C14orf166 Antibody

Abstract

This comprehensive FAQ collection addresses key research questions about C14orf166 antibodies for academic and clinical investigations. C14orf166 (Chromosome 14 Open Reading Frame 166) is a 28-kDa protein that localizes to both the nucleus and cytoplasm, playing crucial roles in RNA metabolism, viral infection responses, and cancer progression. Overexpression of C14orf166 has been associated with poor prognosis in multiple cancers, making it an important research target. This document organizes questions from basic to advanced, providing methodological guidance for researchers utilizing C14orf166 antibodies in their experimental workflows.

What is C14orf166 and why is it significant in cancer research?

C14orf166, also known as CLE, hCLE, CGI-199, or RTRAF, is a highly conserved gene located on chromosome 14 at cytogenetic band 14q22.1. It encodes a 28-kDa protein that localizes to both the nucleus and cytoplasm . This protein serves multiple biological functions:

• Acts as a core element of cytosolic RNA granules in neuronal processes

• Functions in RNA metabolism as part of the human spliceosome and tRNA-splicing ligase complex

• Interacts with 7SK snRNA methylphosphate capping enzyme

• Participates in viral RNA replication and transcriptional activation

C14orf166 has gained significant attention in cancer research due to its:

• Overexpression in multiple cancer types (NSCLC, cervical cancer, bladder cancer, hepatocellular carcinoma)

• Strong correlation with advanced TNM stages, lymph node metastasis, and tumor size

• Association with poor prognosis and shorter survival times

• Potential role in JAK2/STAT3 signaling pathway activation

These characteristics make C14orf166 a promising biomarker for cancer progression and a potential therapeutic target.

What detection methods are available for C14orf166 antibodies?

Based on commercially available antibodies, C14orf166 can be detected using multiple techniques:

Researchers should validate these detection methods in their specific experimental systems, as reactivity may vary across species (human, mouse, rat) and sample types (cell lines, tissue sections) .

How should I optimize antigen retrieval for C14orf166 immunohistochemistry?

For optimal immunohistochemical detection of C14orf166 in formalin-fixed paraffin-embedded (FFPE) tissues:

• Perform heat-mediated antigen retrieval using Tris/EDTA buffer at pH 9.0

• Bring buffer to boiling point, then place sections in the heated buffer

• Maintain at sub-boiling temperature for 15-20 minutes

• Allow sections to cool gradually in the buffer for 20 minutes

• Rinse thoroughly with PBS before proceeding with immunostaining

This protocol has been validated for human tissue samples including astrocytoma, fetal kidney, and various cancer tissues . Alternative approaches using citrate buffer (pH 6.0) may be tested if results are suboptimal, though they appear less effective for C14orf166 detection based on reported literature .

How can I validate the specificity of C14orf166 antibodies?

Thorough validation of C14orf166 antibody specificity is crucial for reliable research results:

• Western blot analysis:

  • Compare reactivity across multiple cell lines (e.g., Ramos, 293T, Jurkat, U87-MG)

  • Confirm the predicted molecular weight (28 kDa)

  • Include positive controls from tissues with known high expression (e.g., fetal brain)

• Knockdown/knockout validation:

  • Perform siRNA knockdown of C14orf166

  • Compare antibody signal in wildtype vs. knockdown samples

  • Signal reduction in knockdown samples confirms specificity

• Peptide competition assay:

  • Pre-incubate antibody with immunizing peptide (e.g., synthetic peptide located between aa85-134 of human C14orf166)

  • Loss of signal indicates specific binding to the target epitope

• Cross-reactivity assessment:

  • Test reactivity across species using BLAST analysis for sequence homology

  • Human C14orf166 shows 100% identity with chimpanzee, orangutan, gibbon, and monkey

  • 92% identity with horse, panda, dog, rabbit, and pig

  • 84-85% identity with rat, elephant, and guinea pig

• Immunoprecipitation followed by mass spectrometry:

  • Perform IP using the C14orf166 antibody

  • Analyze precipitated proteins by mass spectrometry

  • Confirm presence of C14orf166 and known interacting partners

What are the clinical correlations of C14orf166 expression that might impact experimental design?

