Abstract
Background
Colorectal cancer (CRC) is a major global health challenge, creating an urgent need to find new biomarkers that improve early detection and treatment success. While studies suggest that ANXA9 contributes to cancer development, its expression in CRC and its links to prognosis, immune evasion, and resistance are poorly understood.
Methods
This study conducted bioinformatics analysis of gene expression data from the TCGA and GEO to evaluate the prognostic and diagnostic potential of ANXA9. Kaplan-Meier survival analysis and ROC curves were employed to assess these capabilities. Furthermore, ANXA9 gene mutations were examined using cBioPortal, and promoter methylation levels were analyzed via UALCAN. Enrichment, immune infiltration, and treatment response analyses were also performed on ANXA9.
Results
Our research demonstrates that ANXA9 expression is significantly elevated in CRC tissues compared to normal tissues. This elevation is associated with pathological staging and TNM classification. Kaplan-Meier analysis reveals that increased ANXA9 levels are correlated with reduced overall survival and disease-specific survival. Furthermore, several mutations in ANXA9 have been identified, accompanied by a significant reduction in promoter methylation levels. Genes related to ANXA9 are significantly enriched in pathways involved in immune responses, such as cytokine secretion and T cell activation. We also observed a correlation between ANXA9 expression and the infiltration of immune cells, particularly M1 macrophages. ANXA9 expression is linked to increased levels of the immune checkpoint gene IGSF8 and resistance to chemotherapeutic agents.
Conclusion
This study reveals the critical role of ANXA9 in colorectal cancer. Its increased expression is closely associated with poor prognosis, immune evasion, and chemotherapy resistance. This provides valuable evidence for future clinical research and treatment strategies.
Keywords: Colorectal cancer, AnxinA9, Immune infiltration, Drug resistance
Introduction
Colorectal cancer (CRC) is a major global health issue, ranking among the most common cancers and a leading cause of cancer deaths worldwide [1]. This disease presents significant challenges for both patients and healthcare systems due to its high incidence and mortality rates [2]. Recent studies show that CRC is often diagnosed at advanced stages, leading to fewer treatment options and poorer outcomes for patients [3]. While current diagnostic and treatment strategies, such as colonoscopy, surgical resection, chemotherapy, and targeted therapies, have improved, challenges like late-stage diagnosis and treatment resistance still impede effective disease management [4].
Previous studies highlight the need to identify new biomarkers and therapeutic targets for the early detection and better treatment outcomes of CRC [5]. This study examines the expression of ANXA9, a protein linked to cancer progression and metastasis, and its possible role in CRC [6]. ANXA9 is a member of the Annexin family, functioning as a calcium-dependent phospholipid-binding protein that synergistically binds to anionic phospholipids and extracellular matrix proteins [7]. Elevated ANXA9 levels are linked to poor clinical outcomes, indicating its potential as a biomarker for prognosis and treatment response [8]. However, the specific mechanisms and clinical significance remain largely unexplored. Building on these insights, this study aims to investigate the role of ANXA9 in colorectal cancer. This research may help enhance diagnostic and treatment strategies for this difficult disease.
This study investigated the expression of ANXA9 in CRC using comprehensive data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). By employing bioinformatics tools, the research leveraged the advantages of large sample sizes and the integration of genomic, clinical, and immune data to enhance the robustness of the analysis. Given the significant association of ANXA9 with various clinical parameters and its high expression in cancerous tissues, this study aims to provide insights into the potential of ANXA9 as a diagnostic and prognostic biomarker for CRC, as well as to explore the underlying mechanisms by which ANXA9 may influence tumor progression and treatment resistance, thereby potentially guiding future therapeutic strategies. Figure 1 depicts the implemented workflow with comprehensive procedural details.
Fig. 1.
Bioinformatics analysis and flowchart of experiments
Materials and methods
Data collection and processing
We downloaded the gene expression profiles and corresponding clinical information for CRC patients from the TCGA database (https://portal.gdc.cancer.gov/), which included data from 647 CRC tissues and 51 normal tissues. Some patients were excluded because they lacked relevant clinical information on specific factors. For external validation, we retrieved additional CRC data from the GEO (www.ncbi.nlm.nih.gov/geo). We also used the Human Protein Atlas (HPA)(https://www.proteinatlas.org/) to obtain immunohistochemical data for both normal and cancerous colorectal tissues.
