CIAPIN1 Human

What is CIAPIN1 and what are its basic characteristics?

CIAPIN1, also known as anamorsin, is a protein encoded by the CIAPIN1 gene located on chromosome 16q21 with a molecular weight of 33 kDa . It functions as an anti-apoptotic molecule and is a downstream effector of the receptor tyrosine kinase-Ras signaling pathway . CIAPIN1 accumulates in the nucleus and localizes to multiple cellular compartments including the cytoplasm, nucleus, and mitochondria .

Structurally distinct from the caspase family and BCL-2 family proteins, CIAPIN1 serves as a regulator and important effector of non-homologous RAS signaling . It is widely distributed in fetal and adult normal tissues, with particularly high expression in differentiated tissues and metabolically active tissues .

How is CIAPIN1 expression typically measured in research settings?

Researchers commonly employ multiple complementary techniques to measure CIAPIN1 expression:

• mRNA expression analysis: Real-time PCR (RT-PCR) is frequently used to quantify CIAPIN1 mRNA levels. This technique can detect gene silencing efficiency (as demonstrated in studies achieving 88% silencing efficiency) .

• Protein expression analysis: Western blotting provides quantitative assessment of CIAPIN1 protein levels. Studies have achieved 83% inhibition efficiency at the protein level using this method .

• Immunohistochemistry (IHC): IHC can visualize CIAPIN1 protein expression in tissue samples, revealing subcellular localization patterns. The Human Protein Atlas database shows CIAPIN1 protein is significantly expressed in the cytoplasm and membrane of invasive breast cancer cells, while showing weak or no expression in healthy tissues .

• Large-scale genomic/transcriptomic analysis: Databases such as TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) are valuable resources for examining CIAPIN1 expression across multiple cancer types and normal tissues .

What databases and resources are available for CIAPIN1 research?

Several comprehensive databases provide valuable resources for CIAPIN1 research:

• TCGA database (https://portal.gdc.cancer.gov/): Contains RNAseq data and corresponding clinicopathological data for multiple cancer types, including invasive breast cancer (IBC) .

• TIMER database (http://timer.cistrome.org/): Useful for examining CIAPIN1 expression levels across 33 human cancer types and analyzing immune cell infiltration .

• GEO database (https://www.ncbi.nlm.nih.gov/geo/): Provides datasets like GSE45827, GSE65194, GSE1456-GPL96, GSE4922-GPL96, GSE7390, and GSE12276 for validation studies .

• Human Protein Atlas (http://www.proteinatlas.org): Offers immunohistochemical results of CIAPIN1 expression in human tissues .

• CPTAC database (Clinical Proteomic Tumor Analysis Consortium): Provides protein expression data for CIAPIN1 .

• UALCAN database (http://ualcan.path.uab.edu/index.html): Useful for studying the methylation status of the CIAPIN1 promoter .

• MethSurv database (https://biit.cs.ut.ee/methsurv/): Allows analysis of DNA methylation status of CIAPIN1 gene CpG sites and its prognostic value .

How does CIAPIN1 expression differ between normal and cancerous tissues?

CIAPIN1 expression shows distinct patterns between normal and cancerous tissues. According to comprehensive analysis of 33 human cancers from the TCGA database:

• Increased expression: CIAPIN1 is significantly overexpressed in invasive breast cancer compared to healthy tissues (P<0.001), in both paired and unpaired samples . This overexpression is also observed in multiple other cancer types including:

  • Uterine corpus endometrial carcinoma

  • Bladder urothelial carcinoma

  • Colon adenocarcinoma

  • Esophageal carcinoma

  • Head and neck squamous cell carcinoma

  • Renal papillary cell carcinoma

  • Liver hepatocellular carcinoma

  • Lung squamous cell carcinoma

  • Rectal adenocarcinoma

  • Stomach adenocarcinoma

• Decreased expression: Interestingly, CIAPIN1 shows lower expression in chromophobe, clear cell, and thyroid carcinomas compared to healthy tissues (P<0.001) .

