ANXA9 as a novel prognostic biomarker associated with immune infiltrates in gastric cancer

Abstract

Background: Gastric cancer (GC) is the most prevalent malignancy among the digestive system tumors. Increasing evidence has revealed that lower mRNA expression of ANXA9 is associated with a poor prognosis in colorectal cancer. However, the role of ANXA9 in GC remains largely unknown.

Material and methods: The Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas databases were used to investigate the expression of ANXA9 in GC, which was then validated in the four Gene Expression Omnibus (GEO) datasets. The diagnostic value of ANXA9 for GC patients was demonstrated using a receiver operating characteristic (ROC) curve. The correlation between ANXA9 expression and clinicopathological parameters was analyzed in The Cancer Genome Atlas (TCGA) and UALCAN databases. The Kaplan-Meier (K-M) survival curve was used to elucidate the relationship between ANXA9 expression and the survival time of GC patients. We then performed a gene set enrichment analysis (GSEA) to explore the biological functions of ANXA9. The relationship of ANXA9 expression and cancer immune infiltrates was analyzed using the Tumor Immune Estimation Resource (TIMER). In addition, the potential mechanism of ANXA9 in GC was investigated by analyzing its related genes.

Results: ANXA9 was significantly up-regulated in GC tissues and showed obvious diagnostic value. The expression of ANXA9 was related to the age, gender, grade, TP53 mutation, and histological subtype of GC patients. We also found that ANXA9 expression was associated with immune-related biological function. ANXA9 expression was also correlated with the infiltration level of CD8⁺ T cells, neutrophils, and dendritic cells in GC. Additionally, copy number variation (VNV) of ANXA9 occurred in GC patients. Function enrichment analyses revealed that ANXA9 plays a role in the GC progression by interacting with its related genes.

Conclusions: Our results provide strong evidence of ANXA9 expression as a prognostic indicator related to immune responses in GC.

Keywords: ANXA9; GEO; Gastric cancer; Immune infiltrates; Prognosis; TCGA.

Figure 1. The expression level of ANXA9 in pan-cancer and GC.

(A) The ANXA9 mRNA levels in 33 human cancers. (B) The ANXA9 mRNA level in GC was up-regulated compared to the normal tissues based on the TCGA database. (C–F) The ANXA9 mRNA level in GC was up-regulated compared to normal tissues based on GSE13861, GSE13911, GSE19826, and GSE79973. (G) The ANXA9 protein expression in GC was higher than in the normal tissues based on the HPA database. (G–H) The ANXA9 protein expression in GC from the HPA database. (I–J) The ANXA9 protein expression in normal samples from the HPA database.

Figure 2. ROC curves showed that ANXA9 could distinguish the GC patients from normal individuals.

(A–E) ROC curves in the TCGA database (A) and GSE13861 (B), GSE13911 (C), GSE19826 (D), and GSE79973 (E) datasets.

Figure 3. Relationship of ANXA9 expression and clinicopathological characteristics of GC patients based on the TCGA database.

(A–F) The mRNA expression levels by age (A), gender (B), tumor stage (C), pathological T stage (D), pathological N stage (E), pathological M stage (F), and Lauren classification (G) based on the TCGA database.

Figure 4. Relationship of ANXA9 expression and clinicopathological characteristics of GC patients based on the UALCAN online database.

(A–I) the mRNA expression levels between different histological subtypes (A), tumor grade (B), TP53 mutation status (C), age (D), race (E), gender (F), sample types (G), H.pylori infection status (H), nodal metastasis status (I), and tumor stage based on the UALCAN online database.

Figure 5. The correlation between ANXA9 expression and prognosis in GC.

(A–C) The K-M survival curves for OS (A), 3-year survival (B), and 5-year survival (C) of patients with GC.

Figure 6. The biological functions of ANXA9 in GC.

(A) The GO enrichment results based on the genes in the ANXA9 high expression group. (B) The GO enrichment results based on the genes in the ANXA9 low expression group.

Figure 7. Correlation between ANXA9 expression and immune infiltration levels in GC.

(A) The proportion and composition of infiltrating immune cells between ANXA9 high and low expression groups based on 21 gene sets including immune cells and the activity of immune-related pathways. (B) The correlation between ANXA9 expression and immune infiltration in the TIMER database.

Figure 8. The analyses of ANXA9 DNA methylation and CNV.

(A–G), The correlation between DNA methylation level and expression of ANXA9 in different methylation modification sites, including cg04144222 (A), cg07337598 (B), cg07479786 (C), cg13320146 (D), cg13912599 (E), cg20437604 (F), and cg25468058 (G). (H), The correlation between CNV and expression of ANXA9.

Figure 9. The analysis of ANXA9-related genes.

(A) Volcano plot of the DEGs between the ANXA9 high and ANXA9 low expression groups. (B) The PPI network of ANXA9 and ANXA9-related genes. (C) The GO function enriched by ANXA9-related genes.