Clinical characteristics and ANXA9 correlation
The Wilcoxon rank sum test and one-way ANOVA were utilized to examine the relationship between the expression level of ANXA9 and various clinical characteristics, such as age, history of colon polyps, clinical stage, T-, N-, and M-stage. The findings were visually represented using box plots.
Diagnosis and prognosis analysis
For correlation analysis between ANXA9 and prognosis of CRC, Kaplan–Meier plotting was achieved employing the Kaplan–Meier Plotter tool (https://kmplot.com/analysis/) based on the colorectal cancer datasets from TCGA. In addition to the P values, the 95% confidence interval (CI) and hazard ratio (HR) were reported. Variables demonstrating statistical significance at P < 0.1 in univariate Cox regression analyses were advanced to multivariate Cox regression modeling. The prediction power of ANXA9 to separate between CRC patients and healthy individuals was assessed using the receiver operating characteristic (ROC) analysis. Nomograms were created using the rms and survival packages by incorporating variables from multivariate Cox regression analysis. The consistency index (C-index) was utilized to examine the discriminatory ability.
Analysis of genomic alterations and mutation burden
We obtained data on the frequency, type, and location of ANXA9 protein mutations from cBioPortal (https://www.cbioportal.org/). We used the “Mutation” module in the GSCA database to analyze the correlation between ANXA9’s copy number variation (CNV) and mRNA expression levels, along with survival differences related to CNV. The R maftools package was used to assess the correlation between tumor mutation burden (TMB) and microsatellite instability (MSI), and ANXA9 expression. We employed the OCLR algorithm to calculate the mRNA-based stemness index (mRNAsi). Subsequently, we assessed the association between mRNAsi and ANXA9 expression at the mRNA level using the Spearman coefficient. We obtained the correlations between tumor purity, immune score, aneuploidy score, and ANXA9 expression from the IMPACT database (http://www.brimpact.cn/).
DNA methylation analysis
We used the UALCAN platform (http://ualcan.path.uab.edu/) to assess ANXA9 methylation levels in both colon and rectal adenocarcinomas [9]. We evaluated DNA methylation levels using the β value scale. A β value of 0 represents no methylation, while a β value of 1 indicates complete methylation. We utilized the “Mutation” module in the GSCA database to analyze the correlation between ANXA9 methylation and mRNA expression levels, as well as the impact of methylation on prognosis [10]. Additionally, we used the TIDE methylation module to assess the relationship between ANXA9 methylation and cytotoxic T lymphocytes (CTLs) [11]. We downloaded the DNA methylation profile of ANXA9 in colorectal cancer from the MethSurv database(https://biit.cs.ut.ee/methsurv).
DNA repair and epigenetic modification analysis
A visual analysis examined the relationship between ANXA9 and the expression of five mismatch repair (MMR) genes. Additionally, the correlation between 13 homologous recombination repair (HRR) genes and 10 non-homologous end joining (NHEJ) genes with ANXA9 mRNA levels was assessed. The association between ANXA9 and 45 methylation regulatory factors related to N1-methyladenosine (m1A), 5-methylcytosine (m5C), and N6-methyladenosine (m6A) was also evaluated.
Co-expressed genes and gene enrichment analysis
We accessed the LinkedOmics database (http://www.linkedomics.org/login.php) to select the “COADREAD cohort” dataset, using “RNAseq” as the data type and “Pearson correlation test” as the statistical method to analyze ANXA9 co-expressed genes [12]. Next, we utilized the Gene Set Enrichment Analysis (GSEA) tool to conduct enrichment analysis for the related genes in terms of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
Analysis of the immunological role of ANXA9
We utilized the “immuneeconv” R package for immune infiltration analysis. This package integrates five algorithms for estimating immune cell concentrations: xCell, MCPCOUNTER, CIBERSORT, EPIC, and QUANTISEQ. These algorithms estimated the immune cell scores in tumor samples. We used the Spearman correlation coefficient to evaluate the relationship between ANXA9 expression levels and different immune cell types. We also calculated the immune infiltration status using the ssGSEA algorithm from the “GSVA” R package, which utilizes markers from 24 immune cell types. Additionally, we used TIMER2.0 (http://timer.comp-genomics.org/) to evaluate the correlation between ANXA9 and various immune-related genes, including major histocompatibility complex (MHC), chemokines, immunosuppressive genes, and immune-stimulating genes [13]. We used the IMPACT database to analyze the correlation between ANXA9 and cancer-related pathways, TCGA oncogenic pathways, and immune-related pathways.
