TNFRSF10C Human

What is TNFRSF10C and how does it function in the TRAIL signaling pathway?

TNFRSF10C (tumor necrosis factor receptor superfamily, member 10c) is a glycosylphosphatidylinositol (GPI)-linked membrane protein that functions as a decoy receptor for TRAIL (TNF-related apoptosis inducing ligand). It contains a TRAIL-binding extracellular cysteine-rich domain but critically lacks an intracellular signaling domain. This structural configuration enables TNFRSF10C to bind TRAIL with high affinity without triggering pro-apoptotic signaling . The protein, also known as DcR1 (decoy receptor 1), LIT, and TRID, effectively competes with death-inducing receptors (TRAIL-R1/DR4 and TRAIL-R2/DR5) for TRAIL binding. Expression of TNFRSF10C has been demonstrated to protect cells bearing TRAIL-R1 and/or TRAIL-R2 from TRAIL-induced apoptosis, making it an important regulator of apoptotic pathways .

What is the genomic location of TNFRSF10C and why is it significant in cancer research?

TNFRSF10C is located on chromosome 8p21.3 (23.01~23.03 Mb), a region of significant interest in cancer research. This chromosomal region falls within one of the two important consensus prostate cancer-susceptibility regions at 8p and represents one of the most frequently deleted loci in the genome of various cancers including colon, lung, prostate, breast, bladder, and head-and-neck cancers . The location of TNFRSF10C in this commonly deleted genomic segment suggests its potential role as a tumor suppressor gene. Research has indicated that TNFRSF10C is deleted in 74.5% of prostate tumors, and a decreased TNFRSF10C copy number has been shown to accelerate colorectal cancer distant metastasis . This genomic positioning provides a fundamental basis for investigating TNFRSF10C's involvement in cancer development and progression.

How prevalent is TNFRSF10C promoter hypermethylation across different cancer types?

TNFRSF10C promoter CpG island (CGI) hypermethylation occurs with varying frequency across multiple cancer types. Research has documented the following prevalence rates:

• Prostate cancer: 78.0% (46 of 59 primary cancers)

• Neuroblastoma: 21%

• Primary breast cancer: 33%

• Primary lung cancer: 16%

• Malignant mesotheliomas: 63%

• Ependymoma: 50%

• Choroid plexus papillomas: 50%

• Colorectal cancer: Significant association (specific percentage not provided)

In contrast, hypermethylation was detected in only 2.3% of matched normal prostate controls, highlighting the cancer-specific nature of this epigenetic modification . The widespread occurrence of TNFRSF10C methylation across different cancer types suggests it may be a common mechanism in carcinogenesis, making it a valuable epigenetic biomarker for cancer detection and prognosis.

What is the relationship between TNFRSF10C hemizygous deletion and promoter methylation in cancer tissues?

Studies have revealed a striking relationship between TNFRSF10C hemizygous deletion and promoter methylation in cancer tissues, suggesting cooperative mechanisms for gene inactivation. In prostate cancer, research has demonstrated:

This data indicates that in nearly 95% of prostate cancer cases, TNFRSF10C is inactivated through either hemizygous deletion or promoter hypermethylation . The remarkably high combined frequency suggests these two mechanisms function cooperatively to silence TNFRSF10C expression, following the classic "two-hit" model of tumor suppressor gene inactivation. Deletion and/or methylation of the TNFRSF10C gene was correlated with decreased mRNA expression in clinical specimens, confirming the functional impact of these genetic and epigenetic alterations . This pattern indicates strong selective pressure for complete TNFRSF10C inactivation during carcinogenesis.

What methodologies are recommended for analyzing TNFRSF10C methylation status?

Bisulfite sequencing represents the gold standard for analyzing TNFRSF10C methylation status, providing precise methylation mapping across specific CpG sites. The comprehensive methodology involves:

• Bisulfite treatment of DNA: Converting unmethylated cytosines to uracils while preserving methylated cytosines.

• PCR amplification: Using primers designed to amplify the TNFRSF10C promoter CGI region.

• Subcloning of PCR products: Utilizing vectors such as pCR4-TOPO for isolating individual DNA molecules.

• Sequencing of multiple independent clones: Providing the exact methylation status for each CpG site.

