TMEM71 Antibody

What is TMEM71 and what are its key biological functions?

TMEM71 is a transmembrane protein implicated in multiple cellular physiological functions and various cancerous growths. Research has demonstrated that TMEM71 plays significant roles in:

• Immune response regulation in the tumor microenvironment

• Cell proliferation, migration, and invasion in cancer cells

• Potential activation of inflammasomes through mechanisms similar to other TMEM family members

• Modulation of specific signaling pathways including JAK2/STAT3 and NLRP3/Caspase-1/GSDMD pathways

Beyond cancer, TMEM71 has been identified as potentially regulating pyroptosis through mechanisms similar to other TMEM family members, which modulate intracellular calcium levels and promote inflammasome formation .

How does TMEM71 expression vary across different cancer types?

TMEM71 expression patterns vary significantly between cancer types, with opposing roles observed in different malignancies:

• Gliomas/Lower-grade glioma (LGG): TMEM71 is abnormally elevated and associated with poor prognosis . Its expression increases with higher grades of glioma, and is particularly overexpressed in IDH-wild-type and MGMT-unmethylated samples .

• Nasopharyngeal carcinoma (NPC): TMEM71 is downregulated in tumor tissues compared to normal tissues, and higher expression correlates with better progression-free survival .

• Breast cancer: Studies have noted reduced TMEM71 levels, with overexpression shown to inhibit cell proliferation and migration .

This differential expression highlights the context-dependent nature of TMEM71's functions across cancer types.

What are the recommended methods for detecting TMEM71 in research samples?

Multiple complementary techniques should be employed for reliable TMEM71 detection:

• qRT-PCR: For mRNA expression analysis, as demonstrated in studies comparing LGG cell lines (SW1088, SW1783) with normal human astrocyte (NHA) lines .

• Western blotting: For protein-level detection and quantification, using validated antibodies with appropriate positive and negative controls .

• Immunohistochemistry (IHC): For tissue localization and semi-quantitative analysis of expression patterns, as employed in NPC clinical sample studies .

• Multiplex immunofluorescence staining: For co-localization studies, particularly useful when examining interactions with other proteins (e.g., TMEM71 with NLRP3 in NPC tumor cells) .

When implementing these methods, researchers should validate antibody specificity through knockdown/overexpression controls and include appropriate tissue-specific positive and negative controls.

How does TMEM71 contribute to cancer progression mechanisms?

TMEM71's role in cancer progression appears to be cancer-type dependent, with contrasting mechanisms observed:

Pro-oncogenic mechanisms (in gliomas):

• Activates the JAK2/STAT3 pathway

• Promotes CEBPD expression

• Enhances cell proliferation and motility in LGG cells

• Associated with chemoresistance and glioma stem cells

Tumor-suppressive mechanisms (in NPC):

• Activates the NLRP3/Caspase-1/GSDMD pathway

• Inhibits NPC cell viability, proliferation, and invasiveness

• Effects are reversed by NLRP3 silencing, indicating pathway dependence

These contrasting roles underscore the importance of cancer-specific characterization of TMEM71 functions before therapeutic targeting.

What signaling pathways does TMEM71 interact with in cancer cells?

Research has identified several key signaling pathways that interact with TMEM71:

• NLRP3/Caspase-1/GSDMD pathway: In NPC, TMEM71 binds directly to NLRP3 (confirmed through molecular docking and co-immunoprecipitation), activating this pyroptosis-related pathway and suppressing tumor growth .

• JAK2/STAT3 pathway: In LGG, TMEM71 activates this pathway, promoting CEBPD expression and ultimately affecting cancer cell growth and motility .

• Immune regulation pathways: TMEM71 expression correlates with immune characteristics and checkpoint genes (including PD1, PD-L1, CD80, and CD86), suggesting involvement in cancer immunoregulation .

The interaction with these pathways positions TMEM71 as a potential therapeutic target through modulation of downstream signaling events.

How reliable is TMEM71 as a prognostic biomarker in different cancers?

The prognostic value of TMEM71 varies by cancer type:

Lower-grade glioma (LGG):

• Both multivariate and univariate Cox regression analyses confirm TMEM71 as an independent prognostic biomarker

• High expression correlates with poor prognosis

Nasopharyngeal carcinoma (NPC):

The table below summarizes clinical features of NPC patients in relation to TMEM71 expression:

The contrasting prognostic associations highlight the importance of context-specific interpretation of TMEM71 expression data .

