macrophages—in various tumors. We also examined tumor purity to visualize the correlation between FCER1G expression and degree of immune infiltration in different cancer types. Moreover, we analyzed the correlation among the levels of FCER1G, immunostimulatory molecules, and immunosuppressive molecules including CD19, CD20, CD38, CD8A, CD8B, CXCR5, ICOS, BCL-6, IL12RB2, WSX-1,T-BET, CCR3, STAT6, GATA-3, TGFBR2, IRF4, PU.1, IL-21R, IL-23R, STAT3, CCR10, AHR, FOXP3, CCR8, CD25, CD68, CD11b, NOS2, ROS, ARG1, MRC1, HLA-G, CD80, CD86, CD14, CD16, XCL1, KIR3DL1, CD7, CD15, MPO, CD1C, CD141, PD-1 (PDCD1), CTLA4, PD-L1 (CD274), LAG3, TIM3 (HAVCR2) and TIGIT. Gene expression level is expressed in log2 RSEM. We used Spearman method to determine the statistical significance.

KEGG, GO analysis by Cytoscape

Cytoscape is an open source platform with different kinds of attribute data for analyzing and visualizing these data into complex networks. In Cytoscape, we used ClueGO and CluePedia apps to perform pathway enrichment and functional enrichment analyses. ClueGO network could perform single gene analysis and comparison of lists of genes, reflecting the relationships between the associated genes based on kappa statistics. ClueGO visualizes also genes and pathways resulted from other enrichment analyses as functionally grouped networks. CluePedia calculates linear and non-linear statistical dependencies from experimental data. Genes, proteins, and miRNAs linked based on experimental information are integrated into a network with ClueGO pathways. to perform pathway enrichment and functional enrichment analyses (P ≤ 0.05) .

Statistical analysis

The results from the Oncomine database were visualized with P-value, fold change, and gene rank. The threshold was set as a P value of 0.0001, fold change of 2, and gene rank of top 10%. We used Log-rank test, a.k.a the Mantel-Cox test, for the hypothesis test in GEPIA. The cox proportional HR (hazard ratios) and the 95% CI (confidence interval) information were also included in the survival plot. PrognoScan was used to present the survival curves. Survival curves for high (red) and low (blue) expression groups dichotomized at the optimal cutpoint were plotted. The X-axis represented time and the Y-axis represented survival rate. 95% confidence intervals for each group were also indicated by dotted lines. In Kaplan-Meier Plotter, the patient cohorts were compared by a Kaplan-Meier survival plot, and the hazard ratio with 95% confidence intervals and log rank P value were calculated. The correlation between the two genes expression was evaluated by Spearman’s rho correlation.

Statement:

All experiments were performed in accordance with relevant guidelines and regulations.

Acknowledgements

We sincerely thank the public databases, including Kaplan-Meier Plotter, GEPIA, PrognoScan, and Oncomine for providing open access. We would like to thank all participants at Molecular Medicine and Cancer Research Center of Chongqing Medical University.

Authors' contributions

XZ performed the bioinformatics analyses, prepared the figures and wrote the report. JC extracted the information from the databases. FS designed the study and finalised the report. ZY supervised the entire study and did the critical review. All authors support the publication of the manuscript.

Data availability

The datasets generated during and/or analysed during the current study are available in the Oncomine (http://www.oncomine.org), TIMER (http://timer.cistrome.org), GEPIA (http://gepia.cancer-pku.cn), PrognoScan (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html), Kaplan-Meier Plotter (http://kmplot.com), The Human Protein Atlas database (https://www.proteinatlas.org/), and Cytoscape (http://cytoscape.org/).

Conflicts of interest

None.

Funding

This work was supported by the Natural Science Foundation of Chongqing in China under Grant number cstc2021jcyj-bshX0073 project to Xiaoxuan Zhang, and National Natural Science Foundation of China under Grant number 81972851 project to Zhenzhou Yang.

