TNFRSF6B Human

What is TNFRSF6B and what are its alternative names in scientific literature?

TNFRSF6B, also known as Decoy Receptor 3 (DcR3), TR6, and M68, is a member of the TNF receptor superfamily. The gene encodes a 300 amino acid (aa) protein spanning from Val24 to His300 (Accession # O95407). Unlike typical TNF receptors, TNFRSF6B is a soluble secretory protein that lacks the transmembrane structure in its amino acid sequence, functioning primarily as a decoy receptor that competitively binds with several ligands to modulate apoptosis and immune responses .

What experimental methods are most effective for detecting TNFRSF6B expression in clinical samples?

For clinical TNFRSF6B detection, multiple complementary approaches yield comprehensive results:

• Immunohistochemistry (IHC): Using specific antibodies such as Anti-Human DcR3/TNFRSF6B antibodies with heat-induced epitope retrieval for paraffin-embedded sections. In stomach cancer tissue, TNFRSF6B is typically localized to the membrane of epithelial cells in gastric glands .

• ELISA: Direct ELISAs using specific antibodies provide quantitative measurement with minimal cross-reactivity with related proteins like TRAIL receptors .

• Western Blot: For protein size confirmation and semi-quantitative analysis using antibodies with demonstrated specificity .

• Functional Assays: Measuring inhibition of Fas Ligand/TNFSF6-induced apoptosis in Jurkat cells can assess biological activity of TNFRSF6B .

How can researchers differentiate between physiological and pathological levels of TNFRSF6B expression?

While not explicitly detailed in the search results, researchers should approach this distinction methodologically by:

• Establishing baseline expression across normal tissue panels using quantitative methods like qPCR or ELISA

• Comparing expression between matched normal-tumor pairs from the same patient

• Correlating expression with clinical parameters and survival data

• Using tissue microarrays to standardize detection across large sample cohorts

• Implementing statistical thresholds based on population distributions to define "overexpression"

This is particularly important as TNFRSF6B shows differential expression across cancer types, with significant upregulation documented in pancreatic adenocarcinoma, colorectal adenocarcinoma, gastric cancer, and head and neck squamous cell carcinoma compared to corresponding normal tissues .

What molecular mechanisms underlie TNFRSF6B's role in cancer progression?

TNFRSF6B contributes to cancer progression through several interconnected mechanisms:

• Apoptosis Inhibition: By competitively binding with FasL, it prevents Fas-mediated apoptosis, allowing cancer cells to evade programmed cell death .

• Immune Evasion: The molecule interferes with immune surveillance by blocking interactions between death receptors and their ligands .

• Signaling Pathway Modulation: Functional network analysis reveals TNFRSF6B involvement in multiple cancer-related pathways:

  • Cell cycle regulation

  • p53 signaling pathway

  • NF-kappa B signaling

  • ECM-receptor interaction

  • Cytokine-mediated signaling

• Invasion Promotion: Experimental knockdown of TNFRSF6B inhibits pancreatic cancer cell proliferation and invasion in vitro, suggesting its direct role in these processes .

These mechanisms collectively contribute to the aggressive phenotype observed in TNFRSF6B-overexpressing tumors.

How does TNFRSF6B expression correlate with patient prognosis across different cancer types?

TNFRSF6B expression shows variable prognostic significance across cancer types:

Previous literature has consistently reported that overexpression of TNFRSF6B correlates with shortened total survival time in cancer patients generally . This variable impact across tumor types suggests context-dependent functions that may be influenced by tumor microenvironment and molecular subtype.

What experimental approaches have been validated for modulating TNFRSF6B function in cancer models?

Validated experimental approaches for modulating TNFRSF6B include:

• RNA Interference: Short hairpin RNA (shRNA) knockdown experiments have successfully reduced TNFRSF6B expression in pancreatic cancer cell lines, resulting in inhibition of proliferation and invasion .

