The study has been admitted by the Institutional Review Board of the Medical Central Hospital of Qionglai. All written informed consent had already been obtained since all the data were retrieved from the online databases.

ONCOMINE is a publicly accessible online genome-wide expression analysis platform, covering 715 datasets and 86,733 samples of cancer (21). ONCOMINE was utilized to analyze expression differences of the FcγRs gene family in multiple tumor tissues and the corresponding adjacent normal tissues. The threshold was determined according to the following values: p-value of 0.001, fold change of 1.5, and gene ranking the top 10%. In this study, the cell color is determined by the best gene rank percentile for the analysis within the cell, and the Student’s t-test was applied to generate a p-value.

UALCAN is a comprehensive and interactive web resource for analyzing cancer OMICS data (TCGA, MET500, and CPTAC) (22). In our study, UALCAN was used to illustrate the distinct expression levels of tumor and normal tissues of ccRCC. Student’s t-test was used to generate a p-value and the p-value cutoff was 0.05.

Gene Expression Profiling Interactive Analysis (GEPIA) is a newly developed interactive platform for elaborating the RNA sequencing expression data of 9,736 tumors and 8,587 normal samples from the TCGA and the Genotype-tissue Expression dataset, utilizing a standard processing pipeline (23). In this study, GEPIA was used to compare the association with cancer type staging of eight FcγRs members. The Student’s t-test was used to generate a p-value and the p-value cutoff was 0.05.

TIMER is a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types. The 2.0 version of the webserver provides abundances of immune infiltrates estimated by multiple immune deconvolution methods, and allows users to generate high-quality figures dynamically to explore tumor immunological, clinical, and genomic features comprehensively (TIMER2.0 for analysis of tumor-infiltrating immune cells). In this study, we used TIMER2.0 to assess the correlation between FcγRs expression levels and immune cell infiltration and to assess the correlation between clinical outcomes and immune cell infiltration and FcγRs expression.

TISIDB is a web portal for tumor and immune system interaction, and a valuable resource for cancer immunology research and therapy, which integrates multiple heterogeneous data types (TISIDB: an integrated repository portal for tumor-immune system interactions). In this study, we used TISIDB to assess the correlation between FcγRs mRNA expression levels and immunoinhibitors expression levels or cancer grade of ccRCC.

The Kaplan–Meier plotter is an online database to assess the effect of gene expression on survival in 21 cancer types (24). We used this online tool to evaluate the prognostic value of FcγRs mRNA levels in ccRCC patients. The overall survival (OS) and recurrence-free survival (RFS) of patients were analyzed with a 50% (Median) cutoff for both low and high expression groups. The statically significant difference was considered when a p-value is <0.05. Information on the number of patients, median values of mRNA expression, 95% confidence interval (CI), hazard ratio (HR), and P-value can be found on the Kaplan–Meier plotter web page.

We have downloaded RNA-sequencing, clinical, pathological, and follow-up data of 603 ccRCC patients from the TCGA-KIRC dataset. A total of 484 cases with complete data were screened out for multivariate regression analysis.

SurvivalMeth is a web server to investigate the effect of DNA methylation-related functional elements on prognosis, and multiple kinds of commonly used functional elements associated with DNA methylation are considered (25). The frequently used data from the TCGA, the CCLE, and the GEO were prestored into SurvivalMeth, namely, 81 DNA methylation profiles in 13,371 samples across 36 cancers, covering more than 480,000 DNA methylation sites locating in 19,000 coding genes, 1,689,653 super enhancers, 1,304,902 CTCF binding regions, 77,634 repeat elements and multiple functional elements such as CpG island, shore, shelf, promoter, gene body, exon, etc.

STRING is a database of known and predicted protein–protein direct (physical) and indirect (functional) interactions (26). The protein–protein interactions (PPI) network of FcγRs co-expressed genes was visualized using the online tool of STRING with the species setting to Homo sapiens and a combined score of >0.7 was considered statistically significant. The nodes meant proteins; the edges meant the interaction of proteins and we hide disconnected nodes in the network.

Functions of FcγRs and 88 co-expression genes significantly were analyzed by the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) in the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (24). Gene ontology analyses focus on three domains: biological processes (BP), cellular components (CC), and molecular functions (MF), and such analyses are commonly used to predict the functional roles of FcγRs mutations and 80 genes significantly associated with FcγRs mutations, while the KEGG analysis can define the pathways related to the FcγRs mutations and 80 co-expressed genes associated with FcγRs mutations. Only terms with p-value of <0.05 were considered as significant.

Differential mRNA expression levels of FcγRs were profiled in tumor and adjacent normal tissues of multiple cancer types using Oncomine platform. mRNA levels of FcγR family were remarkably upregulated in four cancer types, namely, brain and CNS, breast, head and neck colorectal and kidney, while mRNA levels of FcγRs were downregulated in leukemia and lung cancer ( Figure 1A ). Table 1 shows that mRNA expression levels of FCGR1A/B, FCGR2A/B/C, and FCGR3B were remarkably upregulated in ccRCC in multiple datasets. As shown in Figure 1B , eight FcγRs are expressed abnormally in different tumor tissues. mRNA expression levels of FCGR1A/B/C, FCGR2A/B/C, and FCGR3A were remarkably upregulated in ccRCC tissues compared with normal tissues. The protein expression levels of FcγRs were analyzed using the CPTAC online tool of UALCAN platform. It was observed that only FCGR1A expresses the functional FcγRI, whereas FCGR1B/C represents duplicated pseudogenes of FCGR1A (6). Figure 1C showed that the protein expression levels of FCGR1A, FCGR2A/B, and FCGR3A were downregulated in ccRCC tissues compared with normal tissues.

