Recombinant Human Transmembrane protein 106C (TMEM106C)

What is TMEM106C and where is it primarily located in human cells?

TMEM106C (Transmembrane protein 106C) is a protein encoded by the TMEM106C gene in humans. It is an integral membrane protein primarily localized to the endoplasmic reticulum (ER) membrane, spanning it twice with predicted helical transmembrane regions . The protein contains 250 amino acids, with 230 amino acids comprising a domain of unknown function (DUF1356) . TMEM106C is valine-rich and lacks tryptophan residues, suggesting specific structural characteristics that may influence its function . The protein undergoes several post-translational modifications including phosphorylation, kinase-specific phosphorylation, and N-glycosylation .

What is the genomic location of TMEM106C and its neighboring genes?

The TMEM106C gene is located on the long arm of chromosome 12 at position 12q13.1, spanning from base pair 48357225 to 48362667 . It is positioned between two significant genes: COL2A1 (the human type II collagen gene) and VDR (the human Vitamin D Receptor gene) . This genomic neighborhood may provide insights into potential co-regulation or functional relationships between these genes, which could be relevant for researchers investigating related signaling pathways or disease mechanisms.

What is known about TMEM106C expression patterns in normal human tissues?

TMEM106C demonstrates ubiquitous expression across multiple tissue types, with expression levels approximately 4.9 times higher than the average gene . Particularly high expression has been documented in the adrenal gland, eye, reproductive organs, cervix, and blood . This widespread expression pattern suggests TMEM106C may have fundamental cellular functions across diverse tissue types, making it a potentially important target for understanding basic cellular processes as well as tissue-specific pathologies.

How is TMEM106C implicated in human diseases besides cancer?

Beyond its role in cancer, TMEM106C has been linked to distal arthrogryposis, a condition characterized by stiff joints and irregular muscle development . This connection suggests TMEM106C may play a role in musculoskeletal development and function, opening avenues for research in developmental biology and musculoskeletal disorders. Researchers investigating the pathophysiology of joint stiffness or muscle development abnormalities should consider examining TMEM106C expression and function in relevant tissues and developmental stages.

What methodologies have proven effective for studying TMEM106C expression across different cancer types?

Multiple complementary approaches have demonstrated utility in assessing TMEM106C expression:

• RNA-sequencing analysis: Utilizing normalized pan-cancer RNA-seq data from repositories such as University of California, Santa Cruz Xena for comparative expression analysis across 34 cancer types .

• Integrated multi-platform analysis: Combining gene microarrays, bulk RNA-seq, single-cell RNA-seq, qPCR, and immunohistochemistry (IHC) to validate expression patterns . A robust methodological approach used in recent research incorporated data from 4657 liver hepatocellular carcinoma (LIHC) tissues and 3652 non-LIHC tissues across 14 countries .

• Standardized mean difference (SMD) calculation: For quantitative comparison of expression levels between cancer and normal tissues .

• Receiver operating characteristic (ROC) curve analysis: To evaluate the discriminatory capability of TMEM106C expression between cancer and normal samples .

Researchers should consider employing multiple orthogonal techniques to validate expression findings, with particular attention to tissue-specific controls and normalization methods.

What experimental approaches can be used to investigate the functional role of TMEM106C in cancer cell biology?

Several experimental strategies have proven effective for functional characterization:

• CRISPR-Cas9 gene editing: Construction of TMEM106C-knockout cancer cell lines using guide RNAs targeting specific exons. Published protocols have utilized the following guide RNA sequences:

  • Second exon: Upstream 5'-CACCGTTCTTGCTTTCGCCTGCAGG-3', downstream 5'-AAACCCTGCAGGCGAAAGCAAGAA-3'

  • Fourth exon: Upstream 5'-CACCGTCAGTCCTTGTGGATGATGA-3', downstream 5'-AAACTCATCATCCACAAGGACTGA-3'

• qPCR validation of knockout efficiency using primers:

  • Upstream 5'-AGGGGACAGGCTACATTCCA-3'

  • Downstream 5'-ACCACCAAACCAGATGCCAG-3'

• Proliferation assays using cell counting kit-8 with measurements at 6-hour intervals over 5 days .

• Migration and invasion assays using 24-well transwell chambers to assess metastatic potential .

• Flow cytometry for cell cycle analysis and apoptosis detection using 5 × 10^5 and 1 × 10^5 cells, respectively .

These methodologies provide a comprehensive toolset for investigating how TMEM106C affects cancer cell behavior, with particular relevance to proliferation, migration, invasion, cell cycle progression, and apoptosis.

