SPECC1 Human

What is the basic expression pattern of SPECC1 across normal human tissues?

SPECC1 shows variable expression across normal human tissues, with highest levels observed in the testis and brain. Lower expression levels are found in tissues such as the pancreas. Analysis of the GTEx database has confirmed this tissue-specific expression pattern, with testis showing markedly elevated expression compared to other tissues . Additionally, SPECC1 shows enhanced expression in basophils, suggesting a potential role in immune regulation .

Methodologically, researchers studying tissue-specific expression should:

• Utilize RNA-seq data from repositories like GTEx

• Employ quantitative PCR for validation in tissue samples

• Consider single-cell RNA sequencing to identify cell-type specific expression patterns

• Account for potential splice variants across different tissues

What is the subcellular localization of SPECC1 protein?

SPECC1 demonstrates a complex subcellular distribution pattern, with presence in multiple cellular compartments including the nucleoplasm, vesicles, nucleoli, and cytosol . This diverse distribution suggests involvement in various cellular processes and potential multifunctional roles.

For researchers investigating subcellular localization:

• Immunofluorescence with specific antibodies remains the gold standard

• Subcellular fractionation followed by Western blotting provides quantitative assessment

• Live-cell imaging with fluorescently tagged SPECC1 can reveal dynamic localization patterns

• Proximity labeling approaches can identify interacting partners in specific compartments

How is SPECC1 genetically altered in human cancers?

Genetic alterations in SPECC1 have been observed in multiple cancer types. Analysis of cancer genomics databases has revealed that SPECC1 is frequently mutated in endometrial cancer, sarcoma, and esophageal cancer . The specific mutation patterns and their functional consequences require further investigation.

For mutation analysis research:

• Whole exome or targeted sequencing of tumor samples

• Correlation of mutation status with clinical outcomes

• Functional validation of mutations using site-directed mutagenesis

• Analysis of mutation impact on protein structure and interactions

How does SPECC1 expression correlate with cancer prognosis across different tumor types?

SPECC1 expression shows significant prognostic value across multiple cancer types, with both positive and negative associations depending on the cancer type.

SPECC1 has been identified as a high-risk gene in several cancers including BRCA, LAML, LUAD, LUSC, MESO, PAAD, and LGG, while functioning as a low-risk gene in READ .

For prognostic research:

• Kaplan-Meier survival analysis with log-rank tests

• Cox proportional hazards model for hazard ratio calculation

• Stratification by clinical parameters to identify subgroup-specific effects

• Multivariate analysis to assess independence from established prognostic factors

What is the relationship between SPECC1 expression and tumor immune cell infiltration?

SPECC1 expression correlates significantly with immune cell infiltration across multiple cancer types. Analysis of 25 immune cell types revealed significant associations in 11 tumor types: BLCA, BRCA, CESC, COAD, READ, ESCA, GBM, HNSC, TGCT, THYM, and UVM .

Key patterns include:

• Negative correlation with memory B cells and neutrophils

• Positive correlation with macrophages

• Positive correlation with CD4 memory T cells

• No significant correlation with CD8 T cells or regulatory T cells (Tregs)

For immune infiltration research:

• CIBERSORT analysis of bulk RNA-seq data to estimate immune cell proportions

• Validation with immunohistochemistry on tissue microarrays

• Single-cell RNA sequencing to characterize immune populations more precisely

• Spatial transcriptomics to understand the spatial organization of immune cells in relation to SPECC1-expressing cells

What signaling pathways are regulated by SPECC1 in cancer?

SPECC1 is implicated in the regulation of several critical cancer-related signaling pathways. KEGG pathway enrichment analysis of SPECC1-related genes has identified:

• PI3K-AKT signaling pathway - promoting cell survival, proliferation, and resistance to apoptosis

• WNT signaling pathway - regulating cell fate, stemness, and differentiation

• Cytokine-cytokine receptor interaction

• Focal adhesion signaling

Gene ontology (GO) analysis indicates that SPECC1-related genes are predominantly involved in:

• Cell-substrate junctions

• Collagen-containing extracellular matrix

• Focal adhesion

For signaling pathway research:

• Phosphoproteomic analysis to identify altered phosphorylation events

• Western blotting for key pathway components (e.g., phospho-AKT, phospho-ERK)

• Gene reporter assays to measure pathway activity

• Pharmacological inhibition of pathway components to establish causality

What methodologies are most effective for studying SPECC1 expression across cancer types?

For comprehensive pan-cancer analysis of SPECC1 expression, researchers have successfully employed multiple complementary approaches:

• Multi-database integration approach:

  • TCGA for tumor samples

  • GTEx for normal tissue comparisons

  • CCLE for cancer cell line expression

  • CPTAC for proteomic validation

• Statistical analysis methodology:

  • Log2 transformation of expression data

  • Two-sample t-tests for comparing tumor vs. normal tissues

  • Significance threshold of p < 0.05

  • Box plot visualization using "ggpubr" R package

• Survival analysis framework:

  • Cox proportional hazards model

  • Kaplan-Meier analysis with log-rank tests

  • Analysis of multiple survival metrics (OS, DSS, PFI, DFI)

  • Stratification by age (65 years as cutoff)

This integrated approach provides a comprehensive view of SPECC1's expression patterns and clinical relevance across different cancer types.

