SUGT1 Human

What is SUGT1 and what are its primary cellular functions?

SUGT1 (SGT1 homolog) is a highly conserved protein that functions as a cochaperone of Heat shock protein 90 (Hsp90). Its primary functions include essential roles in G1/S and G2/M transitions in the cell cycle. At the molecular level, SUGT1 is involved in multiple physiological processes including cyclic AMP pathways, immune responses, and ubiquitination processes. Its interaction with Hsp90 is critical for kinetochore assembly and kinetochore-microtubule attachment, which are essential for proper chromosome segregation during cell division .

How is SUGT1 expression regulated in normal human tissues?

SUGT1 expression is tightly regulated in normal human tissues through various mechanisms. Research indicates that SUGT1 is expressed at baseline levels in normal tissues, with expression patterns varying across different tissue types. The regulatory mechanisms involve transcriptional control, post-translational modifications, and protein-protein interactions. When analyzing SUGT1 expression in experimental settings, researchers should consider tissue-specific baseline expression levels to accurately interpret results, particularly when comparing expression between normal and pathological states .

What protein-protein interaction networks involve SUGT1?

SUGT1 participates in complex protein-protein interaction networks that are critical to its function. According to analysis using platforms like STRING and GeneMANIA, SUGT1 exhibits significant interactions with several proteins. In ovarian cancer contexts, studies have identified that SUGT1 expression positively correlates with genes like GTF2F2, NUFIP1, TRIM13, MED4, and GPALPP1, while showing negative correlations with UBE2L6, NUDT8, PLAAT4, LGALS17A, and PIGR. These interaction networks are important for understanding SUGT1's functional roles in various cellular processes and pathological conditions .

How does SUGT1 expression differ across cancer types?

SUGT1 shows differential expression across various cancer types compared to normal tissues. Analysis of TCGA and GTEx data revealed that SUGT1 is significantly overexpressed in 24 out of 33 cancer types, including breast invasive carcinoma, cervical squamous cell carcinoma, esophageal carcinoma, bladder urothelial carcinoma, ovarian cancer, and many others. Conversely, SUGT1 shows lower expression in testicular germ cell tumors, acute myeloid leukemia, and kidney chromophobe. This widespread dysregulation suggests that SUGT1 may function as a tumor promoter in multiple cancer contexts .

What molecular pathways are associated with SUGT1 in cancer progression?

SUGT1 is associated with several critical cancer-related pathways. Gene Set Enrichment Analysis (GSEA) has revealed that high SUGT1 expression is significantly associated with hedgehog signaling, epithelial-mesenchymal transition (EMT), and KRAS signaling. Conversely, low SUGT1 expression correlates with inflammatory response, adipogenesis, TNFα/NF-κB signaling, oxidative phosphorylation, IL6-JAK-STAT3 signaling, apoptosis, mTORC1 signaling, and fatty acid metabolism. These pathways are known to play important roles in cancer cell invasion, metastasis, and proliferation, providing insights into potential mechanisms through which SUGT1 might contribute to cancer progression .

What are the validated techniques for assessing SUGT1 expression in human tissues?

Several validated techniques can be employed to assess SUGT1 expression in human tissues. In research settings, immunohistochemistry (IHC) using specific antibodies (such as 11675-1-AP) has proven effective for evaluating SUGT1 protein expression in tissue samples. Expression can be quantified using the H-SCORE method, calculated as (1 × percentage of weak staining) + (2 × percentage of moderate staining) + (3 × percentage of strong staining). At the transcriptomic level, mRNA expression analysis using RNA sequencing or quantitative PCR is commonly employed. For large-scale studies, researchers can leverage public databases like TCGA and GTEx that contain normalized SUGT1 expression data across multiple tissue types .

How should researchers design experiments to study SUGT1's role in immune infiltration?

When designing experiments to study SUGT1's role in immune infiltration, researchers should employ a multi-faceted approach. Single-sample gene set enrichment analysis (ssGSEA) can be used to evaluate the correlation between SUGT1 expression and immune cell infiltration. Experimental designs should include measurement of infiltration levels of various immune cell types, such as activated dendritic cells, cytotoxic cells, T cells, T central memory cells, natural killer cells, and T gamma delta cells. Spearman correlation analysis is appropriate for assessing the relationship between SUGT1 expression and immune cell infiltration. Additionally, researchers should consider incorporating flow cytometry analysis of tumor-infiltrating immune cells in experimental models or clinical samples to validate bioinformatic findings .

What bioinformatic pipelines are recommended for analyzing SUGT1 in multi-omics datasets?

For comprehensive analysis of SUGT1 in multi-omics datasets, researchers should employ specialized bioinformatic pipelines. Initial data preprocessing should include normalization methods like Transcripts Per Million (TPM) for RNA-seq data. For differential expression analysis, tools like DESeq2 with appropriate statistical thresholds (log fold change > 1, adjusted P < 0.05) are recommended. Functional enrichment analyses should include Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to identify biological processes and pathways associated with SUGT1. For protein-protein interaction analysis, databases like STRING (with interaction score > 0.90) and GeneMANIA can provide valuable insights. For survival analysis, Kaplan-Meier analysis combined with univariate and multivariate Cox regression is essential to evaluate SUGT1's prognostic value. These approaches together provide a comprehensive understanding of SUGT1's biological significance and clinical implications .