• Protein level confirmation: CPATC dataset analysis confirms significantly higher CIAPIN1 protein levels in invasive breast cancer samples (P<0.001) .

These findings suggest that CIAPIN1 expression is cancer-type specific and may play different roles depending on the cancer context.

What clinicopathological features correlate with CIAPIN1 expression in breast cancer?

Analysis of 1,065 samples with clinical information from the TCGA-BRCA dataset revealed several significant associations between CIAPIN1 expression and clinicopathological features:

Statistically significant correlations (P<0.05):

• T stage (P<0.001)

• Race (P=0.001)

• Age (P<0.001)

• Histological type (P<0.001)

• ER (estrogen receptor) status (P<0.001)

• PR (progesterone receptor) status (P<0.001)

• PAM50 molecular subtype (P<0.001)

• Disease-specific survival (DSS) event (P=0.021)

• Progression-free interval (PFI) event (P=0.049)

Features without significant correlation (P>0.05):

The table below summarizes key clinicopathological features according to CIAPIN1 expression levels:

These correlations suggest CIAPIN1 might serve as a potential biomarker for specific breast cancer subtypes and could have prognostic value .

What role does CIAPIN1 play in multidrug resistance in cancer?

CIAPIN1 appears to have a significant role in multidrug resistance (MDR) in cancer, particularly in breast cancer:

• Correlation with P-glycoprotein (P-gp): Immunohistochemical analysis has demonstrated a positive correlation between CIAPIN1 and P-gp expression, suggesting a potential relationship between CIAPIN1 and the MDR phenotype .

• Effect on drug sensitivity: Research using RNA interference (RNAi) to silence CIAPIN1 in MCF7/ADM breast cancer cells showed significantly decreased IC50 values for several clinical chemotherapeutics, including epirubicin, paclitaxel, and gemcitabine. The drug resistance was reduced to levels comparable with the drug-sensitive MCF7 cell line .

• Regulation of MDR1 expression: CIAPIN1 appears to regulate the expression of MDR1 (multidrug resistance protein 1). RNAi targeting CIAPIN1 in MCF7/ADM cells led to significant downregulation of both MDR1 mRNA and its protein product P-gp .

• Potential for resistance reversal: Studies have concluded that RNA interference targeting CIAPIN1 can effectively reverse the MDR properties of breast cancer cells, suggesting CIAPIN1 as a potential therapeutic target for overcoming chemoresistance .

These findings indicate that CIAPIN1 may contribute to the development of multidrug resistance in cancer cells through regulation of MDR1/P-gp expression, thereby affecting cellular response to chemotherapeutic agents.

What are effective methods for modulating CIAPIN1 expression in experimental models?

Several effective methods for modulating CIAPIN1 expression have been validated in research settings:

• RNA interference (RNAi):

  • Lentiviral vector-mediated short hairpin RNA (shRNA) has proven highly effective for CIAPIN1 silencing

  • Design software can be used to analyze the CIAPIN1 gene and design specific siRNA sequences

  • Testing multiple candidate siRNAs is recommended to identify the most effective sequence

  • In published studies, the best interference siRNA sequence achieved 88% silencing efficiency at the mRNA level and 83% at the protein level

• Vector construction and selection:

  • Vectors like pSIH1-H1-copGFP have been successfully used for CIAPIN1 targeting

  • DNA sequencing should confirm correct insertion of oligonucleotide fragments

  • Transient transfection in 293 cells can verify transfection efficiency (90% at 48 hours post-transfection in published studies)

• Stable cell line generation:

  • Lentiviral expression vectors can infect target cells (e.g., MCF7/ADM cells at MOI = 6)

  • Screening can establish stably expressing cell lines

  • Expression of Green Fluorescent Protein (GFP) can serve as a marker for successful transfection

  • Stable CIAPIN1 knockdown lines should show consistent silencing across various clone ages

• Verification methods:

  • Real-time PCR for mRNA expression quantification

  • Western blotting for protein expression analysis

  • Functional assays to confirm phenotypic changes (e.g., drug sensitivity testing)

These methodological approaches provide researchers with reliable tools for studying CIAPIN1 function through experimental manipulation of its expression levels.