How can researchers identify genes functionally associated with SPECC1?

To identify genes functionally related to SPECC1, researchers have employed correlation-based approaches:

• Selection of correlated genes:

  • Analysis of pan-cancer expression data from TCGA and GTEx

  • Identification of top 300 SPECC1-correlated genes from 33 cancer types

  • Use of Pearson correlation coefficient (threshold > 0.5)

  • Statistical significance filtering (p < 0.05)

• Functional enrichment analysis:

  • GO and KEGG pathway analysis using clusterProfiler in R

  • Identification of shared biological processes and pathways

  • Network analysis to visualize gene-gene interactions

This methodology allows for the systematic identification of genes that likely share processes or pathways with SPECC1, providing insights into its functional role in cancer.

What experimental approaches can determine SPECC1's role in drug response?

Research into SPECC1's role in drug response has utilized several approaches:

• Drug sensitivity screening:

  • Analysis of drug response data in high vs. low SPECC1-expressing cell lines

  • Identification of differential sensitivity to compounds like docetaxel and doxorubicin

• In vitro functional validation:

  • Overexpression and knockdown studies in cancer cell lines

  • Drug dose-response curves in modified vs. control cells

  • Cell viability, apoptosis, and cell cycle analysis

• Mechanistic investigation:

  • Protein-protein interaction studies to identify molecular targets

  • Phosphorylation status of downstream effectors

  • Combination therapy approaches in resistant models

For glioblastoma specifically, studies have shown that circSPECC1 can encode SPECC1-415aa, which restores temozolomide (TMZ) sensitivity in resistant cells by inhibiting EGFR activation through ANXA2 binding .

How does circular RNA-encoded SPECC1-415aa function in glioblastoma?

Recent research has uncovered a novel mechanism involving SPECC1 in glioblastoma (GBM). CircSPECC1, a circular RNA derived from the SPECC1 gene, encodes a novel protein called SPECC1-415aa that has significant anti-tumor properties:

For researchers studying circular RNAs and novel protein products:

• CircRNA microarray or RNA-seq with specialized bioinformatic pipelines

• Polysome profiling to confirm translation

• Mass spectrometry to identify novel peptides

• Functional validation through overexpression and knockdown studies

What is the relationship between SPECC1 and RNA methylation (m6A)?

An emerging area of research connects SPECC1 to RNA methylation (m6A) mechanisms:

• In GBM, the m6A reader protein IGF2BP1 can bind to circSPECC1:

  • This binding promotes circSPECC1 expression and stability

  • Consequently affects the production of SPECC1-415aa protein

• Research methodology:

  • m6A RNA immunoprecipitation (MeRIP)

  • RNA immunoprecipitation to identify reader protein interactions

  • Mutation of m6A sites to assess functional consequences

  • Analysis of reader protein expression correlation with SPECC1 in cancer datasets

This connection to RNA epigenetics opens a new dimension for understanding SPECC1 regulation in cancer.

What are the most promising therapeutic applications of SPECC1 research?

Based on current findings, several therapeutic strategies targeting SPECC1 show promise:

• Biomarker applications:

  • Prognostic stratification in multiple cancer types

  • Prediction of drug response, particularly for docetaxel and doxorubicin

• Drug resistance reversal:

  • Upregulation of circSPECC1 to restore TMZ sensitivity in GBM

  • Combination therapy approaches with SPECC1 modulators

• Targeted therapy development:

  • Inhibition of SPECC1-regulated pathways in high-risk cancers

  • Enhancement of SPECC1 function in cancers where it acts as a tumor suppressor

• Immunotherapy enhancement:

  • Leveraging SPECC1's relationship with immune cell infiltration

  • Combination with immune checkpoint inhibitors based on SPECC1 expression status

For translational researchers, prioritizing investigations into circSPECC1-based therapeutic approaches appears particularly promising, given the demonstrated in vivo efficacy in GBM models .

What key methodological challenges must be addressed in future SPECC1 research?

Researchers pursuing SPECC1 studies face several methodological challenges:

• Tissue and cancer specificity:

  • SPECC1 shows context-dependent effects across different cancer types

  • Standardized approaches for context-specific analysis are needed

• Protein isoform characterization:

  • Limited information on potential SPECC1 isoforms

  • Need for comprehensive proteomic characterization across tissues

• Functional validation in appropriate models:

  • Development of physiologically relevant models reflecting SPECC1 biology

  • Consideration of in vivo tumor microenvironment effects

• Integration of multi-omics data:

  • Correlation of genomic, transcriptomic, and proteomic data

  • Machine learning approaches to predict SPECC1 function from integrated datasets

Future studies addressing these challenges will significantly advance our understanding of SPECC1's role in human biology and cancer pathology.