ITGB4 Human

What is the normal tissue distribution pattern of ITGB4 in humans?

ITGB4 shows variable expression across human tissues with low tissue specificity in most normal tissues. According to Human Protein Atlas data, ITGB4 exhibits highest expression in the small intestine, followed by the salivary gland and placenta. It is expressed in most detected tissues (consensus normalized expression values >1) except for monocytes, total PBMC, and NK-cells. Interestingly, ITGB4 shows enhanced expression in plasmacytoid dendritic cells while remaining low or unexpressed in other blood cells, indicating high specificity in this cell type .

How should researchers interpret differences between ITGB4 RNA and protein expression in tumors?

Researchers must carefully consider potential post-transcriptional and post-translational modifications when studying ITGB4. The search results reveal discrepancies between RNA and protein levels in several cancer types. For instance, while UCEC and clear cell RCC show parallel changes in ITGB4 RNA and protein levels (P < 0.001), other cancers including ovarian cancer, breast cancer, colon cancer, and lung adenocarcinoma exhibit protein expression changes that don't correspond to RNA level alterations . This suggests that post-transcriptional regulation plays a significant role in determining ITGB4 protein levels in certain cancer types, necessitating both transcript and protein-level analyses for comprehensive understanding.

What are the recommended methodologies for analyzing ITGB4 expression across different cancer types?

For comprehensive ITGB4 expression analysis across cancer types, researchers should employ multi-platform approaches:

• RNA-seq analysis using TCGA and GEO datasets through platforms like TIMER2 (http://timer.cistrome.org/), comparing tumor vs. adjacent normal tissues

• Protein expression analysis via the CPTAC dataset using the UALCAN portal (http://ualcan.path.uab.edu/analysis-prot.html)

• Verification through immunohistochemistry on tissue samples

• Analysis of phosphoprotein levels (especially at sites S1069, S1180, S1209, S1455, S1454, S1457, T1471, S1547, T1530, T1532, T1583, and S1600) to assess post-translational modifications

This multi-level approach provides more reliable results than single-methodology studies and helps resolve discrepancies between transcriptomic and proteomic data.

How does ITGB4 contribute to cancer progression mechanistically?

ITGB4 promotes cancer progression through multiple mechanisms:

• NF-κB pathway activation: ITGB4 activates the NF-κB signaling pathway by directly interacting with IκBα in lung adenocarcinoma

• Immune suppression: ITGB4 can suppress CD4+ and CD8+ T-cell infiltrations in LUAD cells, potentially contributing to immune evasion

• Transcriptional regulation: TFAP2A can directly bind to the ITGB4 promoter and transcriptionally activate ITGB4 in LUAD cells

• Tumor microenvironment modulation: ITGB4 appears closely related to tumor-associated fibroblasts based on single-cell sequencing analyses

These mechanisms collectively contribute to various hallmarks of cancer including proliferation, invasion, and immune evasion.

In which cancer types is ITGB4 most significantly dysregulated, and how should researchers prioritize their studies?

ITGB4 shows significant upregulation in multiple cancer types. Based on TCGA data, researchers should prioritize studies in:

• Lung cancers (LUAD, LUSC) - shows both expression changes and prognostic significance

• Brain tumors (LGG, GBM) - particularly lower-grade gliomas where ITGB4 accurately predicts prognosis

• Kidney cancers (KIRC, KIRP) - shows expression differences and prognostic value

• Gastrointestinal cancers (COAD, READ, STAD) - consistent upregulation patterns

Researchers should note that ITGB4 is downregulated in some cancers like uterine carcinoma (UCS), breast invasive carcinoma (BRCA), and skin cutaneous melanoma (SKCM) , suggesting context-dependent roles that warrant specific investigation in each cancer type.

How can researchers experimentally validate the oncogenic function of ITGB4 in candidate cancer types?

A comprehensive validation approach should include:

• In vitro functional assays:

  • Knockdown/overexpression studies followed by proliferation, migration, and invasion assays

  • Co-immunoprecipitation to identify protein-protein interactions (e.g., with IκBα)

  • Promoter binding studies to validate transcriptional regulation (e.g., TFAP2A binding)

• In vivo validation:

  • Xenograft models with ITGB4 modulation to assess tumor growth and metastasis

  • Patient-derived xenografts to maintain tumor heterogeneity

  • Orthotopic models for context-specific microenvironment effects

• Mechanism exploration:

  • Pathway analysis focusing on NF-κB signaling

  • Immune infiltration assessment

  • Single-cell analysis to understand cell-type specific effects

This multi-level validation approach provides robust evidence for ITGB4's role in cancer.

How does ITGB4 expression correlate with patient survival across different cancer types?

ITGB4 expression shows variable prognostic significance across cancer types:

Better prognosis associated with high ITGB4 expression:

• OS: UVM, ovarian cancer, liver cancer

• DFS: UCEC

• Relapse-free survival (RFS): Breast cancer

• Disease-specific survival (DSS): Liver cancer

These contradictory findings highlight the context-dependent roles of ITGB4 across different cancer types .

What statistical approaches are recommended for assessing ITGB4's prognostic value in clinical research?