How can researchers effectively analyze the functional impact of CIAPIN1 in cancer cells?

To effectively analyze the functional impact of CIAPIN1 in cancer cells, researchers can employ a multi-faceted approach:

• Cell viability and proliferation assays:

  • MTT or CCK-8 assays to assess cell viability

  • Colony formation assays to evaluate long-term proliferative capacity

  • Growth curve analysis to track proliferation kinetics

  • BrdU incorporation assays to measure DNA synthesis rates

• Drug sensitivity testing:

  • Determination of IC50 values for various chemotherapeutic agents (as demonstrated with epirubicin, paclitaxel, and gemcitabine)

  • Dose-response curves to visualize sensitivity changes

  • Comparison between CIAPIN1-modulated cells and control cells

• Apoptosis assessment:

  • Annexin V/PI staining and flow cytometry

  • TUNEL assay for DNA fragmentation

  • Analysis of apoptotic protein markers (caspases, PARP cleavage)

  • Assessment of mitochondrial membrane potential

• Migration and invasion assays:

  • Wound healing assays to measure cell migration

  • Transwell migration and invasion assays

  • 3D spheroid invasion assays

• Gene expression analysis:

  • Quantitative PCR for specific target genes (e.g., MDR1)

  • Western blot for protein expression (e.g., P-gp)

  • RNA sequencing for genome-wide expression changes

  • Analysis of DEGs (differentially expressed genes) between high and low CIAPIN1 expression groups

• Pathway analysis:

  • Protein-protein interaction (PPI) network construction using databases like STRING

  • Gene Ontology (GO) and KEGG pathway enrichment analysis

  • Gene Set Enrichment Analysis (GSEA)

• In vivo studies:

  • Xenograft models to assess tumor growth and metastasis

  • Patient-derived xenografts for translational relevance

  • Analysis of drug response in vivo

By combining these methodological approaches, researchers can comprehensively characterize the functional impact of CIAPIN1 in cancer cells and elucidate its underlying mechanisms.

What are the best approaches for analyzing the relationship between CIAPIN1 and immune cell infiltration?

To analyze the relationship between CIAPIN1 and immune cell infiltration, researchers can employ several sophisticated approaches:

• Database utilization for large-scale analysis:

  • TIMER database to examine correlations between CIAPIN1 expression and various immune cell types

  • Analysis of cancer purity in relation to CIAPIN1 expression

  • Examination of specific immune cell populations such as CD8+ T cells, NK cells, and neutrophils

• Correlation analysis with immune markers:

  • Spearman's correlation assessment to evaluate associations between CIAPIN1 expression and markers of 24 different immune cell types

  • Investigation of CIAPIN1's relationship with immune checkpoint genes (e.g., PDCD-1, TIGIT)

• Immune scoring methods:

  • Implementation of the ESTIMATE algorithm (Estimation of Stromal and Immune cells in Malignant Tumor tissues Using Expression)

  • Comparison of immune scores between high and low CIAPIN1 expression groups using the Wilcoxon rank-sum test

• Experimental validation:

  • Flow cytometry to quantify immune cell populations in tumor samples

  • Immunohistochemistry to visualize immune cell infiltration in tissue sections

  • Single-cell RNA sequencing to characterize immune cell heterogeneity

  • Co-culture experiments with immune cells and CIAPIN1-modulated cancer cells

• Functional studies:

  • Cytokine profiling to assess immune-related secreted factors

  • Assessment of immune cell activation status in relation to CIAPIN1 expression

  • In vivo studies using immunocompetent mouse models

• Methylation analysis:

  • Examination of CIAPIN1 promoter methylation status using UALCAN database

  • Analysis of CpG site methylation and its correlation with immune parameters

  • Integration of methylation data with immune infiltration profiles

These approaches provide a comprehensive framework for investigating the complex relationship between CIAPIN1 expression and the tumor immune microenvironment, which may yield insights into potential immunotherapeutic strategies.

How does CIAPIN1 interact with the RAS signaling pathway and what are the downstream effects?