Understanding the clinical correlations of C14orf166 expression is essential for designing experiments with clinical relevance:

Bladder cancer:

• C14orf166 expression correlates with:

  • Larger tumor size (p=0.001)

  • Lymph node involvement (p<0.001)

  • Histological differentiation (p<0.001)

  • Poor survival

Hepatocellular carcinoma (HCC):

These correlations suggest experimental designs should consider:

• Stratification of samples based on clinical parameters

• Inclusion of follow-up data when evaluating biomarker potential

• Comparison of expression in primary tumors vs. metastatic sites

• Correlation with other established biomarkers

How can I effectively use C14orf166 antibodies in multiplexed immunofluorescence applications?

Multiplexed immunofluorescence with C14orf166 antibodies allows simultaneous detection of multiple markers and provides valuable information about protein co-localization and cell type-specific expression:

• Antibody selection and validation:

  • Choose antibodies raised in different host species to avoid cross-reactivity

  • Validate each antibody individually before multiplexing

  • For C14orf166, both rabbit polyclonal (e.g., ab188326) and mouse monoclonal (e.g., OTI2A4) antibodies are available

• Optimization steps:

  • Determine optimal dilution for each antibody (typically 1:50-1:500 for IF)

  • Test different fixation methods (4% paraformaldehyde recommended)

  • Optimize blocking solutions to minimize background (5% BSA or normal serum)

  • Sequence antibody applications based on sensitivity

• Multiplexing with markers of interest:

  • Subcellular markers: Combine with nuclear (DAPI), cytoplasmic (β-tubulin), or centrosomal markers to study C14orf166 localization

  • Pathway components: Co-stain with JAK2/STAT3 pathway members to study functional interactions

  • Cell-type markers: Combine with epithelial, stromal, or immune cell markers in tumor samples

• Signal detection and controls:

  • Use spectrally distinct fluorophores (AbBy Fluor® 555, AbBy Fluor® 594 conjugated C14orf166 antibodies are available)

  • Include single-stain controls for spectral unmixing

  • Use automated image analysis for quantification of co-localization

• Special considerations:

  • C14orf166 shuttles between nucleus and cytoplasm; therefore, fixation timing may affect localization patterns

  • In tumors, expression levels vary significantly across cells, requiring careful analysis of heterogeneity

How can C14orf166 antibodies be used to study its role in RNA metabolism and transcriptional regulation?

C14orf166 functions in RNA transcription, splicing, and transport, making it relevant for studies of gene expression regulation:

• RNA immunoprecipitation (RIP):

  • Use C14orf166 antibodies to immunoprecipitate ribonucleoprotein complexes

  • Extract and analyze associated RNAs by RT-PCR or RNA sequencing

  • Focus on mRNAs encoding RNA-binding proteins and microtubule-associated proteins that are transported in neuronal processes

• Chromatin immunoprecipitation (ChIP):

  • Use C14orf166 antibodies to precipitate chromatin fragments

  • Analyze associated DNA sequences to identify genomic binding sites

  • Connect to transcriptional regulation of specific genes

• Proximity ligation assay (PLA):

  • Detect interaction between C14orf166 and known partners (e.g., RNA polymerase II, 7SK snRNA methylphosphate capping enzyme)

  • Visualize interaction events at single-molecule resolution in intact cells

  • Quantify changes in interaction under different conditions

• Mass spectrometry analysis of protein complexes:

  • Immunoprecipitate C14orf166 and associated proteins

  • Identify components of C14orf166-containing complexes

  • Study composition changes during cell cycle or in disease states

• Functional validation:

  • Combine with C14orf166 knockdown/knockout approaches

  • Assess effects on global RNA synthesis using techniques like EU incorporation

  • Analyze splicing patterns using exon junction microarrays or RNA-seq

What experimental approaches can elucidate the role of C14orf166 in cancer progression?

Given the significant associations between C14orf166 overexpression and poor cancer prognosis, several experimental approaches can help understand its mechanistic role:

• Analysis of JAK2/STAT3 pathway activation:

  • C14orf166 interacts with JAK2 as a JH2-interacting protein

  • Use phospho-specific antibodies to detect STAT3 activation (pSTAT3)

  • Compare pSTAT3 levels in cells with normal vs. overexpressed vs. depleted C14orf166

  • In esophageal carcinoma, C14orf166 has been shown to activate the JAK2/STAT3 signaling pathway, potentially initiating carcinogenesis

• Cell cycle analysis:

  • C14orf166 knockdown has been shown to affect cell cycle progression

  • Analyze expression of key G1/S transition proteins (Cyclin D1, P21, P27, and Rb phosphorylation)

  • Flow cytometry can reveal cell cycle distribution changes after C14orf166 manipulation

• Invasion and migration assays:

  • Given its association with metastasis, test effects of C14orf166 modulation on:

    • Transwell migration and invasion

    • Wound healing

    • 3D spheroid invasion models

• In vivo metastasis models:

  • Generate stable cell lines with C14orf166 overexpression or knockdown

  • Assess metastatic potential in animal models

  • Correlate with lymph node involvement observations in clinical samples

• Multi-omics integration:

  • Combine proteomics, transcriptomics, and phosphoproteomics

  • Identify downstream effectors and signaling networks

  • Connect to pathways involved in tumorigenesis and metastasis

What are the technical considerations for developing antibodies against specific functional domains of C14orf166?