• Antibody-Based Neutralization: Anti-TNFRSF6B antibodies can neutralize the inhibitory effect of TNFRSF6B on Fas Ligand-induced apoptosis, as demonstrated in functional assays using Jurkat human acute T cell leukemia cells .

• Recombinant Protein Competition: Recombinant TNFRSF6B proteins (such as DcR3/TNFRSF6B Fc Chimera) can be used to study competitive binding with ligands and downstream effects .

These approaches provide researchers with methodological options for investigating TNFRSF6B's functions and evaluating its potential as a therapeutic target in preclinical models.

How do TNFRSF6B gene polymorphisms influence cancer susceptibility?

TNFRSF6B polymorphisms exhibit complex associations with cancer susceptibility that vary by demographic and environmental factors:

For TNFRSF6B rs2297440:

• Age-dependent effects: Among individuals <62 years old, the TC genotype confers 1.84 times higher risk of gastric cancer compared to TT genotype .

• Interaction with alcohol consumption:

  • In non-drinkers: CC genotype shows protective effects (0.66 times the risk of TT+TC genotypes) .

  • In drinkers: TC genotype associated with 1.67 times higher risk than TT genotype, and TC+CC genotype shows 1.70 times higher risk than TT genotype .

For TNFRSF6B rs2297441:

• Gender-specific effects: In males and non-drinkers, the AG genotype is associated with lower gastric cancer risk compared to GG genotype .

• Consistent protective effect: The AA homozygous mutant appears to be a protective factor for gastric cancer regardless of age .

These findings highlight the importance of stratifying genetic association studies by demographic and lifestyle factors when evaluating TNFRSF6B polymorphisms.

What methodologies are most reliable for genotyping TNFRSF6B variants in large cohort studies?

Based on the search results, the SnapShot method was successfully employed to genotype rs2297440 and rs2297441 polymorphisms in a large hospital-based case-control study involving 577 gastric cancer cases and 678 normal controls .

For comprehensive large-scale genotyping, researchers should consider:

• Initial discovery phase: Using next-generation sequencing to identify all relevant variants

• Validation phase: Employing targeted methods like SnapShot for specific SNPs

• Quality control measures: Including duplicate samples, positive/negative controls, and Hardy-Weinberg equilibrium testing

• Statistical power considerations: Ensuring sufficient sample size for stratified analyses by demographic factors

The study design should also account for potential confounding factors, as demonstrated by the significant interaction between TNFRSF6B genotypes and factors like age and alcohol consumption .

How should researchers interpret contradictory findings in TNFRSF6B genetic association studies?

When faced with contradictory findings in TNFRSF6B genetic association studies, researchers should systematically:

• Examine population stratification: Different ethnic backgrounds may show variable linkage disequilibrium patterns and allele frequencies

• Consider environmental interactions: As seen with rs2297440, where alcohol consumption reverses the direction of association

• Evaluate age and gender effects: The rs2297441 polymorphism shows gender-specific associations

• Assess methodological differences: Genotyping methods, statistical approaches, and sample sizes vary across studies

• Analyze haplotypes rather than single SNPs: Combined effects of multiple polymorphisms may be more informative

• Conduct meta-analyses: Pooling data across studies while accounting for heterogeneity

The complex patterns observed with TNFRSF6B polymorphisms underscore the need for multifactorial analysis when interpreting contradictory genetic findings.

How does TNFRSF6B interact with its ligands to modulate cellular signaling?

TNFRSF6B functions as a decoy receptor that competitively binds with three primary ligands:

• FasL (First apoptosis signal ligand): By binding FasL, TNFRSF6B prevents its interaction with the Fas receptor, inhibiting extrinsic apoptosis pathway activation. This mechanism has been experimentally demonstrated in Jurkat cell models where TNFRSF6B inhibits Fas Ligand/TNFSF6-induced apoptosis in a dose-dependent manner .

• TL1A (Tumor necrosis factor-like cytokine 1A): Binding to TL1A blocks its pro-inflammatory and apoptotic functions.

• LIGHT (TNFSF14): Interaction with LIGHT affects T-cell responses and inflammation .