CIAPIN1 functions as a downstream effector of the receptor tyrosine kinase-Ras signaling pathway, though it operates differently from traditional apoptosis regulators like the caspase family and BCL-2 family proteins . Understanding this interaction requires multi-layered analysis:

Methodological approach for studying CIAPIN1-RAS interactions:

• Protein interaction analysis:

  • Co-immunoprecipitation to detect direct interactions between CIAPIN1 and RAS pathway components

  • Proximity ligation assays to visualize protein interactions in situ

  • FRET/BRET assays to measure real-time interactions in living cells

• Signaling pathway dissection:

  • Western blotting to assess phosphorylation status of downstream effectors (RAF, MEK, ERK)

  • Pharmacological inhibition of specific pathway components to identify dependency relationships

  • Rescue experiments with constitutively active RAS pathway members in CIAPIN1-depleted cells

• Transcriptional regulation analysis:

  • Chromatin immunoprecipitation (ChIP) to identify transcription factors regulated by CIAPIN1

  • Reporter assays to measure transcriptional activity of RAS-responsive elements

  • RNA-seq analysis comparing CIAPIN1-depleted cells with controls, focusing on RAS target genes

• Functional outcome assessment:

  • Cell cycle analysis to determine effects on proliferation

  • Apoptosis assays to evaluate cell survival regulation

  • Migration/invasion assays to assess metastatic potential

• Systems biology approach:

  • Protein-protein interaction networks constructed using databases like STRING

  • Pathway enrichment analysis to identify affected biological processes beyond RAS signaling

  • Integration of multiple -omics data to create comprehensive signaling maps

This methodological framework allows researchers to unravel the complex relationship between CIAPIN1 and RAS signaling, potentially revealing novel therapeutic targets for cancers with dysregulated RAS pathway activity.

What is the relationship between CIAPIN1 and TP53 in cancer progression?

The relationship between CIAPIN1 and TP53 represents an important area of investigation in cancer research. Though specific details about their interaction are not fully described in the provided search results, the methodological approach to studying this relationship would include:

• Correlation analysis in clinical samples:

  • Utilizing Spearman's correlation approach to examine the relationship between CIAPIN1 and TP53 expression levels in datasets like TCGA-BRCA

  • Stratification of samples based on TP53 mutation status to identify potential differential associations

  • Survival analysis comparing patients with different CIAPIN1/TP53 expression patterns

• Mechanistic studies in cellular models:

  • Manipulation of CIAPIN1 expression in TP53 wild-type versus TP53-mutant or null backgrounds

  • Assessment of p53 protein stability, phosphorylation, and transcriptional activity in CIAPIN1-modulated cells

  • Analysis of p53 target gene expression in response to CIAPIN1 alteration

  • Investigation of whether p53 directly or indirectly regulates CIAPIN1 expression

• Stress response analysis:

  • Examination of how DNA damage or other p53-activating stressors affect the CIAPIN1-p53 relationship

  • Analysis of apoptotic responses in cells with different CIAPIN1/p53 status

  • Assessment of cell cycle checkpoint activation and senescence induction

• Protein interaction studies:

  • Co-immunoprecipitation to detect potential physical interactions

  • Identification of common binding partners or regulatory complexes

  • Subcellular localization studies to identify potential co-localization patterns

• Functional impact assessment:

  • Simultaneous manipulation of both CIAPIN1 and TP53 to identify synergistic or antagonistic effects

  • Drug response profiling in cells with different CIAPIN1/p53 configurations

  • In vivo tumor models to assess how the CIAPIN1-p53 axis affects tumor progression

Understanding the relationship between CIAPIN1 and TP53 could provide important insights into cancer biology and potentially reveal novel therapeutic strategies targeting this axis.

How does the methylation status of CIAPIN1 affect its expression and function in different cancer types?