Treatment response analysis
The ESTIMATE algorithm evaluated immune, stromal, and overall ESTIMATE scores in high and low ANXA9 expression groups in CRC. We compared immune checkpoint expression levels between the two groups. The TIDE algorithm predicted the response of ANXA9 to immune checkpoint blockade (ICB) in CRC. The CPADS database (https://robinl-lab.com/CPADS) and “pRRophetic” R package assessed the half-maximal inhibitory concentration (IC50) of commonly used anticancer drugs in high and low ANXA9 expression groups [14].
Statistical analysis
Normal and tumor tissues were compared using Student’s t-test or Wilcoxon rank sum tests. Using Chi-square, Fisher’s exact, one-way ANOVA, Wilcoxon rank sum test, and Cox regression analyses, we examined the association between ANXA9 expression and clinicopathological features. Correlation analysis was performed with the Spearman or Pearson correlation test. A P-value of < 0.05 was considered statistically significant.
Results
Analysis of ANXA9 expression
To assess the expression of ANXA9 in both tumor and normal tissues, mRNA expression data from the TCGA database were obtained using the R package. Analysis of the TCGA datasets revealed elevated expression of ANXA9 in breast invasive carcinoma (BRCA), Cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), rectum adenocarcinoma (READ), and stomach adenocarcinoma (STAD) (P < 0.05, P < 0.01, and P < 0.001). Conversely, decreased expression levels of ANXA9 were observed in head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA) and uterine corpus endometrial carcinoma (UCEC), with statistical significance (P < 0.001; Fig. 2A). The results of the comparison of the paired samples are basically consistent with the above results (P < 0.05; Fig. 2B). Subsequent analysis of ANXA9 expression levels in CRC was conducted utilizing data from TCGA database. The results revealed a significant increase in ANXA9 expression in cancerous tissues compared to non-cancerous tissues (P < 0.001; Fig. 2C). These findings were further validated through analysis of matched cancerous and non-cancerous tissues in two distinct external GEO datasets, specifically GSE146587 and GSE89076, as well as immunohistochemical data which demonstrated upregulation of ANXA9 in CRC tissues (P < 0.01 and P < 0.001; Fig. 2D, E).
Fig. 2.
ANXA9 expression levels. A TCGA analysis of ANXA9 mRNA expression in different cancer types. B mRNA levels of ANXA9 in paired cancers and para-cancerous tissues using the TCGA database. C CRC tumors and paired adjacent normal tissues were examined for ANXA9 mRNA expression in TCGA. D Analysis of GEO datasets (GSE146587 and GSE89076) showed mRNA expression of ANXA9 in CRC. E Data from the Human Protein Atlas database shows that the ANXA9 protein was higher in colon cancer tissues than in normal colon tissues. ANXA9 Annexin A9, TCGA The Cancer Genome Atlas, CRC Colorectal cancer. *P < 0.05, **P < 0.01, ***P < 0.001
Correlation between ANXA9 expression and clinical parameters of patients with CRC
We used the Mann-Whitney U test and one-way ANOVA to evaluate the expression of ANXA9 in a range of clinical stages and TNM staging. The results demonstrated significant differences between ANXA9 expression levels and Pathologic stage, Pathologic M stage, Pathologic N stage, Pathologic T stage (P < 0.05, P < 0.01, P < 0.001; Fig. 3A–D). In addition, we also observed that the expression of ANXA9 was also differentiated in Primary therapy outcome, Neoplasm type, and Anatomic neoplasm subdivision, and OS event (P < 0.05, P < 0.01, P < 0.001; Fig. 3E–H). Conversely, no significant associations were observed between ANXA9 expression and another clinical or pathologic characteristic (Fig. 3I–L).
Fig. 3.
