IGFLR1 Human

What is IGFLR1 and what is its genomic location in humans?

IGFLR1 (Insulin Growth Factor-Like receptor 1), also known as transmembrane protein 149 (TMEM149), is a protein encoding gene located on chromosome 19. It is widely expressed in lymph nodes, spleen, and kidney tissues . The protein has structural similarity to the tumor necrosis factor receptor family (TNFRs), suggesting it may play a role in immune regulation . Unlike many other receptors, IGFLR1 has unique structural characteristics that make it an interesting target for investigation in various disease contexts, particularly in cancer biology.

How does human IGFLR1 compare to murine IGFL in terms of expression patterns?

While human IGFLR1 expression patterns differ somewhat from murine IGFL, both show significant expression in skin tissues. Research indicates that murine IGFL is primarily expressed in normal skin and is further upregulated during inflammatory responses and after skin wounding . In contrast, human IGFLR1 shows variable expression across tissues but is notably elevated in several cancers including breast invasive carcinoma, head and neck squamous cell carcinoma, clear cell renal cell carcinoma (ccRCC), and kidney renal papillary cell carcinoma . Interestingly, studies have shown that expression levels of mouse or human IGFLR1 were not significantly altered in psoriasis models or psoriasis cases , suggesting tissue-specific and context-dependent regulation.

What is the relationship between IGFLR1 and IGFL1 proteins?

IGFLR1 functions as a receptor for IGFL1 (Insulin Growth Factor-Like 1) proteins. Research has demonstrated high-affinity interactions between human IGFL1 and murine IGFL with the IGFLR1 ectodomain . IGFL1 is a secreted protein that contains 11 regularly spaced cysteine residues, 6 of which are conserved within the IGF family . While IGFL1 expression is normally low in human tissues, it shows significant upregulation in certain pathological conditions, particularly in psoriatic skin samples . In ccRCC, researchers have analyzed the correlation between IGFLR1 expression and IGFL1 levels, finding that IGFL1 expression patterns may influence patient outcomes in samples with high IGFLR1 expression .

What techniques are most effective for analyzing IGFLR1 expression in tumor samples?

For analyzing IGFLR1 expression in tumor samples, a multi-modal approach yields the most comprehensive results:

• RNA-Seq Analysis: Utilizing data from databases like TCGA (The Cancer Genome Atlas) through platforms such as GEPIA (Gene Expression Profiling Interactive Analysis) to compare expression levels between tumor and normal tissues .

• In Situ Hybridization: Performing radiographic in situ hybridization on formalin-fixed, paraffin-embedded sections from tumor and adjacent normal tissues. This technique has been effectively used for IGFL1 detection and can be adapted for IGFLR1 .

• Immunohistochemistry: For protein-level detection in tissue sections.

• Flow Cytometry: Particularly useful for analyzing IGFLR1 expression on immune cells within the tumor microenvironment .

• Single-cell RNA Sequencing: To identify cell-specific expression patterns within heterogeneous tumor samples.

These methods should be complemented with appropriate statistical analyses, including survival correlation using Kaplan-Meier methods and log-rank tests to establish clinical relevance .

How can researchers effectively study the relationship between IGFLR1 and tumor-infiltrating immune cells?

To study the relationship between IGFLR1 and tumor-infiltrating immune cells, researchers should implement the following methodological approach:

• Correlation Analysis: Using platforms like TIMER (Tumor Immune Estimation Resource) and TISIDB to analyze correlations between IGFLR1 expression and the abundance of various tumor-infiltrating immune cells .

• Marker Gene Analysis: Investigating correlations between IGFLR1 expression and established marker genes for specific immune cell populations, such as myeloid-derived suppressor cells (MDSCs) .

• Flow Cytometry and Cell Sorting: For direct quantification and isolation of immune cell populations expressing IGFLR1.

• Functional Assays: Co-culture experiments to assess how IGFLR1-expressing cells interact with various immune cell populations.

• Gene Set Enrichment Analysis (GSEA): Using immunologic signature gene sets (C7) to identify immune-related pathways associated with high or low IGFLR1 expression .

This comprehensive approach allows researchers to establish not just correlative but potentially causal relationships between IGFLR1 expression and immune cell function in the tumor microenvironment.

What are the recommended methods for investigating the role of IGFLR1 in chemotherapeutic drug resistance?

Investigating IGFLR1's role in chemotherapeutic drug resistance requires a systematic approach:

• Drug Sensitivity Correlation Analysis: Compare drug sensitivity profiles with IGFLR1 expression levels across cell lines and patient-derived samples .

• Gene Expression Modulation: Use siRNA knockdown, CRISPR/Cas9 knockout, or overexpression systems to modulate IGFLR1 expression, followed by drug sensitivity assays.

• Pathway Analysis: Perform KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and Gene Ontology (GO) enrichment analysis of differentially expressed genes between IGFLR1-high and IGFLR1-low samples to identify potential resistance mechanisms .

• Protein-Protein Interaction Studies: Investigate interactions between IGFLR1 and known drug resistance mediators.

• Patient-Derived Xenograft Models: Test the effect of IGFLR1 modulation on drug response in vivo.

Studies suggest that IGFLR1 may be involved in drug resistance mechanisms in cancer, making this investigation particularly valuable for developing targeted therapeutic strategies .

How reliable is IGFLR1 as a prognostic biomarker in clear cell renal cell carcinoma (ccRCC)?

IGFLR1 has demonstrated significant potential as a prognostic biomarker in ccRCC:

These findings suggest that IGFLR1 is a reliable prognostic biomarker in ccRCC, offering additional predictive value beyond conventional clinical parameters and potentially helping to stratify patients for personalized treatment approaches.

