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Gene summary for RGS1 |
 Gene summary |
| Gene Symbol | RGS1 | Gene ID | 5996 |
| Gene name | regulator of G protein signaling 1 |
| Synonyms | 1R20;IER1;IR20;BL34 |
| Type of gene | protein_coding |
| UniProtAcc | Q08116 |
| Gene ontology (Biological Process only) |
| GO ID | GO term |
| GO:0007165 | signal transduction |
| GO:0007186 | G protein-coupled receptor signaling pathway |
| GO:0009968 | negative regulation of signal transduction |
| GO:0043547 | positive regulation of GTPase activity |
| GO:0006955 | immune response |
| GO:0007193 | adenylate cyclase-inhibiting G protein-coupled receptor signaling pathway |
| GO:0009617 | response to bacterium |
| GO:0061737 | leukotriene signaling pathway |
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Literatures describing the association of RGS1 and immune escape mechanisms |
| The table presents literature evidences demonstrating the involvement of RGS1 in cancer immune escape mechanisms. |
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Comparison of the RGS1 expression level between tumor and normal groups |
| Gene expression level in TCGA (Tumor vs Normal). The threshold for adjusted p-value is shown as : ***, padj < 0.001; **: 0.001 < padj < 0.01; 0.01 < padj < 0.05; NS: padj > 0.05. (Click on the image to enlarge it in a new window.) |
| The table shows the significant results for TCGA cancers with (adjusted P value < 0.05) and (|logFC| > 1). |
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Comparison of the RGS1 methylation level between tumor and normal groups |
| The boxplot shows the mean beta value in normal and tumor group, and the dotplot shows the correlation between methylation level and expression level. Methylation level of the promoter region using TCGA data. The promoter regions were defined as the genomic regions spanning 2000 base pairs upstream and 500 base pairs downstream of the transcription start sites of genes. The average methylation value across all CpG sites within its promoter region was calculated to obtain the gene-level methylation value. |
| The table shows the significant results for TCGA cancers with (adjusted P value < 0.05) and (|diff_beta| > 0.1). |
No significant differences were found in RGS1 methylation in promoter region.
| Methylation level of the genebody region using TCGA data. The genebody regions were defined from 500 base pairs downstream of the transcription start site to the transcription end site. |
| The table shows the significant results for TCGA cancers with (adjusted P value < 0.05) and (|diff_beta| > 0.1). |
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Summary of the copy number in TCGA tumor samples |
| The gene level copy number is annotated as: 0: homozygous deletion; 1: deletion leading to LOH; 2: wild type, including copy-neutral LOH; 3/4: minor gain; 5-8: moderate gain; >=9: high-level amplification, as referenced in [1]. |
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| The violin plot shows the correlation between copy number and expression level. |
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DEGs and the enrichment analysis between the mutated and wild type groups |
| For each cancer type in TCGA, if samples in the mutated group are no less than 5, we performed the differential gene expression analysis. The table shows DEGs with (adjusted p-value < 0.05) and (|logFC| > 1). Then we performed the KEGG, GOBP and Hallmark enrichment analysis for up-regulated and down-regulated DEGs separately (logFC > 1 means the gene is upregulated in the mutated group). |
| Gene ID | Symbol | Log2 Fold Change | P-value | Adjusted P-value |
|---|---|---|---|---|
| ENSG00000255974 | CYP2A6 | -2.53e+00 | 2.81e-04 | 1.70e-02 |
| ENSG00000100095 | SEZ6L | -2.37e+00 | 2.83e-04 | 1.71e-02 |
| ENSG00000249241 | AC195454.1 | -2.20e+00 | 2.86e-04 | 1.73e-02 |
| ENSG00000163499 | CRYBA2 | -3.24e+00 | 2.87e-04 | 1.73e-02 |
| ENSG00000204118 | NAP1L6 | -2.85e+00 | 2.88e-04 | 1.73e-02 |
| ENSG00000245750 | DRAIC | -2.25e+00 | 2.88e-04 | 1.73e-02 |
| ENSG00000183607 | GKN2 | -2.83e+00 | 2.91e-04 | 1.74e-02 |
| ENSG00000124092 | CTCFL | -3.89e+00 | 2.98e-04 | 1.78e-02 |
| ENSG00000251768 | RNA5SP217 | -2.90e+00 | 2.98e-04 | 1.78e-02 |
| ENSG00000185607 | ACTBP7 | 1.12e+00 | 3.00e-04 | 1.78e-02 |
| ENSG00000283422 | RP11-256L6.5 | 1.37e+00 | 3.04e-04 | 1.80e-02 |
| ENSG00000224837 | GCSHP5 | 1.65e+00 | 3.07e-04 | 1.82e-02 |
| ENSG00000091583 | APOH | -2.37e+00 | 3.09e-04 | 1.83e-02 |
| ENSG00000198842 | DUSP27 | -2.48e+00 | 3.11e-04 | 1.84e-02 |
| ENSG00000237390 | RP11-139I14.2 | 2.09e+00 | 3.17e-04 | 1.87e-02 |
| ENSG00000072041 | SLC6A15 | -3.53e+00 | 3.25e-04 | 1.90e-02 |
| ENSG00000231720 | RP11-568A7.3 | -3.94e+00 | 3.25e-04 | 1.90e-02 |
| ENSG00000257818 | C1GALT1P1 | -1.88e+00 | 3.25e-04 | 1.90e-02 |
| ENSG00000196132 | MYT1 | -2.51e+00 | 3.26e-04 | 1.91e-02 |
| ENSG00000285933 | NA | -1.51e+00 | 3.28e-04 | 1.92e-02 |
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Down-regulated GOBP pathways | Down-regulated Hallmark pathways |
Gene expression and mutation differences between non-responders and responders after immunotherapy |
| In ImmunEscpMap, we integrated 10 pre-calculated transcriptomic datasets, and 6 genomic datasets to study the response after immunotherapy [2, 3]. The figure shows the logFC and the -log10(p value) between non-responders and responders in different datasets. The significant results (p value < 0.05 and |logFC| > 1), including the detailed information of datasets are presented in the table. |
| Expression | Mutation |
 |  |
| Expression |
| Mutation |
No significant differences were found in RGS1 mutation.
