Recombinant Human Late secretory pathway protein AVL9 homolog (AVL9)

What is the AVL9 protein and what is its normal biological function?

AVL9 (Late secretory pathway protein AVL9 homolog) is involved in cell migration and secretory pathways. Current research indicates that AVL9 plays roles in single organismal cell-cell adhesion, post-transcriptional regulation of gene expression, and negative regulation of the vascular endothelial growth factor receptor signaling pathway . These functions suggest AVL9's involvement in critical cellular processes related to immune cell migration, tumor cell behavior, and vascular development. The protein has been found to interact with several other genes including KBTBD2, KIAA1147, EPDR1, and RNF216, with positive correlations in expression levels .

How is AVL9 typically detected in experimental settings?

AVL9 can be detected through multiple methodologies. In tissue samples, reverse transcription–quantitative polymerase chain reaction (RT-qPCR) is commonly used to measure mRNA expression levels . For protein-level detection, immunohistochemistry can be employed, as demonstrated in studies using the online tool protein atlas to detect AVL9 expression in different human tumors . When examining plasma samples, RNA isolation followed by RT-qPCR has proven effective for detecting circulating AVL9 expression. For AVL9 detection via RT-qPCR, researchers have used primers with the following sequences: forward 5′-GTGAGGCACGTGACTGAGAA-3′ and reverse 5′-TTGTTGCTGTTCCACACCCT-3′ .

What databases contain information about AVL9 expression in normal and cancer tissues?

Several bioinformatics resources offer AVL9 expression data across tissue types. The Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) provides comparative expression levels between normal and cancerous tissues . The Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) contains microarray datasets such as GSE32323 that show AVL9 expression patterns . The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) offers comprehensive expression data across multiple cancer types . Additionally, the Protein Atlas (https://www.proteinatlas.org/) can be used to examine the expression of AVL9 protein in various human tumors .

What are the recommended methodologies for studying AVL9 expression in patient samples?

For comprehensive AVL9 expression analysis in patient samples, a multi-modal approach is recommended. RNA isolation from tissue samples should be performed using RNAiso Plus reagent or equivalent, with 500 ng of total RNA reverse transcribed to cDNA using appropriate reagents such as PrimeScript RT Master Mix . For RT-qPCR, the SYBR Premix Ex Taq II Kit has been validated for determining AVL9 expression levels, with fold changes calculated using the 2−ΔΔCt method . When analyzing plasma samples, similar RT-qPCR methodology can be applied. For protein detection, immunohistochemistry with appropriate antibodies should be used, with both intensity and quantity analyses performed . Researchers should collect both cancerous tissue and adjacent normal tissue as controls, and in the case of plasma studies, samples from healthy individuals should serve as controls.

What are the key protein-protein interactions of AVL9 and how might they contribute to cancer pathogenesis?

Protein-protein interaction (PPI) network analysis using Cytoscape and the STRING database has revealed that AVL9 significantly interacts with several key proteins. The KBTBD2, KIAA1147, RNF216, EPDR1, and ANKIB1 genes demonstrated the strongest interactions with AVL9 in PPI network analysis . GEPIA data further confirmed positive correlations between AVL9 and these genes according to Pearson's correlation coefficient (P = 0) . These interactions suggest functional relationships in cellular processes relevant to cancer development. The RNF216 gene, which encodes an E3 ubiquitin ligase, may connect AVL9 to the ubiquitin-mediated proteolysis pathway, potentially affecting protein degradation mechanisms in cancer cells . The interaction with EPDR1, previously implicated in tumor progression, suggests AVL9 may participate in extracellular matrix remodeling during cancer invasion . Understanding these interactions provides potential mechanistic insights into how AVL9 upregulation contributes to colorectal cancer pathogenesis and identifies additional therapeutic targets within this network.

What signaling pathways are associated with AVL9 expression in cancer, and how might they be targeted therapeutically?

Bioinformatics analyses using GO function enrichment and KEGG pathway analysis have identified several significant signaling pathways associated with AVL9 expression. GO analysis revealed that AVL9-related genes are functionally concentrated in single organismal cell-cell adhesion, post-transcriptional regulation of gene expression, and negative regulation of the vascular endothelial growth factor receptor signaling pathway (P < 0.05) . KEGG pathway analysis showed involvement in progesterone-mediated oocyte maturation, axon guidance, insulin signaling pathway, and ubiquitin-mediated proteolysis signaling pathways (P < 0.05) .

