1. Pan-Cancer Expression Landscape of GPR183
Among all of the 33 cancer types from the TCGA database, 12 cancer types showed differential GPR183 expression between tumor and normal tissues(Fig. 1C and Figure S1). In BLCA, COAD, LUSC, PRAD, READ and UCEC, GPR183 expression was statistically significant higher in normal tissues(Fig. 1C and Figure S1). While in CHOL, GBM, HNSC, KIRC, KIRP and THCA, GPR183 had an inverse distribution( Fig. 1C and Figure S1). However, after compared to paired normal tissue data from GTEx, cancers with statistically significant high expression in normal sites were BLCA, COAD, LUSC and READ(Fig. 1B). In cancer types including ESCA, GBM, KIRC, KIRP, LAML, LGG and STAD, GPR183 expression was higher in tumor site(Fig. 1B).
2. Pan-Cancer Mutational Landscape of GPR183
In the analysis of GPR183 somatic single-base mutations(SSM) in different cancer types, the top five cancers were UCEC, DLBC, ACC, COAD and SKCM(Fig. 1A). Among the top five cancer types with highest GPR 183 mutations, the overall mutation landscapes were similar(Fig. 2). The most common variant classification, variant type and SNV class were separately missense mutation, SNP and C > T(Fig. 2).
3. Integrated Network Analysis of GPR183
In order to explore the function of GPR183, we applied integrated network analysis of GPR183. 20 genes were closely related to GPR183 in different ways including co-expression, predicted genes and co-localization(Fig. 3A). According to bioprocess analysis, GPR183 involved in various responses to stimulus including peptide, various organic compounds, lipids and bacterium. Besides, GPR183 expression was associated with metabolic and immune process(Fig. 3B). Moreover, GPR183 expression was linked to regulation of programmed cell death(Fig. 3B). The pathway analysis showed that a strong correlation between GPR183 and immune regulation(Fig. 3C). GPR183 participated in regulation of multiple signals such as INF-γ, SMADs family, AKT, EGFR, MECP2 and IGF-2(Fig. 3C). In addition, GPR183 showed association with TP53 regulation(Fig. 3C).
4. Evaluation of prognostic association with GPR183 expression in Pan-cancer analysis
Using cox regression survival analysis, GPR183 expression was associated with prognosis in LGG and SKCM(Table 1, Table S1). After Kaplan-Meier(KM) analysis, we found high GPR183 expression was positively associated with prognosis in BRCA and SKCM(Fig. 4A and 4D), while negatively in LGG, LUSC and UVM(Fig. 4B, 4C and 4E).
5. Overall function analysis of GPR183 in prognosis related cancers
In order to explore the mechanisms of GPR183 in tumor progress and prognosis, we analyzed differentially expressed genes in high and low GPR183 groups in BRCA, SKCM, LUSC, LGG and UVM(|log2 fold-change| > 1, adjusted p-value < 0.01, Table S2). There were 824, 472, 1215, 1077 and 1110 upregulated genes in BRCA, SKCM, LUSC, LGG and UVM. Inversely, there were 284, 52, 180, 366 and 99 downregulated genes in BRCA, SKCM, LUSC, LGG and UVM. We found 18 commonly upregulated expressed genes in high GPR183 groups among these cancer types(Fig. 5A and 5B). The GO analysis demonstrated that GPR183 was related to regulation of immune response and ionic homeostasis like calcium(Fig. 5C). The molecular function of GPR183 were signaling receptor, molecular transducer, immune receptor and CPR activities(Fig. 5D). Besides, GPR183 expressed mainly in the membrane(Fig. 5E). Moreover, according to pathway analysis, GPR183 was involved in immune responses such as innate immune responses, signaling by interleukins and neutrophil degranulation(Fig. 5F).
6. Differential GPR183-related expression patterns in cancers with opposite prognosis
Though associated with prognosis in five cancer types(BRCA, SKCM, LUSC, LGG and UVM), GPR183 seemed to exert a dual function. In order to explore the potential mechanisms causing opposite clinical outcomes in the five cancers, we divided the five cancers into two groups(Prognosis-Positive and Prognosis-Negative) determined by the GPR183-related prognosis. 258 genes were statistically significantly unregulated in Prognosis-Positive group(Fig. 6, Table S3). 222 genes were upregulated in Prognosis-Negative group(Fig. 6, Table S3). Using pathway analysis based on group-specific genes(144 genes in Prognosis-Positive group, 108 genes in Prognosis-Negative group), still we found many immune-related pathways enriched both in the two groups(Table S4). Yet, the detailed regulatory function differed between them. The specific pathways in Prognosis-Positive group were related to signalings of TNFs, Interleukin-2 and CLRs(Table 2). While among the top five specific pathways in Prognosis-Negative group, the function of GPR183 was strongly associated with PD-1 signaling(Table 3).
7. Association between GPR183 expression and tumor immune infiltration
Considering close correlation between GPR183 and immunity, we estimated the correlation between GPR183 expression and immune infiltration levels in the five cancers(Fig. 7). Yet, except for UVM, GPR183 was strongly associated with immune infiltration in other four cancer types. Among all the six lymphocytes, macrophages and dendritic cells showed a quite high correlation.