Clinical characteristics and the expression of ANXA9 in patients with colorectal cancer. A Pathologic stage, B Pathologic M stage, C Pathologic T stage, D Pathologic N stage, E Primary therapy outcome, F Neoplasm type, G Anatomic neoplasm subdivision, H OS event, I History of colon polyps, J Perineural invasion, K Colon polyps present, L Lymphatic invasion. *P < 0.05, **P < 0.01, ***P < 0.001
Diagnostic and survival value of ANXA9 expression in CRC
Those suffering from CRC were categorized into low and high ANXA9 expression groups to examine the relationship between ANXA9 and prognosis. Kaplan-Meier analysis revealed a connection between high ANXA9 levels and poorer outcomes in terms of overall survival (P = 0.003), disease-specific survival (P < 0.001), and progression-free interval (P = 0.002), as shown in Fig. 4A. Additionally, ROC curves were performed to assess the diagnostic utility of ANXA9. The ROC curve analysis showed ANXA9 had an AUC value of 0.741, indicating its potential diagnostic significance as depicted in Fig. 4B. Univariate and multivariate Cox analyses indicated that high ANXA9 expression is significantly associated with poorer overall survival (P = 0.025, P = 0.026; Fig. 4C, D). Furthermore, a nomogram was developed to aid healthcare providers in evaluating the prognosis and predicting the survival likelihood of CRC patients. Pathological stage, Pathological T stage, Pathological N stage, Pathological M stage, age, and ANXA9 were taken into account when developing the nomogram (Fig. 4E). The nomogram model’s predictive accuracy and reliability were confirmed by the calibration plot (Fig. 4F). Clearly, the observed and predicted values are in good agreement since the calibration line of the model is close to the ideal line. This finding aligns with prior studies showing that an increased proportion of ANXA9-positive CRC tissue samples correlates with deeper tumor invasion and a higher incidence of lymph node metastasis. Both univariate and multivariate Cox regression analyses established ANXA9 expression as an independent prognostic factor in CRC [15].
Fig. 4.
Diagnostic and prognostic significance of ANXA9. A High and low ANXA9 expression was associated with poorer prognosis in CRC. B Curves of ROC for ANXA9 expression in colorectal cancer. C, D Univariate and multivariate Cox regression analyses associated with overall survival. E The nomogram survival chart predicts overall survival rates over 1, 3, and 5 years. F Predictive capacity of the nomogram assessed by the calibration plots. ROC Receiver operating characteristic
Genetic alteration analysis and genomic instability of ANXA9 in CRC
The cBioPortal platform was utilized to analyze TCGA data to detect mutations in the ANXA9 gene in CRC. The mutation frequency of ANXA9 was determined to be 1.5%, with common alterations observed including mutations and amplifications in CRC (Fig. 5A, C). The identified mutations encompassed missense, truncating, and splicing mutations (Fig. 5B). Specific missense mutations identified in ANXA9 included T37I, L91P, R208Q, R252H, D202Y, L17R, A23T, S266L, K269N, A28V, A53D, L126M, I54M, and A260V. Truncating mutations identified in ANXA9 included R225, E121, Q243, R232, R300, and Q204. Splicing mutations, including X58. Furthermore, the methylation levels of the ANXA9 promoter were examined in both normal and cancerous tissues. The TIDE analysis results indicated a positive correlation between the expression of ANXA9 CNV and its mRNA levels (Fig. 5D). We further examined the relationship between ANXA9 and several factors, including Tumor Mutation Burden (TMB), Stemness score, Microsatellite Instability (MSI), Tumor Purity, Immune Score, and Aneuploidy score, due to the potential influence of mutations on the prognosis and treatment outcomes of CRC. The analysis revealed that ANXA9 was negatively correlated with TMB, Stemness score, MSI, and Immune Score, while it was positively correlated with Tumor Purity and Aneuploidy score (Fig. 5E, F). Additionally, we also evaluated the impact of ANXA9 CNVs on survival, observing a significant differentiation in DFI for colon adenocarcinoma (Fig. 5G).
Fig. 5.