The insulin signaling pathway presents a promising therapeutic target, as it has been shown to promote tumor growth and invasion . Similarly, dysregulation of ubiquitin activity can promote tumor progression, suggesting ubiquitin pathway inhibitors could counteract AVL9-mediated effects . The negative regulation of VEGFR signaling is particularly significant as it affects angiogenesis, a critical process in tumor growth . Anti-angiogenic therapies that target this pathway might be effective in cancers with high AVL9 expression. Additionally, the involvement in axon guidance pathways suggests potential neuronal connections, as nerves and blood vessels share similar branch network formation mechanisms regulated by axon-guided receptors and ligands .

How do the clinicopathological correlations of AVL9 expression compare across different cancer types?

Current research has primarily established AVL9's clinicopathological correlations in colorectal cancer. In CRC, high AVL9 expression shows statistically significant associations with differentiation status (P = 0.01) and TNM stage (P = 0.028) . The relationship between AVL9 expression and clinical parameters in CRC patients is summarized in the following table:

What are the optimal sample preservation methods for AVL9 protein and RNA analysis?

For optimal preservation of samples for AVL9 analysis, different protocols should be followed depending on the intended analysis type. For RNA-based analyses, tissue samples should be quickly frozen immediately after excision and stored at −80°C to prevent RNA degradation . This rapid freezing is critical for maintaining RNA integrity and ensuring accurate expression measurement. For plasma samples, standard protocols for blood collection in EDTA tubes should be followed, with prompt centrifugation to separate plasma, which should then be stored at −80°C until analysis . For protein-level analyses via immunohistochemistry, tissue samples should be fixed in formalin and embedded in paraffin following standard pathology protocols. The time between tissue excision and preservation should be minimized to prevent degradation of biological molecules. For long-term biobanking of samples for AVL9 research, −80°C storage is recommended for both tissue and liquid samples, with careful documentation of freeze-thaw cycles, as multiple cycles can affect protein and RNA integrity.

How can researchers accurately quantify AVL9 expression in heterogeneous tumor samples?

Accurately quantifying AVL9 expression in heterogeneous tumor samples requires methodological considerations that address tissue complexity. Laser capture microdissection should be employed to isolate specific cell populations from heterogeneous tumor sections, enabling precise analysis of AVL9 expression in cancer cells versus stromal or inflammatory cells . For immunohistochemical analyses, a systematic approach to quantification is necessary, including both intensity scoring (strong, moderate, weak) and quantity assessment (percentage of positive cells) . In the reported studies, intensity analysis found that 2 of 10 samples showed strong staining, while 8 of 10 were moderate; quantity analysis revealed that 7 of 10 samples had >75% positive cells, 2 of 10 had 25-75%, and 1 of 10 had <25% .

For RNA-based measurements, researchers should employ controls for cell-type specific markers to account for varying cellular compositions within samples. Additionally, single-cell RNA sequencing would provide the most detailed assessment of AVL9 expression variance within tumor heterogeneity. When analyzing existing data from public databases, researchers should consider tumor purity estimates and adjust expression values accordingly to avoid confounding effects from non-tumor cells within the sample.

What statistical approaches are most appropriate for analyzing AVL9 expression in relation to multiple clinical variables?

For robust statistical analysis of AVL9 expression in relation to multiple clinical variables, researchers should implement a multi-tiered analytical approach. For baseline comparisons between tumor and normal tissues, paired t-tests or Wilcoxon signed-rank tests should be used depending on data distribution . When analyzing relationships between AVL9 expression and categorical clinical variables (such as TNM stage, differentiation, etc.), Pearson's chi-square test is appropriate, as demonstrated in the reported studies .

For survival analyses, Kaplan-Meier curves with log-rank tests should be generated to compare high versus low AVL9 expression groups . Crucially, multivariate Cox regression analysis must be performed to adjust for potential confounding variables and determine if AVL9 expression is an independent prognostic factor . The studies showed that multivariate survival analysis identified AVL9 expression level as a significant covariate (HR 5.695; 95% CI 1.860–17.442, P = 0.002) .

