ImmunEscpMap: a Knowledgebase of Cancer Immune Escape Mechanisms

Home

Download

Statistics

Landscape

Help

Contact

	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 TGFB1

Gene summary

Gene Symbol	TGFB1
Gene ID	7040
Gene name	transforming growth factor beta 1
Synonyms	TGFBETA;DPD1;CED;TGFB
Type of gene	protein_coding
UniProtAcc	P01137

Gene ontology (Biological Process only)

GO ID	GO term
GO:0010629	negative regulation of gene expression
GO:0071456	cellular response to hypoxia
GO:0010628	positive regulation of gene expression
GO:0045893	positive regulation of DNA-templated transcription
GO:0045944	positive regulation of transcription by RNA polymerase II
GO:0051781	positive regulation of cell division
GO:0045892	negative regulation of DNA-templated transcription
GO:0000122	negative regulation of transcription by RNA polymerase II
GO:0000902	cell morphogenesis
GO:0001570	vasculogenesis
GO:0001657	ureteric bud development
GO:0001666	response to hypoxia
GO:0001763	morphogenesis of a branching structure
GO:0001775	cell activation
GO:0001837	epithelial to mesenchymal transition
GO:0001843	neural tube closure
GO:0002028	regulation of sodium ion transport
GO:0002069	columnar/cuboidal epithelial cell maturation
GO:0002460	adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains
GO:0002513	tolerance induction to self antigen
GO:0003179	heart valve morphogenesis
GO:0003180	aortic valve morphogenesis
GO:0006874	cellular calcium ion homeostasis
GO:0006954	inflammatory response
GO:0007179	transforming growth factor beta receptor signaling pathway
GO:0007219	Notch signaling pathway
GO:0007406	negative regulation of neuroblast proliferation
GO:0007492	endoderm development
GO:0007507	heart development
GO:0007565	female pregnancy
GO:0008283	cell population proliferation
GO:0008354	germ cell migration
GO:0009410	response to xenobiotic stimulus
GO:0009749	response to glucose
GO:0010033	response to organic substance
GO:0010467	gene expression
GO:0010468	regulation of gene expression
GO:0010718	positive regulation of epithelial to mesenchymal transition
GO:0010763	positive regulation of fibroblast migration
GO:0014003	oligodendrocyte development
GO:0014008	positive regulation of microglia differentiation
GO:0014070	response to organic cyclic compound
GO:0016202	regulation of striated muscle tissue development
GO:0021915	neural tube development
GO:0030217	T cell differentiation
GO:0030279	negative regulation of ossification
GO:0030308	negative regulation of cell growth
GO:0030316	osteoclast differentiation
GO:0030879	mammary gland development
GO:0031065	positive regulation of histone deacetylation
GO:0031100	animal organ regeneration
GO:0031536	positive regulation of exit from mitosis
GO:0032355	response to estradiol
GO:0032667	regulation of interleukin-23 production
GO:0032700	negative regulation of interleukin-17 production
GO:0032740	positive regulation of interleukin-17 production
GO:0032943	mononuclear cell proliferation
GO:0032956	regulation of actin cytoskeleton organization
GO:0032967	positive regulation of collagen biosynthetic process
GO:0033280	response to vitamin D
GO:0034616	response to laminar fluid shear stress
GO:0035066	positive regulation of histone acetylation
GO:0035902	response to immobilization stress
GO:0042098	T cell proliferation
GO:0042110	T cell activation
GO:0042127	regulation of cell population proliferation
GO:0042130	negative regulation of T cell proliferation
GO:0042306	regulation of protein import into nucleus
GO:0042475	odontogenesis of dentin-containing tooth
GO:0042482	positive regulation of odontogenesis
GO:0042552	myelination
GO:0043029	T cell homeostasis
GO:0043065	positive regulation of apoptotic process
GO:0043129	surfactant homeostasis
GO:0045066	regulatory T cell differentiation
GO:0045589	regulation of regulatory T cell differentiation
GO:0045591	positive regulation of regulatory T cell differentiation
GO:0046716	muscle cell cellular homeostasis
GO:0048146	positive regulation of fibroblast proliferation
GO:0048286	lung alveolus development
GO:0048535	lymph node development
GO:0048565	digestive tract development
GO:0048839	inner ear development
GO:0050673	epithelial cell proliferation
GO:0050679	positive regulation of epithelial cell proliferation
GO:0050680	negative regulation of epithelial cell proliferation
GO:0050765	negative regulation of phagocytosis
GO:0050832	defense response to fungus
GO:0050868	negative regulation of T cell activation
GO:0051152	positive regulation of smooth muscle cell differentiation
GO:0051280	negative regulation of release of sequestered calcium ion into cytosol
GO:0051402	neuron apoptotic process
GO:0051726	regulation of cell cycle
GO:0055010	ventricular cardiac muscle tissue morphogenesis
GO:0055091	phospholipid homeostasis
GO:0060070	canonical Wnt signaling pathway
GO:0060325	face morphogenesis
GO:0060364	frontal suture morphogenesis
GO:0060391	positive regulation of SMAD protein signal transduction
GO:0060435	bronchiole development
GO:0060744	mammary gland branching involved in thelarche
GO:0060751	branch elongation involved in mammary gland duct branching
GO:0060762	regulation of branching involved in mammary gland duct morphogenesis
GO:0061035	regulation of cartilage development
GO:0061298	retina vasculature development in camera-type eye