What are the optimal experimental approaches for studying TMEM71 function in cancer models?

Comprehensive investigation of TMEM71 function requires multiple complementary approaches:

Genetic manipulation:

• Overexpression using OE-TMEM71 plasmid transfection

• Silencing via siRNA targeting TMEM71 (si-TMEM71)

• Combined approaches (e.g., TMEM71 overexpression with si-NLRP3) to examine pathway dependencies

Functional assays:

• Cell viability assessment (CCK-8 assays at multiple time points: 24h, 48h, 72h)

• Clonogenic assays for proliferation capacity

• Invasion assays for metastatic potential

• Scratch healing experiments for migration assessment

Molecular interaction studies:

• Molecular docking analysis to predict binding sites

• Co-immunoprecipitation to confirm protein-protein interactions

• Western blot and qPCR to verify downstream pathway activation

These methodological approaches should be tailored to the specific cancer type being studied, given TMEM71's context-dependent functions.

How should researchers interpret contradictory findings regarding TMEM71's role in different cancers?

When faced with contradictory findings regarding TMEM71's role:

• Consider cancer-specific contexts: The contrasting roles of TMEM71 in glioma (oncogenic) versus NPC (tumor-suppressive) suggest cancer-type specific functions .

• Examine molecular subtypes: TMEM71 is significantly overexpressed in mesenchymal subtype gliomas, indicating subtype-specific effects .

• Evaluate genetic backgrounds: TMEM71 expression patterns differ between IDH-wild-type and MGMT-unmethylated samples in gliomas .

• Assess pathway interactions: TMEM71's effects depend on interaction with different pathways (NLRP3/Caspase-1/GSDMD in NPC vs. JAK2/STAT3 in LGG) .

• Consider immune context: TMEM71 has differential associations with immune cell populations across cancer types, suggesting microenvironment-dependent functions .

What controls should be included when validating TMEM71 antibodies for cancer research?

Proper antibody validation requires rigorous controls:

Positive controls:

• Cell lines with confirmed high TMEM71 expression (e.g., specific glioma lines)

• Tissues known to express TMEM71 (based on literature)

• Recombinant TMEM71 protein or TMEM71-overexpressing cells

Negative controls:

• TMEM71 knockout or knockdown samples

• Tissues known to have minimal TMEM71 expression

• Secondary antibody-only controls for immunostaining

Specificity controls:

• Peptide competition assays

• Multiple antibodies targeting different epitopes of TMEM71

• Western blotting to confirm expected molecular weight

Application-specific controls:

• For IHC: Include both positive and negative tissue controls on each slide

• For co-localization studies: Include single-stained controls to assess bleed-through

• For Western blotting: Include loading controls and molecular weight markers

These comprehensive controls ensure reliable and reproducible results when employing TMEM71 antibodies in research applications.

How does TMEM71 expression correlate with tumor immune microenvironment?

TMEM71 expression significantly correlates with tumor immune microenvironment characteristics:

In LGG (based on ssGSEA algorithm analysis):

• High TMEM71 expression correlates with increased immune-related characteristics

• Positive correlation with estimation scores, immune scores, and stromal scores

• Negative correlation with tumor purity

The CIBERSORT algorithm analysis revealed TMEM71 expression correlates with specific immune cell populations:

Positive correlations:

• Macrophages M1

• T cells CD4 memory resting

• Macrophages M0

• Activated Dendritic cells

• Neutrophils

• T cells CD8

Negative correlations:

• Activated Mast cells

• Monocytes

• Resting NK cells

• Memory B cells

In NPC, higher TMEM71 expression positively correlates with B cell infiltration, consistent with findings that higher B-cell density correlates with better NPC prognosis .

What is the relationship between TMEM71 and immune checkpoint genes?

TMEM71 demonstrates significant associations with immune checkpoint genes (ICPGs):

• Positive correlation with the majority of ICPGs in both LGG and NPC datasets

• Specifically correlates with key ICPGs including PD1, PD-L1, CD80, and CD86

• In gliomas, PD-1, PD-L1, TIM-3, and B7-H3 are tightly associated with TMEM71 expression

These correlations suggest TMEM71 may be involved in immune regulation mechanisms in the tumor microenvironment, potentially influencing response to immune checkpoint inhibitor therapies.

