Recombinant Human Proline/serine-rich coiled-coil protein 1 (PSRC1)

What is PSRC1 and what are its key structural characteristics?

Proline/serine-rich coiled-coil protein 1 (PSRC1) is a microtubule-associated protein regulated by p53 transcription. The protein contains distinct functional domains with unique properties:

• The C-terminal domain binds to the mitotic spindle

• The regulatory N-terminal domain controls the C-terminal domain's ability to bind to microtubules

• The N-terminal domain determines the cellular activity of the PSRC1 protein

PSRC1's promoter contains a putative p53-binding motif that is responsible for p53-mediated gene suppression, establishing an important regulatory relationship between p53 and PSRC1 expression .

What expression patterns does PSRC1 exhibit in normal versus cancerous tissues?

PSRC1 demonstrates significant differential expression between normal and cancerous tissues, particularly in lung cancer. Based on comprehensive analysis:

• PSRC1 expression is significantly higher in lung adenocarcinoma (LUAD) tissues compared to normal lung tissues

• Similar elevated expression is observed in lung squamous cell carcinoma (LUSC)

• This elevated expression pattern has been consistently verified in multiple datasets, including The Cancer Genome Atlas (TCGA)

• Immunohistochemical validation in 150 patients with non-small cell lung carcinoma confirmed these expression differences

Besides lung cancer, PSRC1 has been found to be overexpressed in colorectal cancer, hepatocellular carcinoma, and oral squamous cell carcinoma, suggesting its broad involvement in various cancers .

How should researchers evaluate PSRC1 expression in tissue samples?

For accurate and reproducible assessment of PSRC1 expression in tissue samples, researchers should employ a standardized semi-quantitative scoring system:

Table 1: Semi-quantitative Scoring System for PSRC1 Expression

The final score is calculated by multiplying the staining intensity by the percentage of stained cells, yielding a range of 0-12. For analytical purposes, scores can be categorized as:

• 0-2: Negative expression

• 3-6: Low PSRC1 expression

• 7-12: High PSRC1 expression

Evaluation should be performed under 400× optical microscopy by independent pathologists with appropriate positive and negative controls run in parallel .

What methods are available for producing recombinant PSRC1 for experimental studies?

For experimental investigations requiring recombinant PSRC1, researchers can employ the following production method:

• Use an E. coli expression system for recombinant production

• Express the target gene encoding E2-T215

• Add a His tag at the C-terminus for purification purposes

• Utilize affinity chromatography for protein isolation

This approach enables the generation of pure recombinant PSRC1 protein suitable for functional studies, antibody production, and protein-protein interaction analyses .

What are the primary challenges in studying PSRC1 function?

When investigating PSRC1 function, researchers should address several methodological challenges:

• Distinguishing between direct and indirect effects of PSRC1 on cellular phenotypes

• Controlling for p53 status in experimental models since PSRC1 is regulated by p53

• Accounting for tissue-specific expression patterns and functions

• Developing appropriate knockout or knockdown models that avoid compensatory mechanisms

• Establishing physiologically relevant experimental conditions that recapitulate in vivo environments

Addressing these challenges requires careful experimental design with appropriate controls and validation across multiple experimental systems.

What experimental design approaches are most effective for investigating PSRC1's role in cancer progression?

Effective investigation of PSRC1's role in cancer progression requires robust experimental design that establishes causal relationships. Based on best practices in experimental design:

• Variable Definition and Control:

  • Independent variable: PSRC1 expression levels (overexpression, knockdown, or knockout)

  • Dependent variables: Cell proliferation, migration, invasion, apoptosis, and in vivo tumor growth

  • Control for confounding variables: p53 status, cell cycle phase, and genetic background

• True Experimental Design Implementation:

  • Utilize random assignment of cells or animals to experimental and control groups

  • Include appropriate negative and positive controls

  • Implement blinding procedures for outcome assessment

• In vitro Functional Studies:

  • PSRC1 gene silencing via siRNA or CRISPR-Cas9

  • Overexpression using expression vectors with standardized promoters

  • Rescue experiments to confirm specificity of observed phenotypes

• In vivo Model Systems:

  • Xenograft models with modulated PSRC1 expression

  • Patient-derived xenografts to preserve tumor heterogeneity

  • Genetically engineered mouse models when appropriate

This systematic approach allows for rigorous testing of hypotheses about PSRC1's mechanistic roles in cancer progression while minimizing experimental bias and confounding factors .

How does PSRC1 expression correlate with clinical outcomes in lung cancer patients?