Analyses of ANXA9 Genomic Alterations. A Genetic alteration in ANXA9 in colorectal cancer. B, C Mutation frequency and mutation sites were displayed using the cBioPortal tool. D Spearman correlation analysis detects the relationship between ANXA9 CNV and its mRNA expression. E Spearman correlation analysis evaluates the correlation between ANXA9 and TMB, stemness score, and MSI. F The IMPACT database shows a close correlation between ANXA9 and Tumor Purity, Immune Score, and Aneuploidy score. G The relationship between ANXA9 CNVs and prognosis in COAD and READ. CNV Copy Number Variation; TMB tumor mutation burden, MSI microsatellite instability, COAD Colon adenocarcinoma, READ Rectum adenocarcinoma
DNA methylation and mRNA regulation analysis of ANXA9
The findings obtained from the GSCA tool indicated a significant inverse correlation between the methylation level of ANXA9 and its mRNA expression in colorectal adenocarcinoma and rectal adenocarcinoma, as illustrated in Fig. 6A. Furthermore, our analysis demonstrated a reduction in methylation of the ANXA9 promoter in both colon and rectal cancers, as depicted in Fig. 6B (P = 1E-12, P = 1.64E-12). The association between ANXA9 promoter methylation and cytotoxic T lymphocyte (CTL) presence in CRC was also assessed using the TIDE (Fig. 6C). Notably, hypermethylation of ANXA9 was associated with an extended disease-free interval in CRC patients (P = 0.023, Fig. 6D). Additionally, seven CpG sites of ANXA9 were identified in colon and rectal cancers using the methylation map from the MethSurv database, as shown in Fig. 6E, F. Correlation analysis of the gene revealed that ANXA9 was positively correlated with several key genes involved in m1A, m5C, and m6A modifications, as presented in Fig. 7A–C.
Fig. 6.
DNA methylation analysis of ANXA9. A Spearman correlation analysis shows the correlation between ANXA9 methylation levels and its mRNA expression. B The promoter methylation level of ANXA9 in COAD and READ. C Scatter plots display the connection between ANXA9 methylation levels and CTL markers in colorectal cancer. D The relationship between ANXA9 methylation and patient prognosis. E, F The heatmap of ANXA9 DNA methylation in COAD and READ from MethSurv. COAD Colon adenocarcinoma, READ Rectum adenocarcinoma, CTL Cytotoxic T lymphocyte
Fig. 7.
ANXA9 is associated with epigenetic modifications and DNA repair (A–C). Correlation analysis between ANXA9 expression and RNA regulatory factors. D The association between ANXA9 and HRR genes. E The association between ANXA9 and MMR genes. F The association between ANXA9 and NHEJ genes. m1A N1-methyladenosine, m5C 5-methylcytosine, m6A N6-methyladenosine, HRR homologous recombination repair, MMR mismatch repair, NHEJ non-homologous end joining. *P < 0.05, **P < 0.01, ***P < 0.001
DNA damage repair response of ANXA9
The DNA damage response (DDR) is essential for repairing DNA damage and ensuring genomic stability. Tumor cells can sometimes avoid cell death by improving their DNA repair capabilities. This enhancement can result in resistance to chemotherapy and radiation therapy [16]. We selected several major pathways involved in DNA repair and analyzed their correlation with ANXA9 using the R package. As shown in Fig. 7D–F, the results indicate that ANXA9 has a positive correlation with genes involved in mismatch repair, homologous recombination, and non-homologous end joining.
ANXA9 co-expression networks correlate with the immune response
To enhance our understanding of ANXA9’s biological function, we analyzed the co-expression of genes associated with ANXA9 in CRC using the LinkedOmics database (Fig. 8A). We then displayed the top 50 genes positively and negatively correlated with ANXA9 in a heatmap (Fig. 8B, C). Next, we conducted gene set enrichment analysis (GSEA) on ANXA9-related genes in CRC. This analysis revealed that the primary GO terms were enriched in several areas, including cytokine secretion, leukocyte cell-cell adhesion, adaptive immune response, and T cell activation, among others (Fig. 8D–F). We subsequently performed KEGG pathway enrichment analysis. This analysis indicated that the main enriched pathways included the Toll-like receptor signaling pathway, natural killer cell-mediated cytotoxicity, and Th17 cell differentiation interactions (Fig. 8G). These results suggest that ANXA9 expression may play a role in the progression of CRC by regulating the immune response. Additionally, we analyzed ANXA9 in connection with cancer-related pathways, TCGA oncogenic pathways, and immune-related pathways (Fig. 9A–C). We also examined immune molecules, including chemokines, MHC, immunostimulators, and immunoinhibitors, using the IMPACT and TIMER2.0 tools. These analyses further suggest that ANXA9 is closely related to tumor immune responses (Fig. 10A–D).