Developing domain-specific antibodies for C14orf166 requires careful consideration of protein structure, function, and experimental goals:

• Domain structure and functional mapping:

  • C14orf166 has distinct functional domains including:

    • RNA-binding regions

    • Nuclear localization signals

    • JAK2-interacting domains

    • Regions involved in centrosome architecture regulation

• Epitope selection strategy:

  • Target unique, accessible epitopes within specific domains

  • Available antibodies target various regions:

    • N-terminal region (aa 1-84)

    • Middle region (aa 85-134)

    • C-terminal region (aa 151-244, aa 244-end)

• Antibody development approaches:

  • Synthetic peptide immunization (e.g., peptide located between aa85-134)

  • Recombinant protein fragments (e.g., full-length human recombinant protein)

  • Phage display technology for specific binding profiles

• Validation requirements:

  • Confirm epitope-specific binding using deletion mutants

  • Verify accessibility of the epitope in native protein conformations

  • Test in multiple applications (WB, IP, IHC, IF)

  • Evaluate cross-reactivity with related proteins

• Functional blocking potential:

  • Design antibodies that can inhibit specific interactions:

    • C14orf166-JAK2 interaction

    • RNA binding

    • Nuclear-cytoplasmic shuttling

  • Test ability to modulate downstream signaling (e.g., STAT3 activation)

How can computational approaches enhance antibody design for targeting C14orf166?

Recent advances in computational biology offer powerful tools for antibody design:

• Biophysics-informed modeling for antibody specificity:

  • Machine learning approaches can predict antibody-antigen interactions

  • Models trained on phage display experiments can predict binding specificity

  • This enables design of antibodies with customized specificity profiles for C14orf166

• Epitope prediction algorithms:

  • Computational tools can identify accessible, immunogenic regions within C14orf166

  • Structures can be predicted using AlphaFold or similar tools

  • Molecular dynamics simulations can reveal conformational flexibility

• Sequence conservation analysis:

  • Identify highly conserved vs. variable regions across species

  • Target conserved regions for broad cross-species reactivity

  • Human C14orf166 shows varying identity with other species:

    • 100% with primates

    • 92% with horse, panda, dog, rabbit, pig

    • 84-85% with rat, elephant, and guinea pig

• Optimization of antibody properties:

  • Computational design of complementarity-determining regions (CDRs)

  • Prediction of physicochemical properties (solubility, stability)

  • Optimization of binding kinetics and affinity

• Integration with experimental validation:

  • Design-build-test cycles combining computational prediction with experimental validation

  • Machine learning models improve with additional data from experimental testing

  • This hybrid approach can efficiently generate antibodies with desired specificities and affinities

What are common challenges when using C14orf166 antibodies and how can they be addressed?

Researchers may encounter several challenges when working with C14orf166 antibodies:

• Inconsistent detection in Western blotting:

  • Problem: Variable band intensity or multiple bands

  • Solution: Optimize lysis conditions to ensure complete protein extraction; fresh samples typically yield better results; use phosphatase inhibitors if phosphorylation affects detection

• Background in immunohistochemistry:

  • Problem: High background staining in FFPE tissues

  • Solution: Ensure proper antigen retrieval (Tris/EDTA buffer pH 9.0 is recommended ); optimize antibody dilution (typically 1:50-1:500); extend blocking step; include appropriate negative controls

• Subcellular localization variability:

  • Problem: Inconsistent nuclear vs. cytoplasmic staining

  • Solution: C14orf166 shuttles between nucleus and cytoplasm; fixation timing may affect localization patterns; compare multiple fixation methods; examine functional state of cells (proliferation, stress)

• Species cross-reactivity issues:

  • Problem: Unexpected results in non-human samples

  • Solution: Verify antibody's predicted reactivity (human antibodies show varying homology with other species); validate antibody in the specific species being studied

• Batch-to-batch variability:

  • Problem: Different results with new antibody lots

  • Solution: Request certificate of analysis; run side-by-side comparisons with previous lots; maintain consistent positive controls across experiments

How can I quantitatively assess C14orf166 expression in clinical samples?