For robust prognostic assessment of ITGB4, researchers should:

• Use Kaplan-Meier survival analysis with log-rank tests to compare high vs. low ITGB4 expression groups

• Apply consistent cutoff thresholds (e.g., 50% for splitting low and high expression cohorts as used in GEPIA2)

• Perform univariate and multivariate Cox regression analyses to control for confounding variables

• Analyze multiple survival endpoints (OS, DFS, PFS, DSS) for comprehensive evaluation

• Validate findings across independent datasets (e.g., TCGA and GEO)

• Consider stage-specific analyses, as ITGB4's prognostic value may vary by disease stage

This multi-faceted approach provides more reliable prognostic assessment than single-method approaches.

How should researchers integrate ITGB4 expression with other molecular and clinical features for improved prognostic models?

To develop integrative prognostic models:

• Combine ITGB4 expression with:

  • Pathological staging data, particularly for cancers showing stage-dependent ITGB4 expression (LUSC, PAAD, THCA, UCS, LIHC, KIRC)

  • Genetic alteration information (mutations, CNAs) from databases like cBioPortal

  • DNA methylation status, particularly at promoter region probes

  • Related signaling pathway markers (e.g., NF-κB pathway components)

• Employ advanced modeling approaches:

  • Machine learning algorithms for pattern recognition

  • Non-negative matrix factorization (NMF) clustering to identify patient subgroups

  • Nomograms for clinical application

  • Time-dependent ROC curve analysis to assess predictive accuracy

This integrative approach enables more precise patient stratification and personalized treatment planning.

How does ITGB4 interact with the immune microenvironment in cancer?

ITGB4 significantly influences the tumor immune microenvironment through:

• T-cell infiltration modulation: Research indicates ITGB4 can suppress CD4+ and CD8+ T-cell infiltrations in LUAD, potentially contributing to immune evasion mechanisms

• Correlation with immune-related genes: Non-negative matrix factorization (NMF) cluster analysis shows ITGB4 is closely associated with immune-related genes

• Interaction with tumor-associated fibroblasts: Single-cell sequencing analyses indicate ITGB4 has a close relationship with tumor-associated fibroblasts in the microenvironment of gliomas

• Potential impact on immunotherapy: Given its influence on T-cell infiltration, ITGB4 may affect responses to immunotherapies, particularly those targeting the PD-1/PD-L1 axis

These relationships suggest ITGB4 could be a potential immunotherapeutic target, particularly in cancers where it shows high expression.

What methodologies are most appropriate for investigating ITGB4's role in the tumor microenvironment?

To comprehensively investigate ITGB4's role in the tumor microenvironment, researchers should employ:

• Single-cell sequencing approaches:

  • scRNA-seq to characterize cell-type specific expression patterns

  • Spatial transcriptomics to understand the geographic distribution of ITGB4-expressing cells

  • Integration with protein analyses (CITE-seq) for surface marker correlation

• Immune cell infiltration analyses:

  • Computational deconvolution methods using bulk RNA-seq data

  • Flow cytometry validation of immune cell populations

  • Multiplex immunohistochemistry to visualize spatial relationships

• Co-culture experimental systems:

  • In vitro co-cultures of cancer cells with fibroblasts and immune cells

  • 3D organoid models incorporating multiple cell types

  • Transwell migration assays to assess immune cell recruitment

• In vivo immune monitoring:

  • Immunocompetent mouse models with ITGB4 modulation

  • Sequential tumor biopsies to track changes in immune infiltration

These complementary approaches provide a comprehensive view of ITGB4's role in the complex tumor ecosystem.

How can contradictory findings about ITGB4's relationship with immune cells across different cancer types be reconciled?

To reconcile contradictory findings about ITGB4 and immune interactions:

• Consider tissue-specific microenvironments:

  • Different cancers have unique immune contexts

  • Baseline immune infiltration varies by tissue origin

  • Stromal composition differs across cancer types

• Apply cancer-specific analysis:

  • Stratify analyses by cancer type rather than pooling data

  • Consider molecular subtypes within each cancer

  • Analyze cancer stage-dependent effects

• Evaluate methodological differences:

  • Distinguish between correlation studies vs. functional validation

  • Consider sample preparation differences (fresh vs. FFPE)

  • Note computational deconvolution algorithm variations

• Integrate multi-omics data:

  • Combine RNA-seq, proteomics, and epigenetic data

  • Include post-translational modification information (phosphorylation status)

  • Consider the impact of genomic alterations on immune interactions

This systematic approach helps researchers understand apparent contradictions and develop more nuanced hypotheses about context-dependent functions.

What are the key considerations when designing knockdown/overexpression experiments for ITGB4?

When manipulating ITGB4 expression in experimental systems:

• Selection of appropriate model systems:

  • Choose cell lines with baseline ITGB4 expression relevant to research question

  • Consider patient-derived primary cultures for clinical relevance

  • Include both 2D and 3D culture systems to capture dimensional effects

• Expression modulation strategies:

  • For knockdown: Compare siRNA (transient) vs. shRNA or CRISPR-Cas9 (stable) approaches

  • For overexpression: Use inducible systems to control expression levels

  • Include rescue experiments to confirm specificity of observed effects

• Functional readouts:

  • Assess multiple cancer hallmarks (proliferation, migration, invasion, etc.)