Research indicates that TMEM71's immune regulatory functions may contribute to its distinct roles in different cancer types, with enhanced immune responses potentially beneficial in some contexts but detrimental in others .

How can single-cell analysis enhance our understanding of TMEM71's role in tumor heterogeneity?

Single-cell analysis would address several limitations in current TMEM71 research:

• Cell-specific expression patterns: Bulk transcriptome data limits cell-specific insights. Single-cell RNA sequencing would reveal TMEM71 expression in specific cell populations within the tumor microenvironment .

• Tumor heterogeneity: Characterizing TMEM71 expression across distinct tumor subpopulations would clarify its potential differential roles within a single tumor mass.

• Spatial context: Combining single-cell transcriptomics with spatial transcriptomics could reveal location-dependent TMEM71 functions within the tumor architecture.

• Dynamic changes: Single-cell analysis at multiple time points could track changes in TMEM71 expression during tumor evolution and treatment response.

• Cell-cell interaction networks: Single-cell approaches would better characterize how TMEM71-expressing cells interact with immune and stromal populations.

These approaches would overcome limitations acknowledged in current studies that rely on bulk RNA sequencing data, potentially resolving contradictory findings regarding TMEM71's functions .

What are the key methodological gaps in current TMEM71 research?

Current research on TMEM71 has several methodological limitations that future studies should address:

• Limited in vivo validation: Most studies have been conducted in cell lines, with insufficient animal experiments to clarify mechanisms, particularly regarding the immune microenvironment .

• Bulk vs. single-cell analysis: The use of bulk transcriptome data limits cell-specific insights, potentially affecting the reliability of observed expression trends .

• Retrospective analysis limitations: Selection bias may be introduced in studies using retrospective clinical data .

• Comparative marker analysis: TMEM71 has not been sufficiently compared with other established diagnostic markers (e.g., EBV DNA in NPC) .

• Limited tissue types: Most research has focused on glioma and NPC, with insufficient data on TMEM71's role in other cancer types.

Addressing these gaps would strengthen the evidence base for TMEM71's potential as a diagnostic, prognostic, and therapeutic target.

How might TMEM71-targeted therapies be developed for cancer treatment?

Development of TMEM71-targeted therapies should consider its context-dependent roles:

For cancers where TMEM71 acts as an oncogene (e.g., gliomas):

• Develop small molecule inhibitors targeting TMEM71-protein interactions

• Design siRNA or shRNA delivery systems for TMEM71 silencing

• Target the JAK2/STAT3 pathway downstream of TMEM71

• Consider combination approaches with immune checkpoint inhibitors given TMEM71's correlation with ICPGs

For cancers where TMEM71 is tumor-suppressive (e.g., NPC):

• Explore strategies to upregulate or restore TMEM71 expression

• Develop therapies that enhance the NLRP3/Caspase-1/GSDMD pathway

• Consider TMEM71 as a potential biomarker for patient stratification rather than a direct target

Methodologically, comprehensive preclinical validation including cell line panels, patient-derived xenografts, and immunocompetent models will be essential before clinical translation of any TMEM71-targeted approach.

What bioinformatic approaches are most valuable for further characterizing TMEM71's role in cancer?

Advanced bioinformatic methods can address knowledge gaps in TMEM71 research:

• Integrated multi-omics analysis: Combine transcriptomics, proteomics, and epigenomics to comprehensively characterize TMEM71 regulation and function.

• Network analysis: Identify TMEM71-centered protein-protein interaction networks and gene regulatory networks to uncover new functional associations.

• Pathway enrichment analysis: Further characterize biological processes associated with TMEM71 expression beyond the GO and KEGG analyses already performed .

• Machine learning approaches: Develop predictive models incorporating TMEM71 with other markers for improved prognostic and diagnostic accuracy.

• Pan-cancer analysis: Systematically compare TMEM71's role across multiple cancer types to better understand its context-specific functions.

These computational approaches, combined with experimental validation, would provide deeper insights into TMEM71's complex roles in cancer pathophysiology and potentially identify novel therapeutic opportunities.