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Correlation between the composition of TIL and gene expression, methylation and CNV |
| The clustered correlation matrix shows the association between the abundance of TIL subpopulations [4] and gene expression, methylation and copy number level. Users can check if the pattern is similar across cancer types or TIL subtypes. The value of each cell represents the spearman correlation of gene expression and an immune cell subtype within one TCGA cancer type. Only cells with p-values < 0.05 were colored, while non-significant correlations were set to NA and displayed as white cells. |
| Expression | Copy number variation |
 |  |
| Promoter methylation | Genebody methylation |
 |  |
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The association between RGS1 expression and immune subtypes/status |
| Thorsson et al identified six immune subtypes: Wound Healing, IFN-gamma Dominant, Inflammatory, Lymphocyte Depleted, Immunologically Quiet, and TGF-b Dominant [5]. Zapata et al classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. In addition, they used immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection [6]. Cortes-Ciriano et al investigated the microsatellite instability (MSI) status, which is related to the antitumour immune responses [7]. The expression level was compared among immune subtype/status. |
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Drugs targeting RGS1 and diseases related to RGS1. |
| The drug-gene interactions are extracted from the Drug-Gene Interaction Database (DGIdb, https://dgidb.org) 5.0 [8], while the disease-gene associations are obtained from Diseases 2.0 (https://diseases.jensenlab.org/Search), which collects disease-gene associations from curated databases, genome-wide association studies (GWAS) and automatic text mining of the biomedical literature [9]. |
| Drug-gene interaction | Disease-gene association |
No drugs targeting RGS1. |  |
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Survival analysis based on RGS1 expression |
| The Kaplan-Meir curves reflects the association of gene expression with overall survival across different cancers. The significance (p value) is assessed by log-rank test. |
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| Reference |
| [1] Steele CD, Abbasi A, Islam SMA, et al. Signatures of copy number alterations in human cancer. Nature. 2022 Jun;606(7916):984-991. doi: 10.1038/s41586-022-04738-6. Epub 2022 Jun 15. PMID: 35705804; PMCID: PMC9242861. [2] Beibei Ru, Ching Ngar Wong, Yin Tong, et al. TISIDB: an integrated repository portal for tumor–immune system interactions, Bioinformatics, Volume 35, Issue 20, October 2019, Pages 4200–4202, https://doi.org/10.1093/bioinformatics/btz210. [3] Zhongyang Liu, Jiale Liu, Xinyue Liu, et al. CTR–DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D1184–D1199, https://doi.org/10.1093/nar/gkab860. [4] Charoentong P, Finotello F, Angelova M, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017 Jan 3;18(1):248–262. doi: 10.1016/j.celrep.2016.12.019. PMID: 28052254. [5] Thorsson V, Gibbs DL, Brown SD, et al. The Immune Landscape of Cancer. Immunity. 2018 Apr 17;48(4):812-830.e14. doi: 10.1016/j.immuni.2018.03.023. Epub 2018 Apr 5. Erratum in: Immunity. 2019 Aug 20;51(2):411-412. doi: 10.1016/j.immuni.2019.08.004. PMID: 29628290; PMCID: PMC5982584. [6] Zapata L, Caravagna G, Williams MJ, et al. Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors. Nat Genet. 2023 Mar;55(3):451-460. doi: 10.1038/s41588-023-01313-1. Epub 2023 Mar 9. PMID: 36894710; PMCID: PMC10011129. [7] Cortes-Ciriano I, Lee S, Park WY, et al. A molecular portrait of microsatellite instability across multiple cancers. Nat Commun. 2017 Jun 6;8:15180. doi: 10.1038/ncomms15180. PMID: 28585546; PMCID: PMC5467167. [8] Cannon M, Stevenson J, Stahl K, et al. DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Res. 2024 Jan 5;52(D1):D1227-D1235. doi: 10.1093/nar/gkad1040. PMID: 37953380; PMCID: PMC10767982. [9] Grissa D, Junge A, Oprea TI, Jensen LJ. Diseases 2.0: a weekly updated database of disease-gene associations from text mining and data integration. Database (Oxford). 2022 Mar 28;2022:baac019. doi: 10.1093/database/baac019. PMID: 35348648; PMCID: PMC9216524. |
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