  • Include pathway activation markers (particularly NF-κB signaling)

  • Measure interactions with extracellular matrix components, especially laminins

• Controls and validation:

  • Verify knockdown/overexpression at both RNA and protein levels

  • Include phosphorylation status assessment for key sites

  • Use multiple independent clones/populations to minimize clonal effects

These considerations ensure robust and reproducible results when manipulating ITGB4 expression.

How should researchers approach the investigation of ITGB4 phosphorylation and its functional implications?

To effectively study ITGB4 phosphorylation:

• Site identification and prioritization:

  • Focus on key phosphorylation sites (S1069, S1180, S1209, S1455, S1454, S1457, T1471, S1547, T1530, T1532, T1583, and S1600)

  • Prioritize sites with clinical correlation data from CPTAC

  • Consider evolutionary conservation of phosphosites

• Detection methodologies:

  • Use phospho-specific antibodies for common sites

  • Apply mass spectrometry for comprehensive phosphosite mapping

  • Implement proximity ligation assays to detect site-specific protein interactions

• Functional analysis approaches:

  • Generate phosphomimetic and phosphodeficient mutants

  • Assess kinase inhibitor panels to identify regulatory kinases

  • Perform temporal analyses to understand phosphorylation dynamics

• Clinical correlation:

  • Compare phosphorylation patterns between normal and tumor tissues

  • Correlate with clinical outcomes and treatment responses

  • Integrate with genomic alteration data

This systematic approach enables meaningful insights into how phosphorylation regulates ITGB4 function in cancer contexts.

What experimental approaches can elucidate the relationship between ITGB4 and the NF-κB pathway?

To investigate ITGB4's interaction with the NF-κB pathway:

• Protein-protein interaction studies:

  • Co-immunoprecipitation to confirm ITGB4-IκBα interaction

  • Proximity ligation assays to visualize interactions in situ

  • Domain mapping to identify specific interaction regions

• Pathway activation assessment:

  • Western blotting for phosphorylated pathway components

  • Nuclear translocation assays for p65/RelA

  • Luciferase reporter assays with NF-κB responsive elements

• Functional validation:

  • Combine ITGB4 manipulation with NF-κB inhibitors

  • Assess rescue effects with constitutively active NF-κB components

  • Evaluate downstream target gene expression changes

• In vivo validation:

  • Tissue analysis for co-localization of ITGB4 and NF-κB components

  • Assessment of pathway component correlation in patient samples

  • Therapeutic targeting studies in animal models

These complementary approaches provide robust evidence for the mechanistic relationship between ITGB4 and NF-κB signaling.

What bioinformatic approaches are recommended for analyzing ITGB4 genetic alterations across cancers?

For comprehensive analysis of ITGB4 genetic alterations:

This systematic approach provides comprehensive insights into the genetic basis of ITGB4 dysregulation in cancer.

How should researchers approach the analysis of ITGB4 DNA methylation in cancer studies?

For effective ITGB4 methylation analysis:

• Methylation probe selection and analysis:

  • Access DNA methylation data from MEXPRESS (https://mexpress.be/)

  • Focus on promoter region probes (specifically highlighted in databases)

  • Analyze multiple probes (e.g., cg12146151, cg16047490, cg23913400, cg0409472)

• Statistical approaches:

  • Apply Benjamini-Hochberg adjustment for multiple testing

  • Calculate Pearson correlation coefficients between methylation and expression

  • Analyze beta values to quantify methylation levels

• Prognostic assessment:

  • Use MethSurv online tool (https://biit.cs.ut.ee/methsurv/) for survival analysis

  • Stratify patients by methylation levels of specific probes

  • Generate Kaplan-Meier plots with statistical significance

• Integrative analysis:

  • Correlate methylation with expression data

  • Examine the relationship between genetic alterations and methylation

  • Consider the impact of copy number on methylation patterns

This approach enables researchers to understand epigenetic regulation of ITGB4 in cancer contexts.

What methodologies are most appropriate for identifying and validating ITGB4-interacting proteins?

To comprehensively identify and validate ITGB4 protein interactions:

• Computational prediction approaches:

  • Use STRING database (https://string-db.org/) with appropriate confidence parameters

  • Set interaction score thresholds (recommended: low confidence 0.150)

  • Filter for experimentally determined interactions

• Correlation-based methods:

  • Utilize "Similar Gene Detection" in GEPIA2 to find top correlated genes

  • Perform pairwise gene Pearson correlation analysis

  • Calculate correlation coefficients and significance values

• Experimental validation techniques:

  • Co-immunoprecipitation followed by mass spectrometry

  • Proximity ligation assays for in situ detection

  • FRET/BRET approaches for dynamic interaction assessment

• Functional validation:

  • Knockdown studies of interacting partners

  • Mutational analysis of interaction domains

  • Competitive peptide inhibition experiments

This multi-layered approach provides high-confidence protein interaction data for mechanistic studies.