How can researchers investigate the relationship between TMEM106C and immune cell infiltration in the tumor microenvironment?

To explore the associations between TMEM106C expression and immune cell infiltration:

• Microenvironment cell populations counter algorithm: This computational approach can quantify infiltration levels of immune and stromal cells in the tumor microenvironment across different cancer types .

• Spearman correlation coefficient analysis: To evaluate the statistical association between TMEM106C expression and various immune cell populations .

• Integrated analysis with public immune cell datasets: Researchers should consider correlating TMEM106C expression with established immune cell markers and signatures to identify potential mechanistic relationships.

• Single-cell RNA sequencing: For high-resolution characterization of immune cell populations and their relationship with TMEM106C expression in specific cancer subtypes.

Current evidence indicates that TMEM106C expression is inversely correlated with the abundance of immune and stromal cells in pan-cancers, suggesting a potential immunomodulatory role that warrants further investigation .

What is known about the transcriptional regulation of TMEM106C and how can this be experimentally validated?

ChIP-seq analysis has predicted CCCTC-binding factor (CTCF) as a pivotal transcriptional factor targeting the TMEM106C gene in pan-cancers . To validate this regulatory relationship:

• ChIP-qPCR: To confirm CTCF binding to the TMEM106C promoter region in specific cell types.

• Luciferase reporter assays: Constructing reporter constructs containing the TMEM106C promoter region to quantify transcriptional activity upon CTCF modulation.

• CTCF knockdown/overexpression experiments: To assess consequent changes in TMEM106C expression levels.

• Single-cell RNA-seq correlation analysis: As performed in GSE112271 to confirm co-expression patterns between CTCF and TMEM106C .

• Motif analysis: To identify specific CTCF binding motifs within the TMEM106C promoter region.

Understanding this regulatory axis is particularly important as CTCF has been shown to upregulate forkhead box protein M1, promoting proliferation and metastasis in hepatocellular carcinoma .

What are the optimal systems and controls for studying TMEM106C function in cancer models?

Researchers should consider the following experimental systems and controls:

• Cell line selection: SMMC-7721 and Huh7 liver hepatocellular carcinoma cell lines have been successfully used for TMEM106C functional studies . Depending on the cancer type of interest, researchers should select cell lines with confirmed TMEM106C expression.

• Knockout validation: Multiple validation methods should be employed to confirm TMEM106C knockout/knockdown, including qPCR and western blot analysis, using the 2^-ΔΔCt algorithm for relative mRNA expression quantification .

• In vivo models: Nude mice xenograft models have been successfully employed, with subcutaneous inoculation of 10^7 cancer cells into the right axilla of six-week-old mice . Both male and female mice should be included to account for potential sex differences.

• Treatment controls: When evaluating therapeutic interventions targeting TMEM106C, appropriate controls should include untreated groups and vehicle controls (e.g., normal saline) alongside experimental treatment groups .

• Statistical analysis: Use paired or unpaired Wilcoxon tests for expression comparison between groups, and address heterogeneity through randomized-effect models when calculating standardized mean difference (SMD) .

How can computational approaches enhance TMEM106C research for therapeutic targeting?

Several computational strategies have proven valuable:

• AlphaFold protein structure prediction: For modeling the three-dimensional structure of TMEM106C protein .

• Molecular docking simulations: To investigate binding interactions between TMEM106C and potential therapeutic compounds, such as nitidine chloride (NC) .

• Discovery Studio Visualizer: For visualization and analysis of molecular interactions in TMEM106C-ligand complexes .

• CellMiner correlation analysis: To identify compounds with expression profiles correlated to TMEM106C, potentially indicating therapeutic efficacy .

• Gene Set Enrichment Analysis (GSEA): For pathway identification associated with TMEM106C across cancer types, helping to pinpoint mechanistic underpinnings .

These computational approaches can significantly accelerate the identification of therapeutic targets and compounds, reducing the time and resources required for experimental validation.

What techniques are recommended for investigating TMEM106C's role in cell cycle regulation and DNA replication?

Based on current evidence linking TMEM106C to cell cycle pathways:

• Flow cytometry with propidium iodide staining: For cell cycle phase distribution analysis following TMEM106C modulation .

• BrdU incorporation assays: To specifically evaluate effects on DNA synthesis phase.

• Co-expression network analysis: To identify cell cycle-related genes correlated with TMEM106C expression .

• Western blot analysis of cell cycle regulators: Including cyclins, CDKs, and checkpoints proteins following TMEM106C knockout/overexpression.

• Chromatin immunoprecipitation sequencing (ChIP-seq): To investigate binding of TMEM106C-associated transcription factors to cell cycle gene promoters .