Through these competitive binding interactions, TNFRSF6B modulates multiple downstream signaling cascades related to cell survival, inflammation, and immune response, creating a complex network of effects that collectively promote cancer progression and immune evasion .

What co-expressed genes and pathways are critical in TNFRSF6B functional networks?

Functional network analysis has identified several key co-expressed genes and pathways associated with TNFRSF6B:

• CEACAM1: Carcinoembryonic antigen-related cell adhesion molecule 1 shows positive correlation with TNFRSF6B in pancreatic cancer, suggesting coordinated functions .

• Enriched pathways associated with TNFRSF6B networks include:

  • Cytokine-mediated signaling

  • Myeloid leukocyte activation

  • ECM organization

  • Response to type I interferon

  • Cellular response to TNF

  • Apoptotic signaling pathways

  • Cell cycle processes

  • p53 signaling pathway

  • NF-kappa B signaling

GSEA analysis revealed TNFRSF6B is widely involved in "ECM organization, cell cycle checkpoints, degradation of the ECM, cell cycle, apoptosis, cytokine signaling in the immune system, and signaling by the interleukin innate immune system" , highlighting its role as a key node in complex regulatory networks.

How does TNFRSF6B contribute to the immunosuppressive tumor microenvironment?

TNFRSF6B creates an immunosuppressive tumor microenvironment through several mechanisms:

• Inhibition of cytotoxic cell function: By preventing FasL-mediated apoptosis, TNFRSF6B protects tumor cells from elimination by cytotoxic T lymphocytes and NK cells that use the Fas/FasL pathway.

• Modulation of immune cell responses: Gene enrichment analysis shows TNFRSF6B involvement in:

  • Immune effector processes

  • Regulation of immune system processes

  • Cytokine signaling

  • Interleukin signaling

• Interference with inflammatory signals: By binding TL1A and LIGHT, TNFRSF6B affects inflammatory processes that would normally contribute to anti-tumor immunity.

These activities collectively suppress anti-tumor immune responses and create a permissive environment for tumor growth and metastasis. The research findings suggest TNFRSF6B as an important immunomodulatory molecule in the tumor microenvironment .

What methodological approaches can resolve conflicting data on TNFRSF6B function?

To resolve conflicting data on TNFRSF6B function, researchers should implement:

• Comprehensive experimental designs:

  • Use multiple cell lines representing diverse tissue origins

  • Employ both gain-of-function and loss-of-function approaches

  • Validate with in vivo models alongside in vitro systems

• Advanced molecular techniques:

  • CRISPR/Cas9 for precise genetic manipulation

  • Inducible expression systems to control timing of TNFRSF6B modulation

  • Single-cell analysis to detect heterogeneous responses

• Integrative data analysis:

  • Combine transcriptomic, proteomic, and functional data

  • Apply network analysis to identify context-dependent effects

  • Use computational modeling to predict pathway interactions

• Standardized reporting:

  • Clearly specify experimental conditions (cell density, passage number, etc.)

  • Report negative results alongside positive findings

  • Share raw data for independent reanalysis

This multifaceted approach can help reconcile seemingly contradictory findings, such as the dual role of TNFRSF6B rs2297440 polymorphism as both risk and protective factor depending on alcohol consumption status .

How can TNFRSF6B be leveraged as a biomarker in personalized oncology?

TNFRSF6B shows promise as a biomarker in personalized oncology through several applications:

This approach aligns with precision medicine by accounting for the demographic and lifestyle factors that influence TNFRSF6B's effects on cancer risk and progression .

What are the most promising therapeutic strategies targeting TNFRSF6B in cancer?