How does SUGT1 expression correlate with immune cell infiltration in tumors?

SUGT1 expression demonstrates significant correlations with immune cell infiltration in tumor microenvironments. Research has identified both positive and negative correlations with specific immune cell populations. Specifically, SUGT1 expression shows significant negative correlations with activated dendritic cells (R = -0.301, P < 0.001), cytotoxic cells (R = -0.271, P < 0.001), and T cells (R = -0.214, P < 0.001). Conversely, SUGT1 expression positively correlates with T central memory cells (R = 0.180, P < 0.001), natural killer cells (R = 0.159, P < 0.001), and T gamma delta cells (R = 0.151, P < 0.001). These findings suggest that SUGT1 may play a role in modulating the tumor immune microenvironment, potentially influencing cancer immunosurveillance and response to immunotherapies .

What molecular mechanisms link SUGT1 to immunomodulation?

The molecular mechanisms linking SUGT1 to immunomodulation involve several key processes. Functional enrichment analyses have revealed that SUGT1-associated differentially expressed genes are enriched in processes related to immunoglobulin complex, phagocytosis, and antigen binding. SUGT1 may influence immune responses through its role as an Hsp90 cochaperone, potentially affecting the stability and function of immune-related client proteins. Additionally, enrichment in pathways related to inflammatory responses and TNFα/NF-κB signaling suggests that SUGT1 may modulate inflammatory processes within the tumor microenvironment. These mechanisms provide potential explanations for the observed correlations between SUGT1 expression and immune cell infiltration patterns in tumors .

How might SUGT1 expression impact response to immunotherapies?

SUGT1 expression could significantly impact response to immunotherapies through its effects on the tumor immune microenvironment. The negative correlation between SUGT1 expression and cytotoxic cells, T cells, and activated dendritic cells suggests that high SUGT1 expression might contribute to an immunosuppressive microenvironment, potentially limiting the efficacy of immunotherapies that rely on these cell populations. Conversely, the positive correlation with natural killer cells might influence response to NK cell-based therapies. Researchers investigating immunotherapy response should consider stratifying patients based on SUGT1 expression levels and analyzing correlations with treatment outcomes. This approach could provide insights into whether SUGT1 could serve as a predictive biomarker for immunotherapy response and whether targeting SUGT1 might enhance immunotherapy efficacy through modulation of the tumor immune microenvironment .

How can researchers reconcile conflicting data on SUGT1's role across different cancer types?

Reconciling conflicting data on SUGT1's role across different cancer types requires systematic approaches. Researchers should:

• Perform meta-analyses combining data from multiple cancer types while adjusting for cancer-specific characteristics

• Investigate tissue-specific cofactors that might modify SUGT1 function

• Consider the genetic and molecular context of each cancer type, including prevalent mutations that might interact with SUGT1

• Examine SUGT1 isoform expression patterns across cancer types

• Analyze SUGT1's interaction partners in different cellular contexts

These approaches can help explain why SUGT1 might function as an oncogene in some cancers while showing tumor-suppressive properties in others. Additionally, researchers should ensure standardized experimental protocols across studies to minimize technical variations that might lead to conflicting results .

What are the methodological challenges in developing SUGT1-targeted therapeutics?

Developing SUGT1-targeted therapeutics presents several methodological challenges:

• Target specificity: As an Hsp90 cochaperone, SUGT1 shares structural similarities with other cochaperones, making selective targeting difficult

• Functional redundancy: Other proteins might compensate for SUGT1 inhibition

• Context-dependent functions: SUGT1's roles vary across tissues and disease states

• Biomarker development: Identifying patient populations most likely to benefit from SUGT1 targeting requires robust biomarkers

• Delivery challenges: Targeting protein-protein interactions often requires larger molecules with potential delivery limitations

• Toxicity concerns: Given SUGT1's roles in normal cellular processes, complete inhibition might cause significant side effects

Researchers should employ structure-based drug design approaches, consider developing proteolysis targeting chimeras (PROTACs) for SUGT1 degradation, and explore combination strategies to mitigate pathway redundancy .

What novel experimental models would advance understanding of SUGT1's function in human disease?

Several novel experimental models could significantly advance understanding of SUGT1's function in human disease:

• Patient-derived organoids: These 3D culture systems recapitulate patient-specific disease characteristics and could help evaluate SUGT1's role in personalized contexts

• CRISPR-engineered cell lines: Generating isogenic cell lines with SUGT1 mutations or expression modulations can provide insights into causal relationships

• Conditional knockout mouse models: Tissue-specific and inducible SUGT1 knockout models would help distinguish SUGT1's roles across different tissues and developmental stages

• Single-cell analysis platforms: Applied to patient samples, these could reveal cell-type-specific functions of SUGT1 and heterogeneity in expression patterns

• Humanized mouse models: These could better recapitulate immune interactions with SUGT1-expressing tumors

• Spatial transcriptomics: This approach could map SUGT1 expression relative to specific tumor microenvironment features

These models would help address current knowledge gaps regarding SUGT1's context-specific functions and potential as a therapeutic target in various human diseases .