GO:0061448	connective tissue development
GO:0061520	Langerhans cell differentiation
GO:0070168	negative regulation of biomineral tissue development
GO:0070173	regulation of enamel mineralization
GO:0070306	lens fiber cell differentiation
GO:0071260	cellular response to mechanical stimulus
GO:0071333	cellular response to glucose stimulus
GO:0071363	cellular response to growth factor stimulus
GO:0071479	cellular response to ionizing radiation
GO:0071549	cellular response to dexamethasone stimulus
GO:0071560	cellular response to transforming growth factor beta stimulus
GO:0071677	positive regulation of mononuclear cell migration
GO:0071895	odontoblast differentiation
GO:0072089	stem cell proliferation
GO:0085029	extracellular matrix assembly
GO:0090190	positive regulation of branching involved in ureteric bud morphogenesis
GO:0097421	liver regeneration
GO:0098586	cellular response to virus
GO:1900182	positive regulation of protein localization to nucleus
GO:1901203	positive regulation of extracellular matrix assembly
GO:1902074	response to salt
GO:1990314	cellular response to insulin-like growth factor stimulus
GO:1990402	embryonic liver development
GO:2000648	positive regulation of stem cell proliferation
GO:0008284	positive regulation of cell population proliferation
GO:0070374	positive regulation of ERK1 and ERK2 cascade
GO:0051897	positive regulation of protein kinase B signaling
GO:0030335	positive regulation of cell migration
GO:0031334	positive regulation of protein-containing complex assembly
GO:0032755	positive regulation of interleukin-6 production
GO:0032760	positive regulation of tumor necrosis factor production
GO:0050729	positive regulation of inflammatory response
GO:0045599	negative regulation of fat cell differentiation
GO:0043117	positive regulation of vascular permeability
GO:0045662	negative regulation of myoblast differentiation
GO:0043123	positive regulation of I-kappaB kinase/NF-kappaB signaling
GO:0050921	positive regulation of chemotaxis
GO:0002062	chondrocyte differentiation
GO:0001933	negative regulation of protein phosphorylation
GO:0006796	phosphate-containing compound metabolic process
GO:1902895	positive regulation of miRNA transcription
GO:0002040	sprouting angiogenesis
GO:0008285	negative regulation of cell population proliferation
GO:0017015	regulation of transforming growth factor beta receptor signaling pathway
GO:0097191	extrinsic apoptotic signaling pathway
GO:0051247	positive regulation of protein metabolic process
GO:0050731	positive regulation of peptidyl-tyrosine phosphorylation
GO:0043536	positive regulation of blood vessel endothelial cell migration
GO:0030214	hyaluronan catabolic process
GO:0043410	positive regulation of MAPK cascade
GO:0048298	positive regulation of isotype switching to IgA isotypes
GO:0010742	macrophage derived foam cell differentiation
GO:0048642	negative regulation of skeletal muscle tissue development
GO:0042307	positive regulation of protein import into nucleus
GO:0071404	cellular response to low-density lipoprotein particle stimulus
GO:0002244	hematopoietic progenitor cell differentiation
GO:1900126	negative regulation of hyaluronan biosynthetic process
GO:0009611	response to wounding
GO:0045742	positive regulation of epidermal growth factor receptor signaling pathway
GO:0010575	positive regulation of vascular endothelial growth factor production
GO:0010716	negative regulation of extracellular matrix disassembly
GO:0048661	positive regulation of smooth muscle cell proliferation
GO:0090263	positive regulation of canonical Wnt signaling pathway
GO:0002248	connective tissue replacement involved in inflammatory response wound healing
GO:0002859	negative regulation of natural killer cell mediated cytotoxicity directed against tumor cell target
GO:0006611	protein export from nucleus
GO:0006754	ATP biosynthetic process
GO:0007435	salivary gland morphogenesis
GO:0010936	negative regulation of macrophage cytokine production
GO:0022408	negative regulation of cell-cell adhesion
GO:0030334	regulation of cell migration
GO:0031293	membrane protein intracellular domain proteolysis
GO:0032570	response to progesterone
GO:0032801	receptor catabolic process
GO:0032930	positive regulation of superoxide anion generation
GO:0035307	positive regulation of protein dephosphorylation
GO:0036446	myofibroblast differentiation
GO:0043537	negative regulation of blood vessel endothelial cell migration
GO:0045216	cell-cell junction organization
GO:0045596	negative regulation of cell differentiation
GO:0045786	negative regulation of cell cycle
GO:0050714	positive regulation of protein secretion
GO:0060312	regulation of blood vessel remodeling
GO:0070723	response to cholesterol
GO:1902462	positive regulation of mesenchymal stem cell proliferation
GO:1902894	negative regulation of miRNA transcription
GO:1903077	negative regulation of protein localization to plasma membrane
GO:1904018	positive regulation of vasculature development
GO:1904894	positive regulation of receptor signaling pathway via STAT
GO:2000343	positive regulation of chemokine (C-X-C motif) ligand 2 production
GO:2000353	positive regulation of endothelial cell apoptotic process
GO:2000636	positive regulation of primary miRNA processing
GO:2000727	positive regulation of cardiac muscle cell differentiation
GO:0007165	signal transduction