For reliable quantitative assessment of C14orf166 in clinical samples:

• Immunohistochemistry scoring systems:

  • Implement standardized scoring based on:

    • Staining intensity (0 = negative, 1 = weak, 2 = moderate, 3 = strong)

    • Percentage of positive cells (0-100%)

    • Calculate H-score (intensity × percentage) or use cutoff values

  • In published studies, high C14orf166 expression was defined based on:

    • NSCLC: H-score >150

    • Cervical cancer: Intensity score ≥2 with >50% positive cells

• Digital image analysis:

  • Use software for automated quantification

  • Segment nuclei and cytoplasm for compartment-specific analysis

  • Generate continuous data rather than categorical scores

  • Standardize image acquisition parameters

• Western blot densitometry:

  • Normalize C14orf166 band intensity to loading controls (α-Tubulin, GAPDH)

  • Include gradient standards for quantification

  • Use technical and biological replicates

• qRT-PCR for mRNA quantification:

  • Design specific primers for C14orf166

  • Use validated reference genes (GAPDH shown to be appropriate)

  • Calculate relative expression using comparative Ct method (ΔΔCt)

  • Validate correlation between protein and mRNA levels

• Statistical analysis recommendations:

  • Use appropriate statistical tests (Chi-square test for categorical data, t-test/ANOVA for continuous data)

  • For survival analysis, use Kaplan-Meier method and log-rank test

  • For multivariate analysis, use Cox proportional hazards model

What emerging applications are being developed for C14orf166 antibodies in cancer research?

Several innovative applications of C14orf166 antibodies are emerging in cancer research:

• Liquid biopsy development:

  • Detection of C14orf166 in circulating tumor cells (CTCs)

  • Analysis in exosomes as potential non-invasive biomarker

  • Correlation with tissue expression patterns and clinical outcomes

• Therapeutic targeting approaches:

  • Antibody-drug conjugates (ADCs) targeting C14orf166-overexpressing cells

  • Blocking antibodies that inhibit C14orf166-JAK2 interaction

  • CAR-T cell development using anti-C14orf166 single-chain variable fragments (scFvs)

• Multi-parameter tissue analysis:

  • Multiplexed immunofluorescence combining C14orf166 with tumor microenvironment markers

  • Spatial transcriptomics integrated with C14orf166 protein detection

  • Single-cell analysis of heterogeneous C14orf166 expression patterns

• Predictive biomarker development:

  • Integration with other biomarkers to improve prognostic models

  • Prediction of therapy response based on C14orf166 expression

  • Combined analysis with serum biomarkers (SCC-Ag, AFP) to enhance predictive accuracy

• Functional antibody applications:

  • Intrabodies targeting specific compartments (nuclear vs. cytoplasmic C14orf166)

  • Antibody-based protein degradation approaches

  • Monitoring therapy-induced changes in C14orf166 expression/localization

How might understanding C14orf166's role in RNA metabolism inform therapeutic strategies?

C14orf166's fundamental role in RNA metabolism offers unique therapeutic opportunities:

• Targeting RNA-dependent processes:

  • C14orf166 is involved in spliceosome function and tRNA-splicing

  • Potential to disrupt aberrant RNA processing in cancer cells

  • Small molecule inhibitors of C14orf166-RNA interactions

• Disruption of protein complexes:

  • C14orf166 interacts with DDX1-HSPC117-FAM98B complex for RNA transport

  • Targeting protein-protein interactions with small molecules or peptides

  • Preventing assembly of functional ribonucleoprotein complexes

• Modulation of gene expression programs:

  • C14orf166 affects RNAP II activity

  • Potential to restore normal transcriptional regulation

  • Combination with epigenetic modifiers to normalize gene expression

• Exploitation of synthetic lethality:

  • Identify genes/pathways that become essential in C14orf166-overexpressing cells

  • Develop targeted therapies based on these dependencies

  • Screen for synthetic lethal interactions using CRISPR-Cas9 libraries

• Integration with existing targeted therapies:

  • C14orf166 activates the JAK2/STAT3 pathway

  • Potential synergy with JAK inhibitors or STAT3 pathway antagonists

  • Rational combination therapy approaches based on molecular mechanisms