The methylation status of CIAPIN1 represents an important epigenetic regulatory mechanism that may significantly impact its expression and function across different cancer types. Researchers can investigate this relationship using the following methodological approaches:

• Comprehensive methylation profiling:

  • Analysis of CIAPIN1 promoter methylation status using the UALCAN database

  • Examination of specific CpG sites within the CIAPIN1 gene using the MethSurv database

  • Comparison of methylation patterns across different cancer types and stages

  • Correlation analysis between methylation levels and clinical outcomes

• Integrated methylation-expression analysis:

  • Correlation between CIAPIN1 promoter methylation and mRNA/protein expression levels

  • Identification of methylation-sensitive CpG sites with the strongest impact on expression

  • Comparison of methylation-expression relationships across cancer types

  • Analysis of potential tissue-specific methylation patterns

• Experimental validation approaches:

  • Bisulfite sequencing to quantify site-specific methylation

  • Methylation-specific PCR to assess targeted regions

  • Pyrosequencing for quantitative methylation analysis

  • Treatment with demethylating agents (e.g., 5-aza-2'-deoxycytidine) to confirm causal relationships

• Functional impact assessment:

  • CRISPR-mediated epigenetic editing to specifically alter methylation at the CIAPIN1 locus

  • Analysis of transcription factor binding affected by differential methylation

  • Chromatin immunoprecipitation (ChIP) to examine histone modifications around methylated regions

  • Reporter assays with methylated versus unmethylated CIAPIN1 promoter constructs

• Clinical correlation studies:

  • Association between CIAPIN1 methylation status and patient survival

  • Correlation with response to specific therapies

  • Potential as a biomarker for disease progression or treatment selection

  • Multivariate analysis accounting for other epigenetic modifications

By implementing these methodological approaches, researchers can gain comprehensive insights into how methylation regulates CIAPIN1 expression and function across different cancer contexts, potentially revealing new therapeutic opportunities targeting epigenetic mechanisms.

What is the potential of CIAPIN1 as a diagnostic or prognostic biomarker for invasive breast cancer?

CIAPIN1 shows considerable promise as both a diagnostic and prognostic biomarker for invasive breast cancer (IBC), based on multiple lines of evidence:

Diagnostic potential:

• Differential expression: CIAPIN1 is significantly overexpressed in IBC compared to normal breast tissue, in both mRNA and protein levels (P<0.001) . This clear distinction makes it a candidate diagnostic marker.

• Comparison to current markers: Current early detection of IBC relies primarily on serum biomarkers CA153 and CEA, which show low sensitivity and specificity . CIAPIN1 could potentially address this limitation.

• Verification across multiple datasets: The differential expression of CIAPIN1 in IBC has been confirmed in independent datasets (GSE45827 and GSE65194), strengthening its reliability as a diagnostic marker .

• Association with specific subtypes: CIAPIN1 expression correlates significantly with various clinicopathological features including histological type, ER/PR status, and molecular subtypes (PAM50) , suggesting potential utility in classifying IBC subtypes.

Prognostic potential:

• Survival correlation: CIAPIN1 expression is significantly associated with disease-specific survival (DSS) events (P=0.021) and progression-free interval (PFI) events (P=0.049) .

• Multivariate analysis: Cox regression analysis on clinicopathological properties and CIAPIN1 expression can identify risk factors related to DSS and progression-free survival (PFS) .

• Validation in multiple cohorts: Prognostic significance can be confirmed using multiple independent datasets, including GSE1456-GPL96, GSE4922-GPL96, GSE7390, and GSE12276 .

• Integration with other markers: CIAPIN1 could be incorporated into prognostic models alongside established markers for improved predictive power.

To fully establish CIAPIN1 as a clinical biomarker, researchers should pursue:

• Prospective clinical validation studies

• Standardization of detection methods

• Determination of optimal cutoff values for clinical decision-making

• Integration into existing diagnostic and prognostic algorithms

How might targeting CIAPIN1 improve treatment outcomes in chemotherapy-resistant cancer?