For diagnostic potential evaluation, ROC curve analysis with calculation of AUC, sensitivity, and specificity at various cutoff values provides comprehensive assessment . Previous research established a cutoff value of 1.296 with 72.9% sensitivity and 64% specificity (AUC = 0.683) for tissue samples, and 0.602 yielding 80.0% sensitivity and 63.3% specificity (AUC = 0.729) for plasma samples . Advanced approaches such as machine learning algorithms that incorporate AVL9 expression with other clinical biomarkers may improve predictive models for patient outcomes.

How reliable is plasma AVL9 as a non-invasive biomarker compared to tissue-based detection?

Plasma AVL9 shows promising reliability as a non-invasive biomarker for colorectal cancer detection. Studies comparing AVL9 expression in 60 CRC patient plasma samples versus healthy control plasma demonstrated that AVL9 expression was significantly upregulated in CRC patients compared to healthy controls (P < 0.01) . ROC curve analysis for plasma AVL9 established a cutoff value of 0.602, yielding 80.0% sensitivity and 63.3% specificity, with an AUC of 0.729 . This compares favorably with tissue-based detection, which showed a cutoff value of 1.296 with 72.9% sensitivity and 64% specificity (AUC = 0.683) .

What experimental approaches would best validate AVL9 as a therapeutic target in cancer?

Validating AVL9 as a therapeutic target requires a comprehensive experimental approach spanning in vitro, in vivo, and ex vivo models. Initially, CRISPR/Cas9-mediated knockout or siRNA-mediated knockdown of AVL9 in colorectal cancer cell lines should be performed to assess effects on proliferation, migration, invasion, and colony formation capacities . These functional assays would establish whether AVL9 inhibition directly affects cancer cell behaviors. Conversely, overexpression studies would confirm if AVL9 upregulation promotes aggressive phenotypes.

In vivo validation should utilize xenograft models with AVL9-knockdown versus control cancer cells, monitoring tumor growth, metastasis, and survival outcomes . Patient-derived xenografts from tumors with varying AVL9 expression levels would provide more clinically relevant models. For therapeutic proof-of-concept, developing specific inhibitors (small molecules or antibodies) targeting AVL9 or its key interaction partners would be necessary . Since AVL9 interacts with several proteins including KBTBD2, KIAA1147, RNF216, and EPDR1, disrupting these interactions might offer alternative therapeutic strategies .

Ex vivo organoid models derived from patient tumors would enable testing of AVL9-targeted therapies in systems that better recapitulate tumor heterogeneity and microenvironment. Throughout these validation studies, monitoring effects on the identified signaling pathways (insulin signaling, ubiquitin-mediated proteolysis, VEGFR signaling) would provide mechanistic insights and potential combination therapy opportunities .

How might AVL9 expression analysis be integrated into current clinical cancer staging systems?

Integrating AVL9 expression analysis into current clinical cancer staging systems represents an important translational goal. The significant correlation between high AVL9 expression and TNM stage (P = 0.028) suggests it could refine current staging approaches, particularly for colorectal cancer . Implementation would require standardization of AVL9 detection methods across clinical laboratories, with established cutoff values for "high" versus "low" expression based on large-scale validation studies.

A proposed integration framework would include AVL9 expression as a molecular subclassifier within existing TNM stages. For instance, patients with Stage II colorectal cancer might be further stratified into AVL9-high and AVL9-low groups, potentially identifying Stage II patients who might benefit from adjuvant therapy despite having node-negative disease . The strong association between AVL9 expression and differentiation status (P = 0.001) suggests it may also enhance tumor grading systems .

For clinical application, a streamlined testing pipeline would be needed—potentially utilizing plasma AVL9 detection for initial screening and monitoring, with tissue confirmation in positive cases . Multivariate survival analysis identified AVL9 expression level as a significant covariate (HR 5.695; 95% CI 1.860–17.442, P = 0.002), indicating substantial prognostic value . Prospective clinical trials would be required to validate whether treatment decisions based on AVL9 expression improve patient outcomes before formal integration into staging systems can be recommended.