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Table 1. Correlations between FCER1G and Gene Markers of Immune Cells in TIMER

Cell Type	Gene Marker	KIRC				THCA
		None		Purity		None		Purity
		Cor	P	Cor	P	Cor	P	Cor	P
B Cell	CD19	0.524	***	0.43	***	0.235	*	0.259	0.061
	CD20	0.485	***	0.331	***	0.148	0.106	0.166	0.076
	CD38	0.629	***	0.526	***	-0.237	*	-0.218	0.019
CD8+ T Cell	CD8A	0.643	***	0.531	***	-0.291	*	-0.267	*
CD8+ T Cell	CD8B	0.637	***	0.537	***	-0.353	***	-0.344	**
Tfh	CXCR5	0.557	***	0.448	***	0.1	0.278	0.14	0.135
	ICOS	0.682	***	0.595	***	-0.193	0.035	-0.173	0.065
	BCL-6	0.143	*	0.187	***	-0.033	0.723	-0.031	0.743
Th1	IL12RB2	0.354	***	0.317	***	-0.119	0.377	-0.106	0.449
	WSX-1	0.558	***	0.483	***	0.22	0.016	0.211	0.024
	T-BET	0.567	***	0.447	***	0.537	***	0.529	***
Th2	CCR3	0.47	***	0.43	***	0.419	***	0.256	***
	STAT6	0.09	0.083	0.106	0.050	-0.006	0.894	-0.047	0.426
	GATA-3	0.637	***	0.521	***	-0.3	**	-0.292	*
Th9	TGFBR2	0.186	**	0.154	*	-0.023	0.605	-0.037	0.412
	IRF4	0.623	***	0.505	***	0.172	0.060	0.203	0.030
	PU.1	0.903	***	0.868	***	0.783	***	0.785	***
Th17	IL-21R	0.756	***	0.674	***	-0.028	0.765	-0.002	0.986
	IL-23R	0.233	***	0.185	**	0.296	*	0.286	*
	STAT3	0.315	***	0.226	***	0.371	***	0.368	***
Th22	CCR10	0.318	***	0.317	***	0.08	0.388	0.093	0.321
Th22	AHR	0.125	0.016	0.107	0.048	0.308	**	0.311	**
Treg	FOXP3	0.316	***	0.257	***	0.377	***	0.402	***
	CCR8	0.569	***	0.499	***	-0.074	0.421	-0.053	0.571
	CD25	0.717	***	0.639	***	0.416	***	0.407	***
Macrophage	CD68	0.705	***	0.632	***	0.876	***	0.877	***
Macrophage	CD11b	0.655	***	0.597	***	0.864	***	0.863	***
M1	NOS2	-0.015	0.778	-0.072	0.18	0.088	0.047	0.083	0.0658
M1	ROS	0.001	0.987	0.031	0.563	0.047	0.612	0.04	0.672
M2	ARG1	-0.113	0.029	-0.19	**	-0.015	0.736	-0.008	0.856
M2	MRC1	0.384	***	0.286	***	0.701	***	0.695	***
TAM	HLA-G	0.305	***	0.214	***	0.522	***	0.529	***
	CD80	0.74	***	0.674	***	0.678	***	0.675	***
	CD86	0.912	***	0.879	***	0.91	***	0.91	***
Monocyte	CD14	-0.108	0.038	-0.178	**	0.663	***	0.671	***
Monocyte	CD16	0.59	***	0.532	***	0.18	0.017	0.135	0.023
NK	XCL1	0.497	***	0.491	***	0.527	***	0.505	***
	KIR3DL1	0.199	**	0.161	*	0.36	***	0.352	**
	CD7	0.605	***	0.524	***	-0.272	*	-0.269	*
Neutrophil	CD15	0.147	*	0.138	*	0.129	*	0.110	*
Neutrophil	MPO	0.349	***	0.215	***	0.013	0.889	0.041	0.665
DC	CD1C	0.444	***	0.316	***	-0.355	***	-0.335	**
DC	CD141	0.358	***	0.162	*	0.33	**	0.347	**
Immunological checkpoint molecules	PD-1	0.6	***	0.562	***	0.611	***	0.622	***
	CTLA4	0.579	***	0.547	***	0.792	***	0.791	***
	PD-L1	0.242	***	0.21	***	0.402	***	0.395	***
	LAG3	0.621	***	0.575	***	0.711	***	0.715	***
	TIM3	0.279	***	0.213	***	0.917	***	0.918	***
	TIGIT	0.689	***	0.651	***	0.785	***	0.784	***

KIRC:kidney renal clear cell carcinoma; THCA:thyroid carcinoma; None: correlation without adjustment; Purity: correlation adjusted for tumor purity; Tfh: follicular helper T cell; Th: T helper cell; Treg: regulatory T cell; TAM: tumor-associated- macrophage; NK: natural killer cell; DC: dendritic cell; Cor: R value of Spearman’s correlation. *P < 0.01; **P < 0.001; ***P < 0.0001.

No competing interests reported.

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Prognostic and Immunological Role of FCER1G in multiple cancers

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