Fig. 8.
The co-expression genes of ANXA9 were analyzed in colorectal cancer using the LinkedOmics database. A Pearson detected genes that are highly correlated with ANXA9 in colorectal cancer. B, C The top 50 positively co-expressed genes of ANXA9 (B) and the negatively co-expressed genes. D–G Gene set enrichment analysis to identify significant GO terms and KEGG pathways associated with the genes co-expressed with ANXA9. GO Gene Ontology, KEGG Kyoto Encyclopedia of Genes and Genomes, BP Biological Process, CC Cellular Component, MF Molecular Function.
Fig. 9.
Correlations between the expression of ANXA9 and cancer pathways (A–C). Cancer-related pathways (A), TCGA oncogenic pathways (B), Immune-related pathways (C). *P < 0.05
Fig. 10.
The relationship between ANXA9 expression and immune gene expression (A–C). Chemokines (A), MHC genes (B), Immune stimulator genes (C), and Immune inhibitor genes (D). MHC: major histocompatibility complex. *P < 0.05
ANXA9 expression is associated with macrophages in the tumor microenvironment
We used single sample GSEA (ssGSEA) to investigate the relationship between ANXA9 expression and immune infiltration in CRC (Fig. 11A). We found that T central memory (Tcm) cells were positively correlated with ANXA9. In contrast, ANXA9 expression was negatively correlated with other immune cells, particularly macrophages. We also employed algorithms like XCELL, MCPCOUNTER, QUANTISEQ, EPIC, CIBERSORT, and to evaluate the relationship between ANXA9 and immune cells (Fig. 11B–F). Given the close association between macrophage polarization and CRC progression, we observed significant negative correlations of ANXA9 expression with M1 macrophage markers, while no correlation was found with M2 macrophage (Fig. 11G). High expression of NOS2 is associated with a better prognosis (Fig. 11H).
Fig. 11.
The association between ANXA9 expression and the level of infiltrating immune cells (A–F). The relationship between ANXA9 expression and immune cell infiltration in colorectal cancer based on ssGSEA (A), XCELL (B), MCP counter (C), QUANTISEQ (D), EPIC (E), and CIBERSORT (F). G The correlation between ANXA9 and the M1 and M2 macrophage-related marker in colorectal cancer. H Kaplan-Meier curves depict the impact of M1 macrophages on the survival of CRC patients. NOS2: Nitric oxide synthase-2. ARG1: Arginase 1. MRC1: Mannose Receptor, also known as CD206. *P < 0.05, **P < 0.01, ***P < 0.001
ANXA9 affects treatment response in CRC and promotes resistance
We investigated the impact of ANXA9 on the treatment response in colorectal cancer. First, we employed the ESTIMATE algorithm to compare the immune, stromal, and overall scores between the high and low ANXA9 expression groups. As shown in Fig. 12A, the low ANXA9 expression group had higher scores, indicating a significant negative correlation (P < 0.01, P < 0.001). This suggests that ANXA9 may affect the treatment response in CRC via the immune system. The TIDE algorithm predicted how ANXA9 expression in CRC correlates with immune checkpoint blockade (ICB) response. The high expression group of ANXA9 had higher TIDE scores and fewer responders than the low expression group, indicating a link between ANXA9 and poor ICB response (Fig. 12B; P < 0.001). Subsequently, we examined the correlation between ANXA9 and immunoglobulin superfamily member 8 (IGSF8). We compared IGSF8 expression between the low and high ANXA9 groups and found that it was significantly elevated in the high ANXA9 group. This suggests that CRC patients with high ANXA9 expression possess enhanced immune evasion capabilities and a more pronounced immunosuppressive microenvironment, potentially linked to increased IGSF8 levels (Fig. 12C–D; P < 0.0001). Furthermore, the high ANXA9 expression group exhibited higher IC50 scores for docetaxel, cyclophosphamide, vinblastine, camptothecin, and sorafenib, suggesting a link between ANXA9 and resistance to these chemotherapeutic agents (Fig. 12E–G).