In detailed studies, this approach allowed researchers to survey 13 CpG sites in and near the TNFRSF10C promoter CGI (12 sites within the CGI and one additional site located outside) . For quantitative analysis of methylation patterns across larger sample sets, researchers may employ methylation-specific PCR, pyrosequencing, or array-based methylation platforms, though these methods provide less comprehensive CpG site resolution compared to bisulfite sequencing.

How can researchers measure TNFRSF10C expression at the mRNA and protein levels?

Comprehensive analysis of TNFRSF10C expression requires assessment at both mRNA and protein levels using complementary techniques:

For mRNA expression analysis:

• Real-time RT-PCR: Using validated TNFRSF10C-specific primers such as:

  • Forward: 5'-CCCTAAAGTTCGTCGTCGTCAT-3'

  • Reverse: 5'-GGGCAGTGGTGGCAGAGTA-3'

  • With housekeeping genes (e.g., GAPDH) as endogenous controls

• Alternative primer sets for validation:

  • Forward: 5'-CACCAACGCTTCCAACAATGAACC-3'

  • Reverse: 5'-TCCGGAAGGTGCCTTCTTTACACT-3'

For protein expression analysis:

• Western blotting: Using specific antibodies like Human TRAIL R3/TNFRSF10C Antibody (Clone #90903)

• Flow cytometry: With fluorescently labeled antibodies such as TRAIL R3/TNFRSF10C Alexa Fluor 700-conjugated Antibody for cellular expression analysis

• ELISA: For quantitative protein measurement in cell lysates or serum samples

Integrating both mRNA and protein analysis provides a comprehensive assessment of TNFRSF10C expression and can establish correlations between methylation status, deletion events, and functional expression levels.

How can researchers experimentally evaluate the functional consequences of TNFRSF10C inactivation?

Multiple experimental approaches can be employed to investigate the functional impacts of TNFRSF10C inactivation:

• Demethylation experiments: Treatment of cancer cell lines (such as PC3 prostate cancer cells) with the demethylating agent 5-aza-2'-deoxycytidine to restore TNFRSF10C expression. Studies have demonstrated that demethylation of the TNFRSF10C promoter CGI was accompanied by transcriptional re-activation of the gene in prostate cancer cell lines . This approach allows researchers to establish a causal relationship between methylation status and gene expression.

• TRAIL sensitivity assays: Characterizing the sensitivity to TRAIL-induced apoptosis in cells with different TNFRSF10C expression levels. Recombinant Human TRAIL R3/TNFRSF10C Fc Chimera can inhibit Recombinant Human TRAIL/TNFSF10-induced cytotoxicity in a dose-dependent manner in experimental systems such as the L-929 mouse fibroblast cell line . The neutralization of this inhibition can be achieved using Human TRAIL R3/TNFRSF10C Monoclonal Antibody, providing a controlled experimental system .

• Genetic manipulation: Using siRNA, shRNA, or CRISPR-Cas9 technology to silence or delete TNFRSF10C in normal cells, followed by phenotypic and molecular characterization.

• Overexpression studies: Introducing TNFRSF10C expression vectors into cancer cell lines with low endogenous expression to assess potential tumor-suppressive effects.

These experimental approaches provide mechanistic insights into how TNFRSF10C alterations contribute to cancer development and progression.

What is the relationship between TNFRSF10C methylation and clinical outcomes in cancer patients?

The relationship between TNFRSF10C methylation and clinical outcomes represents an emerging area of cancer research with significant clinical implications:

• Cancer diagnostic potential: The significantly higher frequency of TNFRSF10C promoter CGI hypermethylation in cancer tissues (78.0% in prostate cancer) compared to normal tissues (2.3%) highlights its potential as a cancer-specific biomarker . This marked differential methylation pattern suggests utility for early cancer detection.

• Correlation with cancer progression: In prostate cancer, a significantly reduced expression of TNFRSF10C in M0 stage tumors compared to benign prostate tissue has been observed, suggesting TNFRSF10C as a potential prostate cancer tumor suppressor gene involved in early carcinogenesis .

• Emerging biomarker for colorectal cancer: Recent research has identified TNFRSF10C methylation as a new epigenetic biomarker for colorectal cancer, expanding its clinical relevance beyond prostate malignancies .

• Integration with clinicopathological parameters: Studies have examined correlations between TNFRSF10C methylation and clinicopathological features such as Gleason score in prostate cancer, though specific associations require further investigation with larger cohorts .

