Recombinant Human Inhibitor of nuclear factor kappa-B kinase-interacting protein (IKBIP)

What is IKBIP and what is its role in cellular function?

IKBIP (Inhibitor of nuclear factor kappa-B kinase-interacting protein) is a gene that has received minimal attention from researchers until recently. It has been identified as one of the target genes of p53, suggesting a potential role in tumor suppression pathways .

The protein encoded by this gene functions within the NF-κB signaling cascade, which regulates various cellular processes including immunity, inflammation, and cell survival. IKBIP has been found to be highly expressed in most cancers and appears to play a crucial role in both carcinogenesis and cancer immunity .

Methodologically, when studying IKBIP's basic function, researchers should consider:

• Performing gene expression analysis across normal human tissues using publicly available databases like Human Proteome Atlas (HPA)

• Conducting knockdown/knockout studies to observe phenotypic changes

• Investigating protein-protein interactions, particularly with other components of the NF-κB pathway

• Examining transcriptional regulation, especially in relation to p53-mediated pathways

How does IKBIP relate to the NF-κB signaling pathway?

IKBIP interacts with the inhibitory-κB kinase (IKK) complex, which is a central regulator of the NF-κB signaling pathway. The NF-κB pathway exists in canonical and non-canonical forms, with IKKα being a key regulator of the non-canonical pathway .

The non-canonical NF-κB pathway is typically activated by members of the TNF superfamily, including lymphotoxin-β (LT-β), TNFSF14 (LIGHT), TWEAK, CD40L, RANKL, and BAFF . IKBIP's interaction with this pathway appears to influence immune cell infiltration and tumor microenvironment composition.

When investigating this relationship, researchers should:

• Study the physical interaction between IKBIP and IKK components using co-immunoprecipitation

• Analyze the impact of IKBIP expression on downstream NF-κB target genes

• Examine pathway activation using phospho-specific antibodies for key pathway components

• Consider the temporal dynamics of pathway activation in response to specific stimuli

What databases and resources are available for IKBIP research?

Several databases provide valuable resources for IKBIP research:

• TIMER database (https://cistrome.shinyapps.io/timer/) - Examines IKBIP expression profiles and immune infiltrates in pan-cancer

• TCGA - Contains expression data for 33 tumor types, TMB data, MSI data, and clinical data, accessible through the UCSC Xena online database (https://xenabrowser.net/)[1]

• UALCAN (http://ualcan.path.uab.edu/index.html) - Provides proteomics information from the CPTAC database

• cBioPortal (https://www.cbioportal.org/) - Offers data on methylation modification and genetic changes in tumor tissues

• Human Proteome Atlas (HPA) (https://www.proteinatlas.org/) - Provides immunohistochemistry images showing protein distribution in human tissues and cells

For miRNA studies related to IKBIP, researchers can utilize:

• DIANA-microT (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=microT_CDS/index)

• miRDB (http://mirdb.org/miRDB/)

• miRWalk (http://mirwalk.umm.uni-heidelberg.de/)

• TargetScan

• StarBase

How does IKBIP function as a pan-cancer biomarker?

IKBIP has emerged as a promising pan-cancer biomarker based on comprehensive multi-database analyses. Research shows that IKBIP is highly expressed in most cancer types and its expression correlates with patient prognosis in several major cancers .

To evaluate IKBIP as a pan-cancer biomarker, researchers should:

What is the relationship between IKBIP expression and tumor-infiltrating immune cells?

IKBIP expression shows significant correlation with tumor-infiltrating immune cells across multiple cancer types. Analysis using the TIMER database revealed substantial correlations between IKBIP expression and:

• B cells in 12 cancer types

• CD4+ T cells in 13 cancer types

• CD8+ T cells in 23 cancer types

• Macrophages in 23 cancer types

• Neutrophils in 24 cancer types

• Dendritic cells in 24 cancer types

To study this relationship, researchers should:

• Use computational tools like TIMER, xCell, or CIBERSORT to estimate immune cell infiltration

• Perform correlation analysis between IKBIP expression and immune cell abundance

• Validate computational findings with immunohistochemistry or flow cytometry

• Investigate the functional impact using co-culture experiments or immune cell depletion models

The relationship between IKBIP and immune cells varies by cancer type. For example, IKBIP expression in COAD, LGG, BLCA, PRAD, STAD, BRCA, and READ was negatively correlated with various immune cell subtypes, while it was positively correlated in THYM, OV, and LAML tissues .

How does IKBIP correlate with tumor mutational burden (TMB) and microsatellite instability (MSI)?

IKBIP expression shows significant correlations with both TMB and MSI across multiple cancer types:

TMB Correlations:

• IKBIP expression positively correlates with TMB in 13 cancer types

• This suggests a potential relationship between IKBIP and genomic instability

MSI Correlations:

• IKBIP expression positively correlates with MSI levels in ACC, COAD, READ, and UCEC

• IKBIP expression negatively correlates with MSI levels in CHOL, LGG, LUAD, and LUSC

To investigate these relationships, researchers should:

• Calculate TMB using Perl scripts to count somatic mutations, normalized by dividing with exon length

• Extract MSI scores from TCGA database

• Use the "cor.test" command with Spearman's method to examine correlations between IKBIP expression and TMB/MSI

• Generate radar plots using the "fmsb" R package to visualize relationships

• Validate findings through experimental models with engineered TMB/MSI status

These correlations suggest that IKBIP may play a role in response to immunotherapy, as both TMB and MSI are established biomarkers for immunotherapy efficacy.