Fig. 12.
The influence of ANXA9 on the treatment response of colorectal cancer. A The application of the ESTIMATE algorithm to determine the association between ANXA9 expression and stroma, immunity, and ESTIMATE scores. B The statistical distribution of immune response and TIDE scores between high and low ANXA9 expression groups. C The correlations between ANXA9 expression and IGSF8 levels. D Gene expression of IGSF8 in high and low ANXA9 expression groups. E Comparison of IC50 scores for vinblastine and sorafenib between high and low ANXA9 expression groups in TCGA. F Distribution of IC50 scores for docetaxel, cyclophosphamide, vinblastine, sorafenib, and camptothecin between high and low ANXA9 expression groups from GDSC. G Using the R package, compare the distribution of IC50 values for docetaxel, vinblastine, and camptothecin between high and low ANXA9 expression groups. IGSF8 immunoglobulin superfamily member 8, IC50 Half maximal inhibitory concentration. **P < 0.01, ***P < 0.001, ****P < 0.0001
Discussions
Colorectal cancer is one of the most common cancers worldwide, leading to significant illness and death from cancer. CRC has multiple causes, including genetic, environmental, and lifestyle factors [17]. These complexities pose significant challenges for timely diagnosis and effective treatment. Many patients do not have symptoms in the early stages of the disease, which often results in late diagnoses and worse outcomes [18]. Current diagnostic methods, such as colonoscopy, and treatment options like surgical resection and chemotherapy have significant limitations, especially related to therapy resistance and the complexity of tumor biology [19]. Therefore, there is a pressing need to explore novel biomarkers and therapeutic targets that can enhance early detection and improve clinical outcomes for CRC patients.
Previous research suggests that ANXA9 demonstrates multi-modal oncogenic functions: promoting proliferation and migration in gastric cancer via TGF-β signaling, facilitating breast cancer progression through STAT3 modulation, and enhancing colorectal carcinogenesis by regulating Wnt pathway activity [8, 20, 21]. This study investigated the expression of ANXA9 and its impact on CRC progression and patient prognosis. Previous studies have suggested that ANXA9 expression correlates with disease severity and promotes cancer cell proliferation and invasion [6, 22]. Analysis of public datasets revealed that ANXA9 expression was significantly higher in CRC tissues than in normal tissues and was closely associated with poor clinical outcomes, indicating its role as a critical factor in CRC progression and prognosis. However, when evaluating ANXA9 as a diagnostic biomarker for CRC, its AUC value was only 0.741, suggesting significant limitations in its utility as a standalone diagnostic biomarker for CRC. Consequently, future studies should focus on combining ANXA9 with other biomarkers to enhance diagnostic accuracy.
To elucidate the role of ANXA9 in CRC, we analyzed genes co-expressed with ANXA9. Genes associated with ANXA9 were significantly enriched in immune response pathways, such as cytokine secretion and T cell activation. Furthermore, KEGG pathway analysis revealed that ANXA9 participates in the Toll-like receptor signaling pathway, which is crucial for mediating tumor immune responses [23]. Although studies specifically investigating ANXA9’s role in CRC pathogenesis remain limited, research on ANXA9 and its family members is well-established in other malignancies. For example, in breast cancer, ANXA9 mediates secretion of pro-angiogenic cytokines IL-6, IL-8, CCL2, and CCL5 [21]. Similarly, ANXA2 drives immune evasion in hepatocellular carcinoma by dysregulating immune mediators: upregulating checkpoint molecules while suppressing effector molecules including perforin, granzyme B, IFN-γ, and TNF-α [24]. Additionally, ANXA1 can bind to FPR1, activating ERK/Akt signaling, enhancing TCR activity, and ultimately promoting T helper cell differentiation via transcription factors NFAT, NF-κB, and AP-1 [25].