What is the relationship between IGFLR1 expression and immune infiltration in the tumor microenvironment?

The relationship between IGFLR1 expression and immune infiltration in the tumor microenvironment is complex and significant:

• Correlation with Immune Cell Populations: IGFLR1 expression shows significant positive correlation with the abundance of specific tumor-infiltrating immune cells, particularly myeloid-derived suppressor cells (MDSCs) .

• Marker Gene Associations: Expression levels of IGFLR1 correlate positively with marker genes for MDSCs, suggesting a functional relationship between IGFLR1 and these immunosuppressive cells .

• T Cell Regulation: Given that IGFLR1 is expressed on the surface of murine T cells, it may influence T cell biology within inflammatory tumor conditions .

• Pathway Enrichment: GSEA reveals that IGFLR1 may be involved in pathways related to regulatory activation and intercellular adhesion of lymphocytes .

This relationship may explain, in part, how IGFLR1 contributes to tumor progression by modulating the immune microenvironment, potentially creating an immunosuppressive niche that facilitates tumor growth and metastasis.

What are the most promising therapeutic targets related to IGFLR1 signaling pathways in cancer?

Based on current research, several promising therapeutic targets related to IGFLR1 signaling pathways in cancer emerge:

• IGFLR1-IGFL1 Interaction: Developing antibodies or small molecules that disrupt the binding between IGFLR1 and its ligand IGFL1 could potentially inhibit downstream signaling .

• Myeloid-Derived Suppressor Cell (MDSC) Modulation: Given the strong correlation between IGFLR1 and MDSCs, targeting this interaction could reduce immunosuppression in the tumor microenvironment .

• T Cell Activation Pathways: Since IGFLR1 is expressed on T cells and bears structural similarity to TNF receptors, modulating its function might enhance anti-tumor immune responses .

• Combination with Existing Immunotherapies: IGFLR1-targeting strategies could potentially enhance the efficacy of checkpoint inhibitors by altering the tumor immune microenvironment.

• Drug Resistance Pathways: Targeting IGFLR1-related mechanisms of drug resistance could improve responses to conventional chemotherapies .

These approaches require further validation through preclinical models and clinical trials but represent promising avenues for future research and therapeutic development.

How can multi-omics approaches be integrated to better understand IGFLR1's role in tumor development?

An integrated multi-omics approach to understanding IGFLR1's role in tumor development should include:

• Genomics: Analyze copy number variations (CNV) and single nucleotide polymorphisms (SNPs) related to IGFLR1 to identify genetic alterations associated with expression changes .

• Epigenomics: Investigate promoter methylation patterns of IGFLR1 to understand mechanisms of expression regulation across different cancer types .

• Transcriptomics: Perform differential gene expression analysis between IGFLR1-high and IGFLR1-low samples to identify co-regulated genes and pathways .

• Proteomics: Characterize protein-protein interactions involving IGFLR1 to map its signaling network.

• Immunomics: Correlate IGFLR1 expression with immune cell infiltration patterns and cytokine profiles .

• Clinical Data Integration: Combine molecular findings with patient outcomes to develop predictive models.

• Single-Cell Analysis: Apply single-cell technologies to understand cell-specific roles of IGFLR1 in heterogeneous tumor samples.

This integrated approach would provide a comprehensive understanding of IGFLR1's role in tumor biology, potentially revealing new therapeutic vulnerabilities and biomarker applications.

What are the challenges in developing reliable antibodies for IGFLR1 detection in human samples?

Developing reliable antibodies for IGFLR1 detection in human samples presents several challenges:

• Protein Structure Complexity: IGFLR1's similarity to TNF receptor family members may lead to cross-reactivity issues .

• Expression Level Variability: The relatively low expression levels in normal tissues make it difficult to validate antibody specificity .

• Isoform Recognition: Potential splice variants of IGFLR1 may not be equally detected by antibodies targeting specific epitopes.

• Post-translational Modifications: These modifications may alter epitope availability and recognition.

• Tissue Fixation Effects: Formalin fixation for histological samples can mask epitopes, requiring optimized antigen retrieval methods.

To overcome these challenges, researchers should consider:

• Validating antibodies using multiple techniques (Western blot, immunohistochemistry, flow cytometry)

• Employing genetic knockdown/knockout controls

• Using recombinant proteins as positive controls

• Developing isoform-specific antibodies when necessary

• Optimizing protocols for different sample types and preparation methods

What statistical approaches are most appropriate for analyzing IGFLR1 expression data in relation to patient outcomes?

When analyzing IGFLR1 expression data in relation to patient outcomes, the following statistical approaches are recommended:

• Survival Analysis:

  • Kaplan-Meier method with log-rank test for comparing survival differences between high and low IGFLR1 expression groups

  • Hazard ratio calculation using Cox proportional hazards models

• Multivariate Analysis:

  • Cox regression to adjust for confounding clinical factors (age, stage, grade, etc.)

  • Creation of nomograms incorporating IGFLR1 with other clinical parameters

• Expression Comparisons:

  • Mann-Whitney U test or t-test (depending on data distribution) for comparing expression between tumor and normal tissues

  • ANOVA or Kruskal-Wallis test for comparing expression across multiple groups (e.g., cancer stages, grades)

• Correlation Analysis:

  • Spearman's or Pearson's correlation coefficients for assessing relationships between IGFLR1 and other continuous variables (e.g., immune cell markers)

• Predictive Modeling:

  • ROC curve analysis to evaluate sensitivity and specificity of IGFLR1 as a biomarker

  • Machine learning approaches for building integrated predictive models

These statistical methods should be accompanied by appropriate significance thresholds (typically p < 0.05) and multiple testing corrections when necessary to ensure reliable results .