Top

Literatures describing the association of TGFB1 and immune escape mechanisms

The table presents literature evidences demonstrating the involvement of TGFB1 in cancer immune escape mechanisms.

PMID	Cancer Type	Mechanism	Evidence Sentences
7121053	Hepatocellular carcinoma	Endothelial cell anergy	We further verified that CD109 knockdown upregulated IL-8 expression through activation of TGF-β/Akt/NF-κB pathway in HUVEC. These results were consistent with previous studies showing that activation of TGF-β, Akt, and NF-κB pathways upregulated the IL-8 expression in EC.
25623554	Breast cancer	Endothelial cell anergy	The western analysis showed Smad5 phosphorylation only in response to treating ECsNorm with Jag1 and TGFβ ligands as was compared with total Smad5 protein. To further verify the synergistic role of Jag1/notch and TGFβ/Smad5 in this process, they treated ECsNorm with both Jag1 and TGFβ ligands and observed increased level of Smad5 phosphorylation confirming the synergistic role for the ligands in activation of Smad5.
37480223	Pancreatic cancer	Matrix barrier	Controls desmoplastic reaction and fibrosis, promotes the production of collagen, increases the production of extracellular matrix proteins, and regulates immune response and inflammatory processes.
31462327	Pancreatic adenocarcinoma	Matrix barrier	CAFs can also produce many growth factors and proinflammatory cytokines, notably, transforming growth factor-β (TGF-β), vascular endothelial growth factor (VEGF), interleukin-6 (IL-6), and CXC-chemokine ligand (CXCL12), to promote angiogenesis and recruit immunosuppressive cells into the TME to assist in immune evasion.
32213632	Melanoma, breast cancer, and bladder cancer	Matrix barrier	Specific targeting of TGFβ1, a molecule secreted by myCAFs, was also effective in tumors expressing more than one TGFβ isoform. Combined SRK-181-mIgG1 and anti-PD-1 treatment resulted in increased intratumoral CD8+ T cells and decreased immunosuppressive myeloid cells.
28232471	Pancreatic cancer	Matrix barrier	Many factors that are present in this organoid media, including Noggin, B27 supplement, and TGBβ inhibitor, are known to be potent inhibitors of fibroblast proliferation.
38574299	Myeloma	T cell dysfunction and exhaustion	Finally, we demonstrate that non-classical monocytes are enriched in the myeloma niche and may induce CAR-T cell dysfunction through mechanisms that include TGFβ.
32753468	Gastric cancer	T cell dysfunction and exhaustion	Our data highlight that GC-derived TGF-β1 promotes PD-1 independent CD8+ T cell dysfunction.
32200421	Gastric cancer	T cell dysfunction and exhaustion	Moreover, such diffuse type-associated CD8+ T cell dysfunction was featured by elevated expression of immunosuppressive factors including interleukin-10 (IL-10), transforming growth factor-β1 (TGF-β1) and indoleamine 2,3-dioxygenase 1 (IDO1).
32117960	Review, pan-cancer	T cell dysfunction and exhaustion	TGF-β stimulates CD39 and CD73 expression, thereby inhibiting autologous CD8+ T cell proliferation and function.
33927375	Review, pan-cancer	Metabolic barrier	Treg cells exert their immunosuppressive effects through the expression of inhibitory molecules such as CTLA4 and LAG3, as well as by secreting immunosuppressive cytokines such as IL-10, TGFβ and IL-35. CD8+ T cells enter the tumour as PD1− or PD1low cells with intact mitochondria.

