ITGAV as a promising diagnostic, immunological, and prognostic biomarker in pan-cancer

Abstract

Integrin αV (ITGAV) plays a key role in cell adhesion, migration, and immune regulation, and is implicated in tumor progression. However, its comprehensive expression profile and functional relevance across different cancers remain poorly understood. We conducted an integrative pan-cancer analysis of ITGAV using data from TCGA, GTEx, CCLE, and other public databases. Expression, diagnostic value (via ROC analysis), and prognostic significance (via Cox and Kaplan-Meier analyses of OS, DSS, PFS, and DFS) were assessed. We further explored ITGAV's correlation with immune cell infiltration and immune-related genes, its predictive role in immunotherapy response based on immunophenoscore (IPS), and its drug-binding potential through molecular docking. (1) ITGAV was significantly overexpressed in multiple cancer types including LIHC, COAD, and STAD. (2) ROC analysis confirmed its strong diagnostic value, particularly in HNSC, UCEC, and ESCA. (3) High ITGAV expression was associated with poorer survival outcomes in most cancers, while a protective role was observed in KIRC. (4) ITGAV expression was positively correlated with immune cell infiltration and co-expressed with immune-activating and immunosuppressive genes. (5) The expression level of ITGAV correlates with the IPS score, suggesting its predictive value for the benefit of immunotherapy. (6) Molecular docking identified strong binding affinities between ITGAV and six candidate compounds, including gemcitabine and pioglitazone. Our findings demonstrate that ITGAV is a promising biomarker for diagnosis, prognosis, and immunotherapy prediction across cancers. Its immunological associations and druggability highlight its potential as a candidate therapeutic target.

Keywords: Biomarker; ITGAV; Immune infiltration; Immunotherapy; Pan-cancer; Prognosis.

Fig. 1

ITGAV expression and localization in pan-cancer. (A) Pan-cancer analysis of ITGAV expression showing elevated levels in CHOL, ESCA, and HNSC. (B) Validation of ITGAV overexpression using TCGA data, especially in CESC and GBM. (C) Tissue-specific expression of ITGAV based on GTEx data, with higher expression in skin, and vascular tissues, and lower levels in spleen, pancreas, liver, and blood. (D) Expression levels of ITGAV across cancer cell lines from the CCLE dataset, with prominent expression in ovarian, lung, liver, and kidney cell lines. (E) RNA expression levels of ITGAV in different cell lines, with highest expression observed in SuSa, RT-4, and hTRET-RPE1 cells. (F-G) Immunofluorescence staining in A-431 and U-251MG cells, demonstrating ITGAV protein localization in the cytoplasm and membrane. Nuclei were counterstained with DAPI (blue), and ITGAV was visualized in green.

Fig. 2

Diagnostic performance of ITGAV across cancers. (A–I) ROC curves assessing the diagnostic value of ITGAV in various tumor types, including CHOL, COAD, ESAD, HNSC, KIRC, LIHC, LUAD, STAD, and UCEC. AUC values are provided for each cancer type, reflecting diagnostic accuracy based on TCGA expression data.

Fig. 3

Association between ITGAV expression and clinical stage. (A–F) Correlation of ITGAV expression with tumor stage in BLCA, BRCA, KIRP, LIHC, STAD, and THCA.

Fig. 4

Correlation between ITGAV expression and overall survival (OS). (A) Univariate Cox regression analysis of ITGAV expression across cancers, showing HRs. (B–G) Kaplan–Meier survival curves comparing OS in high vs. low ITGAV expression groups for CESC, KIRC, LGG, LIHC, MESO, and STAD.

Fig. 5

Correlation between ITGAV expression and progression-free survival (PFS). (A) Cox regression analysis across cancers demonstrating significant associations between ITGAV expression and PFS. (B-G) Kaplan–Meier survival curves for GBM, KIRC, KIRP, LGG, SARC, and STAD, comparing PFS between high and low ITGAV expression groups.

Fig. 6

Correlation between ITGAV expression and disease-specific survival (DSS). (A) Cox regression analysis revealing associations between ITGAV expression and DSS in multiple cancers. (B–G) Kaplan–Meier curves comparing DSS between high and low ITGAV expression groups in KIRC, KIRP, LGG, LIHC, STAD, and MESO.

Fig. 7

Correlation between ITGAV expression and disease-free survival (DFS). (A): Cox regression analysis showing significant associations in PAAD. (B–D) Kaplan–Meier analysis showing differences in DFS between high and low ITGAV expression in KIRP, LUAD, and PAAD.

Fig. 8

PPI network and enrichment analysis of ITGAV. (A) GeneMANIA-derived PPI network illustrating interactions between ITGAV and proteins such as ITGB6, TNC, and SPP1.(B–E) GO and KEGG enrichment analyses revealing ITGAV involvement in integrin signaling, ECM–receptor interaction, and PI3K–Akt pathways.

Fig. 9

Correlation between ITGAV expression and immune infiltration. (A–E) Correlation analysis between ITGAV expression and infiltration of immune cells in COAD, KIRC, LIHC, PRAD, and STAD. Both positive and negative correlations are shown, highlighting the potential immunological role of ITGAV.

Fig. 10

Correlation between ITGAV and immune regulatory genes. (A) Heatmap showing correlation between ITGAV and immune stimulatory genes across cancers. (B) Heatmap showing correlation between ITGAV and immune suppressive genes. Statistically significant results are marked with asterisks.

Fig. 11

Molecular docking of ITGAV with selected drugs. (A–F). Docking of six candidate drugs (Atovaquone, Epinephrine, Gemcitabine, Metolazone, Pioglitazone, Quinpirole) with ITGAV. Binding sites and key interacting residues are visualized for each compound.

Fig. 12

Correlation of ITGAV expression with immunotherapy efficacy across cancer types. (A) In BRCA, low ITGAV expression is associated with significantly higher IPS across all four immune checkpoint inhibitor (ICI) conditions, indicating greater predicted sensitivity to immunotherapy. (B) In COAD, low ITGAV expression also correlates with improved immunotherapy response across all IPS categories.(C) In KIRC, lower ITGAV expression is significantly associated with higher IPS in most ICI settings. (D) In LIHC, no significant difference in IPS is observed between high and low ITGAV expression groups under any ICI condition.