Recent findings indicate that TMEM106C knockout leads to cell cycle arrest specifically at the DNA synthesis phase and increased apoptosis in liver cancer cells, making these methodological approaches particularly relevant .

How might TMEM106C be exploited as a biomarker for cancer prognosis and treatment response?

TMEM106C shows promise as a biomarker in several contexts:

• Prognostic value: TMEM106C overexpression predicts poor prognosis in four cancer types, including liver hepatocellular carcinoma . Researchers should examine survival correlations using Kaplan-Meier and univariate Cox analyses in specific cancer populations.

• Treatment response prediction: Higher TMEM106C expression has been associated with worse survival in liver cancer patients treated with sorafenib (a tyrosine kinase inhibitor) . This suggests utility in predicting treatment responses to specific therapeutic agents.

• Area under the curve (AUC) analysis: Using receiver operating characteristic curves with AUC ≥ 0.70 indicating potential discriminative ability between cancer and normal samples .

• Multivariate analysis: Researchers should incorporate TMEM106C expression into multivariate models alongside established clinical parameters to assess independent prognostic value.

• Liquid biopsy development: Investigation of TMEM106C detection in circulating tumor cells or cell-free DNA could enable minimally invasive monitoring.

What methodological approaches are recommended for exploring TMEM106C as a therapeutic target?

For researchers investigating TMEM106C-targeted therapies:

• Drug sensitivity correlation analysis: Spearman correlation analysis of TMEM106C expression with drug sensitivity data from repositories like CellMiner can identify promising compounds .

• Small molecule screening: Recent research identified erdafitinib (a fibroblast growth factor receptor tyrosine kinase inhibitor) and BPTES as potential therapeutic agents associated with TMEM106C expression .

• Traditional medicine compounds: Nitidine chloride (NC) has demonstrated ability to attenuate TMEM106C upregulation, with molecular docking confirming binding affinity . Research protocols have employed 7.0 mg/kg intraperitoneal injection dosing in animal models .

• Target validation in resistant models: Given TMEM106C's association with tyrosine kinase inhibitor resistance, researchers should establish resistant cell models to evaluate targeted approaches .

• Combination therapy assessment: Evaluating TMEM106C-targeted approaches alongside standard treatments to identify synergistic effects.

How should researchers address heterogeneity when studying TMEM106C across different cancer types?

To account for cancer heterogeneity:

• Multi-cancer analysis: Investigate TMEM106C across diverse cancer types, as current evidence shows significant overexpression in 27 different cancers .

• Single-cell RNA sequencing: To capture cellular heterogeneity within tumors and identify specific cell populations with altered TMEM106C expression.

• Patient-derived xenografts: To maintain tumor heterogeneity when testing TMEM106C-targeted approaches.

• Statistical approaches: Employ randomized-effect models when calculating standardized mean difference to address heterogeneity .

• Stratification by molecular subtypes: Analyze TMEM106C expression and function within established molecular subtypes of each cancer type.

This approach will help determine whether TMEM106C represents a universal therapeutic target or requires cancer-specific contextual understanding.

What are the current limitations in TMEM106C research that need to be addressed in future studies?

Current research limitations include:

• Limited clinical validation: Most findings derive from in vitro studies and computational analyses, requiring further validation in clinical cohorts .

• Incomplete mechanistic understanding: While TMEM106C shows correlation with cell cycle pathways, comprehensive mechanistic details remain to be elucidated .

• CTCF-TMEM106C axis: Additional experimental evidence is needed to establish clear mechanistic links between CTCF regulation and TMEM106C function .

• Cancer type specificity: More detailed investigations across different cancer types are needed to determine universal versus context-specific functions .

• Integration of in vitro and in vivo models: Future research should combine both approaches to establish comprehensive understanding of TMEM106C biology .

Addressing these limitations will be crucial for translating current findings into clinical applications and targeted therapies.

What methodological advances would accelerate TMEM106C research in the next five years?

To advance the field, researchers should consider:

• CRISPR screens: Genome-wide CRISPR screens to identify synthetic lethal interactions with TMEM106C in cancer cells.

• Structural biology approaches: Advanced techniques to fully elucidate TMEM106C protein structure and functional domains.

• Patient-derived organoids: For more physiologically relevant modeling of TMEM106C function in specific cancer types.

• Multi-omics integration: Combining transcriptomics, proteomics, and metabolomics to comprehensively map TMEM106C-associated pathways.

• Immunotherapy combination approaches: Given TMEM106C's inverse correlation with immune cell abundance, investigating combinations with immune checkpoint inhibitors.