Top

Comparison of the TGFB1 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
KIRC	1.57e+00	1.27e-61	5.20e-60
THCA	1.48e+00	1.33e-51	1.34e-49
HNSC	1.56e+00	2.06e-44	4.11e-42
GBM	1.50e+00	1.16e-05	7.85e-05
CHOL	1.33e+00	9.37e-04	3.28e-03

Top

Comparison of the TGFB1 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 TGFB1 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 TGFB1 methylation in genebody region.

Top

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.

Select Cancer Type:

Top

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
ENSG00000156284	CLDN8	-6.80e+00	3.74e-04	3.95e-02
ENSG00000141316	SPACA3	-3.76e+00	3.77e-04	3.96e-02
ENSG00000188459	WASF4P	2.25e+00	3.82e-04	3.99e-02
ENSG00000204386	NEU1	-1.28e+00	3.91e-04	4.07e-02
ENSG00000225706	PTPRD-AS1	-2.70e+00	3.92e-04	4.07e-02
ENSG00000072195	SPEG	-2.78e+00	3.95e-04	4.09e-02
ENSG00000071909	MYO3B	-2.69e+00	3.99e-04	4.09e-02
ENSG00000115263	GCG	-5.17e+00	3.98e-04	4.09e-02
ENSG00000078328	RBFOX1	-4.07e+00	4.05e-04	4.09e-02
ENSG00000104435	STMN2	-2.80e+00	4.05e-04	4.09e-02
ENSG00000112902	SEMA5A	-1.67e+00	4.03e-04	4.09e-02
ENSG00000214417	KRT18P13	2.12e+00	4.05e-04	4.09e-02
ENSG00000048540	LMO3	-3.00e+00	4.07e-04	4.10e-02
ENSG00000156298	TSPAN7	-1.94e+00	4.16e-04	4.16e-02
ENSG00000163435	ELF3	-1.05e+00	4.17e-04	4.16e-02
ENSG00000113805	CNTN3	-3.17e+00	4.24e-04	4.21e-02
ENSG00000185915	KLHL34	-4.35e+00	4.25e-04	4.21e-02
ENSG00000164326	CARTPT	-5.63e+00	4.31e-04	4.25e-02
ENSG00000241635	UGT1A1	-2.79e+00	4.36e-04	4.29e-02
ENSG00000234678	RP11-465N4.4	-1.04e+00	4.41e-04	4.31e-02

Page: 1 2 ... 10 11 12 13 14 ... 14 15

Down-regulated KEGG pathways

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

ImmunEscpMap id	Log2FC	P value	Cancer type	Regimen	Num Resp	Num NonResp	PMID
Exp_4	-1.91e+00	1.00e-03	Lung cancer	Nivolumab	3	6	32879421

Mutation

No significant differences were found in TGFB1 mutation.

Top

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

Top

The association between TGFB1 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.

Select Cancer Type:

Top

Drugs targeting TGFB1 and diseases related to TGFB1.

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

Top

Survival analysis based on TGFB1 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:

Top

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.