We used the GEPIA dataset to analyze the relationship between transcriptional levels of different FcγRs members with tumor stages of ccRCC patients. The results showed that the mRNA levels of FCGR1A/B/C and FCGR3A were correlated with the tumor stages of ccRCC patients, whereas the mRNA levels of FCGR2A/B/C and FCGR3B did not markedly differ among tumor stages ( Figure 2A ). The reason why the mRNA expression of FCGR2A/B/C and FCGR3B in ccRCC did not appear to be significantly different among tumor stages may be their unique roles in anti-tumor immunity. Likewise, cancer grades analysis by TISIDB indicated that mRNA expressions of FCGR1A/B, FCGR2A/B/C, and FCGR3A correlated with cancer grade of ccRCC ( Figure 2B ). In short, the results above suggested that mRNA expressions of FcγRs (except for FCGR3B) were positively correlated with individual tumor stages or cancer grades of patients.

To evaluate the prognostic value of the FCGR gene family in ccRCC progression, we analyze the correlation between FcγRs transcription levels and clinical outcomes including overall survival (OS) and disease-free survival (DFS) using the Kaplan–Meier Plotter database. ccRCC patients were divided into low and high-risk groups based on cutoff value. As shown in Figure 3A , high transcription levels of FcγRs were correlated with shorter OS in ccRCC. Nevertheless, high transcription levels of FCGR2B/C were correlated with longer DFS in ccRCC, and no significant correlation was observed between DFS and other FcγRs ( Figure 3B ). We downloaded and screened the gene expression and clinical data of 485 ccRCC patients from the TCGA database ( Supplementary Table 1 ) for multivariate Cox regression survival analysis. The results showed that the effects of FCGR1A, FCGR1B, and FCGR1C on prognosis were still significant after correcting for conventional prognostic factors ( Table 2 ; Supplementary Table 2 ).

Genome-wide DNA methylation array and clinical outcome profiles of renal tissues were explored on the SurvivalMeth platform to investigate the DNA methylation levels of FcγRs and their relationships with clinical outcomes of ccRCC patients. Methylation levels of ccRCC were tested in Illumina Infnium HumanMethylation 450 array and Illumina Infnium HumanMethylation27 array in 535 tumors versus 357 normal renal tissues (318 tumors vs. 160 normal with HumanMethylation450 array; 217 tumors vs. 197 normal with HumanMethylation27 array). Lower DNA methylation levels of FCGR1A/B/C, FCGR2A, and FCGR3A/B were detected in ccRCC tissues, comparing with normal tissues ( Figure 4A ), whereas, the DNA methylation levels of FCGR2A/B did not differ significantly between tumors and normal tissues. Moreover, lower FCGR1A/B/C and FCGR3A DNA methylation levels were associated with shorter OS, while lower FCGR2A DNA methylation level was associated with longer OS ( Figure 4B ; Table 3 ).

We then analyzed significant coexpression genes with FcγRs using the co-expression analysis module in the UAICAN database and listed in Supplementary Table 3 . A total of 88 upregulated genes were significantly associated with FcγRs expression. Subsequently, the 88 genes were analyzed using GO and KEGG tools in DAVID, and constructed a PPI network by STRING. Figure 5A exposed that the activation of immune response-related genes, namely, C1QA, C1QB and, C1QC and adaptive immune response participant genes, such as LAIR1, LILRB4, CD4, and CD86 were closely connected with FcγRs alterations. The first 21 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of FcγRs and their 88 Co-expression genes are illustrated in Figure 5B . Among them, Phagosom, FcγR-mediated phagocytosis, Cytokine–cytokine receptor interaction, Toll-like receptor signaling pathway and Natural killer cell mediated cytotoxicity are significantly associated with anti-tumor immunity of ccRCC. In addition, GO (Gene Ontology) analysis including molecular functions (MF), cellular components (CC), and biological processes (BP) are shown in Figures 5C – E . Most results of GO analysis were associated with immune responses.

TIMER and TISIDB online analysis tools were used to evaluate the relationship between the expression levels of FcγRs and the level of immune infiltration in ccRCC. It was found that FcγRs are involved in immunosuppression regulation and immune cell infiltration, which might affect the clinical outcome of ccRCC patients. The analysis results showed that CD4⁺ T and NK were negatively correlated with FcγRs expression levels, whereas Treg and M2 macrophage cells were positively correlated with FcγRs expression levels ( Figure 6 ). The result of the TISIDB online analysis shows that immunoinhibitors, namely, IL-10 and CTLA-4 were positively correlated with all the FcγRs expression levels. TGFB1 was positively correlated with FCGR1A/B/C, FCGR2A/B, and FCGR3A ( Figure 7 ).