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. 2025 Aug 7;15(1):28934.
doi: 10.1038/s41598-025-14342-z.

Pan-cancer landscape of ITGAV and its potential role in gastric cancer

Affiliations

Pan-cancer landscape of ITGAV and its potential role in gastric cancer

Bin Ke et al. Sci Rep. .

Abstract

Integrin subunit alpha V (ITGAV), a subunit of the integrin receptor, is involved in many types of cancers. In order to explore the potential mechanisms of ITGAV in cancers, we carried out a comprehensive pan-cancer analysis using public database. In this study, ITGAV expression in different cancers and the relationship between ITGAV and clinic-pathological features, prognosis, genetic alteration, epigenetic modification, and tumor immune microenvironment were systemically analyzed. Gene enrichment analysis was performed to explore potential functions of ITGAV in gastric cancer (GC). GC tissue microarrays and in vitro cell experiments were used to verify the prediction results in GC. The results revealed that ITGAV was variably expression in different cancers, and ITGAV had a certain prognostic and diagnostic value in most cancers, including GC. ITGAV expression was found to be related to genetic alteration, DNA methylation, immune checkpoint gene, and immune cell infiltration in multiple cancers. Functional analyses revealed that ITGAV was involved in the regulation of EMC remodeling, ferroptosis, and cuproptosis in GC. In vitro experiments verified that ITGAV was correlated with GC cell proliferation, apoptosis, migration, and invasion. Our study demonstrated that ITGAV can be used as an effective prognostic and immunological biomarker for multiple cancers. ITGAV can promote GC malignant progression and could serve as a potential therapeutic target for GC treatment.

Keywords: Bioinformatics analysis; Biomarker; Gastric cancer; ITGAV; Pan-cancer.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethical approval: This study was approved by the Ethics Committee of Tianjin cancer hospital (No.bc2023054) and written informed consent was obtained from all patients. All methods in this study were performed in accordance with the principles of the Declaration of Helsinki and current ethical guidelines.

Figures

Fig. 1
Fig. 1
A workflow of the whole study.
Fig. 2
Fig. 2
ITGAV expression in pan-cancer. (A) ITGAV mRNA expression in pan-cancer tissues from TCGA + GTEx. (B) ITGAV mRNA expression in paired tumors and paired adjacent normal tissues from TCGA. (C) Relationship between ITGAV and serosal invasion, lymph node metastasis, and pathologic stage in STAD from TCGA. (D) ITGAV expression in different GC cell lines from CCLE. (E) Single-cell analysis of ITGAV expression in GC from the GSE134520 dataset. P ≥ 0.05, *P < 0.05, **P < 0.01, ***P < 0.001.
Fig. 3
Fig. 3
Prognostic value of ITGAV expression in pan-cancer. (A) Summary of the correlation of ITGAV expression with OS, DSS, and PFI in pan-cancer. (B) Univariate Cox regression analysis of correlation between pan-cancer ITGAV expression and OS. (CF) OS survival curves of ITGAV in LGG (C), PAAD (D), STAD (E), and KIRC (F).
Fig. 4
Fig. 4
Diagnostic value of ITGAV in pan-cancer. (A) Heatmap of diagnostic value of ITGAV. ROC analysis showing predictive efficiency of ITGAV in CHOL (B), ESCA (C), GBM (D), LGG (E), PAAD (F), THYM (G), and STAD (H).
Fig. 5
Fig. 5
Genetic alteration analysis of ITGAV. (A) The genetic alteration of ITGAV in pan-cancer. (B) Alteration frequency and mutation type of ITGAV in different cancers. (C) Correlation between TPM4 mRNA expression and mutation count in pan-cancer. (D). The top 10 genes with the highest frequency of mutations in ITGAV high/low expression groups in GC.
Fig. 6
Fig. 6
DNA methylation analysis of ITGAV. (A) Correlations between ITGAV expression and Methylation level in pan-cancer. (B) Promoter methylation level in ITGAV between normal and tumor tissues in pan-cancer. (C) The heatmap of correlations between ITGAV expression and m6A methylation regulatory factors in pan-cancer. (D) The heatmap of CpG sites methylation of ITGAV in GC.
Fig. 7
Fig. 7
Immunoinfiltration analysis of ITGAV. (A) ITGAV expression and immune cell infiltration in pan-cancer. (B) Correlation between ITGAV expression and ESTIMATE scores in pan-cancer. (C) Differences of enrichment scores in different immune cells between low and high ITGAV expression groups in STAD. (D) Correlation between ITGAV expression and infiltration scores of different immune cell types in STAD. (E) Correlation between ITGAV expression and infiltration scores of six common immune cell type in STAD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Fig. 8
Fig. 8
Relationship between ITGAV expression and immunotherapy response. (A) Relationship between ITGAV expression and immune checkpoints in pan-cancer. (B) Relationship between ITGAV and PD-1, PD-L1, CTLA-4 and LAG-3 in pan-cancer. (C) Relationship between ITGAV expression and MMR markers. (D) Relationship between ITGAV expression and TMB. (E) Relationship between ITGAV expression and MSI. (F) Correlation between ITGAV and TIDE scores in STAD. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Fig. 9
Fig. 9
Functional analysis of ITGAV in GC. (A) A volcano map of DEGs between high and low ITGAV expression groups in TCGA-STAD. (B and C) Heat maps showing the top 30 genes positively (B) and negatively (C) correlated with ITGAV expression in GC. (D) The top 10 hub genes of the network were identified. (E) GO and KEGG enrichment analysis. (F) GSEA enrichment analysis. (G) Correlation analyses of ITGAV expression with ferroptosis- and cuproptosis-related genes in GC.
Fig. 10
Fig. 10
Expression and prognostic value of ITGAV in GC. (A) Representative IHC image of ITGAV expression in GC. (B) Comparisons of ITGAV expression in GC tissues and paracancerous tissues. (C) Survival analysis of ITGAV expression in 110 GC patients after curative resection. (D) Univariate analysis of OS in GC patients. (E) Multivariate analysis of OS in GC patients.
Fig. 11
Fig. 11
Function of ITGAV in GC. (A) Knockdown efficiency of Si-ITGAV was verified in AGS and BGC-823 cells by qRT-PCR analysis. (B) CCK-8 assays indicated ITGAV knockdown suppress the proliferation of GC cells in vitro. (C) Colony formation assay showed knockdown of ITGAV inhibit the cloning ability of GC cells in vitro. (D) Cell apoptosis assays indicated that ITGAV knockdown could increase apoptosis of GC cells in vitro. (E) Cell scratch assays showed the knockdown of ITGAV could inhibit the migration of GC cells in vitro. (F) Transwell migration and invasion assays indicated ITGAV knockdown could restrain the migration and invasion of GC cells in vitro. (G) Western blot analysis indicated that the protein expression levels of ITGAV, E-cad, and VIM were changed after ITGAV knockdown.

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