Based on the molecular understanding of TNFRSF6B, several therapeutic strategies show promise:

• Direct neutralization approaches:

  • Monoclonal antibodies against TNFRSF6B can neutralize its decoy receptor function, as demonstrated in experimental models

  • Aptamers or small peptides targeting the ligand-binding domain

• Inhibition of TNFRSF6B expression:

  • RNA interference strategies, building on successful shRNA knockdown experiments that inhibited pancreatic cancer cell proliferation and invasion

  • Epigenetic modulators targeting TNFRSF6B promoter regions

• Pathway-based approaches:

  • Targeting downstream effectors in TNFRSF6B signaling networks

  • Modulating co-expressed genes like CEACAM1 that show positive correlation with TNFRSF6B

• Combination strategies:

  • Pairing TNFRSF6B inhibition with immune checkpoint blockade

  • Combining with conventional chemotherapy to enhance apoptotic responses

• Personalized targeting:

  • Stratifying patients based on TNFRSF6B polymorphisms (rs2297440, rs2297441)

  • Considering age, gender, and lifestyle factors in treatment decisions

These approaches address TNFRSF6B's critical role in cancer progression and immune evasion, offering multiple intervention points for therapeutic development.

What controls and validation steps are essential when studying TNFRSF6B expression?

Rigorous TNFRSF6B expression studies require comprehensive controls and validation:

• Antibody validation:

  • Confirm specificity using positive and negative control samples

  • Validate across multiple detection methods (Western blot, IHC, ELISA)

  • Test for cross-reactivity with related TNF receptor family members

• Expression analysis controls:

  • Include matched normal-tumor pairs when possible

  • Use multiple housekeeping genes for normalization in qPCR

  • Implement tissue-specific positive controls

• Functional validation:

  • Confirm biological activity using apoptosis inhibition assays

  • Verify neutralization with specific antibodies

  • Demonstrate ligand binding competition

• Genetic knockdown/overexpression validation:

  • Confirm target modulation at both mRNA and protein levels

  • Use multiple independent shRNA/siRNA sequences

  • Include rescue experiments with exogenous expression

These validation steps ensure reliable and reproducible results when investigating TNFRSF6B's complex roles in cancer biology.

How should researchers design experiments to study TNFRSF6B's context-dependent effects?

To effectively study TNFRSF6B's context-dependent effects, researchers should design experiments that:

• Incorporate relevant microenvironmental factors:

  • Co-culture systems with immune cells

  • Use of extracellular matrix components

  • Hypoxic conditions that mimic tumor environments

• Account for demographic variables:

  • Age-stratified analyses (<62 years vs. ≥62 years)

  • Gender-specific evaluations

  • Models that incorporate lifestyle factors like alcohol consumption

• Employ diverse experimental systems:

  • Multiple cell lines representing different tissue origins and molecular subtypes

  • Primary patient-derived cells alongside established lines

  • 3D organoid cultures to better recapitulate tissue architecture

  • In vivo models for systemic effects

• Implement multi-omics approaches:

  • Integrate transcriptomic, proteomic, and metabolomic data

  • Apply network analysis to identify context-specific interactions

  • Use computational modeling to predict conditional dependencies

This comprehensive approach addresses the complex interactions observed in studies like the rs2297440 polymorphism analysis, where effects varied dramatically based on age and alcohol consumption status .

What are the critical considerations for translating TNFRSF6B research findings to clinical applications?

Translating TNFRSF6B research to clinical applications requires addressing several critical considerations:

• Biomarker development:

  • Standardize detection methods and establish clinically relevant cutoff values

  • Validate prognostic significance in large, diverse patient cohorts

  • Develop practical assays suitable for clinical laboratory implementation

• Therapeutic targeting challenges:

  • Address potential off-target effects given TNFRSF6B's interactions with multiple ligands

  • Consider compensatory mechanisms that might emerge following TNFRSF6B inhibition

  • Evaluate combination approaches targeting multiple nodes in TNFRSF6B networks

• Patient stratification:

  • Incorporate polymorphism data (rs2297440, rs2297441) into treatment decisions

  • Consider demographic factors (age, gender) and lifestyle factors (alcohol consumption) that modify TNFRSF6B effects

  • Develop predictive models for treatment response

• Risk-benefit assessment:

  • Evaluate potential immunological side effects of TNFRSF6B modulation

  • Consider tissue-specific effects across different cancer types

  • Establish monitoring protocols for patients receiving TNFRSF6B-targeted therapies

These considerations are essential for successfully bridging the gap between laboratory findings and clinically meaningful applications in TNFRSF6B-focused cancer management.