Genetic alteration analysis revealed a mutation frequency of 1.5% for ANXA9, offering new insights into its role in CRC. Identifying specific mutations and their relationship to clinical outcomes could offer valuable insights for personalized medicine, informing treatment interventions based on individual genomic characteristics. Previous studies have indicated that genetic alterations may affect treatment resistance, suggesting that mutations in ANXA9 might make treating CRC more challenging [26]. These findings offer valuable insights for future research on ANXA9’s role in CRC genomics, which could lead to more effective and personalized treatment options for patients. DNA methylation is crucial in regulating gene expression. Gene expression can be suppressed through the methylation of CpG islands in the promoter region, while demethylation can enhance its expression [27, 28]. We observed low promoter methylation levels in ANXA9, with seven CpG sites exhibiting methylation in colorectal cancer. Additionally, patients with lower levels of methylation exhibited poorer prognoses, aligning with previous findings. The findings suggest that moving from colorectal cancer to high-grade cancer might be because the ANXA9 promoter gets demethylated, causing the gene to be overexpressed.
The TME critically influences cancer progression [29, 30]. In CRC, our analysis demonstrates that ANXA9 expression negatively correlates with macrophage infiltration, particularly M1 macrophages, suggesting high ANXA9 levels may contribute to an immunosuppressive TME characterized by impaired M1 activity. Notably, despite the dynamic plasticity between M1/M2 macrophages, multiple algorithms consistently show no significant ANXA9-M2 association—potentially reflecting bioinformatics limitations and TME complexity. Future studies should therefore integrate experimental validation with single-cell RNA sequencing and spatial transcriptomics to elucidate the underlying mechanisms.
TMB and MSI are parameters that predict tumor sensitivity to immune checkpoint inhibitors, with tumors exhibiting high TMB or MSI more likely to respond to ICIs [31, 32]. In colorectal cancer, ANXA9 expression negatively correlates with both TMB and MSI. This suggests that lower levels of ANXA9 contribute to improved treatment outcomes following immunotherapy. The negative correlation between ANXA9 and immune scoring, as well as the response to immune checkpoint inhibition, suggests that ANXA9 is associated with immune evasion and an immunosuppressive microenvironment in CRC. Investigating the role of ANXA9 in immune cell infiltration may lead to new treatment options, especially in immunotherapy. Targeting ANXA9 alongside immunotherapy may improve patient responses and treatment outcomes in CRC. Future research should focus on these interactions to develop new treatment strategies that utilize the immune system against CRC. Our study reveals that elevated ANXA9 levels are associated with drug resistance, as patients demonstrated reduced sensitivity to chemotherapy and targeted therapies, including docetaxel and sorafenib. This finding highlights ANXA9 as a key factor in drug resistance in colorectal cancer. Future interventions that target ANXA9 could improve treatment outcomes for patients.
The primary limitation of this study is the lack of in vivo/in vitro experimental validation. While bioinformatic analyses offer valuable insights, the inability to biologically validate findings hinders exploration of: (1) ANXA9’s causal role in tumorigenesis, (2) its spatiotemporal dynamics in tumor microenvironments, and (3) therapeutic targeting potential. In vitro models elucidate cellular mechanisms, whereas in vivo systems recapitulate systemic interactions. This experimental validation remains the gold standard for establishing biological plausibility; its absence inherently limits confidence in bioinformatics-derived conclusions. Future work must therefore integrate experimental and bioinformatic approaches to definitively elucidate ANXA9’s role in colorectal carcinogenesis.
In summary, this study reveals the key role of ANXA9 in colorectal cancer, indicating that its elevated expression is closely related to poor prognosis, immune evasion, and chemotherapy resistance in patients. These findings not only enhance our understanding of the biological mechanisms of colorectal cancer but also pave the way for developing early diagnosis and personalized treatment strategies in clinical practice. Future research should explore the functions of ANXA9 and its potential applications in various treatment strategies, which could improve the prognosis for colorectal cancer patients.
Author contributions
H.M.Y. arranged and wrote the content. D.Q. and Z.H.M. analyzed the data and created the figures. H.X. wrote and revised the article. A consensus was reached among all authors on the article’s acceptance for publication.
Funding
The authors funded the study with their own resources.
Data availability
The data presented in this study is openly available from public databases, including the TCGA database (https://portal.gdc.cancer.gov/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Mingyang Hong and Qian Dong contributed equally to this work.
Contributor Information
Huiming Zhu, Email: zhmwpp@163.com.
Xu Huang, Email: Huangxu9414@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data presented in this study is openly available from public databases, including the TCGA database (https://portal.gdc.cancer.gov/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/).