PSRC1 expression demonstrates significant correlations with clinical outcomes in lung adenocarcinoma (LUAD), offering valuable prognostic information:

Table 2: PSRC1 Expression Correlation with Clinical Parameters in LUAD

• T1/T2 stage (p = 0.013)

• N0 stage (p = 0.03)

• M0 stage (p = 0.012)

• Pathological stages I/II/III (p = 0.005)

• Patients aged >65 years (p = 0.003)

• Female patients (p = 0.006)

Interestingly, these prognostic correlations were specific to LUAD and were not observed in lung squamous cell carcinoma (LUSC), highlighting the importance of tumor-type specificity in PSRC1 research .

What bioinformatic approaches are recommended for analyzing PSRC1-associated gene networks?

For comprehensive analysis of PSRC1-associated gene networks, researchers should implement the following bioinformatic approaches:

• Weighted Gene Co-expression Network Analysis (WGCNA):

  • Identify core modules and hub genes associated with PSRC1

  • Cluster genes with similar expression patterns

  • Prioritize key regulatory relationships

• Gene Set Enrichment Analysis (GSEA):

  • Parameters: nPerm = 1000, minGSSize = 10, maxGSSize = 1000, p-value cutoff = 0.05

  • Consider results significant when false discovery rate (FDR) < 0.25 and adjusted p-value < 0.05

  • Group gene sets according to common biological functions, chromosomal regulatory mechanisms, or locations

• Functional Annotation:

  • Gene Ontology (GO) analysis for biological processes, molecular functions, and cellular components

  • Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

  • Implementation using R clusterProfiler package

• Visualization Techniques:

  • Generate volcano plots and heat maps to visualize differential expression

  • Network graphs to display protein-protein interactions

  • Enrichment plots to demonstrate pathway activation or suppression

These approaches enable researchers to uncover the biological mechanisms through which PSRC1 influences cancer development and progression, potentially revealing novel therapeutic targets.

How can researchers investigate the relationship between PSRC1 and immune cell infiltration in the tumor microenvironment?

To investigate PSRC1's relationship with immune infiltration in the tumor microenvironment, researchers should implement the following methodological approach:

• Single-sample Gene Set Enrichment Analysis (ssGSEA):

  • Utilize the GSVA package for implementation

  • Base the analysis on expression profiles of specific immune cell marker genes

  • Derive marker genes from established immune cell signature databases

• Immunoinfiltration Analysis Pipeline:

  • Evaluate the relationship between PSRC1 expression and the abundance of 24 immune cell types

  • Apply Spearman correlation for continuous expression data

  • Implement Wilcoxon rank-sum tests for grouped comparisons

  • Consider p < 0.05 as statistically significant

• Validation Approaches:

  • Multiplex immunohistochemistry on tissue sections

  • Flow cytometry analysis of tumor-infiltrating lymphocytes

  • Spatial transcriptomics to preserve information about cellular locations

• Functional Validation:

  • Co-culture experiments with immune cells and cancer cells with modulated PSRC1 expression

  • Cytokine profiling to assess immune regulatory effects

  • In vivo models with immune-competent animals

This multifaceted approach can reveal how PSRC1 expression influences the immune landscape within tumors, potentially informing immunotherapy strategies for patients with PSRC1-overexpressing cancers.

What methodological considerations are important when assessing PSRC1 as a prognostic biomarker?

When evaluating PSRC1 as a prognostic biomarker, researchers should address several critical methodological considerations:

• Patient Cohort Selection:

  • Ensure adequate sample size with power analysis

  • Include patients across all cancer stages

  • Account for treatment heterogeneity

  • Consider potential confounding clinical variables (age, gender, smoking status)

• Expression Analysis Methods:

  • Standardize tissue collection and processing

  • Employ multiple detection methods (IHC, RT-PCR, RNA-seq)

  • Use validated scoring systems with defined cutoff values

  • Ensure blinded assessment by multiple pathologists

• Statistical Analysis Approach:

  • Univariate analysis with Kaplan-Meier curves and log-rank tests

  • Multivariate analysis using Cox proportional hazards models

  • Time-dependent ROC curve analysis to assess predictive accuracy

  • Stratified analysis for specific patient subgroups

• Validation Strategies:

  • Internal validation with bootstrapping or cross-validation

  • External validation in independent patient cohorts

  • Prospective validation when possible

  • Comparative analysis with established prognostic markers

• Reporting Standards:

  • Adhere to REMARK (REporting recommendations for tumor MARKer prognostic studies) guidelines

  • Report hazard ratios with confidence intervals

  • Disclose all tested hypotheses to address multiple testing issues

  • Provide detailed methodological descriptions for reproducibility

Implementing these considerations ensures robust assessment of PSRC1 as a prognostic biomarker, potentially leading to its clinical application in patient stratification and treatment decision-making.