ImmunEscpMap: a Knowledgebase of Cancer Immune Escape Mechanisms

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	Gene summary
	Literatures describing the association of the gene and immune escape mechanisms
	Comparison of the expression level between tumor and normal groups
	Comparison of the methylation level between tumor and normal groups
	Summary of the copy number in TCGA tumor samples
	The differentially expressed genes (DEGs) and enrichment analysis between mutated and wild type groups
	Expression and mutation differences between non-responders and responders after immunotherapy
	Correlation between the gene expression, copy number, methylation and tumor infiltrating lymphocytes
	The association between gene expression and immune subtypes/status
	Drug-gene interaction and disease-gene association
	Survival analysis based on gene expression

Gene summary for C1QBP

Gene summary

Gene Symbol	C1QBP
Gene ID	708
Gene name	complement C1q binding protein
Synonyms	HABP1;GC1QR;P32;SF2P32;GC1Q-R
Type of gene	protein_coding
UniProtAcc	Q07021

Gene ontology (Biological Process only)

GO ID	GO term
GO:0006397	mRNA processing
GO:0008380	RNA splicing
GO:0006915	apoptotic process
GO:0002376	immune system process
GO:0045087	innate immune response
GO:0002250	adaptive immune response
GO:0042254	ribosome biogenesis
GO:0006958	complement activation, classical pathway
GO:0043065	positive regulation of apoptotic process
GO:0000122	negative regulation of transcription by RNA polymerase II
GO:0032689	negative regulation of type II interferon production
GO:0050687	negative regulation of defense response to virus
GO:0032695	negative regulation of interleukin-12 production
GO:0070131	positive regulation of mitochondrial translation
GO:0006955	immune response
GO:2000510	positive regulation of dendritic cell chemotaxis
GO:0045785	positive regulation of cell adhesion
GO:0043491	protein kinase B signaling
GO:0048025	negative regulation of mRNA splicing, via spliceosome
GO:0042256	cytosolic ribosome assembly
GO:0051897	positive regulation of protein kinase B signaling
GO:0039534	negative regulation of MDA-5 signaling pathway
GO:1900026	positive regulation of substrate adhesion-dependent cell spreading
GO:1901165	positive regulation of trophoblast cell migration
GO:0090023	positive regulation of neutrophil chemotaxis
GO:0030449	regulation of complement activation
GO:0039536	negative regulation of RIG-I signaling pathway

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Literatures describing the association of C1QBP and immune escape mechanisms

The table presents literature evidences demonstrating the involvement of C1QBP in cancer immune escape mechanisms.

Icon	PMID	Cancer Type	Mechanism	Evidence Sentences
	35029648	Lung cancer	Matrix barrier	Lung apCAFs are shown to directly activate CD4 T cells and produce C1q, which rescues T cells from apoptosis. Deletion of MHCII or C1q in fibroblasts impairs CD4 T cell immunity and accelerates tumor growth. Conversely, inducing C1qbp in CD4 T cells enhances their expansion within tumors, suggesting a novel tumor-suppressive function of lung apCAFs and the importance of in situ MHCII antigen presentation for effective antitumor immunity.

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Comparison of the C1QBP 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).

Cancer type	Log2FoldChange	P value	Adjusted P value
LUSC	1.14e+00	6.67e-27	5.89e-26

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Comparison of the C1QBP 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 C1QBP 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).

No significant differences were found in C1QBP methylation in genebody region.

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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].

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
ENSG00000073756	PTGS2	-3.32e+00	2.87e-04	2.76e-02
ENSG00000101443	WFDC2	-2.33e+00	2.89e-04	2.76e-02
ENSG00000225580	RP1-159M24.1	1.68e+00	2.91e-04	2.76e-02
ENSG00000286659	NA	1.66e+00	2.90e-04	2.76e-02
ENSG00000287059	NA	-2.68e+00	2.89e-04	2.76e-02
ENSG00000180638	SLC47A2	-3.27e+00	2.93e-04	2.77e-02
ENSG00000140557	ST8SIA2	-3.23e+00	2.97e-04	2.80e-02
ENSG00000182732	RGS6	-2.53e+00	3.00e-04	2.82e-02
ENSG00000270000	RP3-449M8.9	-3.53e+00	3.07e-04	2.88e-02
ENSG00000113389	NPR3	-2.31e+00	3.10e-04	2.90e-02
ENSG00000228278	ORM2	-3.48e+00	3.11e-04	2.90e-02
ENSG00000255652	RP11-313F23.4	-3.33e+00	3.16e-04	2.94e-02
ENSG00000114948	ADAM23	-3.49e+00	3.19e-04	2.95e-02
ENSG00000142619	PADI3	-3.88e+00	3.21e-04	2.96e-02
ENSG00000251400	ALDH7A1P1	1.98e+00	3.24e-04	2.98e-02
ENSG00000173530	TNFRSF10D	-1.85e+00	3.28e-04	3.00e-02
ENSG00000105642	KCNN1	-2.85e+00	3.32e-04	3.00e-02
ENSG00000224014	RP11-488L18.6	-2.44e+00	3.30e-04	3.00e-02
ENSG00000248485	PCP4L1	-3.30e+00	3.32e-04	3.00e-02
ENSG00000188825	LINC00910	1.22e+00	3.36e-04	3.03e-02

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Down-regulated GOBP 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
	The comparison is not available for C1QBP.

Expression

No significant differences were found in C1QBP expression.

Mutation

No significant differences were found in C1QBP 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 C1QBP 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 C1QBP and diseases related to C1QBP.

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 C1QBP.

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Survival analysis based on C1QBP 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.

Select Cancer Type:

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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.