Further research is needed to establish definitive connections between TNFRSF10C methylation patterns and patient survival, treatment response, and risk of recurrence across different cancer types.

How does TNFRSF10C interact with other components of the TRAIL signaling pathway in cancer cells?

TNFRSF10C engages with the TRAIL signaling pathway through several sophisticated mechanisms:

• Competitive TRAIL binding: TNFRSF10C functions as a high-affinity decoy receptor that sequesters TRAIL, preventing its interaction with death-inducing receptors (TRAIL-R1/DR4 and TRAIL-R2/DR5). This competitive binding effectively neutralizes TRAIL's pro-apoptotic activity without initiating intracellular signaling .

• Neutralization activity: Experimental data demonstrates that recombinant human TRAIL R3/TNFRSF10C Fc chimera can inhibit TRAIL-induced cytotoxicity in a dose-dependent manner. This inhibition can be counteracted using specific TNFRSF10C antibodies, highlighting the receptor's regulatory role in TRAIL signaling .

• Coordination with other decoy receptors: TNFRSF10C works alongside another decoy receptor, TRAIL-R4/DcR2 (TNFRSF10D), which similarly binds TRAIL with high affinity but antagonizes TRAIL-induced apoptosis through a slightly different mechanism .

• GPI-anchored membrane localization: Unlike transmembrane TRAIL receptors, TNFRSF10C is anchored to the cell membrane via a GPI linkage, potentially influencing its spatial distribution and accessibility to TRAIL within the tumor microenvironment .

Understanding these interactions provides insight into how TNFRSF10C inactivation might disrupt normal TRAIL signaling homeostasis and contribute to cancer development.

What statistical approaches should be used for analyzing integrated TNFRSF10C methylation, deletion, and expression data?

Analyzing the complex relationship between TNFRSF10C methylation, deletion, and expression requires sophisticated statistical approaches:

• For categorical comparisons:

  • Fisher's exact test: Appropriate for analyzing differences in methylation frequency between cancer and normal tissues or between different Gleason scores in prostate cancer .

  • Chi-square analysis: For larger sample sizes when comparing categorical methylation or deletion data.

• For expression analysis:

  • General Linear Model analysis: Effective for testing differences in TNFRSF10C gene expression between groups of tissues with different TNFRSF10C status (deletion and/or methylation) .

  • Dunnett's test: Useful for comparing TNFRSF10C expression in different tumor groups against a baseline control group (e.g., normal tissues with intact TNFRSF10C) .

• For integrative analysis:

  • Multivariate regression: To assess the combined impact of methylation and deletion on expression levels.

  • Correlation analysis: To evaluate relationships between methylation intensity, copy number, and expression.

  • Logistic regression: For determining which molecular alterations best predict clinical outcomes.

• For survival analysis:

  • Kaplan-Meier curves with log-rank tests: To evaluate whether TNFRSF10C alterations affect patient survival.

  • Cox proportional hazards models: To adjust for clinical covariates when assessing prognostic significance.

These statistical methods should be selected based on specific research questions and data characteristics to maximize the interpretability and clinical relevance of findings.

What are the most promising future research directions for TNFRSF10C in cancer?

The current understanding of TNFRSF10C in cancer biology presents several promising avenues for future research:

• Development of methylation-based biomarkers: The high frequency and cancer-specificity of TNFRSF10C promoter methylation (78% in prostate cancer vs. 2.3% in normal tissue) makes it a promising candidate for diagnostic, prognostic, and predictive biomarker development across multiple cancer types .

• Therapeutic targeting strategies: Exploring the potential to restore TNFRSF10C expression through demethylating agents or targeted epigenetic editing could sensitize cancer cells to TRAIL-induced apoptosis and enhance immunotherapy efficacy.

• Comprehensive multi-omics profiling: Integrating TNFRSF10C methylation and deletion data with broader genomic, transcriptomic, and proteomic landscapes across cancer types to identify molecular subtypes and targeted intervention opportunities.

• Single-cell and spatial analysis: Investigating TNFRSF10C alterations at single-cell resolution and in spatial context to understand tumor heterogeneity and its implications for treatment response.

• Liquid biopsy applications: Developing methodologies to detect TNFRSF10C methylation in circulating tumor DNA as a minimally invasive approach for cancer detection and monitoring.