Targeting CIAPIN1 shows significant promise for improving treatment outcomes in chemotherapy-resistant cancers, particularly in breast cancer. The methodological approach to developing and evaluating such strategies includes:

• Mechanism-based targeting approaches:

  • RNAi technology has demonstrated effectiveness in reducing CIAPIN1 expression, with lentiviral vector-mediated shRNA achieving up to 88% silencing efficiency at the mRNA level and 83% at the protein level

  • Alternative approaches could include CRISPR/Cas9-mediated gene editing, antisense oligonucleotides, or small molecule inhibitors targeting CIAPIN1 protein function

• Reversal of multidrug resistance:

  • CIAPIN1 silencing significantly reduces IC50 values for multiple chemotherapeutic agents (epirubicin, paclitaxel, gemcitabine) in drug-resistant breast cancer cells

  • Downregulation of CIAPIN1 leads to decreased expression of MDR1 mRNA and P-glycoprotein, directly affecting drug efflux mechanisms

  • Combined therapy approaches coupling CIAPIN1 inhibition with conventional chemotherapeutics could potentially overcome resistance

• Patient stratification for targeted therapy:

  • CIAPIN1 expression correlates with specific clinicopathological features , suggesting potential for identifying patients most likely to benefit from CIAPIN1-targeted therapy

  • Development of companion diagnostics to measure CIAPIN1 expression levels before treatment

• Evaluation in preclinical models:

  • Cell line panels representing diverse cancer types and resistance mechanisms

  • Patient-derived xenografts to assess efficacy in models maintaining tumor heterogeneity

  • Immunocompetent models to evaluate effects on tumor immune microenvironment

  • Pharmacodynamic markers to confirm target engagement in vivo

• Potential combination strategies:

  • Synergistic combinations with established chemotherapeutics

  • Sequential therapy approaches (CIAPIN1 inhibition followed by conventional treatment)

  • Combinations with immunotherapy based on CIAPIN1's potential relationship with immune checkpoints

These approaches provide a roadmap for translating the biological understanding of CIAPIN1 in multidrug resistance into clinically relevant therapeutic strategies that could significantly improve outcomes for patients with chemotherapy-resistant cancers.

What are the key methodological challenges in translating CIAPIN1 research from bench to bedside?

Translating CIAPIN1 research from bench to bedside presents several methodological challenges that researchers must address:

• Target specificity and delivery challenges:

  • Developing highly specific inhibitors for CIAPIN1 that don't affect related pathways

  • Creating effective delivery systems for RNAi or other nucleic acid-based therapeutics

  • Achieving sufficient target engagement in tumor tissue while minimizing off-target effects

  • Overcoming potential redundancy in anti-apoptotic and drug resistance pathways

• Biomarker development and validation:

  • Standardizing CIAPIN1 detection methods across different laboratories and clinical settings

  • Establishing clinically relevant cutoff values for "high" versus "low" expression

  • Developing cost-effective, reproducible assays suitable for routine clinical use

  • Validating prognostic and predictive value in prospective clinical trials

• Patient heterogeneity considerations:

  • Accounting for variations in CIAPIN1 expression across different cancer types and subtypes

  • Understanding the impact of tumor heterogeneity on CIAPIN1-targeted therapy response

  • Identifying patient subpopulations most likely to benefit from CIAPIN1-targeted interventions

  • Developing strategies for monitoring acquired resistance

• Regulatory and clinical trial design challenges:

  • Designing appropriate clinical endpoints to demonstrate efficacy of CIAPIN1-targeted therapies

  • Determining optimal timing for CIAPIN1 intervention (first-line vs. after resistance development)

  • Establishing appropriate patient selection criteria based on CIAPIN1 expression

  • Addressing regulatory requirements for novel therapeutic modalities

• Understanding complex biology:

  • Elucidating the complete signaling network around CIAPIN1 to predict potential compensatory mechanisms

  • Characterizing the interaction between CIAPIN1 and the tumor microenvironment, including immune cells

  • Investigating potential resistance mechanisms to CIAPIN1-targeted therapies

  • Determining the relationship between CIAPIN1 methylation status and therapeutic response

Addressing these methodological challenges requires multidisciplinary collaboration between basic scientists, clinical researchers, biostatisticians, and regulatory experts to successfully translate promising preclinical findings on CIAPIN1 into clinically meaningful advances for cancer patients.