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).