What methodological approaches are recommended for studying IKBIP in cancer research?

Based on current research approaches, the following methodological framework is recommended:

Bioinformatic Analysis:

• Multi-database analysis (TCGA, TIMER, UALCAN, CPTAC, CBIoportAL)

• Expression analysis across 33 tumor types using log2 TPM measurements

• Survival analysis using Kaplan-Meier and Cox regression with R packages "survminer" and "survival"

• Clinical correlation analysis using "ggpubr" and "limma" R packages

• ROC curve analysis with the "pROC" tool

Immune Analysis:

• Immune infiltration analysis using TIMER and xCell

• Correlation analysis with immune checkpoint genes

• ESTIMATE approach to analyze stromal and immune scores

• Analysis of correlation with five types of immune pathways (chemokine, receptor, MHC, immuno-inhibitory, and immunostimulatory)

Functional Analysis:

• Gene Set Enrichment Analysis (GSEA) using GO and KEGG databases

• Pathway analysis using R packages "limma," "org.Hs.eg.db," "clusterProfiler," and "enrichplot"

• ceRNA network construction using Cytoscape to analyze mRNA, miRNA, and ncRNA interactions

Experimental Validation:

• Immunohistochemistry to validate protein expression patterns

• In vitro functional assays to assess impact on proliferation, migration, and invasion

• In vivo models to evaluate impact on tumor growth and immune infiltration

How might IKBIP serve as a therapeutic target?

IKBIP has potential as a therapeutic target based on its role in carcinogenesis and cancer immunity. Several approaches could be considered:

• Direct targeting of IKBIP: Development of small molecule inhibitors or antibodies against IKBIP

• Upstream targeting: Modulating the p53 pathway to influence IKBIP expression

• NF-κB pathway intervention: Using existing IKK inhibitors like BMS-345541, which is a highly selective inhibitor of IκB kinase that binds at an allosteric site of the enzyme and blocks NF-κB-dependent transcription

• Immunotherapy combination: Targeting IKBIP in conjunction with immune checkpoint inhibitors

When designing experiments to validate IKBIP as a therapeutic target, researchers should:

• Perform knockdown/knockout studies to assess impact on cancer cell phenotypes

• Test combination approaches with existing therapies

• Evaluate effects on immune infiltration and the tumor microenvironment

• Conduct patient stratification analysis to identify which patients might benefit most

What is the relationship between IKBIP and the tumor microenvironment (TME)?

IKBIP expression shows significant correlations with both stromal and immune components of the TME. Using the ESTIMATE approach, research has shown:

Stromal Score Correlations:

• IKBIP expression positively correlates with stromal scores in PAAD, BRCA, COAD, READ, ESCA, and BLCA

Immune Score Correlations:

• IKBIP expression positively correlates with immune scores in COAD, BLCA, GBM, PAAD, and PRAD

• IKBIP expression negatively correlates with immune scores in THYM

To investigate this relationship, researchers should:

• Use the ESTIMATE algorithm to calculate stromal and immune scores

• Perform correlation analysis between IKBIP expression and these scores

• Validate findings using spatial transcriptomics or multiplexed immunohistochemistry

• Conduct co-culture experiments with stromal cells and immune cells to assess functional interactions

These findings suggest that elevated IKBIP expression may create an immunosuppressive environment in certain cancer types, which has important implications for immunotherapy strategies.

What challenges exist in translating IKBIP research from bench to bedside?

Several challenges must be addressed to translate IKBIP research into clinical applications:

To address these challenges, researchers should:

• Conduct multi-center validation studies

• Develop reliable biomarker assays for clinical use

• Investigate mechanisms of action through functional studies

• Identify patient subgroups most likely to benefit from IKBIP-targeted approaches

What novel approaches could enhance understanding of IKBIP function?

Future research on IKBIP would benefit from:

• Single-cell RNA sequencing: To understand IKBIP expression heterogeneity within tumors and across cell types

• CRISPR-Cas9 screening: To identify synthetic lethal interactions with IKBIP

• Patient-derived organoids: To test IKBIP-targeted therapies in more physiologically relevant models

• Systems biology approaches: To integrate IKBIP into broader signaling networks

• Structural biology studies: To determine the three-dimensional structure of IKBIP and design targeted inhibitors

How can researchers integrate multi-omics data to better understand IKBIP's role in cancer?

A comprehensive multi-omics approach would include:

• Genomics: Analysis of mutations, copy number variations, and structural variations affecting IKBIP

• Transcriptomics: RNA-seq to evaluate expression levels across cancer types and conditions

• Proteomics: Mass spectrometry to identify IKBIP protein interactions and post-translational modifications

• Epigenomics: Analysis of DNA methylation, histone modifications, and chromatin accessibility at the IKBIP locus

• Metabolomics: Investigation of metabolic changes associated with IKBIP expression

• Integration: Using computational methods to integrate these data types and identify patterns

Researchers should employ advanced bioinformatic tools for integration, including network analysis, machine learning approaches, and causal inference methods.