What emerging technologies could advance TNFRSF6B research?

Several cutting-edge technologies hold promise for advancing TNFRSF6B research:

• Single-cell multi-omics:

  • Revealing cell-specific TNFRSF6B expression patterns within heterogeneous tumors

  • Mapping cell-type specific responses to TNFRSF6B modulation

  • Identifying rare cell populations with unique TNFRSF6B dependencies

• Spatial transcriptomics/proteomics:

  • Visualizing TNFRSF6B expression in the spatial context of the tumor microenvironment

  • Correlating expression with immune cell infiltration patterns

  • Mapping receptor-ligand interactions in tissue sections

• CRISPR-based functional genomics:

  • Genome-wide screens to identify synthetic lethal interactions with TNFRSF6B

  • Base editing for precise manipulation of polymorphisms like rs2297440 and rs2297441

  • In vivo CRISPR screens to identify context-dependent functions

• Advanced computational modeling:

  • Systems biology approaches to model TNFRSF6B network dynamics

  • Machine learning algorithms to predict patient response to TNFRSF6B-targeted therapies

  • Integrative analysis of multi-dimensional datasets

These technologies can help resolve the complex context-dependent effects of TNFRSF6B observed in current research and accelerate translation to clinical applications.

What unexplored aspects of TNFRSF6B biology warrant investigation?

Several unexplored aspects of TNFRSF6B biology deserve focused investigation:

• Isoform-specific functions:

  • Characterization of potential splice variants and their differential activities

  • Investigation of post-translational modifications affecting TNFRSF6B function

• Regulatory mechanisms:

  • Epigenetic regulation of TNFRSF6B expression in different contexts

  • microRNA-mediated control of TNFRSF6B mRNA stability and translation

  • Transcription factor networks governing context-dependent expression

• Metabolic interactions:

  • Relationship between TNFRSF6B signaling and cancer metabolism

  • Impact of metabolic stress on TNFRSF6B expression and function

• Evolutionary conservation:

  • Comparative analysis across species to identify essential functional domains

  • Investigation of species-specific adaptations in TNFRSF6B signaling

• Non-canonical functions:

  • Potential intracellular activities beyond canonical decoy receptor function

  • Role in cell-cell communication beyond direct ligand competition

These research directions could uncover novel mechanisms underlying TNFRSF6B's complex roles in cancer biology and identify new therapeutic opportunities.

How might systems biology approaches advance our understanding of TNFRSF6B networks?

Systems biology approaches offer powerful frameworks for understanding TNFRSF6B's complex regulatory networks:

• Network modeling and analysis:

  • Extension of the current co-expression networks to include dynamic interactions

  • Identification of key nodes and bottlenecks in TNFRSF6B-centered networks

  • Prediction of network perturbations following therapeutic targeting

• Multi-scale integration:

  • Connecting molecular interactions to cellular phenotypes and clinical outcomes

  • Incorporating temporal dynamics of TNFRSF6B signaling during disease progression

  • Linking genetic variants (rs2297440, rs2297441) to network-level changes

• Pathway crosstalk mapping:

  • Characterizing interactions between TNFRSF6B pathways and other cancer-related networks

  • Identifying compensatory mechanisms that might emerge following TNFRSF6B inhibition

  • Revealing synergistic pathway interactions for combination therapy design

• Computational modeling:

  • In silico prediction of TNFRSF6B-targeted therapy responses

  • Patient-specific modeling incorporating demographic and genetic factors

  • Simulation of tumor microenvironment responses to TNFRSF6B modulation

These approaches align with the complex functional network already identified for TNFRSF6B, which includes involvement in "cell cycle processes, apoptosis, apoptosis signaling pathways, immune responses, and responses to interferon" .